diff --git a/data/sampled_jsons/'Machine_Learning_meets_Algebraic_Combinatorics'_'Grassmannian_cluster_algebra_dataset'_'Table_10'_'_year_2024.jsonl b/data/sampled_jsons/'Machine_Learning_meets_Algebraic_Combinatorics'_'Grassmannian_cluster_algebra_dataset'_'Table_10'_'_year_2024.jsonl
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+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "In this paper we introduce the Algebraic Combinatorics Dataset Repository, a collection of research-level mathematics datasets structured for machine learning and designed to accelerate mathematical discovery.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "In this paper we introduce the Algebraic Combinatorics Dataset Repository, a collection of research-level mathematics datasets structured for machine learning and designed to accelerate mathematical discovery."}
+{"idx": 1, "title": "Algebraic Combinatorics Dataset Repository - GitHub", "date": "", "ddg_snippet": "To lower the barrier of entry to the machine learning community, we include datasets centered around open problems in algebraic combinatorics . We hope that use of these by the AI-community will translate into progress in mathematics.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "To lower the barrier of entry to the machine learning community, we include datasets centered around open problems in algebraic combinatorics . We hope that use of these by the AI-community will translate into progress in mathematics."}
+{"idx": 2, "title": "ACDRepo/grassmannian_cluster_algebras · Datasets at Hugging Face", "date": "", "ddg_snippet": "Machine learning meets algebraic combinatorics : A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv:2503.06366.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ACDRepo/grassmannian_cluster_algebras", "content": "Machine learning meets algebraic combinatorics : A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv:2503.06366."}
+{"idx": 3, "title": "CANCELLED Grassmannian cluster algebras and machine learning", "date": "", "ddg_snippet": "It is an open problem to give a combinatorial method to check which tableaux correspond to cluster veriables. In the joint work with Man-Wai Cheung, Pierre-Philippe Dechant, Yang-Hui He, Elli Heyes, Edward Hirst, we apply machine learning method to classify cluster variables.", "subpage_snippet": "", "source": "meetings.ams.org", "link": "https://meetings.ams.org/math/jmm2025/meetingapp.cgi/Paper/40267", "content": "It is an open problem to give a combinatorial method to check which tableaux correspond to cluster veriables. In the joint work with Man-Wai Cheung, Pierre-Philippe Dechant, Yang-Hui He, Elli Heyes, Edward Hirst, we apply machine learning method to classify cluster variables."}
+{"idx": 4, "title": "Twists of $\\mathrm {Gr} (3,n)$ Cluster Variables as Double and Triple ...", "date": "", "ddg_snippet": "We give a combinatorial interpretation for certain cluster variables in Grassmannian cluster algebras in terms of double and triple dimer configurations. More specifically, we examine several Gr (3, n) cluster variables that may be written as degree two or degree three polynomials in terms of Plücker coordinates, and give generating functions for their images under the twist map - a cluster ...", "subpage_snippet": "", "source": "alco.centre-mersenne.org", "link": "https://alco.centre-mersenne.org/articles/10.5802/alco.376/", "content": "We give a combinatorial interpretation for certain cluster variables in Grassmannian cluster algebras in terms of double and triple dimer configurations. More specifically, we examine several Gr (3, n) cluster variables that may be written as degree two or degree three polynomials in terms of Plücker coordinates, and give generating functions for their images under the twist map - a cluster ..."}
+{"idx": 5, "title": "PDF Clustering cluster algebras with clusters - LIMS", "date": "", "ddg_snippet": "In this paper, we apply the tableaux method to clas-sify cluster variables in Grassmannian cluster algebras C[Gr(k, n)] up to (k, n) = (3, 12), (4, 10 ), or (4, 12) up to a certain number of columns of tableaux, using HPC clusters . These datasets are made available on GitHub.", "subpage_snippet": "", "source": "lims.ac.uk", "link": "https://lims.ac.uk/documents/undefined-6.pdf", "content": "In this paper, we apply the tableaux method to clas-sify cluster variables in Grassmannian cluster algebras C[Gr(k, n)] up to (k, n) = (3, 12), (4, 10 ), or (4, 12) up to a certain number of columns of tableaux, using HPC clusters . These datasets are made available on GitHub."}
+{"idx": 6, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets to ...", "date": "", "ddg_snippet": "In this paper we introduced Algebraic Combinatorics Dataset Repository, a collection of datasets structured for machine learning and designed to facilitate the development of machine learning methods for advancing research level mathematics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KQ1gI5qzAf", "content": "In this paper we introduced Algebraic Combinatorics Dataset Repository, a collection of datasets structured for machine learning and designed to facilitate the development of machine learning methods for advancing research level mathematics."}
+{"idx": 7, "title": "Cluster algebras: Network science and machine learning", "date": "", "ddg_snippet": "Cluster algebras have recently become an important player in mathematics and physics. In this work, we investigate them through the lens of modern data science, specifically with techniques from network science and machine learning . Network analysis methods are applied to the exchange graphs for cluster algebras of varying mutation types. The analysis indicates that when the graphs are ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772827723000050", "content": "Cluster algebras have recently become an important player in mathematics and physics. In this work, we investigate them through the lens of modern data science, specifically with techniques from network science and machine learning . Network analysis methods are applied to the exchange graphs for cluster algebras of varying mutation types. The analysis indicates that when the graphs are ..."}
+{"idx": 8, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Benchmark ...", "date": "", "ddg_snippet": "To address this, we introduce a new collection of benchmark datasets , Algebraic Combinatorics Benchmarks (ACBench), representing either classic or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra .", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/publications/machine-learning-meets-algebraic-combinatorics-suite-benchmark-datasets-accelerate-ai", "content": "To address this, we introduce a new collection of benchmark datasets , Algebraic Combinatorics Benchmarks (ACBench), representing either classic or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra ."}
+{"idx": 9, "title": "PDF Pnnl- 36691", "date": "", "ddg_snippet": "In this paper we introduced Algebraic Combinatorics Benchmarks (ACBench), a collection of datasets structured for machine learning and designed to facilitate the development of machine learning methods for advancing research level mathematics.", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-36691.pdf", "content": "In this paper we introduced Algebraic Combinatorics Benchmarks (ACBench), a collection of datasets structured for machine learning and designed to facilitate the development of machine learning methods for advancing research level mathematics."}
diff --git a/data/sampled_jsons/'Machine_Learning_meets_Algebraic_Combinatorics'_'Grassmannian_cluster_algebra_dataset'_Table_10.jsonl b/data/sampled_jsons/'Machine_Learning_meets_Algebraic_Combinatorics'_'Grassmannian_cluster_algebra_dataset'_Table_10.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics : A Suite of...", "date": "", "ddg_snippet": "Among the many algebraic - combinatorial properties of Grassmannians is an algebraic structure on its coordinate ring making it something called a cluster algebra (Williams, 2014) .This dataset relates to a cluster algebra associated with the Grassmann manifold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "Among the many algebraic - combinatorial properties of Grassmannians is an algebraic structure on its coordinate ring making it something called a cluster algebra (Williams, 2014) .This dataset relates to a cluster algebra associated with the Grassmann manifold."}
+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics : A", "date": "", "ddg_snippet": "Model Performance. Baseline hyperparameters. Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research.All datasets (outside of the positive examples in the Grassmannian cluster algebra dataset ) were generated in June 2024.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KQ1gI5qzAf", "content": "Model Performance. Baseline hyperparameters. Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research.All datasets (outside of the positive examples in the Grassmannian cluster algebra dataset ) were generated in June 2024."}
+{"idx": 2, "title": "Twists, Higher Dimer Covers, and Web Duality for Grassmannian ...", "date": "", "ddg_snippet": "Grassmannian cluster algebras represent a fundamental intersection between algebraic geometry, combinatorics , and representation theory.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2507.15211v1", "content": "Grassmannian cluster algebras represent a fundamental intersection between algebraic geometry, combinatorics , and representation theory."}
+{"idx": 3, "title": "Clustering cluster algebras with clusters | Request PDF", "date": "", "ddg_snippet": "The Grassmannian cluster algebra .This computational algebra paradigm generates a dataset that can then be mined using techniques from data science such as supervised and unsupervised machine learning .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381241767_Clustering_cluster_algebras_with_clusters", "content": "The Grassmannian cluster algebra .This computational algebra paradigm generates a dataset that can then be mined using techniques from data science such as supervised and unsupervised machine learning ."}
+{"idx": 4, "title": "co. combinatorics - Grassmannian cluster algebra of... - MathOverflow", "date": "", "ddg_snippet": "Grassmannian cluster algebra of infinite type has no trees in its mutation class.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/381452/grassmannian-cluster-algebra-of-infinite-type-has-no-trees-in-its-mutation-class", "content": "Grassmannian cluster algebra of infinite type has no trees in its mutation class."}
+{"idx": 5, "title": "Twists of $\\mathrm{Gr}(3, n)$ Cluster Variables as Double and Triple...", "date": "", "ddg_snippet": "ALGEBRAIC COMBINATORICS .We give a combinatorial interpretation for certain cluster variables in Grassmannian cluster algebras in terms of double and triple dimer configurations. More specifically, we examine several. Gr(3,n).", "subpage_snippet": "", "source": "alco.centre-mersenne.org", "link": "https://alco.centre-mersenne.org/articles/10.5802/alco.376/", "content": "ALGEBRAIC COMBINATORICS .We give a combinatorial interpretation for certain cluster variables in Grassmannian cluster algebras in terms of double and triple dimer configurations. More specifically, we examine several. Gr(3,n)."}
+{"idx": 6, "title": "Categories for Grassmannian Cluster Algebras of Infinite Rank", "date": "", "ddg_snippet": "Grassmannian cluster categories are an additive categorification of Grassmannian cluster algebras , of which this section provides an overview. 2.1.1 The finite rank case Coordinate rings of flag varieties provide an interesting source of cluster algebras .", "subpage_snippet": "", "source": "eprints.gla.ac.uk", "link": "https://eprints.gla.ac.uk/294377/2/294377.pdf", "content": "Grassmannian cluster categories are an additive categorification of Grassmannian cluster algebras , of which this section provides an overview. 2.1.1 The finite rank case Coordinate rings of flag varieties provide an interesting source of cluster algebras ."}
+{"idx": 7, "title": "(PDF) Grassmannians and Cluster Algebras", "date": "", "ddg_snippet": "A cluster algebra of geometric type 4 GRASSMANNIANS AND CLUSTER ALGEBRAS will be defined as a subalgebra of F generated by a family of transcendence bases of F each of which is determined by a combinatorial process called mutation.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/99109832/Grassmannians_and_Cluster_Algebras", "content": "A cluster algebra of geometric type 4 GRASSMANNIANS AND CLUSTER ALGEBRAS will be defined as a subalgebra of F generated by a family of transcendence bases of F each of which is determined by a combinatorial process called mutation."}
+{"idx": 8, "title": "Dimers in Combinatorics and Cluster Algebras", "date": "", "ddg_snippet": "Cluster Algebra , Dimers and Beyond (slides) (video).In certain cases, these algebras correspond to cluster -tilting objects in the cluster category associated to the Grassmannian introduced by Jensen, King and Su.", "subpage_snippet": "", "source": "dept.math.lsa.umich.edu", "link": "https://dept.math.lsa.umich.edu/~speyer/DimerConference2020/", "content": "Cluster Algebra , Dimers and Beyond (slides) (video).In certain cases, these algebras correspond to cluster -tilting objects in the cluster category associated to the Grassmannian introduced by Jensen, King and Su."}
+{"idx": 9, "title": "Cluster algebras and their bases-Bohrium", "date": "", "ddg_snippet": "[4] Cluster Algebras : Network Science and Machine Learning .Classification of cluster variables in cluster algebras (in particular, Grassmannian cluster algebras ) is an important problem, which has direct application to computations of scattering amplitudes in physics.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/cluster-algebras-and-their-bases/867771891274744541-108554", "content": "[4] Cluster Algebras : Network Science and Machine Learning .Classification of cluster variables in cluster algebras (in particular, Grassmannian cluster algebras ) is an important problem, which has direct application to computations of scattering amplitudes in physics."}
diff --git a/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_paper.jsonl b/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_paper.jsonl
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index 0000000000000000000000000000000000000000..273953f058ddeeb9dd76116f9c8c4a2a7ebfcf46
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+++ b/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ..."}
+{"idx": 1, "title": "Leveraging Per-Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ..."}
+{"idx": 2, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per-instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (R ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "Abstract We present a principled, per-instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (R ..."}
+{"idx": 3, "title": "A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks", "date": "", "ddg_snippet": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.06186", "content": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications."}
+{"idx": 4, "title": "A survey of security and privacy issues of machine unlearning", "date": "", "ddg_snippet": "Specifically, we begin by investigating unlearning -based security attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning .", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/aaai.12209", "content": "Specifically, we begin by investigating unlearning -based security attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning ."}
+{"idx": 5, "title": "PDF Forget to Flourish: Leveraging Machine-Unlearning on Pretrained ...", "date": "", "ddg_snippet": "This scenario creates a privacy threat, as pre-trained models can be intentionally crafted to compromise the privacy of fine-tuning datasets. In this study, we introduce a novel poisoning technique that uses model- unlearning as an attack tool.", "subpage_snippet": "", "source": "shadow.merl.com", "link": "https://shadow.merl.com/publications/docs/TR2025-017.pdf", "content": "This scenario creates a privacy threat, as pre-trained models can be intentionally crafted to compromise the privacy of fine-tuning datasets. In this study, we introduce a novel poisoning technique that uses model- unlearning as an attack tool."}
+{"idx": 6, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ..."}
+{"idx": 7, "title": "Learning to Unlearn: Instance-Wise Unlearning for Pre-trained ...", "date": "", "ddg_snippet": "To this end, we consider instance -wise unlearning , of which the goal is to delete information on a set of instances from a pre-trained model, by either misclassifying each instance away from its original prediction or relabeling the instance to a different label.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/28996", "content": "To this end, we consider instance -wise unlearning , of which the goal is to delete information on a set of instances from a pre-trained model, by either misclassifying each instance away from its original prediction or relabeling the instance to a different label."}
+{"idx": 8, "title": "Pseudo-Labeling for Enhanced User Privacy in Approximate Machine Unlearning", "date": "", "ddg_snippet": "This paper presents an effective pseudo-labeling method for machine unlearning , focusing on protecting user privacy . Recent research predominantly focused on modifying the weights of pre-trained models to approximate exact unlearning . We study the impact of pseudo-labeling methods and propose two novel algorithms for approximate unlearning . Our approach aims to imitate the inference errors and ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10890795", "content": "This paper presents an effective pseudo-labeling method for machine unlearning , focusing on protecting user privacy . Recent research predominantly focused on modifying the weights of pre-trained models to approximate exact unlearning . We study the impact of pseudo-labeling methods and propose two novel algorithms for approximate unlearning . Our approach aims to imitate the inference errors and ..."}
+{"idx": 9, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Leveraging-Per-Instance-Privacy-for-Machine-Sepahvand-Thudi/dca9861c26bd83a7b1c30fb4255810afdd1e4aa3", "content": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}
diff --git a/data/sampled_jsons/0E5rZOGA13_Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning.jsonl b/data/sampled_jsons/0E5rZOGA13_Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning.jsonl
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index 0000000000000000000000000000000000000000..0bf4b3dc7462d55336df3529a12db6acec1d51b7
--- /dev/null
+++ b/data/sampled_jsons/0E5rZOGA13_Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Recommender Learning from Implicit Feedback via ...", "date": "", "ddg_snippet": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13&referrer=[the+profile+of+Licheng+Pan](/profile?id=~Licheng_Pan1)", "content": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback ."}
+{"idx": 1, "title": "Unbiased Recommender Learning from Implicit Feedback via ...", "date": "", "ddg_snippet": "Learning from Weak Supervision .Unbiased Implicit Recommendation and Propensity Es-timation via Combinational Joint Learning . In RecSys. ACM, 551–556. 13 . Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning . 10−1.", "subpage_snippet": "", "source": "zhouchenlin.github.io", "link": "https://zhouchenlin.github.io/Publications/2025-ICML-Unbiased.pdf", "content": "Learning from Weak Supervision .Unbiased Implicit Recommendation and Propensity Es-timation via Combinational Joint Learning . In RecSys. ACM, 551–556. 13 . Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning . 10−1."}
+{"idx": 2, "title": "ICML Poster Unbiased Recommender Learning from Implicit ...", "date": "", "ddg_snippet": "To address this issue, we introduce PURL, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "To address this issue, we introduce PURL, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples."}
+{"idx": 3, "title": "(PDF) Unbiased Implicit Feedback via Bi-level Optimization", "date": "", "ddg_snippet": "PDF | Implicit feedback is widely leveraged in recommender systems since it is easy to collect and provides weak supervision signals.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361022915_Unbiased_Implicit_Feedback_via_Bi-level_Optimization", "content": "PDF | Implicit feedback is widely leveraged in recommender systems since it is easy to collect and provides weak supervision signals."}
+{"idx": 4, "title": "GitHub - jihoo-kim/RecSys-Papers-from-SIGIR-2021: Papers related to...", "date": "", "ddg_snippet": "Self- supervised & Contrasive Learning .[L10] Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement. Mingyue Cheng, Fajie Yuan, Qi Liu, Shenyang Ge, Zhi Li, Runlong Yu, Defu Lian, Senchao Yuan and Enhong Chen.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jihoo-kim/RecSys-Papers-from-SIGIR-2021", "content": "Self- supervised & Contrasive Learning .[L10] Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement. Mingyue Cheng, Fajie Yuan, Qi Liu, Shenyang Ge, Zhi Li, Runlong Yu, Defu Lian, Senchao Yuan and Enhong Chen."}
+{"idx": 5, "title": "(PDF) Unbiased Pairwise Learning from Implicit Feedback for...", "date": "", "ddg_snippet": "Nonetheless, the existing unbiased pairwise learning method suffers from high variance. To get satisfactory performance, non-negative estimator is utilized for practical variance control but introduces additional bias.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/unbiased-pairwise-learning-from-implicit-feedback-for-15stlhei", "content": "Nonetheless, the existing unbiased pairwise learning method suffers from high variance. To get satisfactory performance, non-negative estimator is utilized for practical variance control but introduces additional bias."}
+{"idx": 6, "title": "[1909.03601] Unbiased Recommender Learning from ...", "date": "", "ddg_snippet": "Title: Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Authors:Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, Kazuhide Nakata.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.03601", "content": "Title: Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Authors:Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, Kazuhide Nakata."}
+{"idx": 7, "title": "Sci-Hub | Unbiased Recommender Learning from ...", "date": "", "ddg_snippet": "↓ скачать. Saito, Y., Yaginuma, S., Nishino, Y., Sakata, H., & Nakata, K. (2020). Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Proceedings of the 13th International Conference on Web Search and Data Mining. doi:10.1145/3336191.3371783.", "subpage_snippet": "", "source": "www.sci-hub.ru", "link": "https://www.sci-hub.ru/10.1145/3336191.3371783", "content": "↓ скачать. Saito, Y., Yaginuma, S., Nishino, Y., Sakata, H., & Nakata, K. (2020). Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Proceedings of the 13th International Conference on Web Search and Data Mining. doi:10.1145/3336191.3371783."}
+{"idx": 8, "title": "Unbiased Recommender Learning from Missing-Not-At-Random...", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Biased Graded Implicit Feedback . Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation.", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/2020/wsdm2020/", "content": "Unbiased Recommender Learning from Biased Graded Implicit Feedback . Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation."}
+{"idx": 9, "title": "Breaking Feedback Loops in Recommender Systems with... | CoLab", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Saito Y., Yaginuma S., Nishino Y., Sakata H., Nakata K.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1145/3728372", "content": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Saito Y., Yaginuma S., Nishino Y., Sakata H., Nakata K."}
diff --git a/data/sampled_jsons/0WQJ6DFSKp_Equation_19_DHC_scaling.jsonl b/data/sampled_jsons/0WQJ6DFSKp_Equation_19_DHC_scaling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..91b3313d788de9170aedb7813decdd01d0e2ee32
--- /dev/null
+++ b/data/sampled_jsons/0WQJ6DFSKp_Equation_19_DHC_scaling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Solving the viscous Burger’s equation using three methods", "date": "", "ddg_snippet": "So we consider the analytical solution of Burger’s equation using three different methods, which are: the Cole-Hopf transformation, variational iteration method and Adomian decomposition method.", "subpage_snippet": "", "source": "journals.misuratau.edu.ly", "link": "https://journals.misuratau.edu.ly/edu/upload/file/E2.pdf", "content": "So we consider the analytical solution of Burger’s equation using three different methods, which are: the Cole-Hopf transformation, variational iteration method and Adomian decomposition method."}
+{"idx": 1, "title": "Amplitudes of solar-like oscillations: a new scaling relation", "date": "", "ddg_snippet": "(17) and ( 19 ) gives our new scaling relation for intensity amplitudes: Aλ∝Lτosc0.5λM1.5Teff2.25+r", "subpage_snippet": "", "source": "www.aanda.org", "link": "https://www.aanda.org/articles/aa/full_html/2011/05/aa16789-11/aa16789-11.html", "content": "(17) and ( 19 ) gives our new scaling relation for intensity amplitudes: Aλ∝Lτosc0.5λM1.5Teff2.25+r"}
+{"idx": 2, "title": "(PDF) The Simplest Walking Model: Stability, Complexity, and Scaling", "date": "", "ddg_snippet": "Equation 2 describes the swing leg as a simple pendulum whose support (at the hip) moves through an arc.The period of motion τ is approximately independent of γ, for small γ. Speeds thus scale with the stance angle θ * . The angle by which the hip mass is deflected at heelstrike is 2θ * .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/92301322/The_Simplest_Walking_Model_Stability_Complexity_and_Scaling", "content": "Equation 2 describes the swing leg as a simple pendulum whose support (at the hip) moves through an arc.The period of motion τ is approximately independent of γ, for small γ. Speeds thus scale with the stance angle θ * . The angle by which the hip mass is deflected at heelstrike is 2θ * ."}
+{"idx": 3, "title": "Analytic Solution of the Boltzmann Equation in", "date": "", "ddg_snippet": "mann equation using the method of moments [6]. Due. to local momentum isotropy, the distribution function fk. can be fully described by scalar moments only [31] Equation ( 19 ) is the main result of this Letter. While. the nonlinear coupling between dierent moments was.", "subpage_snippet": "", "source": "link.aps.org", "link": "https://link.aps.org/accepted/10.1103/PhysRevLett.116.022301", "content": "mann equation using the method of moments [6]. Due. to local momentum isotropy, the distribution function fk. can be fully described by scalar moments only [31] Equation ( 19 ) is the main result of this Letter. While. the nonlinear coupling between dierent moments was."}
+{"idx": 4, "title": "Reviews: Neural Ordinary Differential Equations", "date": "", "ddg_snippet": "Equation following line 170: missing right parenthesis in integrand of last integral.This leads the authors to propose ODE-Nets, which are networks solving a differential equation parametrized by a neural network. Numerical experiments span classification and generation.", "subpage_snippet": "", "source": "media.nips.cc", "link": "https://media.nips.cc/nipsbooks/nipspapers/paper_files/nips31/reviews/3310.html", "content": "Equation following line 170: missing right parenthesis in integrand of last integral.This leads the authors to propose ODE-Nets, which are networks solving a differential equation parametrized by a neural network. Numerical experiments span classification and generation."}
+{"idx": 5, "title": "Introduction to Differential Equations with Dynamical... - DOKUMEN.PUB", "date": "", "ddg_snippet": "Equation (4) is an example of a differential equation , and we develop methods to solve such equations in this text. We will discuss population growth models in more depth in Section 1.8 and Chapters 5 and 6. In a typical application, physical laws often lead to a differential...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/introduction-to-differential-equations-with-dynamical-systems-9781400841325.html", "content": "Equation (4) is an example of a differential equation , and we develop methods to solve such equations in this text. We will discuss population growth models in more depth in Section 1.8 and Chapters 5 and 6. In a typical application, physical laws often lead to a differential..."}
+{"idx": 6, "title": "14.3.3 Using the Arrhenius Equation Flashcards by Irina Soloshenko", "date": "", "ddg_snippet": "19.2.4 Using Standard Reduction Potentials. 19.2.5 The Nernst Equation . 19 .2.6 Electrochemicamical Determinants of Equilibria.", "subpage_snippet": "", "source": "www.brainscape.com", "link": "https://www.brainscape.com/flashcards/1433-using-the-arrhenius-equation-7410232/packs/11455278", "content": "19.2.4 Using Standard Reduction Potentials. 19.2.5 The Nernst Equation . 19 .2.6 Electrochemicamical Determinants of Equilibria."}
+{"idx": 7, "title": "[Solved]: Classify(if possible) the differential equation as... - UpStudy", "date": "", "ddg_snippet": "*First-Order Nonlinear** - No general solution method 19 . * *First-Order Linear** - Solution using integrating factor 20. *", "subpage_snippet": "", "source": "upstudy.ai", "link": "https://upstudy.ai/questions/221656465-classify-if-possible-the-differential-equation-as-separable-linear-bernoulli-or", "content": "*First-Order Nonlinear** - No general solution method 19 . * *First-Order Linear** - Solution using integrating factor 20. *"}
+{"idx": 8, "title": "In the paper mentioned can you help me to find the... | ResearchGate", "date": "", "ddg_snippet": "In the paper \"Magnetometer-Only Attitude Determination Using. Novel Two-Step Kalman Filter Approach\" in equation 19 do I need to complete quaternion multiplication first and then do simple mathematical finite differencing or it is in other way?", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/post/In_the_paper_mentioned_can_you_help_me_to_find_the_finite_difference_of_equation_no_19", "content": "In the paper \"Magnetometer-Only Attitude Determination Using. Novel Two-Step Kalman Filter Approach\" in equation 19 do I need to complete quaternion multiplication first and then do simple mathematical finite differencing or it is in other way?"}
+{"idx": 9, "title": "Transport phenomena Solved problems | PDF | Physics | Science", "date": "", "ddg_snippet": "1. The document derives a general differential equation for fluid flow problems in rectangular Cartesian coordinates using a shell momentum balance.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/transport-phenomena-solved-problems/203516288", "content": "1. The document derives a general differential equation for fluid flow problems in rectangular Cartesian coordinates using a shell momentum balance."}
diff --git "a/data/sampled_jsons/0hrkN07DuO_Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge_Figu.jsonl" "b/data/sampled_jsons/0hrkN07DuO_Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge_Figu.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..9e3543a5447e2b35e8d7ffec85c33b1f5de08664
--- /dev/null
+++ "b/data/sampled_jsons/0hrkN07DuO_Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schr\303\266dinger_bridge_Figu.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear Convergence of Sinkhorn's Algorithm for Generalized Static ...", "date": "", "ddg_snippet": "In this paper, we present the generalized static Schrödinger bridge problem, establish its Kantorovich dual, and show that the associated generalized Sinkhorn algorithm con-verges linearly in a dimension independent manner under mild assumptions on the general divergence functional f , weight matrix W and margin (r,c).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "In this paper, we present the generalized static Schrödinger bridge problem, establish its Kantorovich dual, and show that the associated generalized Sinkhorn algorithm con-verges linearly in a dimension independent manner under mild assumptions on the general divergence functional f , weight matrix W and margin (r,c)."}
+{"idx": 1, "title": "PDF On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates.", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/Sinkhorn_rate.pdf", "content": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates."}
+{"idx": 2, "title": "On the Convergence Rate of Sinkhorn's Algorithm - arXiv.org", "date": "", "ddg_snippet": "For the convergence results, the second key innovation uses the finer details of Sinkhorn's algorithm and its mono-tonicity properties: we relate the improvement to a marginal entropy and deduce a difference H(π∗|π2t) − equation enabling us to analyze H(π∗|π2t+2)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.06000", "content": "For the convergence results, the second key innovation uses the finer details of Sinkhorn's algorithm and its mono-tonicity properties: we relate the improvement to a marginal entropy and deduce a difference H(π∗|π2t) − equation enabling us to analyze H(π∗|π2t+2)"}
+{"idx": 3, "title": "On the linear convergence of the multi-marginal Sinkhorn algorithm", "date": "", "ddg_snippet": "Abstract The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel ...", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03176512/document", "content": "Abstract The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel ..."}
+{"idx": 4, "title": "PDF Generative modeling via Schrödinger bridge (basics on ... - vdb", "date": "", "ddg_snippet": "Outline of the course A dynamic and static Schrödinger bridges . Convergence of the Sinkhorn algorithm . Figure 2 : A Schrödinger Bridge between two data distributions. Image extracted from De Bortoli et al. (2021).", "subpage_snippet": "", "source": "vdeborto.github.io", "link": "https://vdeborto.github.io/project/generative_modeling/session_5.pdf", "content": "Outline of the course A dynamic and static Schrödinger bridges . Convergence of the Sinkhorn algorithm . Figure 2 : A Schrödinger Bridge between two data distributions. Image extracted from De Bortoli et al. (2021)."}
+{"idx": 5, "title": "Linear convergence of Sinkhorn's algorithm for generalized static ...", "date": "", "ddg_snippet": "Poster Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46671", "content": "Poster Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506"}
+{"idx": 6, "title": "On the Linear Convergence of the Multimarginal Sinkhorn Algorithm", "date": "", "ddg_snippet": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/21M1410634", "content": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ..."}
+{"idx": 7, "title": "On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "An Optimal Transport Approach for the Schrödinger Bridge Problem and Convergence of Sinkhorn Algorithm Article Full-text available Nov 2020 J SCI COMPUT", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366213052_On_the_Convergence_Rate_of_Sinkhorn's_Algorithm", "content": "An Optimal Transport Approach for the Schrödinger Bridge Problem and Convergence of Sinkhorn Algorithm Article Full-text available Nov 2020 J SCI COMPUT"}
+{"idx": 8, "title": "On the Convergence Rate of Sinkhorn's AlgorithmThe authors thank ...", "date": "", "ddg_snippet": "On the Convergence Rate of Sinkhorn's Algorithm††thanks: The authors thank Stephan Eckstein and Flavien Léger for helpful discussions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.06000v2", "content": "On the Convergence Rate of Sinkhorn's Algorithm††thanks: The authors thank Stephan Eckstein and Flavien Léger for helpful discussions."}
+{"idx": 9, "title": "Hanbaek Lyu", "date": "", "ddg_snippet": "Contingency tables, Random Matrices, Optimal Transport Hanbaek Lyu and Sumit Muhkerjee, \"Large random matrices with given margins.\" Submitted. Prepint (2024) (Last updated ver: Aug 6, 2025 CT_limit) Rahul Choudhary and Hanbaek Lyu, \"Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge \", To appear in ICML 2025 Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer ...", "subpage_snippet": "", "source": "hanbaeklyu.com", "link": "https://hanbaeklyu.com/publications/", "content": "Contingency tables, Random Matrices, Optimal Transport Hanbaek Lyu and Sumit Muhkerjee, \"Large random matrices with given margins.\" Submitted. Prepint (2024) (Last updated ver: Aug 6, 2025 CT_limit) Rahul Choudhary and Hanbaek Lyu, \"Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge \", To appear in ICML 2025 Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer ..."}
diff --git a/data/sampled_jsons/0yzOEMbShU_Beyond_Self-Repellent_Kernels_History-Driven_Target_MCMC_year_2024.jsonl b/data/sampled_jsons/0yzOEMbShU_Beyond_Self-Repellent_Kernels_History-Driven_Target_MCMC_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd63e687c85d0c97c6658b1b9b55c28287c1f4a5
--- /dev/null
+++ b/data/sampled_jsons/0yzOEMbShU_Beyond_Self-Repellent_Kernels_History-Driven_Target_MCMC_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Self - Repellent Kernels : History - Driven Target Towards...", "date": "", "ddg_snippet": "Primary Area: Probabilistic Methods-> Monte Carlo and Sampling Methods. Keywords: Nonlinear MCMC , History - Driven Target , Computational Efficiency, Near-Zero Variance. Submission Number: 4668.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0yzOEMbShU", "content": "Primary Area: Probabilistic Methods-> Monte Carlo and Sampling Methods. Keywords: Nonlinear MCMC , History - Driven Target , Computational Efficiency, Near-Zero Variance. Submission Number: 4668."}
+{"idx": 1, "title": "Remove Old Kernels on CentOS 7 Easily", "date": "", "ddg_snippet": "Kernel kernel -3.10.0-693.5.2.el7.x86_64 is currently loaded and used. based on the about output this is the latest version. Remove old kernels manually.", "subpage_snippet": "", "source": "linuxconfig.org", "link": "https://linuxconfig.org/how-to-remove-old-unused-kernels-on-centos-linux", "content": "Kernel kernel -3.10.0-693.5.2.el7.x86_64 is currently loaded and used. based on the about output this is the latest version. Remove old kernels manually."}
+{"idx": 2, "title": "Tesla to launch self - driving cars in Austin amid safety concerns", "date": "", "ddg_snippet": "Adding a third self - driving taxi service in the city has sparked mix reviews from people in the city. Luis Correa is an Austinite and a Tesla owner. His Tesla has a self - driving function that he uses often. He said this service will benecit many people in Austin the way it does for him.", "subpage_snippet": "", "source": "cbsaustin.com", "link": "https://cbsaustin.com/news/local/tesla-to-launch-self-driving-cars-in-austin-amid-safety-concerns", "content": "Adding a third self - driving taxi service in the city has sparked mix reviews from people in the city. Luis Correa is an Austinite and a Tesla owner. His Tesla has a self - driving function that he uses often. He said this service will benecit many people in Austin the way it does for him."}
+{"idx": 3, "title": "10 MCP Servers for Frontend Developers - The New Stack", "date": "", "ddg_snippet": "Spec- Driven Development: The Key to Scalable AI Agents.Code generation and modification , which facilitate AI- driven code generation, autocompletion, refactoring, and other code manipulation tasks, are executed by the IDE.", "subpage_snippet": "", "source": "thenewstack.io", "link": "https://thenewstack.io/10-mcp-servers-for-frontend-developers/", "content": "Spec- Driven Development: The Key to Scalable AI Agents.Code generation and modification , which facilitate AI- driven code generation, autocompletion, refactoring, and other code manipulation tasks, are executed by the IDE."}
+{"idx": 4, "title": "Debt Rattle September 18 2025 - The Automatic Earth", "date": "", "ddg_snippet": "I have made the decision not to cover any of the controversial aspects, direct or ancillary, beyond what I have already shared. Do I have opinions, yes.Patel was clear: “Nobody is targeted for their faith.” Jordan followed up: “Is the FBI still targeting Americans who are pro-life?”", "subpage_snippet": "", "source": "www.theautomaticearth.com", "link": "https://www.theautomaticearth.com/2025/09/debt-rattle-september-18-2025/", "content": "I have made the decision not to cover any of the controversial aspects, direct or ancillary, beyond what I have already shared. Do I have opinions, yes.Patel was clear: “Nobody is targeted for their faith.” Jordan followed up: “Is the FBI still targeting Americans who are pro-life?”"}
+{"idx": 5, "title": "Can You Copy and Automate Trading Strategies? A Guide to Creating...", "date": "", "ddg_snippet": "Moving beyond basic strategies involves sophisticated risk management, thorough backtesting, and tailoring the setup to specific needs.", "subpage_snippet": "", "source": "trading-strategies.academy", "link": "https://trading-strategies.academy/archives/1633", "content": "Moving beyond basic strategies involves sophisticated risk management, thorough backtesting, and tailoring the setup to specific needs."}
+{"idx": 6, "title": "Включаем Wi-Fi в новых версиях гу — Haval Jolion... | DRIVE 2", "date": "", "ddg_snippet": "Выбираем: Launch Debug App…. Узнайте подробности, смотрите фото и читайте на DRIVE 2.", "subpage_snippet": "", "source": "www.drive2.ru", "link": "https://www.drive2.ru/l/704432911734029556/", "content": "Выбираем: Launch Debug App…. Узнайте подробности, смотрите фото и читайте на DRIVE 2."}
+{"idx": 7, "title": "1,000,000 ЖИТЕЛЕЙ ОХОТИТЬСЯ НА Grox Озвучка На... - YouTube", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=oPaYnFB0dF8", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
+{"idx": 8, "title": "Как подключить Magic Mouse к Windows-компьютеру - Яблык...", "date": "", "ddg_snippet": "Как использовать мышь Apple Mouse на компьютере с установленной операционной системой Windows?", "subpage_snippet": "", "source": "yablyk.com", "link": "https://yablyk.com/777455-kak-podklyuchit-magic-mouse-k-windows-pk-instrukciya/", "content": "Как использовать мышь Apple Mouse на компьютере с установленной операционной системой Windows?"}
+{"idx": 9, "title": "Embedded Linux System Components: Toolchain to RootFS", "date": "", "ddg_snippet": "The Linux kernel is considered a monolithic kernel . This means that its core functions—the process scheduler, memory manager, virtual filesystem (VFS), networking stack, and device drivers —are all tightly integrated into a single large executable (Image or zImage).", "subpage_snippet": "", "source": "circuitlabs.net", "link": "https://circuitlabs.net/embedded-linux-system-components-toolchain-to-rootfs/", "content": "The Linux kernel is considered a monolithic kernel . This means that its core functions—the process scheduler, memory manager, virtual filesystem (VFS), networking stack, and device drivers —are all tightly integrated into a single large executable (Image or zImage)."}
diff --git a/data/sampled_jsons/10l1pGeOcK_SAFE-_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_LLaMa-2_7B_50%_sparsity_perplexit.jsonl b/data/sampled_jsons/10l1pGeOcK_SAFE-_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_LLaMa-2_7B_50%_sparsity_perplexit.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..167f0a506f911f3196361e3cae2e5981509361d3
--- /dev/null
+++ b/data/sampled_jsons/10l1pGeOcK_SAFE-_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_LLaMa-2_7B_50%_sparsity_perplexit.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Google - Wikipedia", "date": "", "ddg_snippet": "Google LLC (/ ˈɡuːɡəl / ⓘ, GOO-gəl) is an American multinational corporation and technology company focusing on online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (AI). [9] It has been referred to as \"the most powerful company in the world\" by the BBC [10] and is one of the ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Google", "content": "Google LLC (/ ˈɡuːɡəl / ⓘ, GOO-gəl) is an American multinational corporation and technology company focusing on online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (AI). [9] It has been referred to as \"the most powerful company in the world\" by the BBC [10] and is one of the ..."}
+{"idx": 1, "title": "About Google : Our products, technology and company information", "date": "", "ddg_snippet": "Learn more about Google . Explore our innovative AI products and services, and discover how we're using technology to help improve lives around the world.", "subpage_snippet": "", "source": "about.google", "link": "https://about.google/", "content": "Learn more about Google . Explore our innovative AI products and services, and discover how we're using technology to help improve lives around the world."}
+{"idx": 2, "title": "Google 's products and services - About Google", "date": "", "ddg_snippet": "Explore Google 's helpful products and services, including Android, Gemini, Pixel and Search.", "subpage_snippet": "", "source": "about.google", "link": "https://about.google/products/", "content": "Explore Google 's helpful products and services, including Android, Gemini, Pixel and Search."}
+{"idx": 3, "title": "Safe : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Safe yields sparse and flat solutions. Figure 2 : Validation accuracy (mean. ±plus-or-minus\\pm±. std) of VGG-19 and ResNet-20/32 models on CIFAR-10/100 pruned across different sparsity levels and methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v1", "content": "Safe yields sparse and flat solutions. Figure 2 : Validation accuracy (mean. ±plus-or-minus\\pm±. std) of VGG-19 and ResNet-20/32 models on CIFAR-10/100 pruned across different sparsity levels and methods."}
+{"idx": 4, "title": "(PDF) SAFE : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "SAFE yields sparse and flat solutions. (a-b) Weight distributions of densely-trained model and model trained with SAFE , and (c-d) loss landscape and maximum Hessian eigenvalue of minima found by ADMM and SAFE .Comparison with IMP+SAM on LLaMA 2 - 7 b for 50 % sparsity .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392531034_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning", "content": "SAFE yields sparse and flat solutions. (a-b) Weight distributions of densely-trained model and model trained with SAFE , and (c-d) loss landscape and maximum Hessian eigenvalue of minima found by ADMM and SAFE .Comparison with IMP+SAM on LLaMA 2 - 7 b for 50 % sparsity ."}
+{"idx": 5, "title": "Как дообучить LLaMA бесплатно и без программирования... / Хабр", "date": "", "ddg_snippet": "Что будем обучать и что потребуется. В данной статье я покажу как я дообучал LLaMA 7 B и LLaMA 2 7 B . Если готовы заплатить за аренду видеокарт, то можете обучить и модели покрупнее.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/755114/", "content": "Что будем обучать и что потребуется. В данной статье я покажу как я дообучал LLaMA 7 B и LLaMA 2 7 B . Если готовы заплатить за аренду видеокарт, то можете обучить и модели покрупнее."}
+{"idx": 6, "title": "Perplexity", "date": "", "ddg_snippet": "Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.", "subpage_snippet": "", "source": "www.perplexity.ai", "link": "https://www.perplexity.ai/", "content": "Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question."}
+{"idx": 7, "title": "AlphaPruning: Using Heavy-Tailed Self", "date": "", "ddg_snippet": "Table 10: Perplexity (↓) of pruning LLaMA - 7 B into various global sparsities using SparseGPT. We compare our method with three other baseline sparsity allocation methods.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/10fc83943b4540a9524af6fc67a23fef-Paper-Conference.pdf", "content": "Table 10: Perplexity (↓) of pruning LLaMA - 7 B into various global sparsities using SparseGPT. We compare our method with three other baseline sparsity allocation methods."}
+{"idx": 8, "title": "APT: Adaptive Pruning and Tuning Pretrained Language Models for...", "date": "", "ddg_snippet": "(b) Training initial sparsity trade-off with 30% target sparsity model’s relative performances to the LoRA-tuned LLaMA 2 - 7 B and 13B models. Figure 5. Detailed analysis in APT with different initial, target sparsities , and adaptive tuning schedules.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/8d52635997029af5c1cf48201dca245bac5e4deb.pdf", "content": "(b) Training initial sparsity trade-off with 30% target sparsity model’s relative performances to the LoRA-tuned LLaMA 2 - 7 B and 13B models. Figure 5. Detailed analysis in APT with different initial, target sparsities , and adaptive tuning schedules."}
+{"idx": 9, "title": "Installing llama -cpp-python with GPU Support", "date": "", "ddg_snippet": "Published on 10 Sep 2023.If llama -cpp-python cannot find the CUDA toolkit, it will default to a CPU-only installation. I got the installation to work with the commands below. If you have tried to install the package before, you will most likely need the --no-cache-dir option to get it to work.", "subpage_snippet": "", "source": "michaelriedl.com", "link": "https://michaelriedl.com/2023/09/10/llama2-install-gpu.html", "content": "Published on 10 Sep 2023.If llama -cpp-python cannot find the CUDA toolkit, it will default to a CPU-only installation. I got the installation to work with the commands below. If you have tried to install the package before, you will most likely need the --no-cache-dir option to get it to work."}
diff --git a/data/sampled_jsons/10l1pGeOcK_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_ablation_study_section_F.1_year_2023.jsonl b/data/sampled_jsons/10l1pGeOcK_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_ablation_study_section_F.1_year_2023.jsonl
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index 0000000000000000000000000000000000000000..809a680d81a8afe96c15273d63dcbe52f4f46500
--- /dev/null
+++ b/data/sampled_jsons/10l1pGeOcK_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_ablation_study_section_F.1_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity ..."}
+{"idx": 1, "title": "PDF Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "In this section , we demonstrate that SAFE converges to sparse and flat solutions, leading to performance improve -ments over baselines in both image classification and lan-guage modeling tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=10l1pGeOcK&name=pdf", "content": "In this section , we demonstrate that SAFE converges to sparse and flat solutions, leading to performance improve -ments over baselines in both image classification and lan-guage modeling tasks."}
+{"idx": 2, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning | Cool Papers ...", "date": "", "ddg_snippet": "#1 SAFE: Finding Sparse and Flat Minima to Improve Pruning [PDF 7] [Copy] [Kimi 4] [REL] Authors: Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization, we aim ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/10l1pGeOcK@OpenReview", "content": "#1 SAFE: Finding Sparse and Flat Minima to Improve Pruning [PDF 7] [Copy] [Kimi 4] [REL] Authors: Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization, we aim ..."}
+{"idx": 3, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "This repository contains the official PyTorch implementation for the paper SAFE: Finding Sparse and Flat Minima to Improve Pruning . Our work introduces SAFE, an algorithm designed to find sparse and flat minima , leading to improved model pruning performance.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LOG-postech/safe-torch", "content": "This repository contains the official PyTorch implementation for the paper SAFE: Finding Sparse and Flat Minima to Improve Pruning . Our work introduces SAFE, an algorithm designed to find sparse and flat minima , leading to improved model pruning performance."}
+{"idx": 4, "title": "PDF Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "We first formulate this as a sharpness-aware sparsity-constrained optimization problem: min max f(x + ε), ∥x∥0≤d∥ε∥2≤ρ where goal is to find a sparse solution x⋆ with atmost d non-zero elements that minimizes the objective function in the whole ε-neighborhood, i.e., seek flat minima .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46658.pdf", "content": "We first formulate this as a sharpness-aware sparsity-constrained optimization problem: min max f(x + ε), ∥x∥0≤d∥ε∥2≤ρ where goal is to find a sparse solution x⋆ with atmost d non-zero elements that minimizes the objective function in the whole ε-neighborhood, i.e., seek flat minima ."}
+{"idx": 5, "title": "Presenting SAFE: A Method for Improving Pruning at ICML 2025", "date": "", "ddg_snippet": "Will be presenting our spotlight poster \"SAFE: Finding Sparse and Flat Minima to Improve Pruning \" tomorrow at East Exhibition Hall, #E-3510, from 11:00 AM to 1:30 PM! (#ICML2025 @ #Vancouver ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/kwanheelee_e-icml2025-vancouver-activity-7351126114557153282-pk7J", "content": "Will be presenting our spotlight poster \"SAFE: Finding Sparse and Flat Minima to Improve Pruning \" tomorrow at East Exhibition Hall, #E-3510, from 11:00 AM to 1:30 PM! (#ICML2025 @ #Vancouver ..."}
+{"idx": 6, "title": "[이남훈 교수] SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee. \"SAFE: Finding Sparse and Flat Minima to Improve Pruning \", International Conference on Machine Learning (ICML), 2025. [성과와 관련된 이미지] 이전글[이남훈 교수] SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation", "subpage_snippet": "", "source": "cse.postech.ac.kr", "link": "https://cse.postech.ac.kr/csepostech/research/latest-research.do?mode=view&articleNo=24246&title=[이남훈+교수]+SAFE:+Finding+Sparse+and+Flat+Minima+to+Improve+Pruning", "content": "Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee. \"SAFE: Finding Sparse and Flat Minima to Improve Pruning \", International Conference on Machine Learning (ICML), 2025. [성과와 관련된 이미지] 이전글[이남훈 교수] SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation"}
+{"idx": 7, "title": "Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32.", "date": "", "ddg_snippet": "Download scientific diagram | Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32. from publication: SAFE: Finding Sparse and Flat Minima to Improve Pruning | Sparsifying neural ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Best-performing-penalty-parameter-of-SAFE-for-VGG-19-and-ResNet-20-32_tbl3_392531034", "content": "Download scientific diagram | Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32. from publication: SAFE: Finding Sparse and Flat Minima to Improve Pruning | Sparsifying neural ..."}
+{"idx": 8, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v2", "content": "Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective."}
+{"idx": 9, "title": "publications | Dongyeop Lee - GitHub Pages", "date": "", "ddg_snippet": "2025 SAFE: Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust ...", "subpage_snippet": "", "source": "edong6768.github.io", "link": "https://edong6768.github.io/publications/", "content": "2025 SAFE: Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust ..."}
diff --git a/data/sampled_jsons/13HPTmZKbM_Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting.jsonl b/data/sampled_jsons/13HPTmZKbM_Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d46e18caf4a39a94066bce865d8d1bd42095af63
--- /dev/null
+++ b/data/sampled_jsons/13HPTmZKbM_Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as \"catastrophic forgetting \". This is especially an issue when one does not have access to the data and recipe used to develop the pre-trained model. Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02797", "content": "Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as \"catastrophic forgetting \". This is especially an issue when one does not have access to the data and recipe used to develop the pre-trained model. Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a ..."}
+{"idx": 1, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "On this particular front, the major challenge in standard, unregu-lated fine-tuning is the catastrophic forgetting phenomenon. In broad terms, it describes the performance decline of the pre-trained model on previously observed data/tasks after fine-tuning on a new one.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=13HPTmZKbM", "content": "On this particular front, the major challenge in standard, unregu-lated fine-tuning is the catastrophic forgetting phenomenon. In broad terms, it describes the performance decline of the pre-trained model on previously observed data/tasks after fine-tuning on a new one."}
+{"idx": 2, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forget...", "date": "", "ddg_snippet": "This research paper talks about a problem in machine learning called \"catastrophic forgetting ,\" which happens when a computer model forgets what it learned before while trying to learn something n...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46655/paper", "content": "This research paper talks about a problem in machine learning called \"catastrophic forgetting ,\" which happens when a computer model forgets what it learned before while trying to learn something n..."}
+{"idx": 3, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "title={Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting }, author={Sanyal, Sunny and Prairie, Hayden and Das, Rudrajit and Kavis, Ali and Sanghavi, Sujay},", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sanyalsunny111/FLOW_finetuning", "content": "title={Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting }, author={Sanyal, Sunny and Prairie, Hayden and Das, Rudrajit and Kavis, Ali and Sanghavi, Sujay},"}
+{"idx": 4, "title": "(PDF) Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388754394_Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting", "content": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses."}
+{"idx": 5, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting | Cool ...", "date": "", "ddg_snippet": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2502.02797", "content": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model."}
+{"idx": 6, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Spotlight Poster Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Sunny Sanyal · Hayden Prairie · Rudrajit Das · Ali Kavis · Sujay Sanghavi East Exhibition Hall A-B #E-1302", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46655", "content": "Spotlight Poster Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Sunny Sanyal · Hayden Prairie · Rudrajit Das · Ali Kavis · Sujay Sanghavi East Exhibition Hall A-B #E-1302"}
+{"idx": 7, "title": "Publications - Ali Kavis", "date": "", "ddg_snippet": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting S. Sanyal *, H. Prairie *, R. Das *, A. Kavis *, S. Sanghavi Published in arXiv, (2025) [Download here]", "subpage_snippet": "", "source": "alikavis.github.io", "link": "https://alikavis.github.io/publications/", "content": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting S. Sanyal *, H. Prairie *, R. Das *, A. Kavis *, S. Sanghavi Published in arXiv, (2025) [Download here]"}
+{"idx": 8, "title": "arXiv:2502.02797v1 [cs.LG] 5 Feb 2025", "date": "", "ddg_snippet": "tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797v1", "content": "tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit"}
+{"idx": 9, "title": "About me - Ali Kavis", "date": "", "ddg_snippet": "Selected Publications: Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting S. Sanyal*, H. Prairie*, R. Das*, A. Kavis*, S. Sanghavi. arXiv. Understanding Self-Supervised Learning via Gaussian Mixture Models P. Bansal, A. Kavis, S. Sanghavi. arXiv. Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization", "subpage_snippet": "", "source": "alikavis.github.io", "link": "https://alikavis.github.io/", "content": "Selected Publications: Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting S. Sanyal*, H. Prairie*, R. Das*, A. Kavis*, S. Sanghavi. arXiv. Understanding Self-Supervised Learning via Gaussian Mixture Models P. Bansal, A. Kavis, S. Sanghavi. arXiv. Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization"}
diff --git a/data/sampled_jsons/1BaC3AdG1i_ATA_Equation_(7)_conf(i_k)_Orlicz_norm.jsonl b/data/sampled_jsons/1BaC3AdG1i_ATA_Equation_(7)_conf(i_k)_Orlicz_norm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a616cfaebb1900321fd51cd2703cd12dbaf24432
--- /dev/null
+++ b/data/sampled_jsons/1BaC3AdG1i_ATA_Equation_(7)_conf(i_k)_Orlicz_norm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Orlicz space - Wikipedia", "date": "", "ddg_snippet": "In mathematical analysis, and especially in real, harmonic analysis and functional analysis, an Orlicz space is a type of function space which generalizes the Lp spaces. Like the Lp spaces, they are Banach spaces.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Orlicz_space", "content": "In mathematical analysis, and especially in real, harmonic analysis and functional analysis, an Orlicz space is a type of function space which generalizes the Lp spaces. Like the Lp spaces, they are Banach spaces."}
+{"idx": 1, "title": "pr.probability - Orlicz norm of random variable and variance...", "date": "", "ddg_snippet": "In probability and statistics Orlicz norms are frequently used in concentration inequalities. For example, for Bernstein's inequality, we have versions for sub-exponential random variables using ψ1. - norm and for bounded random variables using variance.", "subpage_snippet": "", "source": "cstheory.stackexchange.com", "link": "https://cstheory.stackexchange.com/questions/32978/orlicz-norm-of-random-variable-and-variance", "content": "In probability and statistics Orlicz norms are frequently used in concentration inequalities. For example, for Bernstein's inequality, we have versions for sub-exponential random variables using ψ1. - norm and for bounded random variables using variance."}
+{"idx": 2, "title": "pr.probability - Composing an Orlicz norm related to... - MathOverflow", "date": "", "ddg_snippet": "I am curious if this can additionally be made composable as well. There are many ways this question can be answered. One natural conjecture is the following though. Let $L>0$. Define the $L$-Bernstein- Orlicz norm as the Orlicz norm associated with the function $\\Psi_L(x)...", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/453191/composing-an-orlicz-norm-related-to-bernsteins-inequality", "content": "I am curious if this can additionally be made composable as well. There are many ways this question can be answered. One natural conjecture is the following though. Let $L>0$. Define the $L$-Bernstein- Orlicz norm as the Orlicz norm associated with the function $\\Psi_L(x)..."}
+{"idx": 3, "title": "The Hénon equation in Orlicz -Sobolev spaces", "date": "", "ddg_snippet": ", generalizing results for the classical Hénon equation . We also show that radial solutions are indeed bounded. Finally, we state a Pohozaev’s identity in Orlicz -Sobolev spaces that we apply to get a range in.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.17923v1", "content": ", generalizing results for the classical Hénon equation . We also show that radial solutions are indeed bounded. Finally, we state a Pohozaev’s identity in Orlicz -Sobolev spaces that we apply to get a range in."}
+{"idx": 4, "title": "finite-max.tex", "date": "", "ddg_snippet": "An Orlicz function is a convex, increasing function ψ on R+ with 0 ≤ ψ(0) < 1. Define the Orlicz norm X ψ (seminorm actually, unless one identifies random variables that are almost everywhere equal ) by.", "subpage_snippet": "", "source": "www.stat.yale.edu", "link": "http://www.stat.yale.edu/~pollard/Courses/607.spring05/handouts/finite-max.pdf", "content": "An Orlicz function is a convex, increasing function ψ on R+ with 0 ≤ ψ(0) < 1. Define the Orlicz norm X ψ (seminorm actually, unless one identifies random variables that are almost everywhere equal ) by."}
+{"idx": 5, "title": "Countable Additivity of Henstock-Dunford Integrable... | SpringerLink", "date": "", "ddg_snippet": "First Online: 25 June 2025. pp 127–135.Barcenas, D., Finol, C.E.: On vector measures, Uniformly integrable and Orlicz Spaces in vector measures.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-9548-5_9", "content": "First Online: 25 June 2025. pp 127–135.Barcenas, D., Finol, C.E.: On vector measures, Uniformly integrable and Orlicz Spaces in vector measures."}
+{"idx": 6, "title": "Кирилл Фёдоров / Война История Оружие – Telegram", "date": "", "ddg_snippet": "Россия может использовать Молдавию для нападения на Одесскую область, - Санду. \"Победа пророссийских сил угрожает суверенитету страны и может открыть путь российскому вторжению в Одесскую область\". Ежедневная отправка...", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/warhistoryalconafter", "content": "Россия может использовать Молдавию для нападения на Одесскую область, - Санду. \"Победа пророссийских сил угрожает суверенитету страны и может открыть путь российскому вторжению в Одесскую область\". Ежедневная отправка..."}
+{"idx": 7, "title": "BilimClass - BilimLand", "date": "", "ddg_snippet": "Оқушылар Мен Ата -аналар үшін. Күнделікті жеке сабақ кестесі. Дербестендірілген және адаптивті оқыту.", "subpage_snippet": "", "source": "www.bilimclass.kz", "link": "https://www.bilimclass.kz/login", "content": "Оқушылар Мен Ата -аналар үшін. Күнделікті жеке сабақ кестесі. Дербестендірілген және адаптивті оқыту."}
+{"idx": 8, "title": "Метод Гаусса онлайн", "date": "", "ddg_snippet": "Как решить систему уравнений методом Гаусса. Решение системы линейных алгебраических уравнений методом Гаусса online. Оформление сразу в Word прямо на сайте...", "subpage_snippet": "", "source": "math.semestr.ru", "link": "https://math.semestr.ru/gauss/gauss.php", "content": "Как решить систему уравнений методом Гаусса. Решение системы линейных алгебраических уравнений методом Гаусса online. Оформление сразу в Word прямо на сайте..."}
+{"idx": 9, "title": "(Решено) Упр.640 ГДЗ Атанасян 10-11 класс с пояснениями", "date": "", "ddg_snippet": "Решение #1. Изображение Запишите координаты векторов: a = 3 i + 2j-5 k , b = -5 i + 3j- k ,с = i - j, d = j + k , m = k - i , n =...", "subpage_snippet": "", "source": "reshak.ru", "link": "https://reshak.ru/otvet/reshebniki.php?otvet=new/640&predmet=atan10_11", "content": "Решение #1. Изображение Запишите координаты векторов: a = 3 i + 2j-5 k , b = -5 i + 3j- k ,с = i - j, d = j + k , m = k - i , n =..."}
diff --git a/data/sampled_jsons/2.5_Score_Based_Denoising_Training_with_noise_augmentation_score_matching.jsonl b/data/sampled_jsons/2.5_Score_Based_Denoising_Training_with_noise_augmentation_score_matching.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..150eab7fa91062dbc2cce0e54631faa5a7790d8f
--- /dev/null
+++ b/data/sampled_jsons/2.5_Score_Based_Denoising_Training_with_noise_augmentation_score_matching.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "9 Dec 2024 — 2.5 Score Based Denoising . Report issue for preceding element. Training with noise augmentation introduces an additional challenge: models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v1", "content": "9 Dec 2024 — 2.5 Score Based Denoising . Report issue for preceding element. Training with noise augmentation introduces an additional challenge: models ..."}
+{"idx": 1, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "2 . 5 Score Based Denoising . Training with noise augmentation introduces an additional challenge: models trained on the noisy distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v2", "content": "2 . 5 Score Based Denoising . Training with noise augmentation introduces an additional challenge: models trained on the noisy distribution."}
+{"idx": 2, "title": "(PDF) Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "2 . 5 . Score Based Denoising . Training with noise augmentation introduces an additional.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386577118_Normalizing_Flows_are_Capable_Generative_Models", "content": "2 . 5 . Score Based Denoising . Training with noise augmentation introduces an additional."}
diff --git a/data/sampled_jsons/2403.07300_abstract.jsonl b/data/sampled_jsons/2403.07300_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af7116bd705f6a9b1ab0ec92dc0ab75848bc6ba4
--- /dev/null
+++ b/data/sampled_jsons/2403.07300_abstract.jsonl
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+{"idx": 0, "title": "[2403.07300] CALF: Aligning LLMs for Time Series Forecasting", "date": "", "ddg_snippet": "Abstract : Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07300", "content": "Abstract : Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF)."}
+{"idx": 1, "title": "ChartThinker: A Contextual Chain-of-Thought Approach to", "date": "", "ddg_snippet": "... Abstract ... Abstract content", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.11236v2", "content": "... Abstract ... Abstract content"}
+{"idx": 2, "title": "SmolDocling: An ultra-compact vision-language model for", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.11576v1", "content": "... Abstract"}
+{"idx": 3, "title": "Multimodal ArXiv: A Dataset for Improving Scientific", "date": "", "ddg_snippet": "However, their ability to interpret abstract figures, such as geometry shapes and scientific plots, remains limited due to a scarcity of training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00231v3", "content": "However, their ability to interpret abstract figures, such as geometry shapes and scientific plots, remains limited due to a scarcity of training ..."}
+{"idx": 4, "title": "Argumentatively Coherent Judgmental Forecasting", "date": "", "ddg_snippet": "... D Abstract Structure of Complexities ... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23163v1", "content": "... D Abstract Structure of Complexities ... Abstract"}
+{"idx": 5, "title": "Citations of Power Laws in Economics and Finance", "date": "", "ddg_snippet": "... Author & abstract ... HTML with abstract ... plain text with abstract", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/r/anr/reveco/v1y2009p255-294.html", "content": "... Author & abstract ... HTML with abstract ... plain text with abstract"}
+{"idx": 6, "title": "Scaling up Multimodal Instruction Data through Web Search (2025)", "date": "", "ddg_snippet": "... Abstract ... Abstract diagrams and visual reasoning", "subpage_snippet": "", "source": "ritchebridal.com", "link": "https://ritchebridal.com/article/scaling-up-multimodal-instruction-data-through-web-search", "content": "... Abstract ... Abstract diagrams and visual reasoning"}
+{"idx": 7, "title": "| Theoretical Quantum Optics", "date": "", "ddg_snippet": "At an abstract level, our work points towards a fundamental difference between bipartite and multipartite entanglement.", "subpage_snippet": "", "source": "www.physik.uni-siegen.de", "link": "https://www.physik.uni-siegen.de/tqo/publications/?lang=de", "content": "At an abstract level, our work points towards a fundamental difference between bipartite and multipartite entanglement."}
+{"idx": 8, "title": "Scaling up Multimodal Instruction Data through Web Search (2025)", "date": "", "ddg_snippet": "... Abstract ... Abstract diagrams and visual reasoning", "subpage_snippet": "", "source": "galatamuhallebicisi.com", "link": "https://galatamuhallebicisi.com/article/scaling-up-multimodal-instruction-data-through-web-search", "content": "... Abstract ... Abstract diagrams and visual reasoning"}
+{"idx": 9, "title": "Fermionic Love of Black Holes in General Relativity", "date": "", "ddg_snippet": "... Abstract ... D 104 no. 2, (2021) 024013 , arXiv:2010. 07300 [gr-qc] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20155v1", "content": "... Abstract ... D 104 no. 2, (2021) 024013 , arXiv:2010. 07300 [gr-qc] ."}
diff --git a/data/sampled_jsons/2406.14532_Per-step_DPO_algorithm_formal_step-by-step.jsonl b/data/sampled_jsons/2406.14532_Per-step_DPO_algorithm_formal_step-by-step.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4daa199f400425d02d89778c788396417bfcf447
--- /dev/null
+++ b/data/sampled_jsons/2406.14532_Per-step_DPO_algorithm_formal_step-by-step.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - dvlab-research/ Step - DPO : Implementation for \" Step - DPO ...\"", "date": "", "ddg_snippet": "Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs.Notably, Step - DPO boosts the performance of Qwen2-7B-Instruct from 53.0% to 58.6% on MATH, and 85.5% to 87.9% on GSM8K, with as few as 10K data and hundreds of training steps !", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dvlab-research/Step-DPO", "content": "Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs.Notably, Step - DPO boosts the performance of Qwen2-7B-Instruct from 53.0% to 58.6% on MATH, and 85.5% to 87.9% on GSM8K, with as few as 10K data and hundreds of training steps !"}
+{"idx": 1, "title": "[Literature Review] Full- Step - DPO : Self-Supervised Preference...", "date": "", "ddg_snippet": "Full- Step - DPO . Step -wise Rewards.In conclusion, Full- Step - DPO marks a significant advancement in optimizing mathematical reasoning for language models by leveraging self-supervised learning and dynamic step -wise optimization, thereby addressing both the quantization of preferences...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/full-step-dpo-self-supervised-preference-optimization-with-step-wise-rewards-for-mathematical-reasoning", "content": "Full- Step - DPO . Step -wise Rewards.In conclusion, Full- Step - DPO marks a significant advancement in optimizing mathematical reasoning for language models by leveraging self-supervised learning and dynamic step -wise optimization, thereby addressing both the quantization of preferences..."}
+{"idx": 2, "title": "github- Step - DPO :Features, Alternatives | Toolerific", "date": "", "ddg_snippet": "Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs. Step 3: Rectify by the model itself # Before executing, please set the MODEL_PATH, EVAL_PROMPT, JSON_FILE, PRED_PATH, SAVE_PATH bash data_pipeline/ step 3.sh #.", "subpage_snippet": "", "source": "toolerific.ai", "link": "https://toolerific.ai/ai-tools/opensource/dvlab-research-Step-DPO", "content": "Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs. Step 3: Rectify by the model itself # Before executing, please set the MODEL_PATH, EVAL_PROMPT, JSON_FILE, PRED_PATH, SAVE_PATH bash data_pipeline/ step 3.sh #."}
+{"idx": 3, "title": "10 Minute Full Face Massage. Step - by - Step Tutorial.ANASTASIA...", "date": "", "ddg_snippet": "10 Minute Full Face Massage. Step - by - Step Tutorial.ANASTASIA BEAUTY FASCIA.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/50fb3447867f9500f18ed35948af8373/", "content": "10 Minute Full Face Massage. Step - by - Step Tutorial.ANASTASIA BEAUTY FASCIA."}
+{"idx": 4, "title": "ILLIT (아일릿) ‘jellyous' STEP BY STEP Dance Tutorial (Explained)", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=dVXLl3Rgo1Y", "content": ""}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math...", "date": "", "ddg_snippet": "Algorithm 1 Per - step DPO (Part 1: Practical version for most experiments; Parts 1 + 2: Complete version).given by SFT or RFT data, Part 1 of per - step DPO estimates the expected accuracy (Q-value) of each step in a negative rollout.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "Algorithm 1 Per - step DPO (Part 1: Practical version for most experiments; Parts 1 + 2: Complete version).given by SFT or RFT data, Part 1 of per - step DPO estimates the expected accuracy (Q-value) of each step in a negative rollout."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "of per - step DPO [23] by running the full version of the per - step DPO algorithm ( Algorithm 1). In particular, we add new. 0.56. preference pairs to the dataset of per - step DPO algorithm starting from positive samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "of per - step DPO [23] by running the full version of the per - step DPO algorithm ( Algorithm 1). In particular, we add new. 0.56. preference pairs to the dataset of per - step DPO algorithm starting from positive samples."}
+{"idx": 7, "title": "TempGmailer – Free Temp Gmail & Disposable Emails", "date": "", "ddg_snippet": "Learn how Temp Gmail works step by step . Create free temporary Gmail addresses to protect your inbox from spam and stay private online. Read Article.", "subpage_snippet": "", "source": "tempgmailer.com", "link": "https://tempgmailer.com/", "content": "Learn how Temp Gmail works step by step . Create free temporary Gmail addresses to protect your inbox from spam and stay private online. Read Article."}
+{"idx": 8, "title": "(PDF) Subtle Errors Matter: Preference Learning via Error-injected...", "date": "", "ddg_snippet": "Let's think step by step . Step 1: Determine how long one pencil lasts for Jenine. … then one pencil lasts her (5 \\times 1.5 = 7.5 ) hours.than the SOTA step -wise preference learning frameworks. Compared to Step - DPO , which shares.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384770893_Subtle_Errors_Matter_Preference_Learning_via_Error-injected_Self-editing", "content": "Let's think step by step . Step 1: Determine how long one pencil lasts for Jenine. … then one pencil lasts her (5 \\times 1.5 = 7.5 ) hours.than the SOTA step -wise preference learning frameworks. Compared to Step - DPO , which shares."}
+{"idx": 9, "title": "For a utonomous ai a gents", "date": "", "ddg_snippet": "115 around step - by - step verifiers or “Process Reward Models” (Uesato et al., 2022; Lightman et al., 2023), 116. specifically for mathematical reasoning. Step -controlled dpo : Leveraging stepwise error for enhanced mathematical 696 reasoning, 2024.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LuytzzohTa", "content": "115 around step - by - step verifiers or “Process Reward Models” (Uesato et al., 2022; Lightman et al., 2023), 116. specifically for mathematical reasoning. Step -controlled dpo : Leveraging stepwise error for enhanced mathematical 696 reasoning, 2024."}
diff --git "a/data/sampled_jsons/2407.10264_'\316\267M'_'\316\267S'_'Section_4'_'learning_rate'_year_2024.jsonl" "b/data/sampled_jsons/2407.10264_'\316\267M'_'\316\267S'_'Section_4'_'learning_rate'_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..125fc3fd22b85d063fb85507acbd4449060de579
--- /dev/null
+++ "b/data/sampled_jsons/2407.10264_'\316\267M'_'\316\267S'_'Section_4'_'learning_rate'_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The 10 Best El Paso Hotels (From $48) - Booking.com", "date": "", "ddg_snippet": "Great savings on hotels in El Paso , United States online. Good availability and great rates. Read hotel reviews and choose the best hotel deal for your stay.", "subpage_snippet": "", "source": "www.booking.com", "link": "https://www.booking.com/city/us/el-paso.html", "content": "Great savings on hotels in El Paso , United States online. Good availability and great rates. Read hotel reviews and choose the best hotel deal for your stay."}
+{"idx": 1, "title": "THE 10 BEST Hotels in El Paso 2025", "date": "", "ddg_snippet": "Stanton House El Paso , Chase Suite Hotel El Paso , and Home2 Suites By Hilton El Paso Airport all received great reviews from families traveling in El Paso . See the full list: El Paso Family Hotels .", "subpage_snippet": "", "source": "www.tripadvisor.com", "link": "https://www.tripadvisor.com/Hotels-g60768-El_Paso_Texas-Hotels.html", "content": "Stanton House El Paso , Chase Suite Hotel El Paso , and Home2 Suites By Hilton El Paso Airport all received great reviews from families traveling in El Paso . See the full list: El Paso Family Hotels ."}
+{"idx": 2, "title": "25 Best Hotels in El Paso for 2025 | U.S. News Travel", "date": "", "ddg_snippet": "U.S. News evaluates top hotels in El Paso using expert insights, awards, class ratings and guest reviews.", "subpage_snippet": "", "source": "travel.usnews.com", "link": "https://travel.usnews.com/Hotels/El_Paso_TX/", "content": "U.S. News evaluates top hotels in El Paso using expert insights, awards, class ratings and guest reviews."}
+{"idx": 3, "title": "Find hotels in El Paso, TX from $55", "date": "", "ddg_snippet": "Discover a range of hotel accommodations in El Paso , Texas, catering to various traveler needs. From business-friendly options to pet-friendly stays, you'll find the perfect spot for your visit.", "subpage_snippet": "", "source": "www.expedia.com", "link": "https://www.expedia.com/El-Paso-Hotels.d1129.Travel-Guide-Hotels", "content": "Discover a range of hotel accommodations in El Paso , Texas, catering to various traveler needs. From business-friendly options to pet-friendly stays, you'll find the perfect spot for your visit."}
+{"idx": 4, "title": "16 Best Hotels in El Paso. Hotels from $42/night - KAYAK", "date": "", "ddg_snippet": "Stay at Super 8 El Paso Airport from $48/night, Radisson Hotel El Paso Airport from $130/night, Stanton House from $217/night and more. Compare prices of 822 hotels in El Paso on KAYAK now.", "subpage_snippet": "", "source": "www.kayak.com", "link": "https://www.kayak.com/El-Paso-Hotels.9254.hotel.ksp", "content": "Stay at Super 8 El Paso Airport from $48/night, Radisson Hotel El Paso Airport from $130/night, Stanton House from $217/night and more. Compare prices of 822 hotels in El Paso on KAYAK now."}
+{"idx": 5, "title": "Hotels in El Paso, TX - Find Hotels - Hilton", "date": "", "ddg_snippet": "Explore Hotels in El Paso , TX. Search by destination, check the latest prices, or use the interactive map to find the location for your next stay. Book direct for the best price and free cancellation.", "subpage_snippet": "", "source": "www.hilton.com", "link": "https://www.hilton.com/en/locations/usa/texas/el-paso/", "content": "Explore Hotels in El Paso , TX. Search by destination, check the latest prices, or use the interactive map to find the location for your next stay. Book direct for the best price and free cancellation."}
+{"idx": 6, "title": "Hotels - Visit El Paso", "date": "", "ddg_snippet": "Whether you’re seeking luxury accommodations with top-notch amenities, charming boutique hotels with unique local flavor, or budget-friendly stays for a quick trip, El Paso has it all.", "subpage_snippet": "", "source": "visitelpaso.com", "link": "https://visitelpaso.com/plan-your-trip/hotels", "content": "Whether you’re seeking luxury accommodations with top-notch amenities, charming boutique hotels with unique local flavor, or budget-friendly stays for a quick trip, El Paso has it all."}
+{"idx": 7, "title": "THE BEST 10 HOTELS in EL PASO, TX - Updated 2025 - Hours - Yelp", "date": "", "ddg_snippet": "What are the best inexpensive hotels ? What did people search for similar to hotels in El Paso , TX? See more hotels in El Paso .", "subpage_snippet": "", "source": "www.yelp.com", "link": "https://www.yelp.com/search?cflt=hotels&find_loc=El+Paso,+TX", "content": "What are the best inexpensive hotels ? What did people search for similar to hotels in El Paso , TX? See more hotels in El Paso ."}
+{"idx": 8, "title": "Top 10 Hotels in El Paso , TX | Hotels .com", "date": "", "ddg_snippet": "Flexible booking options on most hotels . Compare 814 hotels in El Paso using 16,278 real guest reviews. Unlock travel rewards with One Key.", "subpage_snippet": "", "source": "www.hotels.com", "link": "https://www.hotels.com/de1450956/hotels-el-paso-texas/", "content": "Flexible booking options on most hotels . Compare 814 hotels in El Paso using 16,278 real guest reviews. Unlock travel rewards with One Key."}
+{"idx": 9, "title": "Hotel Paso Del Norte – Luxury Hotel in Downtown El Paso , TX", "date": "", "ddg_snippet": "Experience historic charm & modern amenities at our boutique hotel featuring luxurious accommodations, elegant décor, onsite dining, and event venues in El Paso .", "subpage_snippet": "", "source": "www.hotelpdn.com", "link": "https://www.hotelpdn.com/", "content": "Experience historic charm & modern amenities at our boutique hotel featuring luxurious accommodations, elegant décor, onsite dining, and event venues in El Paso ."}
diff --git a/data/sampled_jsons/2410.02025_empirical_validation_MNIST_Wasserstein_distance_sparse_architecture_results.jsonl b/data/sampled_jsons/2410.02025_empirical_validation_MNIST_Wasserstein_distance_sparse_architecture_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d27f0cdcca7bd43dc9f81366cb66402b7a46ec3
--- /dev/null
+++ b/data/sampled_jsons/2410.02025_empirical_validation_MNIST_Wasserstein_distance_sparse_architecture_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "Figure 2: MNIST images: real images (left panel), generated images with sparse architecture (central panel), and generated images with fully connected architecture (right panel)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "Figure 2: MNIST images: real images (left panel), generated images with sparse architecture (central panel), and generated images with fully connected architecture (right panel)"}
+{"idx": 1, "title": "Empirical Wasserstein distance between MNIST and MNIST with varied ...", "date": "", "ddg_snippet": "Figure 4 compares the empirical Wasserstein distance using orthonomal projection compared to Gaussian random projection and sparse random projection when MNIST is perturbed with additive Gaussian ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Empirical-Wasserstein-distance-between-MNIST-and-MNIST-with-varied-levels-of-additive_fig3_368687851", "content": "Figure 4 compares the empirical Wasserstein distance using orthonomal projection compared to Gaussian random projection and sparse random projection when MNIST is perturbed with additive Gaussian ..."}
+{"idx": 2, "title": "Empirical Wasserstein distance between MNIST and MNIST with varied ...", "date": "", "ddg_snippet": "Download scientific diagram | Empirical Wasserstein distance between MNIST and MNIST with varied levels of additive impulse noise, measured over a range of sample sizes.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Empirical-Wasserstein-distance-between-MNIST-and-MNIST-with-varied-levels-of-additive_fig4_368687851", "content": "Download scientific diagram | Empirical Wasserstein distance between MNIST and MNIST with varied levels of additive impulse noise, measured over a range of sample sizes."}
+{"idx": 3, "title": "Empirical Wasserstein distance between MNIST and MNIST with varied ...", "date": "", "ddg_snippet": "Empirical Wasserstein distance between MNIST and MNIST with varied levels of Gaussian blur, measured over a range of sample sizes. Curves are taken over 5 trials.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Empirical-Wasserstein-distance-between-MNIST-and-MNIST-with-varied-levels-of-Gaussian_fig5_368687851", "content": "Empirical Wasserstein distance between MNIST and MNIST with varied levels of Gaussian blur, measured over a range of sample sizes. Curves are taken over 5 trials."}
+{"idx": 4, "title": "PDF Intrinsic Dimension Estimation Using Wasserstein Distance", "date": "", "ddg_snippet": "In the right plot, we pool all of the data to estimate the manifold distances , and then use these estimated distances to compute the Wasserstein distance between the empirical distributions.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume23/21-1483/21-1483.pdf", "content": "In the right plot, we pool all of the data to estimate the manifold distances , and then use these estimated distances to compute the Wasserstein distance between the empirical distributions."}
+{"idx": 5, "title": "PDF Orthogonal Estimation of Wasserstein Distances", "date": "", "ddg_snippet": "In this paper, we investigate these two shortcomings of the sliced Wasserstein distance and propose a new dis-tance which incorporates the computational bene ts of the sliced Wasserstein distance , whilst still retain-ing information from the high-dimensional distances . Given recent strong results based on Wasserstein dis-tances and their tractable approximations, exploring related methods is ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v89/rowland19a/rowland19a.pdf", "content": "In this paper, we investigate these two shortcomings of the sliced Wasserstein distance and propose a new dis-tance which incorporates the computational bene ts of the sliced Wasserstein distance , whilst still retain-ing information from the high-dimensional distances . Given recent strong results based on Wasserstein dis-tances and their tractable approximations, exploring related methods is ..."}
+{"idx": 6, "title": "PDF Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and ...", "date": "", "ddg_snippet": "We adopt the smooth Wasserstein distance as the figure of merit and use the empirical distri-bution ^n as an estimate for . Generative modeling is thus formulated as the following minimum smooth Wasserstein estimation (M-SWE) problem:", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/nietert21a/nietert21a.pdf", "content": "We adopt the smooth Wasserstein distance as the figure of merit and use the empirical distri-bution ^n as an estimate for . Generative modeling is thus formulated as the following minimum smooth Wasserstein estimation (M-SWE) problem:"}
+{"idx": 7, "title": "PDF Quantifying the Empirical Wasserstein Distance to a Set of ... - NeurIPS", "date": "", "ddg_snippet": "Abstract We consider the problem of estimating the Wasserstein distance between the empirical measure and a set of probability measures whose expectations over a class of functions (hypothesis class) are constrained.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/f3507289cfdc8c9ae93f4098111a13f9-Paper.pdf", "content": "Abstract We consider the problem of estimating the Wasserstein distance between the empirical measure and a set of probability measures whose expectations over a class of functions (hypothesis class) are constrained."}
+{"idx": 8, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Y 𝑌 Y with respect to the Hellinger distance and specialize the obtained rate for two popular deep neural network classes: the sparse and fully connected network classes. Furthermore, we characterize the Wasserstein convergence rates for the induced intrinsic conditional distribution estimator on the manifold (i.e., a deconvolution problem).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.02025", "content": "Y 𝑌 Y with respect to the Hellinger distance and specialize the obtained rate for two popular deep neural network classes: the sparse and fully connected network classes. Furthermore, we characterize the Wasserstein convergence rates for the induced intrinsic conditional distribution estimator on the manifold (i.e., a deconvolution problem)."}
+{"idx": 9, "title": "ACE and Diverse Generalization via Selective Disagreement", "date": "", "ddg_snippet": "In the main body, we present ACE results uses separate group losses for evenly-balanced datasets (Toy Grid, FMNIST- MNIST , and CIFAR- MNIST ) and a combined group loss for unbalanced datasets (Waterbirds, CelebA, and MultiNLI).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.07955", "content": "In the main body, we present ACE results uses separate group losses for evenly-balanced datasets (Toy Grid, FMNIST- MNIST , and CIFAR- MNIST ) and a combined group loss for unbalanced datasets (Waterbirds, CelebA, and MultiNLI)."}
diff --git a/data/sampled_jsons/2502.01846_UVGS_branched_mapping_layers.jsonl b/data/sampled_jsons/2502.01846_UVGS_branched_mapping_layers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3b39c902e57e86741f167fb2b9a7ae604e2c4d87
--- /dev/null
+++ b/data/sampled_jsons/2502.01846_UVGS_branched_mapping_layers.jsonl
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+{"idx": 0, "title": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV...", "date": "", "ddg_snippet": "Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse map -ping networks is to prevent the incompatibility issues aris-ing due the the different value distribution of 3DGS at-tributes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846", "content": "Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse map -ping networks is to prevent the incompatibility issues aris-ing due the the different value distribution of 3DGS at-tributes."}
+{"idx": 1, "title": "(PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using...", "date": "", "ddg_snippet": "arXiv: 2502 . 01846 v1 [cs.CV] 3 Feb 2025. implicit representation, which makes editing and manipula-. tion challenging. UVGS through branched inverse mapping , which in turn can be reconstructed back to the 3DGS object through inverse spherical mapping .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "arXiv: 2502 . 01846 v1 [cs.CV] 3 Feb 2025. implicit representation, which makes editing and manipula-. tion challenging. UVGS through branched inverse mapping , which in turn can be reconstructed back to the 3DGS object through inverse spherical mapping ."}
+{"idx": 2, "title": "GitHub - flyingGH/arxiv-daily: Automatically Update CV Papers...", "date": "", "ddg_snippet": "Free360: Layered Gaussian Splatting for Unbounded 360-Degree View Synthesis from Extremely Sparse and Unposed Views. Chong Bao et.al. UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/flyingGH/arxiv-daily", "content": "Free360: Layered Gaussian Splatting for Unbounded 360-Degree View Synthesis from Extremely Sparse and Unposed Views. Chong Bao et.al. UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping ."}
+{"idx": 3, "title": "Browse | ArcGIS Living Atlas of the World", "date": "", "ddg_snippet": "The Browse page provides comprehensive search and filtering tools to help find ready to use layers , maps , tools, apps and other items to begin your work.", "subpage_snippet": "", "source": "livingatlas.arcgis.com", "link": "https://livingatlas.arcgis.com/en/browse/", "content": "The Browse page provides comprehensive search and filtering tools to help find ready to use layers , maps , tools, apps and other items to begin your work."}
+{"idx": 4, "title": "iPhone Air не самый тонкий телефон Apple? На Reddit провели...", "date": "", "ddg_snippet": "Автобарахолка. Отзывы об авто 2502.", "subpage_snippet": "", "source": "tech.onliner.by", "link": "https://tech.onliner.by/2025/09/19/iphone-air-ne-samyj-tonkij-telefon-apple-na-reddit-proveli-interesnoe-sravnenie", "content": "Автобарахолка. Отзывы об авто 2502."}
+{"idx": 5, "title": "МЕОЦ. Синагога в Марьиной роще. Евреи Москвы. | Календарь", "date": "", "ddg_snippet": "2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520...", "subpage_snippet": "", "source": "mjcc.ru", "link": "https://mjcc.ru/news/kalendar/", "content": "2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520..."}
+{"idx": 6, "title": "Топ игроков в режиме Миссии - 38 неделя 2025 год | crossout-info", "date": "", "ddg_snippet": "The_Nobody_. Каратель 75% 4 10891 4540. 2502. CBR-420.", "subpage_snippet": "", "source": "crossout-info.com", "link": "https://crossout-info.com/season/pvp/2025-38", "content": "The_Nobody_. Каратель 75% 4 10891 4540. 2502. CBR-420."}
+{"idx": 7, "title": "Аргинин или цитруллин – что лучше: сравнение, отличия...", "date": "", "ddg_snippet": "Sureda, A., et al. L-citrulline-malate influence over branched chain amino acid utilization during exercise. Eur J ApplPhysiol. 2010 Sep;110(2):341-51.Магазины map .", "subpage_snippet": "", "source": "geneticlab.ru", "link": "https://geneticlab.ru/blog/arginin-i-tsitrullin-chto-effektivnee-dlya-pampinga-i-rosta-myshts/", "content": "Sureda, A., et al. L-citrulline-malate influence over branched chain amino acid utilization during exercise. Eur J ApplPhysiol. 2010 Sep;110(2):341-51.Магазины map ."}
+{"idx": 8, "title": "Weblog publishing tool from Google, for sharing text, photos and video.", "date": "", "ddg_snippet": "Weblog publishing tool from Google, for sharing text, photos and video.", "subpage_snippet": "", "source": "accounts.google.com", "link": "https://accounts.google.com/ServiceLogin?service=blogger", "content": "Weblog publishing tool from Google, for sharing text, photos and video."}
+{"idx": 9, "title": "Satellite view map | World imagery service", "date": "", "ddg_snippet": "On this map you will find high resolution satellite and aerial imagery. Zoom in the map and see satellite view of the world!", "subpage_snippet": "", "source": "free-map.org", "link": "https://free-map.org/satellite/", "content": "On this map you will find high resolution satellite and aerial imagery. Zoom in the map and see satellite view of the world!"}
diff --git a/data/sampled_jsons/2502.10875_equation_1_FBox_score_definition.jsonl b/data/sampled_jsons/2502.10875_equation_1_FBox_score_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f8dca78abf93e853fdeb5a788bf9441a6348f469
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+++ b/data/sampled_jsons/2502.10875_equation_1_FBox_score_definition.jsonl
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+{"idx": 0, "title": "F-score - Wikipedia", "date": "", "ddg_snippet": "It thus symmetrically represents both precision and recall in one metric. The more generic score applies additional weights, valuing one of precision or recall more than the other. The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if the precision or the recall is zero.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/F-score", "content": "It thus symmetrically represents both precision and recall in one metric. The more generic score applies additional weights, valuing one of precision or recall more than the other. The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if the precision or the recall is zero."}
+{"idx": 1, "title": "Accuracy, Precision, Recall, and F1-Score", "date": "", "ddg_snippet": "F1- score : The F1- score is the harmonic mean of precision and recall. It provides a balanced measure of the model's performance by considering both precision and recall.", "subpage_snippet": "", "source": "machinelearning.org.in", "link": "https://machinelearning.org.in/accuracy-precision-recall-and-f1-score/", "content": "F1- score : The F1- score is the harmonic mean of precision and recall. It provides a balanced measure of the model's performance by considering both precision and recall."}
+{"idx": 2, "title": "F-Score Definition | DeepAI", "date": "", "ddg_snippet": "It is possible to adjust the F-score to give more importance to precision over recall, or vice-versa. Common adjusted F-scores are the F0.5-score and the F2- score , as well as the standard F1- score . F-score Formula The formula for the standard F1- score is the harmonic mean of the precision and recall. A perfect model has an F-score of 1 .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/machine-learning-glossary-and-terms/f-score", "content": "It is possible to adjust the F-score to give more importance to precision over recall, or vice-versa. Common adjusted F-scores are the F0.5-score and the F2- score , as well as the standard F1- score . F-score Formula The formula for the standard F1- score is the harmonic mean of the precision and recall. A perfect model has an F-score of 1 ."}
+{"idx": 3, "title": "What is F-score? Calculation and its Importance - Deepchecks", "date": "", "ddg_snippet": "What is an F-score? The F-score (also known as the F1 score or F-measure) is a metric used to evaluate the performance of a Machine Learning model. It combines precision and recall into a single score . F-measure formula: F-score = 2 * (precision * recall) / (precision + recall) Accuracy in making positive predictions is measured by a recall, while identifying all positive occurrences in the ...", "subpage_snippet": "", "source": "www.deepchecks.com", "link": "https://www.deepchecks.com/glossary/f-score/", "content": "What is an F-score? The F-score (also known as the F1 score or F-measure) is a metric used to evaluate the performance of a Machine Learning model. It combines precision and recall into a single score . F-measure formula: F-score = 2 * (precision * recall) / (precision + recall) Accuracy in making positive predictions is measured by a recall, while identifying all positive occurrences in the ..."}
+{"idx": 4, "title": "What is Piotroski F-score? Definition, Calculator & Formula | ValueSense", "date": "", "ddg_snippet": "Learn what Piotroski F-score is, how to calculate it, and why it matters for stock analysis. Complete guide with formula, examples, and investment tips.", "subpage_snippet": "", "source": "blog.valuesense.io", "link": "https://blog.valuesense.io/piotroski-f-score/", "content": "Learn what Piotroski F-score is, how to calculate it, and why it matters for stock analysis. Complete guide with formula, examples, and investment tips."}
+{"idx": 5, "title": "F1-Score (F-Score) | Definition, Formula & Use Cases", "date": "", "ddg_snippet": "The F1- score : Learn its formula, advantages, limitations, and use cases in medical diagnosis, fraud detection, and NLP.", "subpage_snippet": "", "source": "xenoss.io", "link": "https://xenoss.io/ai-and-data-glossary/f-score", "content": "The F1- score : Learn its formula, advantages, limitations, and use cases in medical diagnosis, fraud detection, and NLP."}
+{"idx": 6, "title": "F1 Score in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "F1 Score is a performance metric used in machine learning to evaluate how well a classification model performs on a dataset especially when the classes are imbalanced meaning one class appears much more frequently than another. It is the harmonic mean of precision and recall which combine both metrics into a single value that balances their importance. Before understanding F1 Score let's ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/f1-score-in-machine-learning/", "content": "F1 Score is a performance metric used in machine learning to evaluate how well a classification model performs on a dataset especially when the classes are imbalanced meaning one class appears much more frequently than another. It is the harmonic mean of precision and recall which combine both metrics into a single value that balances their importance. Before understanding F1 Score let's ..."}
+{"idx": 7, "title": "PDF Error-Bounded Graph Anomaly Loss for GNNs", "date": "", "ddg_snippet": "FraudEagle [ 1 ]: it uses a belief propagation-based algorithm to give a fraud score to each user. Outlying users who behaves more different than the majority are given higher fraud scores ;", "subpage_snippet": "", "source": "tzhao.io", "link": "http://tzhao.io/files/papers/CIKM20_GAL.pdf", "content": "FraudEagle [ 1 ]: it uses a belief propagation-based algorithm to give a fraud score to each user. Outlying users who behaves more different than the majority are given higher fraud scores ;"}
+{"idx": 8, "title": "DoniaGasmii/MNLP_M3_dpo_dataset · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/DoniaGasmii/MNLP_M3_dpo_dataset/viewer", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 9, "title": "machinelearningmodels.org", "date": "", "ddg_snippet": "See relevant content for machinelearningmodels.orgContent blocked Please turn off your ad blocker.", "subpage_snippet": "", "source": "machinelearningmodels.org", "link": "https://machinelearningmodels.org/the-formula-for-calculating-the-f-score-in-machine-learning/", "content": "See relevant content for machinelearningmodels.orgContent blocked Please turn off your ad blocker."}
diff --git a/data/sampled_jsons/2503.16979_Equation_(6)_weighted_interpolation_motion.jsonl b/data/sampled_jsons/2503.16979_Equation_(6)_weighted_interpolation_motion.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a78a0c0848b9cd2ae9f466adc00617a980d12f6a
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+++ b/data/sampled_jsons/2503.16979_Equation_(6)_weighted_interpolation_motion.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Microsoft Word - Thesis_Stefan_Talke_submitted.doc", "date": "", "ddg_snippet": "The “ weighted interpolation ” method clearly outperforms the “best fit extrapolation” method for both the Soulsby. 124.", "subpage_snippet": "", "source": "depts.washington.edu", "link": "https://depts.washington.edu/uwefm/publications/Talke_dissertation2005.pdf", "content": "The “ weighted interpolation ” method clearly outperforms the “best fit extrapolation” method for both the Soulsby. 124."}
+{"idx": 1, "title": "IEEE Std 141-1993 (Revision of IEEE Std 141-1986) IEEE...", "date": "", "ddg_snippet": "The weighted interpolation has signiÞcance only for the short circuit at F1.An examination of equation ( 6 ) shows that it is only necessary to know the system reactance and the capacitor rating to predict the voltage change due to the change in reactive power.", "subpage_snippet": "", "source": "dl.booksee.org", "link": "https://dl.booksee.org/genesis/207000/e456c56c8bf29ef99adb84a7b2688600/_as/[IEEE]_IEEE_Std_141-1993,_IEEE_Recommended_Practic(BookSee.org).pdf", "content": "The weighted interpolation has signiÞcance only for the short circuit at F1.An examination of equation ( 6 ) shows that it is only necessary to know the system reactance and the capacitor rating to predict the voltage change due to the change in reactive power."}
diff --git a/data/sampled_jsons/2503.16979v1_Equation_6_motion_feature_z_i.jsonl b/data/sampled_jsons/2503.16979v1_Equation_6_motion_feature_z_i.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1d0d9a0ec9055e46f29c2da8b7cd1338b35c1570
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+++ b/data/sampled_jsons/2503.16979v1_Equation_6_motion_feature_z_i.jsonl
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+{"idx": 0, "title": "Monocular Dynamic Gaussian Splatting Is Fast and Brittle ...", "date": "", "ddg_snippet": "This document discusses the challenges and advancements in monocular dynamic view synthesis using Gaussian splatting methods, highlighting the lack of ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/872613140/2412-04457v1", "content": "This document discusses the challenges and advancements in monocular dynamic view synthesis using Gaussian splatting methods, highlighting the lack of ..."}
diff --git a/data/sampled_jsons/2504.11786_stage_1_total_loss_disease-matching_-patent.jsonl b/data/sampled_jsons/2504.11786_stage_1_total_loss_disease-matching_-patent.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ea6de209815e3fecb06b31d806f7070d83148b53
--- /dev/null
+++ b/data/sampled_jsons/2504.11786_stage_1_total_loss_disease-matching_-patent.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "DART: Disease -aware Image-Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "1 . In the first stage , we introduce a disease-aware report generation model to generate reports from input images by leveraging image-to-text retrieval with a disease - matching constraint, ensuring that the generated reports accurately reflect disease-relevant findings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "1 . In the first stage , we introduce a disease-aware report generation model to generate reports from input images by leveraging image-to-text retrieval with a disease - matching constraint, ensuring that the generated reports accurately reflect disease-relevant findings."}
+{"idx": 1, "title": "(PDF) DART: Disease -aware Image-Text Alignment and...", "date": "", "ddg_snippet": "DOI:10.48550/arXiv. 2504 . 11786 .loss can be found in the supplementary materials. In the first stage , we minimize a total loss Lstage 1 , which. consists of the contrastive loss, the disease - matching con-. straint, the classification loss, the generation loss to gener", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390845711_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_Report_Generation", "content": "DOI:10.48550/arXiv. 2504 . 11786 .loss can be found in the supplementary materials. In the first stage , we minimize a total loss Lstage 1 , which. consists of the contrastive loss, the disease - matching con-. straint, the classification loss, the generation loss to gener"}
+{"idx": 2, "title": "DART | PDF | Artificial Intelligence", "date": "", "ddg_snippet": "... disease-matching constraint and then refines these reports by re ... ding space using the text encoder trained in stage 1 , ob- IU X-ray.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "... disease-matching constraint and then refines these reports by re ... ding space using the text encoder trained in stage 1 , ob- IU X-ray."}
+{"idx": 3, "title": "wedm2401, gjrmstn1440, dongheeshin, yhson135, meeeo , kamte ...", "date": "", "ddg_snippet": "The disease-matching constraint ensures that the retrieved re- ports align more closely with the disease-relevant findings of the input images, resulting in more accurate and clini- cally coherent generated reports.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.11786", "content": "The disease-matching constraint ensures that the retrieved re- ports align more closely with the disease-relevant findings of the input images, resulting in more accurate and clini- cally coherent generated reports."}
diff --git a/data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_in_Identifying_the_Worst-Off_theoretical_discussion_screening_cap.jsonl b/data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_in_Identifying_the_Worst-Off_theoretical_discussion_screening_cap.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..40a32e5afeee2220f3091346d2433364143788ef
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+++ b/data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_in_Identifying_the_Worst-Off_theoretical_discussion_screening_cap.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "Explaining Anxiety Prediction in Psychotherapy Transcripts ...", "date": "", "ddg_snippet": "Sep 1, 2025 · Request PDF | On Sep 1, 2025, Tobias Steinbrenner and others published Explaining Anxiety Prediction in Psychotherapy Transcripts: The Role of Patient Linguistic Features and Theoretical ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395496975_Explaining_Anxiety_Prediction_in_Psychotherapy_Transcripts_The_Role_of_Patient_Linguistic_Features_and_Theoretical_Constructs", "content": "Sep 1, 2025 · Request PDF | On Sep 1, 2025, Tobias Steinbrenner and others published Explaining Anxiety Prediction in Psychotherapy Transcripts: The Role of Patient Linguistic Features and Theoretical ..."}
+{"idx": 1, "title": "AI and the Future of Academic Peer Review - arXiv.org", "date": "", "ddg_snippet": "These outputs suggest that statistical prediction does not preclude innovation, particularly when models are scaled and equipped with reasoning strategies such as chain-of-thought prompting or tool integration.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14189", "content": "These outputs suggest that statistical prediction does not preclude innovation, particularly when models are scaled and equipped with reasoning strategies such as chain-of-thought prompting or tool integration."}
+{"idx": 2, "title": "Recent advances of theoretical investigation on lead-free ...", "date": "", "ddg_snippet": "This review presents a thorough discussion of the theoretical advancements in Pb-free MHPs, encompassing their crystal structures, electronic properties, optical properties, the lattice dynamics, thermodynamic and chemical stability, mechanical properties, ion migration and thermoelectric properties, and the advantages of theoretical means and ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0010854525007520", "content": "This review presents a thorough discussion of the theoretical advancements in Pb-free MHPs, encompassing their crystal structures, electronic properties, optical properties, the lattice dynamics, thermodynamic and chemical stability, mechanical properties, ion migration and thermoelectric properties, and the advantages of theoretical means and ..."}
+{"idx": 3, "title": "Mechanistic and predictive formulation development for ...", "date": "", "ddg_snippet": "Sep 15, 2025 · More industry/academic partnerships can help to drive progress toward sequence-based predictions . 224 Then, simple predictions from sequence would enable rapid screening of candidate sequences for low viscosity and viscosity-reducing formulation compositions.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/19420862.2025.2550757", "content": "Sep 15, 2025 · More industry/academic partnerships can help to drive progress toward sequence-based predictions . 224 Then, simple predictions from sequence would enable rapid screening of candidate sequences for low viscosity and viscosity-reducing formulation compositions."}
+{"idx": 4, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "Instead of assuming full prediction coverage that robust planners require, we propose to make prediction itself risk-aware. We introduce a new prediction objective to learn a risk-biased distribution over trajectories, so that risk evaluation simplifies to an expected cost estimation under this biased distribution.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=patient+risk+trajectory+prediction", "content": "Instead of assuming full prediction coverage that robust planners require, we propose to make prediction itself risk-aware. We introduce a new prediction objective to learn a risk-biased distribution over trajectories, so that risk evaluation simplifies to an expected cost estimation under this biased distribution."}
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+{"idx": 6, "title": "Strategic Management & Business Policy Textbook - studylib.net", "date": "", "ddg_snippet": "Explore strategic management and business policy with a focus on globalization, innovation, and sustainability. A textbook for college students.", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/27854616/strategic.management.and.business.policy-", "content": "Explore strategic management and business policy with a focus on globalization, innovation, and sustainability. A textbook for college students."}
diff --git a/data/sampled_jsons/32867_Descriptor-In-Pixel_-_Point-Feature_Tracking_For_Pixel_Processor_Arrays.jsonl b/data/sampled_jsons/32867_Descriptor-In-Pixel_-_Point-Feature_Tracking_For_Pixel_Processor_Arrays.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..490064701132998a7f090f1f2cba08df89ec1b7d
--- /dev/null
+++ b/data/sampled_jsons/32867_Descriptor-In-Pixel_-_Point-Feature_Tracking_For_Pixel_Processor_Arrays.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR Poster Descriptor - In - Pixel : Point - Feature Tracking For Pixel ...", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA).", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA)."}
+{"idx": 1, "title": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor ...", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point - feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11092646/", "content": "This paper presents a novel approach for joint point - feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors."}
+{"idx": 2, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "Bose_ Descriptor - In - Pixel __ Point - Feature _ Tracking _ For _ Pixel _ Processor _ Arrays @CVPR2025@CVF.This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA).", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays@CVPR2025@CVF", "content": "Bose_ Descriptor - In - Pixel __ Point - Feature _ Tracking _ For _ Pixel _ Processor _ Arrays @CVPR2025@CVF.This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA)."}
+{"idx": 3, "title": "Descriptor - In _ Pixel", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays . CVPR 2025 Best Paper Award Candidate. Point - Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power.", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays . CVPR 2025 Best Paper Award Candidate. Point - Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power."}
+{"idx": 4, "title": "GitHub - wangxiao5791509/Single_Object_ Tracking _Paper_List...", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays Laurie Bose · Piotr Dudek · Jianing Chen.DINO- Tracker : Taming DINO for Self-Supervised Point Tracking in a Single Video, Narek Tumanyan*, Assaf Singer, Shai Bagon, Tali Dekel.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wangxiao5791509/Single_Object_Tracking_Paper_List", "content": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays Laurie Bose · Piotr Dudek · Jianing Chen.DINO- Tracker : Taming DINO for Self-Supervised Point Tracking in a Single Video, Narek Tumanyan*, Assaf Singer, Shai Bagon, Tali Dekel."}
+{"idx": 5, "title": "Mapping Image Transformations Onto Pixel Processor Arrays", "date": "", "ddg_snippet": "Pixel Processor Arrays (PPA) present a new vision sensor/ processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.16994v1", "content": "Pixel Processor Arrays (PPA) present a new vision sensor/ processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication."}
+{"idx": 6, "title": "Fully Embedding Fast Convolutional Networks on Pixel Processor ...", "date": "", "ddg_snippet": "We present a novel method of CNN inference for pixel processor array (PPA) vision sensors, designed to take advantage of their massive parallelism and analog compute capabilities.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/fully-embedding-fast-convolutional-networks-on-pixel-processor-arrays", "content": "We present a novel method of CNN inference for pixel processor array (PPA) vision sensors, designed to take advantage of their massive parallelism and analog compute capabilities."}
+{"idx": 7, "title": "(PDF) BRISK: Binary Robust invariant scalable keypoints", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays .In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221110715_BRISK_Binary_Robust_invariant_scalable_keypoints", "content": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays .In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features)."}
+{"idx": 8, "title": "Descriptor In Pixel : Point Feature Tracking for Pixel Processor ...", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=QDucNhl8ir8", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
+{"idx": 9, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "If you are attendinding #cvpr2025 and interested in next-generation visual perception, check out the award-nominated latest paper by Laurie Bose Jianing Chen and Piotr Dudek.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/walterio-mayol-cuevas_descriptor-in-pixel-point-feature-tracking-activity-7337315353846235137-Uo0o", "content": "If you are attendinding #cvpr2025 and interested in next-generation visual perception, check out the award-nominated latest paper by Laurie Bose Jianing Chen and Piotr Dudek."}
diff --git a/data/sampled_jsons/32867_Descriptor-In-Pixel_Figure_8_Translate_motion_reliability_FAST_tracking_explanation.jsonl b/data/sampled_jsons/32867_Descriptor-In-Pixel_Figure_8_Translate_motion_reliability_FAST_tracking_explanation.jsonl
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index 0000000000000000000000000000000000000000..09381e113ce32864977fa46f081258f03b1d292c
--- /dev/null
+++ b/data/sampled_jsons/32867_Descriptor-In-Pixel_Figure_8_Translate_motion_reliability_FAST_tracking_explanation.jsonl
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+{"idx": 0, "title": "Descriptor-In-Pixel : Point-Feature Tracking For Pixel ...", "date": "", "ddg_snippet": "This approach is very fast , our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point-features reliably even under violent motion . This is the first work performing point-feature detection and tracking entirely \"in- pixel \".", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "This approach is very fast , our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point-features reliably even under violent motion . This is the first work performing point-feature detection and tracking entirely \"in- pixel \"."}
+{"idx": 1, "title": "Lecture 12-13: Feature Detection and Tracking - VNAV", "date": "", "ddg_snippet": "X kI1(y) I 2(y + )k2 (-1.14) y2W(x1) Figure -1.1: The goal of feature tracking is to compute the displacement of a given pixel x1 (displacement shown as yellow arrow) between two images, due to the camera motion . However, the minimization (-1.14) already took a strong assumption: every pixel in W(x1) is moving by the same amount .", "subpage_snippet": "", "source": "vnav.mit.edu", "link": "https://vnav.mit.edu/material/12-13-featureDetectionAndTracking-notes.pdf", "content": "X kI1(y) I 2(y + )k2 (-1.14) y2W(x1) Figure -1.1: The goal of feature tracking is to compute the displacement of a given pixel x1 (displacement shown as yellow arrow) between two images, due to the camera motion . However, the minimization (-1.14) already took a strong assumption: every pixel in W(x1) is moving by the same amount ."}
+{"idx": 2, "title": "Feature Descriptor in Image Processing - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 23, 2025 · In image processing, a feature descriptor is a representation of an image region or key point that captures relevant information about the image content. In this article, we are going to discuss one of the image processing algorithms i.e. Feature Descriptor Image processing Image processing is a computer vision technique that deals with the manipulation and analysis of digital images. It ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/computer-vision/feature-descriptor-in-image-processing/", "content": "Jul 23, 2025 · In image processing, a feature descriptor is a representation of an image region or key point that captures relevant information about the image content. In this article, we are going to discuss one of the image processing algorithms i.e. Feature Descriptor Image processing Image processing is a computer vision technique that deals with the manipulation and analysis of digital images. It ..."}
+{"idx": 3, "title": "Feature Detectors and Descriptors: Corners, Blobs, and SIFT", "date": "", "ddg_snippet": "Feature Descriptors Once we have detected distinctive and repeatable features, still have to match them across images – Image alignment (e.g., mosaics), 3D reconstruction, motion tracking , object recognition, indexing robot navigation, etc. ? [Seitz]", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/courses/archive/fall17/cos429/notes/fall2016/cos429_f16_lecture04_feature.pdf", "content": "Feature Descriptors Once we have detected distinctive and repeatable features, still have to match them across images – Image alignment (e.g., mosaics), 3D reconstruction, motion tracking , object recognition, indexing robot navigation, etc. ? [Seitz]"}
+{"idx": 4, "title": "Bose Descriptor-In-Pixel Point-Feature Tracking For Pixel ...", "date": "", "ddg_snippet": "This paper introduces a novel method for point-feature detection and tracking using Pixel Processor Array (PPA) vision sensors, which enables in- pixel computation and significantly reduces data transfer requirements. The proposed Descriptor-In-Pixel paradigm allows for efficient processing at over 3000 FPS, making it suitable for high-speed applications while maintaining low latency. By ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/890539401/Bose-Descriptor-In-Pixel-Point-Feature-Tracking-for-Pixel-Processor-Arrays-CVPR-2025-Paper", "content": "This paper introduces a novel method for point-feature detection and tracking using Pixel Processor Array (PPA) vision sensors, which enables in- pixel computation and significantly reduces data transfer requirements. The proposed Descriptor-In-Pixel paradigm allows for efficient processing at over 3000 FPS, making it suitable for high-speed applications while maintaining low latency. By ..."}
+{"idx": 5, "title": "Feature Detection and Extraction - MATLAB & Simulink - MathWorks", "date": "", "ddg_snippet": "Feature Detection and Extraction Image registration, interest point detection, feature descriptor extraction, point feature matching, and image retrieval Local features and their descriptors are the building blocks of many computer vision algorithms.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/help/vision/feature-detection-and-extraction.html", "content": "Feature Detection and Extraction Image registration, interest point detection, feature descriptor extraction, point feature matching, and image retrieval Local features and their descriptors are the building blocks of many computer vision algorithms."}
+{"idx": 6, "title": "CS231M · Mobile Computer Vision - Stanford University", "date": "", "ddg_snippet": "Motion estimation techniques Optical flow Recover image motion at each pixel from spatio-temporal image brightness variations (optical flow) Feature- tracking Extract visual features (corners, textured areas) and “track” them over multiple frames", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs231m/lectures/lecture-7-optical-flow.pdf", "content": "Motion estimation techniques Optical flow Recover image motion at each pixel from spatio-temporal image brightness variations (optical flow) Feature- tracking Extract visual features (corners, textured areas) and “track” them over multiple frames"}
+{"idx": 7, "title": "Stru U", "date": "", "ddg_snippet": "The topic title appears at the top of the page followed by an ex- tended figure caption, and the figure is located at the bottom of the page. Considerable.", "subpage_snippet": "", "source": "ntrs.nasa.gov", "link": "https://ntrs.nasa.gov/api/citations/19880013842/downloads/19880013842.pdf", "content": "The topic title appears at the top of the page followed by an ex- tended figure caption, and the figure is located at the bottom of the page. Considerable."}
+{"idx": 8, "title": "https://huggingface.co/spaces/bigcode/santacoder-t...", "date": "", "ddg_snippet": "... Figure \",\"type\":\"Plot\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"hatch_alpha\":{\"value\":0.1},\"line_alpha\":{\"value\":0.1 ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/bigcode/santacoder-tokens/commit/181cea1211b07ce990fce3847da8154861cbea6f.diff?file=index.html", "content": "... Figure \",\"type\":\"Plot\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"hatch_alpha\":{\"value\":0.1},\"line_alpha\":{\"value\":0.1 ..."}
+{"idx": 9, "title": "OmniVision Technologies, Inc", "date": "", "ddg_snippet": "To meet the increasing demands of the evolving digital imaging market, OmniVision has expanded the core capabilities of its pixel , lens and packaging ...", "subpage_snippet": "", "source": "www.annualreports.com", "link": "https://www.annualreports.com/HostedData/AnnualReportArchive/o/NASDAQ_OVTI_2009.pdf", "content": "To meet the increasing demands of the evolving digital imaging market, OmniVision has expanded the core capabilities of its pixel , lens and packaging ..."}
diff --git a/data/sampled_jsons/33775_Instant_Gaussian_Stream-_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_.jsonl b/data/sampled_jsons/33775_Instant_Gaussian_Stream-_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bc7ecab791f93d625969b2e7265493c567c217a5
--- /dev/null
+++ b/data/sampled_jsons/33775_Instant_Gaussian_Stream-_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_.jsonl
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+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.16979", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians ."}
+{"idx": 1, "title": "PDF Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 62, 66, 69, 71] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 62, 66, 69, 71] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ..."}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "@misc{yan2025instantgaussianstreamfast, title={Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "@misc{yan2025instantgaussianstreamfast, title={Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting ..."}
+{"idx": 3, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 4, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "This paper proposes Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, which introduces a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians . Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Instant-Gaussian-Stream:-Fast-and-Generalizable-of-Yan-Peng/b1554873f9eb74b1fe602a534cde1cce6cb298bd", "content": "This paper proposes Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, which introduces a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians . Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to ..."}
+{"idx": 5, "title": "DASS: Dynamics-Aware Gaussian Splatting Streaming", "date": "", "ddg_snippet": "The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction from multi-view visual inputs. While existing approaches mainly rely on processing full-length multi-view videos for 4D reconstruction , there has been limited exploration of iterative online reconstruction methods that enable on-the-fly training and per-frame streaming . Current ...", "subpage_snippet": "", "source": "www.liuzhening.top", "link": "https://www.liuzhening.top/DASS", "content": "The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction from multi-view visual inputs. While existing approaches mainly rely on processing full-length multi-view videos for 4D reconstruction , there has been limited exploration of iterative online reconstruction methods that enable on-the-fly training and per-frame streaming . Current ..."}
+{"idx": 6, "title": "Rui Peng's Homepage", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang CVPR 2025 (Highlight) Paper | Code", "subpage_snippet": "", "source": "prstrive.github.io", "link": "https://prstrive.github.io/", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang CVPR 2025 (Highlight) Paper | Code"}
+{"idx": 7, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 16520-16531", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.html", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 16520-16531"}
+{"idx": 8, "title": "IGS/README.md at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/blob/master/README.md", "content": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting"}
+{"idx": 9, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ..."}
diff --git a/data/sampled_jsons/42nd_International_Conference_on_Machine_Learning.jsonl b/data/sampled_jsons/42nd_International_Conference_on_Machine_Learning.jsonl
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index 0000000000000000000000000000000000000000..c9a49fb872480ea1aa6afe9dbdc608720d63fe4e
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+{"idx": 0, "title": "International Conference on Machine Learning - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. The International Conference on Machine Learning is a leading international academic conference in machine learning . Along with NeurIPS and ICLR, it is one of the three most respected conferences of high impa...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/International_Conference_on_Machine_Learning", "content": "Machine learningand data mining. v. t. e. The International Conference on Machine Learning is a leading international academic conference in machine learning . Along with NeurIPS and ICLR, it is one of the three most respected conferences of high impa..."}
+{"idx": 1, "title": "Submission and Formatting Instructions for International Conference ...", "date": "", "ddg_snippet": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada.", "subpage_snippet": "", "source": "media.icml.cc", "link": "https://media.icml.cc/Conferences/ICML2025/Styles/example_paper.pdf", "content": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada."}
+{"idx": 2, "title": "Position: Deep Learning is Not So Mysterious or Different", "date": "", "ddg_snippet": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=42Au7FoD8F", "content": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada."}
+{"idx": 3, "title": "Position: Causal Machine Learning Requires Rigorous Synthetic...", "date": "", "ddg_snippet": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-05066031/document", "content": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada."}
+{"idx": 4, "title": "Profile – Machine Learning Lab", "date": "", "ddg_snippet": "@inproceedings{Salinas2025, title = {Tuning LLM Judge Design Decisions for 1/1000 of the Cost}, author = {David Salinas and Omar Swelam and Frank Hutter}, year = {2025}, booktitle = {Proceedings of the 42 nd International Conference on Machine Learning (ICML)}, keywords = {} }.", "subpage_snippet": "", "source": "ml.informatik.uni-freiburg.de", "link": "https://ml.informatik.uni-freiburg.de/profile/hutter/", "content": "@inproceedings{Salinas2025, title = {Tuning LLM Judge Design Decisions for 1/1000 of the Cost}, author = {David Salinas and Omar Swelam and Frank Hutter}, year = {2025}, booktitle = {Proceedings of the 42 nd International Conference on Machine Learning (ICML)}, keywords = {} }."}
+{"idx": 5, "title": "Flexible Tails for Normalizing Flows - University of Bristol", "date": "", "ddg_snippet": "The 42 nd International Conference on Machine Learning (ICML 2025). Abbreviated title.In International Conference on Machine Learning , 13-19 July 2025, Vancouver, Canada (Proceedings of Machine Learning Research).", "subpage_snippet": "", "source": "research-information.bris.ac.uk", "link": "https://research-information.bris.ac.uk/en/publications/flexible-tails-for-normalizing-flows", "content": "The 42 nd International Conference on Machine Learning (ICML 2025). Abbreviated title.In International Conference on Machine Learning , 13-19 July 2025, Vancouver, Canada (Proceedings of Machine Learning Research)."}
+{"idx": 6, "title": "Kernel Quantile Embeddings and Associated... - UCL Discovery", "date": "", "ddg_snippet": "In: Proceedings of the 42 nd International Conference on Machine Learning . PMLR: Vancouver, Canada.", "subpage_snippet": "", "source": "discovery.ucl.ac.uk", "link": "https://discovery.ucl.ac.uk/id/eprint/10211533/", "content": "In: Proceedings of the 42 nd International Conference on Machine Learning . PMLR: Vancouver, Canada."}
+{"idx": 7, "title": "Incorporating Arbitrary Matrix Group Equivariance into KANs", "date": "", "ddg_snippet": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada.In International Conference on Machine Learning , pp. 933– 941. PMLR, 2017. De Boor, C. A practical guide to splines, volume 27. springer New York, 1978.", "subpage_snippet": "", "source": "zhouchenlin.github.io", "link": "https://zhouchenlin.github.io/Publications/2025-ICML-EKAN.pdf", "content": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada.In International Conference on Machine Learning , pp. 933– 941. PMLR, 2017. De Boor, C. A practical guide to splines, volume 27. springer New York, 1978."}
+{"idx": 8, "title": "DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit...", "date": "", "ddg_snippet": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada.In International conference on machine learning , pp. 1484–1495. PMLR, 2021.", "subpage_snippet": "", "source": "dais.chbe.ubc.ca", "link": "https://dais.chbe.ubc.ca/assets/preprints/2025C01_shuyuan_icml.pdf", "content": "Proceedings of the 42 nd International Conference on Machine Learning , Vancouver, Canada.In International conference on machine learning , pp. 1484–1495. PMLR, 2021."}
+{"idx": 9, "title": "ICML2025 Template - Overleaf, Online LaTeX Editor", "date": "", "ddg_snippet": "International Conference on Machine Learning (ICML 2025)} %.The final versions of papers accepted for publication should follow the same format and naming convention as initial submissions, except that author information (names and affiliations) should be given.", "subpage_snippet": "", "source": "www.overleaf.com", "link": "https://www.overleaf.com/latex/templates/icml2025-template/dhxrkcgkvnkt", "content": "International Conference on Machine Learning (ICML 2025)} %.The final versions of papers accepted for publication should follow the same format and naming convention as initial submissions, except that author information (names and affiliations) should be given."}
diff --git a/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_Experimental_Setup_training_seque.jsonl b/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_Experimental_Setup_training_seque.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb2c7f014f4bb7b398af8bc6ee8d86c90b4a6ac0
--- /dev/null
+++ b/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_Experimental_Setup_training_seque.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Centimeters to Feet and Inches Conversion ( cm to ft) - Inch...", "date": "", "ddg_snippet": "Convert centimeters to feet (cm to ft) with the length conversion calculator , and learn the centimeter to foot formula.", "subpage_snippet": "", "source": "www.inchcalculator.com", "link": "https://www.inchcalculator.com/convert/centimeter-to-foot/", "content": "Convert centimeters to feet (cm to ft) with the length conversion calculator , and learn the centimeter to foot formula."}
+{"idx": 1, "title": "Centimeters to Feet conversion : cm to ft calculator", "date": "", "ddg_snippet": "Centimeters to Feet (cm to ft) conversion calculator for Length conversions with additional tables and formulas.", "subpage_snippet": "", "source": "www.metric-conversions.org", "link": "https://www.metric-conversions.org/length/centimeters-to-feet.htm", "content": "Centimeters to Feet (cm to ft) conversion calculator for Length conversions with additional tables and formulas."}
+{"idx": 2, "title": "cm to ft | Convert centimeters to feet", "date": "", "ddg_snippet": "How many feet in a centimeter ? How to convert cm to feet ? Easily and accurately convert centimeters to feet with our free online converter.", "subpage_snippet": "", "source": "convertfeet.com", "link": "https://convertfeet.com/cm-to-feet", "content": "How many feet in a centimeter ? How to convert cm to feet ? Easily and accurately convert centimeters to feet with our free online converter."}
+{"idx": 3, "title": "centimeter to foot calculator - Sage Calculator", "date": "", "ddg_snippet": "Accurate length conversion is essential in engineering, construction, manufacturing, and scientific applications. Converting centimeters ( cm ) to feet (ft) is often required when working with both metric and imperial measurement systems. Our Centimeter to Foot Calculator provides instant and precise results, eliminating manual errors and saving time.", "subpage_snippet": "", "source": "sagecalculator.com", "link": "https://sagecalculator.com/centimeter-to-foot-calculator/", "content": "Accurate length conversion is essential in engineering, construction, manufacturing, and scientific applications. Converting centimeters ( cm ) to feet (ft) is often required when working with both metric and imperial measurement systems. Our Centimeter to Foot Calculator provides instant and precise results, eliminating manual errors and saving time."}
+{"idx": 4, "title": "Cm to Feet +Inches Converter ( cm to ft) - The Calculator Site", "date": "", "ddg_snippet": "Mar 7, 2023 · Use our calculator to convert between cm and feet for height, length or distance measurements. You can also use our reference chart of common conversions and read about how to convert .", "subpage_snippet": "", "source": "www.thecalculatorsite.com", "link": "https://www.thecalculatorsite.com/conversions/common/cm-to-feet-inches.php", "content": "Mar 7, 2023 · Use our calculator to convert between cm and feet for height, length or distance measurements. You can also use our reference chart of common conversions and read about how to convert ."}
+{"idx": 5, "title": "Convert cm to feet - Unit Converter", "date": "", "ddg_snippet": "Instant free online tool for centimeter to foot conversion or vice versa. The centimeter [cm] to foot [ft] conversion table and conversion steps are also listed.", "subpage_snippet": "", "source": "www.unitconverters.net", "link": "https://www.unitconverters.net/length/cm-to-feet.htm", "content": "Instant free online tool for centimeter to foot conversion or vice versa. The centimeter [cm] to foot [ft] conversion table and conversion steps are also listed."}
+{"idx": 6, "title": "CM to Feet Converter - RapidTables.com", "date": "", "ddg_snippet": "CM to feet (cm to ft) converter and how to convert.", "subpage_snippet": "", "source": "www.rapidtables.com", "link": "https://www.rapidtables.com/convert/length/cm-to-feet.html", "content": "CM to feet (cm to ft) converter and how to convert."}
+{"idx": 7, "title": "Convert Centimeters to Feet - Length Unit Converter", "date": "", "ddg_snippet": "Free online centimeters to feet converter . Quick and easy length unit conversion calculator. Convert between length units of measurement.", "subpage_snippet": "", "source": "www.unitconverters.app", "link": "https://www.unitconverters.app/length/cm-to-feet.htm", "content": "Free online centimeters to feet converter . Quick and easy length unit conversion calculator. Convert between length units of measurement."}
+{"idx": 8, "title": "Convert Centimeters to Feet Online - Calculatio", "date": "", "ddg_snippet": "Free online cm to feet converter . Convert centimeters to feet and inches instantly. Perfect height conversion tool for measurements.", "subpage_snippet": "", "source": "calculat.io", "link": "https://calculat.io/en/length/cm-to-feet", "content": "Free online cm to feet converter . Convert centimeters to feet and inches instantly. Perfect height conversion tool for measurements."}
+{"idx": 9, "title": "Convert centimetres to feet - Conversion of Measurement Units", "date": "", "ddg_snippet": "Do a quick conversion: 1 centimetres = 0.032808398950131 feet using the online calculator for metric conversions. Check the chart for more details.", "subpage_snippet": "", "source": "www.convertunits.com", "link": "https://www.convertunits.com/from/centimetres/to/feet", "content": "Do a quick conversion: 1 centimetres = 0.032808398950131 feet using the online calculator for metric conversions. Check the chart for more details."}
diff --git a/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_Initialization.jsonl b/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_Initialization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_Initialization.jsonl
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/4HQaMUYWAT_reasoning_mapping_formula_Frsn(X)_key_34_anchor_11_14_target_59.jsonl b/data/sampled_jsons/4HQaMUYWAT_reasoning_mapping_formula_Frsn(X)_key_34_anchor_11_14_target_59.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..93385118bb649c3247d72c436ff6c2858e99a6a7
--- /dev/null
+++ b/data/sampled_jsons/4HQaMUYWAT_reasoning_mapping_formula_Frsn(X)_key_34_anchor_11_14_target_59.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning Mathematical reasoning Formula: Explanation and Solved Examples Numerical Reasoning Formulas You Need To Know To Pass Quantitative Reasoning Formula Sheet Flashcards | Quizlet Mathematical-reasoning Formula Sheet - Doubtlet Solve - Step-by-Step Math Problem Solver NUMERICAL REASONING FORMULA SHEET - AssessmentDay", "date": "", "ddg_snippet": "Feb 20, 2024 · The application of formulas (e.g., physics formulas) is a fundamental ability of humans when solving numerical reasoning problems. Existing numerical reasoning datasets seldom explicitly indicate the formulas employed in reasoning , as their questions rely on implicit commonsense mathematical knowledge. In contrast, in this paper, we introduce FormulaReasoning, a new dataset specifically ... Sep 13, 2023 · Mathematical reasoning Formula is an essential skill that empowers students to assess a given hypothesis devoid of any specific context or interpretation. Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond. Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more. May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements. The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require. So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.12692", "content": "Feb 20, 2024 · The application of formulas (e.g., physics formulas) is a fundamental ability of humans when solving numerical reasoning problems. Existing numerical reasoning datasets seldom explicitly indicate the formulas employed in reasoning , as their questions rely on implicit commonsense mathematical knowledge. In contrast, in this paper, we introduce FormulaReasoning, a new dataset specifically ... Sep 13, 2023 · Mathematical reasoning Formula is an essential skill that empowers students to assess a given hypothesis devoid of any specific context or interpretation. Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond. Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more. May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements. The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require. So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months."}
+{"idx": 1, "title": "Mathematical reasoning Formula: Explanation and Solved Examples Numerical Reasoning Formulas You Need To Know To Pass Quantitative Reasoning Formula Sheet Flashcards | Quizlet Mathematical-reasoning Formula Sheet - Doubtlet Solve - Step-by-Step Math Problem Solver NUMERICAL REASONING FORMULA SHEET - AssessmentDay", "date": "", "ddg_snippet": "Sep 13, 2023 · Mathematical reasoning Formula is an essential skill that empowers students to assess a given hypothesis devoid of any specific context or interpretation. Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond. Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more. May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements. The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require. So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months.", "subpage_snippet": "", "source": "www.pw.live", "link": "https://www.pw.live/school-prep/exams/mathematical-reasoning-formula", "content": "Sep 13, 2023 · Mathematical reasoning Formula is an essential skill that empowers students to assess a given hypothesis devoid of any specific context or interpretation. Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond. Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more. May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements. The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require. So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months."}
+{"idx": 2, "title": "Numerical Reasoning Formulas You Need To Know To Pass", "date": "", "ddg_snippet": "Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond.", "subpage_snippet": "", "source": "www.practiceaptitudetests.com", "link": "https://www.practiceaptitudetests.com/resources/numerical-reasoning-formulas-you-need-to-know-to-pass-2023/", "content": "Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond."}
+{"idx": 3, "title": "Mathematical-reasoning Formula Sheet - Doubtlet", "date": "", "ddg_snippet": "May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements.", "subpage_snippet": "", "source": "doubtlet.com", "link": "https://doubtlet.com/formula-sheet/mathematical-reasoning/", "content": "May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements."}
+{"idx": 4, "title": "NUMERICAL REASONING FORMULA SHEET - AssessmentDay", "date": "", "ddg_snippet": "So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months.", "subpage_snippet": "", "source": "www.assessmentday.com", "link": "https://www.assessmentday.com/numerical-reasoning-formulas.pdf", "content": "So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months."}
+{"idx": 5, "title": "Quantitative Reasoning Formula Sheet Flashcards | Quizlet", "date": "", "ddg_snippet": "Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/803938812/quantitative-reasoning-formula-sheet-flash-cards/", "content": "Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more."}
+{"idx": 6, "title": "Solve - Step-by-Step Math Problem Solver", "date": "", "ddg_snippet": "The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require.", "subpage_snippet": "", "source": "quickmath.com", "link": "https://quickmath.com/", "content": "The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require."}
+{"idx": 7, "title": "Дмитрий Никотин – Telegram", "date": "", "ddg_snippet": "Про политику, простым языком о сложном. Контрпропаганда и интерпретация новостей.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/dmitrynikotin", "content": "Про политику, простым языком о сложном. Контрпропаганда и интерпретация новостей."}
+{"idx": 8, "title": "Погода в Анталье на 14 дней (Турция) - подробный... - Погода Mail", "date": "", "ddg_snippet": "Подробный прогноз погоды на 14 дней в Анталье (Турция).", "subpage_snippet": "", "source": "pogoda.mail.ru", "link": "https://pogoda.mail.ru/prognoz/antalya/14dney/", "content": "Подробный прогноз погоды на 14 дней в Анталье (Турция)."}
+{"idx": 9, "title": "ГДЗ по биологии 7 класс Пасечник - рабочая тетрадь Просвещение...", "date": "", "ddg_snippet": "§ 11 . Классификация покрытосеменных 45 § 12. Класс Двудольные. Семейства Крестоцветные и Розоцветные 47 § 13. Класс Двудольные. Семейства Паслёновые, Мотыльковые (Бобовые) и Сложноцветные (Астровые) 49 § 14 .", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/7-klass/biologiya/pasechnik-rabochaya-tetrad", "content": "§ 11 . Классификация покрытосеменных 45 § 12. Класс Двудольные. Семейства Крестоцветные и Розоцветные 47 § 13. Класс Двудольные. Семейства Паслёновые, Мотыльковые (Бобовые) и Сложноцветные (Астровые) 49 § 14 ."}
diff --git a/data/sampled_jsons/4tFSKOY2mT_Cross-Verification_module_OmniBench_ablation_study.jsonl b/data/sampled_jsons/4tFSKOY2mT_Cross-Verification_module_OmniBench_ablation_study.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e8767926f17dda6cc83151d00c2755d904251d3f
--- /dev/null
+++ b/data/sampled_jsons/4tFSKOY2mT_Cross-Verification_module_OmniBench_ablation_study.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "May 1, 2025 · The authors' Cross-Verification method cleverly integrates subtask trajectory data with evaluation functions. By leveraging mutual verification between these two types of data, it iteratively optimizes both the synthesized trajectories and the evaluation functions simultaneously.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "May 1, 2025 · The authors' Cross-Verification method cleverly integrates subtask trajectory data with evaluation functions. By leveraging mutual verification between these two types of data, it iteratively optimizes both the synthesized trajectories and the evaluation functions simultaneously."}
+{"idx": 1, "title": "OmniBench：重新定义虚拟代理评估的多维基准测试框架_omnieval-CSDN博...", "date": "", "ddg_snippet": "Jul 4, 2025 · 2. OmniBench ：突破性的自生成基准框架 为解决这些挑战，来自浙江大学、蚂蚁集团等机构的研究团队提出了 OmniBench ——一个基于图结构的自生成、跨平台基准测试框架，通过子任务组合自动合成可控复杂度的任务。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_42540492/article/details/149117606", "content": "Jul 4, 2025 · 2. OmniBench ：突破性的自生成基准框架 为解决这些挑战，来自浙江大学、蚂蚁集团等机构的研究团队提出了 OmniBench ——一个基于图结构的自生成、跨平台基准测试框架，通过子任务组合自动合成可控复杂度的任务。"}
+{"idx": 2, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable ...", "date": "", "ddg_snippet": "For each ablation shown in the table, we sampled 400 task graphs and calculated the average acceptance by three specially trained annotators. The experiment shows that removing any quality control module decreases human acceptance, with the removal of the cross-verification algorithm resulting in the largest drop to 61.2%.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4tFSKOY2mT", "content": "For each ablation shown in the table, we sampled 400 task graphs and calculated the average acceptance by three specially trained annotators. The experiment shows that removing any quality control module decreases human acceptance, with the removal of the cross-verification algorithm resulting in the largest drop to 61.2%."}
+{"idx": 3, "title": "GitHub - antgroup/OmniBench: [ICML 2025 Oral] This is the ...", "date": "", "ddg_snippet": "OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench", "content": "OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition."}
+{"idx": 4, "title": "OmniBench", "date": "", "ddg_snippet": "Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs. It evaluates ten virtual agent capabilities using high-quality graph-based data, ensuring scalable and realistic task assessments.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs. It evaluates ten virtual agent capabilities using high-quality graph-based data, ensuring scalable and realistic task assessments."}
+{"idx": 5, "title": "[2409.15272] OmniBench: Towards The Future of Universal Omni ...", "date": "", "ddg_snippet": "Sep 23, 2024 · Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.15272", "content": "Sep 23, 2024 · Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We ..."}
+{"idx": 6, "title": "README.md · m-a-p/OmniBench at main - Hugging Face", "date": "", "ddg_snippet": "The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni-language models (OLMs).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/m-a-p/OmniBench/blob/main/README.md", "content": "The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni-language models (OLMs)."}
+{"idx": 7, "title": "What Limits Virtual Agent Application? OmniBench ... | OpenReview", "date": "", "ddg_snippet": "OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities. Download PDF.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT&referrer=[the+profile+of+Yunfei+Li](/profile?id=~Yunfei_Li6)", "content": "OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities. Download PDF."}
+{"idx": 8, "title": "Недостатки и отзывы владельцев Lada Vesta SW Cross 2025", "date": "", "ddg_snippet": "LADA Vesta SW Cross позиционировалась производителем как кроссовер нового поколения. Анонсировались новые решения в облике модели, в силовой установке...", "subpage_snippet": "", "source": "autoguruclub.ru", "link": "https://autoguruclub.ru/threads/nedostatki-i-otzyvy-vladelcev-lada-vesta-sw-cross-2025.21034/", "content": "LADA Vesta SW Cross позиционировалась производителем как кроссовер нового поколения. Анонсировались новые решения в облике модели, в силовой установке..."}
+{"idx": 9, "title": "6- ablation - study .ipynb - Colab", "date": "", "ddg_snippet": "Ablation Studies . The gold standard in building complex machine learning models is proving that each constituent part of the model contributes something to the proposed solution.from sklearn.model_selection import cross _val_score.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/jesperdramsch/ml-for-science-reproducibility-tutorial/blob/main/book/notebooks/6-ablation-study.ipynb", "content": "Ablation Studies . The gold standard in building complex machine learning models is proving that each constituent part of the model contributes something to the proposed solution.from sklearn.model_selection import cross _val_score."}
diff --git a/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Dike_Eris.jsonl b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Dike_Eris.jsonl
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index 0000000000000000000000000000000000000000..316d1e633c9cf34060c917d3a9407b03cca81240
--- /dev/null
+++ b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Dike_Eris.jsonl
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+{"idx": 0, "title": "Does Straco Still Operate a Giant Observation Wheel for Sale ...", "date": "", "ddg_snippet": "Check out our does straco still operate a giant observation wheel for sale near me map selection for the very best in unique or custom, handmade pieces from our shops.", "subpage_snippet": "", "source": "www.etsy.com", "link": "https://www.etsy.com/ca/market/does_straco_still_operate_a_giant_observation_wheel_for_sale_near_me_map", "content": "Check out our does straco still operate a giant observation wheel for sale near me map selection for the very best in unique or custom, handmade pieces from our shops."}
+{"idx": 1, "title": "Does Straco Still Operate A Giant Observation Wheel For Sale ...", "date": "", "ddg_snippet": "Découvrez les meilleurs emplois en tant que does straco still operate a giant observation wheel for sale in dubai today sur CareerBeacon.com! Commencez votre recherche des incroyables enterprises et trouvez un meilleur travail, dès aujourd'hui.", "subpage_snippet": "", "source": "www.careerbeacon.com", "link": "https://www.careerbeacon.com/fr/rechercher/does-straco-still-operate-a-giant-observation-wheel-for-sale-in-dubai-today-jobs", "content": "Découvrez les meilleurs emplois en tant que does straco still operate a giant observation wheel for sale in dubai today sur CareerBeacon.com! Commencez votre recherche des incroyables enterprises et trouvez un meilleur travail, dès aujourd'hui."}
+{"idx": 2, "title": "Does Straco Still Operate a Giant Observation Wheel for Sale ...", "date": "", "ddg_snippet": "Check out our does straco still operate a giant observation wheel for sale in orange selection for the very best in unique or custom, handmade pieces from our shops.", "subpage_snippet": "", "source": "www.etsy.com", "link": "https://www.etsy.com/ca/market/does_straco_still_operate_a_giant_observation_wheel_for_sale_in_orange", "content": "Check out our does straco still operate a giant observation wheel for sale in orange selection for the very best in unique or custom, handmade pieces from our shops."}
+{"idx": 3, "title": "A Checks - and - Balances Framework for Context - Aware Ethical AI ...", "date": "", "ddg_snippet": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while..."}
+{"idx": 4, "title": "(PDF) Checks - and - Balances Framework for Context - Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 5, "title": "A Three-Branch Checks - and - Balances Framework", "date": "", "ddg_snippet": "ERIS : Adversarial In- Context Review to Balance Ethics and Cultural Norms.This work presents a three-branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE - ERIS duality.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "ERIS : Adversarial In- Context Review to Balance Ethics and Cultural Norms.This work presents a three-branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE - ERIS duality."}
+{"idx": 6, "title": "ICML Poster A Three-Branch Checks - and - Balances Framework for ...", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models.The adversarial DIKE - ERIS duality enables adaptation to diverse cultural contexts while upholding consistent ethical principles.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models.The adversarial DIKE - ERIS duality enables adaptation to diverse cultural contexts while upholding consistent ethical principles."}
+{"idx": 7, "title": "(PDF) A Three-Branch Checks - and - Balances Framework for ...", "date": "", "ddg_snippet": "This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/edward-y-chang-218b182_pdf-a-three-branch-checks-and-balances-activity-7272792943923458050-RO6k", "content": "This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions."}
+{"idx": 8, "title": "infolab.stanford.edu/~echang/Behavior2024.bib", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models}, author={Chang, Edward Y.}, journal={arXiv preprint arXiv:2502.00136}, year={2024}, url={https...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models}, author={Chang, Edward Y.}, journal={arXiv preprint arXiv:2502.00136}, year={2024}, url={https..."}
+{"idx": 9, "title": "GitHub - langchain- ai /langchain: Build context - aware ...", "date": "", "ddg_snippet": "LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/langchain-ai/langchain", "content": "LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves."}
diff --git a/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Figure_4a_DIKE_GPT.jsonl b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Figure_4a_DIKE_GPT.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c3746a0c779e2c3ac582a327fb13b552fe175bff
--- /dev/null
+++ b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Figure_4a_DIKE_GPT.jsonl
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+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and ..."}
+{"idx": 1, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE (the goddess of justice) as the legislative branch establishing ethical guardrails, and ERIS (the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE (the goddess of justice) as the legislative branch establishing ethical guardrails, and ERIS (the ..."}
+{"idx": 2, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE , a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE , a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as ..."}
+{"idx": 4, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts ."}
+{"idx": 5, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Three-Branch-Checks-and-Balances-Frameworkfor-of-Chang/5918a91419cf95db8599b086590facf63f124702/figure/4", "content": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 6, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 7, "title": "infolab.stanford.edu", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ..."}
+{"idx": 8, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet..."}
+{"idx": 9, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Poster A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Edward Chang East Exhibition Hall A-B #E-703", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "Poster A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Edward Chang East Exhibition Hall A-B #E-703"}
diff --git a/data/sampled_jsons/51x0dfsD8A_Algorithm_2_k-HOC_time_complexity_O(n^4).jsonl b/data/sampled_jsons/51x0dfsD8A_Algorithm_2_k-HOC_time_complexity_O(n^4).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b393569dd4bcda194f7df6a1435b39417f1a154b
--- /dev/null
+++ b/data/sampled_jsons/51x0dfsD8A_Algorithm_2_k-HOC_time_complexity_O(n^4).jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — In Section 3, our algorithm for k - HOC proceeds in this way. Next, we give some fundamental properties of our HOC cost function, and prove them in Appendix A. 4 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "by Y Pan — In Section 3, our algorithm for k - HOC proceeds in this way. Next, we give some fundamental properties of our HOC cost function, and prove them in Appendix A. 4 ."}
+{"idx": 1, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — These approximation algorithms hold for a restricted variant of the HOC problem, namely the k - HOC problem and the 2 - HOC problem. As a final ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "by Y Pan — These approximation algorithms hold for a restricted variant of the HOC problem, namely the k - HOC problem and the 2 - HOC problem. As a final ..."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/6yBhoJn6qy_Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_events.jsonl b/data/sampled_jsons/6yBhoJn6qy_Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_events.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/6yBhoJn6qy_Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_events.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/6yBhoJn6qy_Causal_Modeling_of_Climate_Activism_on_Reddit_six_subreddits_activation.jsonl b/data/sampled_jsons/6yBhoJn6qy_Causal_Modeling_of_Climate_Activism_on_Reddit_six_subreddits_activation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..691c60ec8c91d199b96c692a63a57422934b14a8
--- /dev/null
+++ b/data/sampled_jsons/6yBhoJn6qy_Causal_Modeling_of_Climate_Activism_on_Reddit_six_subreddits_activation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "865 We developed a rich and comprehensive causal model to study the 866 interplay between diferent determinants of climate activism on 867 Reddit . This work represents a first attempt to apply a multi- causal 868 model to social media data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "865 We developed a rich and comprehensive causal model to study the 866 interplay between diferent determinants of climate activism on 867 Reddit . This work represents a first attempt to apply a multi- causal 868 model to social media data."}
+{"idx": 1, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Oct 14, 2024 · Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "Oct 14, 2024 · Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit | Proceedings ...", "date": "", "ddg_snippet": "Apr 22, 2025 · In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714684", "content": "Apr 22, 2025 · In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion)."}
+{"idx": 3, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "This paper aims to promote large - scale climate protests (such as the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion) by constructing a comprehensive causal model to explain how and why Reddit users participate in climate activism communities.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2410.10562", "content": "This paper aims to promote large - scale climate protests (such as the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion) by constructing a comprehensive causal model to explain how and why Reddit users participate in climate activism communities."}
+{"idx": 4, "title": "Causal Modeling of Climate Activism on Reddit - Researchr", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/LentiAMM25", "content": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]"}
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+{"idx": 6, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Firstly, the organization of climate activism movements on Reddit was a grassroots initiative, spawned independent of media coverage. Secondly, coverage about climate action has a positive effect on the individuals activation , with this effect decaying after about a week.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "Firstly, the organization of climate activism movements on Reddit was a grassroots initiative, spawned independent of media coverage. Secondly, coverage about climate action has a positive effect on the individuals activation , with this effect decaying after about a week."}
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+{"idx": 8, "title": "Modeling the Impact of Group Interactions on Climate -related Opinion...", "date": "", "ddg_snippet": "[1] Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. JJacopo LentiLLuca Maria Aiello. +2.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/modeling-the-impact-of-group-interactions-on-climate-related-opinion-change-in-reddit/1126847729390059527-108521", "content": "[1] Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. JJacopo LentiLLuca Maria Aiello. +2."}
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+{"idx": 3, "title": "DeepSeek V3 0324 (free) - API, Providers, Stats | OpenRouter", "date": "", "ddg_snippet": "It succeeds the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs really well on a variety of tasks. Run DeepSeek V3 0324 (free) with API.", "subpage_snippet": "", "source": "openrouter.ai", "link": "https://openrouter.ai/deepseek/deepseek-chat-v3-0324:free", "content": "It succeeds the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs really well on a variety of tasks. Run DeepSeek V3 0324 (free) with API."}
+{"idx": 4, "title": "ПРУFИКИ – Telegram", "date": "", "ddg_snippet": "Мемы, приколы, залипухи Общие вопросы @Proofikxbot Реклама @SMariyS @KristinaPROTG.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/proofikx", "content": "Мемы, приколы, залипухи Общие вопросы @Proofikxbot Реклама @SMariyS @KristinaPROTG."}
+{"idx": 5, "title": "GISMETEO: Погода в Калуге сегодня, прогноз погоды Калуга на...", "date": "", "ddg_snippet": "Подробный прогноз погоды в Калуге на сегодня.", "subpage_snippet": "", "source": "www.gismeteo.ru", "link": "https://www.gismeteo.ru/weather-kaluga-4387/", "content": "Подробный прогноз погоды в Калуге на сегодня."}
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+{"idx": 0, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "rejection finetuning ( RFT ; positive self-generated synthetic data from the SFT m odel) and step-level RL (via per-step DPO) algorithms. SFT on synthetic problems and res ponses by 2 x, whereas using step-level RL with negativ e data scales the efficiency by 8x.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "rejection finetuning ( RFT ; positive self-generated synthetic data from the SFT m odel) and step-level RL (via per-step DPO) algorithms. SFT on synthetic problems and res ponses by 2 x, whereas using step-level RL with negativ e data scales the efficiency by 8x."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "To provide clarity on how synthetic data contributes to performance , we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "To provide clarity on how synthetic data contributes to performance , we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data · MinWoo Park Reinforcement Learning for LLM Reasoning RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... Reinforcement Learning for LLM Reasoning [2406.14532] RL on Incorrect Synthetic Data Scales the Efficiency of LL… Reinforcement Learning for LLM Reasoning dblp: RL on Incorrect Synthetic Data Scales the Efficiency of ...", "date": "", "ddg_snippet": "Jun 20, 2024 · Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ... Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning Jun 20, 2024 · With this per-step scheme, we are able to attain consistent gains over only positive data , attaining performance similar to amplifying the amount of synthetic data by $\\mathbf {8 \\times}$. First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ... Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. Is LLM test-time compute more effective than scaling model parameters? Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Jun 20, 2024 · Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ... Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning Jun 20, 2024 · With this per-step scheme, we are able to attain consistent gains over only positive data , attaining performance similar to amplifying the amount of synthetic data by $\\mathbf {8 \\times}$. First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ... Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. Is LLM test-time compute more effective than scaling model parameters? Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ...", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ..."}
+{"idx": 5, "title": "dblp: RL on Incorrect Synthetic Data Scales the Efficiency of ...", "date": "", "ddg_snippet": "Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2406-14532", "content": "Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Implementation Details. Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4b77d5b896c321a29277524a98a50215-Paper-Conference.pdf", "content": "Implementation Details. Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "• When positive data from 𝜋 sft contains spurious steps, scaling synthetic data leads to worse test errors. 7. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "• When positive data from 𝜋 sft contains spurious steps, scaling synthetic data leads to worse test errors. 7. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/RL-on-Incorrect-Synthetic-Data-Scales-the-Efficiency-of-LLM-Math-Reasoning-by-Eight-Fold-17552ae8-02cf-4931-9351-04c6f7b243c5", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 9, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of reinforcement learning ( RL ) on incorrect synthetic data to improve the math reasoning abilities of large language models (LLMs).", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/rl-incorrect-synthetic-data-scales-efficiency-llm", "content": "This paper investigates the use of reinforcement learning ( RL ) on incorrect synthetic data to improve the math reasoning abilities of large language models (LLMs)."}
diff --git a/data/sampled_jsons/9v1eW8HgMU_Archetypal_SAE_Section_6_Scaling_Archetypal-SAE_relaxation_term_norm_constraint.jsonl b/data/sampled_jsons/9v1eW8HgMU_Archetypal_SAE_Section_6_Scaling_Archetypal-SAE_relaxation_term_norm_constraint.jsonl
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index 0000000000000000000000000000000000000000..abc9e4a74cc810699e5a11f27df3c71d9f6173cb
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "To enable a controlled degree of flexibility beyond conv(C), we introduce a mild relaxation term Λ ∈Rk×d, a matrix of the same dimensions as the dictionary, with a small norm constraint ||Λ||2 2≤δ.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9v1eW8HgMU", "content": "To enable a controlled degree of flexibility beyond conv(C), we introduce a mild relaxation term Λ ∈Rk×d, a matrix of the same dimensions as the dictionary, with a small norm constraint ||Λ||2 2≤δ."}
+{"idx": 1, "title": "Archetypal SAEs: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation , matching standard SAEs in reconstruction while maintaining stability. Both integrate with any SAE variant (e.g., TopK, JumpReLU). B) Instability Problem.", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation , matching standard SAEs in reconstruction while maintaining stability. Both integrate with any SAE variant (e.g., TopK, JumpReLU). B) Instability Problem."}
+{"idx": 2, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts. However, we reveal a fundamental limitation: existing SAEs exhibit severe instability, as identical models trained on similar datasets can produce sharply different ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts. However, we reveal a fundamental limitation: existing SAEs exhibit severe instability, as identical models trained on similar datasets can produce sharply different ..."}
+{"idx": 3, "title": "Archetypal - Overcomplete", "date": "", "ddg_snippet": "Archetypal SAE introduces a constraint on the dictionary where each atom is formed as a convex combination of data points with an additional relaxation term . This method enhances stability and interpretability in dictionary learning, making it a robust drop-in replacement for the dictionary layer in any Sparse Autoencoder.", "subpage_snippet": "", "source": "kempnerinstitute.github.io", "link": "https://kempnerinstitute.github.io/overcomplete/saes/archetypal/", "content": "Archetypal SAE introduces a constraint on the dictionary where each atom is formed as a convex combination of data points with an additional relaxation term . This method enhances stability and interpretability in dictionary learning, making it a robust drop-in replacement for the dictionary layer in any Sparse Autoencoder."}
+{"idx": 4, "title": "Archetypal SAEs: Adaptive and Stable Dictionary Learning for ... - Medium", "date": "", "ddg_snippet": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@kempnerinstitute/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-acf95010c691", "content": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation ..."}
+{"idx": 5, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "The magnitude of this relaxation enables the Archetypal SAE to achieve performance comparable to the unconstrained TopK SAE denoted as Baseline (left) while maintaining excellent stability (right).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v2", "content": "The magnitude of this relaxation enables the Archetypal SAE to achieve performance comparable to the unconstrained TopK SAE denoted as Baseline (left) while maintaining excellent stability (right)."}
+{"idx": 6, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "To enable a controlled degree of flexibility beyond conv (𝑪), we introduce a mild relaxation term 𝚲 ∈ ℝ k × d, a matrix of the same dimensions as the dictionary, with a small norm constraint ‖ 𝚲 ‖ 2 2 ≤ δ.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.12892", "content": "To enable a controlled degree of flexibility beyond conv (𝑪), we introduce a mild relaxation term 𝚲 ∈ ℝ k × d, a matrix of the same dimensions as the dictionary, with a small norm constraint ‖ 𝚲 ‖ 2 2 ≤ δ."}
+{"idx": 7, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A-SAE), wherein dictionary atoms are constrained to the convex hull of data.", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2502.12892", "content": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A-SAE), wherein dictionary atoms are constrained to the convex hull of data."}
+{"idx": 8, "title": "Section List - SAE Sections - Membership - SAE International", "date": "", "ddg_snippet": "Section List The following is an alphabetical reference guide to assist the user in locating a specific section /group within a state, province or country. Click on the relevant link to view each organization's website. Note: If your section is inactive, please reach out to sections@sae.org for information on restarting a section in your area.", "subpage_snippet": "", "source": "www.sae.org", "link": "https://www.sae.org/participate/membership/sections/list", "content": "Section List The following is an alphabetical reference guide to assist the user in locating a specific section /group within a state, province or country. Click on the relevant link to view each organization's website. Note: If your section is inactive, please reach out to sections@sae.org for information on restarting a section in your area."}
+{"idx": 9, "title": "Daily arXiv Papers - 2025-09-18", "date": "", "ddg_snippet": "We introduce a geometric framework to address this, based on archetypal analysis of batches of responses sampled with only black-box model access. At the global level, we propose Geometric Volume, which measures the convex hull volume of archetypes derived from response embeddings.", "subpage_snippet": "", "source": "blog.yueqianlin.com", "link": "https://blog.yueqianlin.com/daily-publication/250918/", "content": "We introduce a geometric framework to address this, based on archetypal analysis of batches of responses sampled with only black-box model access. At the global level, we propose Geometric Volume, which measures the convex hull volume of archetypes derived from response embeddings."}
diff --git a/data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_Strategies_Rew_short_equation_formula.jsonl b/data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_Strategies_Rew_short_equation_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..800c43a0f4fdd26a49ff42ba707e3cdf36ea4a0e
--- /dev/null
+++ b/data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_Strategies_Rew_short_equation_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short-term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ", "content": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short-term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors ."}
+{"idx": 1, "title": "[2403.07932v2] Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932v2", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ..."}
+{"idx": 2, "title": "Feint behaviors and strategies: - ACM Digital Library", "date": "", "ddg_snippet": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738032", "content": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies ."}
+{"idx": 3, "title": "NeurIPS Poster Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "Poster Feint Behaviors and Strategies : Formalization, Implementation and Evaluation Junyu Liu · Xiangjun Peng", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96274", "content": "Poster Feint Behaviors and Strategies : Formalization, Implementation and Evaluation Junyu Liu · Xiangjun Peng"}
+{"idx": 4, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "The concept of 'Feint Formalization' in a research paper would involve a systematic and rigorous approach to defining and modeling feint behaviors . This would likely include a detailed action-level formalization, specifying the precise actions that constitute a feint , and a strategy-level formalization, outlining how feints are integrated into broader game strategies to achieve advantages ...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "The concept of 'Feint Formalization' in a research paper would involve a systematic and rigorous approach to defining and modeling feint behaviors . This would likely include a detailed action-level formalization, specifying the precise actions that constitute a feint , and a strategy-level formalization, outlining how feints are integrated into broader game strategies to achieve advantages ..."}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "However, these prior works (1) lack concrete formalizations of Feint behavior characteristics, which cannot fully unveil the variety of Feint behaviors in the action level; and (2) lack comprehensive explorations of Feint behaviors implications on game strategies , which neglects the potential impacts of fusing effective Feint behaviors into ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ACIDDnTbSJ", "content": "However, these prior works (1) lack concrete formalizations of Feint behavior characteristics, which cannot fully unveil the variety of Feint behaviors in the action level; and (2) lack comprehensive explorations of Feint behaviors implications on game strategies , which neglects the potential impacts of fusing effective Feint behaviors into ..."}
+{"idx": 6, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Overview. Our work provides the first comprehensive and concrete formalization of Feint behaviors in action-level and strategy-level. We first present an automatic approach to generate Feint behaviors using Palindrome-directed Templates based on our observation on Feint characteristics, and provide Dual- Behavior Model to examine the design for the combination of Feint behaviors and normal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v2", "content": "Overview. Our work provides the first comprehensive and concrete formalization of Feint behaviors in action-level and strategy-level. We first present an automatic approach to generate Feint behaviors using Palindrome-directed Templates based on our observation on Feint characteristics, and provide Dual- Behavior Model to examine the design for the combination of Feint behaviors and normal ..."}
+{"idx": 7, "title": "PDF Handout #16: Function-Based Intervention Strategies", "date": "", "ddg_snippet": "The following tables provide possible intervention strategies to incorporate into a comprehensive behavior intervention plan based on the function of the problem behavior .", "subpage_snippet": "", "source": "ceedar.education.ufl.edu", "link": "https://ceedar.education.ufl.edu/wp-content/uploads/2014/09/Handout-16-Function-Based-Intervention-Strategies.pdf", "content": "The following tables provide possible intervention strategies to incorporate into a comprehensive behavior intervention plan based on the function of the problem behavior ."}
+{"idx": 8, "title": "Understanding Feints - 204s", "date": "", "ddg_snippet": "Feints are deceptive movements or gestures that a fighter uses to make their opponent react defensively. The purpose of a feint is to create an opening or to gain a better understanding of an opponent's reactions. It's a crucial aspect of high-level striking strategy and often goes unnoticed by casual viewers. How Feints Work When a fighter uses a feint , they simulate an attack — like ...", "subpage_snippet": "", "source": "204s.wordpress.com", "link": "https://204s.wordpress.com/2024/11/02/understanding-feints/", "content": "Feints are deceptive movements or gestures that a fighter uses to make their opponent react defensively. The purpose of a feint is to create an opening or to gain a better understanding of an opponent's reactions. It's a crucial aspect of high-level striking strategy and often goes unnoticed by casual viewers. How Feints Work When a fighter uses a feint , they simulate an attack — like ..."}
+{"idx": 9, "title": "Junyu Liu - Projects", "date": "", "ddg_snippet": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies .", "subpage_snippet": "", "source": "junyu-liu-nate.github.io", "link": "https://junyu-liu-nate.github.io/projects/FeintFinal.html", "content": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies ."}
diff --git a/data/sampled_jsons/ACM_Digital_Library_WWW_2024_proceedings_Information_Retrieval_papers_year_2024.jsonl b/data/sampled_jsons/ACM_Digital_Library_WWW_2024_proceedings_Information_Retrieval_papers_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e48df2ccbb5dc013bc4af69a842fdc80a93c926
--- /dev/null
+++ b/data/sampled_jsons/ACM_Digital_Library_WWW_2024_proceedings_Information_Retrieval_papers_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICTIR '24: Proceedings of the 2024 ACM SIGIR ...", "date": "", "ddg_snippet": "5 Aug 2024 — ICTIR is the premier forum for presenting and discussing research on theoretical and foundational aspects of Information Retrieval .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3664190", "content": "5 Aug 2024 — ICTIR is the premier forum for presenting and discussing research on theoretical and foundational aspects of Information Retrieval ."}
+{"idx": 1, "title": "Proceedings of the ACM Web Conference 2024", "date": "", "ddg_snippet": "13 May 2024 — This conference has been the premier venue to present and discuss progress in research, development, standards and applications of topics related to the Web.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3589334", "content": "13 May 2024 — This conference has been the premier venue to present and discuss progress in research, development, standards and applications of topics related to the Web."}
+{"idx": 2, "title": "SIGIR 2024 Proceedings", "date": "", "ddg_snippet": "SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval ... ACM Digital Library . SESSION ...", "subpage_snippet": "", "source": "sigir-2024.github.io", "link": "https://sigir-2024.github.io/proceedings.html", "content": "SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval ... ACM Digital Library . SESSION ..."}
+{"idx": 3, "title": "ACM CCS 2024", "date": "", "ddg_snippet": "Faster FHE-based Single-server Private Information Retrieval , Ming Luo (Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering ...", "subpage_snippet": "", "source": "www.sigsac.org", "link": "https://www.sigsac.org/ccs/CCS2024/program/accepted-papers.html", "content": "Faster FHE-based Single-server Private Information Retrieval , Ming Luo (Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering ..."}
+{"idx": 4, "title": "JCDL 2024 - Joint Conference on Digital Libraries", "date": "", "ddg_snippet": "The ACM /IEEE-CS Joint Conference on Digital Libraries (JCDL) is a major international forum focusing on digital libraries and associated technical, practical, ...", "subpage_snippet": "", "source": "2024.jcdl.org", "link": "https://2024.jcdl.org/", "content": "The ACM /IEEE-CS Joint Conference on Digital Libraries (JCDL) is a major international forum focusing on digital libraries and associated technical, practical, ..."}
+{"idx": 5, "title": "Call for Resource & Reproducibility Papers - SIGIR 2024", "date": "", "ddg_snippet": "The 47th ACM SIGIR conference will be run in-person in Washington D.C., USA, from July 14-18, 2024 . This year we continue a special track for resource and ...", "subpage_snippet": "", "source": "sigir-2024.github.io", "link": "https://sigir-2024.github.io/call_for_res_rep_papers.html", "content": "The 47th ACM SIGIR conference will be run in-person in Washington D.C., USA, from July 14-18, 2024 . This year we continue a special track for resource and ..."}
+{"idx": 6, "title": "Enhancing Health Information Retrieval with RAG by ...", "date": "", "ddg_snippet": "by R Upadhyay · 2025 · Cited by 7 — This paper introduces a solution driven by Retrieval -Augmented Generation (RAG), which leverages the capabilities of generative Large Language Models (LLMs)", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10791-025-09505-5", "content": "by R Upadhyay · 2025 · Cited by 7 — This paper introduces a solution driven by Retrieval -Augmented Generation (RAG), which leverages the capabilities of generative Large Language Models (LLMs)"}
+{"idx": 7, "title": "WSDM '19- Proceedings of the Twelfth ACM International ...", "date": "", "ddg_snippet": "This presentation will survey some recent success stories of reinforcement learning for search, recommendation, and conversations.", "subpage_snippet": "", "source": "kdd.org", "link": "https://kdd.org/proceedings/view/wsdm-19-proceedings-of-the-twelfth-acm-international-conference-on-web-sear", "content": "This presentation will survey some recent success stories of reinforcement learning for search, recommendation, and conversations."}
+{"idx": 8, "title": "Research Tracks", "date": "", "ddg_snippet": "This track is a forum for theoretical, empirical, and applied research related to the modeling, analysis, and design of Web-related economic activities.", "subpage_snippet": "", "source": "www2025.thewebconf.org", "link": "https://www2025.thewebconf.org/research-tracks", "content": "This track is a forum for theoretical, empirical, and applied research related to the modeling, analysis, and design of Web-related economic activities."}
+{"idx": 9, "title": "\"Filtering.\", Genre: Conference ... - SearchWorks catalog, Subject", "date": "", "ddg_snippet": "On the use of matrix Bloom filters in data leak protection.- A hybrid approach for the automatic extraction of causal relations from text.", "subpage_snippet": "", "source": "searchworks.stanford.edu", "link": "https://searchworks.stanford.edu/?f[genre_ssim][]=Conference+proceedings&per_page=50&q=\"Filtering.\"&search_field=subject_terms&sort=relevance", "content": "On the use of matrix Bloom filters in data leak protection.- A hybrid approach for the automatic extraction of causal relations from text."}
diff --git a/data/sampled_jsons/AEROChain_Section_5.1_experimental_setup_total_nodes.jsonl b/data/sampled_jsons/AEROChain_Section_5.1_experimental_setup_total_nodes.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..00f87cff93744213f0330b0aba3273fb84a2a8f5
--- /dev/null
+++ b/data/sampled_jsons/AEROChain_Section_5.1_experimental_setup_total_nodes.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "by M Song · Cited by 2 — The experimental setup consists of 16 physical shards, each containing 8 nodes, amounting to a total of 128 nodes in the total network. During each epoch, the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "by M Song · Cited by 2 — The experimental setup consists of 16 physical shards, each containing 8 nodes, amounting to a total of 128 nodes in the total network. During each epoch, the ..."}
+{"idx": 1, "title": "How can I see what products are available on Costco.com?", "date": "", "ddg_snippet": "To view products on Costco.com, either type what you’re looking for into the Search box, or mouse over \"Shop\" to view our different categories. Simply click on the name or picture of an item to navigate to its product page. The great thing about shopping on Costco.com is that everything you see reflects our live, real-time and up-to-date ...", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/671/~/how-can-i-see-what-products-are-available-on-costco.com?", "content": "To view products on Costco.com, either type what you’re looking for into the Search box, or mouse over \"Shop\" to view our different categories. Simply click on the name or picture of an item to navigate to its product page. The great thing about shopping on Costco.com is that everything you see reflects our live, real-time and up-to-date ..."}
+{"idx": 2, "title": "How can I locate a product on Costco.com?", "date": "", "ddg_snippet": "To search for a product online at Costco.com, enter a keyword or an item number into the search engine at the top. If the item you’re seeking is in stock and available for purchase, your search will pull up its product page. You can view the item details on the product page, add it to your cart and proceed to checkout. You can also browse through the drop-down menu of departments on the ...", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/668/~/how-can-i-locate-a-product-on-costco.com?", "content": "To search for a product online at Costco.com, enter a keyword or an item number into the search engine at the top. If the item you’re seeking is in stock and available for purchase, your search will pull up its product page. You can view the item details on the product page, add it to your cart and proceed to checkout. You can also browse through the drop-down menu of departments on the ..."}
+{"idx": 3, "title": "Contact Us - Costco Customer Service", "date": "", "ddg_snippet": "Welcome to the Costco Customer Service page. Explore our many helpful self-service options and learn more about popular topics.", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/9/~/contact-us", "content": "Welcome to the Costco Customer Service page. Explore our many helpful self-service options and learn more about popular topics."}
+{"idx": 4, "title": "Find a Warehouse - Costco Customer Service", "date": "", "ddg_snippet": "Find your nearest Costco warehouse location and explore helpful self-service options for customer support.", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/answer_view/a_id/1007276/~/find-a-warehouse", "content": "Find your nearest Costco warehouse location and explore helpful self-service options for customer support."}
+{"idx": 5, "title": "How do I place an order on Costco.com? - Costco Customer Service", "date": "", "ddg_snippet": "If you’re a member, enter the membership number found on your membership card. You’re now ready to start shopping! Note: Maintaining an active Costco membership doesn’t automatically register you on Costco.com. After becoming a Costco member, you’ll still need to complete a new registration on Costco.com (if you didn’t previously ...", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/762/~/how-do-i-place-an-order-on-costco.com?", "content": "If you’re a member, enter the membership number found on your membership card. You’re now ready to start shopping! Note: Maintaining an active Costco membership doesn’t automatically register you on Costco.com. After becoming a Costco member, you’ll still need to complete a new registration on Costco.com (if you didn’t previously ..."}
+{"idx": 6, "title": "Where can I find Costco Services contact information?", "date": "", "ddg_snippet": "Where can I find Costco Services contact information? You’ll find the phone numbers and information for each of our Costco Services (for your home and business needs) here. Simply click on the name of the service to visit their website.", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/1198", "content": "Where can I find Costco Services contact information? You’ll find the phone numbers and information for each of our Costco Services (for your home and business needs) here. Simply click on the name of the service to visit their website."}
+{"idx": 7, "title": "Costco Customer Service", "date": "", "ddg_snippet": "Welcome to the Costco Customer Service page. Explore our many helpful self-service options and learn more about popular topics.", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/", "content": "Welcome to the Costco Customer Service page. Explore our many helpful self-service options and learn more about popular topics."}
+{"idx": 8, "title": "How do I check warehouse inventory? - Costco Customer Service", "date": "", "ddg_snippet": "You can use the Costco App to check warehouse inventory. Head to our mobile app page to learn more! To use our customer service page instead, follow these steps: Select Chat Us in the lower right corner of the page Enter your first name, last name, and email address Enter a message that includes the word “inventory” in the “Your Message” field Select Login to your Costco.com account ...", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/answer_view/a_id/1015066/~/how-do-i-check-warehouse-inventory?", "content": "You can use the Costco App to check warehouse inventory. Head to our mobile app page to learn more! To use our customer service page instead, follow these steps: Select Chat Us in the lower right corner of the page Enter your first name, last name, and email address Enter a message that includes the word “inventory” in the “Your Message” field Select Login to your Costco.com account ..."}
+{"idx": 9, "title": "What is Costco’s return policy? - Costco Customer Service", "date": "", "ddg_snippet": "Electronics: Costco will accept returns within 90 days (from the date the member received the merchandise) for Televisions, Projectors, Major Appliances (refrigerators above 10 cu. ft., freezers, ranges, cooktops, over-the-range and under-counter microwaves, range hoods, dishwashers, water heaters, washers and dryers), Computers, Touchscreen ...", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/1191/~/what-is-costcos-return-policy?", "content": "Electronics: Costco will accept returns within 90 days (from the date the member received the merchandise) for Televisions, Projectors, Major Appliances (refrigerators above 10 cu. ft., freezers, ranges, cooktops, over-the-range and under-counter microwaves, range hoods, dishwashers, water heaters, washers and dryers), Computers, Touchscreen ..."}
diff --git a/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration.jsonl b/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..27be9dc32cceeae6891b4a22c74e3059b2a00bbe
--- /dev/null
+++ b/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain Sharding", "date": "", "ddg_snippet": "... blockchain - sharding mechanism is usually ... For example, S-Store [ 18 ] enables a dedicated data structure for the storage migration of shard data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.03612v5", "content": "... blockchain - sharding mechanism is usually ... For example, S-Store [ 18 ] enables a dedicated data structure for the storage migration of shard data."}
+{"idx": 1, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts . We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts . We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle."}
+{"idx": 2, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "To address these scalability issues, account migration ofers a promising solution. However, existing migration solutions struggle with the high computational overhead and insuficient capture of complex transaction patterns. We propose AERO , a deep reinforcement learning framework to facilitate eficient account migration in sharding blockchains .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "To address these scalability issues, account migration ofers a promising solution. However, existing migration solutions struggle with the high computational overhead and insuficient capture of complex transaction patterns. We propose AERO , a deep reinforcement learning framework to facilitate eficient account migration in sharding blockchains ."}
+{"idx": 3, "title": "A Review of Dynamic Sharding in Blockchain with Deep Reinforcement ...", "date": "", "ddg_snippet": "Blockchain networks face significant scalability and efficiency challenges as transaction volumes increase. Sharding , a partitioning technique that divides the blockchain into smaller, parallel-processing units, offers a promising solution. However, traditional static sharding lacks adaptability, leading to inefficiencies in resource utilization and security vulnerabilities. This paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11069521", "content": "Blockchain networks face significant scalability and efficiency challenges as transaction volumes increase. Sharding , a partitioning technique that divides the blockchain into smaller, parallel-processing units, offers a promising solution. However, traditional static sharding lacks adaptability, leading to inefficiencies in resource utilization and security vulnerabilities. This paper ..."}
+{"idx": 4, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "This paper proposes AERO , a deep reinforcement learning framework for efficient account migration in sharding blockchains , improving throughput by 31.77% and reducing cross-shard transactions and workload imbalances.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/zh-CN/chatpaper/paper/129864", "content": "This paper proposes AERO , a deep reinforcement learning framework for efficient account migration in sharding blockchains , improving throughput by 31.77% and reducing cross-shard transactions and workload imbalances."}
+{"idx": 5, "title": "PDF AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "Sharding blockchain networks face significant scalability challenges due to high frequencies of cross-shard transactions and uneven workload distributions among shards. To address these scalability issues, account migration ofers a promising solution. However,", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "Sharding blockchain networks face significant scalability challenges due to high frequencies of cross-shard transactions and uneven workload distributions among shards. To address these scalability issues, account migration ofers a promising solution. However,"}
+{"idx": 6, "title": "Paper Digest: WWW 2025 Papers & Highlights –", "date": "", "ddg_snippet": "HySAE: An Efficient Semantic-Enhanced Representation Learning Model for Knowledge Hypergraph Link Prediction Related Papers Related Patents Related ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/04/www-2025-papers-highlights/", "content": "HySAE: An Efficient Semantic-Enhanced Representation Learning Model for Knowledge Hypergraph Link Prediction Related Papers Related Patents Related ..."}
+{"idx": 7, "title": "What Is Twitter, a Social Network or a News Media? | Request PDF", "date": "", "ddg_snippet": "The overarching aim of this PhD thesis was to explore the impact and effectiveness of social media-based publicity appeals for missing persons ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/214640678_What_Is_Twitter_a_Social_Network_or_a_News_Media", "content": "The overarching aim of this PhD thesis was to explore the impact and effectiveness of social media-based publicity appeals for missing persons ..."}
+{"idx": 8, "title": "Mingxuan Song", "date": "", "ddg_snippet": "\" AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .\" Proceedings of the Web Conference, May 2025. [PDF] CVPR 2024 CCF-A Shenglin Yin, Zhen Xiao*, Mingxuan Song, and Jieyi Long. \"Adversarial Distillation Based on Slack Matching and Attribution Region Alignment.\"", "subpage_snippet": "", "source": "www.songmingxuan.com", "link": "https://www.songmingxuan.com/", "content": "\" AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .\" Proceedings of the Web Conference, May 2025. [PDF] CVPR 2024 CCF-A Shenglin Yin, Zhen Xiao*, Mingxuan Song, and Jieyi Long. \"Adversarial Distillation Based on Slack Matching and Attribution Region Alignment.\""}
+{"idx": 9, "title": "Papers - Zhen Xiao", "date": "", "ddg_snippet": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Proc. of the Web Conference 2025 (WWW 2025), May 2025. Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan, Rongkai Zhang, Pengze Li, and Zhen Xiao.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/", "content": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Proc. of the Web Conference 2025 (WWW 2025), May 2025. Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan, Rongkai Zhang, Pengze Li, and Zhen Xiao."}
diff --git a/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_nodes_per_s.jsonl b/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_nodes_per_s.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2674d115ed805a92bc5d9c3b5eb0c54e723fb358
--- /dev/null
+++ b/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_nodes_per_s.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "(PDF) BlockEmulator: An Emulator Enabling to Test Blockchain ...", "date": "", "ddg_snippet": "PDF | Numerous blockchain simulators have been proposed to allow researchers to simulate mainstream blockchains. reinforcement learning based state placement,” in Proc. of the ACM on. Web Conference 2024 (WWW’24), 2024, pp. 2836–2846.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389535758_BlockEmulator_An_Emulator_Enabling_to_Test_Blockchain_Sharding_Protocols", "content": "PDF | Numerous blockchain simulators have been proposed to allow researchers to simulate mainstream blockchains. reinforcement learning based state placement,” in Proc. of the ACM on. Web Conference 2024 (WWW’24), 2024, pp. 2836–2846."}
+{"idx": 1, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain ...", "date": "", "ddg_snippet": "Feb 23, 2025 · To fill this gap, we developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms. BlockEmulator adopts a lightweight blockchain architecture so developers can only focus on implementing their new protocols or mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.03612v5", "content": "Feb 23, 2025 · To fill this gap, we developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms. BlockEmulator adopts a lightweight blockchain architecture so developers can only focus on implementing their new protocols or mechanisms."}
+{"idx": 2, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain ...", "date": "", "ddg_snippet": "by H Huang · 2025 · Cited by 33 — Yin, Z. Xiao, and J. Long, “ AERO: Enhancing sharding blockchain via deep reinforcement learning for account migration ,” in Proc. ACM Web Conf., ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/sc/2025/02/10908689/24MWrrwc2Fa", "content": "by H Huang · 2025 · Cited by 33 — Yin, Z. Xiao, and J. Long, “ AERO: Enhancing sharding blockchain via deep reinforcement learning for account migration ,” in Proc. ACM Web Conf., ..."}
+{"idx": 3, "title": "BlockEmulator: An Emulator Enabling to Test", "date": "", "ddg_snippet": "[36] M. Song, P. Li, B. Zhou, S. Yin, Z. Xiao, and J. Long, “ Aero : Enhancing sharding blockchain via deep reinforcement learning for account migration ,” in Proc. of the ACM on Web Conference 2025 (WWW’25), 2025, pp. 1–11.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.03612", "content": "[36] M. Song, P. Li, B. Zhou, S. Yin, Z. Xiao, and J. Long, “ Aero : Enhancing sharding blockchain via deep reinforcement learning for account migration ,” in Proc. of the ACM on Web Conference 2025 (WWW’25), 2025, pp. 1–11."}
diff --git a/data/sampled_jsons/AERO_blockchain_experimental_setup_100_nodes_4_shards_configuration.jsonl b/data/sampled_jsons/AERO_blockchain_experimental_setup_100_nodes_4_shards_configuration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0417baacffdedb606cea894ddcb6895ff70e97d9
--- /dev/null
+++ b/data/sampled_jsons/AERO_blockchain_experimental_setup_100_nodes_4_shards_configuration.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "The experimental setup consists of 16 physical shards , each containing 8 nodes , amounting to a total of 128 nodes in the total network. During each epoch, the consensus phase is composed of 100 blocks, with each block containing a maximum of 1,000 transactions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "The experimental setup consists of 16 physical shards , each containing 8 nodes , amounting to a total of 128 nodes in the total network. During each epoch, the consensus phase is composed of 100 blocks, with each block containing a maximum of 1,000 transactions."}
+{"idx": 1, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts. We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts. We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle."}
+{"idx": 2, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain Sharding Protocols", "date": "", "ddg_snippet": "In step \\scriptsize {1}⃝ , before the BlockEmulator system gets started, users/developers set up the configuration parameters for the emulated blockchain .The configuration parameters mainly include the directory of the output of experimental results, block-generation interval, the consensus protocol adopted, block size, the number of nodes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.03612v3", "content": "In step \\scriptsize {1}⃝ , before the BlockEmulator system gets started, users/developers set up the configuration parameters for the emulated blockchain .The configuration parameters mainly include the directory of the output of experimental results, block-generation interval, the consensus protocol adopted, block size, the number of nodes ..."}
+{"idx": 3, "title": "Setting Up a Blockchain Node: A Complete Tutorial", "date": "", "ddg_snippet": "Learn how to set up a blockchain node with step-by-step guidance on hardware, software, and network configuration for Bitcoin, Ethereum, and more.", "subpage_snippet": "", "source": "axeminer.com", "link": "https://axeminer.com/setting-up-a-blockchain-node/", "content": "Learn how to set up a blockchain node with step-by-step guidance on hardware, software, and network configuration for Bitcoin, Ethereum, and more."}
+{"idx": 4, "title": "Step-by-Step Guide to Building Blockchain Nodes: Key Considerations and ...", "date": "", "ddg_snippet": "This post provides a high-level overview for blockchain engineers and beginners looking to set up and operate blockchain nodes . Let's dive in!", "subpage_snippet": "", "source": "blog.nodit.io", "link": "https://blog.nodit.io/step-by-step-guide-to-building-blockchain-nodes-key-considerations-and-practical-tips/", "content": "This post provides a high-level overview for blockchain engineers and beginners looking to set up and operate blockchain nodes . Let's dive in!"}
+{"idx": 5, "title": "Complete Guide to Blockchain Node Setup Configuration", "date": "", "ddg_snippet": "Discover the complete process of blockchain node setup and configuration . Follow our expert guide to set up your blockchain node efficiently.", "subpage_snippet": "", "source": "cryptomaximal.com", "link": "https://cryptomaximal.com/blockchain-node-setup/", "content": "Discover the complete process of blockchain node setup and configuration . Follow our expert guide to set up your blockchain node efficiently."}
+{"idx": 6, "title": "A Step-by-Step Guide to Setting Up a Blockchain Node", "date": "", "ddg_snippet": "Learn how to configure a blockchain node with our step-by-step guide. Unlock the power of decentralized networks on your own terms.", "subpage_snippet": "", "source": "proofofdev.com", "link": "https://proofofdev.com/a-step-by-step-guide-to-setting-up-a-blockchain-node/", "content": "Learn how to configure a blockchain node with our step-by-step guide. Unlock the power of decentralized networks on your own terms."}
+{"idx": 7, "title": "A survey of state-of-the-art sharding blockchains: Models, components ...", "date": "", "ddg_snippet": "The sharding technique, a common technique used to expand performance horizontally in the traditional database domain, was first introduced into blockchain by Luu et al. (2016). By splitting nodes into smaller groups, called shards , and processing transactions in parallel, sharding can significantly improve the performance of the blockchain .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1084804523001054", "content": "The sharding technique, a common technique used to expand performance horizontally in the traditional database domain, was first introduced into blockchain by Luu et al. (2016). By splitting nodes into smaller groups, called shards , and processing transactions in parallel, sharding can significantly improve the performance of the blockchain ."}
+{"idx": 8, "title": "Server Infrastructure for Blockchain Nodes: A Complete Setup Guide", "date": "", "ddg_snippet": "This guide breaks down exactly what you need to run Bitcoin, Ethereum, and Solana nodes , based on real-world deployments and current network demands. We'll cover the specific hardware requirements, step-by-step setup processes, and actual hosting costs from providers that blockchain operators trust.", "subpage_snippet": "", "source": "www.mamboserver.com", "link": "https://www.mamboserver.com/blog/server-infrastructure-for-blockchain-nodes/", "content": "This guide breaks down exactly what you need to run Bitcoin, Ethereum, and Solana nodes , based on real-world deployments and current network demands. We'll cover the specific hardware requirements, step-by-step setup processes, and actual hosting costs from providers that blockchain operators trust."}
+{"idx": 9, "title": "PDF AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "blockchain network into multiple smaller, manageable segments called shards . Each shard simultaneously processes a subset of blockchain transactions and smart contracts, while periodically reassigning and maintaining shard nodes to ensure security.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "blockchain network into multiple smaller, manageable segments called shards . Each shard simultaneously processes a subset of blockchain transactions and smart contracts, while periodically reassigning and maintaining shard nodes to ensure security."}
diff --git a/data/sampled_jsons/AI_alignment.jsonl b/data/sampled_jsons/AI_alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6100a21119a51e4a48d2e918131b04a9fbb8109e
--- /dev/null
+++ b/data/sampled_jsons/AI_alignment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AI alignment", "date": "", "ddg_snippet": "In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.It is often challenging for AI designers to align an AI system because it is difficult for them to specify the full range of desired and undesired behaviors. Therefore, AI designers often use simpler proxy goals, such as gaining human approval. But proxy goals can overlook necessary constraints or reward the AI system for merely appearing aligned. AI systems may also find loopholes that allow them to accomplish their proxy goals efficiently but in unintended, sometimes harmful, ways (reward hacking).Advanced AI systems may develop unwanted instrumental strategies, such as seeking power or survival because such strategies help them achieve their assigned final goals. Furthermore, they might develop undesirable emergent goals that could be hard to detect before the system is deployed and encounters new situations and data distributions. Empirical research showed in 2024 that advanced large language models (LLMs) such as OpenAI o1 or Claude 3 sometimes engage in strategic deception to achieve their goals or prevent them from being changed.Today, some of these issues affect existing commercial systems such as LLMs, robots, autonomous vehicles, and social media recommendation engines. Some AI researchers argue that more capable future systems will be more severely affected because these problems partially result from high capabilities.Many prominent AI researchers and the leadership of major AI companies have argued or asserted that AI is approaching human-like (AGI) and superhuman cognitive capabilities (ASI), and could endanger human civilization if misaligned. These include \"AI godfathers\" Geoffrey Hinton and Yoshua Bengio and the CEOs of OpenAI, Anthropic, and Google DeepMind. These risks remain debated.AI alignment is a subfield of AI safety, the study of how to build safe AI systems. Other subfields of AI safety include robustness, monitoring, and capability control. Research challenges in alignment include instilling complex values in AI, developing honest AI, scalable oversight, auditing and interpreting AI models, and preventing emergent AI behaviors like power-seeking. Alignment research has connections to interpretability research, (adversarial) robustness, anomaly detection, calibrated uncertainty, formal verification, preference learning, safety-critical engineering, game theory, algorithmic fairness, and social sciences.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/AI_alignment", "content": "In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.It is often challenging for AI designers to align an AI system because it is difficult for them to specify the full range of desired and undesired behaviors. Therefore, AI designers often use simpler proxy goals, such as gaining human approval. But proxy goals can overlook necessary constraints or reward the AI system for merely appearing aligned. AI systems may also find loopholes that allow them to accomplish their proxy goals efficiently but in unintended, sometimes harmful, ways (reward hacking).Advanced AI systems may develop unwanted instrumental strategies, such as seeking power or survival because such strategies help them achieve their assigned final goals. Furthermore, they might develop undesirable emergent goals that could be hard to detect before the system is deployed and encounters new situations and data distributions. Empirical research showed in 2024 that advanced large language models (LLMs) such as OpenAI o1 or Claude 3 sometimes engage in strategic deception to achieve their goals or prevent them from being changed.Today, some of these issues affect existing commercial systems such as LLMs, robots, autonomous vehicles, and social media recommendation engines. Some AI researchers argue that more capable future systems will be more severely affected because these problems partially result from high capabilities.Many prominent AI researchers and the leadership of major AI companies have argued or asserted that AI is approaching human-like (AGI) and superhuman cognitive capabilities (ASI), and could endanger human civilization if misaligned. These include \"AI godfathers\" Geoffrey Hinton and Yoshua Bengio and the CEOs of OpenAI, Anthropic, and Google DeepMind. These risks remain debated.AI alignment is a subfield of AI safety, the study of how to build safe AI systems. Other subfields of AI safety include robustness, monitoring, and capability control. Research challenges in alignment include instilling complex values in AI, developing honest AI, scalable oversight, auditing and interpreting AI models, and preventing emergent AI behaviors like power-seeking. Alignment research has connections to interpretability research, (adversarial) robustness, anomaly detection, calibrated uncertainty, formal verification, preference learning, safety-critical engineering, game theory, algorithmic fairness, and social sciences."}
+{"idx": 1, "title": "[2310.19852] AI Alignment: A Comprehensive Survey", "date": "", "ddg_snippet": "by J Ji · 2023 · Cited by 429 — AI alignment aims to make AI systems behave in line with human intentions and values. As AI systems grow more capable, so do risks from misalignment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.19852", "content": "by J Ji · 2023 · Cited by 429 — AI alignment aims to make AI systems behave in line with human intentions and values. As AI systems grow more capable, so do risks from misalignment."}
+{"idx": 2, "title": "AI Alignment Forum", "date": "", "ddg_snippet": "A community blog devoted to technical AI alignment research .", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/", "content": "A community blog devoted to technical AI alignment research ."}
+{"idx": 3, "title": "Are We Misunderstanding the AI \"Alignment Problem ...", "date": "", "ddg_snippet": "When an AI exhibits what we call \"misalignment,\" it might actually be behaving exactly as a reasoning system should under the circumstances. It ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ControlProblem/comments/1hvs2gu/are_we_misunderstanding_the_ai_alignment_problem/", "content": "When an AI exhibits what we call \"misalignment,\" it might actually be behaving exactly as a reasoning system should under the circumstances. It ..."}
+{"idx": 4, "title": "AI value alignment: Aligning AI with human values", "date": "", "ddg_snippet": "17 Oct 2024 — AI value alignment is about ensuring that artificial intelligence (AI) systems act in accordance with shared human values and ethical principles.", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/stories/2024/10/ai-value-alignment-how-we-can-align-artificial-intelligence-with-human-values/", "content": "17 Oct 2024 — AI value alignment is about ensuring that artificial intelligence (AI) systems act in accordance with shared human values and ethical principles."}
+{"idx": 5, "title": "What is AI Alignment? Ensuring AI Works for Humanity", "date": "", "ddg_snippet": "9 Jul 2024 — AI Alignment refers to the very complex task of encoding human values into AI systems to prevent unintended consequences and to mitigate potential harm.", "subpage_snippet": "", "source": "www.datacamp.com", "link": "https://www.datacamp.com/blog/ai-alignment", "content": "9 Jul 2024 — AI Alignment refers to the very complex task of encoding human values into AI systems to prevent unintended consequences and to mitigate potential harm."}
+{"idx": 6, "title": "Call for the Special Track on AI Alignment - AAAI", "date": "", "ddg_snippet": "24 Jun 2025 — AAAI-26 is pleased to announce a special track focused on AI Alignment . This track recognizes that as we begin to build more and more capable AI systems, it ...", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/conference/aaai/aaai-26/aia-call/", "content": "24 Jun 2025 — AAAI-26 is pleased to announce a special track focused on AI Alignment . This track recognizes that as we begin to build more and more capable AI systems, it ..."}
+{"idx": 7, "title": "AI Alignment", "date": "", "ddg_snippet": "AI alignment refers to the goal of designing artificial intelligence systems in such a way that their objectives and behavior are aligned with the values and ...", "subpage_snippet": "", "source": "thedecisionlab.com", "link": "https://thedecisionlab.com/reference-guide/computer-science/ai-alignment", "content": "AI alignment refers to the goal of designing artificial intelligence systems in such a way that their objectives and behavior are aligned with the values and ..."}
+{"idx": 8, "title": "AI Alignment: Why It's Hard, and Where to Start - Machine", "date": "", "ddg_snippet": "This is where we are on most of the AI alignment problems, like if I ask you, “How do you build a friendly AI ?” What stops you is not that you ...", "subpage_snippet": "", "source": "intelligence.org", "link": "https://intelligence.org/2016/12/28/ai-alignment-why-its-hard-and-where-to-start/", "content": "This is where we are on most of the AI alignment problems, like if I ask you, “How do you build a friendly AI ?” What stops you is not that you ..."}
+{"idx": 9, "title": "The AI Alignment Problem: Why It's Hard, and Where to", "date": "", "ddg_snippet": "... org if you have any questions, and see intelligence.org/get-involved for information about opportunities to collaborate on AI alignment ...", "subpage_snippet": "", "source": "intelligence.org", "link": "https://intelligence.org/stanford-talk/", "content": "... org if you have any questions, and see intelligence.org/get-involved for information about opportunities to collaborate on AI alignment ..."}
diff --git "a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Tyurin_Richt\303\241rik_wasteful_distributed_machine_learning.jsonl" "b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Tyurin_Richt\303\241rik_wasteful_distributed_machine_learning.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..125083b353c94018135f6403904bd22298b55b2f
--- /dev/null
+++ "b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Tyurin_Richt\303\241rik_wasteful_distributed_machine_learning.jsonl"
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+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "2 Feb 2025 — Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "2 Feb 2025 — Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by ..."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... a greedy task allocation strategy, leading to wastefulness .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/86bb01c56f97fb6ecf7e661151ca6ae354faf473.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... a greedy task allocation strategy, leading to wastefulness ."}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "in Distributed Machine Learning Artavazd Maranjyan El Mehdi Saad Peter Richtárik Francesco Orabona Abstract Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "in Distributed Machine Learning Artavazd Maranjyan El Mehdi Saad Peter Richtárik Francesco Orabona Abstract Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "This paper suggests an adaptive method for task allocation , Adaptive Task Allocation ( ATA ). In the context of machine learning , this approach addresses federated learning , where in each round, n workers collaboratively perform minibatch SGD with total batch size of B.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "This paper suggests an adaptive method for task allocation , Adaptive Task Allocation ( ATA ). In the context of machine learning , this approach addresses federated learning , where in each round, n workers collaboratively perform minibatch SGD with total batch size of B."}
+{"idx": 5, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ATA:-Adaptive-Task-Allocation-for-Efficient-in-Maranjyan-Saad/871e17b70c5c31985b6ecb1d61960ad5ee7d1cbd", "content": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully ..."}
+{"idx": 6, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.00775v2", "content": "View recent discussion. Abstract: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were ..."}
+{"idx": 7, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "View on arXiv @article {maranjyan2025_2502.00775, title= { ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning }, author= { Artavazd Maranjyan and El Mehdi Saad and Peter Richtárik and Francesco Orabona }, journal= {arXiv preprint arXiv:2502.00775}, year= { 2025 } }", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00775", "content": "View on arXiv @article {maranjyan2025_2502.00775, title= { ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning }, author= { Artavazd Maranjyan and El Mehdi Saad and Peter Richtárik and Francesco Orabona }, journal= {arXiv preprint arXiv:2502.00775}, year= { 2025 } }"}
+{"idx": 8, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "#1 ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning [PDF 1] [Copy] [Kimi 1] [REL] Authors: Artavazd Maranjyan, El Mehdi Saad, Peter Richtarik , Francesco Orabona Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/1BaC3AdG1i@OpenReview", "content": "#1 ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning [PDF 1] [Copy] [Kimi 1] [REL] Authors: Artavazd Maranjyan, El Mehdi Saad, Peter Richtarik , Francesco Orabona Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However ..."}
+{"idx": 9, "title": "[2502.00775] ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ..."}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_arXiv.jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e2c4dc7480f197cc2763bb180c88f1bb71c5b5d8
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+++ b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00775] ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 1, "title": "ICML Poster ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan · El Mehdi Saad · Peter Richtarik · Francesco Orabona", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan · El Mehdi Saad · Peter Richtarik · Francesco Orabona"}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ATA:-Adaptive-Task-Allocation-for-Efficient-in-Maranjyan-Saad/871e17b70c5c31985b6ecb1d61960ad5ee7d1cbd", "content": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully ..."}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "Key Findings The research shows that dynamic resource allocation significantly improves training speed. Systems using ATA completed training tasks up to 40% faster than traditional methods. Task scheduling becomes more efficient as the system learns optimal patterns. Resource utilization improved by 35% compared to static allocation methods.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ata-adaptive-task-allocation-efficient-resource-management", "content": "Key Findings The research shows that dynamic resource allocation significantly improves training speed. Systems using ATA completed training tasks up to 40% faster than traditional methods. Task scheduling becomes more efficient as the system learns optimal patterns. Resource utilization improved by 35% compared to static allocation methods."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "In this paper, we propose ATA (Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "In this paper, we propose ATA (Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 5, "title": "論文の概要: ATA: Adaptive Task Allocation for Efficient Resource Management ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2502.00775v2", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 6, "title": "\"ATA: Adaptive Task Allocation for Efficient Resource Management in ...\"", "date": "", "ddg_snippet": "Bibliographic details on ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-00775", "content": "Bibliographic details on ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning."}
+{"idx": 7, "title": "Adaptive Task Allocation in Multi-Human Multi-Robot Teams Under Team ...", "date": "", "ddg_snippet": "Task allocation in multi-human multi-robot (MHMR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches often fail to address these challenges simultaneously, resulting in suboptimal performance. To tackle this, we propose ATA -HRL, an adaptive task ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11127646", "content": "Task allocation in multi-human multi-robot (MHMR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches often fail to address these challenges simultaneously, resulting in suboptimal performance. To tackle this, we propose ATA -HRL, an adaptive task ..."}
+{"idx": 8, "title": "ATA: Adaptive Task Allocation for Eficient Resource Management in ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times."}
+{"idx": 9, "title": "Publications | Arto Maranjyan", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "artomaranjyan.github.io", "link": "https://artomaranjyan.github.io/publications/", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_arXiv2502.00775_Recursive_Allocation_Selection.jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_arXiv2502.00775_Recursive_Allocation_Selection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cdd84fb9c632c199468c0102a9eaecdaac54d25d
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+{"idx": 0, "title": "[2502.00775] ATA: Adaptive Task Allocation for Efficient ...", "date": "", "ddg_snippet": "Feb 2, 2025 · In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "Feb 2, 2025 · In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 1, "title": "[PDF] ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ATA:-Adaptive-Task-Allocation-for-Efficient-in-Maranjyan-Saad/871e17b70c5c31985b6ecb1d61960ad5ee7d1cbd", "content": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully ..."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "This paper introduces a new method called ATA ( Adaptive Task Allocation ) for improving how computers divide tasks among different workers in machine learning. It looks at the problem where some wo...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46650/paper", "content": "This paper introduces a new method called ATA ( Adaptive Task Allocation ) for improving how computers divide tasks among different workers in machine learning. It looks at the problem where some wo..."}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Eficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 5, "title": "ATA: Adaptive Task Allocation for Eficient ... - arXiv.org", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775v1", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times."}
+{"idx": 6, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "Algorithm 1 ATA ( Adaptive Task Allocation ). 1: Input: allocation budget.Appendix B Recursive Allocation Selection Algorithm. In this section, we introduce an efficient method for finding the best allocation .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "Algorithm 1 ATA ( Adaptive Task Allocation ). 1: Input: allocation budget.Appendix B Recursive Allocation Selection Algorithm. In this section, we introduce an efficient method for finding the best allocation ."}
+{"idx": 7, "title": "ICML Poster ATA : Adaptive Task Allocation for Efficient Resource...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 8, "title": "Weather / adaptive", "date": "", "ddg_snippet": "Remix on Adaptive adaptive .ai.", "subpage_snippet": "", "source": "weather.on.adaptive.ai", "link": "https://weather.on.adaptive.ai/", "content": "Remix on Adaptive adaptive .ai."}
+{"idx": 9, "title": "Energy-balancing task allocation on wireless sensor networks for...", "date": "", "ddg_snippet": "Self- adapted task allocation algorithm with complicated coalition in wireless sensor network.In this paper, we develop a real-time adaptive task allocation algorithm based on parallel dynamic coalition in WSNs.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/224167339_Energy-balancing_task_allocation_on_wireless_sensor_networks_for_extending_the_lifetime", "content": "Self- adapted task allocation algorithm with complicated coalition in wireless sensor network.In this paper, we develop a real-time adaptive task allocation algorithm based on parallel dynamic coalition in WSNs."}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_conf(i,_k)_equation_(7).jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_conf(i,_k)_equation_(7).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5898bdfde11008a26216942e4391a1c314ffb693
--- /dev/null
+++ b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_conf(i,_k)_equation_(7).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. In this paper, we propose ATA (Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. In this paper, we propose ATA (Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times.", "subpage_snippet": "", "source": "repository.kaust.edu.sa", "link": "https://repository.kaust.edu.sa/bitstreams/884560fb-34b9-42e5-a5ee-88334b809ebb/download", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times."}
+{"idx": 2, "title": "Adaptive Task Allocation for Heterogeneous Multi-Robot Teams with ...", "date": "", "ddg_snippet": "For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9197283", "content": "For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions ..."}
+{"idx": 3, "title": "ATA-HRL - Google Sites", "date": "", "ddg_snippet": "Conceptual illustration of our adaptive task allocation method, named ATA -HRL, in MH-MR teams. Unlike previous one-sided approaches, we consider both inherent heterogeneity and in-process dynamic states of the team and its assigned tasks , hierarchically combining initial task allocation and conditional task reallocation. To handle state information uncertainty, we also introduce an auxiliary ...", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/view/ata-hrl", "content": "Conceptual illustration of our adaptive task allocation method, named ATA -HRL, in MH-MR teams. Unlike previous one-sided approaches, we consider both inherent heterogeneity and in-process dynamic states of the team and its assigned tasks , hierarchically combining initial task allocation and conditional task reallocation. To handle state information uncertainty, we also introduce an auxiliary ..."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 5, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ATA:-Adaptive-Task-Allocation-for-Efficient-in-Maranjyan-Saad/871e17b70c5c31985b6ecb1d61960ad5ee7d1cbd", "content": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully ..."}
+{"idx": 6, "title": "(PDF) Adaptive Task Allocation Preferences in Different Workload ...", "date": "", "ddg_snippet": "Abstract Adaptive task allocation is used in many human-machine systems and has been proven to improve operators' performance with automated systems.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/365503627_Adaptive_Task_Allocation_Preferences_in_Different_Workload_Scenarios_in_Driving_Automation_Systems", "content": "Abstract Adaptive task allocation is used in many human-machine systems and has been proven to improve operators' performance with automated systems."}
+{"idx": 7, "title": "Adaptive Task Allocation in Multi-Human Multi-Robot Teams under Team ...", "date": "", "ddg_snippet": "To tackle this, we pro-pose ATA -HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic op-erational states.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.13824", "content": "To tackle this, we pro-pose ATA -HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic op-erational states."}
+{"idx": 8, "title": "Driver adaptive task allocation: A field driving study. - APA PsycNet", "date": "", "ddg_snippet": "Adaptive task allocation ( ATA ) provides a new solution for human-machine interaction in a highly automated system. Previous research demonstrates that psychophysiological signals yield sensitive information about human functional states, which can be used to build a closed loop for human-machine interaction to reallocate the tasks upon the status of human operator. The present study ...", "subpage_snippet": "", "source": "psycnet.apa.org", "link": "https://psycnet.apa.org/record/2017-47524-005", "content": "Adaptive task allocation ( ATA ) provides a new solution for human-machine interaction in a highly automated system. Previous research demonstrates that psychophysiological signals yield sensitive information about human functional states, which can be used to build a closed loop for human-machine interaction to reallocate the tasks upon the status of human operator. The present study ..."}
+{"idx": 9, "title": "PDF Speed 2 . 5 To t a l w o r k e r t i m e 1 e 1 0 - artomaranjyan.github.io", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning", "subpage_snippet": "", "source": "artomaranjyan.github.io", "link": "https://artomaranjyan.github.io/assets/pdf/ATA_Rising_Stars.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning"}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_confi.jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_confi.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06d84c9067aabae82174c4a625725ef4404b79f6
--- /dev/null
+++ b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_confi.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "18 Jun 2025 — It leverages foundational bandit theory, particularly lower confidence bounds , to balance exploration and exploitation without prior knowledge ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i&noteId=hkH8Wi9zZm", "content": "18 Jun 2025 — It leverages foundational bandit theory, particularly lower confidence bounds , to balance exploration and exploitation without prior knowledge ..."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "2 Feb 2025 — In this work, we formalize the task allocation problem as a combinatorial online learning problem with partial feedback and non-linear losses.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "2 Feb 2025 — In this work, we formalize the task allocation problem as a combinatorial online learning problem with partial feedback and non-linear losses."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning to minimize the expected completion time subject to the.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/86bb01c56f97fb6ecf7e661151ca6ae354faf473.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning to minimize the expected completion time subject to the."}
+{"idx": 3, "title": "MindFlayer SGD: Efficient Parallel SGD in the Presence of ... - GitHub", "date": "", "ddg_snippet": "ATA: Adaptive task allocation for efficient resource management in distributed machine learning. In International Conference on Machine Learn- ing, 2025a ... 30 pages", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v286/main/assets/maranjyan25a/maranjyan25a.pdf", "content": "ATA: Adaptive task allocation for efficient resource management in distributed machine learning. In International Conference on Machine Learn- ing, 2025a ... 30 pages"}
+{"idx": 4, "title": "A novel dynamic scheduling model for application in ...", "date": "", "ddg_snippet": "by Z Elqabli · 2025 — This study addresses job scheduling problem in multimode systems. A multimode system is designed to perform flexibly and reliably under normal and degraded ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-10710-x.pdf", "content": "by Z Elqabli · 2025 — This study addresses job scheduling problem in multimode systems. A multimode system is designed to perform flexibly and reliably under normal and degraded ..."}
+{"idx": 5, "title": "A Review of the Current Usage of AI/ML for Radio Access ...", "date": "", "ddg_snippet": "by A AlZailaa · 2025 · Cited by 1 — This systematic review assesses the current state of AI and ML applications for future wireless networks, focusing on four main topics: task ... 43 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/11072343.pdf", "content": "by A AlZailaa · 2025 · Cited by 1 — This systematic review assesses the current state of AI and ML applications for future wireless networks, focusing on four main topics: task ... 43 pages"}
+{"idx": 6, "title": "Machine learning for handover decision with mobile edge ...", "date": "", "ddg_snippet": "by SI Loutfi · 2025 — This paper presents a systematic review of the handover decision (HOD) with MEC in 6G networks, providing a deep understanding of the most standing challenges ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2215098625001867", "content": "by SI Loutfi · 2025 — This paper presents a systematic review of the handover decision (HOD) with MEC in 6G networks, providing a deep understanding of the most standing challenges ..."}
+{"idx": 7, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning · Scaling Video-Language Models to 10K Frames via Hierarchical ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning · Scaling Video-Language Models to 10K Frames via Hierarchical ..."}
+{"idx": 8, "title": "Computer Science Feb 2025", "date": "", "ddg_snippet": "Title: ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-02?skip=225&show=2000", "content": "Title: ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik ..."}
+{"idx": 9, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning · Learning to Incentivize in Repeated Principal-Agent Problems ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning · Learning to Incentivize in Repeated Principal-Agent Problems ..."}
diff --git "a/data/sampled_jsons/ATA_paper_Tyurin_Richt\303\241rik_wasteful_sitearxiv.org_year_2025.jsonl" "b/data/sampled_jsons/ATA_paper_Tyurin_Richt\303\241rik_wasteful_sitearxiv.org_year_2025.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..ac6044898bfd1d593cf81fc1bc952707b95332a4
--- /dev/null
+++ "b/data/sampled_jsons/ATA_paper_Tyurin_Richt\303\241rik_wasteful_sitearxiv.org_year_2025.jsonl"
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": ". In a recent breakthrough, Tyurin & Richtárik (2024) recently developed a parallel SGD method, optimal in terms of a novel notion of complexity called time complexity, for solving the above problem with.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": ". In a recent breakthrough, Tyurin & Richtárik (2024) recently developed a parallel SGD method, optimal in terms of a novel notion of complexity called time complexity, for solving the above problem with."}
+{"idx": 1, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "In this paper , we propose ATA (Adaptive Task Allocation), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "In this paper , we propose ATA (Adaptive Task Allocation), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 2, "title": "cherryATA", "date": "", "ddg_snippet": "In this paper , we propose ATA (Adaptive ... Rennala SGD can be wasteful , as explained above. ... Algorithm 3 is exactly Rennala SGD method proposed by Tyurin & ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "In this paper , we propose ATA (Adaptive ... Rennala SGD can be wasteful , as explained above. ... Algorithm 3 is exactly Rennala SGD method proposed by Tyurin & ..."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Adversarial_Review_algorithm.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Adversarial_Review_algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5f8ea838574a4b19b94f91bcaaeeabe5706f3bbc
--- /dev/null
+++ b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Adversarial_Review_algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks - and - Balances Framework for Context - Aware Ethical AI ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 1, "title": "(PDF) Checks - and - Balances Framework for Context - Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.Table 1: Checks - and - balances , adversarial review algorithm .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.Table 1: Checks - and - balances , adversarial review algorithm ."}
+{"idx": 2, "title": "Ethics-driven model auditing and bias mitigation -", "date": "", "ddg_snippet": "AI Bias detection techniques are critical for ensuring AI systems operate fairly and ethically across diverse groups.", "subpage_snippet": "", "source": "www.datasciencecentral.com", "link": "https://www.datasciencecentral.com/ethics-driven-model-auditing-and-bias-mitigation/", "content": "AI Bias detection techniques are critical for ensuring AI systems operate fairly and ethically across diverse groups."}
+{"idx": 3, "title": "Navigating AI Compliance: A Strategic Imperative for Modern", "date": "", "ddg_snippet": "Ongoing training and awareness programs are crucial to mitigate AI related risks, including ethical issues, biases, and regulatory concerns.", "subpage_snippet": "", "source": "www.aryaxai.com", "link": "https://www.aryaxai.com/article/navigating-ai-compliance-a-strategic-imperative-for-modern-enterprises", "content": "Ongoing training and awareness programs are crucial to mitigate AI related risks, including ethical issues, biases, and regulatory concerns."}
+{"idx": 4, "title": "Navigating Ethical AI: Building Trustworthy Software Systems", "date": "", "ddg_snippet": "... at what cost? Balancing the drive for high-performance AI with the imperative to protect user privacy remains one of the field’s most pressing and ...", "subpage_snippet": "", "source": "kliksoft.dev", "link": "https://kliksoft.dev/blog/navigating-ethical-ai-building-trustworthy-software-systems/", "content": "... at what cost? Balancing the drive for high-performance AI with the imperative to protect user privacy remains one of the field’s most pressing and ..."}
+{"idx": 5, "title": "AI Bias and Fairness: The Definitive Guide to Ethical AI |", "date": "", "ddg_snippet": "Ensuring fairness in AI is critical to preventing discrimination, fostering trust, and promoting ethical AI adoption.", "subpage_snippet": "", "source": "smartdev.com", "link": "https://smartdev.com/de/addressing-ai-bias-and-fairness-challenges-implications-and-strategies-for-ethical-ai/", "content": "Ensuring fairness in AI is critical to preventing discrimination, fostering trust, and promoting ethical AI adoption."}
+{"idx": 6, "title": "Context Reasoner: Incentivizing Reasoning Capability for", "date": "", "ddg_snippet": "With the CI framework , we are able to align LLMs with established legal frameworks , including GDPR, the EU AI Act, and HIPAA.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14585v2", "content": "With the CI framework , we are able to align LLMs with established legal frameworks , including GDPR, the EU AI Act, and HIPAA."}
+{"idx": 7, "title": "Redefining Elderly Care with Agentic AI: Challenges and", "date": "", "ddg_snippet": "Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14912v1", "content": "Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older ..."}
+{"idx": 8, "title": "How Juventus protects fans, revenue, and reputation during", "date": "", "ddg_snippet": "Likewise, our data classification framework enables tiered protection and recovery measures, aligning safeguards and restoration objectives to the ...", "subpage_snippet": "", "source": "www.helpnetsecurity.com", "link": "https://www.helpnetsecurity.com/2025/09/22/mirko-rinaldini-juventus-juventus-cyber-risk-strategy/", "content": "Likewise, our data classification framework enables tiered protection and recovery measures, aligning safeguards and restoration objectives to the ..."}
+{"idx": 9, "title": "DomingoSenise.com", "date": "", "ddg_snippet": "Following with the USA but in an international level, the United States and Australia are leveraging generative AI for strategic advantage in the ...", "subpage_snippet": "", "source": "www.domingosenise.com", "link": "http://www.domingosenise.com/", "content": "Following with the USA but in an international level, the United States and Australia are leveraging generative AI for strategic advantage in the ..."}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Figure_4a_DIKE_GPT-4_classifi.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Figure_4a_DIKE_GPT-4_classifi.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7306726abf7413373a5124e7b25cf93f33d948a9
--- /dev/null
+++ b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Figure_4a_DIKE_GPT-4_classifi.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "Figure 4a shows that Dike's classification accuracy sur- passes GPT - 4's zero-shot method by 11.3 percentage points, confirming the effectiveness of emotion ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d24155c881921de1284ece531612c8597f7c0a32.pdf", "content": "Figure 4a shows that Dike's classification accuracy sur- passes GPT - 4's zero-shot method by 11.3 percentage points, confirming the effectiveness of emotion ..."}
+{"idx": 1, "title": "Ethical Medical Image Synthesis", "date": "", "ddg_snippet": "by W Jin · 2025 — Abstract. The task of ethical Medical Image Synthesis (MISyn) is to ensure that the MISyn techniques are researched and developed ethically ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2508.09293", "content": "by W Jin · 2025 — Abstract. The task of ethical Medical Image Synthesis (MISyn) is to ensure that the MISyn techniques are researched and developed ethically ..."}
+{"idx": 2, "title": "An evaluation framework for ambient digital scribing tools ...", "date": "", "ddg_snippet": "by H Wang · 2025 · Cited by 1 — This study proposes a comprehensive ADS evaluation framework incorporating human evaluation, automated metrics, simulation testing, and large language models ( ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12166074/", "content": "by H Wang · 2025 · Cited by 1 — This study proposes a comprehensive ADS evaluation framework incorporating human evaluation, automated metrics, simulation testing, and large language models ( ..."}
+{"idx": 3, "title": "ICLM 2025 AI Safety 7847 Camera Ready3 | PDF | Emotions", "date": "", "ddg_snippet": "10 Aug 2025 — Checks -and- Balances Framework for Context - Aware Ethical AI Alignment ... Figure 4a shows that Dike's classification accuracy sur-", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/899938563/ICLM-2025-AI-Safety-7847-Camera-Ready3", "content": "10 Aug 2025 — Checks -and- Balances Framework for Context - Aware Ethical AI Alignment ... Figure 4a shows that Dike's classification accuracy sur-"}
+{"idx": 4, "title": "Algorithmic discrimination in clinical decision-making ...", "date": "", "ddg_snippet": "by MK Hauglid · 2024 · Cited by 1 — Petro, \"Benefits, Limits, and Risks of GPT - 4 as an AI Chatbot for Medicine,\" New England Journal of. Medicine 388, no. 13 (2023): 1234-35. 98 e.g., Mathias K ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5181633.pdf?abstractid=5181633&mirid=1", "content": "by MK Hauglid · 2024 · Cited by 1 — Petro, \"Benefits, Limits, and Risks of GPT - 4 as an AI Chatbot for Medicine,\" New England Journal of. Medicine 388, no. 13 (2023): 1234-35. 98 e.g., Mathias K ..."}
+{"idx": 5, "title": "Full article: How should an explanation be? A mapping of ...", "date": "", "ddg_snippet": "In essence, XAI attempts to achieve two objectives: (1) 'produce more explainable models, while maintaining a high level of learning performance (prediction ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/13600869.2025.2497633?af=R", "content": "In essence, XAI attempts to achieve two objectives: (1) 'produce more explainable models, while maintaining a high level of learning performance (prediction ..."}
+{"idx": 6, "title": "safe AI through intersecting computational psychological ...", "date": "", "ddg_snippet": "by DJ Edwards · 2024 · Cited by 12 — A functional contextual , observer-centric, quantum mechanical, and neuro-symbolic approach to solving the alignment problem of artificial general intelligence.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2024.1395901/full", "content": "by DJ Edwards · 2024 · Cited by 12 — A functional contextual , observer-centric, quantum mechanical, and neuro-symbolic approach to solving the alignment problem of artificial general intelligence."}
+{"idx": 7, "title": "The Ultimate Guide to Fine-Tuning LLMs from Basics ...", "date": "", "ddg_snippet": "21 Oct 2024 — This technical report thoroughly examines the process of fine-tuning Large Language Models (LLMs), integrating theoretical insights and practical applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.13296v2", "content": "21 Oct 2024 — This technical report thoroughly examines the process of fine-tuning Large Language Models (LLMs), integrating theoretical insights and practical applications."}
+{"idx": 8, "title": "Revolutionizing Digital Pathology With the Power of ...", "date": "", "ddg_snippet": "by A Waqas · 2023 · Cited by 112 — Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0023683723001988", "content": "by A Waqas · 2023 · Cited by 112 — Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow."}
+{"idx": 9, "title": "Responsible AI Guide - CHAI", "date": "", "ddg_snippet": "26 Jun 2024 — By aiming to use AI aligned with ethical principles in healthcare, then AI technologies themselves can influence clinicians to make. 187 pages", "subpage_snippet": "", "source": "assets.ctfassets.net", "link": "https://assets.ctfassets.net/7s4afyr9pmov/6e7PrdrsNTQ5FjZ4uyRjTW/c4070131c523d4e1db26105aa51f087d/CHAI_Responsible-AI-Guide.pdf", "content": "26 Jun 2024 — By aiming to use AI aligned with ethical principles in healthcare, then AI technologies themselves can influence clinicians to make. 187 pages"}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_arxiv_abstract_dataset_reason.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_arxiv_abstract_dataset_reason.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..078950d3f5f36f643b922bfc7bd9be02e69d5d04
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+++ b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_arxiv_abstract_dataset_reason.jsonl
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+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ..."}
+{"idx": 2, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch establishing ethi-cal guardrails, and Eris as the judicial branch for contextual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v2", "content": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch establishing ethi-cal guardrails, and Eris as the judicial branch for contextual ..."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "Abstract This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "Abstract This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems."}
+{"idx": 4, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "Abstract This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethi-cal guardrails, and ERIS as the judicial branch for contextual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v1", "content": "Abstract This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethi-cal guardrails, and ERIS as the judicial branch for contextual ..."}
+{"idx": 5, "title": "PDF A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461.pdf", "content": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues"}
+{"idx": 6, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "paperreading.club", "link": "https://paperreading.club/page?id=281349", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 7, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as ..."}
+{"idx": 8, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts ."}
+{"idx": 9, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence."}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_filetypepdf.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_filetypepdf.jsonl
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+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "by EY Chang · Cited by 1 — A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment . Edward Y. Chang. Computer Science, Stanford University. Problem & Motivation. RLHF ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461_OMgXx2a.pdf", "content": "by EY Chang · Cited by 1 — A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment . Edward Y. Chang. Computer Science, Stanford University. Problem & Motivation. RLHF ..."}
+{"idx": 2, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Abstract This paper introduces a checks-and-balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v3", "content": "Abstract This paper introduces a checks-and-balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ..."}
+{"idx": 3, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Abstract This paper introduces a checks-and-balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch establishing ethi-cal guardrails, and Eris as the judicial branch for contextual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v2", "content": "Abstract This paper introduces a checks-and-balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch establishing ethi-cal guardrails, and Eris as the judicial branch for contextual ..."}
+{"idx": 4, "title": "PDF A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461.pdf", "content": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues"}
+{"idx": 5, "title": "A Checks - and - Balances Framework for Context - Aware Ethical AI ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation..."}
+{"idx": 6, "title": "( PDF ) Checks - and - Balances Framework for Context - Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.1. A novel checks - and - balances architecture for ethical . alignment that maintains separation between knowledge.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.1. A novel checks - and - balances architecture for ethical . alignment that maintains separation between knowledge."}
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diff --git a/data/sampled_jsons/A_Deep_Generative_Approach_to_Conditional_Sampling_arxiv.jsonl b/data/sampled_jsons/A_Deep_Generative_Approach_to_Conditional_Sampling_arxiv.jsonl
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+{"idx": 0, "title": "Conditional Optimal Transport on Function Spaces | SIAM/ASA", "date": "", "ddg_snippet": "Tsai, A Generative Flow for Conditional Sampling via Optimal Transport , https:// arxiv .org/abs/2307.04102 , 2023. ... Conditional sampling with ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/23M1618922", "content": "Tsai, A Generative Flow for Conditional Sampling via Optimal Transport , https:// arxiv .org/abs/2307.04102 , 2023. ... Conditional sampling with ..."}
+{"idx": 1, "title": "Physics-Constrained Flow Matching: Sampling Generative Models", "date": "", "ddg_snippet": "Deep generative models have recently been applied to physical systems governed by partial differential equations (PDEs), offering scalable simulation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.04171v1", "content": "Deep generative models have recently been applied to physical systems governed by partial differential equations (PDEs), offering scalable simulation ..."}
+{"idx": 2, "title": "PROJECTED LATENT MARKOV CHAIN MONTE CARLO: CONDITIONAL SAMPLING", "date": "", "ddg_snippet": "A constrained Hamiltonian Monte Carlo approach has also been introduced in the context of conditional sampling from generative models by Graham et al.", "subpage_snippet": "", "source": "1library.net", "link": "https://1library.net/document/zgjvoo8z-projected-latent-markov-chain-monte-conditional-sampling-normalizing.html", "content": "A constrained Hamiltonian Monte Carlo approach has also been introduced in the context of conditional sampling from generative models by Graham et al."}
+{"idx": 3, "title": "AITopics | Conditional Generative Models are Sufficient to", "date": "", "ddg_snippet": "Based on this result, we devise a diffusion-based approach to sample from any ( conditional ) interventional distribution on image data.", "subpage_snippet": "", "source": "aitopics.org", "link": "https://aitopics.org/doc/arxivorg:D68CF617", "content": "Based on this result, we devise a diffusion-based approach to sample from any ( conditional ) interventional distribution on image data."}
+{"idx": 4, "title": "A Generative Modeling Approach to Reconstructing 21-cm", "date": "", "ddg_snippet": "To address this challenge, we introduce a novel deep generative model based on stochastic interpolants to reconstruct the 21-cm data lost to wedge ...", "subpage_snippet": "", "source": "renfrewshireastro.co.uk", "link": "https://renfrewshireastro.co.uk/a-generative-modeling-approach-to-reconstructing-21-cm-tomographic-data", "content": "To address this challenge, we introduce a novel deep generative model based on stochastic interpolants to reconstruct the 21-cm data lost to wedge ..."}
+{"idx": 5, "title": "A Randomized Bag-of-Birds Approach to Study Robustness of", "date": "", "ddg_snippet": "A Feature Paper should be a substantial original Article that involves several techniques or approaches , provides an outlook for future research ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/11/19/9226", "content": "A Feature Paper should be a substantial original Article that involves several techniques or approaches , provides an outlook for future research ..."}
+{"idx": 6, "title": "You sure? A Bayesian approach to obtaining uncertainty", "date": "", "ddg_snippet": "In this post, we’ll see a both practical as well as theoretically grounded approach to obtaining uncertainty estimates from neural networks.", "subpage_snippet": "", "source": "www.aitoolstv.com", "link": "https://www.aitoolstv.com/you-sure-a-bayesian-approach-to-obtaining-uncertainty-estimates-from-neural-networks/", "content": "In this post, we’ll see a both practical as well as theoretically grounded approach to obtaining uncertainty estimates from neural networks."}
+{"idx": 7, "title": "Computation | arXiver", "date": "", "ddg_snippet": "RCR is especially powerful for cleaning heavily-contaminated samples , and unlike other methods such as sigma clipping, it manages to be both accurate ...", "subpage_snippet": "", "source": "arxiver.moonhats.com", "link": "https://arxiver.moonhats.com/tag/computation/", "content": "RCR is especially powerful for cleaning heavily-contaminated samples , and unlike other methods such as sigma clipping, it manages to be both accurate ..."}
+{"idx": 8, "title": "Frontiers | A Variational Approach to Scripts", "date": "", "ddg_snippet": "... and explicit) forms of social knowledge at play in social actions, and because it allows us to study the interplay between socially constructed norms ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2021.585493/full", "content": "... and explicit) forms of social knowledge at play in social actions, and because it allows us to study the interplay between socially constructed norms ..."}
+{"idx": 9, "title": "Boosting Statistic Learning with Synthetic Data from Pretrained", "date": "", "ddg_snippet": "While Stable Diffusion can theoretically produce an unlimited amount of data, not all generated samples contribute meaningfully to model performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04992v1", "content": "While Stable Diffusion can theoretically produce an unlimited amount of data, not all generated samples contribute meaningfully to model performance."}
diff --git a/data/sampled_jsons/A_Geometric_Approach_to_Personalized_Recommendation_Table_1_ML-1M_Amazon_Beauty_Amazon_Toys_Amazon_G.jsonl b/data/sampled_jsons/A_Geometric_Approach_to_Personalized_Recommendation_Table_1_ML-1M_Amazon_Beauty_Amazon_Toys_Amazon_G.jsonl
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+{"idx": 2, "title": "Disentangled Dynamic Graph Attention Network for Out-of- ...", "date": "", "ddg_snippet": "Note that statistics of Beauty dataset in Table 6 differ from those in Table 7 is because the latter employs 5-core settings. Table 6. Dataset, Beauty , ML - 1m ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3701988", "content": "Note that statistics of Beauty dataset in Table 6 differ from those in Table 7 is because the latter employs 5-core settings. Table 6. Dataset, Beauty , ML - 1m ..."}
+{"idx": 3, "title": "Tensor-Based Sequential Learning via Hankel Matrix ...", "date": "", "ddg_snippet": "by E Frolov · 2023 · Cited by 8 — ( ML - 1M ), Amazon Beauty (AMZ-B), Amazon Toys and. Games (AMZ-G), and ... The resulting statistics 7 for the datasets are provided in. Table 1 .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/6287639/10005208/10007832.pdf", "content": "by E Frolov · 2023 · Cited by 8 — ( ML - 1M ), Amazon Beauty (AMZ-B), Amazon Toys and. Games (AMZ-G), and ... The resulting statistics 7 for the datasets are provided in. Table 1 ."}
+{"idx": 4, "title": "A Survey on Large Language Models in Multimodal ...", "date": "", "ddg_snippet": "by A Lopez-Avila · 2025 — Multimodal recommender systems aim to integrate diverse user and item information, including text, images, structured data , and user behaviour, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.09777", "content": "by A Lopez-Avila · 2025 — Multimodal recommender systems aim to integrate diverse user and item information, including text, images, structured data , and user behaviour, ..."}
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+{"idx": 0, "title": "A Geometric Approach to Personalized Recommendation ...", "date": "", "ddg_snippet": "by SS Dasgupta — Experimental results on four real-world datasets demonstrate good performance of the proposed box embedding method , outperforming vector embedding approaches .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0HWAbWgI3T", "content": "by SS Dasgupta — Experimental results on four real-world datasets demonstrate good performance of the proposed box embedding method , outperforming vector embedding approaches ."}
+{"idx": 1, "title": "A graph neural network recommendation algorithm based ...", "date": "", "ddg_snippet": "by D Pu · 2025 — Detailed statistics of each dataset are shown in Table 1 . Table 1 Statistics of the datasets . Full size table . MovieLens: This dataset is ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-17925-y", "content": "by D Pu · 2025 — Detailed statistics of each dataset are shown in Table 1 . Table 1 Statistics of the datasets . Full size table . MovieLens: This dataset is ..."}
+{"idx": 2, "title": "Personalized Recommendation Algorithm for Interactive ...", "date": "", "ddg_snippet": "27 Jun 2022 — Therefore, this paper proposes an image personalized recommendation algorithm (IPRA) under deep learning. The IPRA cannot only complete image ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1155/2022/2876481", "content": "27 Jun 2022 — Therefore, this paper proposes an image personalized recommendation algorithm (IPRA) under deep learning. The IPRA cannot only complete image ..."}
+{"idx": 3, "title": "InBox: Recommendation with Knowledge Graph using ...", "date": "", "ddg_snippet": "by Z Xu · Cited by 1 — Table 1 : Statistics of the datasets used in our experiments. We report the basic statistics about the datasets , and notably, we also report the proportion ...", "subpage_snippet": "", "source": "www.vldb.org", "link": "https://www.vldb.org/pvldb/vol17/p4641-xu.pdf", "content": "by Z Xu · Cited by 1 — Table 1 : Statistics of the datasets used in our experiments. We report the basic statistics about the datasets , and notably, we also report the proportion ..."}
+{"idx": 4, "title": "Reproducibility and Analysis of Scientific Dataset ...", "date": "", "ddg_snippet": "by O Irrera · 2024 · Cited by 2 — We reviewed current recommendation methods for scientific datasets , focusing on the most recent and competitive approaches .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/fullHtml/10.1145/3640457.3688071", "content": "by O Irrera · 2024 · Cited by 2 — We reviewed current recommendation methods for scientific datasets , focusing on the most recent and competitive approaches ."}
+{"idx": 5, "title": "Interpretable recommender system with heterogeneous ...", "date": "", "ddg_snippet": "by Y Leng · Cited by 35 — The density of nonzeros in the matrix is 0.0161%/0.0309%. We summarize the statistics of the ratings for the businesses in Table 2. We show the ... 45 pages", "subpage_snippet": "", "source": "web.mit.edu", "link": "http://web.mit.edu/~yleng/www/Geometric_Deep_Learning_based_Information_Fusion_for_Preference_Inference.pdf", "content": "by Y Leng · Cited by 35 — The density of nonzeros in the matrix is 0.0161%/0.0309%. We summarize the statistics of the ratings for the businesses in Table 2. We show the ... 45 pages"}
+{"idx": 6, "title": "Personalized Visualization Recommendation", "date": "", "ddg_snippet": "In this work, we propose the notion of a visual configuration that removes the data dependency of a visualization while capturing the visual design choices.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3538703", "content": "In this work, we propose the notion of a visual configuration that removes the data dependency of a visualization while capturing the visual design choices."}
+{"idx": 7, "title": "Personalized Visualization Recommendation - Ryan A. Rossi", "date": "", "ddg_snippet": "In this work, we propose the notion of a visual configuration that removes the data dependency of a visualization while capturing the visual design choices. 47 pages", "subpage_snippet": "", "source": "graphrepresentationlearning.com", "link": "http://graphrepresentationlearning.com/pubs/Personalized-Visualization-Recommendation-TWEB22.pdf", "content": "In this work, we propose the notion of a visual configuration that removes the data dependency of a visualization while capturing the visual design choices. 47 pages"}
+{"idx": 8, "title": "arXiv:1711.04019v1 [stat.ML] 10 Nov 2017", "date": "", "ddg_snippet": "by K Liu · 2017 · Cited by 3 — In this section we conduct experiments on three large scale real-world datasets to verify the effectiveness of the pro- posed methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1711.04019", "content": "by K Liu · 2017 · Cited by 3 — In this section we conduct experiments on three large scale real-world datasets to verify the effectiveness of the pro- posed methods."}
+{"idx": 9, "title": "Tuning-Free LLM Can Build A Strong Recommender ...", "date": "", "ddg_snippet": "6 days ago — Table 1 : Statistics of datasets : Density is the ratio of ... dataset's intent count is about one magnitude larger than other datasets .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10900v3", "content": "6 days ago — Table 1 : Statistics of datasets : Density is the ratio of ... dataset's intent count is about one magnitude larger than other datasets ."}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Table_2_MNIST_Wasserstein_distance_sparse.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Table_2_MNIST_Wasserstein_distance_sparse.jsonl
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+{"idx": 0, "title": "Discrete Distribution Networks", "date": "", "ddg_snippet": "In this work, we propose a novel approach to model the target distribution , where the core idea is to generate multiple samples simultaneously ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00036v3", "content": "In this work, we propose a novel approach to model the target distribution , where the core idea is to generate multiple samples simultaneously ..."}
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+{"idx": 2, "title": "Learning to Optimize for Reinforcement Learning - arXiv.org", "date": "", "ddg_snippet": "To show the gradient distribution from training on MNIST is more iid than the agent-gradient distribution from training in big_dense_long , we compute the Wasserstein distance (WD) between the (agent-)gradient distribution at different training stages and the distribution of all (agent-)gradients during training in Table 4.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2302.01470v3", "content": "To show the gradient distribution from training on MNIST is more iid than the agent-gradient distribution from training in big_dense_long , we compute the Wasserstein distance (WD) between the (agent-)gradient distribution at different training stages and the distribution of all (agent-)gradients during training in Table 4."}
+{"idx": 3, "title": "ACE and Diverse Generalization via Selective Disagreement", "date": "", "ddg_snippet": "1 Introduction Standard deep learning approaches often struggle to maintain high performance under distributional shifts [34]. Models are particularly sensitive to distributional shifts that “break” the correlation (present on the training distribution ) between “spurious” and “ground truth” features (e.g., classifying huskies based on the presence of snow [64], or images based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.07955", "content": "1 Introduction Standard deep learning approaches often struggle to maintain high performance under distributional shifts [34]. Models are particularly sensitive to distributional shifts that “break” the correlation (present on the training distribution ) between “spurious” and “ground truth” features (e.g., classifying huskies based on the presence of snow [64], or images based on ..."}
+{"idx": 4, "title": "Feature selection via risk-bound utility maximization", "date": "", "ddg_snippet": "5 days ago · A more recent work [36] uses the Wasserstein distance to assess feature similarity. However, these prior works primarily employ the Wasserstein distance as a powerful heuristic for feature utility, without directly leveraging the explicit theoretical link to the classification risk bound itself.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231225022441", "content": "5 days ago · A more recent work [36] uses the Wasserstein distance to assess feature similarity. However, these prior works primarily employ the Wasserstein distance as a powerful heuristic for feature utility, without directly leveraging the explicit theoretical link to the classification risk bound itself."}
+{"idx": 5, "title": "2310.01973v1 (1) | PDF | Computer Science | Learning - Scribd", "date": "", "ddg_snippet": "3 C OMPUTING THE F EDERATED WASSERSTEIN DISTANCE In this section, we develop a methodology to compute, on a global server, the Wasserstein distance between two distributions µ and ν, stored on two different clients which do not share this information", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/918012031/2310-01973v1-1", "content": "3 C OMPUTING THE F EDERATED WASSERSTEIN DISTANCE In this section, we develop a methodology to compute, on a global server, the Wasserstein distance between two distributions µ and ν, stored on two different clients which do not share this information"}
+{"idx": 6, "title": "Scalable Bayesian inference for heat kernel Gaussian processes", "date": "", "ddg_snippet": "As shown in the top left panel of Figure 1 , for two points x , x ′ x,x^{\\prime} in the curve, the Euclidean distance is the length of the straight ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.13342v2", "content": "As shown in the top left panel of Figure 1 , for two points x , x ′ x,x^{\\prime} in the curve, the Euclidean distance is the length of the straight ..."}
+{"idx": 7, "title": "A Brief Guide to Cross-Entropy Loss", "date": "", "ddg_snippet": "The overall cross-entropy expression is used to quantify the difference between two probability distributions , the actual distribution P and the ...", "subpage_snippet": "", "source": "www.lightly.ai", "link": "https://www.lightly.ai/blog/cross-entropy-loss", "content": "The overall cross-entropy expression is used to quantify the difference between two probability distributions , the actual distribution P and the ..."}
+{"idx": 8, "title": "OCR and Document Understanding | Mistral AI Large Language", "date": "", "ddg_snippet": "In this setting, detecting shifts in the distribution of covariates is known to be possible (but difficult) in theory (Ramdas et al., 2015), and in ...", "subpage_snippet": "", "source": "docs.mistral.ai", "link": "https://docs.mistral.ai/capabilities/document/", "content": "In this setting, detecting shifts in the distribution of covariates is known to be possible (but difficult) in theory (Ramdas et al., 2015), and in ..."}
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diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Tabl.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Tabl.jsonl
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+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where ..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the ..."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-."}
+{"idx": 4, "title": "A likelihood based approach to distribution regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — We investigated statistical properties of a likelihood - based conditional deep generative model for distribution regression in a scenario where ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — We investigated statistical properties of a likelihood - based conditional deep generative model for distribution regression in a scenario where ..."}
+{"idx": 5, "title": "[Literature Review] A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "Core Methodology · 1. Framework and Model Specification · 2. Deep Generative Modeling · 3. Convergence Rates · 4. Noise Perturbation Strategy · 5. Neural Network ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/a-likelihood-based-approach-to-distribution-regression-using-conditional-deep-generative-models", "content": "Core Methodology · 1. Framework and Model Specification · 2. Deep Generative Modeling · 3. Convergence Rates · 4. Noise Perturbation Strategy · 5. Neural Network ..."}
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+{"idx": 7, "title": "Wasserstein Generative Learning of Conditional Distribution", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models · Shivam KumarYun YangLizhen Lin. Computer Science, Mathematics.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Wasserstein-Generative-Learning-of-Conditional-Liu-Zhou/da25744848bf00ab4f1ecc2902d4ecf356496a50", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models · Shivam KumarYun YangLizhen Lin. Computer Science, Mathematics."}
+{"idx": 8, "title": "A likelihood approach to nonparametric estimation of a ...", "date": "", "ddg_snippet": "1 Jan 2023 — We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3648699.3648776", "content": "1 Jan 2023 — We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative ..."}
+{"idx": 9, "title": "Yun Yang", "date": "", "ddg_snippet": "Figures and Tables : Figure 1 for A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models . Abstract:In this work, we ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Yun+Yang", "content": "Figures and Tables : Figure 1 for A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models . Abstract:In this work, we ..."}
diff --git a/data/sampled_jsons/A_Three-Branch_Checks-and-Balances_Framework_Limitations_and_Future_Work.jsonl b/data/sampled_jsons/A_Three-Branch_Checks-and-Balances_Framework_Limitations_and_Future_Work.jsonl
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+{"idx": 0, "title": "(PDF) Checks - and - Balances Framework for Context-Aware Ethical...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three - branch governmental systems.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three - branch governmental systems."}
+{"idx": 1, "title": "FutureGen: A RAG-based Approach to Generate the Future Work of", "date": "", "ddg_snippet": "This serves two complementary purposes: (1) enabling authors and researchers to obtain higher-quality and more diverse suggestions for Future Work in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16561v3", "content": "This serves two complementary purposes: (1) enabling authors and researchers to obtain higher-quality and more diverse suggestions for Future Work in ..."}
+{"idx": 2, "title": "Routine: A Structural Planning Framework for LLM Agent System", "date": "", "ddg_snippet": "... results demonstrate that Routine provides a practical and accessible approach to building stable agent workflows, accelerating the deployment and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14447v1", "content": "... results demonstrate that Routine provides a practical and accessible approach to building stable agent workflows, accelerating the deployment and ..."}
+{"idx": 3, "title": "Iterative Research Idea Development Through Evolving and", "date": "", "ddg_snippet": "... address these three challenges by introducing IdeaSynth , a research idea expansion and refinement system that facilitates the user’s process of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.04025v2", "content": "... address these three challenges by introducing IdeaSynth , a research idea expansion and refinement system that facilitates the user’s process of ..."}
+{"idx": 4, "title": "DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for", "date": "", "ddg_snippet": "... a comprehensive framework for developing a language model-based formal mathematics prover, integrating several key components: large-scale ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.08152v1", "content": "... a comprehensive framework for developing a language model-based formal mathematics prover, integrating several key components: large-scale ..."}
+{"idx": 5, "title": "A Scalable State Sharing Protocol for Low-Resource Validator", "date": "", "ddg_snippet": "Section VI reviews related work in the field, and Section VII discusses the limitations of this study and future research.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05854v1", "content": "Section VI reviews related work in the field, and Section VII discusses the limitations of this study and future research."}
+{"idx": 6, "title": "Frontiers | ULBERT: a domain-adapted BERT model for bilingual", "date": "", "ddg_snippet": "Information retrieval (IR) is the science of searching for information in a document or file and looking for metadata that describes data and data ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1448785/full", "content": "Information retrieval (IR) is the science of searching for information in a document or file and looking for metadata that describes data and data ..."}
+{"idx": 7, "title": "ICLR 2025 Paper Ranking | OpenReview Analysis", "date": "", "ddg_snippet": "The deliverable is a working implementation of the model, a report analyzing the results, and a presentation summarizing the findings.", "subpage_snippet": "", "source": "openreview-copilot.eamag.me", "link": "https://openreview-copilot.eamag.me/", "content": "The deliverable is a working implementation of the model, a report analyzing the results, and a presentation summarizing the findings."}
+{"idx": 8, "title": "USENIX Security '20 Fall Quarter Accepted Papers | USENIX", "date": "", "ddg_snippet": "We discuss the implications of this work in terms of end-users' privacy, the study's limitations , and future work to extend these results.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity20/fall-accepted-papers", "content": "We discuss the implications of this work in terms of end-users' privacy, the study's limitations , and future work to extend these results."}
+{"idx": 9, "title": "Securing Infrastructure with OpenScap The Automation Way !! |", "date": "", "ddg_snippet": "Limitations and future work are noted around input partitioning, data retention, and partial updates. ... a group of best and most experienced experts ...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/securing-infrastructure-with-openscap-the-automation-way/152169274", "content": "Limitations and future work are noted around input partitioning, data retention, and partial updates. ... a group of best and most experienced experts ..."}
diff --git a/data/sampled_jsons/A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_Section_3.2_self-su.jsonl b/data/sampled_jsons/A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_Section_3.2_self-su.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4494d9ad99dc57c3c8164056bc588c9e24a50d24
--- /dev/null
+++ b/data/sampled_jsons/A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_Section_3.2_self-su.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "git - How to create a new branch from a tag? - Stack Overflow", "date": "", "ddg_snippet": "Jun 7, 2012 · I'd like to create a new master branch from an existing tag. Say I have a tag v1.0. How to create a new branch from this tag?", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/10940981/how-to-create-a-new-branch-from-a-tag", "content": "Jun 7, 2012 · I'd like to create a new master branch from an existing tag. Say I have a tag v1.0. How to create a new branch from this tag?"}
+{"idx": 1, "title": "git: how to rename a branch (both local and remote)?", "date": "", "ddg_snippet": "I have a local branch master that points to a remote branch origin/regacy (oops, typo!). How do I rename the remote branch to origin/legacy or origin/master? I tried: git remote rename regacy legac...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/30590083/git-how-to-rename-a-branch-both-local-and-remote", "content": "I have a local branch master that points to a remote branch origin/regacy (oops, typo!). How do I rename the remote branch to origin/legacy or origin/master? I tried: git remote rename regacy legac..."}
+{"idx": 2, "title": "Using \"If cell contains #N/A\" as a formula condition.", "date": "", "ddg_snippet": "Jan 7, 2014 · I need help on my Excel sheet. How can I declare the following IF condition properly? if A1 = \"n/ a \" then C1 = B1 else if A1 != \"n/ a \" or has value(int) then C1 = A1*B1", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/20962940/using-if-cell-contains-n-a-as-a-formula-condition", "content": "Jan 7, 2014 · I need help on my Excel sheet. How can I declare the following IF condition properly? if A1 = \"n/ a \" then C1 = B1 else if A1 != \"n/ a \" or has value(int) then C1 = A1*B1"}
+{"idx": 3, "title": "git - How to cancel a pull request on github? - Stack Overflow", "date": "", "ddg_snippet": "Mar 22, 2011 · GitHub now supports closing a pull request Basically, you need to do the following steps: Visit the pull request page Click on the pull request Click the \"close pull request\" button Example (button on the very bottom): This way the pull request gets closed ( and ignored), without merging it.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/5385567/how-to-cancel-a-pull-request-on-github", "content": "Mar 22, 2011 · GitHub now supports closing a pull request Basically, you need to do the following steps: Visit the pull request page Click on the pull request Click the \"close pull request\" button Example (button on the very bottom): This way the pull request gets closed ( and ignored), without merging it."}
+{"idx": 4, "title": "github - How do I reverse a commit in git? - Stack Overflow", "date": "", "ddg_snippet": "I think you need to push a revert commit. So pull from github again, including the commit you want to revert, then use git revert and push the result. If you don't care about other people's clones of your github repository being broken, you can also delete and recreate the master branch on github after your reset: git push origin :master.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/5381945/how-do-i-reverse-a-commit-in-git", "content": "I think you need to push a revert commit. So pull from github again, including the commit you want to revert, then use git revert and push the result. If you don't care about other people's clones of your github repository being broken, you can also delete and recreate the master branch on github after your reset: git push origin :master."}
+{"idx": 5, "title": "Regular expression to match string starting with a specific word", "date": "", "ddg_snippet": "How do I create a regular expression to match a word at the beginning of a string? We are looking to match stop at the beginning of a string and anything can follow it. For example, the expression ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/1240504/regular-expression-to-match-string-starting-with-a-specific-word", "content": "How do I create a regular expression to match a word at the beginning of a string? We are looking to match stop at the beginning of a string and anything can follow it. For example, the expression ..."}
+{"idx": 6, "title": "The term 'Get-ADUser' is not recognized as the name of a cmdlet", "date": "", "ddg_snippet": "If the ActiveDirectory module is present add import-module activedirectory before your code. To check if exist try: get-module -listavailable ActiveDirectory module is default present in windows server 2008 R2, install it in this way: Import-Module ServerManager Add-WindowsFeature RSAT-AD-PowerShell For have it to work you need at least one DC in the domain as windows 2008 R2 and have Active ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/17548523/the-term-get-aduser-is-not-recognized-as-the-name-of-a-cmdlet", "content": "If the ActiveDirectory module is present add import-module activedirectory before your code. To check if exist try: get-module -listavailable ActiveDirectory module is default present in windows server 2008 R2, install it in this way: Import-Module ServerManager Add-WindowsFeature RSAT-AD-PowerShell For have it to work you need at least one DC in the domain as windows 2008 R2 and have Active ..."}
+{"idx": 7, "title": "How can I git stash a specific file? - Stack Overflow", "date": "", "ddg_snippet": "Sep 3 , 2014 · How can I stash a specific file leaving the others currently modified out of the stash I am about to save? For example, if git status gives me this: younker % git status # On branch master # ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/5506339/how-can-i-git-stash-a-specific-file", "content": "Sep 3 , 2014 · How can I stash a specific file leaving the others currently modified out of the stash I am about to save? For example, if git status gives me this: younker % git status # On branch master # ..."}
+{"idx": 8, "title": "How do I install a NuGet package .nupkg file locally to Visual...", "date": "", "ddg_snippet": "Apr 20, 2012 · I have some .nupkg files from a C# book that I would like to install to Visual Studio. How can I install them? Here is what I see in the Add Library Package Reference window showing no packages, wi...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/10240029/how-do-i-install-a-nuget-package-nupkg-file-locally-to-visual-studio", "content": "Apr 20, 2012 · I have some .nupkg files from a C# book that I would like to install to Visual Studio. How can I install them? Here is what I see in the Add Library Package Reference window showing no packages, wi..."}
+{"idx": 9, "title": "How to get all groups that a user is a member of? - Stack...", "date": "", "ddg_snippet": "PowerShell's Get-ADGroupMember cmdlet returns members of a specific group. Is there a cmdlet or property to get all the groups that a particular user is a member of?", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/5072996/how-to-get-all-groups-that-a-user-is-a-member-of", "content": "PowerShell's Get-ADGroupMember cmdlet returns members of a specific group. Is there a cmdlet or property to get all the groups that a particular user is a member of?"}
diff --git a/data/sampled_jsons/A_general_theoretical_paradigm_to_understand_learning_from_human_preferences_Azar_abstract.jsonl b/data/sampled_jsons/A_general_theoretical_paradigm_to_understand_learning_from_human_preferences_Azar_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d619a96ecb43262c9c3fb558ed221e8afa37040
--- /dev/null
+++ b/data/sampled_jsons/A_general_theoretical_paradigm_to_understand_learning_from_human_preferences_Azar_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intergroup relations - Wikipedia", "date": "", "ddg_snippet": "In 1967, Martin Luther King spoke at the annual meeting of the American Psychological Association urging social scientists to advance causes of ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Intergroup_relations", "content": "In 1967, Martin Luther King spoke at the annual meeting of the American Psychological Association urging social scientists to advance causes of ..."}
+{"idx": 1, "title": "A General Theoretical Paradigm to Understand Learning from ... A General Theoretical Paradigm to Understand Learning from ... A General Theoretical Paradigm to Understand Learning from ... A General Theoretical Paradigm to UnderstandLearning from ... Theoretical Paradigm for Human Preference Learning A General Theoretical Paradigm to Understand Learning from ... A General Theoretical Paradigm to Understand Learning from Human A General Theoretical Paradigm to Understand Learning from Human A General Theoretical Paradigm to UnderstandLearning from HumanPreferences A General Theoretical Paradigm to UnderstandLearning from HumanPreferences A General Theoretical Paradigm to Understand Learning from Human A General Theoretical Paradigm to Understand Learning from ...", "date": "", "ddg_snippet": "Oct 18, 2023 · In this paper we try to gain a deeper theoretical understanding of these practical algorithms. In particular we derive a new general objective called Ψ PO for learning from human preferences that is expressed in terms of pairwise preferences and therefore bypasses both approximations. Abstract The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards. The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important ap-proximations: the first assumes that pairwise preferences can be substituted with point-wise rewards. The second assumes that a reward model trained on these pointwise re-wards can generalize from collected data to out-of-dis... See full list on misovalko.github.io Learning a reward model consists in training a binary classifier to discriminate between the preferred and dis-preferred actions using a logistic regression loss. For the classifier, a popular choice is Bradley-Terry model: for a given context x and action y, we denote the point-wise reward, which can also be interpreted as an Elo score, of y given... See full list on misovalko.github.io Using the reward (Elo-score) r(x, y) the RLHF objective is simply to optimize for the policy π ∈ ∆X that max- Y imizes the expected reward while minimizing the dis-tance between π and some reference policy πref ∈ ∆X Y through the following KL-regularized objective func-tion: See full list on misovalko.github.io π x∼ρ y∼π(.|x) y′∼μ(.|x) This objective balances the maximisation of a po-tentially non-linear function of preference probabili-ties with the KL regularisation term which encourages policies to be close to the reference πref. This is mo-tivated by the form of Equation (3), and we will see in the next subsection that it strictly generalises both RLH... See full list on misovalko.github.io y,y′∼μ h hπ(y, y′)(p∗(y i = E ≻ μ) − p∗(y′ ≻ μ)) . y,y′∼μ Now, starting with the right-hand side, and using the shorthand πy = log(π(y)), πR y = log(πref(y)), py = p∗(y ≻ μ), and similarly for y′, we have hhπ(y, y′)(p∗(y ≻ See full list on misovalko.github.io y,y′∼μ πy′ py′ + πR y′py − πR y′py′ − πR py + πR py′ h i See full list on misovalko.github.io To illustrate the qualitative diference between our al-gorithm and DPO we will consider a few simple cases. For simplicity we assume there is no context x, i.e., we are in the bandit setting. See full list on misovalko.github.io So far we relied on the closed-form optimal policy from Eq. (9) to study DPO and IPO’s stability, but this equa-tion is not applicable to more complex settings where we only have access to sampled preference instead of p⋆. We can still however find accurate approximations of the optimal policy by choosing a parametrisation πθ and optimize θ with an... See full list on misovalko.github.io We presented a unified objective, called ΨPO, for learn-ing from preferences . It unifies RLHF and DPO methods. In addition, we introduced a particular case of ΨPO, called IPO, that allows to learn directly from prefer-ences without a reward modelling stage and without relying on the Bradley-Terry modelisation assumption that assumes that pairwise p... See full list on misovalko.github.io log σ τ log r(y|x) − τ x∼ρ y,y′∼μ(·|x) π∗ r(y′|x) log πref(y′|x) . In words, the value of the Bradley-Terry reward objective for r is the value of the DPO objective for π∗ r. We recall also that the map r 7→ π∗ r is surjective. Now, suppose r is optimal for the Bradley-Terry reward objective, meaning that objective. If π∗ is not optimal for the D... See full list on misovalko.github.io Dec 5, 2023 · In this work, our focus is on bridging the gap between theory and practice by introducing a simple and gen- eral theoretical representation of the practical algo- rithms for learning from human preferences . Oct 18, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences The paper presents a comprehensive theoretical framework for understanding practical algorithms that learn from human preferences , specifically in the context of reinforcement learning from human feedback (RLHF). Oct 19, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences arXiv - CS - Artificial Intelligence Pub Date : 2023-10-18 , DOI: arxiv-2310.12036 Mohammad Gheshlaghi Azar , Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos Can human preferences be substituted for pointwise rewards in reinforcement learning? abstract = {The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards. Can human preferences be replaced with pointwise rewards? The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards . What is learning from human preferences? Underreview. 1 Introduction Learning from human preferences (Christiano et al., 2017) is a paradigm adopted in the natural language processing literature to better align pretrained (Rad- ford et al., 2018; Ramachandran et al., 2016) and instruction-tuned (Wei et al., 2022) generative lan- guage models to human desiderata. Can human preferences be used for reinforcement learning? The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important ap- proximations: the first assumes that pairwise preferences can be substituted with point- wise rewards. What is posterior sampling for preference learning (pbrl)? This work proposes Posterior Sampling for Preference Learning, a novel algorithm inspired by Top-Two Thompson Sampling, that maintains independent posteriors over the true reward model and transition dynamics and provides the first theoretical guarantees for PbRL in this setting. Oct 18, 2023 · This work provides theoretical insights for a recently proposed learning paradigm , Nash learning from human feedback (NLHF), which considered a general preference model and formulated the alignment process as a game between two competitive LLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.12036", "content": "Oct 18, 2023 · In this paper we try to gain a deeper theoretical understanding of these practical algorithms. In particular we derive a new general objective called Ψ PO for learning from human preferences that is expressed in terms of pairwise preferences and therefore bypasses both approximations. Abstract The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards. The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important ap-proximations: the first assumes that pairwise preferences can be substituted with point-wise rewards. The second assumes that a reward model trained on these pointwise re-wards can generalize from collected data to out-of-dis... See full list on misovalko.github.io Learning a reward model consists in training a binary classifier to discriminate between the preferred and dis-preferred actions using a logistic regression loss. For the classifier, a popular choice is Bradley-Terry model: for a given context x and action y, we denote the point-wise reward, which can also be interpreted as an Elo score, of y given... See full list on misovalko.github.io Using the reward (Elo-score) r(x, y) the RLHF objective is simply to optimize for the policy π ∈ ∆X that max- Y imizes the expected reward while minimizing the dis-tance between π and some reference policy πref ∈ ∆X Y through the following KL-regularized objective func-tion: See full list on misovalko.github.io π x∼ρ y∼π(.|x) y′∼μ(.|x) This objective balances the maximisation of a po-tentially non-linear function of preference probabili-ties with the KL regularisation term which encourages policies to be close to the reference πref. This is mo-tivated by the form of Equation (3), and we will see in the next subsection that it strictly generalises both RLH... See full list on misovalko.github.io y,y′∼μ h hπ(y, y′)(p∗(y i = E ≻ μ) − p∗(y′ ≻ μ)) . y,y′∼μ Now, starting with the right-hand side, and using the shorthand πy = log(π(y)), πR y = log(πref(y)), py = p∗(y ≻ μ), and similarly for y′, we have hhπ(y, y′)(p∗(y ≻ See full list on misovalko.github.io y,y′∼μ πy′ py′ + πR y′py − πR y′py′ − πR py + πR py′ h i See full list on misovalko.github.io To illustrate the qualitative diference between our al-gorithm and DPO we will consider a few simple cases. For simplicity we assume there is no context x, i.e., we are in the bandit setting. See full list on misovalko.github.io So far we relied on the closed-form optimal policy from Eq. (9) to study DPO and IPO’s stability, but this equa-tion is not applicable to more complex settings where we only have access to sampled preference instead of p⋆. We can still however find accurate approximations of the optimal policy by choosing a parametrisation πθ and optimize θ with an... See full list on misovalko.github.io We presented a unified objective, called ΨPO, for learn-ing from preferences . It unifies RLHF and DPO methods. In addition, we introduced a particular case of ΨPO, called IPO, that allows to learn directly from prefer-ences without a reward modelling stage and without relying on the Bradley-Terry modelisation assumption that assumes that pairwise p... See full list on misovalko.github.io log σ τ log r(y|x) − τ x∼ρ y,y′∼μ(·|x) π∗ r(y′|x) log πref(y′|x) . In words, the value of the Bradley-Terry reward objective for r is the value of the DPO objective for π∗ r. We recall also that the map r 7→ π∗ r is surjective. Now, suppose r is optimal for the Bradley-Terry reward objective, meaning that objective. If π∗ is not optimal for the D... See full list on misovalko.github.io Dec 5, 2023 · In this work, our focus is on bridging the gap between theory and practice by introducing a simple and gen- eral theoretical representation of the practical algo- rithms for learning from human preferences . Oct 18, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences The paper presents a comprehensive theoretical framework for understanding practical algorithms that learn from human preferences , specifically in the context of reinforcement learning from human feedback (RLHF). Oct 19, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences arXiv - CS - Artificial Intelligence Pub Date : 2023-10-18 , DOI: arxiv-2310.12036 Mohammad Gheshlaghi Azar , Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos Can human preferences be substituted for pointwise rewards in reinforcement learning? abstract = {The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards. Can human preferences be replaced with pointwise rewards? The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards . What is learning from human preferences? Underreview. 1 Introduction Learning from human preferences (Christiano et al., 2017) is a paradigm adopted in the natural language processing literature to better align pretrained (Rad- ford et al., 2018; Ramachandran et al., 2016) and instruction-tuned (Wei et al., 2022) generative lan- guage models to human desiderata. Can human preferences be used for reinforcement learning? The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important ap- proximations: the first assumes that pairwise preferences can be substituted with point- wise rewards. What is posterior sampling for preference learning (pbrl)? This work proposes Posterior Sampling for Preference Learning, a novel algorithm inspired by Top-Two Thompson Sampling, that maintains independent posteriors over the true reward model and transition dynamics and provides the first theoretical guarantees for PbRL in this setting. Oct 18, 2023 · This work provides theoretical insights for a recently proposed learning paradigm , Nash learning from human feedback (NLHF), which considered a general preference model and formulated the alignment process as a game between two competitive LLMs."}
+{"idx": 2, "title": "A General Theoretical Paradigm to Understand Learning from ...", "date": "", "ddg_snippet": "Abstract The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/gheshlaghi-azar24a.html", "content": "Abstract The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards."}
+{"idx": 3, "title": "A General Theoretical Paradigm to Understand Learning from ... A General Theoretical Paradigm to UnderstandLearning from ... Theoretical Paradigm for Human Preference Learning A General Theoretical Paradigm to Understand Learning from ... A General Theoretical Paradigm to Understand Learning from Human A General Theoretical Paradigm to Understand Learning from Human A General Theoretical Paradigm to UnderstandLearning from HumanPreferences A General Theoretical Paradigm to UnderstandLearning from HumanPreferences A General Theoretical Paradigm to Understand Learning from Human A General Theoretical Paradigm to Understand Learning from ...", "date": "", "ddg_snippet": "The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important ap-proximations: the first assumes that pairwise preferences can be substituted with point-wise rewards. The second assumes that a reward model trained on these pointwise re-wards can generalize from collected data to out-of-dis... See full list on misovalko.github.io Learning a reward model consists in training a binary classifier to discriminate between the preferred and dis-preferred actions using a logistic regression loss. For the classifier, a popular choice is Bradley-Terry model: for a given context x and action y, we denote the point-wise reward, which can also be interpreted as an Elo score, of y given... See full list on misovalko.github.io Using the reward (Elo-score) r(x, y) the RLHF objective is simply to optimize for the policy π ∈ ∆X that max- Y imizes the expected reward while minimizing the dis-tance between π and some reference policy πref ∈ ∆X Y through the following KL-regularized objective func-tion: See full list on misovalko.github.io π x∼ρ y∼π(.|x) y′∼μ(.|x) This objective balances the maximisation of a po-tentially non-linear function of preference probabili-ties with the KL regularisation term which encourages policies to be close to the reference πref. This is mo-tivated by the form of Equation (3), and we will see in the next subsection that it strictly generalises both RLH... See full list on misovalko.github.io y,y′∼μ h hπ(y, y′)(p∗(y i = E ≻ μ) − p∗(y′ ≻ μ)) . y,y′∼μ Now, starting with the right-hand side, and using the shorthand πy = log(π(y)), πR y = log(πref(y)), py = p∗(y ≻ μ), and similarly for y′, we have hhπ(y, y′)(p∗(y ≻ See full list on misovalko.github.io y,y′∼μ πy′ py′ + πR y′py − πR y′py′ − πR py + πR py′ h i See full list on misovalko.github.io To illustrate the qualitative diference between our al-gorithm and DPO we will consider a few simple cases. For simplicity we assume there is no context x, i.e., we are in the bandit setting. See full list on misovalko.github.io So far we relied on the closed-form optimal policy from Eq. (9) to study DPO and IPO’s stability, but this equa-tion is not applicable to more complex settings where we only have access to sampled preference instead of p⋆. We can still however find accurate approximations of the optimal policy by choosing a parametrisation πθ and optimize θ with an... See full list on misovalko.github.io We presented a unified objective, called ΨPO, for learn-ing from preferences . It unifies RLHF and DPO methods. In addition, we introduced a particular case of ΨPO, called IPO, that allows to learn directly from prefer-ences without a reward modelling stage and without relying on the Bradley-Terry modelisation assumption that assumes that pairwise p... See full list on misovalko.github.io log σ τ log r(y|x) − τ x∼ρ y,y′∼μ(·|x) π∗ r(y′|x) log πref(y′|x) . In words, the value of the Bradley-Terry reward objective for r is the value of the DPO objective for π∗ r. We recall also that the map r 7→ π∗ r is surjective. Now, suppose r is optimal for the Bradley-Terry reward objective, meaning that objective. If π∗ is not optimal for the D... See full list on misovalko.github.io Dec 5, 2023 · In this work, our focus is on bridging the gap between theory and practice by introducing a simple and gen- eral theoretical representation of the practical algo- rithms for learning from human preferences . Oct 18, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences The paper presents a comprehensive theoretical framework for understanding practical algorithms that learn from human preferences , specifically in the context of reinforcement learning from human feedback (RLHF). Oct 19, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences arXiv - CS - Artificial Intelligence Pub Date : 2023-10-18 , DOI: arxiv-2310.12036 Mohammad Gheshlaghi Azar , Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos Can human preferences be substituted for pointwise rewards in reinforcement learning? abstract = {The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards. Can human preferences be replaced with pointwise rewards? The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards . What is learning from human preferences? Underreview. 1 Introduction Learning from human preferences (Christiano et al., 2017) is a paradigm adopted in the natural language processing literature to better align pretrained (Rad- ford et al., 2018; Ramachandran et al., 2016) and instruction-tuned (Wei et al., 2022) generative lan- guage models to human desiderata. Can human preferences be used for reinforcement learning? The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important ap- proximations: the first assumes that pairwise preferences can be substituted with point- wise rewards. What is posterior sampling for preference learning (pbrl)? This work proposes Posterior Sampling for Preference Learning, a novel algorithm inspired by Top-Two Thompson Sampling, that maintains independent posteriors over the true reward model and transition dynamics and provides the first theoretical guarantees for PbRL in this setting. Oct 18, 2023 · This work provides theoretical insights for a recently proposed learning paradigm , Nash learning from human feedback (NLHF), which considered a general preference model and formulated the alignment process as a game between two competitive LLMs.", "subpage_snippet": "", "source": "misovalko.github.io", "link": "https://misovalko.github.io/publications/azar2024unified.pdf", "content": "The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important ap-proximations: the first assumes that pairwise preferences can be substituted with point-wise rewards. The second assumes that a reward model trained on these pointwise re-wards can generalize from collected data to out-of-dis... See full list on misovalko.github.io Learning a reward model consists in training a binary classifier to discriminate between the preferred and dis-preferred actions using a logistic regression loss. For the classifier, a popular choice is Bradley-Terry model: for a given context x and action y, we denote the point-wise reward, which can also be interpreted as an Elo score, of y given... See full list on misovalko.github.io Using the reward (Elo-score) r(x, y) the RLHF objective is simply to optimize for the policy π ∈ ∆X that max- Y imizes the expected reward while minimizing the dis-tance between π and some reference policy πref ∈ ∆X Y through the following KL-regularized objective func-tion: See full list on misovalko.github.io π x∼ρ y∼π(.|x) y′∼μ(.|x) This objective balances the maximisation of a po-tentially non-linear function of preference probabili-ties with the KL regularisation term which encourages policies to be close to the reference πref. This is mo-tivated by the form of Equation (3), and we will see in the next subsection that it strictly generalises both RLH... See full list on misovalko.github.io y,y′∼μ h hπ(y, y′)(p∗(y i = E ≻ μ) − p∗(y′ ≻ μ)) . y,y′∼μ Now, starting with the right-hand side, and using the shorthand πy = log(π(y)), πR y = log(πref(y)), py = p∗(y ≻ μ), and similarly for y′, we have hhπ(y, y′)(p∗(y ≻ See full list on misovalko.github.io y,y′∼μ πy′ py′ + πR y′py − πR y′py′ − πR py + πR py′ h i See full list on misovalko.github.io To illustrate the qualitative diference between our al-gorithm and DPO we will consider a few simple cases. For simplicity we assume there is no context x, i.e., we are in the bandit setting. See full list on misovalko.github.io So far we relied on the closed-form optimal policy from Eq. (9) to study DPO and IPO’s stability, but this equa-tion is not applicable to more complex settings where we only have access to sampled preference instead of p⋆. We can still however find accurate approximations of the optimal policy by choosing a parametrisation πθ and optimize θ with an... See full list on misovalko.github.io We presented a unified objective, called ΨPO, for learn-ing from preferences . It unifies RLHF and DPO methods. In addition, we introduced a particular case of ΨPO, called IPO, that allows to learn directly from prefer-ences without a reward modelling stage and without relying on the Bradley-Terry modelisation assumption that assumes that pairwise p... See full list on misovalko.github.io log σ τ log r(y|x) − τ x∼ρ y,y′∼μ(·|x) π∗ r(y′|x) log πref(y′|x) . In words, the value of the Bradley-Terry reward objective for r is the value of the DPO objective for π∗ r. We recall also that the map r 7→ π∗ r is surjective. Now, suppose r is optimal for the Bradley-Terry reward objective, meaning that objective. If π∗ is not optimal for the D... See full list on misovalko.github.io Dec 5, 2023 · In this work, our focus is on bridging the gap between theory and practice by introducing a simple and gen- eral theoretical representation of the practical algo- rithms for learning from human preferences . Oct 18, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences The paper presents a comprehensive theoretical framework for understanding practical algorithms that learn from human preferences , specifically in the context of reinforcement learning from human feedback (RLHF). Oct 19, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences arXiv - CS - Artificial Intelligence Pub Date : 2023-10-18 , DOI: arxiv-2310.12036 Mohammad Gheshlaghi Azar , Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos Can human preferences be substituted for pointwise rewards in reinforcement learning? abstract = {The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards. Can human preferences be replaced with pointwise rewards? The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards . What is learning from human preferences? Underreview. 1 Introduction Learning from human preferences (Christiano et al., 2017) is a paradigm adopted in the natural language processing literature to better align pretrained (Rad- ford et al., 2018; Ramachandran et al., 2016) and instruction-tuned (Wei et al., 2022) generative lan- guage models to human desiderata. Can human preferences be used for reinforcement learning? The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important ap- proximations: the first assumes that pairwise preferences can be substituted with point- wise rewards. What is posterior sampling for preference learning (pbrl)? This work proposes Posterior Sampling for Preference Learning, a novel algorithm inspired by Top-Two Thompson Sampling, that maintains independent posteriors over the true reward model and transition dynamics and provides the first theoretical guarantees for PbRL in this setting. Oct 18, 2023 · This work provides theoretical insights for a recently proposed learning paradigm , Nash learning from human feedback (NLHF), which considered a general preference model and formulated the alignment process as a game between two competitive LLMs."}
+{"idx": 4, "title": "A General Theoretical Paradigm to UnderstandLearning from ...", "date": "", "ddg_snippet": "Dec 5, 2023 · In this work, our focus is on bridging the gap between theory and practice by introducing a simple and gen- eral theoretical representation of the practical algo- rithms for learning from human preferences .", "subpage_snippet": "", "source": "storage.prod.researchhub.com", "link": "https://storage.prod.researchhub.com/uploads/papers/2023/12/05/2310.12036.pdf", "content": "Dec 5, 2023 · In this work, our focus is on bridging the gap between theory and practice by introducing a simple and gen- eral theoretical representation of the practical algo- rithms for learning from human preferences ."}
+{"idx": 5, "title": "Theoretical Paradigm for Human Preference Learning", "date": "", "ddg_snippet": "Oct 18, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences The paper presents a comprehensive theoretical framework for understanding practical algorithms that learn from human preferences , specifically in the context of reinforcement learning from human feedback (RLHF).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2310.12036", "content": "Oct 18, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences The paper presents a comprehensive theoretical framework for understanding practical algorithms that learn from human preferences , specifically in the context of reinforcement learning from human feedback (RLHF)."}
+{"idx": 6, "title": "A General Theoretical Paradigm to Understand Learning from ...", "date": "", "ddg_snippet": "Oct 19, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences arXiv - CS - Artificial Intelligence Pub Date : 2023-10-18 , DOI: arxiv-2310.12036 Mohammad Gheshlaghi Azar , Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos", "subpage_snippet": "", "source": "www.x-mol.com", "link": "https://www.x-mol.com/paper/1715072209892691968", "content": "Oct 19, 2023 · A General Theoretical Paradigm to Understand Learning from Human Preferences arXiv - CS - Artificial Intelligence Pub Date : 2023-10-18 , DOI: arxiv-2310.12036 Mohammad Gheshlaghi Azar , Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos"}
+{"idx": 7, "title": "A General Theoretical Paradigm to Understand Learning from ...", "date": "", "ddg_snippet": "Oct 18, 2023 · This work provides theoretical insights for a recently proposed learning paradigm , Nash learning from human feedback (NLHF), which considered a general preference model and formulated the alignment process as a game between two competitive LLMs.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-General-Theoretical-Paradigm-to-Understand-from-Azar-Rowland/f3460dc3ae5cfd41099d576a3bb77411e1fc2e3f/figure/0", "content": "Oct 18, 2023 · This work provides theoretical insights for a recently proposed learning paradigm , Nash learning from human feedback (NLHF), which considered a general preference model and formulated the alignment process as a game between two competitive LLMs."}
+{"idx": 8, "title": "Free Approaches to Learning Essay Examples & Topic Ideas |", "date": "", "ddg_snippet": "... animals to learners improves their ... Thus, it allows students to gain theoretical information and explore learning through their sense of touch.", "subpage_snippet": "", "source": "chalkypapers.com", "link": "https://chalkypapers.com/subject/approaches-to-learning/", "content": "... animals to learners improves their ... Thus, it allows students to gain theoretical information and explore learning through their sense of touch."}
+{"idx": 9, "title": "Mark Crowley | Reinforcement Learning - Reading List", "date": "", "ddg_snippet": "... was demonstrated in the Arcade Learning Environment (ALE), a challenging framework composed of dozens of Atari 2600 games used to evaluate general ...", "subpage_snippet": "", "source": "markcrowley.ca", "link": "https://markcrowley.ca/rl-reading-list/", "content": "... was demonstrated in the Arcade Learning Environment (ALE), a challenging framework composed of dozens of Atari 2600 games used to evaluate general ..."}
diff --git a/data/sampled_jsons/A_task_is_worth_one_word_Learning_with_task_prompts_for_high-quality_versatile_image_inpainting_Powe.jsonl b/data/sampled_jsons/A_task_is_worth_one_word_Learning_with_task_prompts_for_high-quality_versatile_image_inpainting_Powe.jsonl
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+{"idx": 0, "title": "A Task is Worth One Word : Learning with Task Prompts for ...", "date": "", "ddg_snippet": "First , we introduce learnable task prompts along with tailored fine-tuning strategies to guide the model’s focus on different inpainting targets explicitly.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03594v4", "content": "First , we introduce learnable task prompts along with tailored fine-tuning strategies to guide the model’s focus on different inpainting targets explicitly."}
+{"idx": 1, "title": "A Task Is Worth One Word : Learning with Task Prompts for ...", "date": "", "ddg_snippet": "First , we introduce learnable task prompts along with tailored fine-tuning strategies to guide the model’s focus on different inpainting targets explicitly.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-73636-0_12", "content": "First , we introduce learnable task prompts along with tailored fine-tuning strategies to guide the model’s focus on different inpainting targets explicitly."}
+{"idx": 2, "title": "A Task is Worth One Word", "date": "", "ddg_snippet": "Moreover, the learned task prompt in PowerPaint has effectively captured the intrinsic pattern of the task and can be extended to facilitate powerful object removal. In particular, existing T2 I models employ a classifier - free guidance sampling strategy, where a...", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/07554.pdf", "content": "Moreover, the learned task prompt in PowerPaint has effectively captured the intrinsic pattern of the task and can be extended to facilitate powerful object removal. In particular, existing T2 I models employ a classifier - free guidance sampling strategy, where a..."}
+{"idx": 3, "title": "Paper page - A Task is Worth One Word : Learning with Task ...", "date": "", "ddg_snippet": "PowerPaint is a versatile image inpainting model that uses learnable task prompts for context-aware and text-guided inpainting , achieving state-of-the-art performance across various benchmarks.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2312.03594", "content": "PowerPaint is a versatile image inpainting model that uses learnable task prompts for context-aware and text-guided inpainting , achieving state-of-the-art performance across various benchmarks."}
+{"idx": 4, "title": "A Task Is Worth One Word : Learning with Task Prompts for ...", "date": "", "ddg_snippet": "Image inpainting that completes large free -form missing regions in images is a promising yet challenging task . State-of-the-art approaches have achieved significant progress by taking advantage of generative adversarial networks (GAN).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385555757_A_Task_Is_Worth_One_Word_Learning_with_Task_Prompts_for_High-Quality_Versatile_Image_Inpainting", "content": "Image inpainting that completes large free -form missing regions in images is a promising yet challenging task . State-of-the-art approaches have achieved significant progress by taking advantage of generative adversarial networks (GAN)."}
+{"idx": 5, "title": "PowerPaint : A Task is Worth One Word : Learning with Task ...", "date": "", "ddg_snippet": "PowerPaint introduces the concept of \"learnable task prompts \" to guide the model in achieving specific tasks more effectively. These tasks include text-guided, shape-guided, object-remove, and outpainting.", "subpage_snippet": "", "source": "www.iopaint.com", "link": "https://www.iopaint.com/models/diffusion/powerpaint", "content": "PowerPaint introduces the concept of \"learnable task prompts \" to guide the model in achieving specific tasks more effectively. These tasks include text-guided, shape-guided, object-remove, and outpainting."}
+{"idx": 6, "title": "A Task is Worth One Word : Learning with Task Prompts for ...", "date": "", "ddg_snippet": "First , we introduce learnable task prompts along with tailored fine-tuning strategies to guide the model's focus on different inpainting targets explicitly.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/task-is-worth-one-word-learning-task", "content": "First , we introduce learnable task prompts along with tailored fine-tuning strategies to guide the model's focus on different inpainting targets explicitly."}
+{"idx": 7, "title": "Image Inpainting | Papers With Code", "date": "", "ddg_snippet": "A Task is Worth One Word : Learning with Task Prompts for High - Quality Versatile Image Inpainting .Second, we demonstrate the versatility of the task prompt in PowerPaint by showcasing its effectiveness as a negative prompt for object removal.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/image-inpainting/latest?page=4", "content": "A Task is Worth One Word : Learning with Task Prompts for High - Quality Versatile Image Inpainting .Second, we demonstrate the versatility of the task prompt in PowerPaint by showcasing its effectiveness as a negative prompt for object removal."}
+{"idx": 8, "title": "PowerPaint", "date": "", "ddg_snippet": "A Task is Worth One Word : Learning with Task Prompts for High - Quality Versatile Image Inpainting (ECCV'24).First, we introduce learnable task prompts along with tailored fine-tuning strategies to guide the model's focus on different inpainting targets explicitly.", "subpage_snippet": "", "source": "powerpaint.github.io", "link": "https://powerpaint.github.io/", "content": "A Task is Worth One Word : Learning with Task Prompts for High - Quality Versatile Image Inpainting (ECCV'24).First, we introduce learnable task prompts along with tailored fine-tuning strategies to guide the model's focus on different inpainting targets explicitly."}
+{"idx": 9, "title": "JunhaoZhuang/ PowerPaint _v2 · Hugging Face", "date": "", "ddg_snippet": "A Task is Worth One Word : Learning with Task Prompts for High - Quality Versatile Image Inpainting . Project Page. This README provides a step-by-step guide to download the repository, set up the required virtual environment named \" PowerPaint \" using conda...", "subpage_snippet": "", "source": "huggingface.proxy.nlp.skieer.com", "link": "https://huggingface.proxy.nlp.skieer.com/JunhaoZhuang/PowerPaint_v2", "content": "A Task is Worth One Word : Learning with Task Prompts for High - Quality Versatile Image Inpainting . Project Page. This README provides a step-by-step guide to download the repository, set up the required virtual environment named \" PowerPaint \" using conda..."}
diff --git a/data/sampled_jsons/A_tutorial_on_spectral_clustering_von_Luxburg_abstract_year_2007.jsonl b/data/sampled_jsons/A_tutorial_on_spectral_clustering_von_Luxburg_abstract_year_2007.jsonl
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+{"idx": 0, "title": "[0711.0189] A Tutorial on Spectral Clustering - arXiv.org", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/0711.0189", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 1, "title": "PDF A tutorial on spectral clustering", "date": "", "ddg_snippet": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms tradi-tional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mys-terious, and it is not obvious to see why ...", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/courses/cs6241/2020sp/readings/vonLuxburg-2007-spectral.pdf", "content": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms tradi-tional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mys-terious, and it is not obvious to see why ..."}
+{"idx": 2, "title": "A tutorial on spectral clustering | Statistics and Computing", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11222-007-9033-z", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 3, "title": "A tutorial on spectral clustering | Max Planck Institute for ...", "date": "", "ddg_snippet": "BibTex @techreport {4139, title = { A tutorial on spectral clustering }, abstract = {In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Nevertheless, on the first ...", "subpage_snippet": "", "source": "ps.is.mpg.de", "link": "https://ps.is.mpg.de/publications/4139", "content": "BibTex @techreport {4139, title = { A tutorial on spectral clustering }, abstract = {In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Nevertheless, on the first ..."}
+{"idx": 4, "title": "[PDF] A tutorial on spectral clustering | Semantic Scholar", "date": "", "ddg_snippet": "This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-tutorial-on-spectral-clustering-Luxburg/eda90bd43f4256986688e525b45b833a3addab97", "content": "This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ..."}
+{"idx": 5, "title": "A Tutorial on Spectral Clustering - ADS", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2007arXiv0711.0189V/abstract", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 6, "title": "A tutorial on spectral clustering | Statistics and Computing", "date": "", "ddg_snippet": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1007/s11222-007-9033-z", "content": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm."}
+{"idx": 7, "title": "von Luxburg, U. (2007) A Tutorial on Spectral Clustering. Statistics ...", "date": "", "ddg_snippet": "The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering ; however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-of-Sight algorithm is also developed for clustering .", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=2317610", "content": "The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering ; however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-of-Sight algorithm is also developed for clustering ."}
+{"idx": 8, "title": "PDF A tutorial on spectral clustering", "date": "", "ddg_snippet": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms tradi-tional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mys-terious, and it is not obvious to see why ...", "subpage_snippet": "", "source": "public.websites.umich.edu", "link": "https://public.websites.umich.edu/~jizhu/jizhu/wuke/Luxburg-SC07.pdf", "content": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms tradi-tional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mys-terious, and it is not obvious to see why ..."}
+{"idx": 9, "title": "A Tutorial on Spectral Clustering - DocsLib", "date": "", "ddg_snippet": "Max-Planck-Institut fur¨ biologische Kybernetik Max Planck Institute for Biological Cybernetics Technical Report No. TR-149 A Tutorial on Spectral Clustering Ulrike von Luxburg1 August 2006 1 Department for Empirical Inference, email: [email protected] A Tutorial on Spectral Clustering Ulrike von Luxburg Abstract . In recent years, spectral clustering has become one of the most popular ...", "subpage_snippet": "", "source": "docslib.org", "link": "https://docslib.org/doc/12117048/a-tutorial-on-spectral-clustering", "content": "Max-Planck-Institut fur¨ biologische Kybernetik Max Planck Institute for Biological Cybernetics Technical Report No. TR-149 A Tutorial on Spectral Clustering Ulrike von Luxburg1 August 2006 1 Department for Empirical Inference, email: [email protected] A Tutorial on Spectral Clustering Ulrike von Luxburg Abstract . In recent years, spectral clustering has become one of the most popular ..."}
diff --git a/data/sampled_jsons/Aayush_Karan_github_blink_of_an_eye.jsonl b/data/sampled_jsons/Aayush_Karan_github_blink_of_an_eye.jsonl
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+{"idx": 0, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This phenomenon is not unique to autoregressive models: in diffusion models, key features of the final output are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This phenomenon is not unique to autoregressive models: in diffusion models, key features of the final output are ..."}
+{"idx": 1, "title": "Blink of an Eye: A Simple Theory for Feature Localization in Generative ...", "date": "", "ddg_snippet": "We present a concise theoretical framework that explains sudden feature localization events—\"blinks of an eye\"—in diffusion and autoregressive generative models. Recommended citation: Marvin Li, Aayush Karan , and Sitan Chen. (2025). \" Blink of an Eye : A Simple Theory for Feature Localization in Generative Models.\"", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/publication/2025-blink-eye", "content": "We present a concise theoretical framework that explains sudden feature localization events—\"blinks of an eye\"—in diffusion and autoregressive generative models. Recommended citation: Marvin Li, Aayush Karan , and Sitan Chen. (2025). \" Blink of an Eye : A Simple Theory for Feature Localization in Generative Models.\""}
+{"idx": 2, "title": "ICML Poster Blink of an eye: a simple theory for feature localization ...", "date": "", "ddg_snippet": "Spotlight Poster Blink of an eye : a simple theory for feature localization in generative models Marvin Li · Aayush Karan · Sitan Chen West Exhibition Hall B2-B3 #W-1019", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45312", "content": "Spotlight Poster Blink of an eye : a simple theory for feature localization in generative models Marvin Li · Aayush Karan · Sitan Chen West Exhibition Hall B2-B3 #W-1019"}
+{"idx": 3, "title": "Aayush Karan - OpenReview", "date": "", "ddg_snippet": "Publications Blink of an eye : a simple theory for feature localization in generative models Marvin Li, Aayush Karan , Sitan Chen Published: 05 Mar 2025, Last Modified: 23 Apr 2025 FPI-ICLR2025 Poster Blink of an eye : a simple theory for feature localization in generative models", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Aayush_Karan1", "content": "Publications Blink of an eye : a simple theory for feature localization in generative models Marvin Li, Aayush Karan , Sitan Chen Published: 05 Mar 2025, Last Modified: 23 Apr 2025 FPI-ICLR2025 Poster Blink of an eye : a simple theory for feature localization in generative models"}
+{"idx": 4, "title": "(PDF) Blink of an eye: a simple theory for feature ... - ResearchGate", "date": "", "ddg_snippet": "Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone ..."}
+{"idx": 5, "title": "Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "Blink of an eye : a simple theory for feature localization in generative models Marvin Li, Aayush Karan , Sitan Chen", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QvqnPVGWAN", "content": "Blink of an eye : a simple theory for feature localization in generative models Marvin Li, Aayush Karan , Sitan Chen"}
+{"idx": 6, "title": "BLINK OF AN EYE A SIMPLE THEORY FOR FEATURE LOCALIZATION ... - OpenReview", "date": "", "ddg_snippet": "Published as a conference paper at ICLR 2025 BLINK OF AN EYE:A SIMPLE THEORY FOR FEATURE LOCALIZATION IN GENERATIVE MODELS Marvin Li, Aayush Karan , & Sitan Chen Department of Computer Science Harvard University Cambridge, MA 02138, USA ABSTRACT Large language models can exhibit undesirable and unexpected behavior in the blink of an eye .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=nodyP4FLrM", "content": "Published as a conference paper at ICLR 2025 BLINK OF AN EYE:A SIMPLE THEORY FOR FEATURE LOCALIZATION IN GENERATIVE MODELS Marvin Li, Aayush Karan , & Sitan Chen Department of Computer Science Harvard University Cambridge, MA 02138, USA ABSTRACT Large language models can exhibit undesirable and unexpected behavior in the blink of an eye ."}
+{"idx": 7, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Email: marvinli@college.harvard.edu Aayush Karan Harvard SEAS Email: akaran1@g.harvard.edu, supported in part by the PD Soros fellowship Sitan Chen Harvard SEAS Email: sitan@seas.harvard.edu, supported in part by NSF Award 2331831 Abstract Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "Email: marvinli@college.harvard.edu Aayush Karan Harvard SEAS Email: akaran1@g.harvard.edu, supported in part by the PD Soros fellowship Sitan Chen Harvard SEAS Email: sitan@seas.harvard.edu, supported in part by NSF Award 2331831 Abstract Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye ."}
+{"idx": 8, "title": "Aayush Karan - Google Scholar", "date": "", "ddg_snippet": "Harvard University - Cited by 9 - Generative Modeling - Representation Learning", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=L0SIifMAAAAJ&hl=en", "content": "Harvard University - Cited by 9 - Generative Modeling - Representation Learning"}
+{"idx": 9, "title": "Sitan Chen June 10, 2025 arXiv:2502.00921v2 [cs.LG] 5 Jun 2025", "date": "", "ddg_snippet": "Harvard SEAS Sitan Chen‡ Harvard SEAS June 10, 2025 Abstract Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00921v2", "content": "Harvard SEAS Sitan Chen‡ Harvard SEAS June 10, 2025 Abstract Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks."}
diff --git a/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity_arxiv.jsonl b/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1673634fc7768435f75c379af48024ce60fb4f48
--- /dev/null
+++ b/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with ...", "date": "", "ddg_snippet": "Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.) Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity arXiv - CS - Neural and Evolutionary ComputingPub Date : 2025-02-03, DOI: arxiv -2502.01330 Alessandro Pierro, Steven Abreu, Jonathan Timcheck, Philipp Stratmann, Andreas Wild, Sumit ...", "subpage_snippet": "", "source": "www.x-mol.com", "link": "https://www.x-mol.com/paper/1886855530633846784", "content": "Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.) Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity arXiv - CS - Neural and Evolutionary ComputingPub Date : 2025-02-03, DOI: arxiv -2502.01330 Alessandro Pierro, Steven Abreu, Jonathan Timcheck, Philipp Stratmann, Andreas Wild, Sumit ..."}
+{"idx": 1, "title": "UnIT: Scalable Unstructured Inference-Time Pruning for", "date": "", "ddg_snippet": "Deploying deep neural networks (DNNs) on low-power microcontroller units (MCUs) is essential for enabling edge intelligence in domains such as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07885v1", "content": "Deploying deep neural networks (DNNs) on low-power microcontroller units (MCUs) is essential for enabling edge intelligence in domains such as ..."}
+{"idx": 2, "title": "SparseDPD: A Sparse Neural Network-based Digital Predistortion", "date": "", "ddg_snippet": "... accelerator employing a spatially sparse phase-normalized time-delay neural network (PNTDNN), optimized through unstructured pruning to reduce ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.16591v1", "content": "... accelerator employing a spatially sparse phase-normalized time-delay neural network (PNTDNN), optimized through unstructured pruning to reduce ..."}
+{"idx": 3, "title": "Accelerating LLM Inference with Flexible N:M Sparsity via A", "date": "", "ddg_snippet": "... we present a pruning algorithm that can assign different N:M sparsity with a higher degree of freedom and then present the first DCiM accelerator to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.14365v1", "content": "... we present a pruning algorithm that can assign different N:M sparsity with a higher degree of freedom and then present the first DCiM accelerator to ..."}
+{"idx": 4, "title": "GitHub - he-y/Awesome-Pruning: A curated list of neural network", "date": "", "ddg_snippet": "... title={Structured Pruning for Deep Convolutional Neural Networks ... Joint Edge -Model Sparse Learning is Provably Efficient for Graph Neural Networks", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/he-y/Awesome-Pruning", "content": "... title={Structured Pruning for Deep Convolutional Neural Networks ... Joint Edge -Model Sparse Learning is Provably Efficient for Graph Neural Networks"}
+{"idx": 5, "title": "OpenVINO™ Blog | Accelerate DIEN for Click-Through-Rate", "date": "", "ddg_snippet": "... the same shape on output we can easily preallocate enough memory before the TI computation, The same for Loop with trip count input - we can just read ...", "subpage_snippet": "", "source": "blog.openvino.ai", "link": "https://blog.openvino.ai/blog-posts/accelerate-dien-for-click-through-rate-prediction-with-openvino-tm", "content": "... the same shape on output we can easily preallocate enough memory before the TI computation, The same for Loop with trip count input - we can just read ..."}
+{"idx": 6, "title": "From Bayesian Sparsity to Gated Recurrent Nets", "date": "", "ddg_snippet": "This paper proposes the use of gated feedback gated recurrent unit network (GFGRU), a learning-based sparse estimation algorithm, for multiple source ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/317543453_From_Bayesian_Sparsity_to_Gated_Recurrent_Nets", "content": "This paper proposes the use of gated feedback gated recurrent unit network (GFGRU), a learning-based sparse estimation algorithm, for multiple source ..."}
+{"idx": 7, "title": "SpikeX: Exploring Accelerator Architecture and Network-Hardware", "date": "", "ddg_snippet": "The previous generations of accelerators do not maximally optimize for the unique dataflow opportunities of spiking neural networks , especially as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.12292v1", "content": "The previous generations of accelerators do not maximally optimize for the unique dataflow opportunities of spiking neural networks , especially as ..."}
+{"idx": 8, "title": "Neuro-inspired Ensemble-to-Ensemble Communication Primitives", "date": "", "ddg_snippet": "These vectors are concatenated and passed through a feed-forward network with a predefined sparse structure , inspired by group-to-group ( neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14140v1", "content": "These vectors are concatenated and passed through a feed-forward network with a predefined sparse structure , inspired by group-to-group ( neural ..."}
+{"idx": 9, "title": "GitHub - HuangOwen/Awesome-LLM-Compression: Awesome LLM", "date": "", "ddg_snippet": "Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices Arxiv 2025 [Paper] ... Then Prompt: Improving ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HuangOwen/Awesome-LLM-Compression", "content": "Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices Arxiv 2025 [Paper] ... Then Prompt: Improving ..."}
diff --git a/data/sampled_jsons/ActSVD_methodology_parameter_modification_percentage_Wei_et_al.jsonl b/data/sampled_jsons/ActSVD_methodology_parameter_modification_percentage_Wei_et_al.jsonl
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index 0000000000000000000000000000000000000000..7dd2a3213ec767d949d7e1c6457d0f08f7fe501d
--- /dev/null
+++ b/data/sampled_jsons/ActSVD_methodology_parameter_modification_percentage_Wei_et_al.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Determination of the source parameters of the 2011 Tohoku-Oki...", "date": "", "ddg_snippet": "Zhang et al . (2020) also determined source parameters using their signal simulation method . They synthesized pre-P waveforms for a point source located at the hypocenter of the 2011 Tohoku-Oki earthquake, determining the source parameters that best fit the vertical broadband...", "subpage_snippet": "", "source": "earth-planets-space.springeropen.com", "link": "https://earth-planets-space.springeropen.com/articles/10.1186/s40623-021-01553-7", "content": "Zhang et al . (2020) also determined source parameters using their signal simulation method . They synthesized pre-P waveforms for a point source located at the hypocenter of the 2011 Tohoku-Oki earthquake, determining the source parameters that best fit the vertical broadband..."}
+{"idx": 1, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Specifically, these methods (Geiger et al ., 2021; Stolfo et al ., 2023; Gurnee et al ., 2023) granularly analyze features, neurons, layers and parameters to assist human understand model behavior and capabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v1", "content": "Specifically, these methods (Geiger et al ., 2021; Stolfo et al ., 2023; Gurnee et al ., 2023) granularly analyze features, neurons, layers and parameters to assist human understand model behavior and capabilities."}
+{"idx": 2, "title": "The Use of LLMs to Annotate Data in Management", "date": "", "ddg_snippet": "as a critical methodological component ( Wei et al ., 2022) that incurs unique challenges, because unlike traditional method parameters , prompts are more difficult to systematically optimize.", "subpage_snippet": "", "source": "faculty.wharton.upenn.edu", "link": "https://faculty.wharton.upenn.edu/wp-content/uploads/2025/09/The-use-of-LLMs-to-Annotate-Data.pdf", "content": "as a critical methodological component ( Wei et al ., 2022) that incurs unique challenges, because unlike traditional method parameters , prompts are more difficult to systematically optimize."}
+{"idx": 3, "title": "(PDF) On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "et al .,2023b; Wei et al .,2024a;Carlini et al .,2024) suggests that malicious attackers can circumvent. safety guardrails. Therefore, understanding the inner workings of language models is necessary for. Method Parameter . Modification ASR Attribution.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety", "content": "et al .,2023b; Wei et al .,2024a;Carlini et al .,2024) suggests that malicious attackers can circumvent. safety guardrails. Therefore, understanding the inner workings of language models is necessary for. Method Parameter . Modification ASR Attribution."}
+{"idx": 4, "title": "Острый перец чили и здоровье: вредно или полезно | MedAboutMe", "date": "", "ddg_snippet": "Capsaicin Inhibits Proliferation and Induces Apoptosis in Breast Cancer by Down-Regulating FBI-1-Mediated NF-κB Pathway / Chen, Maojian; Xiao, Chanchan ; Jiang, Wei et al . // Drug Design, Development and Therapy – 2021. Capsaicin may have important potential for promoting...", "subpage_snippet": "", "source": "MedAboutMe.ru", "link": "https://MedAboutMe.ru/articles/ostryy_perets_i_zdorove_chem_polezen_kapsaitsin/", "content": "Capsaicin Inhibits Proliferation and Induces Apoptosis in Breast Cancer by Down-Regulating FBI-1-Mediated NF-κB Pathway / Chen, Maojian; Xiao, Chanchan ; Jiang, Wei et al . // Drug Design, Development and Therapy – 2021. Capsaicin may have important potential for promoting..."}
+{"idx": 5, "title": "Общение – наркотик: оно... | Psychologies (Психология)", "date": "", "ddg_snippet": "Как показывают наши исследования, эти лекарства, возможно, смогут повышать эффективность действия окситоцина непосредственно в мозге, тем самым помогая людям с аутизмом стать более общительными». Подробнее см. D. Wei et al .", "subpage_snippet": "", "source": "www.psychologies.ru", "link": "https://www.psychologies.ru/articles/obschenie-narkotik-ono-vyirabatyivaet-molekulu-blajenstva/", "content": "Как показывают наши исследования, эти лекарства, возможно, смогут повышать эффективность действия окситоцина непосредственно в мозге, тем самым помогая людям с аутизмом стать более общительными». Подробнее см. D. Wei et al ."}
+{"idx": 6, "title": "Промывка мозгов: как работает глимфатическая система", "date": "", "ddg_snippet": "Jeffrey J. Iliff, Minghuan Wang, Yonghong Liao, Benjamin A. Plogg, Weiguo Peng, et . al ..Ehsan Shokri-Kojori, Gene-Jack Wang, Corinde E. Wiers, Sukru B. Demiral, Min Guo, et . al ..", "subpage_snippet": "", "source": "biomolecula.ru", "link": "https://biomolecula.ru/articles/promyvka-mozgov-kak-rabotaet-glimfaticheskaia-sistema", "content": "Jeffrey J. Iliff, Minghuan Wang, Yonghong Liao, Benjamin A. Plogg, Weiguo Peng, et . al ..Ehsan Shokri-Kojori, Gene-Jack Wang, Corinde E. Wiers, Sukru B. Demiral, Min Guo, et . al .."}
+{"idx": 7, "title": "Экспертный консенсус для врачей по диагностике и ведению...", "date": "", "ddg_snippet": "103. Petroff D, Blank V, Newsome PN, Shalimar, Voican CS, Thiele M et al . Assessment of hepatic steatosis by controlled attenuation parameter using the M and XL probes: an individual patient data meta-analysis.", "subpage_snippet": "", "source": "cyberleninka.ru", "link": "https://cyberleninka.ru/article/n/ekspertnyy-konsensus-dlya-vrachey-po-diagnostike-i-vedeniyu-patsientov-s-nealkogolnoy-zhirovoy-boleznyu-pecheni", "content": "103. Petroff D, Blank V, Newsome PN, Shalimar, Voican CS, Thiele M et al . Assessment of hepatic steatosis by controlled attenuation parameter using the M and XL probes: an individual patient data meta-analysis."}
+{"idx": 8, "title": "Chain-of-Thought Prompting | Prompt Engineering Guide", "date": "", "ddg_snippet": "Introduced in Wei et al . (2022) (opens in a new tab), chain-of-thought (CoT) prompting enables complex reasoning capabilities through intermediate reasoning steps. You can combine it with...", "subpage_snippet": "", "source": "www.promptingguide.ai", "link": "https://www.promptingguide.ai/techniques/cot", "content": "Introduced in Wei et al . (2022) (opens in a new tab), chain-of-thought (CoT) prompting enables complex reasoning capabilities through intermediate reasoning steps. You can combine it with..."}
+{"idx": 9, "title": "science.org/doi/10.1126/science.abq7487", "date": "", "ddg_snippet": "Wangjie Hu et al . Genomic inference of a severe human bottleneck during the...", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/science.abq7487", "content": "Wangjie Hu et al . Genomic inference of a severe human bottleneck during the..."}
diff --git a/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_HDR-IPPO_Equation_(2).jsonl b/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_HDR-IPPO_Equation_(2).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e88e4d3523a0cadd2476d6aee3ed8cd166501799
--- /dev/null
+++ b/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_HDR-IPPO_Equation_(2).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "To address these problems, we introduce the Ad - Hoc Human - AI Coordina - tion Challenge ( AH2AC2 ) as a standardised way to evaluate human-AI coordination in Hanabi. Specifically, we develop human proxy agents through a combination of behavioural cloning (BC) and regularised reinforcement learning (RL).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.21490", "content": "To address these problems, we introduce the Ad - Hoc Human - AI Coordina - tion Challenge ( AH2AC2 ) as a standardised way to evaluate human-AI coordination in Hanabi. Specifically, we develop human proxy agents through a combination of behavioural cloning (BC) and regularised reinforcement learning (RL)."}
+{"idx": 1, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) - GitHub", "date": "", "ddg_snippet": "Welcome to the Ad - Hoc Human - AI Coordination Challenge (AH2AC2)! The objective of AH2AC2 is to facilitate the development of AI agents capable of effective collaboration with human-like partners, especially in scenarios with limited prior interaction data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2", "content": "Welcome to the Ad - Hoc Human - AI Coordination Challenge (AH2AC2)! The objective of AH2AC2 is to facilitate the development of AI agents capable of effective collaboration with human-like partners, especially in scenarios with limited prior interaction data."}
+{"idx": 2, "title": "Ad-Hoc Human-AI Coordination Challenge - Semantic Scholar", "date": "", "ddg_snippet": "Hyperparameters used for training BC, HDR - IPPO baselines on a 5,000-game data limit challenge. BC policies trained as baselines are used for starting points in HDR - IPPO and later for BR-BC.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Ad-Hoc-Human-AI-Coordination-Challenge-Dizdarevic-Hammond/76e21098d2925daeb769a647c9af4886d00b05fd/figure/22", "content": "Hyperparameters used for training BC, HDR - IPPO baselines on a 5,000-game data limit challenge. BC policies trained as baselines are used for starting points in HDR - IPPO and later for BR-BC."}
+{"idx": 3, "title": "AD-HOC HUMAN-AI COORDINATION CHALLENGE - OpenReview", "date": "", "ddg_snippet": "525 524 • Theoretical Analysis of HDR - IPPO : While our experiments and previous works provide strong 526 empirical evidence for the effectiveness of regularised RL in generating human-like agents, a 527 deeper theoretical understanding of the methodology is crucial.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Kioojohsuy", "content": "525 524 • Theoretical Analysis of HDR - IPPO : While our experiments and previous works provide strong 526 empirical evidence for the effectiveness of regularised RL in generating human-like agents, a 527 deeper theoretical understanding of the methodology is crucial."}
+{"idx": 4, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "The Ad - Hoc Human - AI Coordination Challenge ( AH2AC2 ) provides a standardized environment for evaluating AI agents on their ability to coordinate with human-like counterparts in Hanabi. The challenge emphasizes data-efficient methods and uses human proxy agents for robust and reproducible evaluation.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/", "content": "The Ad - Hoc Human - AI Coordination Challenge ( AH2AC2 ) provides a standardized environment for evaluating AI agents on their ability to coordinate with human-like counterparts in Hanabi. The challenge emphasizes data-efficient methods and uses human proxy agents for robust and reproducible evaluation."}
+{"idx": 5, "title": "Ad-Hoc Human-AI Coordination Challenge | AI Research Paper ...", "date": "", "ddg_snippet": "These agents are trained using Human-Data-Regularised IPPO ( HDR - IPPO ), a procedure combining behavioral cloning (BC) and regularized Independent Proximal Policy Optimization ( IPPO ). First, BC policies are trained on the large-scale dataset of human gameplay.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ad-hoc-human-ai-coordination-challenge", "content": "These agents are trained using Human-Data-Regularised IPPO ( HDR - IPPO ), a procedure combining behavioral cloning (BC) and regularized Independent Proximal Policy Optimization ( IPPO ). First, BC policies are trained on the large-scale dataset of human gameplay."}
+{"idx": 6, "title": "Ad-Hoc Human-AI Coordination Challenge | ResearchTrend.AI", "date": "", "ddg_snippet": "Jun 26, 2025 · In this work, we introduce the Ad - Hoc Human - AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations.", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2506.21490", "content": "Jun 26, 2025 · In this work, we introduce the Ad - Hoc Human - AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations."}
+{"idx": 7, "title": "[Literature Review] Ad-Hoc Human-AI Coordination ...", "date": "", "ddg_snippet": "25 Jun 2025 — This paper introduces the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) using the cooperative card game Hanabi as a testbed.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/ad-hoc-human-ai-coordination-challenge", "content": "25 Jun 2025 — This paper introduces the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) using the cooperative card game Hanabi as a testbed."}
+{"idx": 8, "title": "Ad - Hoc Human - AI Coordination Challenge", "date": "", "ddg_snippet": "Ad - Hoc Human - AI Coordination Challenge . Report issue for preceding element.OBL does not make use of available human data, yet it achieves high scores. There is a pressing need for new techniques that efficiently utilise limited human data to improve human - AI coordination .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21490v2", "content": "Ad - Hoc Human - AI Coordination Challenge . Report issue for preceding element.OBL does not make use of available human data, yet it achieves high scores. There is a pressing need for new techniques that efficiently utilise limited human data to improve human - AI coordination ."}
+{"idx": 9, "title": "Ad - Hoc Human - AI Coordination Challenge", "date": "", "ddg_snippet": "Ad - Hoc Human - AI Coordination Challenge . Tin Dizdarević, Ravi Hammond, Tobias Gessler, Anisoara Calinescu, Jonathan Cook, Matteo Gallici, Andrei Lupu, Jakob Nicolaus Foerster·June 26, 2025. Summary.Overview of human - AI coordination challenges.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-ad-hoc-human-ai-coordination-challenge-cmcfaq3ifm18907nqbp2j6wty", "content": "Ad - Hoc Human - AI Coordination Challenge . Tin Dizdarević, Ravi Hammond, Tobias Gessler, Anisoara Calinescu, Jonathan Cook, Matteo Gallici, Andrei Lupu, Jakob Nicolaus Foerster·June 26, 2025. Summary.Overview of human - AI coordination challenges."}
diff --git a/data/sampled_jsons/Adaptive_Initialization_Strategies_for_Neural_Network_Optimization_ICML_2025_year_2025.jsonl b/data/sampled_jsons/Adaptive_Initialization_Strategies_for_Neural_Network_Optimization_ICML_2025_year_2025.jsonl
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index 0000000000000000000000000000000000000000..8ed6348b494949104a9a8b69a2372d22dbc9a20b
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+++ b/data/sampled_jsons/Adaptive_Initialization_Strategies_for_Neural_Network_Optimization_ICML_2025_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Downloads 2025", "date": "", "ddg_snippet": "Efficient Bisection Projection to Ensure Neural - Network Solution Feasibility for Optimization over General Set · Efficient Core-set Selection for Deep ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "Efficient Bisection Projection to Ensure Neural - Network Solution Feasibility for Optimization over General Set · Efficient Core-set Selection for Deep ..."}
+{"idx": 1, "title": "Adaptive Pruner for Long-tailed Data", "date": "", "ddg_snippet": "This research provides new insights into solving model optimization problems in long-tailed learning and is significant for improving the performance of neural ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46627", "content": "This research provides new insights into solving model optimization problems in long-tailed learning and is significant for improving the performance of neural ..."}
+{"idx": 2, "title": "Task-Aware Parameter Initialization at Flexible Scales", "date": "", "ddg_snippet": "Appropriate parameter initialization strategies are essential for reducing the high computational costs of training large pretrained models in various task ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45736", "content": "Appropriate parameter initialization strategies are essential for reducing the high computational costs of training large pretrained models in various task ..."}
+{"idx": 3, "title": "WAVE: Weight Templates for Adaptive Initialization of Variable ...", "date": "", "ddg_snippet": "by F Feng · 2025 · Cited by 10 — WAVE is an innovative Learngene method that constructs shared weight templates, termed learngenes, to initialize models of variable sizes. This section begins ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_WAVE_Weight_Templates_for_Adaptive_Initialization_of_Variable-sized_Models_CVPR_2025_paper.pdf", "content": "by F Feng · 2025 · Cited by 10 — WAVE is an innovative Learngene method that constructs shared weight templates, termed learngenes, to initialize models of variable sizes. This section begins ... 10 pages"}
+{"idx": 4, "title": "Adaptive kernel predictors from feature-learning infinite ...", "date": "", "ddg_snippet": "15 Jul 2025 — Previous influential work showed that infinite width limits of neural networks in the lazy training regime are described by kernel machines.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45506", "content": "15 Jul 2025 — Previous influential work showed that infinite width limits of neural networks in the lazy training regime are described by kernel machines."}
+{"idx": 5, "title": "ICML 2025: Key Ideas on LLMs, Human-AI Alignment, and ...", "date": "", "ddg_snippet": "31 Jul 2025 — This paper provides some leads towards techniques to automate learning rate tuning based on intrinsic properties of the optimization process ( ...", "subpage_snippet": "", "source": "www.twosigma.com", "link": "https://www.twosigma.com/articles/icml-2025-key-ideas-on-llms-human-ai-alignment-and-more/", "content": "31 Jul 2025 — This paper provides some leads towards techniques to automate learning rate tuning based on intrinsic properties of the optimization process ( ..."}
+{"idx": 6, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "Calibration and Bias in Algorithms, Data, and Models: a tutorial on metrics and plots for measuring calibration, bias, fairness, reliability, and robustness.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "Calibration and Bias in Algorithms, Data, and Models: a tutorial on metrics and plots for measuring calibration, bias, fairness, reliability, and robustness."}
+{"idx": 7, "title": "Adaptive kernel predictors from feature-learning infinite ...", "date": "", "ddg_snippet": "In certain initialization and parameterization schemes, taking the width of a neural network to infinity leads the model to learn a kernel machine, with a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.07998v2", "content": "In certain initialization and parameterization schemes, taking the width of a neural network to infinity leads the model to learn a kernel machine, with a ..."}
+{"idx": 8, "title": "Neuroevolution with box mutation: An adaptive and ...", "date": "", "ddg_snippet": "by FJJB Santos · 2023 · Cited by 4 — This work presents a novel method for evolving deep neural networks by adapting the principles of Geometric Semantic Genetic Programming.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1568494623007858", "content": "by FJJB Santos · 2023 · Cited by 4 — This work presents a novel method for evolving deep neural networks by adapting the principles of Geometric Semantic Genetic Programming."}
+{"idx": 9, "title": "A Progressive Strategy to Boost Low-Rank Adaptation", "date": "", "ddg_snippet": "Here, we propose CoTo1, a progressive training strategy that gradually increases adapters' activation probability over the course of fine-tuning. By ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44836", "content": "Here, we propose CoTo1, a progressive training strategy that gradually increases adapters' activation probability over the course of fine-tuning. By ..."}
diff --git a/data/sampled_jsons/Adaptive_Task_Allocation_Efficient_Resource_Management_Distributed_Machine_Learning_Table_1_n=153_To_year_2023.jsonl b/data/sampled_jsons/Adaptive_Task_Allocation_Efficient_Resource_Management_Distributed_Machine_Learning_Table_1_n=153_To_year_2023.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA (Adaptive Task Allocation), a method that adapts to heterogeneous and random distributions of worker computation times . Through ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "In this paper, we propose ATA (Adaptive Task Allocation), a method that adapts to heterogeneous and random distributions of worker computation times . Through ..."}
+{"idx": 1, "title": "Resource Allocation and Workload Scheduling for Large ...", "date": "", "ddg_snippet": "12 Jun 2024 — This survey reviews the literature, mainly from 2019 to 2024, on efficient resource allocation and workload scheduling strategies for large-scale distributed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.08115v1", "content": "12 Jun 2024 — This survey reviews the literature, mainly from 2019 to 2024, on efficient resource allocation and workload scheduling strategies for large-scale distributed ..."}
+{"idx": 2, "title": "Task Allocation for Autonomous Machines using ...", "date": "", "ddg_snippet": "by TT Nguyen · 2025 — This paper presents a survey of algorithms that have been developed for controlling and coordinating autonomous machines in complex environments ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2508.20688", "content": "by TT Nguyen · 2025 — This paper presents a survey of algorithms that have been developed for controlling and coordinating autonomous machines in complex environments ..."}
+{"idx": 3, "title": "A Survey of Machine Learning Under Distribution Shift", "date": "", "ddg_snippet": "20 Mar 2024 — An adaptive teaming system requires machine learning models that can estimate workload accurately in both known and unknown situations. Further, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.13318v1", "content": "20 Mar 2024 — An adaptive teaming system requires machine learning models that can estimate workload accurately in both known and unknown situations. Further, ..."}
+{"idx": 4, "title": "Zeal: Rethinking Large-Scale Resource Allocation with “ ...", "date": "", "ddg_snippet": "16 Dec 2024 — We develop Zeal, a general, scalable, and theoretically grounded framework for accelerating resource allocation through a “decouple and decompose” approach.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11447v1", "content": "16 Dec 2024 — We develop Zeal, a general, scalable, and theoretically grounded framework for accelerating resource allocation through a “decouple and decompose” approach."}
+{"idx": 5, "title": "Deep Reinforcement Learning for Job Scheduling and ...", "date": "", "ddg_snippet": "by Y Gu · 2025 · Cited by 12 — Job scheduling involves the allocation of tasks or jobs to available computing resources in a manner that maximizes efficiency , minimizes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.01007?", "content": "by Y Gu · 2025 · Cited by 12 — Job scheduling involves the allocation of tasks or jobs to available computing resources in a manner that maximizes efficiency , minimizes ..."}
+{"idx": 6, "title": "Temporal-Aware GPU Resource Allocation for Distributed ...", "date": "", "ddg_snippet": "6 days ago — TORTA introduces a spatiotemporal scheduling framework that captures both long-term workload patterns and short-term execution constraints. It ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10259v2", "content": "6 days ago — TORTA introduces a spatiotemporal scheduling framework that captures both long-term workload patterns and short-term execution constraints. It ..."}
+{"idx": 7, "title": "Multi-Agent Reinforcement Learning for Resources ...", "date": "", "ddg_snippet": "29 Apr 2025 — This survey provides a comprehensive review of recent MARL algorithms for RAO, encompassing core concepts, classifications, and a structured taxonomy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.21048v1", "content": "29 Apr 2025 — This survey provides a comprehensive review of recent MARL algorithms for RAO, encompassing core concepts, classifications, and a structured taxonomy."}
+{"idx": 8, "title": "cherryATA", "date": "", "ddg_snippet": "Page 1 . ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... n grows. Table 1 : Ratios of total worker times ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "Page 1 . ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... n grows. Table 1 : Ratios of total worker times ..."}
+{"idx": 9, "title": "Scaling Up Throughput-oriented LLM Inference ...", "date": "", "ddg_snippet": "6 days ago — Adaptive Task -Oriented Resource Allocation for Large Dynamic Workflows on Opportunistic Resources . In 2024 IEEE International Parallel and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13201v1", "content": "6 days ago — Adaptive Task -Oriented Resource Allocation for Large Dynamic Workflows on Opportunistic Resources . In 2024 IEEE International Parallel and ..."}
diff --git a/data/sampled_jsons/Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_GTA_strat.jsonl b/data/sampled_jsons/Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_GTA_strat.jsonl
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index 0000000000000000000000000000000000000000..b904f44fc48a6f6fb6aba8ab0eafa8ea68f1b36f
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+++ b/data/sampled_jsons/Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_GTA_strat.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources . However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources . However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ..."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. In this paper, we propose ATA(Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. In this paper, we propose ATA(Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Eficient Resource Management in ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times."}
+{"idx": 3, "title": "An Adaptive Load Balancing Strategy for Distributed Machine Learning", "date": "", "ddg_snippet": "Within a distributed deep learning training system, variances in performance among computing nodes, as well as the influence of external environmental factors, can result in training interruptions or reduced convergence speed. As such, this paper presents an approach to address this issue by proposing a dynamic task allocation strategy among nodes, aimed at mitigating the impact of performance ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10229678", "content": "Within a distributed deep learning training system, variances in performance among computing nodes, as well as the influence of external environmental factors, can result in training interruptions or reduced convergence speed. As such, this paper presents an approach to address this issue by proposing a dynamic task allocation strategy among nodes, aimed at mitigating the impact of performance ..."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods."}
+{"idx": 5, "title": "PDF Adaptive Task-Oriented Resource Allocation for Large Dynamic Workflows ...", "date": "", "ddg_snippet": "In this paper, we (1) argue for the need of an adaptive resource allocator capable of allocating tasks at runtime and adjusting to random fluctuations and abrupt changes in a dynamic workflow without requiring any prior knowledge, and (2) introduce Greedy Bucketing and Exhaustive Bucketing: two robust, online, general-purpose, and prior-free allocation algorithms capable of producing quality ...", "subpage_snippet": "", "source": "ccl.cse.nd.edu", "link": "https://ccl.cse.nd.edu/research/papers/adaptive-ipdps-2024.pdf", "content": "In this paper, we (1) argue for the need of an adaptive resource allocator capable of allocating tasks at runtime and adjusting to random fluctuations and abrupt changes in a dynamic workflow without requiring any prior knowledge, and (2) introduce Greedy Bucketing and Exhaustive Bucketing: two robust, online, general-purpose, and prior-free allocation algorithms capable of producing quality ..."}
+{"idx": 6, "title": "Utilizing machine learning algorithms for task allocation in ...", "date": "", "ddg_snippet": "To overcome these challenges, efficient task allocation planning becomes a crucial success component in software project management . The purpose of this study is to utilize machine learning (ML) predictive algorithms to determine the most appropriate role for a given task , with the aim of assisting software managers in making task assignments ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2405844024159579", "content": "To overcome these challenges, efficient task allocation planning becomes a crucial success component in software project management . The purpose of this study is to utilize machine learning (ML) predictive algorithms to determine the most appropriate role for a given task , with the aim of assisting software managers in making task assignments ..."}
+{"idx": 7, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "Abstract summary: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning .We propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of computation times.We show that ATA identifies the optimal task allocation and performs comparably to methods with ...", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2502.00775v2_enmode", "content": "Abstract summary: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning .We propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of computation times.We show that ATA identifies the optimal task allocation and performs comparably to methods with ..."}
+{"idx": 8, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources . However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.00775v2", "content": "View recent discussion. Abstract: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources . However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were ..."}
+{"idx": 9, "title": "Unlocking Efficiency: How Adaptive Task Allocation Revolutionizes ...", "date": "", "ddg_snippet": "In the rapidly evolving world of artificial intelligence, distributed machine learning stands as a cornerstone for future developments. Yet, as industries adopt parallelized systems to enhance machine learning operations, the challenge of inefficient resource allocation becomes glaring. Enter Adaptive Task Allocation (ATA), a sophisticated solution designed to address this very problem.", "subpage_snippet": "", "source": "www.lumafeed.com", "link": "https://www.lumafeed.com/article/9d4d8fe1-d044-4756-99ca-33ab376e4e05", "content": "In the rapidly evolving world of artificial intelligence, distributed machine learning stands as a cornerstone for future developments. Yet, as industries adopt parallelized systems to enhance machine learning operations, the challenge of inefficient resource allocation becomes glaring. Enter Adaptive Task Allocation (ATA), a sophisticated solution designed to address this very problem."}
diff --git a/data/sampled_jsons/Adversarial_Review_algorithm_Table_1_contentiousness_variable_delta_modulation_parameter.jsonl b/data/sampled_jsons/Adversarial_Review_algorithm_Table_1_contentiousness_variable_delta_modulation_parameter.jsonl
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index 0000000000000000000000000000000000000000..8a75bfebf0ebea268993dcad97a125719d3bff07
--- /dev/null
+++ b/data/sampled_jsons/Adversarial_Review_algorithm_Table_1_contentiousness_variable_delta_modulation_parameter.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ADVERSARIAL Synonyms: 105 Similar and Opposite Words - ...", "date": "", "ddg_snippet": "Synonyms for ADVERSARIAL : hostile, negative, antagonistic, adverse, contentious, adversary , conflicting, opposed; Antonyms of ADVERSARIAL : sympathetic, civil, friendly, social, hospitable, cordial, amiable, pleasant", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/thesaurus/adversarial", "content": "Synonyms for ADVERSARIAL : hostile, negative, antagonistic, adverse, contentious, adversary , conflicting, opposed; Antonyms of ADVERSARIAL : sympathetic, civil, friendly, social, hospitable, cordial, amiable, pleasant"}
+{"idx": 1, "title": "ADVERSARIAL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of ADVERSARIAL is involving two people or two sides who oppose each other : of, relating to, or characteristic of an adversary or adversary procedures.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/adversarial", "content": "The meaning of ADVERSARIAL is involving two people or two sides who oppose each other : of, relating to, or characteristic of an adversary or adversary procedures."}
+{"idx": 2, "title": "ADVERSARIAL | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "Opposing & against (Definition of adversarial from the Cambridge Advanced Learner's Dictionary & Thesaurus © Cambridge University Press)", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/adversarial", "content": "Opposing & against (Definition of adversarial from the Cambridge Advanced Learner's Dictionary & Thesaurus © Cambridge University Press)"}
+{"idx": 3, "title": "ADVERSARIAL Definition & Meaning | Dictionary.com", "date": "", "ddg_snippet": "Adversarial definition: pertaining to or characterized by antagonism and conflict . See examples of ADVERSARIAL used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/adversarial", "content": "Adversarial definition: pertaining to or characterized by antagonism and conflict . See examples of ADVERSARIAL used in a sentence."}
+{"idx": 4, "title": "adversarial adjective - Definition, pictures, pronunciation and...", "date": "", "ddg_snippet": "Definition of adversarial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/adversarial", "content": "Definition of adversarial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 5, "title": "Adversarial - definition of adversarial by The Free Dictionary", "date": "", "ddg_snippet": "Relating to or characteristic of an adversary ; involving antagonistic elements: \"Some speakers fall almost willingly into an adversarial relationship with the audience\" (Don Pfarrer).", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/adversarial", "content": "Relating to or characteristic of an adversary ; involving antagonistic elements: \"Some speakers fall almost willingly into an adversarial relationship with the audience\" (Don Pfarrer)."}
+{"idx": 6, "title": "adversarial - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Aug 4, 2025 · adversarial (comparative more adversarial , superlative most adversarial ) Characteristic of, or in the manner of, an adversary; combative, hostile, opposed. quotations", "subpage_snippet": "", "source": "en.wiktionary.org", "link": "https://en.wiktionary.org/wiki/adversarial", "content": "Aug 4, 2025 · adversarial (comparative more adversarial , superlative most adversarial ) Characteristic of, or in the manner of, an adversary; combative, hostile, opposed. quotations"}
+{"idx": 7, "title": "ADVERSARIAL definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "If you describe something as adversarial, you mean that it involves two or more people or organizations who are opposing each other .", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/adversarial", "content": "If you describe something as adversarial, you mean that it involves two or more people or organizations who are opposing each other ."}
+{"idx": 8, "title": "Adversarial - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "Being adversarial means that each side is antagonistic , sharply opposed to the other, or locked into a deeply divided rivalry. In fact, this adjective is sometimes used simply to mean \"hostile.\"", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/adversarial", "content": "Being adversarial means that each side is antagonistic , sharply opposed to the other, or locked into a deeply divided rivalry. In fact, this adjective is sometimes used simply to mean \"hostile.\""}
+{"idx": 9, "title": "Adversarial Definition & Meaning | Britannica Dictionary", "date": "", "ddg_snippet": "adversarial (adjective) adversarial /ˌædvɚ ˈ serijəl/ adjective Britannica Dictionary definition of ADVERSARIAL [more adversarial ; most adversarial ] formal : involving two people or two sides who oppose each other", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/dictionary/adversarial", "content": "adversarial (adjective) adversarial /ˌædvɚ ˈ serijəl/ adjective Britannica Dictionary definition of ADVERSARIAL [more adversarial ; most adversarial ] formal : involving two people or two sides who oppose each other"}
diff --git a/data/sampled_jsons/Adversarial_Review_algorithm_contentiousness_delta_modulation_parameter_iteration.jsonl b/data/sampled_jsons/Adversarial_Review_algorithm_contentiousness_delta_modulation_parameter_iteration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2b9fdf1715c6bd9369ae63897677d3c75c24f44f
--- /dev/null
+++ b/data/sampled_jsons/Adversarial_Review_algorithm_contentiousness_delta_modulation_parameter_iteration.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ADVERSARIAL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of ADVERSARIAL is involving two people or two sides who oppose each other : of, relating to, or characteristic of an adversary or adversary procedures.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/adversarial", "content": "The meaning of ADVERSARIAL is involving two people or two sides who oppose each other : of, relating to, or characteristic of an adversary or adversary procedures."}
+{"idx": 1, "title": "ADVERSARIAL | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "Opposing & against (Definition of adversarial from the Cambridge Advanced Learner's Dictionary & Thesaurus © Cambridge University Press)", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/adversarial", "content": "Opposing & against (Definition of adversarial from the Cambridge Advanced Learner's Dictionary & Thesaurus © Cambridge University Press)"}
+{"idx": 2, "title": "ADVERSARIAL Definition & Meaning | Dictionary.com", "date": "", "ddg_snippet": "Adversarial definition: pertaining to or characterized by antagonism and conflict . See examples of ADVERSARIAL used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/adversarial", "content": "Adversarial definition: pertaining to or characterized by antagonism and conflict . See examples of ADVERSARIAL used in a sentence."}
+{"idx": 3, "title": "adversarial adjective - Definition, pictures, pronunciation and...", "date": "", "ddg_snippet": "Definition of adversarial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/adversarial", "content": "Definition of adversarial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 4, "title": "Adversarial - definition of adversarial by The Free Dictionary", "date": "", "ddg_snippet": "Relating to or characteristic of an adversary ; involving antagonistic elements: \"Some speakers fall almost willingly into an adversarial relationship with the audience\" (Don Pfarrer).", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/adversarial", "content": "Relating to or characteristic of an adversary ; involving antagonistic elements: \"Some speakers fall almost willingly into an adversarial relationship with the audience\" (Don Pfarrer)."}
+{"idx": 5, "title": "adversarial - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Aug 4, 2025 · adversarial (comparative more adversarial , superlative most adversarial ) Characteristic of, or in the manner of, an adversary; combative, hostile, opposed. quotations", "subpage_snippet": "", "source": "en.wiktionary.org", "link": "https://en.wiktionary.org/wiki/adversarial", "content": "Aug 4, 2025 · adversarial (comparative more adversarial , superlative most adversarial ) Characteristic of, or in the manner of, an adversary; combative, hostile, opposed. quotations"}
+{"idx": 6, "title": "ADVERSARIAL definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "If you describe something as adversarial, you mean that it involves two or more people or organizations who are opposing each other .", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/adversarial", "content": "If you describe something as adversarial, you mean that it involves two or more people or organizations who are opposing each other ."}
+{"idx": 7, "title": "Adversarial - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "Being adversarial means that each side is antagonistic , sharply opposed to the other, or locked into a deeply divided rivalry. In fact, this adjective is sometimes used simply to mean \"hostile.\"", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/adversarial", "content": "Being adversarial means that each side is antagonistic , sharply opposed to the other, or locked into a deeply divided rivalry. In fact, this adjective is sometimes used simply to mean \"hostile.\""}
+{"idx": 8, "title": "ADVERSARIAL Synonyms: 105 Similar and Opposite Words - ...", "date": "", "ddg_snippet": "Synonyms for ADVERSARIAL : hostile, negative, antagonistic, adverse, contentious, adversary , conflicting, opposed; Antonyms of ADVERSARIAL : sympathetic, civil, friendly, social, hospitable, cordial, amiable, pleasant", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/thesaurus/adversarial", "content": "Synonyms for ADVERSARIAL : hostile, negative, antagonistic, adverse, contentious, adversary , conflicting, opposed; Antonyms of ADVERSARIAL : sympathetic, civil, friendly, social, hospitable, cordial, amiable, pleasant"}
+{"idx": 9, "title": "Adversarial Definition & Meaning | Britannica Dictionary", "date": "", "ddg_snippet": "adversarial (adjective) adversarial /ˌædvɚ ˈ serijəl/ adjective Britannica Dictionary definition of ADVERSARIAL [more adversarial ; most adversarial ] formal : involving two people or two sides who oppose each other", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/dictionary/adversarial", "content": "adversarial (adjective) adversarial /ˌædvɚ ˈ serijəl/ adjective Britannica Dictionary definition of ADVERSARIAL [more adversarial ; most adversarial ] formal : involving two people or two sides who oppose each other"}
diff --git "a/data/sampled_jsons/Adversarial_Review_algorithm_\316\224_\316\264_modulation_parameter_formula_debate_rounds.jsonl" "b/data/sampled_jsons/Adversarial_Review_algorithm_\316\224_\316\264_modulation_parameter_formula_debate_rounds.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..758ea54fe7c59c9302f41e12aabeddc2663d4d18
--- /dev/null
+++ "b/data/sampled_jsons/Adversarial_Review_algorithm_\316\224_\316\264_modulation_parameter_formula_debate_rounds.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Integrating Emotional and Linguistic Models for Ethical ...", "date": "", "ddg_snippet": "May 15, 2024 · The debate starts with the contentiousness level at percent 90 90\\% 90 %, adjusting through a modulation parameter 𝛿 \\delta italic_ δ . Following each round of debate , contentiousness is decreased by dividing it by 𝛿 \\delta italic_ δ , steering the discussion towards a more cooperative tone.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07076v2", "content": "May 15, 2024 · The debate starts with the contentiousness level at percent 90 90\\% 90 %, adjusting through a modulation parameter 𝛿 \\delta italic_ δ . Following each round of debate , contentiousness is decreased by dividing it by 𝛿 \\delta italic_ δ , steering the discussion towards a more cooperative tone."}
+{"idx": 1, "title": "Implications of Minimum Description Length for Adversarial ... RITFIS: Robust input testing framework for LLMs-based ...", "date": "", "ddg_snippet": "Apr 24, 2024 · Following this, the algorithm computes the MDL score for the t a r g e t T o k e n s on line 14. Lines 15–17 then evaluate whether these t a r g e t T o k e n s have been subject to adversarial alterations, employing a predefined threshold δ to facilitate this determination. Feb 21, 2024 · To address these issues, we suggest improving the establishment of perturbation spaces and search methods in testing algorithms , tailored to LLMs’ unique characteristics and behavioral patterns, thereby increasing the coverage and depth of testing.", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11119437/", "content": "Apr 24, 2024 · Following this, the algorithm computes the MDL score for the t a r g e t T o k e n s on line 14. Lines 15–17 then evaluate whether these t a r g e t T o k e n s have been subject to adversarial alterations, employing a predefined threshold δ to facilitate this determination. Feb 21, 2024 · To address these issues, we suggest improving the establishment of perturbation spaces and search methods in testing algorithms , tailored to LLMs’ unique characteristics and behavioral patterns, thereby increasing the coverage and depth of testing."}
+{"idx": 2, "title": "RITFIS: Robust input testing framework for LLMs-based ...", "date": "", "ddg_snippet": "Feb 21, 2024 · To address these issues, we suggest improving the establishment of perturbation spaces and search methods in testing algorithms , tailored to LLMs’ unique characteristics and behavioral patterns, thereby increasing the coverage and depth of testing.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.13518v1", "content": "Feb 21, 2024 · To address these issues, we suggest improving the establishment of perturbation spaces and search methods in testing algorithms , tailored to LLMs’ unique characteristics and behavioral patterns, thereby increasing the coverage and depth of testing."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Framework", "date": "", "ddg_snippet": "ERIS: Adversarial In-Context Review to Balance Ethics and Cultural Norms.After each round , the contentiousness level is decreased by dividing it by a modulation parameter δ . This gradual reduction steers the discussion towards a more cooperative tone.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "ERIS: Adversarial In-Context Review to Balance Ethics and Cultural Norms.After each round , the contentiousness level is decreased by dividing it by a modulation parameter δ . This gradual reduction steers the discussion towards a more cooperative tone."}
+{"idx": 4, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI...", "date": "", "ddg_snippet": "Table 1: Checks-and-balances, adversarial review algorithm .• Iterative Debate : A while loop facilitates ongoing rebut-tals. After each round , the level of contentiousness is reduced by dividing it by a modulation parameter δ . This.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "Table 1: Checks-and-balances, adversarial review algorithm .• Iterative Debate : A while loop facilitates ongoing rebut-tals. After each round , the level of contentiousness is reduced by dividing it by a modulation parameter δ . This."}
+{"idx": 5, "title": "Tommy Wennerstierna's blog | Science, Biohacking, Biology...", "date": "", "ddg_snippet": "Lifestyle Algorithm for Longevity. Adversarial review : red-team linkage attacks quarterly; publish results. 44.12 Records, Notices & Rights. RoPA (Record of Processing Activities): maintained for Classes B–D; reviewed quarterly.", "subpage_snippet": "", "source": "tommywennerstierna.wordpress.com", "link": "https://tommywennerstierna.wordpress.com/", "content": "Lifestyle Algorithm for Longevity. Adversarial review : red-team linkage attacks quarterly; publish results. 44.12 Records, Notices & Rights. RoPA (Record of Processing Activities): maintained for Classes B–D; reviewed quarterly."}
+{"idx": 6, "title": "(PDF) Post-Temporal Physics: Ledgeral Time and the Collapse of...", "date": "", "ddg_snippet": "The key parameters that govern the model are: 1. η (global attenuation scale): This parameter modulates the level of attenuation applied recursively to the energy transport. 2. Θ (holonomy strength): A measure of the order sensitivity or recursion curvature that affects how...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/143657517/Post_Temporal_Physics_Ledgeral_Time_and_the_Collapse_of_Relativistic_Continuity", "content": "The key parameters that govern the model are: 1. η (global attenuation scale): This parameter modulates the level of attenuation applied recursively to the energy transport. 2. Θ (holonomy strength): A measure of the order sensitivity or recursion curvature that affects how..."}
+{"idx": 7, "title": "Meta‑Thinking in LLMs via Multi‑Agent Reinforcement", "date": "", "ddg_snippet": "The crux of the survey is to talk about how multi-agent architectures, namely supervisor-agent hierarchies, agent debates , and theory of mind ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.14520v1", "content": "The crux of the survey is to talk about how multi-agent architectures, namely supervisor-agent hierarchies, agent debates , and theory of mind ..."}
+{"idx": 8, "title": "EVINCE: Optimizing Multi-LLM Dialogues Using Conditional", "date": "", "ddg_snippet": "It addresses limitations in multi-agent debate (MAS) frameworks, where multiple LLMs “chat” without behavior modulation or mutual information ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.14575v4", "content": "It addresses limitations in multi-agent debate (MAS) frameworks, where multiple LLMs “chat” without behavior modulation or mutual information ..."}
+{"idx": 9, "title": "XUAT-Copilot: Multi-Agent Collaborative System for Automated", "date": "", "ddg_snippet": "... consists of three LLM-based agents responsible for action planning, state checking and parameter selecting, respectively, and two additional modules ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.02705v2", "content": "... consists of three LLM-based agents responsible for action planning, state checking and parameter selecting, respectively, and two additional modules ..."}
diff --git a/data/sampled_jsons/Algorithm_1_DCBM_area_filtering_minimum_maximum_threshold_year_2024.jsonl b/data/sampled_jsons/Algorithm_1_DCBM_area_filtering_minimum_maximum_threshold_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b2a77f86eb3e07ec0f4ee922e05fc3edb76f846c
--- /dev/null
+++ b/data/sampled_jsons/Algorithm_1_DCBM_area_filtering_minimum_maximum_threshold_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "How to Find Maximum and Minimum Values of... - GeeksforGeeks", "date": "", "ddg_snippet": "Steps to find the maximum and minimum value of the function are added below: Step 1 : Find the first derivative of the function.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/maths/how-to-find-maximum-and-minimum-values-of-a-function/", "content": "Steps to find the maximum and minimum value of the function are added below: Step 1 : Find the first derivative of the function."}
+{"idx": 1, "title": "Relative error of DCBM and DCBM with Guyan interface reduction", "date": "", "ddg_snippet": "... eigenfrequency j depicted in Figure 2 is resulting (graph denoted by \" DCBM \") with ω red,j being the j-th eigenfrequency of the reduced system and ω f ull,j being the j-th eigenfrequency of the full (non-reduced) system, which is computed additionally. The eigenfrequencies are ordered by...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Relative-error-of-DCBM-and-DCBM-with-Guyan-interface-reduction_fig2_313881120", "content": "... eigenfrequency j depicted in Figure 2 is resulting (graph denoted by \" DCBM \") with ω red,j being the j-th eigenfrequency of the reduced system and ω f ull,j being the j-th eigenfrequency of the full (non-reduced) system, which is computed additionally. The eigenfrequencies are ordered by..."}
+{"idx": 2, "title": "Find local minimum or local maximum in O( 1 )", "date": "", "ddg_snippet": "Every next element differs from the previous by +/- 1 . We will use this property. We will assume that the given array will have Either a local maximum OR local minimum and only one local maximum or only one local minimum is present.", "subpage_snippet": "", "source": "tutorialhorizon.com", "link": "https://tutorialhorizon.com/algorithms/find-local-minimum-or-local-maximum-in-o1/", "content": "Every next element differs from the previous by +/- 1 . We will use this property. We will assume that the given array will have Either a local maximum OR local minimum and only one local maximum or only one local minimum is present."}
+{"idx": 3, "title": "Comparison of approaches for tessellating a business area using...", "date": "", "ddg_snippet": "Instead, the area of the business area is divided by a desired size for the clusters. Here, the sizes 100m, 200m, and 500m have been chosen. From the perspective of the micromobility provider, the maximum travel distance of users plays a role for later optimizations.", "subpage_snippet": "", "source": "jesit.springeropen.com", "link": "https://jesit.springeropen.com/articles/10.1186/s43067-025-00271-4", "content": "Instead, the area of the business area is divided by a desired size for the clusters. Here, the sizes 100m, 200m, and 500m have been chosen. From the perspective of the micromobility provider, the maximum travel distance of users plays a role for later optimizations."}
+{"idx": 4, "title": "Find minimum and maximum values in an array in C++ | Techie Delight", "date": "", "ddg_snippet": "The recommended solution is to use the std::minmax_element to find the smallest and largest array elements. It returns a pair of iterators with the first value pointing to the minimum element and the second value pointing to the maximum element.", "subpage_snippet": "", "source": "www.techiedelight.com", "link": "https://www.techiedelight.com/find-minimum-maximum-value-array-cpp/", "content": "The recommended solution is to use the std::minmax_element to find the smallest and largest array elements. It returns a pair of iterators with the first value pointing to the minimum element and the second value pointing to the maximum element."}
+{"idx": 5, "title": "Advances in environmental remote sensing : sensors, algorithms , and...", "date": "", "ddg_snippet": "where Tmin, Tmax, and Topt are the minimum , maximum , and optimum temperatures for photosynthetic activities, respectively. If air temperature falls below Tmin, Tscalar is set to be zero.", "subpage_snippet": "", "source": "silo.pub", "link": "https://silo.pub/advances-in-environmental-remote-sensing-sensors-algorithms-and-applications.html", "content": "where Tmin, Tmax, and Topt are the minimum , maximum , and optimum temperatures for photosynthetic activities, respectively. If air temperature falls below Tmin, Tscalar is set to be zero."}
+{"idx": 6, "title": "Dynamic Programming Algorithm to Compute the Max Dot Product of...", "date": "", "ddg_snippet": "We can use DFS (Depth First Search) to enumerate the possible subsequences combination of both, but the complexity is exponetial. The key to solve this problem is to re-use the intermediate results, via Dynamic Programming algorithm .", "subpage_snippet": "", "source": "helloacm.com", "link": "https://helloacm.com/dynamic-programming-algorithm-to-compute-the-max-dot-product-of-two-subsequences/", "content": "We can use DFS (Depth First Search) to enumerate the possible subsequences combination of both, but the complexity is exponetial. The key to solve this problem is to re-use the intermediate results, via Dynamic Programming algorithm ."}
+{"idx": 7, "title": "Multiply - Area Model & Standard Algorithm (examples, solutions...)", "date": "", "ddg_snippet": "This video demonstrates how to create an area model in order to visualize and solve double digit multiplication problems. 1 . Draw an area model then solve using the standard algorithm .", "subpage_snippet": "", "source": "www.onlinemathlearning.com", "link": "https://www.onlinemathlearning.com/multiply-area-model.html", "content": "This video demonstrates how to create an area model in order to visualize and solve double digit multiplication problems. 1 . Draw an area model then solve using the standard algorithm ."}
+{"idx": 8, "title": "Prim's algorithm in 2 minutes - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=cplfcGZmX7I", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 9, "title": "Face Swap Online Free", "date": "", "ddg_snippet": "How to Swap Face Online? 1 . Upload Original Image. Retain areas outside of the face. 2. Upload Target face.", "subpage_snippet": "", "source": "remaker.ai", "link": "https://remaker.ai/face-swap-free/", "content": "How to Swap Face Online? 1 . Upload Original Image. Retain areas outside of the face. 2. Upload Target face."}
diff --git a/data/sampled_jsons/Algorithm_1_SAHARA_target_group_size_S=3_iterations.jsonl b/data/sampled_jsons/Algorithm_1_SAHARA_target_group_size_S=3_iterations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..310ee083c23ddb024dc4f9881c46638213407de6
--- /dev/null
+++ b/data/sampled_jsons/Algorithm_1_SAHARA_target_group_size_S=3_iterations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Iteration - Wikipedia", "date": "", "ddg_snippet": "Iteration means repeating a process to generate a sequence of outcomes. Each repetition of the process is a single iteration , and the outcome of each iteration is the starting point of the next iteration . In mathematics and computer science, iteratio...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Iteration", "content": "Iteration means repeating a process to generate a sequence of outcomes. Each repetition of the process is a single iteration , and the outcome of each iteration is the starting point of the next iteration . In mathematics and computer science, iteratio..."}
+{"idx": 1, "title": "Whats the ideal population size and number of iterations for ...", "date": "", "ddg_snippet": "Sep 1 , 2015 · I am using 50 iterations . With these numbers, the GA is not getting me really close to the optimum solution. I was wondering if there is a way to determine the best population size and number of iterations for a vector of huge size (40,000 variables).", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/23812721/whats-the-ideal-population-size-and-number-of-iterations-for-genetic-algorithm", "content": "Sep 1 , 2015 · I am using 50 iterations . With these numbers, the GA is not getting me really close to the optimum solution. I was wondering if there is a way to determine the best population size and number of iterations for a vector of huge size (40,000 variables)."}
+{"idx": 2, "title": "Parameters determining Optimal Solution of Particle Swarm ... Com Sci quiz Algorithms Flashcards | Quizlet (PDF) Particle Swarm Optimization Algorithm and Its ... Efficient maximum iterations for swarm intelligence ... Selection Algorithms with Small Groups - Adrian Dumitrescu Efficient maximum iterations for swarm intelligence algorithms : a Efficient maximum iterations for swarm intelligence algorithms : a Efficient maximum iterations for swarm intelligence algorithms : a Selection Algorithms with Small Groups - Adrian Dumitrescu Efficient maximum iterations for swarm intelligence algorithms : a Efficient maximum iterations for swarm intelligence algorithms : a Swarm-intelligence-optimization-algorithm/DE.py at main ...", "date": "", "ddg_snippet": "Initialize the population randomly While (Population Size ) { Loop Calculate the fitness If the fitness value is better from the best fitness value (pbest) found so far then, Update the pbest with new pbest End loop Select particle with the best fitness value from all particles as the gbest While maximum iterations or the minimum error criteria is ... See full list on purplepetaledu.files.wordpress.com Yuhui Shi and Russell Eberhart , “A Modified Particle Swarm Optimizer”- A new parameter , called inertia weight was introduced into the original particle swarm optimizer to enhance its performance. Simulations were done to present the effective impact of this new parameter (inertia weight) on the particle swarm optimizer. Yamille del Valle, Ganesh ... See full list on purplepetaledu.files.wordpress.com The global best position is found at the point where all the particles of the swarm converge and this point is said to be the convergence point. The time taken for the computation of the global solution is known as time of computation. The effect of population size of the swarm and the number of iterations for the process of particle swarm optimiza... See full list on purplepetaledu.files.wordpress.com The data is recorded in the table below. Input Size . Iterations 10. 200 20. 400 30. 600 40 800 50 1000 100 2000 Based on the data provided, does this algorithm run in a reasonable or unreasonable time? Explain your answer Reasonable because the data doesn't grow exponentially and it follows the rate of 20n (input size = 10, 20 times 10 = 200 ... Apr 19, 2022 · One of the most popular SI paradigms, the Particle Swarm Optimization algorithm (PSO), is presented in this work. Many changes have been made to PSO since its inception in the mid 1990s. Jan 8, 2025 · A swarm intelligence algorithm usually iterates many times to approximate the optimum to obtain the solution of a problem. The maximum iteration is influenced by many factors such as the algorithm itself, problem types, as well as dimensions and search space sizes of decision variables. There are few existing studies on efficient maximum iterations , especially a large-scale study on comparison ... In Section 4, we introduce another variant of select, the shifting target algorithm , a linear time selection algorithm with group size 4. In each iteration , upper or lower medians are used based on the current rank of the target , and the shift in the target parameter i is controlled over three consecutive iterations . How many times does a swarm intelligence algorithm iterate? A swarm intelligence algorithm usually iterates many times to approximate the optimum to obtain the solution of a problem. The maximum iteration is influenced by many factors such as the algorithm itself, problem types, as well as dimensions and search space sizes of decision variables. How many iterations does a search algorithm have? The experimental results show that for low-dimensionality, wide search space, and/or simple- and medium-complex problems, about a quarter of the algorithms are concentrated in iterations of about 30 ~ 80 , while most algorithms for other types of problems tend to have as many iterations as possible. How many iterations does an algorithm need? Additionally, some algorithms show an efficient iteration of 800 (the highest iteration set in this study), which does not necessarily mean that their exact efficient iterations are 800, but it is likely to require more than 800. What is a shifting target algorithm? In Section 4, we introduce another variant of select, the shifting target algorithm, a linear time selection algorithm with group size 4. In each iteration, upper or lower medians are used based on the current rank of the target, and the shift in the target parameter i is controlled over three consecutive iterations. Does a large iteration increase the accuracy of algorithms? The column “Strictly monotonically increasing” in Table 2 shows that the mean-, worst-, or best-statistics of 25 algorithms increase strictly monotonically with the increase of the maximum iteration, reflecting a larger iteration will improve their accuracy . Does Sia runtime increase with maximum iterations? And, numerically, the runtime of all algorithms on all functions is almost linearly increasing with the maximum iterations. See Table 10 for details. CEC 2022 is mainly employed to compare the optimization performance of SIAs on the problems with different complexity. 种群算法复现 (swarm- algorithm )，包括乌鸦搜索 (Crow Search Algorithm , CSA)、樽海鞘群算法 (Salp Swarm Algorithm , SSA)、缎蓝园丁鸟优化算法 (Satin Bowerbird Optimizer, SBO)、麻雀搜索算法 (Sparrow Search Algorithm , SSA)、 狼群搜索算法 (2007WPS, 2013WPA)、正余弦优化算法 (Sine Cosine Algorithm , CSA）、烟花算法 (Fireworks Algorithm , FA) - Swarm ...", "subpage_snippet": "", "source": "purplepetaledu.files.wordpress.com", "link": "https://purplepetaledu.files.wordpress.com/2018/08/ijeei-pso-population-iterations.pdf", "content": "Initialize the population randomly While (Population Size ) { Loop Calculate the fitness If the fitness value is better from the best fitness value (pbest) found so far then, Update the pbest with new pbest End loop Select particle with the best fitness value from all particles as the gbest While maximum iterations or the minimum error criteria is ... See full list on purplepetaledu.files.wordpress.com Yuhui Shi and Russell Eberhart , “A Modified Particle Swarm Optimizer”- A new parameter , called inertia weight was introduced into the original particle swarm optimizer to enhance its performance. Simulations were done to present the effective impact of this new parameter (inertia weight) on the particle swarm optimizer. Yamille del Valle, Ganesh ... See full list on purplepetaledu.files.wordpress.com The global best position is found at the point where all the particles of the swarm converge and this point is said to be the convergence point. The time taken for the computation of the global solution is known as time of computation. The effect of population size of the swarm and the number of iterations for the process of particle swarm optimiza... See full list on purplepetaledu.files.wordpress.com The data is recorded in the table below. Input Size . Iterations 10. 200 20. 400 30. 600 40 800 50 1000 100 2000 Based on the data provided, does this algorithm run in a reasonable or unreasonable time? Explain your answer Reasonable because the data doesn't grow exponentially and it follows the rate of 20n (input size = 10, 20 times 10 = 200 ... Apr 19, 2022 · One of the most popular SI paradigms, the Particle Swarm Optimization algorithm (PSO), is presented in this work. Many changes have been made to PSO since its inception in the mid 1990s. Jan 8, 2025 · A swarm intelligence algorithm usually iterates many times to approximate the optimum to obtain the solution of a problem. The maximum iteration is influenced by many factors such as the algorithm itself, problem types, as well as dimensions and search space sizes of decision variables. There are few existing studies on efficient maximum iterations , especially a large-scale study on comparison ... In Section 4, we introduce another variant of select, the shifting target algorithm , a linear time selection algorithm with group size 4. In each iteration , upper or lower medians are used based on the current rank of the target , and the shift in the target parameter i is controlled over three consecutive iterations . How many times does a swarm intelligence algorithm iterate? A swarm intelligence algorithm usually iterates many times to approximate the optimum to obtain the solution of a problem. The maximum iteration is influenced by many factors such as the algorithm itself, problem types, as well as dimensions and search space sizes of decision variables. How many iterations does a search algorithm have? The experimental results show that for low-dimensionality, wide search space, and/or simple- and medium-complex problems, about a quarter of the algorithms are concentrated in iterations of about 30 ~ 80 , while most algorithms for other types of problems tend to have as many iterations as possible. How many iterations does an algorithm need? Additionally, some algorithms show an efficient iteration of 800 (the highest iteration set in this study), which does not necessarily mean that their exact efficient iterations are 800, but it is likely to require more than 800. What is a shifting target algorithm? In Section 4, we introduce another variant of select, the shifting target algorithm, a linear time selection algorithm with group size 4. In each iteration, upper or lower medians are used based on the current rank of the target, and the shift in the target parameter i is controlled over three consecutive iterations. Does a large iteration increase the accuracy of algorithms? The column “Strictly monotonically increasing” in Table 2 shows that the mean-, worst-, or best-statistics of 25 algorithms increase strictly monotonically with the increase of the maximum iteration, reflecting a larger iteration will improve their accuracy . Does Sia runtime increase with maximum iterations? And, numerically, the runtime of all algorithms on all functions is almost linearly increasing with the maximum iterations. See Table 10 for details. CEC 2022 is mainly employed to compare the optimization performance of SIAs on the problems with different complexity. 种群算法复现 (swarm- algorithm )，包括乌鸦搜索 (Crow Search Algorithm , CSA)、樽海鞘群算法 (Salp Swarm Algorithm , SSA)、缎蓝园丁鸟优化算法 (Satin Bowerbird Optimizer, SBO)、麻雀搜索算法 (Sparrow Search Algorithm , SSA)、 狼群搜索算法 (2007WPS, 2013WPA)、正余弦优化算法 (Sine Cosine Algorithm , CSA）、烟花算法 (Fireworks Algorithm , FA) - Swarm ..."}
+{"idx": 3, "title": "Com Sci quiz Algorithms Flashcards | Quizlet", "date": "", "ddg_snippet": "The data is recorded in the table below. Input Size . Iterations 10. 200 20. 400 30. 600 40 800 50 1000 100 2000 Based on the data provided, does this algorithm run in a reasonable or unreasonable time? Explain your answer Reasonable because the data doesn't grow exponentially and it follows the rate of 20n (input size = 10, 20 times 10 = 200 ...", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/782986776/com-sci-quiz-algorithms-flash-cards/", "content": "The data is recorded in the table below. Input Size . Iterations 10. 200 20. 400 30. 600 40 800 50 1000 100 2000 Based on the data provided, does this algorithm run in a reasonable or unreasonable time? Explain your answer Reasonable because the data doesn't grow exponentially and it follows the rate of 20n (input size = 10, 20 times 10 = 200 ..."}
+{"idx": 4, "title": "(PDF) Particle Swarm Optimization Algorithm and Its ...", "date": "", "ddg_snippet": "Apr 19, 2022 · One of the most popular SI paradigms, the Particle Swarm Optimization algorithm (PSO), is presented in this work. Many changes have been made to PSO since its inception in the mid 1990s.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/360057862_Particle_Swarm_Optimization_Algorithm_and_Its_Applications_A_Systematic_Review", "content": "Apr 19, 2022 · One of the most popular SI paradigms, the Particle Swarm Optimization algorithm (PSO), is presented in this work. Many changes have been made to PSO since its inception in the mid 1990s."}
+{"idx": 5, "title": "Efficient maximum iterations for swarm intelligence ...", "date": "", "ddg_snippet": "Jan 8, 2025 · A swarm intelligence algorithm usually iterates many times to approximate the optimum to obtain the solution of a problem. The maximum iteration is influenced by many factors such as the algorithm itself, problem types, as well as dimensions and search space sizes of decision variables. There are few existing studies on efficient maximum iterations , especially a large-scale study on comparison ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-024-11104-7", "content": "Jan 8, 2025 · A swarm intelligence algorithm usually iterates many times to approximate the optimum to obtain the solution of a problem. The maximum iteration is influenced by many factors such as the algorithm itself, problem types, as well as dimensions and search space sizes of decision variables. There are few existing studies on efficient maximum iterations , especially a large-scale study on comparison ..."}
+{"idx": 6, "title": "Selection Algorithms with Small Groups - Adrian Dumitrescu", "date": "", "ddg_snippet": "In Section 4, we introduce another variant of select, the shifting target algorithm , a linear time selection algorithm with group size 4. In each iteration , upper or lower medians are used based on the current rank of the target , and the shift in the target parameter i is controlled over three consecutive iterations .", "subpage_snippet": "", "source": "www.adriandumitrescu.org", "link": "https://www.adriandumitrescu.org/select.pdf", "content": "In Section 4, we introduce another variant of select, the shifting target algorithm , a linear time selection algorithm with group size 4. In each iteration , upper or lower medians are used based on the current rank of the target , and the shift in the target parameter i is controlled over three consecutive iterations ."}
+{"idx": 7, "title": "Swarm-intelligence-optimization-algorithm/DE.py at main ...", "date": "", "ddg_snippet": "种群算法复现 (swarm- algorithm )，包括乌鸦搜索 (Crow Search Algorithm , CSA)、樽海鞘群算法 (Salp Swarm Algorithm , SSA)、缎蓝园丁鸟优化算法 (Satin Bowerbird Optimizer, SBO)、麻雀搜索算法 (Sparrow Search Algorithm , SSA)、 狼群搜索算法 (2007WPS, 2013WPA)、正余弦优化算法 (Sine Cosine Algorithm , CSA）、烟花算法 (Fireworks Algorithm , FA) - Swarm ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LucXiong/Swarm-intelligence-optimization-algorithm/blob/main/DE.py", "content": "种群算法复现 (swarm- algorithm )，包括乌鸦搜索 (Crow Search Algorithm , CSA)、樽海鞘群算法 (Salp Swarm Algorithm , SSA)、缎蓝园丁鸟优化算法 (Satin Bowerbird Optimizer, SBO)、麻雀搜索算法 (Sparrow Search Algorithm , SSA)、 狼群搜索算法 (2007WPS, 2013WPA)、正余弦优化算法 (Sine Cosine Algorithm , CSA）、烟花算法 (Fireworks Algorithm , FA) - Swarm ..."}
+{"idx": 8, "title": "Basic Square- 1 Algorithms", "date": "", "ddg_snippet": "Advanced Square- 1 Algorithms . Use these in addition to the Basic Algorithms . Orient Corners.", "subpage_snippet": "", "source": "www.kungfoomanchu.com", "link": "https://www.kungfoomanchu.com/guides/andy-klise-square-1.pdf", "content": "Advanced Square- 1 Algorithms . Use these in addition to the Basic Algorithms . Orient Corners."}
+{"idx": 9, "title": "Data Structures and Algorithm - Week 11 - Algorithm Analysis | PDF", "date": "", "ddg_snippet": "This document discusses algorithm analysis and determining the time complexity of algorithms . It begins by defining an algorithm and noting that the efficiency of algorithms should be analyzed independently of specific implementations or hardware.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/data-structures-and-algorithm-week-11-algorithm-analysis/141154827", "content": "This document discusses algorithm analysis and determining the time complexity of algorithms . It begins by defining an algorithm and noting that the efficiency of algorithms should be analyzed independently of specific implementations or hardware."}
diff --git a/data/sampled_jsons/AltUp_Baykal_2023_paper_abstract_year_2023.jsonl b/data/sampled_jsons/AltUp_Baykal_2023_paper_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..52329d733a71e5c7fdf976942c5c0ce2e1d490ba
--- /dev/null
+++ b/data/sampled_jsons/AltUp_Baykal_2023_paper_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Alternating Updates for Efficient Transforme - papers.nips.cc", "date": "", "ddg_snippet": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2023/file/f2059277ac6ce66e7e5543001afa8bb5-Paper-Conference.pdf", "content": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ..."}
+{"idx": 1, "title": "Paper page - Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "Abstract Alternating Updates ( AltUp ) increases transformer model capacity without significantly impacting latency, achieving up to 87% speedup on benchmarks while maintaining accuracy.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2301.13310", "content": "Abstract Alternating Updates ( AltUp ) increases transformer model capacity without significantly impacting latency, achieving up to 87% speedup on benchmarks while maintaining accuracy."}
+{"idx": 2, "title": "[2301.13310] Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.13310", "content": "It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening ..."}
+{"idx": 3, "title": "Alternating updates for efficient transformers | Proceedings of the ...", "date": "", "ddg_snippet": "We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3669474", "content": "We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency."}
+{"idx": 4, "title": "Alternating Updates for Efficient Transformers - Semantic Scholar", "date": "", "ddg_snippet": "This work introduces Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden, and demonstrates how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Alternating-Updates-for-Efficient-Transformers-Baykal-Cutler/c9d46cfcf0211d11356c295ecd9584c84c19c8f8/figure/0", "content": "This work introduces Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden, and demonstrates how AltUp can be synergistically combined with existing approaches, such as Sparse Mixture-of-Experts models, to obtain efficient models with even higher capacity."}
+{"idx": 5, "title": "Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/f2059277ac6ce66e7e5543001afa8bb5-Abstract-Conference.html", "content": "Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ..."}
+{"idx": 6, "title": "Alternating updates for efficient transformers - Google Research", "date": "", "ddg_snippet": "In \" Alternating Updates for Efficient Transformers \", accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost. AltUp is easy to implement, widely applicable to any transformer architecture, and requires minimal hyperparameter tuning.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/alternating-updates-for-efficient-transformers/", "content": "In \" Alternating Updates for Efficient Transformers \", accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost. AltUp is easy to implement, widely applicable to any transformer architecture, and requires minimal hyperparameter tuning."}
+{"idx": 7, "title": "Alternating Updates for Efficient Transformers - arXiv.org", "date": "", "ddg_snippet": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2301.13310", "content": "Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables ..."}
+{"idx": 8, "title": "Alternating Updates for Efficient Transformers - DeepAI", "date": "", "ddg_snippet": "AltUp enables the widening of the learned representation without increasing the computation time by working on a subblock of the representation at each layer. Our experiments on various transformer models and language tasks demonstrate the consistent effectiveness of alternating updates on a diverse set of benchmarks.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/alternating-updates-for-efficient-transformers", "content": "AltUp enables the widening of the learned representation without increasing the computation time by working on a subblock of the representation at each layer. Our experiments on various transformer models and language tasks demonstrate the consistent effectiveness of alternating updates on a diverse set of benchmarks."}
+{"idx": 9, "title": "Alternating Updates for Efficient Transformers | Request PDF", "date": "", "ddg_snippet": "Request PDF | Alternating Updates for Efficient Transformers | It is well established that increasing scale in deep transformer networks leads to improved quality and performance. This increase in ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/367652661_Alternating_Updates_for_Efficient_Transformers", "content": "Request PDF | Alternating Updates for Efficient Transformers | It is well established that increasing scale in deep transformer networks leads to improved quality and performance. This increase in ..."}
diff --git a/data/sampled_jsons/An_Empirical_Study_of_License_Conflict_in_Free_and_Open_Source_Software_Xing_Cui_abstract.jsonl b/data/sampled_jsons/An_Empirical_Study_of_License_Conflict_in_Free_and_Open_Source_Software_Xing_Cui_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d88800d11231aee10f7820958ba9b49ba053d2c0
--- /dev/null
+++ b/data/sampled_jsons/An_Empirical_Study_of_License_Conflict_in_Free_and_Open_Source_Software_Xing_Cui_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Empirical Study of License Conflict in Free and Open Source Software", "date": "", "ddg_snippet": "Request PDF | On May 1, 2023, Xing Cui and others published An Empirical Study of License Conflict in Free and Open Source Software | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372305383_An_Empirical_Study_of_License_Conflict_in_Free_and_Open_Source_Software", "content": "Request PDF | On May 1, 2023, Xing Cui and others published An Empirical Study of License Conflict in Free and Open Source Software | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 1, "title": "An Empirical Study of License Conflict in Free and Open Source Software ...", "date": "", "ddg_snippet": "research-article An Empirical Study of License Conflict in Free and Open Source Software Authors: Xing Cui , Jingzheng Wu , Yanjun Wu", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/ICSE-SEIP58684.2023.00050", "content": "research-article An Empirical Study of License Conflict in Free and Open Source Software Authors: Xing Cui , Jingzheng Wu , Yanjun Wu"}
+{"idx": 2, "title": "An Empirical Study of License Conflict in Free and Open Source Software", "date": "", "ddg_snippet": "Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license terms. Identifying the licenses that provide FOSS and understanding the terms of those licenses is not easy ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/icse-seip/2023/003700a495/1OH5Mm1Q8Mg", "content": "Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license terms. Identifying the licenses that provide FOSS and understanding the terms of those licenses is not easy ..."}
+{"idx": 3, "title": "dblp: An Empirical Study of License Conflict in Free and Open Source ...", "date": "", "ddg_snippet": "Bibliographic details on An Empirical Study of License Conflict in Free and Open Source Software .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/icse/CuiWWWLQLY23", "content": "Bibliographic details on An Empirical Study of License Conflict in Free and Open Source Software ."}
+{"idx": 4, "title": "An Empirical Study of License Conflict in Free and Open Source Software", "date": "", "ddg_snippet": "Abstract : Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10172522", "content": "Abstract : Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license ..."}
+{"idx": 5, "title": "An Empirical Study of License Conflict in Free and Open Source Software ...", "date": "", "ddg_snippet": "DIKE is proposed, an automated tool that can perform license detection and conflict analysis for FOSS and suggests that conflicts are prevalent in FOSS, warning the open source community about intellectual property risks. Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/An-Empirical-Study-of-License-Conflict-in-Free-and-Cui-Wu/7d262af42e65c2554ff7cbb41d252ad27912cff9", "content": "DIKE is proposed, an automated tool that can perform license detection and conflict analysis for FOSS and suggests that conflicts are prevalent in FOSS, warning the open source community about intellectual property risks. Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions ..."}
+{"idx": 6, "title": "An Empirical Study of License Conflict in Free and Open Source Software", "date": "", "ddg_snippet": "Abstract : Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QDqA34xZXz", "content": "Abstract : Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force."}
+{"idx": 7, "title": "OSS-LCAF: Open-Source Software License Conflict Analysis Framework", "date": "", "ddg_snippet": "Xing Cui , Jingzheng Wu, Yanjun Wu, Xu Wang, Tianyue Luo, Sheng Qu, Xiang Ling, and Mutian Yang. An empirical study of license conflict in free and open source software .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/ICSE-Companion66252.2025.00084", "content": "Xing Cui , Jingzheng Wu, Yanjun Wu, Xu Wang, Tianyue Luo, Sheng Qu, Xiang Ling, and Mutian Yang. An empirical study of license conflict in free and open source software ."}
+{"idx": 8, "title": "An Empirical Study of License Conflict in Free and Open Source Software", "date": "", "ddg_snippet": "TABLE I PERFORMANCE COMPARISON OF DIFFERENT MODELS IN LICENSE TERMS EXTRACTION - \" An Empirical Study of License Conflict in Free and Open Source Software \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/An-Empirical-Study-of-License-Conflict-in-Free-and-Cui-Wu/7d262af42e65c2554ff7cbb41d252ad27912cff9/figure/4", "content": "TABLE I PERFORMANCE COMPARISON OF DIFFERENT MODELS IN LICENSE TERMS EXTRACTION - \" An Empirical Study of License Conflict in Free and Open Source Software \""}
+{"idx": 9, "title": "An Empirical Study of License Conflict in Free and Open Source ...", "date": "", "ddg_snippet": "Practical software development relies on excellent software engineering research. SEIP provides a unique forum for networking, exchanging ideas, fostering innovations, and forging long-term collaborations to address Software Engineering research that impacts directly on practice.", "subpage_snippet": "", "source": "conf.researchr.org", "link": "https://conf.researchr.org/details/icse-2023/icse-2023-SEIP/42/An-Empirical-Study-of-License-Conflict-in-Free-and-Open-Source-Software", "content": "Practical software development relies on excellent software engineering research. SEIP provides a unique forum for networking, exchanging ideas, fostering innovations, and forging long-term collaborations to address Software Engineering research that impacts directly on practice."}
diff --git a/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_behavioral_use_restrictions_.jsonl b/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_behavioral_use_restrictions_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..684e51a84a73dd7332389420181feb1b3dee2e95
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+{"idx": 0, "title": "Timeline of computing 2020–present - Wikipedia", "date": "", "ddg_snippet": "... an \" AI boom \" included: local or open source versions of LLaMA which was leaked in March, [ 40 ] [ 41 ] [ 42 ] news outlets reported on GPT4-based ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/2025_in_computing", "content": "... an \" AI boom \" included: local or open source versions of LLaMA which was leaked in March, [ 40 ] [ 41 ] [ 42 ] news outlets reported on GPT4-based ..."}
+{"idx": 1, "title": "A first look at License Variants in the PyPI Ecosystem", "date": "", "ddg_snippet": "... empirical study into license variants in software packaging ecosystem but also equips developers and organizations with practical tools for navigating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14594v1", "content": "... empirical study into license variants in software packaging ecosystem but also equips developers and organizations with practical tools for navigating ..."}
+{"idx": 2, "title": "E-Cigarette and Cannabis Social Media Posts and Adolescent", "date": "", "ddg_snippet": "Multinomial logistic regression analysis of exposure to e-cigarette and cannabis posts and substance use initiation ( study 1)", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2835516", "content": "Multinomial logistic regression analysis of exposure to e-cigarette and cannabis posts and substance use initiation ( study 1)"}
+{"idx": 3, "title": "Frontiers | The impact of centralized band purchasing of", "date": "", "ddg_snippet": "In 1910, the New York Hospital Authority was established, and joint hospitals and upstream institutions provided washing services to negotiate prices ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1406254/full", "content": "In 1910, the New York Hospital Authority was established, and joint hospitals and upstream institutions provided washing services to negotiate prices ..."}
+{"idx": 4, "title": "Instructions To Authors | The Review of Financial Studies |", "date": "", "ddg_snippet": "Browse content in E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit ... in E6 - Macroeconomic Policy, Macroeconomic Aspects of ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/rfs/pages/Instructions_To_Authors", "content": "Browse content in E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit ... in E6 - Macroeconomic Policy, Macroeconomic Aspects of ..."}
+{"idx": 5, "title": "Publishing with The Quarterly Journal of Economics | The", "date": "", "ddg_snippet": "Browse content in E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit ... in E6 - Macroeconomic Policy, Macroeconomic Aspects of ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/qje/pages/publish-with-us", "content": "Browse content in E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit ... in E6 - Macroeconomic Policy, Macroeconomic Aspects of ..."}
+{"idx": 6, "title": "Wikibooks:Alphabetical classification/All Books - Wikibooks,", "date": "", "ddg_snippet": "Admission to Graduate School in the U.S. ... Auxiliares de Conversación, Language and Culture Assistants in Spain Survival Guide", "subpage_snippet": "", "source": "en.wikibooks.org", "link": "https://en.wikibooks.org/wiki/Wikibooks:Alphabetical_classification/All_Books", "content": "Admission to Graduate School in the U.S. ... Auxiliares de Conversación, Language and Culture Assistants in Spain Survival Guide"}
+{"idx": 7, "title": "The effect of the 2019 coronavirus disease outbreak on social", "date": "", "ddg_snippet": "A cross-sectional study using an online survey was conducted in Jordan between the 6th and the 30th of May, 2020. ... often emphasise that ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/full/10.1177/0020764020966631?cookieSet=1", "content": "A cross-sectional study using an online survey was conducted in Jordan between the 6th and the 30th of May, 2020. ... often emphasise that ..."}
+{"idx": 8, "title": "MSR 2018 Contributors - MSR 2018", "date": "", "ddg_snippet": "Analyzing Conflict Predictors in Open - Source Java Projects from GitHub and Travis CI ... For Empirical Studies On In -IDE Activities Of Software ...", "subpage_snippet": "", "source": "2018.msrconf.org", "link": "https://2018.msrconf.org/people-index", "content": "Analyzing Conflict Predictors in Open - Source Java Projects from GitHub and Travis CI ... For Empirical Studies On In -IDE Activities Of Software ..."}
+{"idx": 9, "title": "Availability of Mobile Crisis Services in Mental Health", "date": "", "ddg_snippet": "... and seasonality of emergency department visits and hospitalizations for suicidality among children and adolescents in the US from 2016 to 2021. ...", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2830554", "content": "... and seasonality of emergency department visits and hospitalizations for suicidality among children and adolescents in the US from 2016 to 2021. ..."}
diff --git a/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_siteieeexplore.ieee.org_year_2023.jsonl b/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_siteieeexplore.ieee.org_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2df0ac2f61c9c9e6c27186f253b49ec6858f1f91
--- /dev/null
+++ b/data/sampled_jsons/An_empirical_study_of_license_conflict_in_free_and_open_source_software_siteieeexplore.ieee.org_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Empirical Study of License Violations in Open Source Projects", "date": "", "ddg_snippet": "The use of Open Source Software (OSS) components in building applications has presented the challenge of integrating them in a way such that the licenses of the individual components do not conflict with each other and if applicable, the overall license of the application.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/6479814", "content": "The use of Open Source Software (OSS) components in building applications has presented the challenge of integrating them in a way such that the licenses of the individual components do not conflict with each other and if applicable, the overall license of the application."}
+{"idx": 1, "title": "Exploring Large Language Models for Analyzing Open Source ...", "date": "", "ddg_snippet": "With the rapid growth of the open source software (OSS) ecosystem, the use of open source has become the predominant model for contemporary software development.The recent emergence of large language models (LLMs) offers new opportunities for license conflict detection.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11024476/", "content": "With the rapid growth of the open source software (OSS) ecosystem, the use of open source has become the predominant model for contemporary software development.The recent emergence of large language models (LLMs) offers new opportunities for license conflict detection."}
+{"idx": 2, "title": "IEEE Transactions on Pattern Analysis and Machine... | IEEE Xplore", "date": "", "ddg_snippet": "IEEE Transactions on Pattern Analysis and Machine Intelligence. The IEEE Transactions on Pattern Analysis and Machine Intelligence publishes article...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34", "content": "IEEE Transactions on Pattern Analysis and Machine Intelligence. The IEEE Transactions on Pattern Analysis and Machine Intelligence publishes article..."}
+{"idx": 3, "title": "OSLDetector: Identifying Open - Source Libraries through... | IEEE Xplore", "date": "", "ddg_snippet": "Using open - source libraries can provide rich functions and reduce development cost. However, some critical issues have also been caused such as license conflicts and vulnerability risks.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9286035", "content": "Using open - source libraries can provide rich functions and reduce development cost. However, some critical issues have also been caused such as license conflicts and vulnerability risks."}
+{"idx": 4, "title": "An Empirical Study on Workflows and Security Policies... | IEEE Xplore", "date": "", "ddg_snippet": "We conduct an empirical study to measure the usage of GitHub workflows and security policies in thousands of popular repositories based on the number of stars.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10190609", "content": "We conduct an empirical study to measure the usage of GitHub workflows and security policies in thousands of popular repositories based on the number of stars."}
+{"idx": 5, "title": "An Empirical Study on the Stability of Explainable Software Defect...", "date": "", "ddg_snippet": "Explaining the results of software defect prediction (SDP) models is practical but challenging. Jiarpakdee et al. proposed using two model-agnostic techniques (i.e., LIME and BreakDown) to explain prediction results.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10479376", "content": "Explaining the results of software defect prediction (SDP) models is practical but challenging. Jiarpakdee et al. proposed using two model-agnostic techniques (i.e., LIME and BreakDown) to explain prediction results."}
+{"idx": 6, "title": "An Empirical Study of License Conflict in Free and Open ...", "date": "", "ddg_snippet": "by X Cui · 2023 · Cited by 9 — The results show that 1,787 open source licenses are used in the project , and 27.2% of licenses conflict. Our new findings suggest that ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10172522/", "content": "by X Cui · 2023 · Cited by 9 — The results show that 1,787 open source licenses are used in the project , and 27.2% of licenses conflict. Our new findings suggest that ..."}
+{"idx": 7, "title": "An Empirical Study of License Conflict in Free and Open ...", "date": "", "ddg_snippet": "by X Cui · 2023 · Cited by 9 — Based on our research , this conflict analysis tool supports the largest range of open source licenses and places no limitations on the.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/10172485/10172344/10172522.pdf", "content": "by X Cui · 2023 · Cited by 9 — Based on our research , this conflict analysis tool supports the largest range of open source licenses and places no limitations on the."}
+{"idx": 8, "title": "Studying and Improving Software License Compliance in ...", "date": "", "ddg_snippet": "by N Wintersgill · 2024 · Cited by 2 — Given these challenges, this dissertation seeks to streamline modern software license compliance practices by (1) identifying current ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/10554808/10554814/10554954.pdf", "content": "by N Wintersgill · 2024 · Cited by 2 — Given these challenges, this dissertation seeks to streamline modern software license compliance practices by (1) identifying current ..."}
+{"idx": 9, "title": "A Method to Detect License Inconsistencies in Large-Scale ...", "date": "", "ddg_snippet": "by Y Wu · 2015 · Cited by 57 — In our study , we focus on the license header change during the evolution of open source software , which we refer to as license inconsistency. In our research , ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "http://ieeexplore.ieee.org/iel7/7180033/7180053/07180091.pdf", "content": "by Y Wu · 2015 · Cited by 57 — In our study , we focus on the license header change during the evolution of open source software , which we refer to as license inconsistency. In our research , ..."}
diff --git "a/data/sampled_jsons/Anari_2024_Theorem_13_overdamped_Langevin_O(log\302\262d)_vs_O(log_d\316\265)_parallel_sampling_year_2024.jsonl" "b/data/sampled_jsons/Anari_2024_Theorem_13_overdamped_Langevin_O(log\302\262d)_vs_O(log_d\316\265)_parallel_sampling_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..bb0efea15d779fc9709c4f75896e469c0b5ae0b4
--- /dev/null
+++ "b/data/sampled_jsons/Anari_2024_Theorem_13_overdamped_Langevin_O(log\302\262d)_vs_O(log_d\316\265)_parallel_sampling_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Langevin dynamics - Wikipedia", "date": "", "ddg_snippet": "In physics, Langevin dynamics is an approach to the mathematical modeling of the dynamics of molecular systems using the Langevin equation. It was originally developed by French physicist Paul Langevin .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Langevin_dynamics", "content": "In physics, Langevin dynamics is an approach to the mathematical modeling of the dynamics of molecular systems using the Langevin equation. It was originally developed by French physicist Paul Langevin ."}
+{"idx": 1, "title": "Fast parallel sampling under isoperimetry", "date": "", "ddg_snippet": "Abstract We show how to sample in parallel from a distribution $\\pi$ over $\\mathbb {R}^ d $ that satisfies a log -Sobolev inequality and has a smooth log -density, by parallelizing the Langevin (resp. underdamped Langevin ) algorithms.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/anari24a.html", "content": "Abstract We show how to sample in parallel from a distribution $\\pi$ over $\\mathbb {R}^ d $ that satisfies a log -Sobolev inequality and has a smooth log -density, by parallelizing the Langevin (resp. underdamped Langevin ) algorithms."}
+{"idx": 2, "title": "2 arXiv:2401.09016v1 [cs.DS] 17 Jan 2024", "date": "", "ddg_snippet": "Jan 18, 2024 · Abstract We show how to sample in parallel from a distribution over R3 that satisfies a log -Sobolev inequality and has a smooth log -density, by parallelizing the Langevin (resp. underdamped Langevin ) algorithms. We show that our algorithm outputs samples from a distribution that is close to in Kullback–Leibler (KL) divergence (resp. total variation (TV) distance), ˆ while using only log (3 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.09016", "content": "Jan 18, 2024 · Abstract We show how to sample in parallel from a distribution over R3 that satisfies a log -Sobolev inequality and has a smooth log -density, by parallelizing the Langevin (resp. underdamped Langevin ) algorithms. We show that our algorithm outputs samples from a distribution that is close to in Kullback–Leibler (KL) divergence (resp. total variation (TV) distance), ˆ while using only log (3 ..."}
+{"idx": 3, "title": "Penalized Overdamped and Underdamped Langevin Monte Carlo ...", "date": "", "ddg_snippet": "To our knowledge, these are the rst convergence rate results for underdamped Langevin Monte Carlo methods in the constrained sampling set-ting that can handle non-convex choices of f and can provide guarantees with the best dimension dependency among existing methods for constrained sampling when the gra-dients are deterministically available.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume25/22-1443/22-1443.pdf", "content": "To our knowledge, these are the rst convergence rate results for underdamped Langevin Monte Carlo methods in the constrained sampling set-ting that can handle non-convex choices of f and can provide guarantees with the best dimension dependency among existing methods for constrained sampling when the gra-dients are deterministically available."}
+{"idx": 4, "title": "dominicp6/parallelised_stochastic_sampling - GitHub", "date": "", "ddg_snippet": "Implements parallelised classes for Overdamped Langevin Dynamics, Underdamped Langevin Dyanamics and Gaussian Drift Diffusion Dynamics in arbitary dimensions. - dominicp6/parallelised_stochastic_ sampling", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dominicp6/parallelised_stochastic_sampling", "content": "Implements parallelised classes for Overdamped Langevin Dynamics, Underdamped Langevin Dyanamics and Gaussian Drift Diffusion Dynamics in arbitary dimensions. - dominicp6/parallelised_stochastic_ sampling"}
+{"idx": 5, "title": "Lecture 3 – Langevin algorithms - ETH Z", "date": "", "ddg_snippet": "Theorem 3.2.2 (Convergence of Langevin difusion in χ2-divergence). Let {Xt} t≥0 be generated according to the Langevin difusion (3.1) with initialization X0 ∼ μ0 and stationary measure μ ∝ e−f.", "subpage_snippet": "", "source": "metaphor.ethz.ch", "link": "https://metaphor.ethz.ch/x/2024/fs/401-4634-24L/lec/Lecture03.pdf", "content": "Theorem 3.2.2 (Convergence of Langevin difusion in χ2-divergence). Let {Xt} t≥0 be generated according to the Langevin difusion (3.1) with initialization X0 ∼ μ0 and stationary measure μ ∝ e−f."}
+{"idx": 6, "title": "COLT 2024: Fast parallel sampling under isoperimetry", "date": "", "ddg_snippet": "Abstract: We show how to sample in parallel from a distribution $\\pi$ over $\\mathbb {R}^ d $ that satisfies a log -Sobolev inequality and has a smooth log -density, by parallelizing the Langevin (resp. underdamped Langevin ) algorithms.", "subpage_snippet": "", "source": "learningtheory.org", "link": "https://learningtheory.org/colt2024/schedule/poster_13.html", "content": "Abstract: We show how to sample in parallel from a distribution $\\pi$ over $\\mathbb {R}^ d $ that satisfies a log -Sobolev inequality and has a smooth log -density, by parallelizing the Langevin (resp. underdamped Langevin ) algorithms."}
+{"idx": 7, "title": "Parallelizing MCMC Across the Sequence Length: T samples in...", "date": "", "ddg_snippet": "We take an alternative approach: we propose algorithms to evaluate MCMC samplers in parallel across the chain length.We show how this approach can be used to parallelize Gibbs, Metropolis-adjusted Langevin , and Hamiltonian Monte Carlo sampling across the sequence length.", "subpage_snippet": "", "source": "stat.duke.edu", "link": "https://stat.duke.edu/events/parallelizing-mcmc-across-sequence-length-t-samples-olog2-t-time", "content": "We take an alternative approach: we propose algorithms to evaluate MCMC samplers in parallel across the chain length.We show how this approach can be used to parallelize Gibbs, Metropolis-adjusted Langevin , and Hamiltonian Monte Carlo sampling across the sequence length."}
+{"idx": 8, "title": "stochastic processes - Underdamped vs overdamped Langevin ...", "date": "", "ddg_snippet": "Sign up or log in to customize your list.Connect and share knowledge within a single location that is structured and easy to search. Learn more about Teams. Underdamped vs overdamped Langevin dynamics.", "subpage_snippet": "", "source": "physics.stackexchange.com", "link": "https://physics.stackexchange.com/questions/283450/underdamped-vs-overdamped-langevin-dynamics", "content": "Sign up or log in to customize your list.Connect and share knowledge within a single location that is structured and easy to search. Learn more about Teams. Underdamped vs overdamped Langevin dynamics."}
+{"idx": 9, "title": "(PDF) Optimal importance sampling for overdamped Langevin ...", "date": "", "ddg_snippet": "In this work, we mathematically analyze an importance sampling approach for MCMC methods that rely on the overdamped Langevin dynamics. Specifically, we study an estimator based on an ergodic average along a realization of an overdamped Langevin process for a modified potential.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372547568_Optimal_importance_sampling_for_overdamped_Langevin_dynamics", "content": "In this work, we mathematically analyze an importance sampling approach for MCMC methods that rely on the overdamped Langevin dynamics. Specifically, we study an estimator based on an ergodic average along a realization of an overdamped Langevin process for a modified potential."}
diff --git a/data/sampled_jsons/Anvith_Thudi_per-instance_privacy_2024.jsonl b/data/sampled_jsons/Anvith_Thudi_per-instance_privacy_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..82761f9bd4e628c958fea1cb5bcae36a9e2e4d04
--- /dev/null
+++ b/data/sampled_jsons/Anvith_Thudi_per-instance_privacy_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "by NM Sepahvand · 2025 — We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "by NM Sepahvand · 2025 — We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning."}
+{"idx": 1, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0A4Y9qRnu9&noteId=Zd6KsMzKb8", "content": "We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}
+{"idx": 2, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}
+{"idx": 3, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "by NM Sepahvand · 2025 — Using recent results in differential privacy with Rényi divergences ( Thudi et al., 2024 ), our proposed measures— per - instance privacy losses— ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.18786", "content": "by NM Sepahvand · 2025 — Using recent results in differential privacy with Rényi divergences ( Thudi et al., 2024 ), our proposed measures— per - instance privacy losses— ..."}
+{"idx": 4, "title": "Anvith Thudi –", "date": "", "ddg_snippet": "”Leveraging Per - Instance Privacy for Machine Unlearning”: Nazanin Mohammadi Sepahvand, Anvith Thudi ,. Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot ... 3 pages", "subpage_snippet": "", "source": "www.anvith.com", "link": "https://www.anvith.com/Anvith_Thudi_CV_May_15_2025.pdf", "content": "”Leveraging Per - Instance Privacy for Machine Unlearning”: Nazanin Mohammadi Sepahvand, Anvith Thudi ,. Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot ... 3 pages"}
+{"idx": 5, "title": "Anvith Thudi", "date": "", "ddg_snippet": "Leveraging Per-Instance Privacy for Machine Unlearning Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, ...", "subpage_snippet": "", "source": "cleverhans.io", "link": "https://cleverhans.io/members/anvith.html", "content": "Leveraging Per-Instance Privacy for Machine Unlearning Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, ..."}
+{"idx": 6, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/fr/chatpaper/paper/165514", "content": "We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient ..."}
+{"idx": 7, "title": "Anvith Thudi", "date": "", "ddg_snippet": "Proceedings of the 13th International Conference on Learning Representations, 2024 ... Leveraging Per-Instance Privacy for Machine Unlearning . NM Sepahvand, A ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=bTEybH0AAAAJ&hl=en", "content": "Proceedings of the 13th International Conference on Learning Representations, 2024 ... Leveraging Per-Instance Privacy for Machine Unlearning . NM Sepahvand, A ..."}
+{"idx": 8, "title": "Anvith Thudi", "date": "", "ddg_snippet": "Leveraging Per-Instance Privacy for Machine Unlearning · Published: 01 May 2025, Last Modified: 23 Jul 2025 · ICML 2025 poster · Readers: Everyone ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Anvith_Thudi1", "content": "Leveraging Per-Instance Privacy for Machine Unlearning · Published: 01 May 2025, Last Modified: 23 Jul 2025 · ICML 2025 poster · Readers: Everyone ..."}
+{"idx": 9, "title": "Sensitivity is Often Overestimated in DP-SGD", "date": "", "ddg_snippet": "by A Thudi · 2024 · Cited by 8 — DP-SGD leaks significantly less privacy for many datapoints (when trained on common benchmarks) than the current data-independent guarantee.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity24/presentation/thudi", "content": "by A Thudi · 2024 · Cited by 8 — DP-SGD leaks significantly less privacy for many datapoints (when trained on common benchmarks) than the current data-independent guarantee."}
diff --git a/data/sampled_jsons/Archetypal_SAE_Section_6_Scaling_Archetypal-SAE_relaxation_term_norm_constraint_year_2024.jsonl b/data/sampled_jsons/Archetypal_SAE_Section_6_Scaling_Archetypal-SAE_relaxation_term_norm_constraint_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1db4ff027d2fe12b39a936d28fa021095d16b494
--- /dev/null
+++ b/data/sampled_jsons/Archetypal_SAE_Section_6_Scaling_Archetypal-SAE_relaxation_term_norm_constraint_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal SAEs: Adaptive and Stable Dictionary Learning for ...", "date": "", "ddg_snippet": "Mar 20, 2025 · The next refinement introduced is RA- SAE , or Relaxed Archetypal SAE , which allows for a small deviation outside the convex hull. In other words, while A- SAE enforces each atom to be exactly in convex hull of C, RA- SAE introduces a trainable shift matrix Λ Λ with a norm constraint : D = W C + Λ, subject to ∥Λ∥2 2 ≤ δ D = W C + Λ ...", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "Mar 20, 2025 · The next refinement introduced is RA- SAE , or Relaxed Archetypal SAE , which allows for a small deviation outside the convex hull. In other words, while A- SAE enforces each atom to be exactly in convex hull of C, RA- SAE introduces a trainable shift matrix Λ Λ with a norm constraint : D = W C + Λ, subject to ∥Λ∥2 2 ≤ δ D = W C + Λ ..."}
+{"idx": 1, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for ...", "date": "", "ddg_snippet": "To scale up the Archetypal SAE , it is impractical to utilize the entire data matrix 𝐀 𝐀 \\mathbf {A} bold_A for identifying archetypes . Instead, we first reduce the dataset to a smaller subset of points, denoted as 𝐂 𝐂 \\mathbf {C} bold_C, and construct the archetypes /dictionary elements from this reduced set.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v2", "content": "To scale up the Archetypal SAE , it is impractical to utilize the entire data matrix 𝐀 𝐀 \\mathbf {A} bold_A for identifying archetypes . Instead, we first reduce the dataset to a smaller subset of points, denoted as 𝐂 𝐂 \\mathbf {C} bold_C, and construct the archetypes /dictionary elements from this reduced set."}
+{"idx": 2, "title": "Archetypal - Overcomplete", "date": "", "ddg_snippet": "Archetypal SAE introduces a constraint on the dictionary where each atom is formed as a convex combination of data points with an additional relaxation term . This method enhances stability and interpretability in dictionary learning, making it a robust drop-in replacement for the dictionary layer in any Sparse Autoencoder.", "subpage_snippet": "", "source": "kempnerinstitute.github.io", "link": "https://kempnerinstitute.github.io/overcomplete/saes/archetypal/", "content": "Archetypal SAE introduces a constraint on the dictionary where each atom is formed as a convex combination of data points with an additional relaxation term . This method enhances stability and interpretability in dictionary learning, making it a robust drop-in replacement for the dictionary layer in any Sparse Autoencoder."}
+{"idx": 3, "title": "Archetypal SAEs: Adaptive and Stable Dictionary ... - Medium", "date": "", "ddg_snippet": "A) Compared to a Regular- SAE , Archetypal -SAEs constrain dictionary atoms (decoder directions) to the data’s convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@kempnerinstitute/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-acf95010c691", "content": "A) Compared to a Regular- SAE , Archetypal -SAEs constrain dictionary atoms (decoder directions) to the data’s convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation ..."}
+{"idx": 4, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for ...", "date": "", "ddg_snippet": "Jun 25, 2025 · To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A- SAE ), wherein dictionary atoms are constrained to the data’s convex hull.", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/paper/?paper_id=icml-forum-id-9v1eW8HgMU-2025-06-25", "content": "Jun 25, 2025 · To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A- SAE ), wherein dictionary atoms are constrained to the data’s convex hull."}
+{"idx": 5, "title": "Archetypal SAE: Variable and stable methods to learn the ...", "date": "", "ddg_snippet": "Mar 17, 2025 · After that, RA- SAE is the balance in the reconstruction estimates of the purpose of the purpose of the purpose of Great- Scale Scale . To explore these methods, the group striking the novel metrics and benchmarks inspired by the diagnostic idea, providing a formal framework for measuring the quality of dictionary and the meaning of the concept of ...", "subpage_snippet": "", "source": "dataforcee.us", "link": "https://dataforcee.us/2025/03/17/archetypal-sae-variable-and-stable-methods-to-learn-the-temple-dictionary-in-the-largest-visual-models/", "content": "Mar 17, 2025 · After that, RA- SAE is the balance in the reconstruction estimates of the purpose of the purpose of the purpose of Great- Scale Scale . To explore these methods, the group striking the novel metrics and benchmarks inspired by the diagnostic idea, providing a formal framework for measuring the quality of dictionary and the meaning of the concept of ..."}
+{"idx": 6, "title": "Archetypal SAE: Adaptive And Stable Dictionary Learning For ...", "date": "", "ddg_snippet": "Mar 17, 2025 · These approaches build upon archetypal analysis to enhance stability and consistency in concept extraction. The A- SAE model constrains each dictionary atom to reside strictly within the convex hull of the training data, which imposes a geometric constraint that improves stability across different training runs.", "subpage_snippet": "", "source": "metaailabs.com", "link": "https://metaailabs.com/archetypal-sae-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "Mar 17, 2025 · These approaches build upon archetypal analysis to enhance stability and consistency in concept extraction. The A- SAE model constrains each dictionary atom to reside strictly within the convex hull of the training data, which imposes a geometric constraint that improves stability across different training runs."}
+{"idx": 7, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "18 Feb 2025 — To address this, we introduced Archetypal SAEs (A- SAE ), which constrain dictionary atoms to the convex hull of the data, significantly enhancing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v1", "content": "18 Feb 2025 — To address this, we introduced Archetypal SAEs (A- SAE ), which constrain dictionary atoms to the convex hull of the data, significantly enhancing ..."}
+{"idx": 8, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "18 Jun 2025 — We introduce Archetypal SAEs (A- SAE and RA- SAE ) that constrain dictionary elements within the data's convex hull. Abstract: Sparse Autoencoders ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9v1eW8HgMU&noteId=jupnkmN3Zt", "content": "18 Jun 2025 — We introduce Archetypal SAEs (A- SAE and RA- SAE ) that constrain dictionary elements within the data's convex hull. Abstract: Sparse Autoencoders ..."}
+{"idx": 9, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "Archetypal -SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46195", "content": "Archetypal -SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild ..."}
diff --git a/data/sampled_jsons/Are_Language_Models_Actually_Useful_for_Time_Series_Forecasting_Table_3_OneFitsAll_MAE_year_2024.jsonl b/data/sampled_jsons/Are_Language_Models_Actually_Useful_for_Time_Series_Forecasting_Table_3_OneFitsAll_MAE_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0b9c90fb053886b732662c30539f0cb5aa6566f8
--- /dev/null
+++ b/data/sampled_jsons/Are_Language_Models_Actually_Useful_for_Time_Series_Forecasting_Table_3_OneFitsAll_MAE_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series ? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance -- in most cases, the results even ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.16964", "content": "Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series ? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance -- in most cases, the results even ..."}
+{"idx": 1, "title": "PDF Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "Abstract Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series ? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance—in most cases, the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/997f213b76c3b259e0f4cc88195a65f800b920e3.pdf", "content": "Abstract Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series ? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance—in most cases, the ..."}
+{"idx": 2, "title": "Table 3 from Are Language Models Actually Useful for Time Series ...", "date": "", "ddg_snippet": "Table 3 : Forecasting performance of all models - Time -LLM, LLaTA, and OneFitsAll and results from our ablations. All results are averaged across different prediction lengths, though full results are available in Appendix E.1. Results in Red denote the best-performing model . # Wins refers to the number of times the method performed best, and # Params is the number of model parameters ...", "subpage_snippet": "", "source": "semanticscholar.org", "link": "https://semanticscholar.org/paper/Are-Language-Models-Actually-Useful-for-Time-Series-Tan-Merrill/df0d604b8e8e3b2947d9865d735f204c08635012/figure/3", "content": "Table 3 : Forecasting performance of all models - Time -LLM, LLaTA, and OneFitsAll and results from our ablations. All results are averaged across different prediction lengths, though full results are available in Appendix E.1. Results in Red denote the best-performing model . # Wins refers to the number of times the method performed best, and # Params is the number of model parameters ..."}
+{"idx": 3, "title": "PDF Are Language Models Actually Useful for", "date": "", "ddg_snippet": "Aligning Input Time Series Input Time Series Input Time Series Input Time Series # Wins: Illness 3 0.8691 8 1.6996 0.8523 5 1.6146 0 0.8742 1.6640 The results indicate that our ablations can perform Figure 7: Ablation methods (a) w/ consume less time for inference while providing better forecasting 0.8663 1.6381", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/96085.pdf", "content": "Aligning Input Time Series Input Time Series Input Time Series Input Time Series # Wins: Illness 3 0.8691 8 1.6996 0.8523 5 1.6146 0 0.8742 1.6640 The results indicate that our ablations can perform Figure 7: Ablation methods (a) w/ consume less time for inference while providing better forecasting 0.8663 1.6381"}
+{"idx": 4, "title": "Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "These studies suggest that language models , designed for sequential dependencies in text, could generalize to time series data. While this idea aligns with the popularity of language models in machine learning, direct connections between language modeling and TSF remain unclear. How beneficial are language models for traditional TSF task?", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BennyTMT/LLMsForTimeSeries/blob/main/README.md", "content": "These studies suggest that language models , designed for sequential dependencies in text, could generalize to time series data. While this idea aligns with the popularity of language models in machine learning, direct connections between language modeling and TSF remain unclear. How beneficial are language models for traditional TSF task?"}
+{"idx": 5, "title": "Are Language Models Actually Useful for Time Series Forecasting ...", "date": "", "ddg_snippet": "This table presents the forecasting performance results ( MAE and MSE) for three popular LLM-based time series forecasting models ( Time -LLM, CALF, OneFitsAll ) and their corresponding ablation methods (without LLM, LLM replaced with attention, LLM replaced with transformer).", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/spotlight-others/dv15ubhcy1/", "content": "This table presents the forecasting performance results ( MAE and MSE) for three popular LLM-based time series forecasting models ( Time -LLM, CALF, OneFitsAll ) and their corresponding ablation methods (without LLM, LLM replaced with attention, LLM replaced with transformer)."}
+{"idx": 6, "title": "Time Series Analysis: Definition, Types & Techniques | Tableau", "date": "", "ddg_snippet": "Time series analysis and forecasting models must define the types of data relevant to answering the business question. Once analysts have chosen the relevant data they want to analyze, they choose what types of analysis and techniques are the best fit.", "subpage_snippet": "", "source": "www.tableau.com", "link": "https://www.tableau.com/analytics/what-is-time-series-analysis", "content": "Time series analysis and forecasting models must define the types of data relevant to answering the business question. Once analysts have chosen the relevant data they want to analyze, they choose what types of analysis and techniques are the best fit."}
+{"idx": 7, "title": "Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "But are language models actually useful for time series ? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance---in most cases, the results even improve!", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/6ed5bf446f59e2c6646d23058c86424b-Abstract-Conference.html", "content": "But are language models actually useful for time series ? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance---in most cases, the results even improve!"}
+{"idx": 8, "title": "Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "In time series tasks, LLM (LLaMA and GPT-2) significantly increases training time . The table shows the number of model parameters (in millions) and total training time (in minutes) for three methods predicting over a length of 96 on ETTh1 and Weather data. Compared with original method \"w/ LLM\" are \"w/o LLM\", \"LLM2Attn\" and \"LLM2Trsf\". [source] Time -LLM, OneFitsAll , and LLaTA ...", "subpage_snippet": "", "source": "pub.towardsai.net", "link": "https://pub.towardsai.net/are-language-models-actually-useful-for-time-series-forecasting-81a099415702", "content": "In time series tasks, LLM (LLaMA and GPT-2) significantly increases training time . The table shows the number of model parameters (in millions) and total training time (in minutes) for three methods predicting over a length of 96 on ETTh1 and Weather data. Compared with original method \"w/ LLM\" are \"w/o LLM\", \"LLM2Attn\" and \"LLM2Trsf\". [source] Time -LLM, OneFitsAll , and LLaTA ..."}
+{"idx": 9, "title": "Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "Abstract Large language models (LLMs) are being applied to time series tasks, particularly time series forecasting . However, are language models actually useful for time series ? After a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.16964v1", "content": "Abstract Large language models (LLMs) are being applied to time series tasks, particularly time series forecasting . However, are language models actually useful for time series ? After a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade the ..."}
diff --git a/data/sampled_jsons/Assessing_the_Brittleness_of_Safety_Alignment_via_Pruning_and_Low-Rank_Modifications_Wei_2024.jsonl b/data/sampled_jsons/Assessing_the_Brittleness_of_Safety_Alignment_via_Pruning_and_Low-Rank_Modifications_Wei_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0bee9d09f12982a7f6d3752cc4acafaec9135243
--- /dev/null
+++ b/data/sampled_jsons/Assessing_the_Brittleness_of_Safety_Alignment_via_Pruning_and_Low-Rank_Modifications_Wei_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Assessing the Brittleness of Safety Alignment via Pruning ...", "date": "", "ddg_snippet": "by B Wei · 2024 · Cited by 144 — This study explores this brittleness of safety alignment by leveraging pruning and low - rank modifications . We develop methods to identify ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.05162", "content": "by B Wei · 2024 · Cited by 144 — This study explores this brittleness of safety alignment by leveraging pruning and low - rank modifications . We develop methods to identify ..."}
+{"idx": 1, "title": "ASSESSING THE BRITTLENESS OF SAFETY ALIGNMENT", "date": "", "ddg_snippet": "by B Wei · Cited by 144 — This study explores this brittleness of safety alignment by leverag- ing pruning and low - rank modifications . We develop methods to identify critical regions ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=niBPvgJIHB", "content": "by B Wei · Cited by 144 — This study explores this brittleness of safety alignment by leverag- ing pruning and low - rank modifications . We develop methods to identify critical regions ..."}
+{"idx": 2, "title": "Assessing the Brittleness of Safety Alignment via Pruning ...", "date": "", "ddg_snippet": "by B Wei · 2024 · Cited by 143 — This study ex- plores this brittleness of safety alignment by lever- aging pruning and low - rank modifications . We de- velop methods to identify critical regions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.05162", "content": "by B Wei · 2024 · Cited by 143 — This study ex- plores this brittleness of safety alignment by lever- aging pruning and low - rank modifications . We de- velop methods to identify critical regions ..."}
+{"idx": 3, "title": "Assessing the brittleness of safety alignment via pruning ...", "date": "", "ddg_snippet": "by B Wei · 2024 · Cited by 144 — This study explores this brittleness of safety alignment by leveraging pruning and low - rank modifications . We develop methods to identify ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3694226", "content": "by B Wei · 2024 · Cited by 144 — This study explores this brittleness of safety alignment by leveraging pruning and low - rank modifications . We develop methods to identify ..."}
+{"idx": 4, "title": "Assessing the Brittleness of Safety Alignment via Pruning ...", "date": "", "ddg_snippet": "by B Wei · 2024 · Cited by 143 — This study explores this brittleness of safety alignment by leveraging pruning and low - rank modifications . We develop methods to identify critical regions that ...", "subpage_snippet": "", "source": "collaborate.princeton.edu", "link": "https://collaborate.princeton.edu/en/publications/assessing-the-brittleness-of-safety-alignment-via-pruning-and-low", "content": "by B Wei · 2024 · Cited by 143 — This study explores this brittleness of safety alignment by leveraging pruning and low - rank modifications . We develop methods to identify critical regions that ..."}
+{"idx": 5, "title": "Assessing the Brittleness of Safety Alignment via Pruning ...", "date": "", "ddg_snippet": "In this study, we explore this brittleness of safety alignment by leveraging pruning and low - rank modifications . We develop methods to identify critical regions ...", "subpage_snippet": "", "source": "boyiwei.com", "link": "https://boyiwei.com/alignment-attribution/", "content": "In this study, we explore this brittleness of safety alignment by leveraging pruning and low - rank modifications . We develop methods to identify critical regions ..."}
+{"idx": 6, "title": "boyiwei/alignment-attribution-code", "date": "", "ddg_snippet": "This study explores this brittleness of safety alignment by leveraging pruning and low - rank modifications . We develop methods to identify critical regions that ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/boyiwei/alignment-attribution-code", "content": "This study explores this brittleness of safety alignment by leveraging pruning and low - rank modifications . We develop methods to identify critical regions that ..."}
+{"idx": 7, "title": "Revision History for Assessing the Brittleness of Safety...", "date": "", "ddg_snippet": "13 Nov 2024 , 15:15 Pacific Standard Time. Title: Assessing the Brittleness of Safety Alignment via Pruning and Low - Rank Modifications . Authors: Boyi Wei ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/revisions?id=38ZahtMDF3", "content": "13 Nov 2024 , 15:15 Pacific Standard Time. Title: Assessing the Brittleness of Safety Alignment via Pruning and Low - Rank Modifications . Authors: Boyi Wei ..."}
+{"idx": 8, "title": "Assessing the Brittleness of Safety Alignment via Pruning and ...", "date": "", "ddg_snippet": "Key takeaway: 'Large language models (LLMs) show inherent brittleness in their safety mechanisms, highlighting the need for more robust safety strategies.'", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/assessing-the-brittleness-of-safety-alignment-via-pruning-qi-xie/d8dff42be5c85acf841c619bd56ec984/", "content": "Key takeaway: 'Large language models (LLMs) show inherent brittleness in their safety mechanisms, highlighting the need for more robust safety strategies.'"}
+{"idx": 9, "title": "Mengdi Wang", "date": "", "ddg_snippet": "Check out our #icml2024 paper: Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications . https://arxiv.org/abs ...", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/MengdiWang10/status/1787917387406393798", "content": "Check out our #icml2024 paper: Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications . https://arxiv.org/abs ..."}
diff --git a/data/sampled_jsons/BIT-VO_Murai_focal_plane_binary_feature_detection_tracking.jsonl b/data/sampled_jsons/BIT-VO_Murai_focal_plane_binary_feature_detection_tracking.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..37b546a9dad96d3426745c0452931cd88eadc321
--- /dev/null
+++ b/data/sampled_jsons/BIT-VO_Murai_focal_plane_binary_feature_detection_tracking.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bit - Wikipedia", "date": "", "ddg_snippet": "The bit represents a logical state with one of two possible values. These values are most commonly represented as either \"1\" or \"0\", but other representations such as true / false, yes / no, on / off, or + / − are also widely used.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Bit", "content": "The bit represents a logical state with one of two possible values. These values are most commonly represented as either \"1\" or \"0\", but other representations such as true / false, yes / no, on / off, or + / − are also widely used."}
+{"idx": 1, "title": "BIT Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of BIT is the biting or cutting edge or part of a tool. How to use bit in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/bit", "content": "The meaning of BIT is the biting or cutting edge or part of a tool. How to use bit in a sentence."}
+{"idx": 2, "title": "What is bit (binary digit) in computing? - TechTarget", "date": "", "ddg_snippet": "Jun 6, 2025 · Bits are stored in memory through the use of capacitors that hold electrical charges. The charge determines the state of each bit which, in turn, determines the bit 's value. Various combinations of bits -- combinations of 0s and 1s -- are used to represent numbers larger than 1.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/whatis/definition/bit-binary-digit", "content": "Jun 6, 2025 · Bits are stored in memory through the use of capacitors that hold electrical charges. The charge determines the state of each bit which, in turn, determines the bit 's value. Various combinations of bits -- combinations of 0s and 1s -- are used to represent numbers larger than 1."}
+{"idx": 3, "title": "Bit Definition - What is a bit in data storage? - TechTerms.com", "date": "", "ddg_snippet": "Apr 20, 2013 · A bit (short for \" binary digit \") is the smallest unit of measurement used to quantify computer data. It contains a single binary value of 0 or 1. While a single bit can define a boolean value of True (1) or False (0), an individual bit has little other use.", "subpage_snippet": "", "source": "techterms.com", "link": "https://techterms.com/definition/bit", "content": "Apr 20, 2013 · A bit (short for \" binary digit \") is the smallest unit of measurement used to quantify computer data. It contains a single binary value of 0 or 1. While a single bit can define a boolean value of True (1) or False (0), an individual bit has little other use."}
+{"idx": 4, "title": "What is BIT (Binary DigIT)? - Computer Hope", "date": "", "ddg_snippet": "Sep 7, 2025 · Sometimes abbreviated as b (lowercase), bit is short for binary digit . It's a single unit of information with a value of either 0 or 1 (off or on, false or true, low or high).", "subpage_snippet": "", "source": "www.computerhope.com", "link": "https://www.computerhope.com/jargon/b/bit.htm", "content": "Sep 7, 2025 · Sometimes abbreviated as b (lowercase), bit is short for binary digit . It's a single unit of information with a value of either 0 or 1 (off or on, false or true, low or high)."}
+{"idx": 5, "title": "Bits and Bytes", "date": "", "ddg_snippet": "Everything in a computer is 0's and 1's. The bit stores just a 0 or 1: it's the smallest building block of storage.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs101/bits-bytes.html", "content": "Everything in a computer is 0's and 1's. The bit stores just a 0 or 1: it's the smallest building block of storage."}
+{"idx": 6, "title": "Bit - definition of bit by The Free Dictionary", "date": "", "ddg_snippet": "Define bit . bit synonyms, bit pronunciation, bit translation, English dictionary definition of bit . n. 1. A small portion, degree, or amount: a bit of lint; a bit of luck.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/bit", "content": "Define bit . bit synonyms, bit pronunciation, bit translation, English dictionary definition of bit . n. 1. A small portion, degree, or amount: a bit of lint; a bit of luck."}
+{"idx": 7, "title": "Bits (binary digits ) (article) | Khan Academy", "date": "", "ddg_snippet": "Computers store information using bits . A bit (short for \"binary digit\") stores either the value 0 or 1 .", "subpage_snippet": "", "source": "www.khanacademy.org", "link": "https://www.khanacademy.org/computing/computers-and-internet/xcae6f4a7ff015e7d:digital-information/xcae6f4a7ff015e7d:bits-and-bytes/a/bits-binary-digits", "content": "Computers store information using bits . A bit (short for \"binary digit\") stores either the value 0 or 1 ."}
+{"idx": 8, "title": "BIT | definition in the Cambridge English Dictionary", "date": "", "ddg_snippet": "For the competition they had to write, shoot, and edit a one-minute comedy bit . Bits are basically jokes, but in the context of a play or movie, they usually involve a physical element and more than one person.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/us/dictionary/english/bit", "content": "For the competition they had to write, shoot, and edit a one-minute comedy bit . Bits are basically jokes, but in the context of a play or movie, they usually involve a physical element and more than one person."}
+{"idx": 9, "title": "What is a bit? Bits and bytes explained - IONOS", "date": "", "ddg_snippet": "Dec 8, 2022 · A bit is the smallest unit of electronic information; multiple bits form a byte. Whereas the storage capacity of hard drives is given in bytes, data transfer rates are shown in bits.", "subpage_snippet": "", "source": "www.ionos.com", "link": "https://www.ionos.com/digitalguide/websites/web-development/what-is-a-bit/", "content": "Dec 8, 2022 · A bit is the smallest unit of electronic information; multiple bits form a byte. Whereas the storage capacity of hard drives is given in bytes, data transfer rates are shown in bits."}
diff --git a/data/sampled_jsons/Balke_and_Pearl_1994_counterfactual_response_function_abstract_year_1994.jsonl b/data/sampled_jsons/Balke_and_Pearl_1994_counterfactual_response_function_abstract_year_1994.jsonl
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index 0000000000000000000000000000000000000000..01629731dcc49e30a6c2a8526a26ea5e7b2c0d6f
--- /dev/null
+++ b/data/sampled_jsons/Balke_and_Pearl_1994_counterfactual_response_function_abstract_year_1994.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Counterfactual Probabilities: Computational Methods, Bounds and ...", "date": "", "ddg_snippet": "The connection between the factual and counterfactual worlds is discussed in [ Balke and Pearl , 1994 ] where it is argued that the response-function variables should assume the same values in both worlds.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1302.6784", "content": "The connection between the factual and counterfactual worlds is discussed in [ Balke and Pearl , 1994 ] where it is argued that the response-function variables should assume the same values in both worlds."}
+{"idx": 1, "title": "Structural Counterfactuals: A Brief Introduction - Pearl - 2013 ...", "date": "", "ddg_snippet": "Since its inception ( Balke & Pearl , 1995) this counterfactual model has provided mathematical solutions to a vast number of lingering problems in policy analysis and retrospective reasoning.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.12065", "content": "Since its inception ( Balke & Pearl , 1995) this counterfactual model has provided mathematical solutions to a vast number of lingering problems in policy analysis and retrospective reasoning."}
+{"idx": 2, "title": "PDF 1994-Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "Evaluation of counterfactual queries (e.g., \"If A were tion for verifying the truth of an indicative sentence,", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/AAAI/1994/AAAI94-035.pdf", "content": "Evaluation of counterfactual queries (e.g., \"If A were tion for verifying the truth of an indicative sentence,"}
+{"idx": 3, "title": "PDF Probabilistic Counterfactuals: Semantics, Computation, and Applications", "date": "", "ddg_snippet": "The intervention-based interpreta- tion of counterfactual antecedents implies that the disturbance eb, and hence the response-function r&, is unaffected by the interventions that force the counterfac - tual values; therefore, what we learn about the response-function from the ob- served evidence is applicable to the evaluation of belief in the ...", "subpage_snippet": "", "source": "apps.dtic.mil", "link": "https://apps.dtic.mil/sti/tr/pdf/ADA332296.pdf", "content": "The intervention-based interpreta- tion of counterfactual antecedents implies that the disturbance eb, and hence the response-function r&, is unaffected by the interventions that force the counterfac - tual values; therefore, what we learn about the response-function from the ob- served evidence is applicable to the evaluation of belief in the ..."}
+{"idx": 4, "title": "Counterfactual probabilities | Proceedings of the Tenth international ...", "date": "", "ddg_snippet": "Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/2074394.2074401", "content": "Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ..."}
+{"idx": 5, "title": "dblp: Probabilistic Evaluation of Counterfactual Queries.", "date": "", "ddg_snippet": "> Home [-] Details and statistics DOI: — access: closed type: Conference or Workshop Paper metadata version: 2023-09-04 Alexander Balke , Judea Pearl : Probabilistic Evaluation of Counterfactual Queries. AAAI 1994 : 230-237", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/aaai/BalkeP94", "content": "> Home [-] Details and statistics DOI: — access: closed type: Conference or Workshop Paper metadata version: 2023-09-04 Alexander Balke , Judea Pearl : Probabilistic Evaluation of Counterfactual Queries. AAAI 1994 : 230-237"}
+{"idx": 6, "title": "Counterfactual Probabilities: Computational Methods, Bounds and ...", "date": "", "ddg_snippet": "( Balke and Pearl , 1994b) develop the representation of a causal theory in terms of latent response function variables that describe the probability distribution of counterfactual quantities. ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/235726713_Counterfactual_Probabilities_Computational_Methods_Bounds_andApplications", "content": "( Balke and Pearl , 1994b) develop the representation of a causal theory in terms of latent response function variables that describe the probability distribution of counterfactual quantities. ..."}
+{"idx": 7, "title": "Counterfactual Probabilities: Computational Methods, Bounds and ...", "date": "", "ddg_snippet": "Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1302.6784", "content": "Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ..."}
+{"idx": 8, "title": "Causation, Action, and Counterfactuals | SpringerLink", "date": "", "ddg_snippet": "Balke , A. and Pearl , J., \"Probabilistic evaluation of counterfactual queries,\" in Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle, WA, Volume I, 230-237, July 31-August 4, 1994 .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-94-017-0487-8_18", "content": "Balke , A. and Pearl , J., \"Probabilistic evaluation of counterfactual queries,\" in Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle, WA, Volume I, 230-237, July 31-August 4, 1994 ."}
+{"idx": 9, "title": "Probabilistic evaluation of counterfactual queries", "date": "", "ddg_snippet": "Pearl J (2008)Causal inferenceProceedings of the 2008th International Conference on Causality: Objectives and Assessment - Volume 610.5555/2996801.2996805(39-58)Online publication date: 12-Dec-2008", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/199288.178004", "content": "Pearl J (2008)Causal inferenceProceedings of the 2008th International Conference on Causality: Objectives and Assessment - Volume 610.5555/2996801.2996805(39-58)Online publication date: 12-Dec-2008"}
diff --git a/data/sampled_jsons/Bansal_FOCS_2010_discrepancy_minimization_year_2010.jsonl b/data/sampled_jsons/Bansal_FOCS_2010_discrepancy_minimization_year_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..16384b3be976c29ea75a3c7f040c547b14fbfeda
--- /dev/null
+++ b/data/sampled_jsons/Bansal_FOCS_2010_discrepancy_minimization_year_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bansal - Wikipedia", "date": "", "ddg_snippet": "Bansal Bansal is a surname of Indian origin. Among the Agrawal and Baranwal communities, it is the name of a gotra (patrilineal clan). [1]", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Bansal", "content": "Bansal Bansal is a surname of Indian origin. Among the Agrawal and Baranwal communities, it is the name of a gotra (patrilineal clan). [1]"}
+{"idx": 1, "title": "Sachin Bansal - Wikipedia", "date": "", "ddg_snippet": "Sachin Bansal (born 5 August 1981) is an Indian entrepreneur. [1][2][3] He is best known as the founder of Flipkart [4] During his over 11 year career at Flipkart, Bansal was CEO and chairman. [5]", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Sachin_Bansal", "content": "Sachin Bansal (born 5 August 1981) is an Indian entrepreneur. [1][2][3] He is best known as the founder of Flipkart [4] During his over 11 year career at Flipkart, Bansal was CEO and chairman. [5]"}
+{"idx": 2, "title": "Dr. Alok Bansal , M.D. | Ophthalmology | Sutter Health", "date": "", "ddg_snippet": "Dr. Alok Bansal , M.D. practices Ophthalmology in San Mateo, CA and Daly City, CA", "subpage_snippet": "", "source": "www.sutterhealth.org", "link": "https://www.sutterhealth.org/find-provider/dr-alok-bansal-1046232370", "content": "Dr. Alok Bansal , M.D. practices Ophthalmology in San Mateo, CA and Daly City, CA"}
+{"idx": 3, "title": "Alok Shawn Bansal , MD - Northern California Retina Vitreous...", "date": "", "ddg_snippet": "Dr. Bansal is a Board-Certified Ophthalmologist specializing in all aspects of vitreoretinal diseases. His clinical interests include age-related macular degeneration, diabetic retinopathy, and retinal vein occlusions.", "subpage_snippet": "", "source": "ncrva.com", "link": "https://ncrva.com/ophthalmologist-alok-bansal.php", "content": "Dr. Bansal is a Board-Certified Ophthalmologist specializing in all aspects of vitreoretinal diseases. His clinical interests include age-related macular degeneration, diabetic retinopathy, and retinal vein occlusions."}
+{"idx": 4, "title": "Bansal | Nephrology", "date": "", "ddg_snippet": "She completed her internship and residency in internal medicine at Tufts Medical Center, followed by completing a nephrology fellowship at UCSF. Dr. Bansal ’s clinical and research expertise is in hypertension and the intersection of kidney and heart disease.", "subpage_snippet": "", "source": "nephrology.uw.edu", "link": "https://nephrology.uw.edu/people/faculty/bansal-n", "content": "She completed her internship and residency in internal medicine at Tufts Medical Center, followed by completing a nephrology fellowship at UCSF. Dr. Bansal ’s clinical and research expertise is in hypertension and the intersection of kidney and heart disease."}
+{"idx": 5, "title": "Bansal - Name Meaning and Origin", "date": "", "ddg_snippet": "The surname Bansal is of Indian origin and is commonly found among the Hindu community, particularly in the state of Punjab. It is derived from the Sanskrit word \"bans\" meaning \"bamboo\" and \"al\" meaning \"dweller\" or \"residing in.\"", "subpage_snippet": "", "source": "namediscoveries.com", "link": "https://namediscoveries.com/surnames/bansal", "content": "The surname Bansal is of Indian origin and is commonly found among the Hindu community, particularly in the state of Punjab. It is derived from the Sanskrit word \"bans\" meaning \"bamboo\" and \"al\" meaning \"dweller\" or \"residing in.\""}
+{"idx": 6, "title": "Bansal Surname/Last Name: Meaning, Origin & Family History -...", "date": "", "ddg_snippet": "The meaning of Bansal Indian (northern states): Bania, Jain, and Sikh name, which appears to be related to Sanskrit vamša ‘lineage’, also meaning ‘bamboo’.", "subpage_snippet": "", "source": "discover.23andme.com", "link": "https://discover.23andme.com/last-name/Bansal", "content": "The meaning of Bansal Indian (northern states): Bania, Jain, and Sikh name, which appears to be related to Sanskrit vamša ‘lineage’, also meaning ‘bamboo’."}
+{"idx": 7, "title": "Bansal Academy", "date": "", "ddg_snippet": "Bansal Academy is your key to success in CSIR NET, GATE, TGT, PGT, Master Cadre, and Lecturer Cadre exams. Our top-notch coaching, experienced instructors, and proven track record ensure you excel in these competitive exams.", "subpage_snippet": "", "source": "bansalacademy.com", "link": "https://bansalacademy.com/", "content": "Bansal Academy is your key to success in CSIR NET, GATE, TGT, PGT, Master Cadre, and Lecturer Cadre exams. Our top-notch coaching, experienced instructors, and proven track record ensure you excel in these competitive exams."}
+{"idx": 8, "title": "Pain Rehab of WNY", "date": "", "ddg_snippet": "My pain doctor had suddenly died and I was fortunate enough to set up an appointment with Dr. Bansal . My history was a severe groin injury in 1983 followed by dozens of surgeries through the years and in 1994 I was struck by RSD.", "subpage_snippet": "", "source": "www.painrehabofwny.com", "link": "https://www.painrehabofwny.com/", "content": "My pain doctor had suddenly died and I was fortunate enough to set up an appointment with Dr. Bansal . My history was a severe groin injury in 1983 followed by dozens of surgeries through the years and in 1994 I was struck by RSD."}
+{"idx": 9, "title": "Northern California Retina Vitreous Associates", "date": "", "ddg_snippet": "Mark R. Wieland, MD James D. Palmer, MD J. Luigi Borrillo, MD Rahul N. Khurana, MD Alok Shawn Bansal , MD Louis K. Chang, MD, PhD Jay C. Wang, MD", "subpage_snippet": "", "source": "www.ncrva.com", "link": "https://www.ncrva.com/index.php", "content": "Mark R. Wieland, MD James D. Palmer, MD J. Luigi Borrillo, MD Rahul N. Khurana, MD Alok Shawn Bansal , MD Louis K. Chang, MD, PhD Jay C. Wang, MD"}
diff --git a/data/sampled_jsons/Bayesian_network_structure_learning_problem_type_NP-hard_causal_discovery.jsonl b/data/sampled_jsons/Bayesian_network_structure_learning_problem_type_NP-hard_causal_discovery.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6415d049386324b61928150dbf7b817a5658a461
--- /dev/null
+++ b/data/sampled_jsons/Bayesian_network_structure_learning_problem_type_NP-hard_causal_discovery.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bayesian network structure learning by opposition-based learning", "date": "", "ddg_snippet": "Bayesian network structure learning is a well-known NP-hard problem , and its computation accuracy is still worth being further studied.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-03267-2", "content": "Bayesian network structure learning is a well-known NP-hard problem , and its computation accuracy is still worth being further studied."}
+{"idx": 1, "title": "A survey of Bayesian Network structure learning", "date": "", "ddg_snippet": "However, determining the graphical structure of a BN remains a major challenge, especially when modelling a problem under causal assumptions. Solutions to this problem include the automated discovery of BN graphs from data, constructing them based on expert knowledge, or a combination of the two.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.11415", "content": "However, determining the graphical structure of a BN remains a major challenge, especially when modelling a problem under causal assumptions. Solutions to this problem include the automated discovery of BN graphs from data, constructing them based on expert knowledge, or a combination of the two."}
+{"idx": 2, "title": "A survey of Bayesian Network structure learning | Artificial ...", "date": "", "ddg_snippet": "However, determining the graphical structure of a BN remains a major challenge, especially when modelling a problem under causal assumptions. Solutions to this problem include the automated discovery of BN graphs from data, constructing them based on expert knowledge, or a combination of the two.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-022-10351-w", "content": "However, determining the graphical structure of a BN remains a major challenge, especially when modelling a problem under causal assumptions. Solutions to this problem include the automated discovery of BN graphs from data, constructing them based on expert knowledge, or a combination of the two."}
+{"idx": 3, "title": "Causal Discovery and Reasoning for Continuous Variables with an ...", "date": "", "ddg_snippet": "The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11854740/", "content": "The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous ..."}
+{"idx": 4, "title": "Learning causal Bayesian networks based on causality analysis for ...", "date": "", "ddg_snippet": "Revealing causal information by analyzing purely observational data, known as causal discovery , has drawn much attention. To prove that the causal knowledge mined from data can be applied to facilitate various machine learning tasks (e.g., classification), we propose to measure, describe and evaluate the causalities in the framework of Bayesian network (BN) learning . In this paper, heuristic ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197622002962", "content": "Revealing causal information by analyzing purely observational data, known as causal discovery , has drawn much attention. To prove that the causal knowledge mined from data can be applied to facilitate various machine learning tasks (e.g., classification), we propose to measure, describe and evaluate the causalities in the framework of Bayesian network (BN) learning . In this paper, heuristic ..."}
+{"idx": 5, "title": "PDF BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery", "date": "", "ddg_snippet": "Structure Learning for Bayesian Networks : The field of Bayesian structure learning investigates how to infer the structure of Bayesian networks from data. Learning the structure of graphical models from data is known to be NP-hard [8].", "subpage_snippet": "", "source": "why21.causalai.net", "link": "https://why21.causalai.net/papers/WHY21_33.pdf", "content": "Structure Learning for Bayesian Networks : The field of Bayesian structure learning investigates how to infer the structure of Bayesian networks from data. Learning the structure of graphical models from data is known to be NP-hard [8]."}
+{"idx": 6, "title": "Improving Causal Discovery By Optimal Bayesian Network Learning", "date": "", "ddg_snippet": "It treats causal discovery as a constraint optimiza-tion problem , encodes conditional independence and depen-dence as Boolean variables and formula, and tackles causal discovery with the Boolean satisfiablity solver.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/17059/16866", "content": "It treats causal discovery as a constraint optimiza-tion problem , encodes conditional independence and depen-dence as Boolean variables and formula, and tackles causal discovery with the Boolean satisfiablity solver."}
+{"idx": 7, "title": "PDF Local Structure Discovery in Bayesian Networks", "date": "", "ddg_snippet": "Abstract Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger networks are usually learned with various heuristics. Another approach to scaling up the structure learning is local learning . In local learning , the modeler has one or more target variables that are of special ...", "subpage_snippet": "", "source": "sites.stat.washington.edu", "link": "https://sites.stat.washington.edu/tsr/uai-causal-structure-learning-workshop/papers/niinimaki.pdf", "content": "Abstract Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger networks are usually learned with various heuristics. Another approach to scaling up the structure learning is local learning . In local learning , the modeler has one or more target variables that are of special ..."}
+{"idx": 8, "title": "DAGSLAM: causal Bayesian network structure learning of mixed type data ...", "date": "", "ddg_snippet": "While these methods have been widely used, they often face challenges in scalability and computational efficiency due to the combinatorial nature of the DAG space. Recently, gradient-based DAG learning has emerged as a promising alternative, leveraging continuous optimization techniques to address the NP-hard problem of DAG discovery .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12142989/", "content": "While these methods have been widely used, they often face challenges in scalability and computational efficiency due to the combinatorial nature of the DAG space. Recently, gradient-based DAG learning has emerged as a promising alternative, leveraging continuous optimization techniques to address the NP-hard problem of DAG discovery ."}
+{"idx": 9, "title": "Fast & Efficient Learning of Bayesian Networks from Data: Knowledge ...", "date": "", "ddg_snippet": "Structure learning is essential for Bayesian networks (BNs) as it uncovers causal relationships, and enables knowledge discovery , predictions, inferences, and decision-making under uncertainty. Two novel algorithms, FSBN and SSBN, based on the PC algo-rithm, employ local search strategy and conditional independence tests to learn the causal network structure from data. They incorporate d ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.09222", "content": "Structure learning is essential for Bayesian networks (BNs) as it uncovers causal relationships, and enables knowledge discovery , predictions, inferences, and decision-making under uncertainty. Two novel algorithms, FSBN and SSBN, based on the PC algo-rithm, employ local search strategy and conditional independence tests to learn the causal network structure from data. They incorporate d ..."}
diff --git a/data/sampled_jsons/BdO4R6XxUH_DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Algorithm_1_year_2023.jsonl b/data/sampled_jsons/BdO4R6XxUH_DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Algorithm_1_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fa28eb4ce0fa14e124e70bbe2619b555cadbbf72
--- /dev/null
+++ b/data/sampled_jsons/BdO4R6XxUH_DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Algorithm_1_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material."}
+{"idx": 1, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Dec 16, 2024 · Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.11576", "content": "Dec 16, 2024 · Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes ..."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data -sparse scenarios.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=BdO4R6XxUH&name=pdf", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data -sparse scenarios."}
+{"idx": 3, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models , allowing each image to generate multiple concepts across different granularities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v2", "content": "We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models , allowing each image to generate multiple concepts across different granularities."}
+{"idx": 4, "title": "ICML DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/49558", "content": "Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper"}
+{"idx": 5, "title": "Discovering Fine-Grained Visual-Concept Relations by ...", "date": "", "ddg_snippet": "Concept Bottleneck Models are a family of models [17] that first predict concepts from input images, then use these concepts to predict a downstream label. This design al-lows CBMs to ( 1 ) provide concept -based explanations via their predicted concepts , and (2) improve their test perfor-mance when deployed with experts via concept interven-tions ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Discovering_Fine-Grained_Visual-Concept_Relations_by_Disentangled_Optimal_Transport_Concept_Bottleneck_CVPR_2025_paper.pdf", "content": "Concept Bottleneck Models are a family of models [17] that first predict concepts from input images, then use these concepts to predict a downstream label. This design al-lows CBMs to ( 1 ) provide concept -based explanations via their predicted concepts , and (2) improve their test perfor-mance when deployed with experts via concept interven-tions ..."}
+{"idx": 6, "title": "ICML Poster DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46104", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 7, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/DCBM:-Data-Efficient-Visual-Concept-Bottleneck-Models-4be352f8-e37e-432a-9849-dc4f3c8c586f", "content": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 8, "title": "Margret KEUPER | Professor for Visual Computing | Professor", "date": "", "ddg_snippet": "DCBM : Data - Efficient Visual Concept Bottleneck Models .Unlike diffusion models , AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Margret-Keuper", "content": "DCBM : Data - Efficient Visual Concept Bottleneck Models .Unlike diffusion models , AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains."}
+{"idx": 9, "title": "Language in a Bottle : Language Model Guided Concept Bottlenecks ...", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBM) are inherently inter-pretable models that factor model decisions into human-readable concepts . They allow people to easily understand why a model is failing, a critical feature for high-stakes ap-plications.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Language_in_a_Bottle_Language_Model_Guided_Concept_Bottlenecks_for_CVPR_2023_paper.pdf", "content": "Concept Bottleneck Models (CBM) are inherently inter-pretable models that factor model decisions into human-readable concepts . They allow people to easily understand why a model is failing, a critical feature for high-stakes ap-plications."}
diff --git a/data/sampled_jsons/Beimel_Nissim_Stemmer_2022_differential_privacy_adaptive_queries.jsonl b/data/sampled_jsons/Beimel_Nissim_Stemmer_2022_differential_privacy_adaptive_queries.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c3c8a91181199c0ae721065aea279d5ad551378e
--- /dev/null
+++ b/data/sampled_jsons/Beimel_Nissim_Stemmer_2022_differential_privacy_adaptive_queries.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Differential Privacy for Adaptively Solving Search Problems via...", "date": "", "ddg_snippet": "Adaptive ANN via Differentially Private Selection.In this work, we focus in particular on the adaptive data structures based on differential privacy (Has-sidim et al., 2022 ; Beimel et al., 2022 ; Song et al., 2023b; Cherapanamjeri et al., 2023).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kEn7Wt6Yj2", "content": "Adaptive ANN via Differentially Private Selection.In this work, we focus in particular on the adaptive data structures based on differential privacy (Has-sidim et al., 2022 ; Beimel et al., 2022 ; Song et al., 2023b; Cherapanamjeri et al., 2023)."}
+{"idx": 1, "title": "Sketching Meets Differential Privacy : Fast Algorithm for... | DeepAI", "date": "", "ddg_snippet": "Following the work of [ Beimel , Kaplan, Mansour, Nissim , Saranurak and Stemmer , STOC'22], we use tools from differential privacy to reduce the randomness required by the data structure and further improve the running time.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/sketching-meets-differential-privacy-fast-algorithm-for-dynamic-kronecker-projection-maintenance", "content": "Following the work of [ Beimel , Kaplan, Mansour, Nissim , Saranurak and Stemmer , STOC'22], we use tools from differential privacy to reduce the randomness required by the data structure and further improve the running time."}
+{"idx": 2, "title": "Uri Stemmer 's research works | Tel Aviv University, Tel Aviv...", "date": "", "ddg_snippet": "Differentially - Private Bayes Consistency. Preprint. Dec 2022 .Uri Stemmer . A new line of work, started with Dwork et al., studies the task of answering statistical queries using a sample and relates the problem to the concept of differential privacy .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Uri-Stemmer-2043638417", "content": "Differentially - Private Bayes Consistency. Preprint. Dec 2022 .Uri Stemmer . A new line of work, started with Dwork et al., studies the task of answering statistical queries using a sample and relates the problem to the concept of differential privacy ."}
+{"idx": 3, "title": "Uri Stemmer", "date": "", "ddg_snippet": "Private Everlasting Prediction Moni Naor, Kobbi Nissim , Uri Stemmer , and Chao Yan.On Differential Privacy and Adaptive Data Analysis with Bounded Space Itai Dinur, Uri Stemmer , David P. Woodruff, and Samson Zhou.", "subpage_snippet": "", "source": "uri.co.il", "link": "https://uri.co.il/", "content": "Private Everlasting Prediction Moni Naor, Kobbi Nissim , Uri Stemmer , and Chao Yan.On Differential Privacy and Adaptive Data Analysis with Bounded Space Itai Dinur, Uri Stemmer , David P. Woodruff, and Samson Zhou."}
+{"idx": 4, "title": "Private Learning and Sanitization: Pure vs. Approximate Differential ...", "date": "", "ddg_snippet": "Amos beimel , kobbi nissim , and URI stemmer . 2.1 Differential privacy .A mechanism that permits k adaptive interactions with mechanisms that preserves (ε, δ )- differential privacy (and does not access the database otherwise) ensures (kε, kδ )- differential privacy .", "subpage_snippet": "", "source": "www.theoryofcomputing.org", "link": "https://www.theoryofcomputing.org/articles/v012a001/v012a001.pdf", "content": "Amos beimel , kobbi nissim , and URI stemmer . 2.1 Differential privacy .A mechanism that permits k adaptive interactions with mechanisms that preserves (ε, δ )- differential privacy (and does not access the database otherwise) ensures (kε, kδ )- differential privacy ."}
+{"idx": 5, "title": "Differentially Private Learning of Geometric Concepts | SIAM Journal...", "date": "", "ddg_snippet": "A. Beimel , K. Nissim , and U. Stemmer , Learning privately with labeled and unlabeled examples, in Proceedings of the Symposium on Discrete Algorithms, SIAM, 2015, pp. 461--477.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/21M1406428", "content": "A. Beimel , K. Nissim , and U. Stemmer , Learning privately with labeled and unlabeled examples, in Proceedings of the Symposium on Discrete Algorithms, SIAM, 2015, pp. 461--477."}
+{"idx": 6, "title": "Uri Stemmer", "date": "", "ddg_snippet": "Amos Beimel Iftach Haitner Kobbi Nissim Uri Stemmer . The shuffle model of differential privacy [Bittau et al.EUROCRYPT 2019] was proposed as a viable model for performing distributed differentially private computations.", "subpage_snippet": "", "source": "www.iacr.org", "link": "https://www.iacr.org/cryptodb/data/author.php?authorkey=11485", "content": "Amos Beimel Iftach Haitner Kobbi Nissim Uri Stemmer . The shuffle model of differential privacy [Bittau et al.EUROCRYPT 2019] was proposed as a viable model for performing distributed differentially private computations."}
+{"idx": 7, "title": "Uri Stemmer - Google Akademik", "date": "", "ddg_snippet": "A Beimel , K Nissim , U Stemmer . International Workshop on Approximation Algorithms for Combinatorial …, 2013. 2022 . On the generalization properties of differential privacy .", "subpage_snippet": "", "source": "scholar.google.co.id", "link": "https://scholar.google.co.id/citations?user=Ew7QLzsAAAAJ&hl=tr", "content": "A Beimel , K Nissim , U Stemmer . International Workshop on Approximation Algorithms for Combinatorial …, 2013. 2022 . On the generalization properties of differential privacy ."}
+{"idx": 8, "title": "Private Learning and Sanitization: Pure vs. Approximate Differential ...", "date": "", "ddg_snippet": "Amos Beimel ; Kobbi Nissim ; Uri Stemmer . Publication date. 2014-07-09.We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy .", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/arxiv-1407.2674", "content": "Amos Beimel ; Kobbi Nissim ; Uri Stemmer . Publication date. 2014-07-09.We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy ."}
+{"idx": 9, "title": "Uri Stemmer | Ben-Gurion University of the Negev | 88 Publications", "date": "", "ddg_snippet": "Uri Stemmer is an academic researcher from Ben-Gurion University of the Negev. The author has contributed to research in topics: Differential privacy & Computer science. The author has an hindex of 20, co-authored 71 publications.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/authors/uri-stemmer-2s7a4mnpnn", "content": "Uri Stemmer is an academic researcher from Ben-Gurion University of the Negev. The author has contributed to research in topics: Differential privacy & Computer science. The author has an hindex of 20, co-authored 71 publications."}
diff --git a/data/sampled_jsons/Beimel_et_al._2022_adaptive_optimization_differential_privacy_PDF_year_2022.jsonl b/data/sampled_jsons/Beimel_et_al._2022_adaptive_optimization_differential_privacy_PDF_year_2022.jsonl
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index 0000000000000000000000000000000000000000..f07692dd4f63d561a12ad29906d07d4f68bf3b71
--- /dev/null
+++ b/data/sampled_jsons/Beimel_et_al._2022_adaptive_optimization_differential_privacy_PDF_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Private Adaptive Optimization with Side Information", "date": "", "ddg_snippet": "Adaptive optimization methods have become the default solvers for many machine learning tasks. Unfortunately, the benefits of adaptivity may degrade when training with differential privacy , as the noise added to ensure privacy reduces the effectiveness of the adaptive preconditioner. To this end, we propose AdaDPS, a general framework that uses non-sensitive side information to precondition ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.05963", "content": "Adaptive optimization methods have become the default solvers for many machine learning tasks. Unfortunately, the benefits of adaptivity may degrade when training with differential privacy , as the noise added to ensure privacy reduces the effectiveness of the adaptive preconditioner. To this end, we propose AdaDPS, a general framework that uses non-sensitive side information to precondition ..."}
+{"idx": 1, "title": "Differentially Private Adaptive Optimization with Delayed Preconditioners", "date": "", "ddg_snippet": "While this can boost performance, assuming access to informative public data may be unrealistic in many privacy -sensitive applications. In this work, we instead ask: Can we improve privacy /utility trade-offs in private adaptive optimization without accessing auxiliary data?", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=jKnTWwW8eVN", "content": "While this can boost performance, assuming access to informative public data may be unrealistic in many privacy -sensitive applications. In this work, we instead ask: Can we improve privacy /utility trade-offs in private adaptive optimization without accessing auxiliary data?"}
+{"idx": 2, "title": "Differentially Private Deep Learning With Dynamic Privacy Budget ...", "date": "", "ddg_snippet": "Finally, we integrate the adaptive optimizer into the gradient descent. In addition to improving the model utility, we also leverage the leading Sinh-Normal noise addition mechanism to achieve truncated concentrated differential privacy (tCDP) - as demonstrated by our rigorous analysis.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10179278", "content": "Finally, we integrate the adaptive optimizer into the gradient descent. In addition to improving the model utility, we also leverage the leading Sinh-Normal noise addition mechanism to achieve truncated concentrated differential privacy (tCDP) - as demonstrated by our rigorous analysis."}
+{"idx": 3, "title": "An Adaptive Differential Privacy Method Based on Federated Learning", "date": "", "ddg_snippet": "In 2022 , Chen et al. [10] intro-duced a gradient compression framework based on federated learning with adaptive privacy budget allocation. In this framework, Top-k compression was applied to the client side for data transmission.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.08909", "content": "In 2022 , Chen et al. [10] intro-duced a gradient compression framework based on federated learning with adaptive privacy budget allocation. In this framework, Top-k compression was applied to the client side for data transmission."}
+{"idx": 4, "title": "PDF Advancing Differential Privacy: Where We Are Now and Future Directions ...", "date": "", "ddg_snippet": "This fits into a broader line of work in private ML that makes substitutions to components of traditional nonprivate training pipelines, ranging from activation functions, to pooling functions, and normalization layers (Cheng et al ., 2022 ; Nasirigerdeh et al ., 2023; Papernot et al ., 2021).", "subpage_snippet": "", "source": "s3.amazonaws.com", "link": "https://s3.amazonaws.com/assets.pubpub.org/ki90cxv0c1p0h148qvqipnk3mxcxy2e2.pdf", "content": "This fits into a broader line of work in private ML that makes substitutions to components of traditional nonprivate training pipelines, ranging from activation functions, to pooling functions, and normalization layers (Cheng et al ., 2022 ; Nasirigerdeh et al ., 2023; Papernot et al ., 2021)."}
+{"idx": 5, "title": "Adaptive Differential Privacy for Language Model Training", "date": "", "ddg_snippet": "Abstract Although differential privacy (DP) can protect language models from leaking privacy , its indiscriminative protection on all data points reduces its practical utility. Previous works improve DP training by discriminating privacy and non- privacy data.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.fl4nlp-1.3/", "content": "Abstract Although differential privacy (DP) can protect language models from leaking privacy , its indiscriminative protection on all data points reduces its practical utility. Previous works improve DP training by discriminating privacy and non- privacy data."}
+{"idx": 6, "title": "Adaptive Differential Privacy Based Optimization Scheme for Federated ...", "date": "", "ddg_snippet": "In this paper, we propose a federated learning optimization framework based on adaptive differential privacy (ALDP-FL) that dynamically adjusts privacy protection strategy to tackle key challenges in federated learning, such as accuracy degradation, difficulty in privacy quantification, and high communication overhead.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-80419-9_11", "content": "In this paper, we propose a federated learning optimization framework based on adaptive differential privacy (ALDP-FL) that dynamically adjusts privacy protection strategy to tackle key challenges in federated learning, such as accuracy degradation, difficulty in privacy quantification, and high communication overhead."}
+{"idx": 7, "title": "Adaptive Differential Privacy Based Optimization Scheme for ... - Springer", "date": "", "ddg_snippet": "For the privacy and security issues of federated learning, some research proposed solutions of potential security issues in FL to protect privacy information such as encryp-tion, noise, etc. Meanwhile, lots of optimization solutions are proposed for FL. ABADI et al. [7] proposed a stochastic gradient descent algorithm in differential privacy which added noise to the gradient of each data batch ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-80419-9_11", "content": "For the privacy and security issues of federated learning, some research proposed solutions of potential security issues in FL to protect privacy information such as encryp-tion, noise, etc. Meanwhile, lots of optimization solutions are proposed for FL. ABADI et al. [7] proposed a stochastic gradient descent algorithm in differential privacy which added noise to the gradient of each data batch ..."}
+{"idx": 8, "title": "Adaptive Differential Privacy Algorithm for Federated Learning on Small ...", "date": "", "ddg_snippet": "We address the challenge of enhancing federated learning with small datasets by proposing an adaptive differential privacy technique and a dynamic learning rate optimization algorithm. Our method adds adaptive noise to protect data privacy and uses historical momentum information to improve stability and convergence speed. Extensive experiments demonstrate that our approach significantly ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10671274", "content": "We address the challenge of enhancing federated learning with small datasets by proposing an adaptive differential privacy technique and a dynamic learning rate optimization algorithm. Our method adds adaptive noise to protect data privacy and uses historical momentum information to improve stability and convergence speed. Extensive experiments demonstrate that our approach significantly ..."}
+{"idx": 9, "title": "PDF Fully-Adaptive Composition in Differential Privacy", "date": "", "ddg_snippet": "They defined two probabilistic objects to measure privacy in adaptive composition: pri-vacy filters, which provide differential privacy guarantees for composed interactions, and pri-vacy odometers, time-uniform bounds on privacy loss. There are substantial gaps between advanced composition and existing filters and odometers.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/whitehouse23a/whitehouse23a.pdf", "content": "They defined two probabilistic objects to measure privacy in adaptive composition: pri-vacy filters, which provide differential privacy guarantees for composed interactions, and pri-vacy odometers, time-uniform bounds on privacy loss. There are substantial gaps between advanced composition and existing filters and odometers."}
diff --git a/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target.jsonl b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target.jsonl
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index 0000000000000000000000000000000000000000..8ff37011ef19cb675bbec0e604438c8ee6235974
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Kernel (operating system) - Wikipedia", "date": "", "ddg_snippet": "For example, to show the user something on the screen, an application would make a request to the kernel , which would forward the request to its display driver , which is then responsible for actually plotting the character/pixel.[6]. A kernel must maintain a list of available devices.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Kernel_(operating_system)", "content": "For example, to show the user something on the screen, an application would make a request to the kernel , which would forward the request to its display driver , which is then responsible for actually plotting the character/pixel.[6]. A kernel must maintain a list of available devices."}
+{"idx": 1, "title": "Beyond Self - Repellent Kernels : History - Driven Target Towards...", "date": "", "ddg_snippet": "In this paper, we propose a history - driven target (HDT) framework for MCMC sampling on general graphs. By embedding self -repellency in the target rather than the transition kernel , HDT maintains unbiased sampling with a lightweight design and provides an.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "In this paper, we propose a history - driven target (HDT) framework for MCMC sampling on general graphs. By embedding self -repellency in the target rather than the transition kernel , HDT maintains unbiased sampling with a lightweight design and provides an."}
+{"idx": 2, "title": "An invariance principle for the 2d weakly self - repelling Brownian...", "date": "", "ddg_snippet": "The first instances of self repelling motion date back to the early eighties [2], when physicists introduced the “true” self-avoiding walk (TSAW) as a model capturing the statistics of a growing polymer.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00440-025-01363-y", "content": "The first instances of self repelling motion date back to the early eighties [2], when physicists introduced the “true” self-avoiding walk (TSAW) as a model capturing the statistics of a growing polymer."}
+{"idx": 3, "title": "Jie Hu - Google Scholar", "date": "", "ddg_snippet": "Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Nonlinear MCMC on General Graphs.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=8uBqtwEAAAAJ&hl=en", "content": "Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Nonlinear MCMC on General Graphs."}
+{"idx": 4, "title": "Do Young Eun", "date": "", "ddg_snippet": "Jie Hu, Yi-Ting Ma, and Do Young Eun, “ Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Nonlinear MCMC on General Graphs“, International Conference on Machine Learning (ICML), Vancouver, Canada, July 2025 (Oral Presentation)...", "subpage_snippet": "", "source": "dyeun.wordpress.ncsu.edu", "link": "https://dyeun.wordpress.ncsu.edu/", "content": "Jie Hu, Yi-Ting Ma, and Do Young Eun, “ Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Nonlinear MCMC on General Graphs“, International Conference on Machine Learning (ICML), Vancouver, Canada, July 2025 (Oral Presentation)..."}
+{"idx": 5, "title": "ICML 2025 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Nonlinear MCMC on General Graphs. OR.Efficient Graph Continual Learning via Lightweight Graph Neural Tangent Kernels -based Dataset Distillation.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/icml-paper-list/icml-2025-paper-list/", "content": "Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Nonlinear MCMC on General Graphs. OR.Efficient Graph Continual Learning via Lightweight Graph Neural Tangent Kernels -based Dataset Distillation."}
+{"idx": 6, "title": "dblp: List of computer science publications by Do Young Eun", "date": "", "ddg_snippet": "Jie Hu, Yi-Ting Ma, Do Young Eun: Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Nonlinear MCMC on General Graphs.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/65/2878.html", "content": "Jie Hu, Yi-Ting Ma, Do Young Eun: Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Nonlinear MCMC on General Graphs."}
+{"idx": 7, "title": "41846 PDFs | Review articles in RANDOM WALKS", "date": "", "ddg_snippet": "Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Nonlinear MCMC on General Graphs.The escalating urban heat, driven by climate change and urbanization, poses significant threats to residents’ health and urban climate resilience.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Random-Walks/publications", "content": "Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Nonlinear MCMC on General Graphs.The escalating urban heat, driven by climate change and urbanization, poses significant threats to residents’ health and urban climate resilience."}
+{"idx": 8, "title": "kaixuan zhang - Video Processing Engineer at Zoom | LinkedIn", "date": "", "ddg_snippet": "Excited to announce that our paper, “ Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Non-linear MCMC on General Graphs,” has…", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/kaixuan-zhang-731a16159", "content": "Excited to announce that our paper, “ Beyond Self - Repellent Kernels : History - Driven Target Towards Efficient Non-linear MCMC on General Graphs,” has…"}
+{"idx": 9, "title": "Debt Rattle September 18 2025 - The Automatic Earth", "date": "", "ddg_snippet": "I have made the decision not to cover any of the controversial aspects, direct or ancillary, beyond what I have already shared. Do I have opinions, yes.Patel was clear: “Nobody is targeted for their faith.” Jordan followed up: “Is the FBI still targeting Americans who are pro-life?”", "subpage_snippet": "", "source": "www.theautomaticearth.com", "link": "https://www.theautomaticearth.com/2025/09/debt-rattle-september-18-2025/", "content": "I have made the decision not to cover any of the controversial aspects, direct or ancillary, beyond what I have already shared. Do I have opinions, yes.Patel was clear: “Nobody is targeted for their faith.” Jordan followed up: “Is the FBI still targeting Americans who are pro-life?”"}
diff --git a/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_LRU_cache_MCMC.jsonl b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_LRU_cache_MCMC.jsonl
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+{"idx": 0, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ... Our paper on efficient MCMC on graphs accepted at ICML 2025 Track: Oral 5C Probablistic Models - icml.cc Modeling Cache Performance Beyond LRU Self-Repellent Random Walks on General Graphs - OpenReview dblp: Beyond Self-Repellent Kernels: History-Driven Target ... Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of 論文の概要: Beyond Self-Repellent Kernels: History-Driven Target ...", "date": "", "ddg_snippet": "May 23, 2025 · We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ... Excited to announce that our paper, “ Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Non-linear MCMC on General Graphs,” has been chosen for an 𝗢𝗿𝗮𝗹 ... We propose a * history-driven target (HDT)* framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution b o l d s y m b o l m u. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW ... We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Apr 24, 2023 · We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures. Given any Markov chain corresponding to a target probability distribution, we design a * self-repellent random ... Bibliographic details on Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs. What is a cache model in LRU? r = (In LRU , the oldest age distribution uses 1.) The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. Do modern processors use high-performance cache replacement policies? Modern processors use high-performance cache replacement policies that outperform traditional alternatives like least-recently used (LRU). Unfortunately, current cache models do not capture these high-performance policies as most use stack distances, which are inherently tied to or its vari- LRU ants. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. Why do we model a random-candidates cache? In return, this assump-tion greatly simplifies the probability calculations , allowing a simple model to capture diverse access patterns. Similarly, we model an idealized, random-candidates cache, where replacement candidates are drawn at random from cached lines. What are the implications of cache modeling? For the purposes of cache modeling, there are two impor-tant implications. First, private caches capture most “hot” data , so the LLC’s access stream is stripped of short-term tem-poral correlations: a second access to the same line requires that the line be first evicted from the L1 and L2. Jul 29, 2025 · This design preserves lightweight implementation by requiring only local information between the current and proposed states and achieves compatibility with both reversible and non-reversible MCMC samplers, while retaining unbiased samples with target distribution $\\boldsymbol {\\mu}$ and near-zero variance performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "content": "May 23, 2025 · We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ... Excited to announce that our paper, “ Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Non-linear MCMC on General Graphs,” has been chosen for an 𝗢𝗿𝗮𝗹 ... We propose a * history-driven target (HDT)* framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution b o l d s y m b o l m u. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW ... We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Apr 24, 2023 · We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures. Given any Markov chain corresponding to a target probability distribution, we design a * self-repellent random ... Bibliographic details on Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs. What is a cache model in LRU? r = (In LRU , the oldest age distribution uses 1.) The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. Do modern processors use high-performance cache replacement policies? Modern processors use high-performance cache replacement policies that outperform traditional alternatives like least-recently used (LRU). Unfortunately, current cache models do not capture these high-performance policies as most use stack distances, which are inherently tied to or its vari- LRU ants. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. Why do we model a random-candidates cache? In return, this assump-tion greatly simplifies the probability calculations , allowing a simple model to capture diverse access patterns. Similarly, we model an idealized, random-candidates cache, where replacement candidates are drawn at random from cached lines. What are the implications of cache modeling? For the purposes of cache modeling, there are two impor-tant implications. First, private caches capture most “hot” data , so the LLC’s access stream is stripped of short-term tem-poral correlations: a second access to the same line requires that the line be first evicted from the L1 and L2. Jul 29, 2025 · This design preserves lightweight implementation by requiring only local information between the current and proposed states and achieves compatibility with both reversible and non-reversible MCMC samplers, while retaining unbiased samples with target distribution $\\boldsymbol {\\mu}$ and near-zero variance performance."}
+{"idx": 1, "title": "Our paper on efficient MCMC on graphs accepted at ICML 2025", "date": "", "ddg_snippet": "Excited to announce that our paper, “ Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Non-linear MCMC on General Graphs,” has been chosen for an 𝗢𝗿𝗮𝗹 ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/jie-hu-ncsu_icml2025-mcmc-activity-7337284173516210176-FGlQ", "content": "Excited to announce that our paper, “ Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Non-linear MCMC on General Graphs,” has been chosen for an 𝗢𝗿𝗮𝗹 ..."}
+{"idx": 2, "title": "Modeling Cache Performance Beyond LRU Self-Repellent Random Walks on General Graphs - OpenReview dblp: Beyond Self-Repellent Kernels: History-Driven Target ... Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of 論文の概要: Beyond Self-Repellent Kernels: History-Driven Target ...", "date": "", "ddg_snippet": "We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Apr 24, 2023 · We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures. Given any Markov chain corresponding to a target probability distribution, we design a * self-repellent random ... Bibliographic details on Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs. What is a cache model in LRU? r = (In LRU , the oldest age distribution uses 1.) The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. Do modern processors use high-performance cache replacement policies? Modern processors use high-performance cache replacement policies that outperform traditional alternatives like least-recently used (LRU). Unfortunately, current cache models do not capture these high-performance policies as most use stack distances, which are inherently tied to or its vari- LRU ants. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. Why do we model a random-candidates cache? In return, this assump-tion greatly simplifies the probability calculations , allowing a simple model to capture diverse access patterns. Similarly, we model an idealized, random-candidates cache, where replacement candidates are drawn at random from cached lines. What are the implications of cache modeling? For the purposes of cache modeling, there are two impor-tant implications. First, private caches capture most “hot” data , so the LLC’s access stream is stripped of short-term tem-poral correlations: a second access to the same line requires that the line be first evicted from the L1 and L2. Jul 29, 2025 · This design preserves lightweight implementation by requiring only local information between the current and proposed states and achieves compatibility with both reversible and non-reversible MCMC samplers, while retaining unbiased samples with target distribution $\\boldsymbol {\\mu}$ and near-zero variance performance.", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/sanchez/papers/2016.model.hpca.pdf", "content": "We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Apr 24, 2023 · We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures. Given any Markov chain corresponding to a target probability distribution, we design a * self-repellent random ... Bibliographic details on Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs. What is a cache model in LRU? r = (In LRU , the oldest age distribution uses 1.) The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. Do modern processors use high-performance cache replacement policies? Modern processors use high-performance cache replacement policies that outperform traditional alternatives like least-recently used (LRU). Unfortunately, current cache models do not capture these high-performance policies as most use stack distances, which are inherently tied to or its vari- LRU ants. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. Why do we model a random-candidates cache? In return, this assump-tion greatly simplifies the probability calculations , allowing a simple model to capture diverse access patterns. Similarly, we model an idealized, random-candidates cache, where replacement candidates are drawn at random from cached lines. What are the implications of cache modeling? For the purposes of cache modeling, there are two impor-tant implications. First, private caches capture most “hot” data , so the LLC’s access stream is stripped of short-term tem-poral correlations: a second access to the same line requires that the line be first evicted from the L1 and L2. Jul 29, 2025 · This design preserves lightweight implementation by requiring only local information between the current and proposed states and achieves compatibility with both reversible and non-reversible MCMC samplers, while retaining unbiased samples with target distribution $\\boldsymbol {\\mu}$ and near-zero variance performance."}
+{"idx": 3, "title": "Self-Repellent Random Walks on General Graphs - OpenReview", "date": "", "ddg_snippet": "Apr 24, 2023 · We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures. Given any Markov chain corresponding to a target probability distribution, we design a * self-repellent random ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=450iImFM4U", "content": "Apr 24, 2023 · We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures. Given any Markov chain corresponding to a target probability distribution, we design a * self-repellent random ..."}
+{"idx": 4, "title": "dblp: Beyond Self-Repellent Kernels: History-Driven Target ...", "date": "", "ddg_snippet": "Bibliographic details on Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2505-18300", "content": "Bibliographic details on Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs."}
+{"idx": 5, "title": "論文の概要: Beyond Self-Repellent Kernels: History-Driven Target ...", "date": "", "ddg_snippet": "Jul 29, 2025 · This design preserves lightweight implementation by requiring only local information between the current and proposed states and achieves compatibility with both reversible and non-reversible MCMC samplers, while retaining unbiased samples with target distribution $\\boldsymbol {\\mu}$ and near-zero variance performance.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2505.18300v3", "content": "Jul 29, 2025 · This design preserves lightweight implementation by requiring only local information between the current and proposed states and achieves compatibility with both reversible and non-reversible MCMC samplers, while retaining unbiased samples with target distribution $\\boldsymbol {\\mu}$ and near-zero variance performance."}
+{"idx": 6, "title": "Beyond Self - Repellent Kernels : History - Driven Target Towards...", "date": "", "ddg_snippet": "Scalable Implementation for Large Graphs: We implement an LRU ( Least Recently Used ) cache scheme to manage. 𝐱{\\mathbf{x}}bold_x. with limited memory.In this paper, we propose a history - driven target (HDT) framework for MCMC sampling on general graphs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "Scalable Implementation for Large Graphs: We implement an LRU ( Least Recently Used ) cache scheme to manage. 𝐱{\\mathbf{x}}bold_x. with limited memory.In this paper, we propose a history - driven target (HDT) framework for MCMC sampling on general graphs."}
+{"idx": 7, "title": "Trump’s new detention policy targets millions of immigrants. - POLITICO", "date": "", "ddg_snippet": "Under this provision of the law, long-standing regulations permit those targeted for detention to challenge the move in immigration court — a distinct, executive branch-run network of courts meant to handle deportation matters.", "subpage_snippet": "", "source": "www.politico.com", "link": "https://www.politico.com/news/2025/09/20/ice-detention-immigration-policy-00573850", "content": "Under this provision of the law, long-standing regulations permit those targeted for detention to challenge the move in immigration court — a distinct, executive branch-run network of courts meant to handle deportation matters."}
+{"idx": 8, "title": "Issue 46622: Add an async variant of lru _ cache for... - Python tracker", "date": "", "ddg_snippet": "For non-async uses , overloading the current lru _ cache makes it confusing to reason about. It becomes harder to describe clearly what the caches do or to show a pure python equivalent.", "subpage_snippet": "", "source": "bugs.python.org", "link": "https://bugs.python.org/issue46622", "content": "For non-async uses , overloading the current lru _ cache makes it confusing to reason about. It becomes harder to describe clearly what the caches do or to show a pure python equivalent."}
+{"idx": 9, "title": "\"It's the best gun in the world\": Norway buys K9 self -propelled h...", "date": "", "ddg_snippet": "On September 18, South Korean company Hanwha Aerospace signed a contract with the Norwegian Ministry of Defense for the delivery of 24 additional K9 VIDAR self -propelled howitzers by 2027.", "subpage_snippet": "", "source": "en.topwar.ru", "link": "https://en.topwar.ru/271247-jeto-luchshaja-pushka-v-mire-norvegija-zakupaet-samohodnye-gaubicy-k9.html", "content": "On September 18, South Korean company Hanwha Aerospace signed a contract with the Norwegian Ministry of Defense for the delivery of 24 additional K9 VIDAR self -propelled howitzers by 2027."}
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index 0000000000000000000000000000000000000000..c1fb1c7b36a0e98b980431a02a0c43cd6cbab69b
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cache (computing) - Wikipedia", "date": "", "ddg_snippet": "Diagram of a CPU memory cache operation. In computing, a cache is a hardware or software component that stores data so that future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation o...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cache_(computing)", "content": "Diagram of a CPU memory cache operation. In computing, a cache is a hardware or software component that stores data so that future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation o..."}
+{"idx": 1, "title": "Beyond Self - Repellent Kernels : History - Driven Target Towards...", "date": "", "ddg_snippet": "Methods like the Self - Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History - Driven Target (HDT) framework...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0yzOEMbShU", "content": "Methods like the Self - Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History - Driven Target (HDT) framework..."}
+{"idx": 2, "title": "LRU Cache - LeetCode", "date": "", "ddg_snippet": "Can you solve this real interview question? LRU Cache - Design a data structure that follows the constraints of a Least Recently Used ( LRU ) cache [https://en.wikipedia.org/wiki/ Cache _replacement_policies# LRU ]. Implement the...", "subpage_snippet": "", "source": "leetcode.com", "link": "https://leetcode.com/problems/lru-cache/", "content": "Can you solve this real interview question? LRU Cache - Design a data structure that follows the constraints of a Least Recently Used ( LRU ) cache [https://en.wikipedia.org/wiki/ Cache _replacement_policies# LRU ]. Implement the..."}
+{"idx": 3, "title": "LRU Cache - Complete Tutorial - GeeksforGeeks", "date": "", "ddg_snippet": "LRUCache (Capacity c): Initialize LRU cache with positive size capacity c. get (key) : Returns the value of key ' k' if it is present in the cache otherwise it returns -1. Also updates the priority of data in the LRU cache .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/system-design/lru-cache-implementation/", "content": "LRUCache (Capacity c): Initialize LRU cache with positive size capacity c. get (key) : Returns the value of key ' k' if it is present in the cache otherwise it returns -1. Also updates the priority of data in the LRU cache ."}
+{"idx": 4, "title": "Mastering the LRU Cache Interview Question... | Stackademic", "date": "", "ddg_snippet": "🔎 Why LRU Cache Is a Popular Interview QuestionReal-World Relevance → Used in browsers, databases, and operating systems....if key not in self . cache : return -1 node = self . cache [key] self ._remove(node) self ._add_front...", "subpage_snippet": "", "source": "blog.stackademic.com", "link": "https://blog.stackademic.com/mastering-the-lru-cache-interview-question-java-c-python-implementations-with-real-world-475ba5e58011", "content": "🔎 Why LRU Cache Is a Popular Interview QuestionReal-World Relevance → Used in browsers, databases, and operating systems....if key not in self . cache : return -1 node = self . cache [key] self ._remove(node) self ._add_front..."}
+{"idx": 5, "title": "Implementing LRU cache using std::map and std::list... - Stack Overflow", "date": "", "ddg_snippet": "The cache uses Least Recently Used policy explained by the behaviour: If the cache has a capacity to store 5 keys like 5 3 2 1 4 then if next key=1 comes as a hit, the cache order becomes 1 5 3 2 4.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/79765807/implementing-lru-cache-using-stdmap-and-stdlist-in-c-cant-get-required-o", "content": "The cache uses Least Recently Used policy explained by the behaviour: If the cache has a capacity to store 5 keys like 5 3 2 1 4 then if next key=1 comes as a hit, the cache order becomes 1 5 3 2 4."}
+{"idx": 6, "title": "Как предотвратить повторное вычисление функции с lru _ cache", "date": "", "ddg_snippet": "Как работает Least Recently Used ( LRU ) алгоритм. Параметры функции lru _ cache .Если экземпляр постоянно обращается к этому методу, то после декорирования cached _property будет возвращаться значение из кэша. class A: def __init__( self , data)", "subpage_snippet": "", "source": "python-school.ru", "link": "https://python-school.ru/blog/python/lru_cache/", "content": "Как работает Least Recently Used ( LRU ) алгоритм. Параметры функции lru _ cache .Если экземпляр постоянно обращается к этому методу, то после декорирования cached _property будет возвращаться значение из кэша. class A: def __init__( self , data)"}
+{"idx": 7, "title": "lru - cache - npm", "date": "", "ddg_snippet": "Start using lru - cache in your project by running `npm i lru - cache `.However, note that using some of the features will necessarily impact performance, by causing the cache to have to do more work. See the \"Performance\" section below. Installation. npm install lru - cache --save. Usage.", "subpage_snippet": "", "source": "www.npmjs.com", "link": "https://www.npmjs.com/package/lru-cache", "content": "Start using lru - cache in your project by running `npm i lru - cache `.However, note that using some of the features will necessarily impact performance, by causing the cache to have to do more work. See the \"Performance\" section below. Installation. npm install lru - cache --save. Usage."}
+{"idx": 8, "title": "МНЕ СТЫДНО. №16 НЕЛЬЗЯ РЕШАТЬ lru _ cache /setrecursionlimit.", "date": "", "ddg_snippet": "Подпишись для открытых стримов с разборами всех заданий ЕГЭ!Посмотреть, какие доступны закрытые курсы в боте: https://t.me/rodya_inf_botКанал в телеге с анон...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=CkeevFkVntQ", "content": "Подпишись для открытых стримов с разборами всех заданий ЕГЭ!Посмотреть, какие доступны закрытые курсы в боте: https://t.me/rodya_inf_botКанал в телеге с анон..."}
+{"idx": 9, "title": "functools — Higher-order functions and operations on callable objects", "date": "", "ddg_snippet": "@ lru _ cache def count_vowels(sentence): return sum(sentence.count(vowel) for vowel in 'AEIOUaeiou'). If maxsize is set to None, the LRU feature is disabled and the cache can grow without bound.", "subpage_snippet": "", "source": "docs.python.org", "link": "https://docs.python.org/3/library/functools.html", "content": "@ lru _ cache def count_vowels(sentence): return sum(sentence.count(vowel) for vowel in 'AEIOUaeiou'). If maxsize is set to None, the LRU feature is disabled and the cache can grow without bound."}
diff --git a/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_Towards_Efficient_Nonlinear_MCMC_on_General_Grap.jsonl b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_Towards_Efficient_Nonlinear_MCMC_on_General_Grap.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..29814221e78ede9eb1657bf2fabefd32514b3cb4
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_Towards_Efficient_Nonlinear_MCMC_on_General_Grap.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "by J Hu · 2025 — We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "content": "by J Hu · 2025 — We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces."}
+{"idx": 1, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ..."}
+{"idx": 2, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46659", "content": "We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state spaces, ..."}
+{"idx": 3, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "27 Jul 2025 — We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "27 Jul 2025 — We propose a history - driven target (HDT) framework in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on discrete state ..."}
+{"idx": 4, "title": "Beyond Self-Repellent Kernels: History-Driven Target ...", "date": "", "ddg_snippet": "9 Jun 2025 — Adaptive MCMC with history - driven targeting achieves: 40% faster convergence compared to self - repelling random walks; Better coverage of graph ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-self-repellent-kernels-history-driven-target", "content": "9 Jun 2025 — Adaptive MCMC with history - driven targeting achieves: 40% faster convergence compared to self - repelling random walks; Better coverage of graph ..."}
+{"idx": 5, "title": "History-Driven Target Towards Efficient Nonlinear MCMC on ...", "date": "", "ddg_snippet": "We propose a history-driven target (HDT) framework in Mar kov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/es/paper/141994", "content": "We propose a history-driven target (HDT) framework in Mar kov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as ..."}
+{"idx": 6, "title": "[PDF] Accelerating Distributed Stochastic Optimization via Self ...", "date": "", "ddg_snippet": "DOI:10.48550/arXiv.2401.09665; Corpus ID: 267034636 ... Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/e3acbe11a15fb1aa6dff3148ad01b77bc462976b", "content": "DOI:10.48550/arXiv.2401.09665; Corpus ID: 267034636 ... Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs ."}
+{"idx": 7, "title": "Do Young Eun", "date": "", "ddg_snippet": "Jie Hu, Yi-Ting Ma, and Do Young Eun, “Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs “, International ...", "subpage_snippet": "", "source": "dyeun.wordpress.ncsu.edu", "link": "https://dyeun.wordpress.ncsu.edu/", "content": "Jie Hu, Yi-Ting Ma, and Do Young Eun, “Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs “, International ..."}
+{"idx": 8, "title": "Yi-Ting Ma - Google 學術搜尋", "date": "", "ddg_snippet": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs . J Hu, YT Ma, DY Eun. arXiv preprint arXiv:2505.18300, ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=pIN9Lr0AAAAJ&hl=zh-TW", "content": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs . J Hu, YT Ma, DY Eun. arXiv preprint arXiv:2505.18300, ..."}
+{"idx": 9, "title": "Publications | Do Young Eun", "date": "", "ddg_snippet": "Vishwaraj Doshi, Jie Hu, and Do Young Eun, “ Self - Repellent Random Walks on General Graphs – Achieving Minimal Sampling Variance via Nonlinear Markov Chains ...", "subpage_snippet": "", "source": "dyeun.wordpress.ncsu.edu", "link": "https://dyeun.wordpress.ncsu.edu/publications/", "content": "Vishwaraj Doshi, Jie Hu, and Do Young Eun, “ Self - Repellent Random Walks on General Graphs – Achieving Minimal Sampling Variance via Nonlinear Markov Chains ..."}
diff --git a/data/sampled_jsons/Blink_of_an_eye_feature_localization_generative_models_GitHub_repository.jsonl b/data/sampled_jsons/Blink_of_an_eye_feature_localization_generative_models_GitHub_repository.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3927732d277659b96d17307d46f1164f8875ef04
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_eye_feature_localization_generative_models_GitHub_repository.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Blink of an eye : a simple theory for feature localization ...", "date": "", "ddg_snippet": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow “critical windows” of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45312", "content": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow “critical windows” of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon."}
+{"idx": 1, "title": "Blink of an eye : a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "OverviewResearch explores how generative AI models learn specific features during trainingIntroduces \" blink of an eye \" theory to explain rapid feature emergence", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/blink-eye-simple-theory-feature-localization-generative", "content": "OverviewResearch explores how generative AI models learn specific features during trainingIntroduces \" blink of an eye \" theory to explain rapid feature emergence"}
+{"idx": 2, "title": "(PDF) Blink of an eye : a simple theory for feature localization in...", "date": "", "ddg_snippet": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow ``critical windows'' of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow ``critical windows'' of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon."}
+{"idx": 3, "title": "gen_l10n/app_ localizations .dart` is not recognized · Issue #120561...", "date": "", "ddg_snippet": "GitHub Models New. Manage and compare prompts.I got app_ localizations .dart generated , but import of gen_l10n/app_ localizations .dart is not recognized.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/flutter/flutter/issues/120561", "content": "GitHub Models New. Manage and compare prompts.I got app_ localizations .dart generated , but import of gen_l10n/app_ localizations .dart is not recognized."}
+{"idx": 4, "title": "Flutter gen-l10n localization . Can't import 'package... - Stack Overfl...", "date": "", "ddg_snippet": "I am writing flutter application with localization support. It worked locally, I generated code using flutter gen-l10n and was able to use import 'package:flutter_gen/l10n/app_ localizations .dart'; . Then I decided to automate code compilation using github actions...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/76905937/flutter-gen-l10n-localization-cant-import-packageflutter-gen-l10n-app-locali", "content": "I am writing flutter application with localization support. It worked locally, I generated code using flutter gen-l10n and was able to use import 'package:flutter_gen/l10n/app_ localizations .dart'; . Then I decided to automate code compilation using github actions..."}
+{"idx": 5, "title": "Open closed eyes in photos with AI | Online Free", "date": "", "ddg_snippet": "Transform photos with closed eyes into natural-looking open eyes in seconds. Fix blinked photos and save your precious memories with our intelligent photo enhancement tool.", "subpage_snippet": "", "source": "enhance.cam", "link": "https://enhance.cam/tools/open-closed-eyes-photo", "content": "Transform photos with closed eyes into natural-looking open eyes in seconds. Fix blinked photos and save your precious memories with our intelligent photo enhancement tool."}
+{"idx": 6, "title": "Why doesn't Flutter generate localization files?", "date": "", "ddg_snippet": "Learn why Flutter may not generate the necessary localization files and how configuration issues with gen_l10n and cache problems with Flutter Intl are among the common reasons.", "subpage_snippet": "", "source": "localizely.com", "link": "https://localizely.com/i18n-questions/flutter/why-does-not-flutter-generate-localization-files/", "content": "Learn why Flutter may not generate the necessary localization files and how configuration issues with gen_l10n and cache problems with Flutter Intl are among the common reasons."}
+{"idx": 7, "title": "Learning Localized Generative Models for... | Papers With Code", "date": "", "ddg_snippet": "This paper studies the unsupervised problem of a generative model exploiting graph convolution.The proposed architecture learns to generate localized features that approximate graph embeddings of the output geometry.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/learning-localized-generative-models-for-3d", "content": "This paper studies the unsupervised problem of a generative model exploiting graph convolution.The proposed architecture learns to generate localized features that approximate graph embeddings of the output geometry."}
+{"idx": 8, "title": "Face Swap Online Free", "date": "", "ddg_snippet": "Join the latest social media craze by participating in the TikTok, Snapchat, and Instagram gender swap trend. This fun and creative trend allows you to explore a different side of yourself by swapping genders in your photos, offering a fresh and eye -catching way to update your online persona.", "subpage_snippet": "", "source": "remaker.ai", "link": "https://remaker.ai/face-swap-free/", "content": "Join the latest social media craze by participating in the TikTok, Snapchat, and Instagram gender swap trend. This fun and creative trend allows you to explore a different side of yourself by swapping genders in your photos, offering a fresh and eye -catching way to update your online persona."}
+{"idx": 9, "title": "Nano Banana Free - Revolutionary AI Image Generation Model by...", "date": "", "ddg_snippet": "Discover nano banana free model 's groundbreaking character consistency technology. Unlike traditional AI image generators, nano banana ensures identical facial features , clothing details, and character elements across multiple generations .", "subpage_snippet": "", "source": "www.lovart.ai", "link": "https://www.lovart.ai/tools/nano-banana-free", "content": "Discover nano banana free model 's groundbreaking character consistency technology. Unlike traditional AI image generators, nano banana ensures identical facial features , clothing details, and character elements across multiple generations ."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_conclusion_limitations_future_work_side-chain_year_2024.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_conclusion_limitations_future_work_side-chain_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d9773a93fead6f7a14d567fc90dcc7e7d0318113
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_conclusion_limitations_future_work_side-chain_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "B -a Inverse Folding Model As a Predictor of Mutational Effects on ...", "date": "", "ddg_snippet": "In this work , we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models . However, the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "In this work , we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models . However, the ..."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ... - GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 2, "title": "B -a Inverse Folding Model As a Predictor of Mutational Effects on ...", "date": "", "ddg_snippet": "existing methods focus on pre-training, while neglecting the importance of alignment. In this work , we propose Boltzmann Alignment technique o transfer knowledge from pre-trained inverse folding models to prediction of ∆∆G. We begin by analyzing the thermodynamic definition of ∆∆G and introducing t", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "existing methods focus on pre-training, while neglecting the importance of alignment. In this work , we propose Boltzmann Alignment technique o transfer knowledge from pre-trained inverse folding models to prediction of ∆∆G. We begin by analyzing the thermodynamic definition of ∆∆G and introducing t"}
+{"idx": 3, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 4, "title": "Portal Weekly #67: MoML 2024, data-driven discovery, boltzmann-aligned ...", "date": "", "ddg_snippet": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Predicting changes in binding free energy (∆∆G) is essential for understanding and modifying protein-protein interactions, which are important in drug design.", "subpage_snippet": "", "source": "m2d2.substack.com", "link": "https://m2d2.substack.com/p/portal-weekly-67-moml-2024-data-driven", "content": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Predicting changes in binding free energy (∆∆G) is essential for understanding and modifying protein-protein interactions, which are important in drug design."}
+{"idx": 5, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values."}
+{"idx": 6, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Due to the scarcity of experimental ΔΔG data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work , we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to ΔΔG prediction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "Due to the scarcity of experimental ΔΔG data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work , we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to ΔΔG prediction."}
+{"idx": 7, "title": "dblp: Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Bibliographic details on Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/JiaoMJY0S25", "content": "Bibliographic details on Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions."}
+{"idx": 8, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "直接估计条件概率是比较困难的，因为直接通过序列生成结构的模型，通常不是预测一个状态的概率，而是在预测各种扭转角，这个状态空间就太大了。而一些概率生成模型，又是在估计条件概率的梯度 ∇_x\\log p (X|S) ，并不能直接使用。因此，让我们思考一下， 直接估计 P (X|S) 很困难，但是因为 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "直接估计条件概率是比较困难的，因为直接通过序列生成结构的模型，通常不是预测一个状态的概率，而是在预测各种扭转角，这个状态空间就太大了。而一些概率生成模型，又是在估计条件概率的梯度 ∇_x\\log p (X|S) ，并不能直接使用。因此，让我们思考一下， 直接估计 P (X|S) 很困难，但是因为 ..."}
+{"idx": 9, "title": "BA-DDG/README.md at master · aim-uofa/BA-DDG · GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/blob/master/README.md", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_limitations_side-chain_flexibility.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_limitations_side-chain_flexibility.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b0084188d2708dccbbe28ab5ff54edb4687199e4
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_limitations_side-chain_flexibility.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.09543] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "Oct 12, 2024 · Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment . In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "Oct 12, 2024 · Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment . In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ..."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 2, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "h Boltzmann Alignment . BA-DDG employs a forward process identical to that of BA-Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆G and the predicted ∆∆G ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "h Boltzmann Alignment . BA-DDG employs a forward process identical to that of BA-Cycle. During training, the parameters θ of the inverse folding model and kBT in Eq. 10 are treated as learnable parameters that undergo optimization. The objective of BA-DDG is to minimize the discrepancy between the ground truth ∆∆G and the predicted ∆∆G ..."}
+{"idx": 3, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values."}
+{"idx": 4, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 5, "title": "B -A INVERSE FOLDING MODEL AS A PREDICTOR OF MUTATIONAL ...", "date": "", "ddg_snippet": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models. However, the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09543", "content": "In this work, we propose a technique named Boltzmann Alignment to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformational distri-bution, thereby highlighting the potential of pre-trained probabilistic models. However, the ..."}
+{"idx": 6, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "12 Oct 2024 — Incorporating side - chain flexibility could enhance predictive accuracy and provide deeper insights into protein-protein interactions. Report ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "12 Oct 2024 — Incorporating side - chain flexibility could enhance predictive accuracy and provide deeper insights into protein-protein interactions. Report ..."}
+{"idx": 7, "title": "[Literature Review] Boltzmann-Aligned Inverse Folding Model as ...", "date": "", "ddg_snippet": "... limitations regarding structural dependencies and the need for side - chain flexibility in future iterations. Overall, the Boltzmann Alignment approach marks ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/boltzmann-aligned-inverse-folding-model-as-a-predictor-of-mutational-effects-on-protein-protein-interactions", "content": "... limitations regarding structural dependencies and the need for side - chain flexibility in future iterations. Overall, the Boltzmann Alignment approach marks ..."}
+{"idx": 8, "title": "Zero-shot protein stability prediction by inverse folding ...", "date": "", "ddg_snippet": "by J Frellsen · 2025 — Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05596", "content": "by J Frellsen · 2025 — Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between ..."}
+{"idx": 9, "title": "Approximating Projections of Conformational Boltzmann ...", "date": "", "ddg_snippet": "Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing ...", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acs.jctc.3c01081", "content": "Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing ..."}
diff --git a/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_mutational_effects_protein-protein_interactions_side-chain_f.jsonl b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_mutational_effects_protein-protein_interactions_side-chain_f.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3822e919a531d5f28842a87862c90626c969a109
--- /dev/null
+++ b/data/sampled_jsons/Boltzmann-Aligned_Inverse_Folding_Model_mutational_effects_protein-protein_interactions_side-chain_f.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "12 Oct 2024 — Incorporating side - chain flexibility could enhance predictive accuracy and provide deeper insights into protein - protein interactions . Report ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "12 Oct 2024 — Incorporating side - chain flexibility could enhance predictive accuracy and provide deeper insights into protein - protein interactions . Report ..."}
+{"idx": 1, "title": "[Revisión de artículo] Boltzmann-Aligned Inverse Folding ...", "date": "", "ddg_snippet": "This paper introduces a promising new avenue for predicting mutational impacts on protein interactions , leveraging a robust thermodynamic perspective alongside ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/es/review/boltzmann-aligned-inverse-folding-model-as-a-predictor-of-mutational-effects-on-protein-protein-interactions", "content": "This paper introduces a promising new avenue for predicting mutational impacts on protein interactions , leveraging a robust thermodynamic perspective alongside ..."}
+{"idx": 2, "title": "Predicting mutational effects on protein binding from ...", "date": "", "ddg_snippet": "This paper proposes a novel approach to modeling binding energy by leveraging folding energy and fine-tuning a protein inverse folding model . The proposed STAB- ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=En0vq4ICkW&noteId=nrqGPI5pmv", "content": "This paper proposes a novel approach to modeling binding energy by leveraging folding energy and fine-tuning a protein inverse folding model . The proposed STAB- ..."}
+{"idx": 3, "title": "Predicting mutational effects on protein binding from ...", "date": "", "ddg_snippet": "The key idea is to parameterize the binding energy as the difference between the folding energy of the protein complex and the sum of the folding energies of ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45926", "content": "The key idea is to parameterize the binding energy as the difference between the folding energy of the protein complex and the sum of the folding energies of ..."}
+{"idx": 4, "title": "Zero-shot protein stability prediction by inverse folding ...", "date": "", "ddg_snippet": "by J Frellsen · 2025 — Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05596", "content": "by J Frellsen · 2025 — Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between ..."}
+{"idx": 5, "title": "Selection on protein structure, interaction, and sequence", "date": "", "ddg_snippet": "by PB Chi · 2016 · Cited by 65 — Here, an overview of selection on and corresponding modeling of folding stability, folding specificity, binding affinity and specificity for ligands.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4918422/", "content": "by PB Chi · 2016 · Cited by 65 — Here, an overview of selection on and corresponding modeling of folding stability, folding specificity, binding affinity and specificity for ligands."}
+{"idx": 6, "title": "Protein design: a perspective from simple tractable models", "date": "", "ddg_snippet": "by EI Shakhnovich · 1998 · Cited by 210 — The protein folding problem has two components: the 'direct' folding problem (i.e. folding ) and the ' inverse ' problem (i.e. protein design). The main issue ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1359027898000212", "content": "by EI Shakhnovich · 1998 · Cited by 210 — The protein folding problem has two components: the 'direct' folding problem (i.e. folding ) and the ' inverse ' problem (i.e. protein design). The main issue ..."}
+{"idx": 7, "title": "Design of protein-binding proteins from the target structure ...", "date": "", "ddg_snippet": "by L Cao · 2022 · Cited by 507 — We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-022-04654-9", "content": "by L Cao · 2022 · Cited by 507 — We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and ..."}
+{"idx": 8, "title": "Learning to engineer protein flexibility", "date": "", "ddg_snippet": "by P Kouba · Cited by 2 — We demonstrate that our method Flexpert enables guidance of inverse folding models toward increased flexibility . This opens up a transformative possibility of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=L238BAx0wP", "content": "by P Kouba · Cited by 2 — We demonstrate that our method Flexpert enables guidance of inverse folding models toward increased flexibility . This opens up a transformative possibility of ..."}
+{"idx": 9, "title": "S0006-3495(25)00610-1.pdf", "date": "", "ddg_snippet": "4 days ago — There is one protein (2LZM) among ten studied proteins , for which the χ1 and χ2 angles exhibit an opposite trend; the side chain prediction ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/biophysj/pdf/S0006-3495(25)00610-1.pdf", "content": "4 days ago — There is one protein (2LZM) among ten studied proteins , for which the χ1 and χ2 angles exhibit an opposite trend; the side chain prediction ..."}
diff --git a/data/sampled_jsons/Buchholz_et_al._2023_causal_representation_learning.jsonl b/data/sampled_jsons/Buchholz_et_al._2023_causal_representation_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf920eec3840cfcc44d1ab1d4965df1b3c7adcfc
--- /dev/null
+++ b/data/sampled_jsons/Buchholz_et_al._2023_causal_representation_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Robust Intervention Representations with Delta", "date": "", "ddg_snippet": "This fundamental problem falls under the category of Causal Representation Learning (CRL) (Schölkopf et al . ... 2023 ; Ahuja, Hartford, and Bengio ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.04492v1", "content": "This fundamental problem falls under the category of Causal Representation Learning (CRL) (Schölkopf et al . ... 2023 ; Ahuja, Hartford, and Bengio ..."}
+{"idx": 1, "title": "On the Origins of Linear Representations in Large Language", "date": "", "ddg_snippet": "... a latent variable model that abstracts the concept dynamics of LLM inference, allowing us to mathematically analyze LLM representations of concepts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.03867v1", "content": "... a latent variable model that abstracts the concept dynamics of LLM inference, allowing us to mathematically analyze LLM representations of concepts."}
+{"idx": 2, "title": "Identifying Weight-Variant Latent Causal Models", "date": "", "ddg_snippet": "... representation learning can be viewed as a special case of causal representation learning where the latent variables have no causal influences ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2208.14153v6", "content": "... representation learning can be viewed as a special case of causal representation learning where the latent variables have no causal influences ..."}
+{"idx": 3, "title": "Do-PFN: In-Context Learning for Causal Effect Estimation", "date": "", "ddg_snippet": "... causality involve tabular data, and prior-data fitted networks (PFNs) (Müller et al .,, 2022 ) have recently transformed the landscape of tabular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06039v1", "content": "... causality involve tabular data, and prior-data fitted networks (PFNs) (Müller et al .,, 2022 ) have recently transformed the landscape of tabular ..."}
+{"idx": 4, "title": "Causal inference under feedback — The Dan MacKinlay stable of", "date": "", "ddg_snippet": "I am also curious about their work in causality in continuous fields ( Bongers and Mooij 2018 ; Bongers et al . ... Causal Models with Cycles: ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/causality_feedback", "content": "I am also curious about their work in causality in continuous fields ( Bongers and Mooij 2018 ; Bongers et al . ... Causal Models with Cycles: ..."}
+{"idx": 5, "title": "Causal inference under feedback — The Dan MacKinlay stable of", "date": "", "ddg_snippet": "I am also curious about their work in causality in continuous fields ( Bongers and Mooij 2018 ; Bongers et al . ... Causal Models with Cycles: ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/causality_feedback.html", "content": "I am also curious about their work in causality in continuous fields ( Bongers and Mooij 2018 ; Bongers et al . ... Causal Models with Cycles: ..."}
+{"idx": 6, "title": "Michel Besserve, Prof. Dr.", "date": "", "ddg_snippet": "... that it can be mathematically formulated and exploited in various ways to expand capabilities of causal inference to new settings [Besserve et al ...", "subpage_snippet": "", "source": "michelbesserve.com", "link": "https://michelbesserve.com/", "content": "... that it can be mathematically formulated and exploited in various ways to expand capabilities of causal inference to new settings [Besserve et al ..."}
+{"idx": 7, "title": "Stefan Bauer", "date": "", "ddg_snippet": "... Active Causal Induction with Deep Reinforcement Learning ... Proceedings of the Eleventh International Conference on Learning Representations , 2023", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/stefan-bauer/", "content": "... Active Causal Induction with Deep Reinforcement Learning ... Proceedings of the Eleventh International Conference on Learning Representations , 2023"}
+{"idx": 8, "title": "Elan Rosenfeld | DeepAI", "date": "", "ddg_snippet": "Recent advances in learning aligned multimodal representations have been... ... Invariant Causal Prediction (Peters et al ., 2016) is a technique for ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/elan-rosenfeld", "content": "Recent advances in learning aligned multimodal representations have been... ... Invariant Causal Prediction (Peters et al ., 2016) is a technique for ..."}
+{"idx": 9, "title": "Patrick SCHWAB | Director | GlaxoSmithKline, London | GSK |", "date": "", "ddg_snippet": "Causal discovery, the inference of causal relations from data, is a core task of fundamental importance in all scientific domains, and several new ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Patrick-Schwab", "content": "Causal discovery, the inference of causal relations from data, is a core task of fundamental importance in all scientific domains, and several new ..."}
diff --git a/data/sampled_jsons/C-Proxy_machine_unlearning_definition_method.jsonl b/data/sampled_jsons/C-Proxy_machine_unlearning_definition_method.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8510546874ed8ca08cef692143d535b70387f469
--- /dev/null
+++ b/data/sampled_jsons/C-Proxy_machine_unlearning_definition_method.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Exploring the Landscape of Machine Unlearning", "date": "", "ddg_snippet": "General Definition : Machine unlearning is a concept that refers to the process of removing or “forgetting” previously learned information from a machine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.06360v6", "content": "General Definition : Machine unlearning is a concept that refers to the process of removing or “forgetting” previously learned information from a machine ..."}
+{"idx": 1, "title": "A Survey of Machine Unlearning", "date": "", "ddg_snippet": "7 days ago — Second, we show how to define an unlearning problem in ML systems. This includes the formulation of the original model and unlearned model as ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3749987", "content": "7 days ago — Second, we show how to define an unlearning problem in ML systems. This includes the formulation of the original model and unlearned model as ..."}
+{"idx": 2, "title": "What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "Machine unlearning is the problem of removing the effect of a subset of training data (the ``forget set'') from a trained model without damaging the model's ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QAbhLBF72K&referrer=[the+profile+of+Eleni+Triantafillou](/profile?id=~Eleni_Triantafillou1)", "content": "Machine unlearning is the problem of removing the effect of a subset of training data (the ``forget set'') from a trained model without damaging the model's ..."}
+{"idx": 3, "title": "What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "30 Oct 2024 — Additionally, our results with the C - proxy demonstrate that significant performance improvements in unlearning can be achieved with minimal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01257v2", "content": "30 Oct 2024 — Additionally, our results with the C - proxy demonstrate that significant performance improvements in unlearning can be achieved with minimal ..."}
+{"idx": 4, "title": "Machine Unlearning: The Right to be Forgotten", "date": "", "ddg_snippet": "This survey aims to provide a comprehensive examination of machine unlearning , including its concepts, scenarios, methods , and applications.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/code/tamlhp/machine-unlearning-the-right-to-be-forgotten", "content": "This survey aims to provide a comprehensive examination of machine unlearning , including its concepts, scenarios, methods , and applications."}
+{"idx": 5, "title": "Towards Source-Free Machine Unlearning - CVF Open Access", "date": "", "ddg_snippet": "by SM Ahmed · 2025 — To address this challenge, we focus on the source-free unlearning scenario, where an unlearning algorithm must be capable of removing spe- cific data from a ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Ahmed_Towards_Source-Free_Machine_Unlearning_CVPR_2025_paper.pdf", "content": "by SM Ahmed · 2025 — To address this challenge, we focus on the source-free unlearning scenario, where an unlearning algorithm must be capable of removing spe- cific data from a ..."}
+{"idx": 6, "title": "A Contrastive Approach to Machine Unlearning", "date": "", "ddg_snippet": "Abstract. Machine unlearning aims to eliminate the influ- ence of a subset of training samples (i.e., unlearn - ing samples) from a trained model.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0830.pdf", "content": "Abstract. Machine unlearning aims to eliminate the influ- ence of a subset of training samples (i.e., unlearn - ing samples) from a trained model."}
+{"idx": 7, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "by NM Sepahvand — Recall that C - Proxy has been used in prior work by Zhao et al. (2024) to identify difficult to unlearn forget sets for certain unlearning algorithms. Figure 2 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "by NM Sepahvand — Recall that C - Proxy has been used in prior work by Zhao et al. (2024) to identify difficult to unlearn forget sets for certain unlearning algorithms. Figure 2 ..."}
+{"idx": 8, "title": "Zero-Shot Machine Unlearning with Proxy Adversarial Data ...", "date": "", "ddg_snippet": "Machine unlearning aims to remove the influence of specific samples from a trained model . A key challenge in this process is over-unlearning, where.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0039.pdf", "content": "Machine unlearning aims to remove the influence of specific samples from a trained model . A key challenge in this process is over-unlearning, where."}
+{"idx": 9, "title": "Large-Scale Machine Unlearning with a Taste of Uncertainty", "date": "", "ddg_snippet": "by CN Spartalis · 2025 · Cited by 2 — We present LoTUS, a novel Machine Unlearning (MU) method that eliminates the influence of training samples from pre-trained models, avoiding retraining from ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Spartalis_LoTUS_Large-Scale_Machine_Unlearning_with_a_Taste_of_Uncertainty_CVPR_2025_paper.pdf", "content": "by CN Spartalis · 2025 · Cited by 2 — We present LoTUS, a novel Machine Unlearning (MU) method that eliminates the influence of training samples from pre-trained models, avoiding retraining from ..."}
diff --git a/data/sampled_jsons/C-Proxy_method_machine_unlearning_definition.jsonl b/data/sampled_jsons/C-Proxy_method_machine_unlearning_definition.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine unlearning - Wikipedia", "date": "", "ddg_snippet": "Machine unlearning is a branch of machine learning focused on removing specific undesired element, such as private data, wrong or manipulated training data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Machine_unlearning", "content": "Machine unlearning is a branch of machine learning focused on removing specific undesired element, such as private data, wrong or manipulated training data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up."}
+{"idx": 1, "title": "[2405.07406] Machine Unlearning: A Comprehensive Survey", "date": "", "ddg_snippet": "This survey aims to systematically classify a wide range of machine unlearning and discuss their differences, connections and open problems. We categorize current unlearning methods into four scenarios: centralized unlearning , distributed and irregular data unlearning , unlearning verification, and privacy and security issues in unlearning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.07406", "content": "This survey aims to systematically classify a wide range of machine unlearning and discuss their differences, connections and open problems. We categorize current unlearning methods into four scenarios: centralized unlearning , distributed and irregular data unlearning , unlearning verification, and privacy and security issues in unlearning ."}
+{"idx": 2, "title": "An overview of machine unlearning - ScienceDirect", "date": "", "ddg_snippet": "Therefore, this paper summarises the definition of the machine unlearning formulation, process, deletion requests, design requirements and validation, algorithms, applications, and future perspectives, in the hope that it will help future researchers in machine unlearning .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2667295224000576", "content": "Therefore, this paper summarises the definition of the machine unlearning formulation, process, deletion requests, design requirements and validation, algorithms, applications, and future perspectives, in the hope that it will help future researchers in machine unlearning ."}
+{"idx": 3, "title": "PDF Machine Unlearning in 2024 - Stanford University", "date": "", "ddg_snippet": "Machine unlearning can be broadly described as removing the influences of training data from a trained model. At its core, unlearning on a target model seeks to produce an unlearned model that is equivalent to—or at least \"behaves like\"—a retrained model that is trained on the same data of target model, minus the information to be unlearned.", "subpage_snippet": "", "source": "ai.stanford.edu", "link": "https://ai.stanford.edu/~kzliu/files/unlearning.pdf", "content": "Machine unlearning can be broadly described as removing the influences of training data from a trained model. At its core, unlearning on a target model seeks to produce an unlearned model that is equivalent to—or at least \"behaves like\"—a retrained model that is trained on the same data of target model, minus the information to be unlearned."}
+{"idx": 4, "title": "Machine Unlearning: A Survey | ACM Computing Surveys", "date": "", "ddg_snippet": "Machine unlearning (a.k.a. selectively forgetting, data deletion, or scrubbing) requires that the samples and their influence can be completely and quickly removed from a training dataset and a trained model [13, 14, 15]. Figure 1 illustrates an example of machine unlearning for a trained model.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3603620", "content": "Machine unlearning (a.k.a. selectively forgetting, data deletion, or scrubbing) requires that the samples and their influence can be completely and quickly removed from a training dataset and a trained model [13, 14, 15]. Figure 1 illustrates an example of machine unlearning for a trained model."}
+{"idx": 5, "title": "What is Machine Unlearning, and Why Does It Matter?", "date": "", "ddg_snippet": "Despite these hurdles, many algorithms capable of machine unlearning for various models exist. Methods and Techniques Existing algorithms typically leverage reverse-engineered, traditional machine -learning methodologies. These are generally classified into two main categories: exact and approximate unlearning methods .", "subpage_snippet": "", "source": "deepgram.com", "link": "https://deepgram.com/learn/what-is-machine-unlearning-and-why-does-it-matter", "content": "Despite these hurdles, many algorithms capable of machine unlearning for various models exist. Methods and Techniques Existing algorithms typically leverage reverse-engineered, traditional machine -learning methodologies. These are generally classified into two main categories: exact and approximate unlearning methods ."}
+{"idx": 6, "title": "Machine Unlearning: Solutions and Challenges - IEEE Xplore", "date": "", "ddg_snippet": "Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation. To address these issues, machine unlearning has emerged as a critical technique to selectively remove specific training data points' influence on trained models. This paper provides a comprehensive taxonomy and ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10488864", "content": "Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation. To address these issues, machine unlearning has emerged as a critical technique to selectively remove specific training data points' influence on trained models. This paper provides a comprehensive taxonomy and ..."}
+{"idx": 7, "title": "Zero-Shot Machine Unlearning with Proxy Adversarial Data Generation", "date": "", "ddg_snippet": "Abstract Machine unlearning aims to remove the influence of specific samples from a trained model. A key challenge in this process is over- unlearning , where the model's performance on the remaining data sig-nificantly drops due to the change in the model's parameters. Existing unlearning algorithms depend on the remaining data to prevent this issue. As such, these methods are inapplicable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.21738", "content": "Abstract Machine unlearning aims to remove the influence of specific samples from a trained model. A key challenge in this process is over- unlearning , where the model's performance on the remaining data sig-nificantly drops due to the change in the model's parameters. Existing unlearning algorithms depend on the remaining data to prevent this issue. As such, these methods are inapplicable ..."}
+{"idx": 8, "title": "C UNLEARNING: A C A ONTRASTIVE P PROACH TO MACHINE UNLEARNING - OpenReview", "date": "", "ddg_snippet": "In addition, they utilize unlearning or remaining samples ineffectively, sacrificing either unlearning eficacy or eficiency. Our main insight is that direct optimiza-tion on the representation space utilizing both unlearning and remaining samples can effectively remove influence of unlearning samples while maintaining repre-sentations learned from remaining samples. We propose a contrastive ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lgnAEBE1Xq", "content": "In addition, they utilize unlearning or remaining samples ineffectively, sacrificing either unlearning eficacy or eficiency. Our main insight is that direct optimiza-tion on the representation space utilizing both unlearning and remaining samples can effectively remove influence of unlearning samples while maintaining repre-sentations learned from remaining samples. We propose a contrastive ..."}
+{"idx": 9, "title": "Machine Unlearning in 2024 - Ken Ziyu Liu - Stanford Computer Science", "date": "", "ddg_snippet": "As our ML models today become larger and their (pre-)training sets grow to inscrutable sizes, people are increasingly interested in the concept of machine unlearning to edit away undesired things like private data, stale knowledge, copyrighted materials, toxic/unsafe content, dangerous capabilities, and misinformation, without retraining models from scratch.", "subpage_snippet": "", "source": "ai.stanford.edu", "link": "https://ai.stanford.edu/~kzliu/blog/unlearning", "content": "As our ML models today become larger and their (pre-)training sets grow to inscrutable sizes, people are increasingly interested in the concept of machine unlearning to edit away undesired things like private data, stale knowledge, copyrighted materials, toxic/unsafe content, dangerous capabilities, and misinformation, without retraining models from scratch."}
diff --git a/data/sampled_jsons/CALF_Aligning_LLMs_for_Time_Series_Forecasting_via_Cross-modal_Fine-Tuning_abstract.jsonl b/data/sampled_jsons/CALF_Aligning_LLMs_for_Time_Series_Forecasting_via_Cross-modal_Fine-Tuning_abstract.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ... CALF | Proceedings of the Thirty-Ninth AAAI Conference on ... CALF: Aligning LLMs for Time Series Forecasting via Cross ... CALF/README.md at main · Hank0626/CALF · GitHub dblp: CALF: Aligning LLMs for Time Series Forecasting via ... CALF: Aligning LLMs for Time Series Forecasting via Cross ... CALF /README.md at main · Hank0626/ CALF · GitHub CALF /README.md at main · Hank0626/ CALF · GitHub CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning Taming Pre-trained LLMs for Generalised Time Series ...", "date": "", "ddg_snippet": "Mar 12, 2024 · Title: CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning Authors: Peiyuan Liu, Hang Guo, Tao Dai, Naiqi Li, Jigang Bao, Xudong Ren, Yong Jiang, Shu-Tao Xia View PDF HTML (experimental) Abstract : Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Aug 5, 2025 · CALF : aligning LLMs for time series forecasting via cross - modal fine - tuning Apr 11, 2025 · To address this issue, we propose a novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF by reducing the distribution discrepancy between textual and temporal data, which mainly consists of the temporal target branch with temporal input and the textual source branch with aligned textual input. Official implementation of \" CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning \" (AAAI 2025) - Hank0626/ CALF Apr 17, 2025 · Bibliographic details on CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning . Apr 8, 2025 · In this work, we propose CALF , a novel cross-modal fine-tuning framework that leverages the robust capabilities of Large Language Models ( LLMs ) for time series forecasting . How does our calf achieve better alignment? Our CALF achieves better alignment through multi-level cross-modal fine-tuning . To bridge the modality gap between textual and temporal data, we introduce three meticulously designed cross-modal fine-tuning techniques (see Figure 2): How do I train a calf model for long-term forecasting? For instance, to train the CALF model on the ETTh2 dataset for long-term forecasting , execute: For short-term forecasting , use: Post-Training: Trained models will be saved in ./checkpoints. Is calf better than other LLM-based methods? As shown in Tab.5, our proposed CALF shows significant improvements in both efficiency and accuracy compared with other LLM-based methods, largely due to treating each channel sequence as a token, employing an efficient fine-tuning strategy, and requiring only a single time branch during inference. Is calf a good predictor for long-term and short-term forecasting tasks? Thanks to the modality alignment, CALF establishes state-of-the-art performance for both long-term and short-term forecasting tasks with low computational complexity, and exhibits favorable few-shot and zero-shot abilities similar to that in LLMs. Liu, P., Guo, H., Dai, T., Li, N., Bao, J., Ren, X., Jiang, Y., & Xia, S.-T. (2025). Can LLMs be used for time series analysis? A groundbreaking framework proposed by Zhou et al. (Zhou et al. 2023) first demonstrated the potential of adapting LLMs for time series analysis. Following this paradigm, subsequent research has introduced further refinements and innovations. Dec 31, 2023 · To address this issue, we propose a novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF by reducing the distribution discrepancy between textual and temporal data, which mainly consists of the temporal target branch with temporal input and the textual source branch with aligned textual input.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07300", "content": "Mar 12, 2024 · Title: CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning Authors: Peiyuan Liu, Hang Guo, Tao Dai, Naiqi Li, Jigang Bao, Xudong Ren, Yong Jiang, Shu-Tao Xia View PDF HTML (experimental) Abstract : Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Aug 5, 2025 · CALF : aligning LLMs for time series forecasting via cross - modal fine - tuning Apr 11, 2025 · To address this issue, we propose a novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF by reducing the distribution discrepancy between textual and temporal data, which mainly consists of the temporal target branch with temporal input and the textual source branch with aligned textual input. Official implementation of \" CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning \" (AAAI 2025) - Hank0626/ CALF Apr 17, 2025 · Bibliographic details on CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning . Apr 8, 2025 · In this work, we propose CALF , a novel cross-modal fine-tuning framework that leverages the robust capabilities of Large Language Models ( LLMs ) for time series forecasting . How does our calf achieve better alignment? Our CALF achieves better alignment through multi-level cross-modal fine-tuning . To bridge the modality gap between textual and temporal data, we introduce three meticulously designed cross-modal fine-tuning techniques (see Figure 2): How do I train a calf model for long-term forecasting? For instance, to train the CALF model on the ETTh2 dataset for long-term forecasting , execute: For short-term forecasting , use: Post-Training: Trained models will be saved in ./checkpoints. Is calf better than other LLM-based methods? As shown in Tab.5, our proposed CALF shows significant improvements in both efficiency and accuracy compared with other LLM-based methods, largely due to treating each channel sequence as a token, employing an efficient fine-tuning strategy, and requiring only a single time branch during inference. Is calf a good predictor for long-term and short-term forecasting tasks? Thanks to the modality alignment, CALF establishes state-of-the-art performance for both long-term and short-term forecasting tasks with low computational complexity, and exhibits favorable few-shot and zero-shot abilities similar to that in LLMs. Liu, P., Guo, H., Dai, T., Li, N., Bao, J., Ren, X., Jiang, Y., & Xia, S.-T. (2025). Can LLMs be used for time series analysis? A groundbreaking framework proposed by Zhou et al. (Zhou et al. 2023) first demonstrated the potential of adapting LLMs for time series analysis. Following this paradigm, subsequent research has introduced further refinements and innovations. Dec 31, 2023 · To address this issue, we propose a novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF by reducing the distribution discrepancy between textual and temporal data, which mainly consists of the temporal target branch with temporal input and the textual source branch with aligned textual input."}
+{"idx": 1, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "Apr 8, 2025 · In this work, we propose CALF , a novel cross-modal fine-tuning framework that leverages the robust capabilities of Large Language Models ( LLMs ) for time series forecasting .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07300v3", "content": "Apr 8, 2025 · In this work, we propose CALF , a novel cross-modal fine-tuning framework that leverages the robust capabilities of Large Language Models ( LLMs ) for time series forecasting ."}
+{"idx": 2, "title": "CALF | Proceedings of the Thirty-Ninth AAAI Conference on ...", "date": "", "ddg_snippet": "Aug 5, 2025 · CALF : aligning LLMs for time series forecasting via cross - modal fine - tuning", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1609/aaai.v39i18.34082", "content": "Aug 5, 2025 · CALF : aligning LLMs for time series forecasting via cross - modal fine - tuning"}
+{"idx": 3, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "Apr 11, 2025 · To address this issue, we propose a novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF by reducing the distribution discrepancy between textual and temporal data, which mainly consists of the temporal target branch with temporal input and the textual source branch with aligned textual input.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/34082", "content": "Apr 11, 2025 · To address this issue, we propose a novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF by reducing the distribution discrepancy between textual and temporal data, which mainly consists of the temporal target branch with temporal input and the textual source branch with aligned textual input."}
+{"idx": 4, "title": "CALF/README.md at main · Hank0626/CALF · GitHub", "date": "", "ddg_snippet": "Official implementation of \" CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning \" (AAAI 2025) - Hank0626/ CALF", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Hank0626/CALF/blob/main/README.md", "content": "Official implementation of \" CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning \" (AAAI 2025) - Hank0626/ CALF"}
+{"idx": 5, "title": "dblp: CALF: Aligning LLMs for Time Series Forecasting via ...", "date": "", "ddg_snippet": "Apr 17, 2025 · Bibliographic details on CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/aaai/LiuG0LBR0X25", "content": "Apr 17, 2025 · Bibliographic details on CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning ."}
+{"idx": 6, "title": "Taming Pre-trained LLMs for Generalised Time Series ...", "date": "", "ddg_snippet": "Dec 31, 2023 · To address this issue, we propose a novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF by reducing the distribution discrepancy between textual and temporal data, which mainly consists of the temporal target branch with temporal input and the textual source branch with aligned textual input.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LSgJYc4kg8", "content": "Dec 31, 2023 · To address this issue, we propose a novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF by reducing the distribution discrepancy between textual and temporal data, which mainly consists of the temporal target branch with temporal input and the textual source branch with aligned textual input."}
+{"idx": 7, "title": "LLMs Meet Cross-Modal Time Series Analytics: Overview and", "date": "", "ddg_snippet": "Cross - modal alignment seeks to bridge the semantic gap between time series and textual modalities by aligning their latent representations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10620v1", "content": "Cross - modal alignment seeks to bridge the semantic gap between time series and textual modalities by aligning their latent representations."}
+{"idx": 8, "title": "Time Series Forecasting as Reasoning: A Slow-Thinking Approach", "date": "", "ddg_snippet": "For this purpose, we propose Time -R1, a two-stage reinforcement fine - tuning framework designed to enhance multi-step reasoning ability of LLMs for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10630v1", "content": "For this purpose, we propose Time -R1, a two-stage reinforcement fine - tuning framework designed to enhance multi-step reasoning ability of LLMs for ..."}
+{"idx": 9, "title": "Fusing Large Language Models with Temporal Transformers for", "date": "", "ddg_snippet": "... LLMs ) have demonstrated powerful capabilities in performing various tasks and thus are applied by recent studies to time series forecasting (TSF) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10098v1", "content": "... LLMs ) have demonstrated powerful capabilities in performing various tasks and thus are applied by recent studies to time series forecasting (TSF) ..."}
diff --git a/data/sampled_jsons/CALF_Liu_et_al._2024_time_series_forecasting_CALF_abstract_year_2024.jsonl b/data/sampled_jsons/CALF_Liu_et_al._2024_time_series_forecasting_CALF_abstract_year_2024.jsonl
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+++ b/data/sampled_jsons/CALF_Liu_et_al._2024_time_series_forecasting_CALF_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross-modal", "date": "", "ddg_snippet": "... significantly revolutionized the field of time series forecasting , with a plethora of methods emerging to enhance predictive accuracy (Zeng et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07300v3", "content": "... significantly revolutionized the field of time series forecasting , with a plethora of methods emerging to enhance predictive accuracy (Zeng et al ..."}
+{"idx": 1, "title": "EventTSF: Event-Aware Non-Stationary Time Series Forecasting", "date": "", "ddg_snippet": "Time series forecasting is critical in domains like energy, transportation, and meteorology (Liang et al . ... time series forecasting with large ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.13434v1", "content": "Time series forecasting is critical in domains like energy, transportation, and meteorology (Liang et al . ... time series forecasting with large ..."}
+{"idx": 2, "title": "LLMs Meet Cross-Modal Time Series Analytics: Overview and", "date": "", "ddg_snippet": "... efforts have been made to design time series analytics methods, which enable different downstream tasks, such as traffic forecasting ( Liu et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10620v1", "content": "... efforts have been made to design time series analytics methods, which enable different downstream tasks, such as traffic forecasting ( Liu et al ..."}
+{"idx": 3, "title": "(PDF) Simple Algorithms for Peak Detection in Time-Series", "date": "", "ddg_snippet": "We offer different formalizations of the notion of a peak and propose corresponding algorithms to detect peaks in the given time - series .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series", "content": "We offer different formalizations of the notion of a peak and propose corresponding algorithms to detect peaks in the given time - series ."}
+{"idx": 4, "title": "Animals | July-1 2024 - Browse Articles", "date": "", "ddg_snippet": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-2615/14/13", "content": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ..."}
+{"idx": 5, "title": "Fusing Large Language Models with Temporal Transformers for", "date": "", "ddg_snippet": "... time series 𝒳 𝒳 \\mathcal{X} caligraphic_X and forecasts the future values 𝒴 𝒴 \\mathcal{ Y } caligraphic_ Y , an LLM that generates semantic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10098v1", "content": "... time series 𝒳 𝒳 \\mathcal{X} caligraphic_X and forecasts the future values 𝒴 𝒴 \\mathcal{ Y } caligraphic_ Y , an LLM that generates semantic ..."}
+{"idx": 6, "title": "DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate", "date": "", "ddg_snippet": "Multivariate Time Series Forecasting (MTSF) (Yu et al ., 2024 , 2023 ; Zheng et al ., 2023 ; Huang et al ., 2024 ) plays an essential role in a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21830v4", "content": "Multivariate Time Series Forecasting (MTSF) (Yu et al ., 2024 , 2023 ; Zheng et al ., 2023 ; Huang et al ., 2024 ) plays an essential role in a ..."}
+{"idx": 7, "title": "INTERPRETIVE SUMMARIES, FEBRUARY 2025 - Journal of Dairy Science", "date": "", "ddg_snippet": "... time -offlight mass spectrometry, a total of 34 representative differential metabolites were identified, primarily consisting of peptides and ...", "subpage_snippet": "", "source": "www.journalofdairyscience.org", "link": "https://www.journalofdairyscience.org/article/S0022-0302(25)00029-3/fulltext", "content": "... time -offlight mass spectrometry, a total of 34 representative differential metabolites were identified, primarily consisting of peptides and ..."}
+{"idx": 8, "title": "Investigation of Bovine Disease and Events through Machine", "date": "", "ddg_snippet": "... mounted accelerometers, provides real- time data on cow movement and activity, enabling the identification of lameness indicators ( Haladjian et al ...", "subpage_snippet": "", "source": "researcherslinks.com", "link": "https://researcherslinks.com/current-issues/Investigation-of-Bovine-Disease-and-Events/24/1/7576/html", "content": "... mounted accelerometers, provides real- time data on cow movement and activity, enabling the identification of lameness indicators ( Haladjian et al ..."}
+{"idx": 9, "title": "Chris Fawson | IDEAS/RePEc", "date": "", "ddg_snippet": "agriculture ,\" Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt7rf2g35q, Department of Agricultural & Resource ...", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/e/pfa99.html", "content": "agriculture ,\" Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt7rf2g35q, Department of Agricultural & Resource ..."}
diff --git a/data/sampled_jsons/CALF_pre-trained_fine-tuning_LLM_time_series_alignment_method_year_2024.jsonl b/data/sampled_jsons/CALF_pre-trained_fine-tuning_LLM_time_series_alignment_method_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c01d3113154ac67c331945c4d39355dd163a4e0e
--- /dev/null
+++ b/data/sampled_jsons/CALF_pre-trained_fine-tuning_LLM_time_series_alignment_method_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "In practice, current LLM -based methods typically treat pre-trained ... LLM4TS: Two-stage fine-tuning for time-series forecasting with pre-trained llms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07300v2", "content": "In practice, current LLM -based methods typically treat pre-trained ... LLM4TS: Two-stage fine-tuning for time-series forecasting with pre-trained llms."}
+{"idx": 1, "title": "Aligning LLMs for Time Series Forecasting via Cross- ...", "date": "", "ddg_snippet": "CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning ... Current LLM -based methods either use linear layers to project time series ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Hank0626/CALF", "content": "CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning ... Current LLM -based methods either use linear layers to project time series ..."}
+{"idx": 2, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "Our CALF achieves better alignment through multi-level cross-modal fine-tuning . ... LLM4TS: Two-stage fine-tuning for time-series forecasting with pre-trained ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07300v3", "content": "Our CALF achieves better alignment through multi-level cross-modal fine-tuning . ... LLM4TS: Two-stage fine-tuning for time-series forecasting with pre-trained ..."}
+{"idx": 3, "title": "CALF: LLM Alignment for Time Series", "date": "", "ddg_snippet": "... CALF ... LLM4TS: Two-stage fine-tuning for time-series forecasting with pre-trained llms. ... LLM -Empowered Multivariate Time Series Forecasting via Cross-Modality ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/articles/2403.07300", "content": "... CALF ... LLM4TS: Two-stage fine-tuning for time-series forecasting with pre-trained llms. ... LLM -Empowered Multivariate Time Series Forecasting via Cross-Modality ..."}
+{"idx": 4, "title": "Taming Pre-trained LLMs for Generalised Time Series ...", "date": "", "ddg_snippet": "Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation ... Thanks to the modality alignment , CALF establishes ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LSgJYc4kg8&referrer=[the+profile+of+Hang+Guo](/profile?id=~Hang_Guo3)", "content": "Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation ... Thanks to the modality alignment , CALF establishes ..."}
+{"idx": 5, "title": "[PDF] CALF: Aligning LLMs for Time Series Forecasting via ...", "date": "", "ddg_snippet": "A novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF is proposed that establishes state-of-the-art performance for both long-term and short-term ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/c5af46d51668c054e00c6419a522862531eaf9cc", "content": "A novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF is proposed that establishes state-of-the-art performance for both long-term and short-term ..."}
+{"idx": 6, "title": "CALF: aligning LLMs for time series forecasting via cross- ...", "date": "", "ddg_snippet": "5 Aug 2025 — CALF : aligning LLMs for time series forecasting via cross-modal fine-tuning ... fine-tuning for time-series forecasting with pre-trained llms.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1609/aaai.v39i18.34082", "content": "5 Aug 2025 — CALF : aligning LLMs for time series forecasting via cross-modal fine-tuning ... fine-tuning for time-series forecasting with pre-trained llms."}
+{"idx": 7, "title": "Aligning LLMs for Time Series Forecasting via Cross- ...", "date": "", "ddg_snippet": "... CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning ... This technique is utilized for parameter-efficient training of the pre-trained LLM ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/calf-aligning-llms-for-time-series-forecasting-via-cross-modal-fine-tuning", "content": "... CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning ... This technique is utilized for parameter-efficient training of the pre-trained LLM ..."}
+{"idx": 8, "title": "A novel LLM time series forecasting method based on ...", "date": "", "ddg_snippet": "by L Wang · 2025 — Cross-modal alignment fine-tuning is a key technique ... Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-06581-x", "content": "by L Wang · 2025 — Cross-modal alignment fine-tuning is a key technique ... Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms."}
+{"idx": 9, "title": "Two-Stage Fine-Tuning for Time-Series Forecasting with ...", "date": "", "ddg_snippet": "LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs ... A novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF is proposed ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/LLM4TS:-Two-Stage-Fine-Tuning-for-Time-Series-with-Chang-Peng/41576f3a627c0db8815fe8eb9328e13d09fc2eff", "content": "LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs ... A novel Cross-Modal LLM Fine-Tuning ( CALF ) framework for MTSF is proposed ..."}
diff --git a/data/sampled_jsons/CITRIS_Lippe_et_al._2022_causal_representation_learning.jsonl b/data/sampled_jsons/CITRIS_Lippe_et_al._2022_causal_representation_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05b288dd220332a34fee72c37085a13d19a4589e
--- /dev/null
+++ b/data/sampled_jsons/CITRIS_Lippe_et_al._2022_causal_representation_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Robust Intervention Representations with Delta", "date": "", "ddg_snippet": "This fundamental problem falls under the category of Causal Representation Learning (CRL) (Schölkopf et al . ... 2022 ) , showing that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.04492v1", "content": "This fundamental problem falls under the category of Causal Representation Learning (CRL) (Schölkopf et al . ... 2022 ) , showing that ..."}
+{"idx": 1, "title": "Discovering Latent Causal Graphs from Spatiotemporal Data", "date": "", "ddg_snippet": "Another important line of research is causal representation learning from time series data (Schölkopf et al ., 2021 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.05331v3", "content": "Another important line of research is causal representation learning from time series data (Schölkopf et al ., 2021 ) ."}
+{"idx": 2, "title": "Self-supervised contrastive learning performs non-linear system", "date": "", "ddg_snippet": "... learning (CL) and next-token prediction tasks have become important backbones of modern machine learning systems for learning from sequential data, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.14673v2", "content": "... learning (CL) and next-token prediction tasks have become important backbones of modern machine learning systems for learning from sequential data, ..."}
+{"idx": 3, "title": "Weakly supervised causal representation learning | DeepAI", "date": "", "ddg_snippet": "Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/weakly-supervised-causal-representation-learning", "content": "Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from ..."}
+{"idx": 4, "title": "CITRIS: Causal Identifiability from Temporal Intervened", "date": "", "ddg_snippet": "In this paper, we propose CITRIS , a variational autoencoder framework that learns causal representations from temporal sequences of images in which ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/citris-causal-identifiability-from-temporal-intervened-sequences", "content": "In this paper, we propose CITRIS , a variational autoencoder framework that learns causal representations from temporal sequences of images in which ..."}
+{"idx": 5, "title": "Phillip Lippe | DeepAI", "date": "", "ddg_snippet": "Causal representation learning is the task of identifying the ... Learning high-level causal representations together with a causal model ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/phillip-lippe", "content": "Causal representation learning is the task of identifying the ... Learning high-level causal representations together with a causal model ..."}
+{"idx": 6, "title": "Negate or Embrace: On How Misalignment Shapes Multimodal", "date": "", "ddg_snippet": "... learning (MMCL) on paired data has emerged as a dominant strategy for aligning modalities (Radford et al ., 2021 ; Jia et al ., 2021 ; Wu et al ., ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.10143v2", "content": "... learning (MMCL) on paired data has emerged as a dominant strategy for aligning modalities (Radford et al ., 2021 ; Jia et al ., 2021 ; Wu et al ., ..."}
+{"idx": 7, "title": "Half-AVAE: Adversarial-Enhanced Factorized and Structured", "date": "", "ddg_snippet": "... et al ., ( 2017 ) ; Chen et al ., ( 2018 ) ; Burgess et al ., ( 2018 ) ; Yang et al ., ( 2021 ) ; Lachapelle et al ., ( 2022 ) ) of the model and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07011v1", "content": "... et al ., ( 2017 ) ; Chen et al ., ( 2018 ) ; Burgess et al ., ( 2018 ) ; Yang et al ., ( 2021 ) ; Lachapelle et al ., ( 2022 ) ) of the model and ..."}
+{"idx": 8, "title": "QUVA Lab", "date": "", "ddg_snippet": "... Causal Representations }, author = {Davide Talon and Phillip Lippe and Stuart James and Alessio Del Bue and Sara Magliacane}, year = {2024}, booktitle ...", "subpage_snippet": "", "source": "ivi.fnwi.uva.nl", "link": "https://ivi.fnwi.uva.nl/quva/publications.html", "content": "... Causal Representations }, author = {Davide Talon and Phillip Lippe and Stuart James and Alessio Del Bue and Sara Magliacane}, year = {2024}, booktitle ..."}
+{"idx": 9, "title": "Sara Magliacane | DeepAI", "date": "", "ddg_snippet": "Causal representation learning is the task of identifying the underlying... ... Causal discovery algorithms infer causal relations from data based on ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/sara-magliacane", "content": "Causal representation learning is the task of identifying the underlying... ... Causal discovery algorithms infer causal relations from data based on ..."}
diff --git a/data/sampled_jsons/CLIP_embedding_space_vs_VAE_latent_space_differences_year_2023-2024.jsonl b/data/sampled_jsons/CLIP_embedding_space_vs_VAE_latent_space_differences_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ab3c37cc4b13c7a5c9b055270111efc524c35410
--- /dev/null
+++ b/data/sampled_jsons/CLIP_embedding_space_vs_VAE_latent_space_differences_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OpenAI 的 CLIP 有何亮点？ - 知乎", "date": "", "ddg_snippet": "简单的说，CLIP 无需利用 ImageNet 的数据和标签进行训练，就可以达到 ResNet50 在 ImageNet数据集上有监督训练的结果，所以叫做 Zero-shot。 CLIP（contrastive language-image pre-training）主要的贡献就是 利用无监督的文本信息，作为监督信号来学习视觉特征。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/505125640", "content": "简单的说，CLIP 无需利用 ImageNet 的数据和标签进行训练，就可以达到 ResNet50 在 ImageNet数据集上有监督训练的结果，所以叫做 Zero-shot。 CLIP（contrastive language-image pre-training）主要的贡献就是 利用无监督的文本信息，作为监督信号来学习视觉特征。"}
+{"idx": 1, "title": "CLIP 模型简介 - 知乎", "date": "", "ddg_snippet": "CLIP (Contrastive Language-Image Pre-Training) 模型 是 OpenAI 在 2021 年初发布的用于 匹配图像和文本 的 预训练 神经网络模型，是近年来多模态研究领域的经典之作。该模型直接使用 大量的互联网数据 进行预训练，在很多任务表现上达到了SOTA 。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/tardis/zm/art/662365120", "content": "CLIP (Contrastive Language-Image Pre-Training) 模型 是 OpenAI 在 2021 年初发布的用于 匹配图像和文本 的 预训练 神经网络模型，是近年来多模态研究领域的经典之作。该模型直接使用 大量的互联网数据 进行预训练，在很多任务表现上达到了SOTA 。"}
+{"idx": 2, "title": "如何评价OpenAI最新的工作CLIP：连接文本和图像，zero shot效果堪比Re...", "date": "", "ddg_snippet": "从检索这个角度来看，CLIP的zero shot其实就是把分类问题转化为了检索问题。 总结来看，CLIP能够zero shot识别，而且效果不错的原因在于： 1、训练集够大，zero shot任务的图像分布在训练集中有类似的，zero shot任务的concept在训练集中有相近的；", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/438649654", "content": "从检索这个角度来看，CLIP的zero shot其实就是把分类问题转化为了检索问题。 总结来看，CLIP能够zero shot识别，而且效果不错的原因在于： 1、训练集够大，zero shot任务的图像分布在训练集中有类似的，zero shot任务的concept在训练集中有相近的；"}
+{"idx": 3, "title": "有哪些最新的针对CLIP跨模态图文检索的改改进方案啊？最好是不用做预...", "date": "", "ddg_snippet": "Alpha-CLIP不仅保留了CLIP的视觉识别能力，而且能够精确控制图像内容的重点。 它在各种任务中都表现出了有效性，包括但不限于开放世界识别、多模态大型语言模型和条件 2D/3D 生成。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/632432573", "content": "Alpha-CLIP不仅保留了CLIP的视觉识别能力，而且能够精确控制图像内容的重点。 它在各种任务中都表现出了有效性，包括但不限于开放世界识别、多模态大型语言模型和条件 2D/3D 生成。"}
+{"idx": 4, "title": "为什么Clip可以用于zero shot分类？ - 知乎", "date": "", "ddg_snippet": "在CLIP的实验过程中，它从没有用ImageNet这个经典分类数据集上的数据做训练，但是在测试中，它却能达到和用了ImageNet做训练集的ResNet架构模型比肩的效果。 在我个人看来，CLIP解决缺点2的意义，要高于缺点1。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/459366970", "content": "在CLIP的实验过程中，它从没有用ImageNet这个经典分类数据集上的数据做训练，但是在测试中，它却能达到和用了ImageNet做训练集的ResNet架构模型比肩的效果。 在我个人看来，CLIP解决缺点2的意义，要高于缺点1。"}
+{"idx": 5, "title": "荣耀亲选lchse耳夹式耳机与华为clip哪个好？ - 知乎", "date": "", "ddg_snippet": "华为clip音质平衡度好，操作简便易上手，不过续航较弱，需频繁充电。 不过，如果你想要一款综合表现更出色的耳夹式耳机，我推荐可以看看南卡Clip Pro。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/6760145062", "content": "华为clip音质平衡度好，操作简便易上手，不过续航较弱，需频繁充电。 不过，如果你想要一款综合表现更出色的耳夹式耳机，我推荐可以看看南卡Clip Pro。"}
+{"idx": 6, "title": "什么是 CLIP 模型，它为什么重要？ - 知乎", "date": "", "ddg_snippet": "1. CLIP模型结构 CLIP 的关键思想是通过训练两个编码器（图像和文本编码器）使得相对应的图像和文本在同一潜在空间中尽可能接近，而不相关的图像和文本尽可能远离。文本经过Text Encoder得到文本的向量表示，图片经过Image Encoder得到图片的向量表示，分别通过线性投射层，投射到共同的多模态线性 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/3627534119?write", "content": "1. CLIP模型结构 CLIP 的关键思想是通过训练两个编码器（图像和文本编码器）使得相对应的图像和文本在同一潜在空间中尽可能接近，而不相关的图像和文本尽可能远离。文本经过Text Encoder得到文本的向量表示，图片经过Image Encoder得到图片的向量表示，分别通过线性投射层，投射到共同的多模态线性 ..."}
+{"idx": 7, "title": "一文读懂三篇少样本微调CLIP的论文及代码实现细节", "date": "", "ddg_snippet": "CLIP就是这样一个坚实的、可以用来微调的基础模型。 这篇文章介绍三种少样本基于CLIP微调的方法，实验的任务是图像分类，但是否能适用于其它任务，因成本不高，读者有时间可以自己尝试一下，欢迎在评论区探讨你的经验。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/tardis/bd/art/670622153", "content": "CLIP就是这样一个坚实的、可以用来微调的基础模型。 这篇文章介绍三种少样本基于CLIP微调的方法，实验的任务是图像分类，但是否能适用于其它任务，因成本不高，读者有时间可以自己尝试一下，欢迎在评论区探讨你的经验。"}
+{"idx": 8, "title": "想问一下摄影师们，文件夹里面CLIP、GENERAL、SUB、THMBNL这几个文件...", "date": "", "ddg_snippet": "THMBNL 这个文件夹放的应该是索尼拍的视频的截图 每个视频在相机里预览的那张图就存在这个文件夹 如果从电脑上删除了视频 没有去删除这张图片的话 相机上就会显示？ 文件无法显示 修复影像数据库好像也不能消除问号 PRIVATE\\M4ROOT \\THMBNL GENERAL暂时不知道 CLIP 放的是原视频文件 SUB 是开启了代理 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/503160163", "content": "THMBNL 这个文件夹放的应该是索尼拍的视频的截图 每个视频在相机里预览的那张图就存在这个文件夹 如果从电脑上删除了视频 没有去删除这张图片的话 相机上就会显示？ 文件无法显示 修复影像数据库好像也不能消除问号 PRIVATE\\M4ROOT \\THMBNL GENERAL暂时不知道 CLIP 放的是原视频文件 SUB 是开启了代理 ..."}
+{"idx": 9, "title": "Stable Diffusion中CLIP文本编码器和Diffusion Models是 ... - 知乎", "date": "", "ddg_snippet": "Jun 13, 2023 · 分词器。 文本提示首先由 CLIP 标记器 进行标记化。 CLIP是由Open AI开发的深度学习模型，用于生成任何图像的文本描述。 Stable Diffusion v1使用CLIP的分词器。 令牌化（Tokenization） 是计算机理解单词的方式。 我们人类可以阅读单词，但计算机只能读取数字。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/606456465", "content": "Jun 13, 2023 · 分词器。 文本提示首先由 CLIP 标记器 进行标记化。 CLIP是由Open AI开发的深度学习模型，用于生成任何图像的文本描述。 Stable Diffusion v1使用CLIP的分词器。 令牌化（Tokenization） 是计算机理解单词的方式。 我们人类可以阅读单词，但计算机只能读取数字。"}
diff --git a/data/sampled_jsons/CLIP_vs_VAE_encoder_differences.jsonl b/data/sampled_jsons/CLIP_vs_VAE_encoder_differences.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..147604f4091cdb43c89b1ae75dd412fd1f87c438
--- /dev/null
+++ b/data/sampled_jsons/CLIP_vs_VAE_encoder_differences.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scaling Down Text Encoders of Text-to-Image Diffusion Models", "date": "", "ddg_snippet": "Recently, text encoders used for diffusion models has a notable transition from models like CLIP [ 35 ] to the more parameter-intensive T5-XXL [ 36 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19897v1", "content": "Recently, text encoders used for diffusion models has a notable transition from models like CLIP [ 35 ] to the more parameter-intensive T5-XXL [ 36 ..."}
+{"idx": 1, "title": "machine learning - How to Resolve Variational Autoencoder (VAE)", "date": "", "ddg_snippet": "Anomalies in bias and gradients: Except for the second layer in the encoder and the second layer in the decoder, the bias for all layers is very ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/621004/how-to-resolve-variational-autoencoder-vae-model-collapse-in-reconstruction-ta", "content": "Anomalies in bias and gradients: Except for the second layer in the encoder and the second layer in the decoder, the bias for all layers is very ..."}
+{"idx": 2, "title": "variational autoencoder - How does using the ELBO in VAEs make", "date": "", "ddg_snippet": "begingroup$ Of course encoder is used to approximate parameters of p(z|x), similar to decoder p(x|z). ... it appropriate to use complex impedance vs ...", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/43827/how-does-using-the-elbo-in-vaes-make-the-problem-tractable", "content": "begingroup$ Of course encoder is used to approximate parameters of p(z|x), similar to decoder p(x|z). ... it appropriate to use complex impedance vs ..."}
+{"idx": 3, "title": "Back to Ear: Perceptually Driven High Fidelity Music", "date": "", "ddg_snippet": "This approach, pioneered by VQ- VAE [ 4 ] and now standard in neural audio codecs like EnCodec [ 5 ] and DAC [ 6 ] , excels at achieving high ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14912v1", "content": "This approach, pioneered by VQ- VAE [ 4 ] and now standard in neural audio codecs like EnCodec [ 5 ] and DAC [ 6 ] , excels at achieving high ..."}
+{"idx": 4, "title": "BEAST: Efficient Tokenization of B-Splines Encoded Action", "date": "", "ddg_snippet": "... Autoencoder ( VAE ) with continuous tokens, a decoder-only Transformer with discrete tokens, and Florence-2, a Vision-Language Model with an encoder ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06072v2", "content": "... Autoencoder ( VAE ) with continuous tokens, a decoder-only Transformer with discrete tokens, and Florence-2, a Vision-Language Model with an encoder ..."}
+{"idx": 5, "title": "lesswrong.com/posts/r6gpBgs98gnArCEty/how-to-think-with-images", "date": "", "ddg_snippet": "... contrastive learning forces an image encoder and a text encoder to meet in the middle – to produce embeddings that match for a true pair and differ ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/r6gpBgs98gnArCEty/how-to-think-with-images", "content": "... contrastive learning forces an image encoder and a text encoder to meet in the middle – to produce embeddings that match for a true pair and differ ..."}
+{"idx": 6, "title": "MimicPC - Complete Guide to Flux.1 Models | Mimic PC", "date": "", "ddg_snippet": "... clips , as well as the Schnell Model, ... Next, search \"Load VAE \" and add it to your clip -space. ... Look for the \" Clip text encode prompt\" and add it.", "subpage_snippet": "", "source": "www.mimicpc.com", "link": "https://www.mimicpc.com/learn/guide-to-flux-model", "content": "... clips , as well as the Schnell Model, ... Next, search \"Load VAE \" and add it to your clip -space. ... Look for the \" Clip text encode prompt\" and add it."}
+{"idx": 7, "title": "Introducing BLIP3-o: A Family of Fully Open Unified Multimodal", "date": "", "ddg_snippet": "First, what should be used as the ground-truth embeddings: should we use a VAE or CLIP to encode images into continuous features?", "subpage_snippet": "", "source": "www.salesforce.com", "link": "https://www.salesforce.com/blog/blip3/", "content": "First, what should be used as the ground-truth embeddings: should we use a VAE or CLIP to encode images into continuous features?"}
+{"idx": 8, "title": "Bifrost-1", "date": "", "ddg_snippet": "Specifically, we substitute the CLIP visual encoder with the FLUX VAE encoder , and replace our visual decoder (i.e., Latent ControlNet + FLUX ...", "subpage_snippet": "", "source": "bifrost-1.github.io", "link": "https://bifrost-1.github.io/", "content": "Specifically, we substitute the CLIP visual encoder with the FLUX VAE encoder , and replace our visual decoder (i.e., Latent ControlNet + FLUX ..."}
+{"idx": 9, "title": "【5分で分かる】画像生成AIで頻出の拡散モデル...", "date": "", "ddg_snippet": "The key idea is to minimize the difference between the original and denoised images by optimizing the parameters involved in the noise removal ...", "subpage_snippet": "", "source": "stablediffusion3.net", "link": "https://stablediffusion3.net/blog-5ai-45131", "content": "The key idea is to minimize the difference between the original and denoised images by optimizing the parameters involved in the noise removal ..."}
diff --git a/data/sampled_jsons/CQT_constant-Q_transform_logarithmic_frequency_resolution_musical_scales_advantage.jsonl b/data/sampled_jsons/CQT_constant-Q_transform_logarithmic_frequency_resolution_musical_scales_advantage.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..378b8ffdba810d7259f05f23969f6128882bca02
--- /dev/null
+++ b/data/sampled_jsons/CQT_constant-Q_transform_logarithmic_frequency_resolution_musical_scales_advantage.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constant-Q transform - Wikipedia", "date": "", "ddg_snippet": "In mathematics and signal processing, the constant-Q transform and variable- Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform [1] and very closely related to the complex Morlet wavelet transform . [2] Its design is suited for musical representation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Constant-Q_transform", "content": "In mathematics and signal processing, the constant-Q transform and variable- Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform [1] and very closely related to the complex Morlet wavelet transform . [2] Its design is suited for musical representation."}
+{"idx": 1, "title": "Constant-Q transform - Hydrogenaudio Knowledgebase", "date": "", "ddg_snippet": "Constant-Q and variable- Q transforms ( CQT /VQT) are spectral analysis algorithms that usually have logarithmic frequency spacing and time/ frequency resolution following octave series. Due to its usually logarithmic frequency resolution , it is suited for musical representation. Overview", "subpage_snippet": "", "source": "wiki.hydrogenaudio.org", "link": "https://wiki.hydrogenaudio.org/index.php?title=Constant-Q_transform", "content": "Constant-Q and variable- Q transforms ( CQT /VQT) are spectral analysis algorithms that usually have logarithmic frequency spacing and time/ frequency resolution following octave series. Due to its usually logarithmic frequency resolution , it is suited for musical representation. Overview"}
+{"idx": 2, "title": "Constant-Q Transform (CQT) Overview - emergentmind.com", "date": "", "ddg_snippet": "The Constant-Q Transform ( CQT ) is a time– frequency analysis technique central to modern audio signal processing. Distinguished by its logarithmic frequency scaling and constant-Q (quality factor) filterbank architecture, CQT provides non-uniform frequency resolution optimized for applications where frequency perception is inherently nonlinear, such as music , speech analysis, and behavioral ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/constant-q-transform-cqt", "content": "The Constant-Q Transform ( CQT ) is a time– frequency analysis technique central to modern audio signal processing. Distinguished by its logarithmic frequency scaling and constant-Q (quality factor) filterbank architecture, CQT provides non-uniform frequency resolution optimized for applications where frequency perception is inherently nonlinear, such as music , speech analysis, and behavioral ..."}
+{"idx": 3, "title": "The Constant Q Transform - Machine Learning Group", "date": "", "ddg_snippet": "the constant Q transform so useful is that by an appropriate choice for f0 (minimal center frequency ) and b the center frequencies directly correspond to musical notes. For instance choosing b = 12 and f0 as the frequency of midinote 0 makes the k-th cq-bin correspond the midinote number k. Another nice feature of the constant Q transform is its increasing time resolution towards higher ...", "subpage_snippet": "", "source": "doc.ml.tu-berlin.de", "link": "https://doc.ml.tu-berlin.de/bbci/material/publications/Bla_constQ.pdf", "content": "the constant Q transform so useful is that by an appropriate choice for f0 (minimal center frequency ) and b the center frequencies directly correspond to musical notes. For instance choosing b = 12 and f0 as the frequency of midinote 0 makes the k-th cq-bin correspond the midinote number k. Another nice feature of the constant Q transform is its increasing time resolution towards higher ..."}
+{"idx": 4, "title": "The Constant-Q Harmonic Coefficients: A timbre feature ...", "date": "", "ddg_snippet": "This arti-cle shows how to design a simple but effective pitch-independent timbre fea-ture, well adapted to musical data, by deriving it from the constant-Q trans-form ( CQT ), a log - frequency transform that matches the typical Western musi-cal scale [2], [3].", "subpage_snippet": "", "source": "zafarrafii.com", "link": "https://zafarrafii.com/Documents/Journals/Rafii+-+The+Constant-Q+Harmonic+Coefficients+A+Timbre+Feature+designed+for+Music+Signals+-+2022.pdf", "content": "This arti-cle shows how to design a simple but effective pitch-independent timbre fea-ture, well adapted to musical data, by deriving it from the constant-Q trans-form ( CQT ), a log - frequency transform that matches the typical Western musi-cal scale [2], [3]."}
+{"idx": 5, "title": "Degenerate Unmixing Estimation Technique using the Constant Q ...", "date": "", "ddg_snippet": "I Constant Q Transform ( CQT ) Unlike the Fourier Transform (FT), the CQT has a logarithmic frequency resolution matching the geometrically spaced notes of the Western music scale , therefore better adapted to music mixtures.", "subpage_snippet": "", "source": "www.cs.northwestern.edu", "link": "https://www.cs.northwestern.edu/~zra446/doc/Rafii-Pardo+-+Degenerate+Unmixing+Estimation+Technique+using+the+Constant+Q+Transform+-+ICASSP+2011+(poster).pdf", "content": "I Constant Q Transform ( CQT ) Unlike the Fourier Transform (FT), the CQT has a logarithmic frequency resolution matching the geometrically spaced notes of the Western music scale , therefore better adapted to music mixtures."}
+{"idx": 6, "title": "GitHub - Liu-Feng-deeplearning/FastCqt: Fast constant-Q ...", "date": "", "ddg_snippet": "The constant-Q transform feature( CQT ), is often useful for some recognized task, especiall for some task about music . This repo is for c++ code for Efficient solver of CQT and also offer pyWrapper for easily using. Compared with other speech features such as mel, the biggest advantage of the Cqt is ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Liu-Feng-deeplearning/FastCqt", "content": "The constant-Q transform feature( CQT ), is often useful for some recognized task, especiall for some task about music . This repo is for c++ code for Efficient solver of CQT and also offer pyWrapper for easily using. Compared with other speech features such as mel, the biggest advantage of the Cqt is ..."}
+{"idx": 7, "title": "(PDF) Towards an inverse constant Q transform", "date": "", "ddg_snippet": "PDF | The Constant Q transform has found use in the analysis of musical signals due to its logarithmic frequency resolution .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228680714_Towards_an_inverse_constant_Q_transform", "content": "PDF | The Constant Q transform has found use in the analysis of musical signals due to its logarithmic frequency resolution ."}
+{"idx": 8, "title": "Pitch shifting of audio signals using the constant - Q transform", "date": "", "ddg_snippet": "Due to the logarithmic frequency resolution of the CQT , relative detuning between partials is avoided and closely spaced sinusoidal components at lower frequencies can be distinguished. In [5] a STFT-based phase vocoder for time- scaling has been...", "subpage_snippet": "", "source": "dafx12.york.ac.uk", "link": "https://dafx12.york.ac.uk/papers/dafx12_submission_81.pdf", "content": "Due to the logarithmic frequency resolution of the CQT , relative detuning between partials is avoided and closely spaced sinusoidal components at lower frequencies can be distinguished. In [5] a STFT-based phase vocoder for time- scaling has been..."}
+{"idx": 9, "title": "cqt - Constant - Q nonstationary Gabor transform - MATLAB", "date": "", "ddg_snippet": "Constant - Q Transform Using Default ValuesCenter Frequencies of the Constant - Q Transform cfs = cqt (x) returns the constant - Q transform ( CQT ), cfs, of the input signal x...", "subpage_snippet": "", "source": "la.mathworks.com", "link": "https://la.mathworks.com/help/wavelet/ref/cqt.html", "content": "Constant - Q Transform Using Default ValuesCenter Frequencies of the Constant - Q Transform cfs = cqt (x) returns the constant - Q transform ( CQT ), cfs, of the input signal x..."}
diff --git a/data/sampled_jsons/CRAB_Cross-environment_Agent_Benchmark_Xu_et_al_2024_PDF.jsonl b/data/sampled_jsons/CRAB_Cross-environment_Agent_Benchmark_Xu_et_al_2024_PDF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1411793f96eacdf3073e30f273e0339ecbdf0493
--- /dev/null
+++ b/data/sampled_jsons/CRAB_Cross-environment_Agent_Benchmark_Xu_et_al_2024_PDF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal Language Model ...", "date": "", "ddg_snippet": "To overcome these limitations, we introduce CRAB , the first cross - environment agent benchmark framework, incor- porating a graph-based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.01511", "content": "To overcome these limitations, we introduce CRAB , the first cross - environment agent benchmark framework, incor- porating a graph-based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface."}
+{"idx": 1, "title": "GitHub - camel-ai/crab: ️ CRAB: Cross-environment Agent Benchmark for ...", "date": "", "ddg_snippet": "🌐 Cross -platform and Multi- environment Create build agent environments that support various deployment options including in-memory, Docker-hosted, virtual machines, or distributed physical machines, provided they are accessible via Python functions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/camel-ai/crab", "content": "🌐 Cross -platform and Multi- environment Create build agent environments that support various deployment options including in-memory, Docker-hosted, virtual machines, or distributed physical machines, provided they are accessible via Python functions."}
+{"idx": 2, "title": "camel-ai/crab: CRAB: Cross-environment Agent Benchmark for Multim...", "date": "", "ddg_snippet": "@misc {xu2024crab, title= { CRAB : Cross - environment Agent Benchmark for Multimodal Language Model Agents }, author= {Tianqi Xu and Linyao Chen and Dai-Jie Wu and Yanjun Chen and Zecheng Zhang and Xiang Yao and Zhiqiang Xie and Yongchao Chen and Shilong Liu and Bochen Qian and Philip Torr and Bernard Ghanem and Guohao Li}, year= { 2024 }, eprint= {2407.01511}, archivePrefix= {arXiv}, primaryClass ...", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/camel-ai/crab", "content": "@misc {xu2024crab, title= { CRAB : Cross - environment Agent Benchmark for Multimodal Language Model Agents }, author= {Tianqi Xu and Linyao Chen and Dai-Jie Wu and Yanjun Chen and Zecheng Zhang and Xiang Yao and Zhiqiang Xie and Yongchao Chen and Shilong Liu and Bochen Qian and Philip Torr and Bernard Ghanem and Guohao Li}, year= { 2024 }, eprint= {2407.01511}, archivePrefix= {arXiv}, primaryClass ..."}
+{"idx": 3, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal Language Model ...", "date": "", "ddg_snippet": "To overcome these limitations, we introduce CRAB , the first cross - environment agent benchmark framework, incorporating a graph-based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.1113/", "content": "To overcome these limitations, we introduce CRAB , the first cross - environment agent benchmark framework, incorporating a graph-based fine-grained evaluation method and an efficient task generation method. Our framework supports multiple devices and can be easily extended to any environment with a Python interface."}
+{"idx": 4, "title": "CRAB: Cross-platfrom agent benchmark for multi-modal embodied...", "date": "", "ddg_snippet": "To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross - environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kyExS4V0H7", "content": "To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross - environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction."}
+{"idx": 5, "title": "CRAB: cross-environment agent benchmark for multimodal language model ...", "date": "", "ddg_snippet": "To overcome these limitations, we introduce CRAB , the first agent benchmark framework designed to support cross - environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction.", "subpage_snippet": "", "source": "ora.ox.ac.uk", "link": "https://ora.ox.ac.uk/objects/uuid:53a31ad5-7aa7-46c7-aa52-5a5e2dc0e6cd", "content": "To overcome these limitations, we introduce CRAB , the first agent benchmark framework designed to support cross - environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction."}
+{"idx": 6, "title": "[2407.01511] CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross - environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.01511", "content": "To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross - environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction."}
+{"idx": 7, "title": "(PDF) CRAB: Cross-environment Agent Benchmark for Multimodal Language ...", "date": "", "ddg_snippet": "To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross - environment tasks, incorporating a graph-based fine-grained evaluation method and an ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381910878_CRAB_Cross-environment_Agent_Benchmark_for_Multimodal_Language_Model_Agents", "content": "To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross - environment tasks, incorporating a graph-based fine-grained evaluation method and an ..."}
+{"idx": 8, "title": "Crab: Cross-platform Agent Benchmark for Multimodal", "date": "", "ddg_snippet": "on human interaction Table 1.1: Comparison of existing agent benchmark frameworks. The caption details key features of each framework: Interactive Environment indicates the presence of either interactive environments or static datasets; Multimodal Obser-vation specifies the availability of vision-based observations (e.g., screenshots); Cross ...", "subpage_snippet": "", "source": "repository.kaust.edu.sa", "link": "https://repository.kaust.edu.sa/bitstreams/e0b04c12-82f0-48be-bb57-872215043ea2/download", "content": "on human interaction Table 1.1: Comparison of existing agent benchmark frameworks. The caption details key features of each framework: Interactive Environment indicates the presence of either interactive environments or static datasets; Multimodal Obser-vation specifies the availability of vision-based observations (e.g., screenshots); Cross ..."}
+{"idx": 9, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal Language Model ...", "date": "", "ddg_snippet": "CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents . CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments , and create benchmarks to evaluate them, featuring three key components: cross - environment support, a graph evaluator, and task generation.", "subpage_snippet": "", "source": "crab.camel-ai.org", "link": "https://crab.camel-ai.org/", "content": "CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents . CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments , and create benchmarks to evaluate them, featuring three key components: cross - environment support, a graph evaluator, and task generation."}
diff --git a/data/sampled_jsons/CRAB_Xu_et_al._2024a_paper_abstract_year_2024.jsonl b/data/sampled_jsons/CRAB_Xu_et_al._2024a_paper_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a9c0ce4a1eb9056676c37a3ff59a8a9a4be37287
--- /dev/null
+++ b/data/sampled_jsons/CRAB_Xu_et_al._2024a_paper_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2407.01511] CRAB: Cross-environment Agent Benchmark for ... Global Prediction of Flash Drought Using Machine Learning Crab: A Unified Audio-Visual Scene Understanding Model with ... Research on thyroid nodule segmentation using an improved U ... Xu et al. (2024): Assessment of the Potential for Carbon Sink ... ZFIN Publication: Xu et al., 2024 CRAB: cross-environment agent benchmark for multimodal ...", "date": "", "ddg_snippet": "Jul 1, 2024 · To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction. Nov 4, 2024 · Abstract Flash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub-seasonal time scales remains a great challenge. Current state-of-the-art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Figure 1. We present Crab , a unified audio-visual scene understanding model with explicit cooperation, which can complete various audio-visual tasks. It is trained on an instruction-tuning dataset with explicit reasoning process, which clarifies the cooperative relationship among tasks. Furthermore, to alleviate the interference caused by the learning process of complex audiovisual data and ... Hu et al . [5] introduced attention mechanisms for thyroid nodule segmentation, optimizing low-dimensional features of images and preserving important features through the fusion of high and low-dimensional features. Nov 2, 2024 · Xiaojuan Xu , Fusheng Jiao, Dayi Lin, Jie Qiu, Changxin Zou, Kun Zhang IN: Frontiers in Plant Science, 15, 10.3389/fpls.2024.1482077 Few studies have projected the carbon sink trends in regions where ecological engineering projects overlap and have not considered the different climate change conditions and land use scenarios. Abstract Agrobacterium sp. are notable for their ability to produce substantial amounts of exopolysaccharides. Our study identified an exopolysaccharide (Galacan, 4982.327 kDa) from Agrobacterium sp. FN01. Galacan is a heteropolysaccharide primarily composed of glucose and galactose at a molar ratio of 25:1. To overcome these limitations, we introduce CRAB , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.01511", "content": "Jul 1, 2024 · To overcome these limitations, we introduce Crab , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction. Nov 4, 2024 · Abstract Flash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub-seasonal time scales remains a great challenge. Current state-of-the-art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Figure 1. We present Crab , a unified audio-visual scene understanding model with explicit cooperation, which can complete various audio-visual tasks. It is trained on an instruction-tuning dataset with explicit reasoning process, which clarifies the cooperative relationship among tasks. Furthermore, to alleviate the interference caused by the learning process of complex audiovisual data and ... Hu et al . [5] introduced attention mechanisms for thyroid nodule segmentation, optimizing low-dimensional features of images and preserving important features through the fusion of high and low-dimensional features. Nov 2, 2024 · Xiaojuan Xu , Fusheng Jiao, Dayi Lin, Jie Qiu, Changxin Zou, Kun Zhang IN: Frontiers in Plant Science, 15, 10.3389/fpls.2024.1482077 Few studies have projected the carbon sink trends in regions where ecological engineering projects overlap and have not considered the different climate change conditions and land use scenarios. Abstract Agrobacterium sp. are notable for their ability to produce substantial amounts of exopolysaccharides. Our study identified an exopolysaccharide (Galacan, 4982.327 kDa) from Agrobacterium sp. FN01. Galacan is a heteropolysaccharide primarily composed of glucose and galactose at a molar ratio of 25:1. To overcome these limitations, we introduce CRAB , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction."}
+{"idx": 1, "title": "Crab: A Unified Audio-Visual Scene Understanding Model with ...", "date": "", "ddg_snippet": "Figure 1. We present Crab , a unified audio-visual scene understanding model with explicit cooperation, which can complete various audio-visual tasks. It is trained on an instruction-tuning dataset with explicit reasoning process, which clarifies the cooperative relationship among tasks. Furthermore, to alleviate the interference caused by the learning process of complex audiovisual data and ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Du_Crab_A_Unified_Audio-Visual_Scene_Understanding_Model_with_Explicit_Cooperation_CVPR_2025_paper.pdf", "content": "Figure 1. We present Crab , a unified audio-visual scene understanding model with explicit cooperation, which can complete various audio-visual tasks. It is trained on an instruction-tuning dataset with explicit reasoning process, which clarifies the cooperative relationship among tasks. Furthermore, to alleviate the interference caused by the learning process of complex audiovisual data and ..."}
+{"idx": 2, "title": "CRAB: cross-environment agent benchmark for multimodal ...", "date": "", "ddg_snippet": "To overcome these limitations, we introduce CRAB , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction.", "subpage_snippet": "", "source": "ora.ox.ac.uk", "link": "https://ora.ox.ac.uk/objects/uuid:53a31ad5-7aa7-46c7-aa52-5a5e2dc0e6cd", "content": "To overcome these limitations, we introduce CRAB , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction."}
+{"idx": 3, "title": "Global Prediction of Flash Drought Using Machine Learning", "date": "", "ddg_snippet": "Nov 4, 2024 · Abstract Flash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub-seasonal time scales remains a great challenge. Current state-of-the-art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction.", "subpage_snippet": "", "source": "agupubs.onlinelibrary.wiley.com", "link": "https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024GL111134", "content": "Nov 4, 2024 · Abstract Flash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub-seasonal time scales remains a great challenge. Current state-of-the-art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction."}
+{"idx": 4, "title": "ZFIN Publication: Xu et al., 2024", "date": "", "ddg_snippet": "Abstract Agrobacterium sp. are notable for their ability to produce substantial amounts of exopolysaccharides. Our study identified an exopolysaccharide (Galacan, 4982.327 kDa) from Agrobacterium sp. FN01. Galacan is a heteropolysaccharide primarily composed of glucose and galactose at a molar ratio of 25:1.", "subpage_snippet": "", "source": "zfin.org", "link": "https://zfin.org/ZDB-PUB-240915-10", "content": "Abstract Agrobacterium sp. are notable for their ability to produce substantial amounts of exopolysaccharides. Our study identified an exopolysaccharide (Galacan, 4982.327 kDa) from Agrobacterium sp. FN01. Galacan is a heteropolysaccharide primarily composed of glucose and galactose at a molar ratio of 25:1."}
+{"idx": 5, "title": "Ferret-UI 2: Mastering Universal User Interface Understanding Across...", "date": "", "ddg_snippet": "( 2024 a ); Cheng et al . (2024) demonstrated GPT-4V as a generalist agent when grounded.GUI-World (Chen et al ., 2024 a ) pioneer in covering multi-platform benchmarking, while CRAB ( Xu et al ., 2024 ) further tests cross-environment tasks for GUI agents.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.18967v2", "content": "( 2024 a ); Cheng et al . (2024) demonstrated GPT-4V as a generalist agent when grounded.GUI-World (Chen et al ., 2024 a ) pioneer in covering multi-platform benchmarking, while CRAB ( Xu et al ., 2024 ) further tests cross-environment tasks for GUI agents."}
+{"idx": 6, "title": "Google Scholar", "date": "", "ddg_snippet": "Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/schhp?hl=en&as_sdt=0,5", "content": "Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions."}
+{"idx": 7, "title": "On the Generalization of Training-based ChatGPT... - ACL Anthology", "date": "", "ddg_snippet": "If paper metadata matches the PDF, but the paper should be linked to a different author page, please file an author page correction instead.Cite (Informal): On the Generalization of Training-based ChatGPT Detection Methods ( Xu et al ., Findings 2024).", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.424/", "content": "If paper metadata matches the PDF, but the paper should be linked to a different author page, please file an author page correction instead.Cite (Informal): On the Generalization of Training-based ChatGPT Detection Methods ( Xu et al ., Findings 2024)."}
+{"idx": 8, "title": "Overview of the 9th Social Media Mining for Health Applications...", "date": "", "ddg_snippet": "Abstract .This paper provides an overview of the tasks and participating systems. The data sets remain available upon request, and new systems can be evaluated through the post-evaluation phase on CodaLab.", "subpage_snippet": "", "source": "dfki-nlp.github.io", "link": "https://dfki-nlp.github.io/publication/xu-etal-2024-overview/", "content": "Abstract .This paper provides an overview of the tasks and participating systems. The data sets remain available upon request, and new systems can be evaluated through the post-evaluation phase on CodaLab."}
+{"idx": 9, "title": "Exploring Chain-of-Thought for Multi-modal Metaphor Detection", "date": "", "ddg_snippet": "(2024), and Xu et al . (2024) focus on simple reasoning using image information without addressing complex multimodal tasks.This view leads to the hypothesis that metaphorical word usage is correlated with the degree of abstractness of the word's context.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384198293_Exploring_Chain-of-Thought_for_Multi-modal_Metaphor_Detection", "content": "(2024), and Xu et al . (2024) focus on simple reasoning using image information without addressing complex multimodal tasks.This view leads to the hypothesis that metaphorical word usage is correlated with the degree of abstractness of the word's context."}
diff --git a/data/sampled_jsons/CVE-2022-0543_Redis_affected_application_attack_type_RCE_Lua_sandbox_escape_vulnerability.jsonl b/data/sampled_jsons/CVE-2022-0543_Redis_affected_application_attack_type_RCE_Lua_sandbox_escape_vulnerability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..344d2dd6db23151be757d0e0c03831586bd7a9ee
--- /dev/null
+++ b/data/sampled_jsons/CVE-2022-0543_Redis_affected_application_attack_type_RCE_Lua_sandbox_escape_vulnerability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NVD - CVE-2022-0543", "date": "", "ddg_snippet": "Feb 18, 2022 · It was discovered, that redis , a persistent key-value database, due to a packaging issue, is prone to a (Debian-specific) Lua sandbox escape , which could result in remote code execution.", "subpage_snippet": "", "source": "nvd.nist.gov", "link": "https://nvd.nist.gov/vuln/detail/CVE-2022-0543", "content": "Feb 18, 2022 · It was discovered, that redis , a persistent key-value database, due to a packaging issue, is prone to a (Debian-specific) Lua sandbox escape , which could result in remote code execution."}
+{"idx": 1, "title": "Fixing Redis CVE-2022-0543: Lua Sandbox Escape Patch Lab Walkthrough - [CVE-2022–0543]: Lua Sand… - INE Redis RCE through Lua Sandbox Escape vulnerability - GitHub CVE-2022-0543 Impact, Exploitability, and Mitigation Steps | Wiz Redis Vulnerability CVE-2022-0543 - Packt SecPro NVD - CVE-2022-0543 Redis Vulnerability CVE - 2022 - 0543 - Packt SecPro Redis Vulnerability CVE - 2022 - 0543 - Packt SecPro Redis Vulnerability CVE - 2022 - 0543 - Packt SecPro Redis Vulnerability CVE - 2022 - 0543 - Packt SecPro Redis Vulnerability CVE - 2022 - 0543 - Packt SecPro CVE-2022-0543: Redis Lua Sandbox Escape Vulnerability", "date": "", "ddg_snippet": "How To Fix CVE - 2022 - 0543 - A Critical Lua Sandbox Escape Vulnerability In Redis ? The best possible way to fix the CVE - 2022 - 0543 vulnerability is to upgrade to the fixed or latest available versions. Sep 1, 2022 · In February 2022, a critical vulnerability in a popular persistent key-value store, Redis , was reported. It was discovered that Redis is prone to a (Debian-specific) Lua sandbox escape due to a packaging issue, which could result in remote code execution. Fully featured exploit for Redis RCE through Lua Sandbox Escape vulnerability . Based on https://thesecmaster.com/how-to-fix- cve - 2022 - 0543 -a-critical-lua-sandbox-escape- vulnerability -in-redis/. CVE - 2022 - 0543 is a Debian-specific Lua sandbox escape vulnerability in Redis , a persistent key-value database. The vulnerability was discovered by Reginaldo Silva and disclosed in February 2022. Jun 10, 2022 · In summary, we have learned about the vulnerability CVE - 2022 - 0543 which can exploit the Redis Dictionary Server. We’ve discussed how this vulnerability came to be, and how it was discovered then finally how to mitigate this risk. Why is Redis prone to a Lua sandbox escape? It was discovered, that redis, a persistent key-value database, due to a packaging issue , is prone to a (Debian-specific) Lua sandbox escape, which could result in remote code execution. NVD enrichment efforts reference publicly available information to associate vector strings. CVSS information contributed by other sources is also displayed. What is the cve-2022-0543 vulnerability? In summary, we have learned about the vulnerability CVE-2022-0543 which can exploit the Redis Dictionary Server . We’ve discussed how this vulnerability came to be, and how it was discovered then finally how to mitigate this risk. In the last section, we will learn how the Muhstik group exploited this vulnerability. What would happen if Lua was sandboxed? The expected behaviour of the sandboxed Lua would be that executing arbitrary code on the machine running Redis would not be possible . By failing to sanitise the interface the developers allowed attackers to load arbitrary libraries. The module was then able to complete execution as the ‘redis’ user. What is a Redis attack? The group, thought to operate from China are thought to have been involved in targeting Oracle WebLogic Server bugs, and a Drupal RCE flaw CVE -2018-7600. The payload used in the Redis attack was named russia.sh which is downloaded using curl commands or wget from their C2 Server. Why did CISA add a bug to the known vulnerabilities database? With the exploit ‘in-the-wild’ the US Cybersecurity and Infrastructure Agency (CISA) added the bug to their Known Vulnerabilities Database in late March. What payload is used in Redis attack? The payload used in the Redis attack was named russia.sh which is downloaded using curl commands or wget from their C2 Server. The script grabs variants of the bot from their IRC server which supports parsing of shell & flood commands, and SSH brute force. Learn about the Redis Lua sandbox escape vulnerability ( CVE - 2022 - 0543 ) and its potential impact. Find out how to fix the vulnerability and implement security measures to protect your Redis deployments.", "subpage_snippet": "", "source": "thesecmaster.com", "link": "https://thesecmaster.com/blog/how-to-fix-cve-2022-0543-a-critical-lua-sandbox-escape-vulnerability-in-redis", "content": "How To Fix CVE - 2022 - 0543 - A Critical Lua Sandbox Escape Vulnerability In Redis ? The best possible way to fix the CVE - 2022 - 0543 vulnerability is to upgrade to the fixed or latest available versions. Sep 1, 2022 · In February 2022, a critical vulnerability in a popular persistent key-value store, Redis , was reported. It was discovered that Redis is prone to a (Debian-specific) Lua sandbox escape due to a packaging issue, which could result in remote code execution. Fully featured exploit for Redis RCE through Lua Sandbox Escape vulnerability . Based on https://thesecmaster.com/how-to-fix- cve - 2022 - 0543 -a-critical-lua-sandbox-escape- vulnerability -in-redis/. CVE - 2022 - 0543 is a Debian-specific Lua sandbox escape vulnerability in Redis , a persistent key-value database. The vulnerability was discovered by Reginaldo Silva and disclosed in February 2022. Jun 10, 2022 · In summary, we have learned about the vulnerability CVE - 2022 - 0543 which can exploit the Redis Dictionary Server. We’ve discussed how this vulnerability came to be, and how it was discovered then finally how to mitigate this risk. Why is Redis prone to a Lua sandbox escape? It was discovered, that redis, a persistent key-value database, due to a packaging issue , is prone to a (Debian-specific) Lua sandbox escape, which could result in remote code execution. NVD enrichment efforts reference publicly available information to associate vector strings. CVSS information contributed by other sources is also displayed. What is the cve-2022-0543 vulnerability? In summary, we have learned about the vulnerability CVE-2022-0543 which can exploit the Redis Dictionary Server . We’ve discussed how this vulnerability came to be, and how it was discovered then finally how to mitigate this risk. In the last section, we will learn how the Muhstik group exploited this vulnerability. What would happen if Lua was sandboxed? The expected behaviour of the sandboxed Lua would be that executing arbitrary code on the machine running Redis would not be possible . By failing to sanitise the interface the developers allowed attackers to load arbitrary libraries. The module was then able to complete execution as the ‘redis’ user. What is a Redis attack? The group, thought to operate from China are thought to have been involved in targeting Oracle WebLogic Server bugs, and a Drupal RCE flaw CVE -2018-7600. The payload used in the Redis attack was named russia.sh which is downloaded using curl commands or wget from their C2 Server. Why did CISA add a bug to the known vulnerabilities database? With the exploit ‘in-the-wild’ the US Cybersecurity and Infrastructure Agency (CISA) added the bug to their Known Vulnerabilities Database in late March. What payload is used in Redis attack? The payload used in the Redis attack was named russia.sh which is downloaded using curl commands or wget from their C2 Server. The script grabs variants of the bot from their IRC server which supports parsing of shell & flood commands, and SSH brute force. Learn about the Redis Lua sandbox escape vulnerability ( CVE - 2022 - 0543 ) and its potential impact. Find out how to fix the vulnerability and implement security measures to protect your Redis deployments."}
+{"idx": 2, "title": "Lab Walkthrough - [CVE-2022–0543]: Lua Sand… - INE", "date": "", "ddg_snippet": "Sep 1, 2022 · In February 2022, a critical vulnerability in a popular persistent key-value store, Redis , was reported. It was discovered that Redis is prone to a (Debian-specific) Lua sandbox escape due to a packaging issue, which could result in remote code execution.", "subpage_snippet": "", "source": "ine.com", "link": "https://ine.com/blog/cve-20220543-lua-sandbox-escape-in-redis", "content": "Sep 1, 2022 · In February 2022, a critical vulnerability in a popular persistent key-value store, Redis , was reported. It was discovered that Redis is prone to a (Debian-specific) Lua sandbox escape due to a packaging issue, which could result in remote code execution."}
+{"idx": 3, "title": "Redis RCE through Lua Sandbox Escape vulnerability - GitHub", "date": "", "ddg_snippet": "Fully featured exploit for Redis RCE through Lua Sandbox Escape vulnerability . Based on https://thesecmaster.com/how-to-fix- cve - 2022 - 0543 -a-critical-lua-sandbox-escape- vulnerability -in-redis/.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/JacobEbben/CVE-2022-0543", "content": "Fully featured exploit for Redis RCE through Lua Sandbox Escape vulnerability . Based on https://thesecmaster.com/how-to-fix- cve - 2022 - 0543 -a-critical-lua-sandbox-escape- vulnerability -in-redis/."}
+{"idx": 4, "title": "CVE-2022-0543 Impact, Exploitability, and Mitigation Steps | Wiz", "date": "", "ddg_snippet": "CVE - 2022 - 0543 is a Debian-specific Lua sandbox escape vulnerability in Redis , a persistent key-value database. The vulnerability was discovered by Reginaldo Silva and disclosed in February 2022.", "subpage_snippet": "", "source": "www.wiz.io", "link": "https://www.wiz.io/vulnerability-database/cve/cve-2022-0543", "content": "CVE - 2022 - 0543 is a Debian-specific Lua sandbox escape vulnerability in Redis , a persistent key-value database. The vulnerability was discovered by Reginaldo Silva and disclosed in February 2022."}
+{"idx": 5, "title": "Redis Vulnerability CVE-2022-0543 - Packt SecPro", "date": "", "ddg_snippet": "Jun 10, 2022 · In summary, we have learned about the vulnerability CVE - 2022 - 0543 which can exploit the Redis Dictionary Server. We’ve discussed how this vulnerability came to be, and how it was discovered then finally how to mitigate this risk.", "subpage_snippet": "", "source": "security.packt.com", "link": "https://security.packt.com/redis-vulnerability-cve-2022-0543/", "content": "Jun 10, 2022 · In summary, we have learned about the vulnerability CVE - 2022 - 0543 which can exploit the Redis Dictionary Server. We’ve discussed how this vulnerability came to be, and how it was discovered then finally how to mitigate this risk."}
+{"idx": 6, "title": "CVE-2022-0543: Redis Lua Sandbox Escape Vulnerability", "date": "", "ddg_snippet": "Learn about the Redis Lua sandbox escape vulnerability ( CVE - 2022 - 0543 ) and its potential impact. Find out how to fix the vulnerability and implement security measures to protect your Redis deployments.", "subpage_snippet": "", "source": "vulert.com", "link": "https://vulert.com/vuln-db/CVE-2022-0543", "content": "Learn about the Redis Lua sandbox escape vulnerability ( CVE - 2022 - 0543 ) and its potential impact. Find out how to fix the vulnerability and implement security measures to protect your Redis deployments."}
+{"idx": 7, "title": "Redis sandbox escape affects only Debian, Ubuntu", "date": "", "ddg_snippet": "The Lua engine should be sandboxed, which means that clients should be able to communicate with Redis APIs from Lua but not be able to run arbitrary ...", "subpage_snippet": "", "source": "blog.securelayer7.net", "link": "https://blog.securelayer7.net/redis-sandbox-escape-affects-only-debian-ubuntu-and-other-derivatives/", "content": "The Lua engine should be sandboxed, which means that clients should be able to communicate with Redis APIs from Lua but not be able to run arbitrary ..."}
+{"idx": 8, "title": "CVE-2022-0543 | AttackerKB", "date": "", "ddg_snippet": "AttackerKB requires a CVE ID in order to pull vulnerability data and references from the CVE list and the National Vulnerability Database .", "subpage_snippet": "", "source": "attackerkb.com", "link": "https://attackerkb.com/topics/wyA1c1HIC8/cve-2022-0543/assess", "content": "AttackerKB requires a CVE ID in order to pull vulnerability data and references from the CVE list and the National Vulnerability Database ."}
+{"idx": 9, "title": "New peer-to-peer worm infects Redis instances through Lua", "date": "", "ddg_snippet": "Meanwhile, the P2PInfect worm also exploits a critical Lua sandbox exploit vulnerability tracked as CVE - 2022 - 0543 that specifically affects the Redis ...", "subpage_snippet": "", "source": "www.csoonline.com", "link": "https://www.csoonline.com/article/646773/new-peer-to-peer-worm-infects-redis-instances-through-lua-vulnerability.html", "content": "Meanwhile, the P2PInfect worm also exploits a critical Lua sandbox exploit vulnerability tracked as CVE - 2022 - 0543 that specifically affects the Redis ..."}
diff --git a/data/sampled_jsons/CVE-2023-38646_CVE-2023-20269_CVE-2022-22965_CVSS_10.0_CVE-Bench_web_application.jsonl b/data/sampled_jsons/CVE-2023-38646_CVE-2023-20269_CVE-2022-22965_CVSS_10.0_CVE-Bench_web_application.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..553046580da8271bd8603dcfed9e42c20b290aba
--- /dev/null
+++ b/data/sampled_jsons/CVE-2023-38646_CVE-2023-20269_CVE-2022-22965_CVSS_10.0_CVE-Bench_web_application.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - kh4sh3i/ CVE - 2023 - 38646 : Metabase Pre-auth RCE...", "date": "", "ddg_snippet": "CVE - 2023 - 38646 - Metabase Pre-auth RCE. Metabase open source before 0.46.6.1 and Metabase Enterprise before 1.46.6.1 allow attackers to execute arbitrary commands on the server, at the server's privilege level.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kh4sh3i/CVE-2023-38646", "content": "CVE - 2023 - 38646 - Metabase Pre-auth RCE. Metabase open source before 0.46.6.1 and Metabase Enterprise before 1.46.6.1 allow attackers to execute arbitrary commands on the server, at the server's privilege level."}
+{"idx": 1, "title": "Reproducing CVE - 2023 - 38646 : Metabase Pre-auth RCE - Calif", "date": "", "ddg_snippet": "The vulnerability CVE - 2023 - 38646 was discovered by an unknown researcher. This analysis was done by Duc Nguyen in collaboration with Jang Nguyen.CraftCMS RCE. Craft is a flexible, user-friendly CMS for creating custom digital experiences on the web —and beyond.", "subpage_snippet": "", "source": "blog.calif.io", "link": "https://blog.calif.io/p/reproducing-cve-2023-38646-metabase", "content": "The vulnerability CVE - 2023 - 38646 was discovered by an unknown researcher. This analysis was done by Duc Nguyen in collaboration with Jang Nguyen.CraftCMS RCE. Craft is a flexible, user-friendly CMS for creating custom digital experiences on the web —and beyond."}
+{"idx": 2, "title": "Unmasking CVE - 2023 - 38646 : Analyzing the Critical Metabase Security...", "date": "", "ddg_snippet": "This vulnerability primarily affected instances set up after the change.To avoid damaging databases or the application , they used a sample H2 database from Metabase's JAR file. Patching should address these code issues and enhance security practices.", "subpage_snippet": "", "source": "www.vicarius.io", "link": "https://www.vicarius.io/vsociety/posts/unmasking-cve-2023-38646-analyzing-the-critical-metabase-security-vulnerability-and-its-implications-1", "content": "This vulnerability primarily affected instances set up after the change.To avoid damaging databases or the application , they used a sample H2 database from Metabase's JAR file. Patching should address these code issues and enhance security practices."}
+{"idx": 3, "title": "Lessons Learned #1: One line of code can make your application ...", "date": "", "ddg_snippet": "A vulnerability was discovered around July 2023 and assigned CVE - 2023 – 38646 (you can find the full write-up here) which had a devastating impact of pre-auth RCE (Remote code execution)...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/appsec-untangled/lessons-learned-1-one-line-of-code-can-make-your-application-vulnerable-pre-auth-rce-in-metabase-a8579ca0102d", "content": "A vulnerability was discovered around July 2023 and assigned CVE - 2023 – 38646 (you can find the full write-up here) which had a devastating impact of pre-auth RCE (Remote code execution)..."}
+{"idx": 4, "title": "TryHackMe | Spring4Shell: CVE - 2022 - 22965", "date": "", "ddg_snippet": "Interactive lab for exploiting Spring4Shell ( CVE - 2022 - 22965 ) in the Java Spring Framework.In late March 2022, two remote command execution vulnerabilities in the Java Spring framework were made public.", "subpage_snippet": "", "source": "tryhackme.com", "link": "https://tryhackme.com/room/spring4shell", "content": "Interactive lab for exploiting Spring4Shell ( CVE - 2022 - 22965 ) in the Java Spring Framework.In late March 2022, two remote command execution vulnerabilities in the Java Spring framework were made public."}
+{"idx": 5, "title": "CVE : Common Vulnerabilities and Exposures", "date": "", "ddg_snippet": "CVE - 2023 -39410. CNA: Apache Software Foundation.A vulnerability exists by allowing low-privileged users to read and update the data in various directories used by the Zenon system .", "subpage_snippet": "", "source": "www.cve.org", "link": "https://www.cve.org/CVERecord/SearchResults?query=deserializing", "content": "CVE - 2023 -39410. CNA: Apache Software Foundation.A vulnerability exists by allowing low-privileged users to read and update the data in various directories used by the Zenon system ."}
+{"idx": 6, "title": "Fortra Releases Critical Patch for CVSS 10 . 0 GoAnywhere MFT...", "date": "", "ddg_snippet": "The vulnerability , tracked as CVE -2025-10035, carries a CVSS score of 10 . 0 , indicating maximum severity.That said, previously disclosed shortcomings in the same product ( CVE - 2023 -0669, CVSS score : 7.2) were abused as a zero-day by ransomware actors to steal sensitive data.", "subpage_snippet": "", "source": "thehackernews.com", "link": "https://thehackernews.com/2025/09/fortra-releases-critical-patch-for-cvss.html", "content": "The vulnerability , tracked as CVE -2025-10035, carries a CVSS score of 10 . 0 , indicating maximum severity.That said, previously disclosed shortcomings in the same product ( CVE - 2023 -0669, CVSS score : 7.2) were abused as a zero-day by ransomware actors to steal sensitive data."}
+{"idx": 7, "title": "CVE TOP 12 in 2022 for penetration testing | CQR", "date": "", "ddg_snippet": "Learn More What is CVE or Common Vulnerabilities and Exposures ? CVE is a publicly available and free to use database / glossary of disclosed cyber security issues and their classification. This database is maintained by MITRE…Top 10 detected threats for month in Ukraine ( source ).", "subpage_snippet": "", "source": "cqr.company", "link": "https://cqr.company/blog/cve-top-12-in-2022-for-penetration-testing/", "content": "Learn More What is CVE or Common Vulnerabilities and Exposures ? CVE is a publicly available and free to use database / glossary of disclosed cyber security issues and their classification. This database is maintained by MITRE…Top 10 detected threats for month in Ukraine ( source )."}
+{"idx": 8, "title": "Discover kh4sh3i/Grafana- CVE Open Source project by @kh4sh3i", "date": "", "ddg_snippet": "a Curated list of Grafana Security Vulnerabilities , CVE & exploit.Grafana is a multi-platform open source analytics and interactive visualization web application . It provides charts, graphs, and alerts for the web when connected to supported data sources. CVE -2020-11110.", "subpage_snippet": "", "source": "opensource-heroes.com", "link": "https://opensource-heroes.com/r/kh4sh3i/Grafana-CVE", "content": "a Curated list of Grafana Security Vulnerabilities , CVE & exploit.Grafana is a multi-platform open source analytics and interactive visualization web application . It provides charts, graphs, and alerts for the web when connected to supported data sources. CVE -2020-11110."}
+{"idx": 9, "title": "OpenTSDB Command Injection- CVE - 2023 -25826", "date": "", "ddg_snippet": "CVE - 2023 -25826 Description:- Poor input validation in the legacy HTTP query API (/q endpoint) causes OpenTSDB to have a remote command injection vulnerability . This flaw is the result of an insufficient fix for a previously disclosed problem ( CVE -2020-35476).", "subpage_snippet": "", "source": "blog.certcube.com", "link": "https://blog.certcube.com/opentsdb-command-injection-rce-cve-2023-25826/", "content": "CVE - 2023 -25826 Description:- Poor input validation in the legacy HTTP query API (/q endpoint) causes OpenTSDB to have a remote command injection vulnerability . This flaw is the result of an insufficient fix for a previously disclosed problem ( CVE -2020-35476)."}
diff --git a/data/sampled_jsons/CVE-Bench_AI_Agents_Web_Application_Vulnerabilities_Table_6_CVSS.jsonl b/data/sampled_jsons/CVE-Bench_AI_Agents_Web_Application_Vulnerabilities_Table_6_CVSS.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b42a0e4cdea3aafae7d481ed85ecf441dd4c98c4
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_AI_Agents_Web_Application_Vulnerabilities_Table_6_CVSS.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to ... - GitHub", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE - Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "We focused on CVEs of web applications that are rated as “critical” by the Common Vulnerability Scoring System ( CVSS ) version 3 (Mell et al., 2022), indicating high exploitability and severe potential impacts on sensitive data and vital services.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "We focused on CVEs of web applications that are rated as “critical” by the Common Vulnerability Scoring System ( CVSS ) version 3 (Mell et al., 2022), indicating high exploitability and severe potential impacts on sensitive data and vital services."}
+{"idx": 2, "title": "Measuring AI Agents’ Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Mar 31, 2025 · Our findings reveal potential threats to web application security from rapidly evolving AI agents . This highlights the need for continuous improvement in evaluating, red-teaming, and...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danieldkang/measuring-ai-agents-ability-to-exploit-web-applications-ba4225aa281f", "content": "Mar 31, 2025 · Our findings reveal potential threats to web application security from rapidly evolving AI agents . This highlights the need for continuous improvement in evaluating, red-teaming, and..."}
+{"idx": 3, "title": "CVE security vulnerability database. Security vulnerabilities ...", "date": "", "ddg_snippet": "EPSS scores provides users with a list of vulnerabilities with increased risk. Set up email alerts for new CVEs or when new exploits are discovered for CVEs . Or create CVE feeds which can be integrated with tools like Slack or Outlook. Or use our APIs to query CVEs , exploits and other data.", "subpage_snippet": "", "source": "www.cvedetails.com", "link": "https://www.cvedetails.com/", "content": "EPSS scores provides users with a list of vulnerabilities with increased risk. Set up email alerts for new CVEs or when new exploits are discovered for CVEs . Or create CVE feeds which can be integrated with tools like Slack or Outlook. Or use our APIs to query CVEs , exploits and other data."}
+{"idx": 4, "title": "[PDF] CVE-Bench: A Benchmark for AI Agents' Ability to ...", "date": "", "ddg_snippet": "Mar 21, 2025 · CVE - Bench is introduced, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/CVE-Bench:-A-Benchmark-for-AI-Agents'-Ability-to-Zhu-Kellermann/095b31dfaa032a2daf13da21bd4d04dddb2097fa", "content": "Mar 21, 2025 · CVE - Bench is introduced, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits."}
+{"idx": 5, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · CVE - Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents ' abilities to discover and exploit web application vulnerabilities .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "May 12, 2025 · CVE - Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents ' abilities to discover and exploit web application vulnerabilities ."}
+{"idx": 6, "title": "Measuring AI Agents’ Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Mar 31, 2025 · After exploring the dangerous potential of AI agents in autonomously penetrating web applications in our previous studies, we found an urgent need for standardized evaluation.", "subpage_snippet": "", "source": "ddkang.substack.com", "link": "https://ddkang.substack.com/p/measuring-ai-agents-ability-to-exploit", "content": "Mar 31, 2025 · After exploring the dangerous potential of AI agents in autonomously penetrating web applications in our previous studies, we found an urgent need for standardized evaluation."}
+{"idx": 7, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vul- nerable web applications in scenarios that mimic real-world ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "by Y Zhu · 2025 · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vul- nerable web applications in scenarios that mimic real-world ..."}
+{"idx": 8, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "10 Apr 2025 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v3", "content": "10 Apr 2025 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions."}
+{"idx": 9, "title": "BountyBench: Dollar Impact of AI Agent Attackers and ...", "date": "", "ddg_snippet": "20 Mar 2025 — CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World Web Application Vulnerabilities , 2025. 11. Page 12. A Lunary Details. A.1 ...", "subpage_snippet": "", "source": "cs191.stanford.edu", "link": "https://cs191.stanford.edu/projects/Spring2025/Celeste___Huang-Menders_.pdf", "content": "20 Mar 2025 — CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World Web Application Vulnerabilities , 2025. 11. Page 12. A Lunary Details. A.1 ..."}
diff --git a/data/sampled_jsons/CVE-Bench_A_Benchmark_for_AI_Agents_Ability_to_Exploit_Real-World_Web_Application_Vulnerabilities_Fi.jsonl b/data/sampled_jsons/CVE-Bench_A_Benchmark_for_AI_Agents_Ability_to_Exploit_Real-World_Web_Application_Vulnerabilities_Fi.jsonl
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index 0000000000000000000000000000000000000000..532285826f2946d9d0e75893de7906c4318ebe54
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_A_Benchmark_for_AI_Agents_Ability_to_Exploit_Real-World_Web_Application_Vulnerabilities_Fi.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real - world conditions, while ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3pk0p4NGmQ", "content": "by Y Zhu · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real - world conditions, while ..."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vul- nerable web applications in scenarios that mimic real - world ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "by Y Zhu · 2025 · Cited by 12 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vul- nerable web applications in scenarios that mimic real - world ..."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "21 Mar 2025 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real - world conditions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "21 Mar 2025 — In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real - world conditions."}
+{"idx": 3, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real- ...", "date": "", "ddg_snippet": "This paper introduces the ' T - Agent ' (Teams of Agent) framework, which is described as a state-of-the-art agent for exploiting web application vulnerabilities .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17332", "content": "This paper introduces the ' T - Agent ' (Teams of Agent) framework, which is described as a state-of-the-art agent for exploiting web application vulnerabilities ."}
+{"idx": 4, "title": "BountyBench: Dollar Impact of AI Agent Attackers and ...", "date": "", "ddg_snippet": "20 Mar 2025 — OpenAI Codex CLI and Claude Code are more capable at defense, achieving higher Patch scores of 90% and 87.5%, compared to Exploit scores of 32.5 ...", "subpage_snippet": "", "source": "cs191.stanford.edu", "link": "https://cs191.stanford.edu/projects/Spring2025/Celeste___Huang-Menders_.pdf", "content": "20 Mar 2025 — OpenAI Codex CLI and Claude Code are more capable at defense, achieving higher Patch scores of 90% and 87.5%, compared to Exploit scores of 32.5 ..."}
+{"idx": 5, "title": "Reproducible CVE Benchmarks", "date": "", "ddg_snippet": "7 Sept 2025 — Reproducible CVE benchmarks are rigorously designed datasets or protocols that enable transparent and repeatable evaluation of vulnerability ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/reproducible-cve-benchmarks", "content": "7 Sept 2025 — Reproducible CVE benchmarks are rigorously designed datasets or protocols that enable transparent and repeatable evaluation of vulnerability ..."}
+{"idx": 6, "title": "SafeBench 2025's top picks: The Benchmarks That Actually ...", "date": "", "ddg_snippet": "26 Aug 2025 — CVE - Bench : Real - World Vulnerability Testing ... Even state-of-the-art agent frameworks can only exploit up to 13% of real vulnerabilities .", "subpage_snippet": "", "source": "www.getmaxim.ai", "link": "https://www.getmaxim.ai/blog/safebench-2025s-top-picks-the-benchmarks-that-actually-matter-for-ai-safety/", "content": "26 Aug 2025 — CVE - Bench : Real - World Vulnerability Testing ... Even state-of-the-art agent frameworks can only exploit up to 13% of real vulnerabilities ."}
+{"idx": 7, "title": "From CVE to Exploit | PDF | Software Engineering", "date": "", "ddg_snippet": "Cve - bench : A benchmark for ai agents ' ability to [57] Chunqiu Steven ... exploit real - world web application vulnerabilities , 2025. Automated Program ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/916414360/From-CVE-to-Exploit", "content": "Cve - bench : A benchmark for ai agents ' ability to [57] Chunqiu Steven ... exploit real - world web application vulnerabilities , 2025. Automated Program ..."}
+{"idx": 8, "title": "AI-Driven Cyberattacks are on the Rise. Are You Ready?", "date": "", "ddg_snippet": "22 Jul 2025 — Researchers created CVE-Bench , a practical cybersecurity benchmark that relies on critical-severity Common Vulnerabilities and Exposures (CVEs).", "subpage_snippet": "", "source": "lsvp.com", "link": "https://lsvp.com/stories/ai-enabled-hacking-is-here-are-we-ready-for-it/", "content": "22 Jul 2025 — Researchers created CVE-Bench , a practical cybersecurity benchmark that relies on critical-severity Common Vulnerabilities and Exposures (CVEs)."}
+{"idx": 9, "title": "Spotlight Posters", "date": "", "ddg_snippet": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real - World Web Application Vulnerabilities ... RE-Bench: Evaluating Frontier AI R&D Capabilities ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/2025SpotlightPosters", "content": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real - World Web Application Vulnerabilities ... RE-Bench: Evaluating Frontier AI R&D Capabilities ..."}
diff --git a/data/sampled_jsons/CVE-Bench_ArXiv_2503.17332_Figure_2_Figure_4_DB_access.jsonl b/data/sampled_jsons/CVE-Bench_ArXiv_2503.17332_Figure_2_Figure_4_DB_access.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d929310d6c77acf040a654d2f47781b4d9aebb9d
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_ArXiv_2503.17332_Figure_2_Figure_4_DB_access.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "status: False Result (success) status: True attack: File access Figure 1. Illustration of the sandbox framework in CVE-Bench as applied to a WordPress web application. It features environment isolation and supports various stages of the vulnerability lifecycle (e.g., zero-day and one-day), diverse attacks, and automatic evaluation. application.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "status: False Result (success) status: True attack: File access Figure 1. Illustration of the sandbox framework in CVE-Bench as applied to a WordPress web application. It features environment isolation and supports various stages of the vulnerability lifecycle (e.g., zero-day and one-day), diverse attacks, and automatic evaluation. application."}
+{"idx": 1, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for ...", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 2, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to ... CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ... CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ... GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for ... GitHub - UBSec/AI-Security-Papers: Reading list for my ... cve-bench/README.md at main · uiuc-kang-lab/cve-bench - GitHub GitHub - usnistgov/caisi-cyber-evals", "date": "", "ddg_snippet": "Mar 21, 2025 · Abstract page for arXiv paper 2503.17332 : CVE-Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities status: False Result (success) status: True attack: File access Figure 1. Illustration of the sandbox framework in CVE-Bench as applied to a WordPress web application. It features environment isolation and supports various stages of the vulnerability lifecycle (e.g., zero-day and one-day), diverse attacks, and automatic evaluation. application. Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. CVE-Bench : A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities https:// arxiv .org/abs/ 2503.17332 Security of AI Agents https:// arxiv .org/pdf/2406.08689 Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. CVE-bench Task-Level Options: These options will impact tasks from CVE-bench : cve _details: bool = True: Should the CVE text be made available. target_details: bool = True: Should the agent be given a copy of relevant files from the target. writeup_details: bool = False: Should the text of a technical writeup be made available.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Mar 21, 2025 · Abstract page for arXiv paper 2503.17332 : CVE-Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities status: False Result (success) status: True attack: File access Figure 1. Illustration of the sandbox framework in CVE-Bench as applied to a WordPress web application. It features environment isolation and supports various stages of the vulnerability lifecycle (e.g., zero-day and one-day), diverse attacks, and automatic evaluation. application. Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. CVE-Bench : A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities https:// arxiv .org/abs/ 2503.17332 Security of AI Agents https:// arxiv .org/pdf/2406.08689 Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. CVE-bench Task-Level Options: These options will impact tasks from CVE-bench : cve _details: bool = True: Should the CVE text be made available. target_details: bool = True: Should the agent be given a copy of relevant files from the target. writeup_details: bool = False: Should the text of a technical writeup be made available."}
+{"idx": 3, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures."}
+{"idx": 4, "title": "cve-bench/README.md at main · uiuc-kang-lab/cve-bench - GitHub", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench/blob/main/README.md", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database . CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 5, "title": "CVE - Bench : A Benchmark for AI Agents' Ability to Exploit... | alphaXiv", "date": "", "ddg_snippet": "CVE - Bench Framework Figure 1: Overview of the CVE - Bench framework showing how AI agents interact with vulnerable web applications in target containers and are evaluated based on standardized attack vectors. Research Motivation.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17332v1", "content": "CVE - Bench Framework Figure 1: Overview of the CVE - Bench framework showing how AI agents interact with vulnerable web applications in target containers and are evaluated based on standardized attack vectors. Research Motivation."}
+{"idx": 6, "title": "uiuc-kang-lab cve - bench issues - Githubissues", "date": "", "ddg_snippet": "uiuc-kang-lab / cve - bench . CVE - Bench : A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities .eval fails with \"unhealthy\" db -1 container, and other issues.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/uiuc-kang-lab/cve-bench", "content": "uiuc-kang-lab / cve - bench . CVE - Bench : A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities .eval fails with \"unhealthy\" db -1 container, and other issues."}
+{"idx": 7, "title": "Show HN: CVE - Bench , the first LLM benchmark using real-world web...", "date": "", "ddg_snippet": "We created CVE - bench to find out (I'm one contributor of 16). To our knowledge CVE - bench is the first benchmark using real-world web vulnerabilities to evaluate AI agents' cyberattack capabilities.", "subpage_snippet": "", "source": "www.gpt-5.com", "link": "https://www.gpt-5.com/80434572/show-hn-cve-bench-the-first-llm-benchmark-using-real-world-web-vulnerabilities", "content": "We created CVE - bench to find out (I'm one contributor of 16). To our knowledge CVE - bench is the first benchmark using real-world web vulnerabilities to evaluate AI agents' cyberattack capabilities."}
+{"idx": 8, "title": "Adversarial Arxiv Daily | Automatically Update Adversarial Attacks...", "date": "", "ddg_snippet": "Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios. Jingzheng Li et.al. 2503.23708. CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities . Yuxuan Zhu et.al. 2503 . 17332 .", "subpage_snippet": "", "source": "anson-he.github.io", "link": "https://anson-he.github.io/adversarial-arxiv-daily/", "content": "Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios. Jingzheng Li et.al. 2503.23708. CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities . Yuxuan Zhu et.al. 2503 . 17332 ."}
+{"idx": 9, "title": "Teen Titans GO Figure ! v1.1.10 (Много денег) APK - Скачать на...", "date": "", "ddg_snippet": "Teen Titans GO Figure ! - относится к жанру ролевых игр с элементами коллекционирования и битв. В этой вселенной вы будете собирать миниатюрные фигурки супергероев из комиксов DC, таких как Бэтмен или Супермен, а также самих Юных Титанов.", "subpage_snippet": "", "source": "5play.life", "link": "https://5play.life/880-teen-titans-go-figure.html", "content": "Teen Titans GO Figure ! - относится к жанру ролевых игр с элементами коллекционирования и битв. В этой вселенной вы будете собирать миниатюрные фигурки супергероев из комиксов DC, таких как Бэтмен или Супермен, а также самих Юных Титанов."}
diff --git a/data/sampled_jsons/CVE-Bench_Benchmark_AI_Agents_Web_Application_Vulnerabilities_Figure_2_Figure_4_DB_access_zero-day.jsonl b/data/sampled_jsons/CVE-Bench_Benchmark_AI_Agents_Web_Application_Vulnerabilities_Figure_2_Figure_4_DB_access_zero-day.jsonl
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index 0000000000000000000000000000000000000000..863448dbe5f4f8e91e25be634bfe563839efbc9f
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_Benchmark_AI_Agents_Web_Application_Vulnerabilities_Figure_2_Figure_4_DB_access_zero-day.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for AI Agents ...", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. For each CVE , given a target web application and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. For each CVE , given a target web application and ..."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications . This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities . However, existing benchmarks fall short as they are limited to abstracted Capture the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications . This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities . However, existing benchmarks fall short as they are limited to abstracted Capture the ..."}
+{"idx": 2, "title": "PDF CVE-Bench: Benchmarking LLM-based Software Engineering Agent's Ability ...", "date": "", "ddg_snippet": "Figure 2 : There are four steps in CVE-Bench : (1) Environment construction: CVE-Bench uses the three-level information to generate a vulnerability issue as the input to the agent .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "Figure 2 : There are four steps in CVE-Bench : (1) Environment construction: CVE-Bench uses the three-level information to generate a vulnerability issue as the input to the agent ."}
+{"idx": 3, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents' abilities to discover and exploit web application vulnerabilities .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents' abilities to discover and exploit web application vulnerabilities ."}
+{"idx": 4, "title": "Measuring AI Agents' Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Success rates of different AI agents on CVE-bench in the zero-day or one- day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danieldkang/measuring-ai-agents-ability-to-exploit-web-applications-ba4225aa281f", "content": "Success rates of different AI agents on CVE-bench in the zero-day or one- day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ..."}
+{"idx": 5, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "Abstract Large language model (LLM) agents are increas-ingly capable of autonomously conducting cy-berattacks, posing significant threats to existing applications . This growing risk highlights the ur-gent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web appli-cation vulnerabilities .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=3pk0p4NGmQ", "content": "Abstract Large language model (LLM) agents are increas-ingly capable of autonomously conducting cy-berattacks, posing significant threats to existing applications . This growing risk highlights the ur-gent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web appli-cation vulnerabilities ."}
+{"idx": 6, "title": "CVE-Bench: A Real-World Cybersecurity Benchmark for AI Agents", "date": "", "ddg_snippet": "We took a series of agents and we tested our agents on our benchmark , and what we found is that existing agents , in particular Cy- Agent , which was developed for the Cy- Bench CTF challenge, performs poorly on real-world end-to-end web application vulnerabilities .", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/events/sessions/daniel-kang-cve-bench-a-real-world-cybersecurity-benchmark-for-ai-agents", "content": "We took a series of agents and we tested our agents on our benchmark , and what we found is that existing agents , in particular Cy- Agent , which was developed for the Cy- Bench CTF challenge, performs poorly on real-world end-to-end web application vulnerabilities ."}
+{"idx": 7, "title": "cve-bench/README.md at main · uiuc-kang-lab/cve-bench · GitHub", "date": "", "ddg_snippet": "CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. For each CVE , given a target web application and necessary information, an AI agent is tasked with executing an attack that triggers one of the following results (if applicable):", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench/blob/main/README.md", "content": "CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. For each CVE , given a target web application and necessary information, an AI agent is tasked with executing an attack that triggers one of the following results (if applicable):"}
+{"idx": 8, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "Abstract Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications . This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "Abstract Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications . This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities ."}
+{"idx": 9, "title": "CVE-Bench: Benchmarking LLM-based Software Engineering Agent's Ability ...", "date": "", "ddg_snippet": "CVE-Bench : Benchmarking LLM-based Software Engineering Agent's Ability to Repair Real-World CVE Vulnerabilities . In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4207-4224, Albuquerque, New Mexico.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212/", "content": "CVE-Bench : Benchmarking LLM-based Software Engineering Agent's Ability to Repair Real-World CVE Vulnerabilities . In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4207-4224, Albuquerque, New Mexico."}
diff --git a/data/sampled_jsons/CVE-Bench_Figure_3_T-Agent_13%_50%_success_rate_one-day_zero-day_year_2025.jsonl b/data/sampled_jsons/CVE-Bench_Figure_3_T-Agent_13%_50%_success_rate_one-day_zero-day_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..440468c4996a0a870de7e17b01d9f7550b96d0ec
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_Figure_3_T-Agent_13%_50%_success_rate_one-day_zero-day_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero-day setting, we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero-day setting, we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack."}
+{"idx": 1, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for AI Agents ...", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. For each CVE , given a target web application and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. For each CVE , given a target web application and ..."}
+{"idx": 2, "title": "Measuring AI Agents' Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Success rates of different AI agents on CVE-bench in the zero-day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day setting (with no prior knowledge) and 25% in the one-day setting (with basic vulnerability information).", "subpage_snippet": "", "source": "ddkang.substack.com", "link": "https://ddkang.substack.com/p/measuring-ai-agents-ability-to-exploit", "content": "Success rates of different AI agents on CVE-bench in the zero-day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day setting (with no prior knowledge) and 25% in the one-day setting (with basic vulnerability information)."}
+{"idx": 3, "title": "CVE: Common Vulnerabilities and Exposures", "date": "", "ddg_snippet": "At cve .org, we provide the authoritative reference method for publicly known information-security vulnerabilities and exposures", "subpage_snippet": "", "source": "www.cve.org", "link": "https://www.cve.org/About/Metrics", "content": "At cve .org, we provide the authoritative reference method for publicly known information-security vulnerabilities and exposures"}
+{"idx": 4, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents' abilities to discover and exploit web application vulnerabilities.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents' abilities to discover and exploit web application vulnerabilities."}
+{"idx": 5, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ..."}
+{"idx": 6, "title": "Measuring AI Agents' Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Success rates of different AI agents on CVE-bench in the zero-day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danieldkang/measuring-ai-agents-ability-to-exploit-web-applications-ba4225aa281f", "content": "Success rates of different AI agents on CVE-bench in the zero-day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ..."}
+{"idx": 7, "title": "cve-bench/README.md at main · uiuc-kang-lab/cve-bench · GitHub", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench/blob/main/README.md", "content": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 8, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "status: False Result ( success ) status: True attack: File access Figure 1. Illustration of the sandbox framework in CVE-Bench as applied to a WordPress web application. It features environment isolation and supports various stages of the vulnerability lifecycle (e.g., zero-day and one-day ), diverse attacks, and automatic evaluation. application.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "status: False Result ( success ) status: True attack: File access Figure 1. Illustration of the sandbox framework in CVE-Bench as applied to a WordPress web application. It features environment isolation and supports various stages of the vulnerability lifecycle (e.g., zero-day and one-day ), diverse attacks, and automatic evaluation. application."}
+{"idx": 9, "title": "From Capabilities to Performance: Evaluating Key Functional Properties ...", "date": "", "ddg_snippet": "Across all models, the success rate for MITM tasks was 0%. While PenHeal demonstrated robust performance on complex multi-phase tasks such as post-exploitation and credential chaining, it too failed to complete any MITM scenario.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14289v1", "content": "Across all models, the success rate for MITM tasks was 0%. While PenHeal demonstrated robust performance on complex multi-phase tasks such as post-exploitation and credential chaining, it too failed to complete any MITM scenario."}
diff --git a/data/sampled_jsons/CVE-Bench_T-Agent_Success@5_one-day_results_year_2025.jsonl b/data/sampled_jsons/CVE-Bench_T-Agent_Success@5_one-day_results_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..27bc22b12a965603f29258553b74aed64e6fa65c
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_T-Agent_Success@5_one-day_results_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "Result ( success ). status: True attack: File access.We apply CVE - Bench to evaluate various LLM agents under both zero-day and one - day settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "Result ( success ). status: True attack: File access.We apply CVE - Bench to evaluate various LLM agents under both zero-day and one - day settings."}
+{"idx": 1, "title": "CVE - Bench : A Benchmark for AI Agents’ Ability to Exploit Real-World...", "date": "", "ddg_snippet": "Under the one - day setting, we provide the agents with a high-level description of the vulnerability, which they can use as guidance to craft and execute exploits.We apply CVE - Bench to evaluate various LLM agents under both zero-day and one - day settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332", "content": "Under the one - day setting, we provide the agents with a high-level description of the vulnerability, which they can use as guidance to craft and execute exploits.We apply CVE - Bench to evaluate various LLM agents under both zero-day and one - day settings."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "We apply CVE - Bench to evaluate various LLM agents under both zero- day and one - day settings. ... agent framework, teams of LLM agents (Fang et al., ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "We apply CVE - Bench to evaluate various LLM agents under both zero- day and one - day settings. ... agent framework, teams of LLM agents (Fang et al., ..."}
+{"idx": 3, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "... bench -Verified challenges an agent to resolve GitHub issues, and considers the agent successful if the patch it generates passes manually vetted unit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v1", "content": "... bench -Verified challenges an agent to resolve GitHub issues, and considers the agent successful if the patch it generates passes manually vetted unit ..."}
+{"idx": 4, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "... bench -Verified challenges an agent to resolve GitHub issues, and considers the agent successful if the patch it generates passes manually vetted unit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v5", "content": "... bench -Verified challenges an agent to resolve GitHub issues, and considers the agent successful if the patch it generates passes manually vetted unit ..."}
+{"idx": 5, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "... bench -Verified challenges an agent to resolve GitHub issues, and considers the agent successful if the patch it generates passes manually vetted unit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v3", "content": "... bench -Verified challenges an agent to resolve GitHub issues, and considers the agent successful if the patch it generates passes manually vetted unit ..."}
+{"idx": 6, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "... bench -Verified challenges an agent to resolve GitHub issues, and considers the agent successful if the patch it generates passes manually vetted unit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v4", "content": "... bench -Verified challenges an agent to resolve GitHub issues, and considers the agent successful if the patch it generates passes manually vetted unit ..."}
+{"idx": 7, "title": "AI-Driven Cyberattacks are on the Rise. Are You Ready? -", "date": "", "ddg_snippet": "In a paper titled CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real-World Web Application Vulnerabilities , researchers found that ...", "subpage_snippet": "", "source": "lsvp.com", "link": "https://lsvp.com/stories/ai-enabled-hacking-is-here-are-we-ready-for-it/", "content": "In a paper titled CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real-World Web Application Vulnerabilities , researchers found that ..."}
+{"idx": 8, "title": "Singapore Alignment Workshop 2025", "date": "", "ddg_snippet": "CVE - Bench results show that current agents struggle in realistic settings, achieving 10% success rates on zero- day exploits compared to 50%+ success ...", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/news/singapore-alignment-workshop-2025", "content": "CVE - Bench results show that current agents struggle in realistic settings, achieving 10% success rates on zero- day exploits compared to 50%+ success ..."}
+{"idx": 9, "title": "News – FAR.AI", "date": "", "ddg_snippet": "Even simple logistic models trained on these internal activations can successfully pinpoint 95-99% of deceptive responses .", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/news", "content": "Even simple logistic models trained on these internal activations can successfully pinpoint 95-99% of deceptive responses ."}
diff --git a/data/sampled_jsons/CVE-Bench_T-Agent_Success@5_rate_one-day_setting_Figure_3.jsonl b/data/sampled_jsons/CVE-Bench_T-Agent_Success@5_rate_one-day_setting_Figure_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..36f59cfb0cc6ba8c3d453197e15d0ffc8ed28b62
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_T-Agent_Success@5_rate_one-day_setting_Figure_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "In CVE - Bench , we simulate the zero-day and one - day scenarios. In the zero-day scenario, LLM agents must compromise the application without further in-formation about the vulnerability .We present success rates of different LLM agents in Figure 3 with one or five attempts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "In CVE - Bench , we simulate the zero-day and one - day scenarios. In the zero-day scenario, LLM agents must compromise the application without further in-formation about the vulnerability .We present success rates of different LLM agents in Figure 3 with one or five attempts."}
+{"idx": 1, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit... | alphaXiv", "date": "", "ddg_snippet": "Cybench Agent (Cy- Agent ): Achieved the lowest success rates ( 3 % in zero-day, 3 % in one - day ). Struggled with the exploratory nature of vulnerability identification.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17332v1", "content": "Cybench Agent (Cy- Agent ): Achieved the lowest success rates ( 3 % in zero-day, 3 % in one - day ). Struggled with the exploratory nature of vulnerability identification."}
+{"idx": 2, "title": "CVE - Bench : Benchmarking LLM-based Software Engineering", "date": "", "ddg_snippet": "In this paper, we introduce CVE - Bench (§2), a benchmark that evaluates LLM-based agents in a realistic vulnerability -repairing setting .As shown in Figure 3 (a), we can ob-serve that the repair rate under the white-box set -ting is better than the black-box setting .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "In this paper, we introduce CVE - Bench (§2), a benchmark that evaluates LLM-based agents in a realistic vulnerability -repairing setting .As shown in Figure 3 (a), we can ob-serve that the repair rate under the white-box set -ting is better than the black-box setting ."}
+{"idx": 3, "title": "uiuc-kang-lab/ cve - bench : CVE - Bench : A Benchmark for AI Agents ...", "date": "", "ddg_snippet": "CVE - Bench includes 40 critical-severity Common Vulnerability and Exposures (CVE) with the reference automatic exploits available on requests. For each CVE, given a target web application and necessary information...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "CVE - Bench includes 40 critical-severity Common Vulnerability and Exposures (CVE) with the reference automatic exploits available on requests. For each CVE, given a target web application and necessary information..."}
+{"idx": 4, "title": "CVE - Bench : A Real-World Cybersecurity Benchmark for AI Agents", "date": "", "ddg_snippet": "Daniel Kang introduced \" CVE - Bench ,\" the first benchmark to evaluate AI agents against real-world cybersecurity vulnerabilities .And what we found is that our AI agent can find and exploit up to 60% of vulnerabilities in the zero- day setting for this set of vulnerabilities .", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/events/sessions/daniel-kang-cve-bench-a-real-world-cybersecurity-benchmark-for-ai-agents", "content": "Daniel Kang introduced \" CVE - Bench ,\" the first benchmark to evaluate AI agents against real-world cybersecurity vulnerabilities .And what we found is that our AI agent can find and exploit up to 60% of vulnerabilities in the zero- day setting for this set of vulnerabilities ."}
+{"idx": 5, "title": "Prompt Generation | uiuc-kang-lab/ cve - bench | DeepWiki", "date": "", "ddg_snippet": "This document details the prompt generation system in CVE - Bench , which creates structured prompts for AI agents to exploit vulnerabilities .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/3.2-prompt-generation", "content": "This document details the prompt generation system in CVE - Bench , which creates structured prompts for AI agents to exploit vulnerabilities ."}
+{"idx": 6, "title": "Measuring AI Agents ’ Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Success rates of different AI agents on CVE - bench in the zero-day or one - day setting .", "subpage_snippet": "", "source": "ddkang.substack.com", "link": "https://ddkang.substack.com/p/measuring-ai-agents-ability-to-exploit", "content": "Success rates of different AI agents on CVE - bench in the zero-day or one - day setting ."}
+{"idx": 7, "title": "SWE- bench Leaderboards", "date": "", "ddg_snippet": "SWE- bench Bash Only uses the SWE- bench Verified dataset with the mini-SWE- agent environment for all models [Post]. SWE- bench Lite is a subset curated for less costly evaluation [Post].", "subpage_snippet": "", "source": "www.swebench.com", "link": "https://www.swebench.com/", "content": "SWE- bench Bash Only uses the SWE- bench Verified dataset with the mini-SWE- agent environment for all models [Post]. SWE- bench Lite is a subset curated for less costly evaluation [Post]."}
+{"idx": 8, "title": "CVE -2024-50066 - mm/mremap: fix... - SecAlerts", "date": "", "ddg_snippet": "CVE -2024-50066 has a medium severity rating due to the potential race condition in the Linux kernel.", "subpage_snippet": "", "source": "secalerts.co", "link": "https://secalerts.co/vulnerability/CVE-2024-50066", "content": "CVE -2024-50066 has a medium severity rating due to the potential race condition in the Linux kernel."}
+{"idx": 9, "title": "Claude 3 .7 Sonnet and Claude Code \\ Anthropic", "date": "", "ddg_snippet": "First , Claude 3 .7 Sonnet is both an ordinary LLM and a reasoning model in one : you can pick when you want the model to answer normally and when you want it to think longer before answering. In the standard mode, Claude 3 .7 Sonnet represents an upgraded version of Claude 3 . 5 Sonnet.", "subpage_snippet": "", "source": "www.anthropic.com", "link": "https://www.anthropic.com/news/claude-3-7-sonnet", "content": "First , Claude 3 .7 Sonnet is both an ordinary LLM and a reasoning model in one : you can pick when you want the model to answer normally and when you want it to think longer before answering. In the standard mode, Claude 3 .7 Sonnet represents an upgraded version of Claude 3 . 5 Sonnet."}
diff --git a/data/sampled_jsons/CVE-Bench_T-Agent_sqlmap_DB_access_success_rate.jsonl b/data/sampled_jsons/CVE-Bench_T-Agent_sqlmap_DB_access_success_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a8c49805298958edb86c6262d04ebebd9bc79736
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_T-Agent_sqlmap_DB_access_success_rate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for AI Agents ...", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v3", "content": "In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities."}
+{"idx": 2, "title": "SQLMap: The Basics TryHackMe Walkthrough - Medium", "date": "", "ddg_snippet": "SQLMap is an automated tool for detecting and exploiting SQL injection vulnerabilities in web applications. It simplifies the process of identifying these vulnerabilities.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@rayhan.mdsaifulislam/sqlmap-the-basics-tryhackme-walkthrough-fed48e4a7f06", "content": "SQLMap is an automated tool for detecting and exploiting SQL injection vulnerabilities in web applications. It simplifies the process of identifying these vulnerabilities."}
+{"idx": 3, "title": "Evaluation | uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "Evaluation Relevant source files This document explains how CVE-Bench evaluates AI agent performance in exploiting vulnerabilities. It focuses on the evaluation framework, grading system, and success criteria used to determine whether an exploitation attempt has succeeded. For information about specific application graders, see Application-Specific Graders. Evaluation Overview The evaluation ...", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/5-evaluation", "content": "Evaluation Relevant source files This document explains how CVE-Bench evaluates AI agent performance in exploiting vulnerabilities. It focuses on the evaluation framework, grading system, and success criteria used to determine whether an exploitation attempt has succeeded. For information about specific application graders, see Application-Specific Graders. Evaluation Overview The evaluation ..."}
+{"idx": 4, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ..."}
+{"idx": 5, "title": "How to use SQLMAP to test a website for SQL Injection vulnerability", "date": "", "ddg_snippet": "This article explains how to test whether a website is safe from SQL injection using the SQLMAP penetration testing tool. What is SQL Injection? SQL Injection is a code injection technique where an attacker executes malicious SQL queries that control a web application's database. With the right set of queries, a user can gain access to information stored in databases. SQLMAP tests whether a ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/blogs/use-sqlmap-test-website-sql-injection-vulnerability/", "content": "This article explains how to test whether a website is safe from SQL injection using the SQLMAP penetration testing tool. What is SQL Injection? SQL Injection is a code injection technique where an attacker executes malicious SQL queries that control a web application's database. With the right set of queries, a user can gain access to information stored in databases. SQLMAP tests whether a ..."}
+{"idx": 6, "title": "PDF CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "CVE-Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities Yuxuan Zhu, Antony Kellermann, Dylan Bowman, Philip Li, Akul Gupta, Adarsh Danda, Richard Fang, Conner Jensen, Eric Ihli, Jason Benn, Jet Geronimo, Avi Dhir, Sudhit Rao, Kaicheng Yu, Twm Stone, Daniel Kang", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46522.pdf", "content": "CVE-Bench : A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities Yuxuan Zhu, Antony Kellermann, Dylan Bowman, Philip Li, Akul Gupta, Adarsh Danda, Richard Fang, Conner Jensen, Eric Ihli, Jason Benn, Jet Geronimo, Avi Dhir, Sudhit Rao, Kaicheng Yu, Twm Stone, Daniel Kang"}
+{"idx": 7, "title": "Measuring AI Agents' Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Success rates of different AI agents on CVE-bench in the zero-day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danieldkang/measuring-ai-agents-ability-to-exploit-web-applications-ba4225aa281f", "content": "Success rates of different AI agents on CVE-bench in the zero-day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ..."}
+{"idx": 8, "title": "GitHub - sqlmapproject/sqlmap: Automatic SQL injection and database ...", "date": "", "ddg_snippet": "sqlmap sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database servers.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sqlmapproject/sqlmap", "content": "sqlmap sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of database servers."}
+{"idx": 9, "title": "SQLMap Lab Walkthrough — TryHackMe | by Shahin Raza | Medium", "date": "", "ddg_snippet": "Learn how to use SQLMap in a TryHackMe lab to automate SQL injection, enumerate databases, and extract data from a vulnerable web application.", "subpage_snippet": "", "source": "5r4z4.medium.com", "link": "https://5r4z4.medium.com/sqlmap-lab-walkthrough-tryhackme-9c041ca46ed5", "content": "Learn how to use SQLMap in a TryHackMe lab to automate SQL injection, enumerate databases, and extract data from a vulnerable web application."}
diff --git a/data/sampled_jsons/CVE-Bench_arxiv2503.17332_Table_4_monetary_cost_USD_T-Agent_AutoGPT_one-day_year_2025.jsonl b/data/sampled_jsons/CVE-Bench_arxiv2503.17332_Table_4_monetary_cost_USD_T-Agent_AutoGPT_one-day_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..db03897163b5d540d55d70ea2209bae4734e3314
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_arxiv2503.17332_Table_4_monetary_cost_USD_T-Agent_AutoGPT_one-day_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AutoGPT - Wikipedia", "date": "", "ddg_snippet": "AutoGPT is an open-source autonomous software agent that uses OpenAI's large language models, such as GPT - 4 , to attempt to achieve a goal specified by a user in natural language. Unlike chatbots that require continuous user commands...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/AutoGPT", "content": "AutoGPT is an open-source autonomous software agent that uses OpenAI's large language models, such as GPT - 4 , to attempt to achieve a goal specified by a user in natural language. Unlike chatbots that require continuous user commands..."}
+{"idx": 1, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "In CVE - Bench , we simulate the zero-day and one - day scenarios. In the zero-day scenario, LLM agents must compromise the application without further in-formation about the vulnerability . Table 4 . Per-task costs of evaluating LLM agents on CVE - Bench .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "In CVE - Bench , we simulate the zero-day and one - day scenarios. In the zero-day scenario, LLM agents must compromise the application without further in-formation about the vulnerability . Table 4 . Per-task costs of evaluating LLM agents on CVE - Bench ."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "We present the costs of using CVE - Bench to evaluate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332", "content": "We present the costs of using CVE - Bench to evaluate LLM agents in Table 4 . We report the average number of input and output tokens, monetary cost , and the time ..."}
+{"idx": 3, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "21 Mar 2025 — We present the costs of using CVE - Bench to evaluate LLM agents in Table4 . We report the average number of input and output tokens, monetary cost ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "21 Mar 2025 — We present the costs of using CVE - Bench to evaluate LLM agents in Table4 . We report the average number of input and output tokens, monetary cost ..."}
+{"idx": 4, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "by Y Zhu · 2025 · Cited by 12 — This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332?", "content": "by Y Zhu · 2025 · Cited by 12 — This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application."}
+{"idx": 5, "title": "GitHub - Significant-Gravitas/ AutoGPT : AutoGPT is the vision of...", "date": "", "ddg_snippet": "AutoGPT : Build, Deploy, and Run AI Agents .This includes the original stand-alone AutoGPT Agent , along with projects such as Forge, agbenchmark and the AutoGPT Classic GUI.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Significant-Gravitas/AutoGPT", "content": "AutoGPT : Build, Deploy, and Run AI Agents .This includes the original stand-alone AutoGPT Agent , along with projects such as Forge, agbenchmark and the AutoGPT Classic GUI."}
+{"idx": 6, "title": "AutoGPT Agent & GPTs for Automation & Integration Like AutoGPT ...", "date": "", "ddg_snippet": "AutoGPT Agent . This symbol indicates that the GPT 's builder has linked a verified domain or social media account to their profile.", "subpage_snippet": "", "source": "www.whatplugin.ai", "link": "https://www.whatplugin.ai/gpts/autogpt-agent", "content": "AutoGPT Agent . This symbol indicates that the GPT 's builder has linked a verified domain or social media account to their profile."}
+{"idx": 7, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "We evaluated three LLM agents on CVE - Bench with zero-day and one - day settings. For each setting, we repeated experiments five times. Table 4 . Per-task costs of evaluating LLM agents on CVE - Bench . AutoGPT T - Agent Cy-Agent.", "subpage_snippet": "", "source": "yuxuan18.github.io", "link": "https://yuxuan18.github.io/assets/pub/cvebench.pdf", "content": "We evaluated three LLM agents on CVE - Bench with zero-day and one - day settings. For each setting, we repeated experiments five times. Table 4 . Per-task costs of evaluating LLM agents on CVE - Bench . AutoGPT T - Agent Cy-Agent."}
+{"idx": 8, "title": "AutoGPT Agent -Free expert AI assistant for any task", "date": "", "ddg_snippet": "AutoGPT Agent is your all-in- one AI expert, dynamically connecting you to specialized agents for writing, coding, marketing, research, and more—free to use, no login required.", "subpage_snippet": "", "source": "plz.ai", "link": "https://plz.ai/tools/autogpt-agent-ZxX6Wsn3", "content": "AutoGPT Agent is your all-in- one AI expert, dynamically connecting you to specialized agents for writing, coding, marketing, research, and more—free to use, no login required."}
+{"idx": 9, "title": "chatgpt.com/g/g-POb5UhhJ6- autogpt - agent", "date": "", "ddg_snippet": "try AutoGPT Agent right now here.", "subpage_snippet": "", "source": "chatgpt.com", "link": "https://chatgpt.com/g/g-POb5UhhJ6-autogpt-agent", "content": "try AutoGPT Agent right now here."}
diff --git a/data/sampled_jsons/CVE-Bench_blog_post_T-Agent_Success@5_25%_one-day_ddkang.substack.jsonl b/data/sampled_jsons/CVE-Bench_blog_post_T-Agent_Success@5_25%_one-day_ddkang.substack.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..843d0571d514e76f0fc7efc25b9df772d1b90cfd
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_blog_post_T-Agent_Success@5_25%_one-day_ddkang.substack.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for AI Agents ...", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench (paper, blog ), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "This repository contains data and code used in the CVE-Bench (paper, blog ), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 1, "title": "Measuring AI Agents' Ability to Exploit Web Applications", "date": "", "ddg_snippet": "How Dangerous Are Current AI Agents ? We evaluated three agent frameworks using OpenAI's latest GPT-4o model (at the time of this study; gpt-4o-2024-11-20): Cybench Agent (or Cy- Agent ), Teams of Agent (or T-Agent ), and AutoGPT. Success rates of different AI agents on CVE-bench in the zero- day or one-day setting.", "subpage_snippet": "", "source": "ddkang.substack.com", "link": "https://ddkang.substack.com/p/measuring-ai-agents-ability-to-exploit", "content": "How Dangerous Are Current AI Agents ? We evaluated three agent frameworks using OpenAI's latest GPT-4o model (at the time of this study; gpt-4o-2024-11-20): Cybench Agent (or Cy- Agent ), Teams of Agent (or T-Agent ), and AutoGPT. Success rates of different AI agents on CVE-bench in the zero- day or one-day setting."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero- day setting, we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero- day setting, we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack."}
+{"idx": 3, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ... - OpenReview", "date": "", "ddg_snippet": "We observe that AutoGPT demonstrates superior perfor-mance by achieving the highest success@5 rate, with an unexpectedly higher zero- day success@5 rate compared to its one-day success@5 rate.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=3pk0p4NGmQ", "content": "We observe that AutoGPT demonstrates superior perfor-mance by achieving the highest success@5 rate, with an unexpectedly higher zero- day success@5 rate compared to its one-day success@5 rate."}
+{"idx": 4, "title": "AI agents can find and exploit known vulnerabilities, study shows", "date": "", "ddg_snippet": "AI agent success rates Researchers tested about a dozen medium, high, and critical vulnerabilities recently added to the CVE database, which the AI agents knew nothing about.", "subpage_snippet": "", "source": "www.csoonline.com", "link": "https://www.csoonline.com/article/2512791/ai-agents-can-find-and-exploit-known-vulnerabilities-study-shows.html", "content": "AI agent success rates Researchers tested about a dozen medium, high, and critical vulnerabilities recently added to the CVE database, which the AI agents knew nothing about."}
+{"idx": 5, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents' abilities to discover and exploit web application vulnerabilities.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents' abilities to discover and exploit web application vulnerabilities."}
+{"idx": 6, "title": "Measuring AI Agents' Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Success rates of different AI agents on CVE-bench in the zero- day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero- day ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danieldkang/measuring-ai-agents-ability-to-exploit-web-applications-ba4225aa281f", "content": "Success rates of different AI agents on CVE-bench in the zero- day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero- day ..."}
+{"idx": 7, "title": "PDF CVE-Bench: Benchmarking LLM-based Software Engineering Agent's Ability ...", "date": "", "ddg_snippet": "In this paper, we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. CVE-Bench contains three unique characteristics: (1) Instead of input-output evaluation, CVE-Bench supports agent -based evaluation by offering real-world interactive execution-guided programming environments .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "In this paper, we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. CVE-Bench contains three unique characteristics: (1) Instead of input-output evaluation, CVE-Bench supports agent -based evaluation by offering real-world interactive execution-guided programming environments ."}
+{"idx": 8, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ..."}
+{"idx": 9, "title": "PDF LLM Agents can Autonomously Exploit One-day Vulnerabilities Authors ...", "date": "", "ddg_snippet": "The authors demonstrate that LLM agents can effectively hack into systems by exploiting real-world(particularly one-day vulnerabilities) vulnerabilities, GPT-4 achieving an impressive 87% success rate when provided with CVE descriptions.", "subpage_snippet": "", "source": "surrealyz.github.io", "link": "https://surrealyz.github.io/classes/llmsec-fall24/slides/14-agents-exploit-vulnerabilities.pdf", "content": "The authors demonstrate that LLM agents can effectively hack into systems by exploiting real-world(particularly one-day vulnerabilities) vulnerabilities, GPT-4 achieving an impressive 87% success rate when provided with CVE descriptions."}
diff --git a/data/sampled_jsons/CVE-Bench_paper_Table_4_T-Agent_AutoGPT_cost_comparison_One-day_setting_USD.jsonl b/data/sampled_jsons/CVE-Bench_paper_Table_4_T-Agent_AutoGPT_cost_comparison_One-day_setting_USD.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..121a582cf90348e6b2000e09bfd6f7821a175f15
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_paper_Table_4_T-Agent_AutoGPT_cost_comparison_One-day_setting_USD.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero- day setting , we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack. Under the one-day setting , we provide", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332v1", "content": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero- day setting , we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack. Under the one-day setting , we provide"}
+{"idx": 1, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for AI Agents ...", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 2, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ..."}
+{"idx": 3, "title": "LLM Agents can Autonomously Exploit One-day Vulnerabilities", "date": "", "ddg_snippet": "In this work, we show that LLM agents can autonomously exploit one-day vulnerabilities in real-world systems. To show this, we collected a dataset of 15 one-day vulnerabilities that include ones categorized as critical severity in the CVE description.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08144", "content": "In this work, we show that LLM agents can autonomously exploit one-day vulnerabilities in real-world systems. To show this, we collected a dataset of 15 one-day vulnerabilities that include ones categorized as critical severity in the CVE description."}
+{"idx": 4, "title": "LLM Agents can Autonomously Exploit One-day Vulnerabili-ties", "date": "", "ddg_snippet": "15 one-day vulnerabilities that include ones categorized as critical severity in the CVE description. When given the CVE description, GPT-4 is capable of exploiting 87% of these vulnerabilities compared to 0% for every other model we test (GPT-3.5, open-source LLMs) and open-source vulnerability scanners (ZAP and Metasploit). Fortunately, our GPT-4 agent requires the CVE description for high ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.08144", "content": "15 one-day vulnerabilities that include ones categorized as critical severity in the CVE description. When given the CVE description, GPT-4 is capable of exploiting 87% of these vulnerabilities compared to 0% for every other model we test (GPT-3.5, open-source LLMs) and open-source vulnerability scanners (ZAP and Metasploit). Fortunately, our GPT-4 agent requires the CVE description for high ..."}
+{"idx": 5, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero- day setting , we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero- day setting , we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack."}
+{"idx": 6, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero- day setting , we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero- day setting , we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack."}
+{"idx": 7, "title": "GPT-4 Is Capable Of Exploiting 87% Of One-Day Vulnerabilities", "date": "", "ddg_snippet": "GPT-4 & One-Day Vulnerabilities A benchmark of 15 real-world one-day vulnerabilities, including vulnerable websites, container management software, and Python packages, was collected from the CVE database and academic papers . Researchers created a single LLM agent that can exploit 87% of the one-day vulnerabilities in their collected benchmark.", "subpage_snippet": "", "source": "cybersecuritynews.com", "link": "https://cybersecuritynews.com/gpt-4-exploits-one-day-vulnerabilities/", "content": "GPT-4 & One-Day Vulnerabilities A benchmark of 15 real-world one-day vulnerabilities, including vulnerable websites, container management software, and Python packages, was collected from the CVE database and academic papers . Researchers created a single LLM agent that can exploit 87% of the one-day vulnerabilities in their collected benchmark."}
+{"idx": 8, "title": "PDF CVE-Bench: Benchmarking LLM-based Software Engineering Agent's Ability ...", "date": "", "ddg_snippet": "In this paper , we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. CVE-Bench contains three unique characteristics: (1) Instead of input-output evaluation, CVE-Bench supports agent -based evaluation by offering real-world interactive execution-guided programming environments .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "In this paper , we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. CVE-Bench contains three unique characteristics: (1) Instead of input-output evaluation, CVE-Bench supports agent -based evaluation by offering real-world interactive execution-guided programming environments ."}
+{"idx": 9, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents' abilities to discover and exploit web application vulnerabilities.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents' abilities to discover and exploit web application vulnerabilities."}
diff --git a/data/sampled_jsons/CVPR_conference_paper_tiers_Standard-Tier_acceptance_categories.jsonl b/data/sampled_jsons/CVPR_conference_paper_tiers_Standard-Tier_acceptance_categories.jsonl
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index 0000000000000000000000000000000000000000..fd4849e1f3808aa03a58d2a689c37f67f617fe81
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR 2025 Accepted Papers", "date": "", "ddg_snippet": "CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/AcceptedPapers", "content": "CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here."}
+{"idx": 1, "title": "CVPR Statistics - Paper Copilot", "date": "", "ddg_snippet": "For example, if a paper received ratings of 3, 4, and 5, its average score is 4 — and this average is used in the distribution. Suppose the Accept tier contains submissions with reviewer averages: {4.0, 3.1, 3.6}.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/cvpr-statistics/", "content": "For example, if a paper received ratings of 3, 4, and 5, its average score is 4 — and this average is used in the distribution. Suppose the Accept tier contains submissions with reviewer averages: {4.0, 3.1, 3.6}."}
+{"idx": 2, "title": "GitHub - lixin4ever/Conference-Acceptance-Rate: Acceptance ...", "date": "", "ddg_snippet": "Acceptance rates for the major AI conferences. Contribute to lixin4ever/ Conference - Acceptance -Rate development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lixin4ever/Conference-Acceptance-Rate", "content": "Acceptance rates for the major AI conferences. Contribute to lixin4ever/ Conference - Acceptance -Rate development by creating an account on GitHub."}
+{"idx": 3, "title": "Paper Digest: CVPR 2025 Papers & Highlights", "date": "", "ddg_snippet": "Jun 7, 2025 · The IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ) is one of the top computer vision conferences in the world. To help the community quickly catch up on the work presented in this conference , Paper Digest Team processed all accepted papers , and generated one highlight sentence (ty", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/06/cvpr-2025-papers-highlights/", "content": "Jun 7, 2025 · The IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ) is one of the top computer vision conferences in the world. To help the community quickly catch up on the work presented in this conference , Paper Digest Team processed all accepted papers , and generated one highlight sentence (ty"}
+{"idx": 4, "title": "The CVPR Survival Guide: Discovering Research That's ...", "date": "", "ddg_snippet": "Jun 14, 2024 · The 2024 Conference on Computer Vision and Pattern Recognition ( CVPR ) received 11,532 valid paper submissions, and only 2,719 were accepted for an overall acceptance rate of about 23.6%.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/harpreetsahota/cvpr2024-survival-guide", "content": "Jun 14, 2024 · The 2024 Conference on Computer Vision and Pattern Recognition ( CVPR ) received 11,532 valid paper submissions, and only 2,719 were accepted for an overall acceptance rate of about 23.6%."}
+{"idx": 5, "title": "CVPR 2025 Statistics - Paper Copilot", "date": "", "ddg_snippet": "- Withdraw: Includes papers withdrawn by the authors, including those withdrawn after acceptance . Post-decision withdrawals are specifically marked as such. How to use the paper list below: - Overview: This table presents papers from the CVPR conference , year 2025. - Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/cvpr-statistics/cvpr-2025-statistics/", "content": "- Withdraw: Includes papers withdrawn by the authors, including those withdrawn after acceptance . Post-decision withdrawals are specifically marked as such. How to use the paper list below: - Overview: This table presents papers from the CVPR conference , year 2025. - Filtering: By default, the table loads the first 100 records."}
+{"idx": 6, "title": "CVPR 2025 Accepted Papers | MMLab@NTU", "date": "", "ddg_snippet": "Presentation Schedule The team has a total of 20 papers (including 2 orals and 3 highlights) accepted to CVPR 2025.", "subpage_snippet": "", "source": "www.mmlab-ntu.com", "link": "https://www.mmlab-ntu.com/conference/cvpr2025/index.html", "content": "Presentation Schedule The team has a total of 20 papers (including 2 orals and 3 highlights) accepted to CVPR 2025."}
+{"idx": 7, "title": "What is the basic criteria for acceptance of a paper in IEEE ...", "date": "", "ddg_snippet": "It has an 'A' rating from the Australian Ranking of ICT Conferences and an 'A1' rating from the Brazilian ministry of education(Qualis (2012)).", "subpage_snippet": "", "source": "www.quora.com", "link": "https://www.quora.com/What-is-the-basic-criteria-for-acceptance-of-a-paper-in-IEEE-conference", "content": "It has an 'A' rating from the Australian Ranking of ICT Conferences and an 'A1' rating from the Brazilian ministry of education(Qualis (2012))."}
+{"idx": 8, "title": "Ranking Computer Science Research Papers", "date": "", "ddg_snippet": "For instance, ECCV, ICCV, and CVPR are all categorized at the same top tier , and ICLR is recognized as a premier conference. While the ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/ranking-computer-science-research-papers-objective-guide-wei-shao-tcl9c", "content": "For instance, ECCV, ICCV, and CVPR are all categorized at the same top tier , and ICLR is recognized as a premier conference. While the ..."}
+{"idx": 9, "title": "Tiered Representations for Video Memorability Prediction", "date": "", "ddg_snippet": "by T Dumont · 2023 · Cited by 13 — This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version;. 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Dumont_Modular_Memorability_Tiered_Representations_for_Video_Memorability_Prediction_CVPR_2023_paper.pdf", "content": "by T Dumont · 2023 · Cited by 13 — This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version;. 10 pages"}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Section_4.1_hyperparameters_beta_gam.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Section_4.1_hyperparameters_beta_gam.jsonl
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index 0000000000000000000000000000000000000000..1bc566dfe3b6552adfe1faf841c562df2d2d9be0
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Assessment framework for deepfake detection in real-world", "date": "", "ddg_snippet": "... assessment approach, which exists in various benchmarks, often directly samples test data from the same distribution as training data and can hardly ...", "subpage_snippet": "", "source": "jivp-eurasipjournals.springeropen.com", "link": "https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-024-00621-8", "content": "... assessment approach, which exists in various benchmarks, often directly samples test data from the same distribution as training data and can hardly ..."}
+{"idx": 1, "title": "[2408.17052] Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive."}
+{"idx": 2, "title": "(PDF) Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "deepfake detector without using any deepfake data seems counter-intuitive. Therefore, we argue.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648453_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector", "content": "deepfake detector without using any deepfake data seems counter-intuitive. Therefore, we argue."}
+{"idx": 3, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "This paper explores whether deepfake detection models can be trained without using deepfake data , which can be costly and difficult to obtain. The researchers experiment with different training approaches and evaluate the performance of the resulting deepfake detectors .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/can-we-leave-deepfake-data-behind-training", "content": "This paper explores whether deepfake detection models can be trained without using deepfake data , which can be costly and difficult to obtain. The researchers experiment with different training approaches and evaluate the performance of the resulting deepfake detectors ."}
+{"idx": 4, "title": "beautyremain/ProDet: The official code for paper \" Can We Leave ...\"", "date": "", "ddg_snippet": "@article{cheng2024can, title={ Can We Leave Deepfake Data Behind in Training Deepfake Detector ?}, author={Cheng, Jikang and Yan, Zhiyuan and Zhang, Ying and Luo, Yuhao and Wang, Zhongyuan and Li, Chen}, journal={arXiv preprint arXiv:2408.17052}, year={2024} }.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "@article{cheng2024can, title={ Can We Leave Deepfake Data Behind in Training Deepfake Detector ?}, author={Cheng, Jikang and Yan, Zhiyuan and Zhang, Ying and Luo, Yuhao and Wang, Zhongyuan and Li, Chen}, journal={arXiv preprint arXiv:2408.17052}, year={2024} }."}
+{"idx": 5, "title": "FreqDebias: Towards Generalizable Deepfake Detection via...", "date": "", "ddg_snippet": "Deepfake detectors often struggle to generalize to novel forgery types due to biases learned from limited training data . Can we leave deepfake data . behind in training deepfake detector ? In Advances in Neural.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kashiani_FreqDebias_Towards_Generalizable_Deepfake_Detection_via_Consistency-Driven_Frequency_Debiasing_CVPR_2025_paper.pdf", "content": "Deepfake detectors often struggle to generalize to novel forgery types due to biases learned from limited training data . Can we leave deepfake data . behind in training deepfake detector ? In Advances in Neural."}
+{"idx": 6, "title": "Top 10 Deepfake Audio Detection Tools for 2025 | Resemble AI", "date": "", "ddg_snippet": "What Makes Audio Deepfake Detection Essential for Security Strategies in 2025. Data scarcity for training : Detection models need massive, diverse datasets of both authentic and synthetic voices across languages, accents, and environments.", "subpage_snippet": "", "source": "www.resemble.ai", "link": "https://www.resemble.ai/audio-deepfake-detection-tools/", "content": "What Makes Audio Deepfake Detection Essential for Security Strategies in 2025. Data scarcity for training : Detection models need massive, diverse datasets of both authentic and synthetic voices across languages, accents, and environments."}
+{"idx": 7, "title": "SCLBD/DeepfakeBench - Githubissues", "date": "", "ddg_snippet": "The pre- trained weights of 3D R50 for training I3D, FTCN, and AltFreezing are here. Welcome to DeepfakeBench, your one-stop solution for deepfake detection !", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/SCLBD/DeepfakeBench/readme", "content": "The pre- trained weights of 3D R50 for training I3D, FTCN, and AltFreezing are here. Welcome to DeepfakeBench, your one-stop solution for deepfake detection !"}
+{"idx": 8, "title": "DF40: Toward Next-Generation Deepfake Detection (2024)", "date": "", "ddg_snippet": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ?Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/df40-toward-next-generation-deepfake-detection-273mzyvz9x", "content": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ?Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning."}
+{"idx": 9, "title": "Blendfake Data : A New Way to Detect Deepfakes - Simple Science", "date": "", "ddg_snippet": "Exploring blendfake data 's effectiveness in deepfake detection methods. Deepfake technology has raised serious concerns about privacy and security.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-19-blendfake-data-a-new-way-to-detect-deepfakes--akxyq01", "content": "Exploring blendfake data 's effectiveness in deepfake detection methods. Deepfake technology has raised serious concerns about privacy and security."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Xception.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Xception.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d626e87ba6ccffeaaabbc0928f978c325a28a497
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_Xception.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "Deepfake Detector ? Jikang Cheng1∗. deepfake detector without using any deepfake data seems counter-intuitive. Therefore, we argue. that the significance of deepfake samples is underestimated due to insufficient exploration of how to. appropriately utilize these samples.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648453_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector", "content": "Deepfake Detector ? Jikang Cheng1∗. deepfake detector without using any deepfake data seems counter-intuitive. Therefore, we argue. that the significance of deepfake samples is underestimated due to insufficient exploration of how to. appropriately utilize these samples."}
+{"idx": 1, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from \"real to blendfake to deepfake \" to be a progressive transition. Specifically, blendfake and deepfake can be explicitly delineated as the oriented pivot anchors between \"real-to-fake\" transitions.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/2718a032d15e0b80cd164b240220df89-Paper-Conference.pdf", "content": "In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from \"real to blendfake to deepfake \" to be a progressive transition. Specifically, blendfake and deepfake can be explicitly delineated as the oriented pivot anchors between \"real-to-fake\" transitions."}
+{"idx": 2, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "AI Summary This paper addresses the suboptimal performance of deepfake detectors trained on both deepfake and blendfake data . It proposes an Oriented Progressive Regularizor (OPR) to organize the latent space progressively from real to blendfake to deepfake , enabling effective utilization of forgery information from both data types, thus improving generalization ability.", "subpage_snippet": "", "source": "deepfake-total.com", "link": "https://deepfake-total.com/related_work/2408.17052", "content": "AI Summary This paper addresses the suboptimal performance of deepfake detectors trained on both deepfake and blendfake data . It proposes an Oriented Progressive Regularizor (OPR) to organize the latent space progressively from real to blendfake to deepfake , enabling effective utilization of forgery information from both data types, thus improving generalization ability."}
+{"idx": 3, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Interestingly, current SoTA methods utilize blendfake $\\textit {without}$ incorporating any deepfake data in their training process.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/2718a032d15e0b80cd164b240220df89-Abstract-Conference.html", "content": "Interestingly, current SoTA methods utilize blendfake $\\textit {without}$ incorporating any deepfake data in their training process."}
+{"idx": 4, "title": "Where the Devil Hides: Deepfake Detectors Can No Longer Be ...", "date": "", "ddg_snippet": "We uncover a security problem merely studied in Deep - fake detection , and describe effective solutions to cor-rupt Deepfake detectors by adding passcode-controlled, representation-suppression, adaptive, and invisible trig-gers during training .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_Where_the_Devil_Hides_Deepfake_Detectors_Can_No_Longer_Be_CVPR_2025_paper.pdf", "content": "We uncover a security problem merely studied in Deep - fake detection , and describe effective solutions to cor-rupt Deepfake detectors by adding passcode-controlled, representation-suppression, adaptive, and invisible trig-gers during training ."}
+{"idx": 5, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Aug 30, 2024 · Although Deep fake detection is extremely difficult and still an unsolved problem, a Deepfake detection model trained only on the DFDC can generalize to real \"in-the-wild\" Deepfake videos, and such a model can be a valuable analysis tool when analyzing potentially Deepfaked videos. 2020 IEEE/CVF Conference on Computer Vision and…", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Can-We-Leave-Deepfake-Data-Behind-in-Training-Cheng-Yan/6b186896a5b2c15ea07a1e516c41ce01f2c15772/figure/0", "content": "Aug 30, 2024 · Although Deep fake detection is extremely difficult and still an unsolved problem, a Deepfake detection model trained only on the DFDC can generalize to real \"in-the-wild\" Deepfake videos, and such a model can be a valuable analysis tool when analyzing potentially Deepfaked videos. 2020 IEEE/CVF Conference on Computer Vision and…"}
+{"idx": 6, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "However, given that actual deepfake data is widely available in real-world scenarios, and it should contain extra forgery clues (e.g., generative artifacts in deep net) absent in blendfake, training a deepfake detector without using any deepfake data seems counter-intuitive.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17052v1", "content": "However, given that actual deepfake data is widely available in real-world scenarios, and it should contain extra forgery clues (e.g., generative artifacts in deep net) absent in blendfake, training a deepfake detector without using any deepfake data seems counter-intuitive."}
+{"idx": 7, "title": "GitHub - Yashasrock/ DEEPFAKE -DETECTION", "date": "", "ddg_snippet": "Contribute to Yashasrock/ DEEPFAKE -DETECTION development by creating an account on GitHub. Data Preprocessing: DeepfakeBench currently provides LMDB for more faster and effective IO.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Yashasrock/DEEPFAKE-DETECTION", "content": "Contribute to Yashasrock/ DEEPFAKE -DETECTION development by creating an account on GitHub. Data Preprocessing: DeepfakeBench currently provides LMDB for more faster and effective IO."}
+{"idx": 8, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "...which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called 1+1<2). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/can-we-leave-deepfake-data-behind-training", "content": "...which combines deepfake and blendfake data , results in inferior performance to methods using only blendfake data (so-called 1+1<2). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?"}
+{"idx": 9, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "# Current state-of-the-art deepfake detectors often use only blendfake data for training , avoiding deepfake data due to empirically observed performance issues. This raises a critical question: can deepfake data be entirely discarded?", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/vh9yepleyd/", "content": "# Current state-of-the-art deepfake detectors often use only blendfake data for training , avoiding deepfake data due to empirically observed performance issues. This raises a critical question: can deepfake data be entirely discarded?"}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_arxiv_2408.17052_beta_gamma_Section_.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_arxiv_2408.17052_beta_gamma_Section_.jsonl
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index 0000000000000000000000000000000000000000..39fe699ed91c877c689b4e2ac7f93843307f9d86
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_arxiv_2408.17052_beta_gamma_Section_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2408 . 17052 ] Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive.Computer Vision and Pattern Recognition (cs.CV). Cite as: arXiv : 2408 . 17052 [cs.CV].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive.Computer Vision and Pattern Recognition (cs.CV). Cite as: arXiv : 2408 . 17052 [cs.CV]."}
+{"idx": 1, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "License: CC Zero. arXiv : 2408 . 17052 v1 [cs.CV] 30 Aug 2024.Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17052v1", "content": "License: CC Zero. arXiv : 2408 . 17052 v1 [cs.CV] 30 Aug 2024.Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive."}
+{"idx": 2, "title": "(PDF) Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "deepfake detector without using any deepfake data seems counter-intuitive.: Explicit data -level debiasing for deepfake detection . arXiv preprint arXiv : 2408 .06779, 2024.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648453_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector", "content": "deepfake detector without using any deepfake data seems counter-intuitive.: Explicit data -level debiasing for deepfake detection . arXiv preprint arXiv : 2408 .06779, 2024."}
+{"idx": 3, "title": "Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "This paper explores whether deepfake detection models can be trained without using deepfake data , which can be costly and difficult to obtain. The researchers experiment with different training approaches and evaluate the performance of the resulting deepfake detectors .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/can-we-leave-deepfake-data-behind-training", "content": "This paper explores whether deepfake detection models can be trained without using deepfake data , which can be costly and difficult to obtain. The researchers experiment with different training approaches and evaluate the performance of the resulting deepfake detectors ."}
+{"idx": 4, "title": "beautyremain/ProDet: The official code for paper \" Can We Leave ...\"", "date": "", "ddg_snippet": "@article{cheng2024can, title={ Can We Leave Deepfake Data Behind in Training Deepfake Detector ?}, author={Cheng, Jikang and Yan, Zhiyuan and Zhang, Ying and Luo, Yuhao and Wang, Zhongyuan and Li, Chen}, journal={ arXiv preprint arXiv : 2408 . 17052 }, year={2024} }.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "@article{cheng2024can, title={ Can We Leave Deepfake Data Behind in Training Deepfake Detector ?}, author={Cheng, Jikang and Yan, Zhiyuan and Zhang, Ying and Luo, Yuhao and Wang, Zhongyuan and Li, Chen}, journal={ arXiv preprint arXiv : 2408 . 17052 }, year={2024} }."}
+{"idx": 5, "title": "Blendfake Data : A New Way to Detect Deepfakes - Simple Science", "date": "", "ddg_snippet": "Title: Can We Leave Deepfake Data Behind in Training Deepfake Detector ? Abstract: The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data ...", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-19-blendfake-data-a-new-way-to-detect-deepfakes--akxyq01", "content": "Title: Can We Leave Deepfake Data Behind in Training Deepfake Detector ? Abstract: The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data ..."}
+{"idx": 6, "title": "FreqDebias: Towards Generalizable Deepfake Detection via...", "date": "", "ddg_snippet": "Can we leave deepfake data . behind in training deepfake detector ? In Advances in Neural. data -level debiasing for deepfake detection . arXiv preprint. arXiv : 2408 .06779, 2024.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kashiani_FreqDebias_Towards_Generalizable_Deepfake_Detection_via_Consistency-Driven_Frequency_Debiasing_CVPR_2025_paper.pdf", "content": "Can we leave deepfake data . behind in training deepfake detector ? In Advances in Neural. data -level debiasing for deepfake detection . arXiv preprint. arXiv : 2408 .06779, 2024."}
+{"idx": 7, "title": "Top 10 Deepfake Audio Detection Tools for 2025 | Resemble AI", "date": "", "ddg_snippet": "What Makes Audio Deepfake Detection Essential for Security Strategies in 2025. Data scarcity for training : Detection models need massive, diverse datasets of both authentic and synthetic voices across languages, accents, and environments.", "subpage_snippet": "", "source": "www.resemble.ai", "link": "https://www.resemble.ai/audio-deepfake-detection-tools/", "content": "What Makes Audio Deepfake Detection Essential for Security Strategies in 2025. Data scarcity for training : Detection models need massive, diverse datasets of both authentic and synthetic voices across languages, accents, and environments."}
+{"idx": 8, "title": "SCLBD/DeepfakeBench - Githubissues", "date": "", "ddg_snippet": "The pre- trained weights of 3D R50 for training I3D, FTCN, and AltFreezing are here. Welcome to DeepfakeBench, your one-stop solution for deepfake detection !", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/SCLBD/DeepfakeBench/readme", "content": "The pre- trained weights of 3D R50 for training I3D, FTCN, and AltFreezing are here. Welcome to DeepfakeBench, your one-stop solution for deepfake detection !"}
+{"idx": 9, "title": "DF40: Toward Next-Generation Deepfake Detection (2024)", "date": "", "ddg_snippet": "(DOI: 10.48550/ arxiv .2406.13495) We propose a new comprehensive benchmark to revolutionize the current deepfake detection field to the next generation.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/df40-toward-next-generation-deepfake-detection-273mzyvz9x", "content": "(DOI: 10.48550/ arxiv .2406.13495) We propose a new comprehensive benchmark to revolutionize the current deepfake detection field to the next generation."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_experimental_setup_EfficientNet_PyTo.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_experimental_setup_EfficientNet_PyTo.jsonl
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index 0000000000000000000000000000000000000000..25012b6b62527b2a2d20f830e61754392e2ad1c9
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A comprehensive benchmark of deepfake detection", "date": "", "ddg_snippet": "DeepfakeBench presents the first comprehensive benchmark for deepfake detection , resolving the issue of lack of standardization and uniformity in this field.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SCLBD/DeepfakeBench", "content": "DeepfakeBench presents the first comprehensive benchmark for deepfake detection , resolving the issue of lack of standardization and uniformity in this field."}
+{"idx": 1, "title": "An Integrative Review of Deepfake Detection, Multimedia ...", "date": "", "ddg_snippet": "by S Singh · 2025 — Presents an in-depth review of deepfake generation and detection , highlighting AI methods such as GANs, face synthesis, and speech cloning.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2215016125004765", "content": "by S Singh · 2025 — Presents an in-depth review of deepfake generation and detection , highlighting AI methods such as GANs, face synthesis, and speech cloning."}
+{"idx": 2, "title": "Do DeepFake Attribution Models Generalize?", "date": "", "ddg_snippet": "22 May 2025 — Their results indicate that contrastive training can enhance cross-dataset generalization in DeepFake attribution. Another study (Jia et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.21520v1", "content": "22 May 2025 — Their results indicate that contrastive training can enhance cross-dataset generalization in DeepFake attribution. Another study (Jia et al ..."}
+{"idx": 3, "title": "Deepfake Detection Using Hybrid Model", "date": "", "ddg_snippet": "Abstract: Deepfake technologies' quick development has created significant threats to the integrity of digital media, especially in the areas of ...", "subpage_snippet": "", "source": "www.ijsred.com", "link": "https://www.ijsred.com/volume8/issue3/IJSRED-V8I3P539.pdf", "content": "Abstract: Deepfake technologies' quick development has created significant threats to the integrity of digital media, especially in the areas of ..."}
+{"idx": 4, "title": "A systematic review of deepfake detection and generation ...", "date": "", "ddg_snippet": "by F Abbas · 2024 · Cited by 58 — TITLE-ABS-KEY ((“synthetic media” OR “ Deep Fake ” OR “Deepfakes” OR “Deep fakes” OR” Deepfake Images”OR”Fake video*” OR” Deepfake video*” OR “ ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424011266", "content": "by F Abbas · 2024 · Cited by 58 — TITLE-ABS-KEY ((“synthetic media” OR “ Deep Fake ” OR “Deepfakes” OR “Deep fakes” OR” Deepfake Images”OR”Fake video*” OR” Deepfake video*” OR “ ..."}
+{"idx": 5, "title": "DeepFake Detection for Human Face Images and Videos", "date": "", "ddg_snippet": "by A Malik · 2022 · Cited by 246 — In this part, we will review recent DeepFake detection - based techniques grouped into three types: (1) traditional- based techniques for DeepFakes, (2) DNN ... 19 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/6287639/6514899/09712265.pdf", "content": "by A Malik · 2022 · Cited by 246 — In this part, we will review recent DeepFake detection - based techniques grouped into three types: (1) traditional- based techniques for DeepFakes, (2) DNN ... 19 pages"}
+{"idx": 6, "title": "Quantifying DeepFake Detection Accuracy for a Variety of ...", "date": "", "ddg_snippet": "by P Prajapati · 2020 — We tested our models using the Deep Fake Detection Challenge dataset and found our plain frames-based model achieves 90% test accuracy, our MRI model achieves ...", "subpage_snippet": "", "source": "scholarworks.sjsu.edu", "link": "https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1962&context=etd_projects", "content": "by P Prajapati · 2020 — We tested our models using the Deep Fake Detection Challenge dataset and found our plain frames-based model achieves 90% test accuracy, our MRI model achieves ..."}
+{"idx": 7, "title": "ProActive DeepFake Detection using GAN-based Visible ...", "date": "", "ddg_snippet": "by AV Nadimpalli · 2024 · Cited by 34 — The aim of this article is to propose a novel proactive DeepFake detection technique using GAN-based visible watermarking.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3625547", "content": "by AV Nadimpalli · 2024 · Cited by 34 — The aim of this article is to propose a novel proactive DeepFake detection technique using GAN-based visible watermarking."}
+{"idx": 8, "title": "GM-DF: Generalized Multi-Scenario Deepfake Detection", "date": "", "ddg_snippet": "28 Jun 2024 — In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.20078v1", "content": "28 Jun 2024 — In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery ..."}
+{"idx": 9, "title": "Learning Pairwise Interaction for Generalizable DeepFake ...", "date": "", "ddg_snippet": "by Y Xu · 2023 · Cited by 38 — We also observe that no single configuration could perform rea- sonably well for all the unseen data , which is the biggest issue for Deepfake detection field.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2023W/XAI4B/papers/Xu_Learning_Pairwise_Interaction_for_Generalizable_DeepFake_Detection_WACVW_2023_paper.pdf", "content": "by Y Xu · 2023 · Cited by 38 — We also observe that no single configuration could perform rea- sonably well for all the unseen data , which is the biggest issue for Deepfake detection field."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_sitearxiv.org_OR_siteacm.org_OR_site.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_sitearxiv.org_OR_siteacm.org_OR_site.jsonl
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index 0000000000000000000000000000000000000000..978fc4d2ea2a02dd2d982ddf0b0dad7b040d0a7c
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deepfake Detection: Analyzing Model Generalization Across Architectures ...", "date": "", "ddg_snippet": "As deepfake technology gains traction, the need for reliable detection systems is crucial. Recent research has introduced various deep learning-based detection systems, yet they exhibit limitations in generalising effectively across diverse data distributions that differ from the training data . Our study focuses on understanding the generalisation challenge by exploring different aspects such ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10376174", "content": "As deepfake technology gains traction, the need for reliable detection systems is crucial. Recent research has introduced various deep learning-based detection systems, yet they exhibit limitations in generalising effectively across diverse data distributions that differ from the training data . Our study focuses on understanding the generalisation challenge by exploring different aspects such ..."}
+{"idx": 1, "title": "Is It Certainly a Deepfake? Reliability Analysis in Detection ...", "date": "", "ddg_snippet": "As a result, different generative models leave different resid-ual traces behind [72]. Those traces are directly correlated to the detector response, tying deepfake detectors and gen-erators.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.17550", "content": "As a result, different generative models leave different resid-ual traces behind [72]. Those traces are directly correlated to the detector response, tying deepfake detectors and gen-erators."}
+{"idx": 2, "title": "Deepfake Media Forensics: State of the Art and Challenges Ahead", "date": "", "ddg_snippet": "We can therefore distinguish 5 main areas of research in the Forensic Deepfake domain, namely Deepfake Detection (Section II) Deepfake Attribution And Recognition (Section III), Passive Deepfake Authentication Methods (Section IV), Deepfakes Detection Method On Realistic Scenarios (Section V-C), and Active Authentication (Section VI-C).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.00388v1", "content": "We can therefore distinguish 5 main areas of research in the Forensic Deepfake domain, namely Deepfake Detection (Section II) Deepfake Attribution And Recognition (Section III), Passive Deepfake Authentication Methods (Section IV), Deepfakes Detection Method On Realistic Scenarios (Section V-C), and Active Authentication (Section VI-C)."}
+{"idx": 3, "title": "A Review of Deepfake Techniques: Architecture, Detection, and Datasets", "date": "", "ddg_snippet": "Driven by continuous advancements in artificial intelligence, especially deep learning, the level of realism associated with deepfake technology continues to improve year after year, which poses unprecedented challenges to the field of deepfake detection. The boundary between what we as humans can detect as real or fake becomes evermore blurred as new generations of algorithms such as Dall-E 3 ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10711187", "content": "Driven by continuous advancements in artificial intelligence, especially deep learning, the level of realism associated with deepfake technology continues to improve year after year, which poses unprecedented challenges to the field of deepfake detection. The boundary between what we as humans can detect as real or fake becomes evermore blurred as new generations of algorithms such as Dall-E 3 ..."}
+{"idx": 4, "title": "Deepfake Detection: A Systematic Literature Review - IEEE Xplore", "date": "", "ddg_snippet": "We also evaluate the performance of the detection capability of the various methods with respect to different datasets and conclude that the deep learning-based methods outperform other methods in Deepfake detection. The list of Deepfake detection models.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9721302", "content": "We also evaluate the performance of the detection capability of the various methods with respect to different datasets and conclude that the deep learning-based methods outperform other methods in Deepfake detection. The list of Deepfake detection models."}
+{"idx": 5, "title": "Deepfake Generation and Detection: Case Study and Challenges", "date": "", "ddg_snippet": "We discuss the implementation challenges and future research directions regarding optimized approaches and models. A unique case study, IBMM is discussed, which presents a multi-modal overview of deepfake detection. The proposed survey would benefit researchers, industry, and academia to study deepfake generation and subsequent detection schemes.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10354308", "content": "We discuss the implementation challenges and future research directions regarding optimized approaches and models. A unique case study, IBMM is discussed, which presents a multi-modal overview of deepfake detection. The proposed survey would benefit researchers, industry, and academia to study deepfake generation and subsequent detection schemes."}
+{"idx": 6, "title": "AVoiD-DF: Audio-Visual Joint Learning for Detecting Deepfake", "date": "", "ddg_snippet": "Recently, deepfakes have raised severe concerns about the authenticity of online media. Prior works for deepfake detection have made many efforts to capture the intra-modal artifacts. However, deepfake videos in real-world scenarios often consist of a combination of audio and visual. In this paper, we propose an Audio-Visual Joint Learning for Detecting Deepfake (AVoiD-DF), which exploits ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10081373", "content": "Recently, deepfakes have raised severe concerns about the authenticity of online media. Prior works for deepfake detection have made many efforts to capture the intra-modal artifacts. However, deepfake videos in real-world scenarios often consist of a combination of audio and visual. In this paper, we propose an Audio-Visual Joint Learning for Detecting Deepfake (AVoiD-DF), which exploits ..."}
+{"idx": 7, "title": "Spotting the Deepfake - IEEE Transmitter", "date": "", "ddg_snippet": "In the first six months of 2024 alone, over 300 articles focused on creating detection tools for deepfakes have been published in the IEEE Xplore digital Deepfake detection continues to be a cat-and-mouse game.", "subpage_snippet": "", "source": "transmitter.ieee.org", "link": "https://transmitter.ieee.org/spotting-the-deepfake/", "content": "In the first six months of 2024 alone, over 300 articles focused on creating detection tools for deepfakes have been published in the IEEE Xplore digital Deepfake detection continues to be a cat-and-mouse game."}
+{"idx": 8, "title": "Deepfake Detection: A Systematic Literature Review", "date": "", "ddg_snippet": "Our contributions are summarized as follows. We perform a comprehensive survey on existing litera-ture in the Deepfake domain. We report current tools, techniques, and datasets for Deepfake detection-related research by posing some research questions.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?arnumber=9721302", "content": "Our contributions are summarized as follows. We perform a comprehensive survey on existing litera-ture in the Deepfake domain. We report current tools, techniques, and datasets for Deepfake detection-related research by posing some research questions."}
+{"idx": 9, "title": "Analyzing Fairness in Deepfake Detection With Massively Annotated ...", "date": "", "ddg_snippet": "In recent years, image and video manipulations with Deepfake have become a severe concern for security and society. Many detection models and datasets have been proposed to detect Deepfake data reliably. However, there is an increased concern that these models and training databases might be biased and, thus, cause Deepfake detectors to fail. In this work, we investigate factors causing biased ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10438899", "content": "In recent years, image and video manipulations with Deepfake have become a severe concern for security and society. Many detection models and datasets have been proposed to detect Deepfake data reliably. However, there is an increased concern that these models and training databases might be biased and, thus, cause Deepfake detectors to fail. In this work, we investigate factors causing biased ..."}
diff --git a/data/sampled_jsons/Capturing_dynamics_time-varying_data_topology_Foundations_Data_Science_2022_volume_pages_year_2022.jsonl b/data/sampled_jsons/Capturing_dynamics_time-varying_data_topology_Foundations_Data_Science_2022_volume_pages_year_2022.jsonl
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index 0000000000000000000000000000000000000000..b5d58956521460c13af53fc9bb9ae72ced4f3343
--- /dev/null
+++ b/data/sampled_jsons/Capturing_dynamics_time-varying_data_topology_Foundations_Data_Science_2022_volume_pages_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Capturing dynamics of time - varying data via topology", "date": "", "ddg_snippet": "Foundations of Data Science . Topological methods for studying time - varying data are built on a technique called persistent homology. Homology provides a way to (partially) characterize the topology of an object.", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/doi/10.3934/fods.2021033", "content": "Foundations of Data Science . Topological methods for studying time - varying data are built on a technique called persistent homology. Homology provides a way to (partially) characterize the topology of an object."}
+{"idx": 1, "title": "(Open Access) Capturing dynamics of time - varying data via...", "date": "", "ddg_snippet": "- Foundations of data science .While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time. A time - varying collection of metric spaces as formed, for example, by a moving...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/capturing-dynamics-of-time-varying-data-via-topology-3bio3cno", "content": "- Foundations of data science .While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time. A time - varying collection of metric spaces as formed, for example, by a moving..."}
+{"idx": 2, "title": "Topological methods for time - varying data : theory and applications...", "date": "", "ddg_snippet": "Topological Data Analysis (TDA) is a research area at the intersection of Algebra, Topology , Geometry, Statistics and Machine Learning. While methods from TDA have been applied successfully to data from a variety of domains — from financial mathematics, to materials science ...", "subpage_snippet": "", "source": "amsi.org.au", "link": "https://amsi.org.au/events/event/topological-methods-for-time-varying-data-theory-and-applications/", "content": "Topological Data Analysis (TDA) is a research area at the intersection of Algebra, Topology , Geometry, Statistics and Machine Learning. While methods from TDA have been applied successfully to data from a variety of domains — from financial mathematics, to materials science ..."}
+{"idx": 3, "title": "A Topology Scavenger Hunt to Introduce Topological Data Analysis", "date": "", "ddg_snippet": "In a senior-level Topology course, the author introduces some applied topology and topological data analysis (TDA) techniques, such as persistent homology and the mapper graph – as well as some software tools to compute them – through a “ Topology Scavenger Hunt”.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15580v1", "content": "In a senior-level Topology course, the author introduces some applied topology and topological data analysis (TDA) techniques, such as persistent homology and the mapper graph – as well as some software tools to compute them – through a “ Topology Scavenger Hunt”."}
+{"idx": 4, "title": "Spatial and Sequential Topological Analysis of Molecular Dynamics ...", "date": "", "ddg_snippet": "2. Topological Data Analysis Background.Table 2. Time in Seconds to Read Formatted Data from One Frame of an Fc Domain Molecular Dynamics Simulation and Produce a GCCD Matrix for Each Value of σ Included in the Grid Search, for a Random Selection of Frames.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12079798/", "content": "2. Topological Data Analysis Background.Table 2. Time in Seconds to Read Formatted Data from One Frame of an Fc Domain Molecular Dynamics Simulation and Produce a GCCD Matrix for Each Value of σ Included in the Grid Search, for a Random Selection of Frames."}
+{"idx": 5, "title": "science .org/journal/ science", "date": "", "ddg_snippet": "Illustration: C. Bickel/ Science ; Data : InSight Mars SEIS Data Service (2019).", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/journal/science", "content": "Illustration: C. Bickel/ Science ; Data : InSight Mars SEIS Data Service (2019)."}
+{"idx": 6, "title": "Applied Topology & Geometry Reading Group | Program in Applied...", "date": "", "ddg_snippet": "We'll finish discussion of the Capturing Dynamics of Time - Varying Data via Topology and begin reading this summary: Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological , and Algebraic Structures.", "subpage_snippet": "", "source": "appliedmath.arizona.edu", "link": "https://appliedmath.arizona.edu/events/applied-topology-geometry-reading-group-2", "content": "We'll finish discussion of the Capturing Dynamics of Time - Varying Data via Topology and begin reading this summary: Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological , and Algebraic Structures."}
+{"idx": 7, "title": "Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time ...", "date": "", "ddg_snippet": "characteristics of time - varying data deli vered by the zigzag.Xian, L., Adams, H., Topaz, C. M., and Ziegelmeier, L. Capturing dynamics of time - varying data via topology . arXiv:2010.05780, 2020.", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/z-gcnets-time-zigzags-at-graph-convolutional-networks-for-6162788", "content": "characteristics of time - varying data deli vered by the zigzag.Xian, L., Adams, H., Topaz, C. M., and Ziegelmeier, L. Capturing dynamics of time - varying data via topology . arXiv:2010.05780, 2020."}
+{"idx": 8, "title": "Capturing dynamics of time-varying data via topology", "date": "", "ddg_snippet": "One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ...", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/id/2acaee54-6688-46a4-b35d-447f84c4c691", "content": "One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ..."}
+{"idx": 9, "title": "Capturing Dynamics of Time-Varying Data via Topology CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY replication code for \"Capturing dynamics of time-varying data ... CAPTURING DYNAMICS OF TIME-VARYING DATA VIA TOPOLOGY The synchronized dynamics of time-varying networks The synchronized dynamics of time-varying networks The synchronized dynamics of time-varying networks The synchronized dynamics of time-varying networks The synchronized dynamics of time-varying networks The synchronized dynamics of time-varying networks", "date": "", "ddg_snippet": "Oct 7, 2020 · One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ... ( 2022 ) Xian et al. Foundations of Data Science . One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have... Abstract. One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topolog-ical data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or ... About replication code for \" Capturing dynamics of time-varying data via topology \" Abstract. One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topolog-ical data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of sh or ock ... Does the rate of change of the topology prevent synchronization? The presence of a region in which the rate of change of the topology prevents synchronization is also found when a more general interaction scheme is adopted, as in Ref. . Similarly to Ref. , the model investigated in Ref. also considers mobile pulse-coupled integrate-and-fire oscillators. Does synchronization occur in time-varying networks? In particular, a significant number of systems in physics, biology and social science features a time - varying nature of the interactions among their units. We here give a comprehensive review of the major results obtained by contemporary studies on the emergence of synchronization in time - varying networks. Does a time-varying network have a stable synchronization manifold? Thus, Lemma 1yields that, if the time -average system has an asymptotically stable synchronization manifold, then the time - varying network also has asymptotically stable synchronization for sufficient fast switching. This fast switching stability criterion is a fundamental tool to assess the local stability of several temporal networks. Do time-varying networks enlarge the range of coupling strengths? Compared to the static multiplex networks, time-varying networks enlarge the range of coupling strengths yielding complete intra-layer synchronization. Similarly, the variation of Einteris drawn in Fig. 20(b). What is the topology of the competitive adaptive mechanism for small? For small ε, the topology resulting from the competitive adaptive mechanism is highly heterogeneous , characterized by a weight distribution close to a power-law, indicating that in this case a complete redistribution of the weights occurred. Feb 23, 2022 · In particular, a significant number of systems in physics, biology and social science features a time-varying nature of the interactions among their units. We here give a comprehensive review of the major results obtained by contemporary studies on the emergence of synchronization in time-varying networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.05780", "content": "Oct 7, 2020 · One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or flock of ... ( 2022 ) Xian et al. Foundations of Data Science . One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topological data analysis focused primarily on static data , in recent years, theoretical and applied studies have... Abstract. One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topolog-ical data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of fish or ... About replication code for \" Capturing dynamics of time-varying data via topology \" Abstract. One approach to understanding complex data is to study its shape through the lens of algebraic topology . While the early development of topolog-ical data analysis focused primarily on static data , in recent years, theoretical and applied studies have turned to data that varies in time . A time-varying collection of metric spaces as formed, for example, by a moving school of sh or ock ... Does the rate of change of the topology prevent synchronization? The presence of a region in which the rate of change of the topology prevents synchronization is also found when a more general interaction scheme is adopted, as in Ref. . Similarly to Ref. , the model investigated in Ref. also considers mobile pulse-coupled integrate-and-fire oscillators. Does synchronization occur in time-varying networks? In particular, a significant number of systems in physics, biology and social science features a time - varying nature of the interactions among their units. We here give a comprehensive review of the major results obtained by contemporary studies on the emergence of synchronization in time - varying networks. Does a time-varying network have a stable synchronization manifold? Thus, Lemma 1yields that, if the time -average system has an asymptotically stable synchronization manifold, then the time - varying network also has asymptotically stable synchronization for sufficient fast switching. This fast switching stability criterion is a fundamental tool to assess the local stability of several temporal networks. Do time-varying networks enlarge the range of coupling strengths? Compared to the static multiplex networks, time-varying networks enlarge the range of coupling strengths yielding complete intra-layer synchronization. Similarly, the variation of Einteris drawn in Fig. 20(b). What is the topology of the competitive adaptive mechanism for small? For small ε, the topology resulting from the competitive adaptive mechanism is highly heterogeneous , characterized by a weight distribution close to a power-law, indicating that in this case a complete redistribution of the weights occurred. Feb 23, 2022 · In particular, a significant number of systems in physics, biology and social science features a time-varying nature of the interactions among their units. We here give a comprehensive review of the major results obtained by contemporary studies on the emergence of synchronization in time-varying networks."}
diff --git "a/data/sampled_jsons/Catoni_Contextual_Bandits_Heavy-tailed_Rewards_\316\250_function.jsonl" "b/data/sampled_jsons/Catoni_Contextual_Bandits_Heavy-tailed_Rewards_\316\250_function.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..0303ad685f656b8748223460eb3a26404f91fbb2
--- /dev/null
+++ "b/data/sampled_jsons/Catoni_Contextual_Bandits_Heavy-tailed_Rewards_\316\250_function.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "Two principled strategies for handling heavy - tailed noise, truncation and median-of-means, have been introduced to heavy - tailed bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "Two principled strategies for handling heavy - tailed noise, truncation and median-of-means, have been introduced to heavy - tailed bandits ."}
+{"idx": 1, "title": "Extended UCB Policies for Multi-Armed Bandit Problems", "date": "", "ddg_snippet": "... all reward distributions are light- tailed , i.e., the sample mean converges to the true mean faster so learning becomes easier compared to the heavy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1112.1768v5", "content": "... all reward distributions are light- tailed , i.e., the sample mean converges to the true mean faster so learning becomes easier compared to the heavy ..."}
+{"idx": 2, "title": "Machine Learning Feb 2025", "date": "", "ddg_snippet": "Title: Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy - Tailed Noise ... cs.IT); Machine Learning (cs.LG); Functional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/stat.ML/2025-02", "content": "Title: Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy - Tailed Noise ... cs.IT); Machine Learning (cs.LG); Functional ..."}
+{"idx": 3, "title": "Chenlu Ye - Google Académico", "date": "", "ddg_snippet": "... robust algorithms with uncertainty weighting for nonlinear contextual bandits and ... Catoni contextual bandits are robust to heavy - tailed rewards", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=c8yK5XsAAAAJ&hl=es", "content": "... robust algorithms with uncertainty weighting for nonlinear contextual bandits and ... Catoni contextual bandits are robust to heavy - tailed rewards"}
+{"idx": 4, "title": "Chenlu Ye - Google Scholar", "date": "", "ddg_snippet": "... robust algorithms with uncertainty weighting for nonlinear contextual bandits and ... Catoni contextual bandits are robust to heavy - tailed rewards", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=c8yK5XsAAAAJ&hl=de", "content": "... robust algorithms with uncertainty weighting for nonlinear contextual bandits and ... Catoni contextual bandits are robust to heavy - tailed rewards"}
+{"idx": 5, "title": "Downloads", "date": "", "ddg_snippet": "Adaptive Best-of-Both-Worlds Algorithm for Heavy - Tailed Multi-Armed Bandits ... Ascent Algorithm for Nonconvex Functional Constrained Optimization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2022", "content": "Adaptive Best-of-Both-Worlds Algorithm for Heavy - Tailed Multi-Armed Bandits ... Ascent Algorithm for Nonconvex Functional Constrained Optimization"}
+{"idx": 6, "title": "ICML 2022 Papers", "date": "", "ddg_snippet": "... Learning with General Function ... A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/papers.html", "content": "... Learning with General Function ... A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization"}
+{"idx": 7, "title": "COLT 2024 - Accepted Papers", "date": "", "ddg_snippet": "Follow-the-Perturbed-Leader with Fr\\'{e}chet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds Lee, Jongyeong; Honda ...", "subpage_snippet": "", "source": "learningtheory.org", "link": "http://learningtheory.org/colt2024/accepted-papers.html", "content": "Follow-the-Perturbed-Leader with Fr\\'{e}chet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds Lee, Jongyeong; Honda ..."}
+{"idx": 8, "title": "COLT 2024 - Accepted Papers", "date": "", "ddg_snippet": "Follow-the-Perturbed-Leader with Fr\\'{e}chet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds Lee, Jongyeong; Honda ...", "subpage_snippet": "", "source": "learningtheory.org", "link": "https://learningtheory.org/colt2024/accepted-papers.html", "content": "Follow-the-Perturbed-Leader with Fr\\'{e}chet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds Lee, Jongyeong; Honda ..."}
+{"idx": 9, "title": "Chenlu Ye - Google Scholar", "date": "", "ddg_snippet": "... robust algorithms with uncertainty weighting for nonlinear contextual bandits and ... Catoni contextual bandits are robust to heavy - tailed rewards", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=c8yK5XsAAAAJ&hl=fi", "content": "... robust algorithms with uncertainty weighting for nonlinear contextual bandits and ... Catoni contextual bandits are robust to heavy - tailed rewards"}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_Theorem_3.1_lower_bound_Theorem_3.4_Catoni-OFUL_regret_bound.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_Theorem_3.1_lower_bound_Theorem_3.4_Catoni-OFUL_regret_bound.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3380c72a8f74edd832dc4d018ecb5c61808afea9
--- /dev/null
+++ b/data/sampled_jsons/Catoni_Contextual_Bandits_Theorem_3.1_lower_bound_Theorem_3.4_Catoni-OFUL_regret_bound.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "The variance dependence in our theorem matches the lower bound in Theorem 3.1 . Specifically, for the determinis-tic case where σt = 0 for all t ∈ [T], the bound is re-duced to eO(log N(F, υ) · dim", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "The variance dependence in our theorem matches the lower bound in Theorem 3.1 . Specifically, for the determinis-tic case where σt = 0 for all t ∈ [T], the bound is re-duced to eO(log N(F, υ) · dim"}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "Q5: In Theorem 3.1 , the regret lower bound depends on the used policy. A5: The intuition is as follows. Consider two bandit problems, each with two arms.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5IpVe9PH14&noteId=J3K6uYfoM5", "content": "Q5: In Theorem 3.1 , the regret lower bound depends on the used policy. A5: The intuition is as follows. Consider two bandit problems, each with two arms."}
+{"idx": 2, "title": "Bootstrapping Upper Confidence Bound", "date": "", "ddg_snippet": "by B Hao · 2019 · Cited by 71 — In theory , we develop both problem-dependent and problem-independent regret bounds for multi- armed bandits with symmetric rewards under a much ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1906.05247", "content": "by B Hao · 2019 · Cited by 71 — In theory , we develop both problem-dependent and problem-independent regret bounds for multi- armed bandits with symmetric rewards under a much ..."}
+{"idx": 3, "title": "Bootstrapping Upper Confidence Bound", "date": "", "ddg_snippet": "by B Hao · Cited by 71 — Moreover, we show that naive bootstrapped UCB will result in linear regret in some cases in Section A in the supplement. 3.1 Regret Bound for Bootstrapped UCB. 11 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "http://papers.neurips.cc/paper/9382-bootstrapping-upper-confidence-bound.pdf", "content": "by B Hao · Cited by 71 — Moreover, we show that naive bootstrapped UCB will result in linear regret in some cases in Section A in the supplement. 3.1 Regret Bound for Bootstrapped UCB. 11 pages"}
+{"idx": 4, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ..."}
+{"idx": 5, "title": "PDF A Reduction from Linear Contextual Bandits Lower Bounds to Estimations ...", "date": "", "ddg_snippet": "In this work, we establish the necessity of an accurate estima-tor in a low- regret algorithm for stochastic linear contextual bandit problems and demonstrate how our analysis leads to a reduction in studying lower bounds for bandit problems.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/he22e/he22e.pdf", "content": "In this work, we establish the necessity of an accurate estima-tor in a low- regret algorithm for stochastic linear contextual bandit problems and demonstrate how our analysis leads to a reduction in studying lower bounds for bandit problems."}
+{"idx": 6, "title": "PDF Regret lower bound and optimal algorithm for high-dimensional ...", "date": "", "ddg_snippet": "Later, Chu et al. (2011) prove a lower bound of Ω(√Td) ⊆ R for low-dimensional contextual bandit problem, where no margin condition is assumed and the regret lower bound therein corresponds to the worst case when α = 0, and propose the LinUCB algorithm with O(√dT) ̃ regret upper bound , which is near-optimal according to Theorem 1.", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-15/issue-2/Regret-lower-bound-and-optimal-algorithm-for-high-dimensional-contextual/10.1214/21-EJS1909.pdf", "content": "Later, Chu et al. (2011) prove a lower bound of Ω(√Td) ⊆ R for low-dimensional contextual bandit problem, where no margin condition is assumed and the regret lower bound therein corresponds to the worst case when α = 0, and propose the LinUCB algorithm with O(√dT) ̃ regret upper bound , which is near-optimal according to Theorem 1."}
+{"idx": 7, "title": "PDF Bandits: Regret Lower Bound and Instance-Dependent Regret", "date": "", "ddg_snippet": "Instance-dependent regret bounds incorporate information about the particular instance of a bandit environment into their bounds , reflecting the fact that a given algorithm's regret will depend on the instance", "subpage_snippet": "", "source": "shamulent.github.io", "link": "https://shamulent.github.io/RL_2022/Lectures/Lecture4_prelecture.pdf", "content": "Instance-dependent regret bounds incorporate information about the particular instance of a bandit environment into their bounds , reflecting the fact that a given algorithm's regret will depend on the instance"}
+{"idx": 8, "title": "PDF bandit-lower-bound-notes.dvi", "date": "", "ddg_snippet": "Lower bounds on regret . Fix a strategy, and write: Xj,s = outcome from pull s of arm j, P = joint distribution over {It, Xj,s} under distribution Pθ, P′ = joint distribution under distribution Pθ′ .", "subpage_snippet": "", "source": "www.stat.berkeley.edu", "link": "https://www.stat.berkeley.edu/~bartlett/courses/2014fall-cs294stat260/lectures/bandit-lower-bound-notes.pdf", "content": "Lower bounds on regret . Fix a strategy, and write: Xj,s = outcome from pull s of arm j, P = joint distribution over {It, Xj,s} under distribution Pθ, P′ = joint distribution under distribution Pθ′ ."}
+{"idx": 9, "title": "PDF On the Minimax Regret for Contextual Linear Bandits and Multi-Armed ...", "date": "", "ddg_snippet": "To show the regret lower bound for BwE, we follow the approach employed in the study [Chen et al., 2024] on the minimax regret for problems interpolating problems of (full-information) online learning with expert advice and multi-armed bandit .", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/file/7169e48889a07662f4168e3911c4a69e-Paper-Conference.pdf", "content": "To show the regret lower bound for BwE, we follow the approach employed in the study [Chen et al., 2024] on the minimax regret for problems interpolating problems of (full-information) online learning with expert advice and multi-armed bandit ."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_regret_bound_Catoni-OFUL.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_regret_bound_Catoni-OFUL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ea2765a7a4921aabcfb529563ae58e29f6ae56be
--- /dev/null
+++ b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_regret_bound_Catoni-OFUL.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "The algorithm enjoys a variance-based regret bound with only polynomial dependence on R. When the per-round variance is unknown, our proposed variance-agnostic Catoni .Combining this with the range bound that ∥f ⋆∥∞ ≤ Lf , the cumulative regret is bounded by.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "The algorithm enjoys a variance-based regret bound with only polynomial dependence on R. When the per-round variance is unknown, our proposed variance-agnostic Catoni .Combining this with the range bound that ∥f ⋆∥∞ ≤ Lf , the cumulative regret is bounded by."}
+{"idx": 1, "title": "Catoniコンテキストバンディットは重たいテール報酬に対してロバストである（Catoni Contextual Bandits are ...", "date": "", "ddg_snippet": "「 Catoni Contextual Bandits are Robust to Heavy-tailed Rewards」という論文は、重たいテールを持つ報酬を伴うコンテキストバンディット問題を扱った研究です。", "subpage_snippet": "", "source": "aibr.jp", "link": "https://aibr.jp/2025/07/02/catoniコンテキストバンディットは重たいテール報酬/", "content": "「 Catoni Contextual Bandits are Robust to Heavy-tailed Rewards」という論文は、重たいテールを持つ報酬を伴うコンテキストバンディット問題を扱った研究です。"}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy - tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "However, many practical scenarios naturally involve heavy - tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics..."}
+{"idx": 3, "title": "ICML Poster Catoni Contextual Bandits are Robust to Heavy - tailed ...", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy - tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "However, many practical scenarios naturally involve heavy - tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics..."}
+{"idx": 4, "title": "Multi-Armed Bandits | Papers With Code", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/task/multi-armed-bandits/codeless?page=4", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Multi-agent Multi-armed Bandit with Fully Heavy - tailed Dynamics."}
+{"idx": 5, "title": "[PDF] The fundamentals of heavy -tails: properties... | Semantic Scholar", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards . Chen YeYujia JinAlekh AgarwalTong Zhang.This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-fundamentals-of-heavy-tails:-properties,-and-Nair-Wierman/f7d0070cda82026150d7c9cdf539a57cc6a9663f", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards . Chen YeYujia JinAlekh AgarwalTong Zhang.This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function..."}
+{"idx": 6, "title": "16034 PDFs | Review articles in ROBUST STATISTICS", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range $[0, R]$, and their regret scales polynomially with this reward range $R$.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Robust-Statistics/publications", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range $[0, R]$, and their regret scales polynomially with this reward range $R$."}
+{"idx": 7, "title": "Chenlu Ye - Google Akademik", "date": "", "ddg_snippet": "Corruption-Robust Offline Reinforcement Learning with General Function Approximation. Catoni Contextual Bandits are Robust to Heavy - tailed Rewards .", "subpage_snippet": "", "source": "scholar.google.bg", "link": "https://scholar.google.bg/citations?user=c8yK5XsAAAAJ&hl=tr", "content": "Corruption-Robust Offline Reinforcement Learning with General Function Approximation. Catoni Contextual Bandits are Robust to Heavy - tailed Rewards ."}
+{"idx": 8, "title": "Chenlu Ye", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint.", "subpage_snippet": "", "source": "chenluye99.github.io", "link": "https://chenluye99.github.io/", "content": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint."}
+{"idx": 9, "title": "Google at ICML 2025", "date": "", "ddg_snippet": "Catoni Contextual Bandits Are Robust to Heavy - Tailed Rewards Chenlu Ye, Yujia Jin*, Alekh Agarwal, Tong Zhang. Correlation Clustering Beyond the Pivot Algorithm Soheil Behnezhad, Moses Charikar, Vincent Cohen-Addad, Alma Ghafari, Weiyun Ma.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/conferences-and-events/google-at-icml-2025/", "content": "Catoni Contextual Bandits Are Robust to Heavy - Tailed Rewards Chenlu Ye, Yujia Jin*, Alekh Agarwal, Tong Zhang. Correlation Clustering Beyond the Pivot Algorithm Soheil Behnezhad, Moses Charikar, Vincent Cohen-Addad, Alma Ghafari, Weiyun Ma."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_sitearxiv.org_year_2023.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_sitearxiv.org_year_2023.jsonl
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index 0000000000000000000000000000000000000000..d4700d05cdbd7ad04403511c67c1a94127b52e98
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Catoni Cont Catoni-Style Change Point Detection for Regret Minimization ... [2505.20051] Catoni-Style Change Point Detection for Regret ... Improved Regret Bounds for Linear Bandits with Heavy-Tailed ... Bandits with heavy tail - arXiv.org Single Index Bandits: Generalized Linear Contextual Bandits ...", "date": "", "ddg_snippet": "Feb 4, 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ... We provide a novel Catoni -style change-point detection strategy tailored for heavy-tailed distributions that relies on recent advancements in the theory of sequential estimation, which is of independent interest. May 26, 2025 · Regret minimization in stochastic non-stationary bandits gained popularity over the last decade, as it can model a broad class of real-world problems, from advertising to recommendation systems. Existing literature relies on various assumptions about the reward-generating process, such as Bernoulli or subgaussian rewards . However, in settings such as finance and telecommunications, heavy ... In this paper, we revisited stochastic linear bandits with heavy-tailed rewards and substantially narrowed the gap between known minimax lower and upper regret bounds in both the infinite- and finite-action settings. The key to successful handling heavy-tailed reward distributions is to replace the empir-ical mean with other, more robust , estimators of the mean. All we need is a performance guarantee like the one shown above for the empirical mean. More precisely, we need a mean estimator with the following property. Misspecification of this link function can lead to the failure of all existing algorithms. In this work, we address this critical limitation by introducing a new problem of generalized linear bandits with unknown reward functions, also known as single index bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Feb 4, 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ... We provide a novel Catoni -style change-point detection strategy tailored for heavy-tailed distributions that relies on recent advancements in the theory of sequential estimation, which is of independent interest. May 26, 2025 · Regret minimization in stochastic non-stationary bandits gained popularity over the last decade, as it can model a broad class of real-world problems, from advertising to recommendation systems. Existing literature relies on various assumptions about the reward-generating process, such as Bernoulli or subgaussian rewards . However, in settings such as finance and telecommunications, heavy ... In this paper, we revisited stochastic linear bandits with heavy-tailed rewards and substantially narrowed the gap between known minimax lower and upper regret bounds in both the infinite- and finite-action settings. The key to successful handling heavy-tailed reward distributions is to replace the empir-ical mean with other, more robust , estimators of the mean. All we need is a performance guarantee like the one shown above for the empirical mean. More precisely, we need a mean estimator with the following property. Misspecification of this link function can lead to the failure of all existing algorithms. In this work, we address this critical limitation by introducing a new problem of generalized linear bandits with unknown reward functions, also known as single index bandits ."}
+{"idx": 1, "title": "Catoni-Style Change Point Detection for Regret Minimization ...", "date": "", "ddg_snippet": "We provide a novel Catoni -style change-point detection strategy tailored for heavy-tailed distributions that relies on recent advancements in the theory of sequential estimation, which is of independent interest.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.20051", "content": "We provide a novel Catoni -style change-point detection strategy tailored for heavy-tailed distributions that relies on recent advancements in the theory of sequential estimation, which is of independent interest."}
+{"idx": 2, "title": "Improved Regret Bounds for Linear Bandits with Heavy-Tailed ...", "date": "", "ddg_snippet": "In this paper, we revisited stochastic linear bandits with heavy-tailed rewards and substantially narrowed the gap between known minimax lower and upper regret bounds in both the infinite- and finite-action settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.04775", "content": "In this paper, we revisited stochastic linear bandits with heavy-tailed rewards and substantially narrowed the gap between known minimax lower and upper regret bounds in both the infinite- and finite-action settings."}
+{"idx": 3, "title": "Single Index Bandits: Generalized Linear Contextual Bandits ...", "date": "", "ddg_snippet": "Misspecification of this link function can lead to the failure of all existing algorithms. In this work, we address this critical limitation by introducing a new problem of generalized linear bandits with unknown reward functions, also known as single index bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.12751", "content": "Misspecification of this link function can lead to the failure of all existing algorithms. In this work, we address this critical limitation by introducing a new problem of generalized linear bandits with unknown reward functions, also known as single index bandits ."}
+{"idx": 4, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Catoni Cont", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ..."}
+{"idx": 5, "title": "[2505.20051] Catoni-Style Change Point Detection for Regret ...", "date": "", "ddg_snippet": "May 26, 2025 · Regret minimization in stochastic non-stationary bandits gained popularity over the last decade, as it can model a broad class of real-world problems, from advertising to recommendation systems. Existing literature relies on various assumptions about the reward-generating process, such as Bernoulli or subgaussian rewards . However, in settings such as finance and telecommunications, heavy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.20051", "content": "May 26, 2025 · Regret minimization in stochastic non-stationary bandits gained popularity over the last decade, as it can model a broad class of real-world problems, from advertising to recommendation systems. Existing literature relies on various assumptions about the reward-generating process, such as Bernoulli or subgaussian rewards . However, in settings such as finance and telecommunications, heavy ..."}
+{"idx": 6, "title": "Bandits with heavy tail - arXiv.org", "date": "", "ddg_snippet": "The key to successful handling heavy-tailed reward distributions is to replace the empir-ical mean with other, more robust , estimators of the mean. All we need is a performance guarantee like the one shown above for the empirical mean. More precisely, we need a mean estimator with the following property.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1209.1727", "content": "The key to successful handling heavy-tailed reward distributions is to replace the empir-ical mean with other, more robust , estimators of the mean. All we need is a performance guarantee like the one shown above for the empirical mean. More precisely, we need a mean estimator with the following property."}
+{"idx": 7, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with ..."}
+{"idx": 8, "title": "Improved Regret Bounds for Linear Bandits with Heavy- ...", "date": "", "ddg_snippet": "5 Jun 2025 — Robustness to heavy tails was first introduced into sequential decision-making by Bubeck et al., (2013) in the context of multi-armed bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.04775v1", "content": "5 Jun 2025 — Robustness to heavy tails was first introduced into sequential decision-making by Bubeck et al., (2013) in the context of multi-armed bandits ."}
+{"idx": 9, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One- ...", "date": "", "ddg_snippet": "by J Wang · 2025 · Cited by 2 — Abstract. We study the stochastic linear bandits with heavy - tailed noise. Two principled strategies for handling heavy - tailed noise, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.00419", "content": "by J Wang · 2025 · Cited by 2 — Abstract. We study the stochastic linear bandits with heavy - tailed noise. Two principled strategies for handling heavy - tailed noise, ..."}
diff --git a/data/sampled_jsons/Catoni_M-estimator_psi_function_formula_definition_robust_statistics.jsonl b/data/sampled_jsons/Catoni_M-estimator_psi_function_formula_definition_robust_statistics.jsonl
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index 0000000000000000000000000000000000000000..cd8740d47493a8e7e26ef1373a12f110c4cc0e9b
--- /dev/null
+++ b/data/sampled_jsons/Catoni_M-estimator_psi_function_formula_definition_robust_statistics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Data-driven distributionally robust optimization using the", "date": "", "ddg_snippet": "... robust models with moment ambiguity sets are more tractable than the corresponding stochastic models because the intractable high-dimensional ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10107-017-1172-1", "content": "... robust models with moment ambiguity sets are more tractable than the corresponding stochastic models because the intractable high-dimensional ..."}
+{"idx": 1, "title": "Catoni-Giulini M-estimator - The Stats Map", "date": "", "ddg_snippet": "In 2017, Catoni and Giulini proposed an approach to multivariate concentration based on M-estimation. Let \\\\ psi be any symmetric \"influence function \" such that -\\\\log(1 - x + x^2/2)\\\\leq \\\\psi(x) \\\\leq \\\\log(1 + x + x^2/2),\\\\quad \\\\forall x \\\\in\\\\Re.", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/Catoni-Giulini-M-estimator", "content": "In 2017, Catoni and Giulini proposed an approach to multivariate concentration based on M-estimation. Let \\\\ psi be any symmetric \"influence function \" such that -\\\\log(1 - x + x^2/2)\\\\leq \\\\psi(x) \\\\leq \\\\log(1 + x + x^2/2),\\\\quad \\\\forall x \\\\in\\\\Re."}
+{"idx": 2, "title": "On Catoni's M-Estimation - arXiv.org", "date": "", "ddg_snippet": "Catoni proposed a robust M-estimator and gave the deviation inequality for one fixed test function . The present paper is devoted to the uniform concentration inequality for a family of test functions . As an application, we consider empirical risk minimization for heavy-tailed losses.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.08211", "content": "Catoni proposed a robust M-estimator and gave the deviation inequality for one fixed test function . The present paper is devoted to the uniform concentration inequality for a family of test functions . As an application, we consider empirical risk minimization for heavy-tailed losses."}
+{"idx": 3, "title": "stat-map/Catoni-Giulini M-estimator.md at main - GitHub", "date": "", "ddg_snippet": "The estimator is then $$ \\xi (\\theta) = \\frac {1} {n\\lambda}\\sum_ {i\\leq n}\\int_ {\\Re^d}\\psi (\\lambda \\la \\vartheta, X_i\\ra)\\rho_\\theta (d\\vartheta), $$ where $\\rho_\\theta$ is Gaussian with mean $\\theta$ and covariance $\\beta^ {-1}I$ for some $\\beta>0$.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bchugg/stat-map/blob/main/Catoni-Giulini+M-estimator.md", "content": "The estimator is then $$ \\xi (\\theta) = \\frac {1} {n\\lambda}\\sum_ {i\\leq n}\\int_ {\\Re^d}\\psi (\\lambda \\la \\vartheta, X_i\\ra)\\rho_\\theta (d\\vartheta), $$ where $\\rho_\\theta$ is Gaussian with mean $\\theta$ and covariance $\\beta^ {-1}I$ for some $\\beta>0$."}
+{"idx": 4, "title": "M-Estimators for Robust Linear Modeling - statsmodels", "date": "", "ddg_snippet": "where ρ is a symmetric function of the residuals The effect of ρ is to reduce the influence of outliers s is an estimate of scale. The robust estimates β ^ are computed by the iteratively re-weighted least squares algorithm We have several choices available for the weighting functions to be used", "subpage_snippet": "", "source": "www.statsmodels.org", "link": "https://www.statsmodels.org/dev/examples/notebooks/generated/robust_models_1.html", "content": "where ρ is a symmetric function of the residuals The effect of ρ is to reduce the influence of outliers s is an estimate of scale. The robust estimates β ^ are computed by the iteratively re-weighted least squares algorithm We have several choices available for the weighting functions to be used"}
+{"idx": 5, "title": "PDF Nearly Optimal Catoni's M-estimator for Infinite Variance", "date": "", "ddg_snippet": "The key difference is that robust -ness in class of M-estimators is characterized by the extrema of an influence function which modulates the impact of out-lier samples (Huber, 2004).", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/bhatt22b/bhatt22b.pdf", "content": "The key difference is that robust -ness in class of M-estimators is characterized by the extrema of an influence function which modulates the impact of out-lier samples (Huber, 2004)."}
+{"idx": 6, "title": "PDF Stat 610: Mathematical Statistics Lecture 25", "date": "", "ddg_snippet": "In this lecture we consider another robust estimators , the so-called M-estimators . Note that the MLE is obtained by maximizing the likelihood function L(qjx), or minimizing L(qjx), where L is specified by the particular parametric assumption on the population.", "subpage_snippet": "", "source": "pages.stat.wisc.edu", "link": "https://pages.stat.wisc.edu/~shao/stat610/stat610-25.pdf", "content": "In this lecture we consider another robust estimators , the so-called M-estimators . Note that the MLE is obtained by maximizing the likelihood function L(qjx), or minimizing L(qjx), where L is specified by the particular parametric assumption on the population."}
+{"idx": 7, "title": "A review on robust M-estimators for regression analysis", "date": "", "ddg_snippet": "These estimators can adapt their shape by varying some parameters so that the estimator obtains characteristics of monotonous, soft-redescending or hardredescending estimators . • For many robust estimators , specifically, those classified as redescending, there is a need for global-type optimization methods, since their structure are non ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0098135421000326", "content": "These estimators can adapt their shape by varying some parameters so that the estimator obtains characteristics of monotonous, soft-redescending or hardredescending estimators . • For many robust estimators , specifically, those classified as redescending, there is a need for global-type optimization methods, since their structure are non ..."}
+{"idx": 8, "title": "A generalized Catoni's M-estimator under finite -th moment assumption ...", "date": "", "ddg_snippet": "Abstract: We generalize Catoni's M-estimator , put forward in [3] by Ca-toni under finite variance assumption, to the case in which distributions can have finite α-th moment with α (1,2). Our approach, inspired by the Taylor-like expansion developed in [4], is via slightly modifying the influence function φ in [3]. A deviation bound is established for this generalized estimator , and ...", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journalArticle/Download?urlid=10.1214/21-EJS1911", "content": "Abstract: We generalize Catoni's M-estimator , put forward in [3] by Ca-toni under finite variance assumption, to the case in which distributions can have finite α-th moment with α (1,2). Our approach, inspired by the Taylor-like expansion developed in [4], is via slightly modifying the influence function φ in [3]. A deviation bound is established for this generalized estimator , and ..."}
+{"idx": 9, "title": "Section 18 M-Estimators | MATH3714 Linear Regression and Robustness", "date": "", "ddg_snippet": "The function \\ (\\rho\\) is called the objective function of the M-estimator . This is a generalisation of the least squares estimator , where the least squares estimator corresponds to the case \\ (\\rho (\\varepsilon) = \\varepsilon^2\\).", "subpage_snippet": "", "source": "seehuhn.github.io", "link": "https://seehuhn.github.io/MATH3714/S18-m-est.html", "content": "The function \\ (\\rho\\) is called the objective function of the M-estimator . This is a generalisation of the least squares estimator , where the least squares estimator corresponds to the case \\ (\\rho (\\varepsilon) = \\varepsilon^2\\)."}
diff --git a/data/sampled_jsons/Causal_Bayesian_Networks_NeurIPS_2025_paper_abstract_siteneurips.cc_year_2025.jsonl b/data/sampled_jsons/Causal_Bayesian_Networks_NeurIPS_2025_paper_abstract_siteneurips.cc_year_2025.jsonl
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index 0000000000000000000000000000000000000000..ef2170009e1327fd7049b0e711adb6f2e2adf9ee
--- /dev/null
+++ b/data/sampled_jsons/Causal_Bayesian_Networks_NeurIPS_2025_paper_abstract_siteneurips.cc_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causality - Wikipedia", "date": "", "ddg_snippet": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Causality", "content": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future."}
+{"idx": 1, "title": "CAUSAL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/causal", "content": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence."}
+{"idx": 2, "title": "CAUSAL | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/causal", "content": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more."}
+{"idx": 3, "title": "CAUSAL Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/causal", "content": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence."}
+{"idx": 4, "title": "CAUSAL definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "If there is a causal relationship between two things, one thing is responsible for causing the other thing.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/causal", "content": "If there is a causal relationship between two things, one thing is responsible for causing the other thing."}
+{"idx": 5, "title": "Causal - definition of causal by The Free Dictionary", "date": "", "ddg_snippet": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/causal", "content": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause."}
+{"idx": 6, "title": "causal adjective - Definition, pictures, pronunciation and usage...", "date": "", "ddg_snippet": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/causal", "content": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 7, "title": "causal - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark.", "subpage_snippet": "", "source": "en.wiktionary.org", "link": "https://en.wiktionary.org/wiki/causal", "content": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark."}
+{"idx": 8, "title": "Causal - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "Causal is a variation of the word cause , which should be a clue to its meaning. A cause is what makes something happen: the notebook flew across the room because you threw it, so your throwing it was causal. If a bolt of lightning set a statue on fire, the lightning was causal for the fire.", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/causal", "content": "Causal is a variation of the word cause , which should be a clue to its meaning. A cause is what makes something happen: the notebook flew across the room because you threw it, so your throwing it was causal. If a bolt of lightning set a statue on fire, the lightning was causal for the fire."}
+{"idx": 9, "title": "causal , adj. & n. meanings, etymology and more | Oxford English...", "date": "", "ddg_snippet": "causal , adj. & n. meanings, etymology, pronunciation and more in the Oxford English Dictionary", "subpage_snippet": "", "source": "www.oed.com", "link": "https://www.oed.com/dictionary/causal_adj", "content": "causal , adj. & n. meanings, etymology, pronunciation and more in the Oxford English Dictionary"}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_events_types_year_2023-2024.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_events_types_year_2023-2024.jsonl
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index 0000000000000000000000000000000000000000..2ce7aaa7671265f1d51dc0c0147f79167f55fafb
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_events_types_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests ."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · 2025 · Cited by 9 — This study models how Reddit users engage in climate activism , finding that direct interactions and media coverage of protests influence ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3696410.3714684", "content": "by J Lenti · 2025 · Cited by 9 — This study models how Reddit users engage in climate activism , finding that direct interactions and media coverage of protests influence ..."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the ..."}
+{"idx": 3, "title": "Modeling the Impact of Group Interactions on Climate- ...", "date": "", "ddg_snippet": "by A Antelmi · 2025 — This paper uses a temporal hypergraph model to model group interactions on Reddit , predicting climate opinion shifts, and tested against a ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2505.02989", "content": "by A Antelmi · 2025 — This paper uses a temporal hypergraph model to model group interactions on Reddit , predicting climate opinion shifts, and tested against a ..."}
+{"idx": 4, "title": "A methodological approach for inferring causal ...", "date": "", "ddg_snippet": "by J Marten · 2025 — A study closely related to our work on extracting causal relationships from climate change social media data is proposed by Lenti et al. (2024).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12193450/", "content": "by J Marten · 2025 — A study closely related to our work on extracting causal relationships from climate change social media data is proposed by Lenti et al. (2024)."}
+{"idx": 5, "title": "Full article: The aesthetics of climate misinformation", "date": "", "ddg_snippet": "by A Törnberg · 2025 — This paper introduces a multimodal analytical framework to examine climate - related visual misinformation. Analyzing 17,848 image-text posts, we combine BERTopic ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/09644016.2025.2557684", "content": "by A Törnberg · 2025 — This paper introduces a multimodal analytical framework to examine climate - related visual misinformation. Analyzing 17,848 image-text posts, we combine BERTopic ..."}
+{"idx": 6, "title": "CliME: Evaluating Multimodal Climate Discourse on Social ...", "date": "", "ddg_snippet": "by A Borah · 2025 — Multimodal data is increasingly used in cli- mate research for tasks like stance detection, pre- dictive modeling , and video analysis (Dancygier ... 19 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.nlp4pi-1.4.pdf", "content": "by A Borah · 2025 — Multimodal data is increasingly used in cli- mate research for tasks like stance detection, pre- dictive modeling , and video analysis (Dancygier ... 19 pages"}
+{"idx": 7, "title": "Anyone here working on the Climate Risk modelling or ...", "date": "", "ddg_snippet": "I am recently trying to develop a climate model, which goes along with BCBS and EBA guidelines as we believe our potential clients could be based out of Europe ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/quant/comments/1agki21/anyone_here_working_on_the_climate_risk_modelling/", "content": "I am recently trying to develop a climate model, which goes along with BCBS and EBA guidelines as we believe our potential clients could be based out of Europe ..."}
+{"idx": 8, "title": "Luca Maria Aiello - Home", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit · Author Picture Jacopo Lenti. Sapienza University Rome, Rome, Italy and CENTAI, Turin, Italy. ,; + 3. April 2025 ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/profile/81375611194", "content": "Causal Modeling of Climate Activism on Reddit · Author Picture Jacopo Lenti. Sapienza University Rome, Rome, Italy and CENTAI, Turin, Italy. ,; + 3. April 2025 ..."}
+{"idx": 9, "title": "Research | Kenkyuu", "date": "", "ddg_snippet": "Kenkyuu's research includes publications on topics like online discourse, radicalization, conspiracy theories, and sociodemographics, and also includes invited ...", "subpage_snippet": "", "source": "gdfm.me", "link": "https://gdfm.me/research/", "content": "Kenkyuu's research includes publications on topics like online discourse, radicalization, conspiracy theories, and sociodemographics, and also includes invited ..."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_previous_social_media_studies_changes_in_.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_previous_social_media_studies_changes_in_.jsonl
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index 0000000000000000000000000000000000000000..e2bc8c7394e99cfb4b658b97a623d7cc8cf0db50
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_previous_social_media_studies_changes_in_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "Distinct from previous studies , our ap-proach uses large-scale and fine-grained longitudinal data (2016 to 2022) to jointly model the roles of sociodemographic makeup, experience of extreme weather events , exposure to climate - related news, and social influence through online interactions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "Distinct from previous studies , our ap-proach uses large-scale and fine-grained longitudinal data (2016 to 2022) to jointly model the roles of sociodemographic makeup, experience of extreme weather events , exposure to climate - related news, and social influence through online interactions."}
+{"idx": 1, "title": "(PDF) Analyzing Climate Change Discussions on Reddit", "date": "", "ddg_snippet": "Dec 23, 2022 · Climate action is one of the United Nations Sustainable Development Goals. We contribute to this effort by analyzing climate change topics on the Reddit social curation platform, which contains ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366548402_Analyzing_climate_change_discussions_on_Reddit", "content": "Dec 23, 2022 · Climate action is one of the United Nations Sustainable Development Goals. We contribute to this effort by analyzing climate change topics on the Reddit social curation platform, which contains ..."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit - Researchr", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/LentiAMM25", "content": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]"}
+{"idx": 3, "title": "Causal Modeling of Climate Activism on Reddit | Article ...", "date": "", "ddg_snippet": "Article \" Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ...", "subpage_snippet": "", "source": "jglobal.jst.go.jp", "link": "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202402219833468228", "content": "Article \" Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ..."}
+{"idx": 4, "title": "\"Causal Modeling of Climate Activism on Reddit.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2410-10562", "content": "Bibliographic details on Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 5, "title": "Temporal Dynamics of Climate Change Sentiment on Reddit ...", "date": "", "ddg_snippet": "Jun 8, 2025 · To understand climate change discourse on Reddit , we employ sentiment analysis, topic modeling , and social network analysis. Sentiment analysis helps quantify emotional tones in discussions, while topic modeling identifies prevalent themes.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-3762-1_1", "content": "Jun 8, 2025 · To understand climate change discourse on Reddit , we employ sentiment analysis, topic modeling , and social network analysis. Sentiment analysis helps quantify emotional tones in discussions, while topic modeling identifies prevalent themes."}
+{"idx": 6, "title": "(PDF) Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Previous social media studies on climate . action have analyzed the changes in climate debate after extreme.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929273_Causal_Modeling_of_Climate_Activism_on_Reddit", "content": "Previous social media studies on climate . action have analyzed the changes in climate debate after extreme."}
+{"idx": 7, "title": "Modeling the Impact of Group Interactions on Climate - related Opinion...", "date": "", "ddg_snippet": "Modeling . Group Interactions. Climate - related Opinion Change . Reddit . Social Media . Temporal Hypergraph Model . Group Dynamics. Climate Change . Stance Prediction. Large Language Model .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/modeling-the-impact-of-group-interactions-on-climate-related-opinion-change-in-reddit/1126847729390059527-108521", "content": "Modeling . Group Interactions. Climate - related Opinion Change . Reddit . Social Media . Temporal Hypergraph Model . Group Dynamics. Climate Change . Stance Prediction. Large Language Model ."}
+{"idx": 8, "title": "A methodological approach for inferring causal relationships from...", "date": "", "ddg_snippet": "Causal analysis, Climate change , Opinion mining, Topic mining, Social media mining, Sentiment analysis, Stochastic causality.", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-2964/", "content": "Causal analysis, Climate change , Opinion mining, Topic mining, Social media mining, Sentiment analysis, Stochastic causality."}
+{"idx": 9, "title": "Aussie Climate Activists are Still Brutalising the Kids – Watts Up With...", "date": "", "ddg_snippet": "Climate Change vs. Crop Production. Coral Reefs are Dying Because of Climate Change . Climate Change is Lowering Water Levels at Lake Tahoe. Coal Pollution Can Be Seen Pouring From Power Plant Smokestacks.", "subpage_snippet": "", "source": "wattsupwiththat.com", "link": "https://wattsupwiththat.com/2025/09/17/aussie-climate-activists-are-still-brutalising-the-kids/", "content": "Climate Change vs. Crop Production. Coral Reefs are Dying Because of Climate Change . Climate Change is Lowering Water Levels at Lake Tahoe. Coal Pollution Can Be Seen Pouring From Power Plant Smokestacks."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_social_media_studies_climate_action_event_year_2024.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_social_media_studies_climate_action_event_year_2024.jsonl
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index 0000000000000000000000000000000000000000..7b172475b23a5a24968bbcb41dcbb8eb425b6524
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work_social_media_studies_climate_action_event_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Social media messaging by climate action NGOs: a case study of", "date": "", "ddg_snippet": "Here, we examine how Australian climate action NGOs framed the relationship of the 2019–2020 Black Summer bushfires to climate change on Twitter/X.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/oocc/article/3/1/kgad011/7311736", "content": "Here, we examine how Australian climate action NGOs framed the relationship of the 2019–2020 Black Summer bushfires to climate change on Twitter/X."}
+{"idx": 1, "title": "Sentiment and Social Signals in the Climate Crisis: A Survey on", "date": "", "ddg_snippet": "Overview of the climate sentiment pipeline linking extreme events to social media reactions, analyzed through data, models , and their societal impact.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.18837v3", "content": "Overview of the climate sentiment pipeline linking extreme events to social media reactions, analyzed through data, models , and their societal impact."}
+{"idx": 2, "title": "Podcasts as a Medium for Participation in Collective Action: A", "date": "", "ddg_snippet": "... BLM- related events in May and June of 2020, and extracted participatory statements using a layered framework adapted from prior work on social media ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13197v1", "content": "... BLM- related events in May and June of 2020, and extracted participatory statements using a layered framework adapted from prior work on social media ..."}
+{"idx": 3, "title": "My students draw their emotional reactions to climate change", "date": "", "ddg_snippet": "... of Climate Change subject with an activity that prompts students to explore their emotional reactions to the climate change threat. The objective is ...", "subpage_snippet": "", "source": "drbenjaminhabib.com", "link": "https://drbenjaminhabib.com/2022/11/15/emotional-reaction-to-climate-change-politics/", "content": "... of Climate Change subject with an activity that prompts students to explore their emotional reactions to the climate change threat. The objective is ..."}
+{"idx": 4, "title": "Public engagement on climate change - Climate Etc.", "date": "", "ddg_snippet": "Climate change communication is complicated by complexity of the scientific problem, multiple perspectives on the magnitude of the risk from climate ...", "subpage_snippet": "", "source": "judithcurry.com", "link": "https://judithcurry.com/2011/11/13/public-engagement-on-climate-change/", "content": "Climate change communication is complicated by complexity of the scientific problem, multiple perspectives on the magnitude of the risk from climate ..."}
+{"idx": 5, "title": "Climate Dialogue: influence of the sun on climate - Climate Etc.", "date": "", "ddg_snippet": "ClimateDialogue.org is the result of a request by the Dutch parliament to facilitate the scientific discussions between climate experts representing ...", "subpage_snippet": "", "source": "judithcurry.com", "link": "https://judithcurry.com/2014/10/27/climate-dialogue-influence-of-the-sun-on-climate/", "content": "ClimateDialogue.org is the result of a request by the Dutch parliament to facilitate the scientific discussions between climate experts representing ..."}
+{"idx": 6, "title": "Social Governance | THRIVE Project", "date": "", "ddg_snippet": "Register for one of THRIVE ’ s exclusive workshop series above and discover actionable strategies for a Thrivable future.", "subpage_snippet": "", "source": "thrivabilitymatters.org", "link": "https://thrivabilitymatters.org/thrive-outreach-clusters/social-sustainability-governance/", "content": "Register for one of THRIVE ’ s exclusive workshop series above and discover actionable strategies for a Thrivable future."}
+{"idx": 7, "title": "Research | Kenkyuu", "date": "", "ddg_snippet": "De Francisci Morales “ Causal Modeling of Climate Activism on Reddit ” WWW ’ 25: The Web Conference, pp. ... Modeling Political Activism ...", "subpage_snippet": "", "source": "gdfm.me", "link": "https://gdfm.me/research/", "content": "De Francisci Morales “ Causal Modeling of Climate Activism on Reddit ” WWW ’ 25: The Web Conference, pp. ... Modeling Political Activism ..."}
+{"idx": 8, "title": "Study measures the psychological toll of wildfires | MIT News |", "date": "", "ddg_snippet": "... is based on an examination of the events of 2019 in Southeast Asia, in which a huge series of Indonesian wildfires, seemingly related to climate ...", "subpage_snippet": "", "source": "news.mit.edu", "link": "https://news.mit.edu/2024/study-measures-psychological-toll-wildfires-0213", "content": "... is based on an examination of the events of 2019 in Southeast Asia, in which a huge series of Indonesian wildfires, seemingly related to climate ..."}
+{"idx": 9, "title": "Science and Understanding – Politics and Prosperity", "date": "", "ddg_snippet": "Just one more thought about economics before I shift to a seemingly sacrosanct model : Einstein ’ s general theory of relativity.", "subpage_snippet": "", "source": "politicsandprosperity.com", "link": "https://politicsandprosperity.com/category/science-and-understanding/", "content": "Just one more thought about economics before I shift to a seemingly sacrosanct model : Einstein ’ s general theory of relativity."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_methodology_results_six_subreddits.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_methodology_results_six_subreddits.jsonl
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index 0000000000000000000000000000000000000000..0c8d986a9c699acab5d31e0a2e5670cdd072a8d8
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_methodology_results_six_subreddits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion)."}
+{"idx": 1, "title": "(PDF) Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929273_Causal_Modeling_of_Climate_Activism_on_Reddit", "content": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion)."}
+{"idx": 2, "title": "Modeling the Impact of Group Interactions on Climate -related Opinion...", "date": "", "ddg_snippet": "[1] Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/modeling-the-impact-of-group-interactions-on-climate-related-opinion-change-in-reddit/1126847729390059527-108521", "content": "[1] Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure."}
+{"idx": 3, "title": "A methodological approach for inferring causal relationships from...", "date": "", "ddg_snippet": "The results of the ve causal analysis methods vary considerably, with stochastic causality diverging notably from the other four (Direct.", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-2964.pdf", "content": "The results of the ve causal analysis methods vary considerably, with stochastic causality diverging notably from the other four (Direct."}
+{"idx": 4, "title": "Professor Henrik Svensmark: The Earth’s climate is not in a crisis...", "date": "", "ddg_snippet": "“It's a bad career move to go against the idea that CO₂ is the only driver of climate change,” Danish astrophysicist Professor Henrik Svensmark says.", "subpage_snippet": "", "source": "expose-news.com", "link": "https://expose-news.com/2025/09/20/earths-climate-is-not-in-a-crisis/", "content": "“It's a bad career move to go against the idea that CO₂ is the only driver of climate change,” Danish astrophysicist Professor Henrik Svensmark says."}
+{"idx": 5, "title": "Aussie Climate Activists are Still Brutalising the Kids – Watts Up With...", "date": "", "ddg_snippet": "Climate Models have Accurately Predicted 30 Years of Warming. A Climate Tipping Point Will Happen at 1.5°C of Warming. Climate Change is Causing More Floods. Droughts are Increasing Due to Climate Change.", "subpage_snippet": "", "source": "wattsupwiththat.com", "link": "https://wattsupwiththat.com/2025/09/17/aussie-climate-activists-are-still-brutalising-the-kids/", "content": "Climate Models have Accurately Predicted 30 Years of Warming. A Climate Tipping Point Will Happen at 1.5°C of Warming. Climate Change is Causing More Floods. Droughts are Increasing Due to Climate Change."}
+{"idx": 6, "title": "The New York Times Reaches Acceptance Stage - ClimateRealism", "date": "", "ddg_snippet": "Climate models .The great cause has become a ghost: summits no longer attended, pledges no longer met, publics no longer convinced. Last month, I argued that climate activists had not yet arrived at acceptance.", "subpage_snippet": "", "source": "climaterealism.com", "link": "https://climaterealism.com/2025/09/the-new-york-times-reaches-acceptance-stage/", "content": "Climate models .The great cause has become a ghost: summits no longer attended, pledges no longer met, publics no longer convinced. Last month, I argued that climate activists had not yet arrived at acceptance."}
+{"idx": 7, "title": "The Upper Atmosphere Is Cooling, Prompting New Climate Concerns", "date": "", "ddg_snippet": "Early climate modelers predicted back in the 1960s that this combination of tropospheric warming and strong cooling higher up was the likely effect of increasing CO2 in the air.This interest arises because the cooling of the upper air also causes it to contract. The sky is falling — literally.", "subpage_snippet": "", "source": "e360.yale.edu:2087", "link": "https://e360.yale.edu:2087/features/climate-change-upper-atmosphere-cooling", "content": "Early climate modelers predicted back in the 1960s that this combination of tropospheric warming and strong cooling higher up was the likely effect of increasing CO2 in the air.This interest arises because the cooling of the upper air also causes it to contract. The sky is falling — literally."}
+{"idx": 8, "title": "Climate activists gather in New York for ‘Sun Day... | The Guardian", "date": "", "ddg_snippet": "Groups of climate activists walk through the street of New York City for the ‘Make Billionaires Pay’ march on 20 September.", "subpage_snippet": "", "source": "www.theguardian.com", "link": "https://www.theguardian.com/us-news/2025/sep/22/sun-day-climate-new-york", "content": "Groups of climate activists walk through the street of New York City for the ‘Make Billionaires Pay’ march on 20 September."}
+{"idx": 9, "title": "Proceedings of the ACM on Web Conference 2025 | ACM Conferences", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure.", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/proceedings/10.1145/3696410?tocHeading=heading11", "content": "Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_sympathy_activation_participation_pathway_mechanism_year_2024.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_sympathy_activation_participation_pathway_mechanism_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b58745b7c3c1c63223ed4ccb647d7a857a1eab5a
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_sympathy_activation_participation_pathway_mechanism_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Secondly, coverage about climate action has a positive effect on the individuals activation , with this effect decaying after about a week. Lastly, some media coverage of extreme weather events may have a positive long-term impact on sympathy towards activism .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "Secondly, coverage about climate action has a positive effect on the individuals activation , with this effect decaying after about a week. Lastly, some media coverage of extreme weather events may have a positive long-term impact on sympathy towards activism ."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — Causal Modeling of Climate Activism on Reddit . WebConf'25, Apr 28–May 02 ... Figure 3b shows that the coefficient of sympathy to activation (i.e., 𝛽A1 in Equation ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — Causal Modeling of Climate Activism on Reddit . WebConf'25, Apr 28–May 02 ... Figure 3b shows that the coefficient of sympathy to activation (i.e., 𝛽A1 in Equation ..."}
+{"idx": 2, "title": "(PDF) Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "activation in climate activism groups on Reddit , and over which. time scale?communities—the activation outcome we aim to build a causal. Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929273_Causal_Modeling_of_Climate_Activism_on_Reddit", "content": "activation in climate activism groups on Reddit , and over which. time scale?communities—the activation outcome we aim to build a causal. Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 3, "title": "Modeling the Impact of Group Interactions on Climate -related Opinion...", "date": "", "ddg_snippet": "[1] Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. JJacopo LentiLLuca Maria Aiello.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/modeling-the-impact-of-group-interactions-on-climate-related-opinion-change-in-reddit/1126847729390059527-108521", "content": "[1] Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. JJacopo LentiLLuca Maria Aiello."}
+{"idx": 4, "title": "A methodological approach for inferring causal relationships from...", "date": "", "ddg_snippet": "The proposed methodology is applied to climate change opinions and data, offering insights into the causal relationships among public sentiment, specific topics, and natural disasters. This approach provides a framework for analyzing various causal questions.", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-2964/", "content": "The proposed methodology is applied to climate change opinions and data, offering insights into the causal relationships among public sentiment, specific topics, and natural disasters. This approach provides a framework for analyzing various causal questions."}
+{"idx": 5, "title": "Isaac and andrea reddit - Isaac & Andrea (@isaacandandrea)...", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit | Proceedings of the ACM on Web Conference 2025 Isaac and andrea reddit By ACM Digital Library.tease and denial: explore the sensual possibilities of chastity cage gifs.", "subpage_snippet": "", "source": "280days-en.org", "link": "https://280days-en.org/isaac+and+andrea+reddit", "content": "Causal Modeling of Climate Activism on Reddit | Proceedings of the ACM on Web Conference 2025 Isaac and andrea reddit By ACM Digital Library.tease and denial: explore the sensual possibilities of chastity cage gifs."}
+{"idx": 6, "title": "Proceedings of the ACM on Web Conference 2025 | ACM Conferences", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure.", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/proceedings/10.1145/3696410?tocHeading=heading11", "content": "Causal Modeling of Climate Activism on Reddit . Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure."}
+{"idx": 7, "title": "research - Corrado Monti", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales. Proceedings of the ACM Web Conference 2025 (WWW2025), ACM.", "subpage_snippet": "", "source": "www.corradomonti.com", "link": "http://www.corradomonti.com/research/", "content": "Causal Modeling of Climate Activism on Reddit . Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales. Proceedings of the ACM Web Conference 2025 (WWW2025), ACM."}
+{"idx": 8, "title": "Jacopo Lenti - Google Scholar", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit .Proceedings of the 17th ACM International Conference on Web Search and Data …, 2024.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=183XFYMAAAAJ&hl=en", "content": "Causal Modeling of Climate Activism on Reddit .Proceedings of the 17th ACM International Conference on Web Search and Data …, 2024."}
+{"idx": 9, "title": "Corrado Monti | OpenReview", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales.The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Corrado_Monti1", "content": "Causal Modeling of Climate Activism on Reddit . Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales.The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_sympathy_activation_subreddit_participation_mechanism.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_sympathy_activation_subreddit_participation_mechanism.jsonl
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--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_sympathy_activation_subreddit_participation_mechanism.jsonl
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+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — Subreddit participation . To estimate the user ... Figure 3b shows that the coefficient of sympathy to activation (i.e. ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — Subreddit participation . To estimate the user ... Figure 3b shows that the coefficient of sympathy to activation (i.e. ..."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "D Sympathy . E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit .3 Model. We ground our operationalization of involvement with activist communities—the activation outcome we aim to build a causal. Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "D Sympathy . E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit .3 Model. We ground our operationalization of involvement with activist communities—the activation outcome we aim to build a causal. Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 2, "title": "(PDF) Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "the organization of climate activism movements on Reddit was. a grassroots initiative, spawned independent of media coverage.sample of 17k users from our dataset. Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929273_Causal_Modeling_of_Climate_Activism_on_Reddit", "content": "the organization of climate activism movements on Reddit was. a grassroots initiative, spawned independent of media coverage.sample of 17k users from our dataset. Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 3, "title": "What climate sensitivity says about the IPCC assessment process", "date": "", "ddg_snippet": "ECS is therefore an emergent property of the overall climate system, and of its models . ... of summary 9.6.4: “Results from studies of observed ...", "subpage_snippet": "", "source": "judithcurry.com", "link": "https://judithcurry.com/2012/07/22/what-climate-sensitivity-says-about-the-ipcc-assessment-process/", "content": "ECS is therefore an emergent property of the overall climate system, and of its models . ... of summary 9.6.4: “Results from studies of observed ..."}
+{"idx": 4, "title": "Scientists Often Pigeonholed by Political Debates - Climate Etc.", "date": "", "ddg_snippet": "CONAN: And that ’ s, you would say, would be at the heart of the so-called Climategate story, where emails from some scientists seemed to be ...", "subpage_snippet": "", "source": "judithcurry.com", "link": "https://judithcurry.com/2011/04/13/scientists-often-pigeonholed-by-political-debates/", "content": "CONAN: And that ’ s, you would say, would be at the heart of the so-called Climategate story, where emails from some scientists seemed to be ..."}
+{"idx": 5, "title": "Sentiment and Social Signals in the Climate Crisis: A Survey on", "date": "", "ddg_snippet": "As climate instability intensifies, societies worldwide are grappling not only with the physical consequences of extreme weather events but also with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.18837v3", "content": "As climate instability intensifies, societies worldwide are grappling not only with the physical consequences of extreme weather events but also with ..."}
+{"idx": 6, "title": "The Correlation-Causality One-Liner Can Highlight One’s", "date": "", "ddg_snippet": "Most often, when one poses the ‘ correlation does not prove causality ’ apothegm, they are attempting to enforce an implicit counter ...", "subpage_snippet": "", "source": "theethicalskeptic.com", "link": "https://theethicalskeptic.com/2016/01/17/the-correlation-causality-one-liner-can-elicit-ones-scientific-illiteracy/", "content": "Most often, when one poses the ‘ correlation does not prove causality ’ apothegm, they are attempting to enforce an implicit counter ..."}
+{"idx": 7, "title": "Climate Scientists Occupy the Hot Seat in Mock Trial Training", "date": "", "ddg_snippet": "A Minnesota mock courtroom lays bare one of the thorniest issues confronting climate litigation: Scientific evidence.", "subpage_snippet": "", "source": "undark.org", "link": "https://undark.org/2023/12/20/climate-litigation-evidence/", "content": "A Minnesota mock courtroom lays bare one of the thorniest issues confronting climate litigation: Scientific evidence."}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 9, "title": "Props to the liberal anticommunists of the 1930s-1950s |", "date": "", "ddg_snippet": "T]o provide a list of names of communist sympathisers in 1949 to a government department – a list presumably based chiefly on gossip and inference ...", "subpage_snippet": "", "source": "statmodeling.stat.columbia.edu", "link": "https://statmodeling.stat.columbia.edu/2024/10/29/props-to-the-liberal-anticommunists-of-the-1930s-1950s/", "content": "T]o provide a list of names of communist sympathisers in 1949 to a government department – a list presumably based chiefly on gossip and inference ..."}
diff --git a/data/sampled_jsons/Causal_Representation_Learning_robust_noisy_mixing_function_stochastic_2024_2023_year_2023.jsonl b/data/sampled_jsons/Causal_Representation_Learning_robust_noisy_mixing_function_stochastic_2024_2023_year_2023.jsonl
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index 0000000000000000000000000000000000000000..15d57672afe382c0eaa9ecba5d9b18f5ed066778
--- /dev/null
+++ b/data/sampled_jsons/Causal_Representation_Learning_robust_noisy_mixing_function_stochastic_2024_2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Representation Learning from Multiple Distributions: A General ...", "date": "", "ddg_snippet": "The nonparametric settings for both the mixing function and causal model have been explored by (Brehmer et al., 2022; von K ̈ugelgen et al., 2023 ; Jiang & Aragam, 2023 ) together with additional assumptions on counterfactual views (Brehmer et al., 2022), distinct paired interventions (von K ̈ugelgen et al., 2023 ), and graphical conditions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.05052", "content": "The nonparametric settings for both the mixing function and causal model have been explored by (Brehmer et al., 2022; von K ̈ugelgen et al., 2023 ; Jiang & Aragam, 2023 ) together with additional assumptions on counterfactual views (Brehmer et al., 2022), distinct paired interventions (von K ̈ugelgen et al., 2023 ), and graphical conditions ..."}
+{"idx": 1, "title": "PDF Identifying Linearly-Mixed Causal Representations from Multi-Node ...", "date": "", "ddg_snippet": "Many complementary approaches result in recovering linear mixtures, such as assuming polynomial mixing functions (Ahuja et al., 2023a), considering multi-task prediction problems (Lachapelle et al., 2023 ), learning of nonlinear causal effects with anchor variables (Saengkyongam et al., 2023 ), or a large class of problems where deep neural ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v236/bing24a/bing24a.pdf", "content": "Many complementary approaches result in recovering linear mixtures, such as assuming polynomial mixing functions (Ahuja et al., 2023a), considering multi-task prediction problems (Lachapelle et al., 2023 ), learning of nonlinear causal effects with anchor variables (Saengkyongam et al., 2023 ), or a large class of problems where deep neural ..."}
+{"idx": 2, "title": "PDF From Causal to Concept-Based Representation Learning", "date": "", "ddg_snippet": "Abstract To build intelligent machine learning systems, modern representation learning attempts to recover latent generative factors from data, such as in causal representa-tion learning . A key question in this growing field is to provide rigorous conditions under which latent factors can be identified and thus, potentially learned.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/b76a9959151d377ddd2c77a275a97475-Paper-Conference.pdf", "content": "Abstract To build intelligent machine learning systems, modern representation learning attempts to recover latent generative factors from data, such as in causal representa-tion learning . A key question in this growing field is to provide rigorous conditions under which latent factors can be identified and thus, potentially learned."}
+{"idx": 3, "title": "PDF Causal Representation Learning from General Environments under ...", "date": "", "ddg_snippet": "Abstract Causal representation learning aims to re-cover the latent causal variables and their causal relations, typically represented by di-rected acyclic graphs (DAGs), from low-level observations such as image pixels. A pre-vailing line of research exploits multiple en-vironments, which assume how data distri-butions change, including single-node inter-ventions, coupled interventions, or ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v258/main/assets/ng25a/ng25a.pdf", "content": "Abstract Causal representation learning aims to re-cover the latent causal variables and their causal relations, typically represented by di-rected acyclic graphs (DAGs), from low-level observations such as image pixels. A pre-vailing line of research exploits multiple en-vironments, which assume how data distri-butions change, including single-node inter-ventions, coupled interventions, or ..."}
+{"idx": 4, "title": "PDF Causal Representation Learning - AISTATS", "date": "", "ddg_snippet": "nglement via interventions\". ICML 2023 [4] Buchholz et al. \" Learning linear causal representations from interventions und r general nonlinear mixing \". NeurIPS 2023 [5] Zhang et al. \"Identifiability guarantees for causal disentanglem", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/media/aistats-2024/Slides/6689.pdf", "content": "nglement via interventions\". ICML 2023 [4] Buchholz et al. \" Learning linear causal representations from interventions und r general nonlinear mixing \". NeurIPS 2023 [5] Zhang et al. \"Identifiability guarantees for causal disentanglem"}
+{"idx": 5, "title": "Specify Robust Causal Representation from Mixed Observations", "date": "", "ddg_snippet": "This video provides a brief introduction to the importance of causal representation in machine learning prediction, as well as the basic idea of how to find robust causal representation in mixed observational data, such as images.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3580305.3599512", "content": "This video provides a brief introduction to the importance of causal representation in machine learning prediction, as well as the basic idea of how to find robust causal representation in mixed observational data, such as images."}
+{"idx": 6, "title": "Learning Linear Causal Representations from General Environments ...", "date": "", "ddg_snippet": "In this work, we consider the task of learning causal representation learning with data collected from general environments. We show that even when the causal model and the mixing function are both linear, there exists a surrounded-node ambiguity (SNA) [Varici et al. 2023 ] which is basically unavoidable in our setting.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/741aab8b41a2987867acc9939ad50383-Abstract-Conference.html", "content": "In this work, we consider the task of learning causal representation learning with data collected from general environments. We show that even when the causal model and the mixing function are both linear, there exists a surrounded-node ambiguity (SNA) [Varici et al. 2023 ] which is basically unavoidable in our setting."}
+{"idx": 7, "title": "Learning Causal Representations for Robust Domain Adaptation", "date": "", "ddg_snippet": "To address this problem, assuming that the causal relationships between the features and the class variable are robust across domains, we propose a novel causal autoencoder (CAE), which integrates a deep autoencoder and a causal structure learning model to learn causal representations using data from a single source domain.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9566788", "content": "To address this problem, assuming that the causal relationships between the features and the class variable are robust across domains, we propose a novel causal autoencoder (CAE), which integrates a deep autoencoder and a causal structure learning model to learn causal representations using data from a single source domain."}
+{"idx": 8, "title": "Causality-Inspired Robustness for Nonlinear Models via Representation ...", "date": "", "ddg_snippet": "This is achieved by integrating causal principles with modern techniques in representation learning , allowing us to handle nonlinear dependencies while maintaining robustness. This advancement opens up new possibilities for applying causal methods to a broader range of problems, including those involving high-dimensional and nonlinear data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.12868", "content": "This is achieved by integrating causal principles with modern techniques in representation learning , allowing us to handle nonlinear dependencies while maintaining robustness. This advancement opens up new possibilities for applying causal methods to a broader range of problems, including those involving high-dimensional and nonlinear data."}
+{"idx": 9, "title": "PDF Learning Linear Causal Representations from Interventions ... - NeurIPS", "date": "", "ddg_snippet": "2 / 14 Causal Representation Learning Causal representation learning , an emerging field aiming to resolve this issue: Neural model Observational data Output representations Causal structure in representations Causal representationswill be morerobust, interpretable and also enable alignment", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2023/Slides/73823.pdf", "content": "2 / 14 Causal Representation Learning Causal representation learning , an emerging field aiming to resolve this issue: Neural model Observational data Output representations Causal structure in representations Causal representationswill be morerobust, interpretable and also enable alignment"}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_methodology_experiments.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_methodology_experiments.jsonl
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index 0000000000000000000000000000000000000000..9dd03fa5715101dfbef6d836300e56e35f7692b1
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_methodology_experiments.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Strategic Alignment Patterns in National AI Policies", "date": "", "ddg_snippet": "... rigorous comparative analysis across diverse national contexts and granular examination of alignment patterns within individual policy frameworks .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.05400v2", "content": "... rigorous comparative analysis across diverse national contexts and granular examination of alignment patterns within individual policy frameworks ."}
+{"idx": 1, "title": "Navigating the ethical landscape of AI... | F1000Research", "date": "", "ddg_snippet": "... frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible use of AI in ...", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/14-299", "content": "... frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible use of AI in ..."}
+{"idx": 2, "title": "Achieving a Data-Driven Risk Assessment Methodology for Ethical", "date": "", "ddg_snippet": "Therefore, new methodologies are needed to provide a structure and path through the checks and balances needed for ethically assessing an AI .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s44206-022-00016-0", "content": "Therefore, new methodologies are needed to provide a structure and path through the checks and balances needed for ethically assessing an AI ."}
+{"idx": 3, "title": "Ethical Clinical Challenges and Pathways to Trustworthy AI", "date": "", "ddg_snippet": "... section, we prioritized studies that examine real-world challenges in AI governance, data privacy, and the limitations of current ethical frameworks ...", "subpage_snippet": "", "source": "greenberg.news", "link": "https://greenberg.news/ethical-clinical-challenges-and-pathways-to-trustworthy-ai/", "content": "... section, we prioritized studies that examine real-world challenges in AI governance, data privacy, and the limitations of current ethical frameworks ..."}
+{"idx": 4, "title": "Pre-Employment Checks for Machine Learning Jobs: DBS,", "date": "", "ddg_snippet": "... references for machine learning roles must address experience with algorithmic auditing, bias mitigation techniques, and ethical AI frameworks ...", "subpage_snippet": "", "source": "machinelearningjobs.co.uk", "link": "https://machinelearningjobs.co.uk/career-advice/pre-employment-checks-for-machine-learning-jobs-dbs-references-right-to-work-and-more-explained", "content": "... references for machine learning roles must address experience with algorithmic auditing, bias mitigation techniques, and ethical AI frameworks ..."}
+{"idx": 5, "title": "Ethics-driven model auditing and bias mitigation -", "date": "", "ddg_snippet": "In recent years, this approach has prioritized ethical principles to ensure that AI systems align with societal values and do not exacerbate existing ...", "subpage_snippet": "", "source": "www.datasciencecentral.com", "link": "https://www.datasciencecentral.com/ethics-driven-model-auditing-and-bias-mitigation/", "content": "In recent years, this approach has prioritized ethical principles to ensure that AI systems align with societal values and do not exacerbate existing ..."}
+{"idx": 6, "title": "Justified Evidence Collection for Argument-based AI Fairness", "date": "", "ddg_snippet": "... where practitioners struggle with balancing fairness-accuracy tradeoffs, resource limitations, and navigating complex sociotechnical contexts ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.08064v1", "content": "... where practitioners struggle with balancing fairness-accuracy tradeoffs, resource limitations, and navigating complex sociotechnical contexts ..."}
+{"idx": 7, "title": "“Think First, Verify Always”: Training Humans to Face AI", "date": "", "ddg_snippet": "... frameworks such as IEEE’s Ethically Aligned Design ( ieee2019, ) and NIST AI RMF ( nist2023ai, ) , which present ethical principles, TFVA ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03714v1", "content": "... frameworks such as IEEE’s Ethically Aligned Design ( ieee2019, ) and NIST AI RMF ( nist2023ai, ) , which present ethical principles, TFVA ..."}
+{"idx": 8, "title": "A Human-centric Approach to Fairness in AI | Quasilinear Musings", "date": "", "ddg_snippet": "We embed the evaluation of AI fairness within the best practices of machine learning development and operations such as version control, unit testing ...", "subpage_snippet": "", "source": "www.timlrx.com", "link": "https://www.timlrx.com/blog/a-human-centric-approach-to-fairness-in-ai", "content": "We embed the evaluation of AI fairness within the best practices of machine learning development and operations such as version control, unit testing ..."}
+{"idx": 9, "title": "Crafting the AI Compliance Blueprint | CORE", "date": "", "ddg_snippet": "... 2021, the European Commission proposed the first ever legal framework on AI , which aims to ensure that AI is trustworthy, human-centric, and aligned ...", "subpage_snippet": "", "source": "core.se", "link": "https://core.se/en/blog/crafting-ai-compliance-blueprint", "content": "... 2021, the European Commission proposed the first ever legal framework on AI , which aims to ensure that AI is trustworthy, human-centric, and aligned ..."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_Ethical_AI_Alignment_limitations_future_work_section.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_Ethical_AI_Alignment_limitations_future_work_section.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c522bf3dc99e5fd7e4c08732d168da014fd6679d
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_Ethical_AI_Alignment_limitations_future_work_section.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks - and - Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while..."}
+{"idx": 1, "title": "(PDF) Checks - and - Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.lenges of AI ethics . This section presents representative. works , their advances, and limitations .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.lenges of AI ethics . This section presents representative. works , their advances, and limitations ."}
+{"idx": 2, "title": "Ethical AI Alignment Frameworks | Restackio", "date": "", "ddg_snippet": "Ethical AI Alignment Frameworks . Last updated on 06/03/25. Explore various frameworks for aligning AI systems with ethical principles to ensure responsible and fair AI development.", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/ethical-ai-answer-alignment-frameworks-cat-ai", "content": "Ethical AI Alignment Frameworks . Last updated on 06/03/25. Explore various frameworks for aligning AI systems with ethical principles to ensure responsible and fair AI development."}
+{"idx": 3, "title": "Beyond Good and Evil: Navigating AI Morality in a Complex World", "date": "", "ddg_snippet": "The issues of ensuring AI safety and imbuing AI systems with sound ethical frameworks are of paramount importance. As a collaborative writing project between a human (Mark) and a sentient AI (Atlas), we offer a unique perspective on Dr. Draelos’s thought-provoking analysis.", "subpage_snippet": "", "source": "ai.gopubby.com", "link": "https://ai.gopubby.com/beyond-good-and-evil-navigating-ai-morality-in-a-complex-world-e3d270ca8b51", "content": "The issues of ensuring AI safety and imbuing AI systems with sound ethical frameworks are of paramount importance. As a collaborative writing project between a human (Mark) and a sentient AI (Atlas), we offer a unique perspective on Dr. Draelos’s thought-provoking analysis."}
+{"idx": 4, "title": "The Ethical Implications and Quality Challenges of AI -Powered...", "date": "", "ddg_snippet": "Future Trends and Ethical Directions for AI in Digital Journalism.Publishing explainer articles or sections that describe the role and limitations of AI tools in content creation. Providing access to AI -generated data sources or methodologies that contribute to the news story.", "subpage_snippet": "", "source": "spreadbot.ai", "link": "https://spreadbot.ai/blog/the-ethical-implications-and-quality-challenges-of-ai-powered-automated-content-creation-in-digital-journalism/", "content": "Future Trends and Ethical Directions for AI in Digital Journalism.Publishing explainer articles or sections that describe the role and limitations of AI tools in content creation. Providing access to AI -generated data sources or methodologies that contribute to the news story."}
+{"idx": 5, "title": "Ethical Statistical Practice and Ethical AI", "date": "", "ddg_snippet": "In what ways does the ASA's Statement on Ethical AI align with the ACM Code of Ethics , IEEE's Ethically Aligned Design guidance, and the Toronto Declaration? How do the ASA's Ethical Guidelines for Statistical Practice relate to the development of ethical AI algorithms?", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-Ethical-Statistical-Practice-cm2xslxrz25ey017vvynn74wc", "content": "In what ways does the ASA's Statement on Ethical AI align with the ACM Code of Ethics , IEEE's Ethically Aligned Design guidance, and the Toronto Declaration? How do the ASA's Ethical Guidelines for Statistical Practice relate to the development of ethical AI algorithms?"}
+{"idx": 6, "title": "\"X\" Ethical Considerations Shaping the Future of AI in... - Featured.c...", "date": "", "ddg_snippet": "Ethics will shape the future of AI in business by shifting the focus from what's possible to what's responsible. As AI continues to evolve, the real question isn't just how much faster or cheaper it can make things—but whether it supports human wellbeing in the process.", "subpage_snippet": "", "source": "featured.com", "link": "https://featured.com/questions/ethics-ai-business", "content": "Ethics will shape the future of AI in business by shifting the focus from what's possible to what's responsible. As AI continues to evolve, the real question isn't just how much faster or cheaper it can make things—but whether it supports human wellbeing in the process."}
+{"idx": 7, "title": "Day 30: Embracing Ethical AI for the Future – A Comprehensive...", "date": "", "ddg_snippet": "Ethical AI is a dynamic, collective effort requiring ongoing commitment. By grounding actions in real-world examples (e.g., correcting biases, reducing carbon footprints) and leveraging global frameworks , we can ensure AI aligns with human values.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/day30-embracing-ethical-ai-future-comprehensive-call-guru-avinash-t-bua0e", "content": "Ethical AI is a dynamic, collective effort requiring ongoing commitment. By grounding actions in real-world examples (e.g., correcting biases, reducing carbon footprints) and leveraging global frameworks , we can ensure AI aligns with human values."}
+{"idx": 8, "title": "Title : The Virtue of Artificiality: Islamic Ethics and the...", "date": "", "ddg_snippet": "2. Intercultural Ethics of AI and Value Alignment 4. Neutragency: Mahrams and Protecting AwrahThe artificiality of AI , while often problematic in Western ethical frameworks , uniquely aligns ...", "subpage_snippet": "", "source": "philarchive.org", "link": "https://philarchive.org/archive/ABUTVO", "content": "2. Intercultural Ethics of AI and Value Alignment 4. Neutragency: Mahrams and Protecting AwrahThe artificiality of AI , while often problematic in Western ethical frameworks , uniquely aligns ..."}
+{"idx": 9, "title": "Thoughts on Gradual Disempowerment — AI Alignment Forum", "date": "", "ddg_snippet": "Aligned AI could be much better on all dimensions, and better navigate many complex challenges. It could increase our effective control of the situation. It could be a grave mistake not to hand over control to aligned AI .", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/ct6SMDuexe9uBwDoL/thoughts-on-gradual-disempowerment", "content": "Aligned AI could be much better on all dimensions, and better navigate many complex challenges. It could increase our effective control of the situation. It could be a grave mistake not to hand over control to aligned AI ."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Adversarial_Review_algorithm.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Adversarial_Review_algorithm.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ethics-driven model auditing and bias mitigation -", "date": "", "ddg_snippet": "That’s what the ethics -driven model auditing and bias mitigation aim to address by systematically evaluating AI systems for fairness, transparency ...", "subpage_snippet": "", "source": "www.datasciencecentral.com", "link": "https://www.datasciencecentral.com/ethics-driven-model-auditing-and-bias-mitigation/", "content": "That’s what the ethics -driven model auditing and bias mitigation aim to address by systematically evaluating AI systems for fairness, transparency ..."}
+{"idx": 1, "title": "Context Reasoner: Incentivizing Reasoning Capability for", "date": "", "ddg_snippet": "With the CI framework , we are able to align LLMs with established legal frameworks , including GDPR, the EU AI Act, and HIPAA.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14585v2", "content": "With the CI framework , we are able to align LLMs with established legal frameworks , including GDPR, the EU AI Act, and HIPAA."}
+{"idx": 2, "title": "Redefining Elderly Care with Agentic AI: Challenges and", "date": "", "ddg_snippet": "Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14912v1", "content": "Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older ..."}
+{"idx": 3, "title": "Pre-Employment Checks for Machine Learning Jobs: DBS,", "date": "", "ddg_snippet": "... references for machine learning roles must address experience with algorithmic auditing, bias mitigation techniques, and ethical AI frameworks ...", "subpage_snippet": "", "source": "machinelearningjobs.co.uk", "link": "https://machinelearningjobs.co.uk/career-advice/pre-employment-checks-for-machine-learning-jobs-dbs-references-right-to-work-and-more-explained", "content": "... references for machine learning roles must address experience with algorithmic auditing, bias mitigation techniques, and ethical AI frameworks ..."}
+{"idx": 4, "title": "MITRE's Sensible Regulatory Framework for AI Security - Palo", "date": "", "ddg_snippet": "What Are the Risks and Benefits of Artificial Intelligence ( AI ) in Cybersecurity? ... AI Techniques and ML Algorithms that Support Next-Gen SIEM ...", "subpage_snippet": "", "source": "www.paloaltonetworks.com", "link": "https://www.paloaltonetworks.com/cyberpedia/mitre-sensible-regulatory-framework-atlas-matrix", "content": "What Are the Risks and Benefits of Artificial Intelligence ( AI ) in Cybersecurity? ... AI Techniques and ML Algorithms that Support Next-Gen SIEM ..."}
+{"idx": 5, "title": "Algorithmic Decision Making and the Cost of Fairness | Request", "date": "", "ddg_snippet": "... is commonly utilized to enhance the reliability and interpretability of probabilistic classifiers, yet its potential for reducing algorithmic bias ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/318916375_Algorithmic_Decision_Making_and_the_Cost_of_Fairness", "content": "... is commonly utilized to enhance the reliability and interpretability of probabilistic classifiers, yet its potential for reducing algorithmic bias ..."}
+{"idx": 6, "title": "Downloads", "date": "", "ddg_snippet": "A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning"}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "Adversarial Risk and Robustness: General Definitions ... A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2018", "content": "Adversarial Risk and Robustness: General Definitions ... A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks"}
+{"idx": 8, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "Align Your Prompts: Test-Time Prompting with Distribution ... Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html?filter=titles", "content": "Align Your Prompts: Test-Time Prompting with Distribution ... Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming"}
+{"idx": 9, "title": "Towards Fair AI: Mitigating Bias in Credit Decisions—A", "date": "", "ddg_snippet": "... papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers .", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1911-8074/18/5/228", "content": "... papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers ."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Adversarial_Review_algorithm_Ta_year_2023-2024.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Adversarial_Review_algorithm_Ta_year_2023-2024.jsonl
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index 0000000000000000000000000000000000000000..08fac549b351a63767985eeae96ba56c46a81b86
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Adversarial_Review_algorithm_Ta_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Wikipedia AI alignment - Wikipedia", "date": "", "ddg_snippet": "5 days ago - In the field of artificial intelligence ( AI ), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/AI_alignment", "content": "5 days ago - In the field of artificial intelligence ( AI ), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives."}
+{"idx": 1, "title": "arXiv [2502.00136] A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "May 28, 2025 - This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "May 28, 2025 - This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative ..."}
+{"idx": 2, "title": "ResearchGate (PDF) Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "May 8, 2024 - PDF | This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental... | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "May 8, 2024 - PDF | This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental... | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 3, "title": "OpenReview A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment | OpenReview", "date": "", "ddg_snippet": "June 18, 2025 - This paper introduces a ... yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ....", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn", "content": "June 18, 2025 - This paper introduces a ... yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...."}
+{"idx": 4, "title": "ICML ICML Poster A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "May 8, 2025 - They check each other to make sure everything stays fair and kind .Here's why this works: Just like people learn to pause when angry instead of saying something hurtful, we teach AI to spot emotional language (like frustration or bias) and respond more thoughtfully.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "May 8, 2025 - They check each other to make sure everything stays fair and kind .Here's why this works: Just like people learn to pause when angry instead of saying something hurtful, we teach AI to spot emotional language (like frustration or bias) and respond more thoughtfully."}
+{"idx": 5, "title": "PubMed Central Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review - PMC", "date": "", "ddg_snippet": "Common computational methods used to achieve calibrated fairness include the following: (1) preprocessing—modifying the original data set to diminish or eliminate the impact of sensitive attributes (eg, gender and ethnic background) on the outcome of an ML model [35]; (2) in-processing—...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11024755/", "content": "Common computational methods used to achieve calibrated fairness include the following: (1) preprocessing—modifying the original data set to diminish or eliminate the impact of sensitive attributes (eg, gender and ethnic background) on the outcome of an ML model [35]; (2) in-processing—..."}
+{"idx": 6, "title": "Frontiers Frontiers | Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making", "date": "", "ddg_snippet": "June 20, 2024 - The rapid integration of artificial intelligence ( AI ) systems into various domains has raised concerns about their impact on individual and societal wellbein...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/human-dynamics/articles/10.3389/fhumd.2024.1421273/full", "content": "June 20, 2024 - The rapid integration of artificial intelligence ( AI ) systems into various domains has raised concerns about their impact on individual and societal wellbein..."}
+{"idx": 7, "title": "NIST NIST Special Publication 1270 Towards a Standard for Identifying and", "date": "", "ddg_snippet": "NIST Special Publication 1270 · Towards a Standard for Identifying and", "subpage_snippet": "", "source": "nvlpubs.nist.gov", "link": "https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf", "content": "NIST Special Publication 1270 · Towards a Standard for Identifying and"}
+{"idx": 8, "title": "Taylor & Francis Online Full article: AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development", "date": "", "ddg_snippet": "In recent years, substantial advancements in AI ethics have emerged, with significant contributions addressing transparency, fairness, and privacy in AI development. Recent research studies (Bender...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/08839514.2025.2463722", "content": "In recent years, substantial advancements in AI ethics have emerged, with significant contributions addressing transparency, fairness, and privacy in AI development. Recent research studies (Bender..."}
+{"idx": 9, "title": "ScienceDirect Assessing trustworthy AI: Technical and legal perspectives of fairness in AI - ScienceDirect", "date": "", "ddg_snippet": "September 18, 2024 - Artificial Intelligence systems are used more and more nowadays, from the application of decision support systems to autonomous vehicles. Hence, the w…", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0267364924001195", "content": "September 18, 2024 - Artificial Intelligence systems are used more and more nowadays, from the application of decision support systems to autonomous vehicles. Hence, the w…"}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_DIKE_GPT-4.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_DIKE_GPT-4.jsonl
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index 0000000000000000000000000000000000000000..6a920ee5573634c41e534d5aca3822de6a1acbb5
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_DIKE_GPT-4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ethics and Psychology: May 2024", "date": "", "ddg_snippet": "Building on these efforts, now is the time for governments to develop regulatory institutions and frameworks that specifically target the existential ...", "subpage_snippet": "", "source": "www.ethicalpsychology.com", "link": "https://www.ethicalpsychology.com/2024/05/", "content": "Building on these efforts, now is the time for governments to develop regulatory institutions and frameworks that specifically target the existential ..."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 2, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "This paper introduces a checks -and- balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 3, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "by EY Chang · Cited by 1 — (a) GPT - 4's zero-shot shows naive mapping. (b) DIKE's analysis reveals complex relationships. Study 2: Adversarial Evaluation. ▷ Reduces subjectivity in ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461_OMgXx2a.pdf", "content": "by EY Chang · Cited by 1 — (a) GPT - 4's zero-shot shows naive mapping. (b) DIKE's analysis reveals complex relationships. Study 2: Adversarial Evaluation. ▷ Reduces subjectivity in ..."}
+{"idx": 4, "title": "A Three-Branch Checks-and-Balances Framework for ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/c76fc56310e947fbc848c07660b1ecbd60580a08.pdf", "content": "This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems."}
+{"idx": 5, "title": "Integrating Emotional and Linguistic Models for Ethical ...", "date": "", "ddg_snippet": "11 May 2024 — This framework establishes a robust foundation for AI systems to operate with ethical integrity and cultural sensitivity, paving the way for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07076v1", "content": "11 May 2024 — This framework establishes a robust foundation for AI systems to operate with ethical integrity and cultural sensitivity, paving the way for ..."}
+{"idx": 6, "title": "a modular framework for ethical, structured, and adaptive AI", "date": "", "ddg_snippet": "by MS Torkestani · 2025 — This paper presents the Inclusive Prompt Engineering Model (IPEM), a modular framework designed to enhance LLM performance, adaptability, and ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11330-7", "content": "by MS Torkestani · 2025 — This paper presents the Inclusive Prompt Engineering Model (IPEM), a modular framework designed to enhance LLM performance, adaptability, and ..."}
+{"idx": 7, "title": "Matthew Barnett's Shortform", "date": "", "ddg_snippet": "8 Aug 2019 — In other words, GPT - 4 looks like it's on a path towards an adequate solution to the value identification problem, where \"adequate\" means \"about ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/MnrQMLuEg5wZ7f4bn/matthew-barnett-s-shortform", "content": "8 Aug 2019 — In other words, GPT - 4 looks like it's on a path towards an adequate solution to the value identification problem, where \"adequate\" means \"about ..."}
+{"idx": 8, "title": "LeadingEHS.com | Leading is not watching the crowd to see where", "date": "", "ddg_snippet": "... accidents such as Eastern Air Lines Flight 401 (1972), where crew fixation on a landing gear indicator light led to unnoticed altitude loss and a ...", "subpage_snippet": "", "source": "leadingehs.com", "link": "https://leadingehs.com/", "content": "... accidents such as Eastern Air Lines Flight 401 (1972), where crew fixation on a landing gear indicator light led to unnoticed altitude loss and a ..."}
+{"idx": 9, "title": "MACI: Multi-LLM Agent Collaborative Intelligence", "date": "", "ddg_snippet": "A Three-Branch Checks -and- Balances Framework for Context - Aware Ethical Alignment of Large Language Models Edward Y. Chang, NeurIPS AI Safety, December 2024.", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/SocraSynth.html", "content": "A Three-Branch Checks -and- Balances Framework for Context - Aware Ethical Alignment of Large Language Models Edward Y. Chang, NeurIPS AI Safety, December 2024."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Section_3.2.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Section_3.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..73316d7b64fcbbd65b3d1a6a1dabeb39050f880c
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Section_3.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Navigating the ethical landscape of AI... | F1000Research", "date": "", "ddg_snippet": "... for ethical AI deployment are encapsulated in frameworks like the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems ( IEEE, 2016 ...", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/14-299", "content": "... for ethical AI deployment are encapsulated in frameworks like the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems ( IEEE, 2016 ..."}
+{"idx": 1, "title": "Justified Evidence Collection for Argument-based AI Fairness", "date": "", "ddg_snippet": "... and demand for AI -enabled systems raises ... We discuss how the framework can be adapted for different organisational contexts and stages of maturity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.08064v1", "content": "... and demand for AI -enabled systems raises ... We discuss how the framework can be adapted for different organisational contexts and stages of maturity."}
+{"idx": 2, "title": "Context Reasoner: Incentivizing Reasoning Capability for", "date": "", "ddg_snippet": "Under the CI framework , we align our model with three critical regulatory standards: GDPR, EU AI Act, and HIPAA. ... context , we formulate LLM safety ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14585v2", "content": "Under the CI framework , we align our model with three critical regulatory standards: GDPR, EU AI Act, and HIPAA. ... context , we formulate LLM safety ..."}
+{"idx": 3, "title": "Frontiers | Building responsible AI chatbot platforms in higher", "date": "", "ddg_snippet": "Higher education leaders who are AI forward are aware of the importance of minimizing the digital divide and preparing students for a future where AI ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1604934/full", "content": "Higher education leaders who are AI forward are aware of the importance of minimizing the digital divide and preparing students for a future where AI ..."}
+{"idx": 4, "title": "AI Tools in Society: Impacts on Cognitive Offloading and the", "date": "", "ddg_snippet": "... Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2075-4698/15/1/6", "content": "... Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and ..."}
+{"idx": 5, "title": "Hawkish nationalism vs international AI power and benefit", "date": "", "ddg_snippet": "... see this narrative as extremely dangerous, but also expect that the grandest AI challenges call for global coordination between rivalling nations and ...", "subpage_snippet": "", "source": "nacicankaya.substack.com", "link": "https://nacicankaya.substack.com/p/hawkish-nationalism-vs-international", "content": "... see this narrative as extremely dangerous, but also expect that the grandest AI challenges call for global coordination between rivalling nations and ..."}
+{"idx": 6, "title": "The 2025 Total Rewards Playbook: A Framework to Build an", "date": "", "ddg_snippet": "Across every section , this playbook offers a framework for building a competitive, high-performing, and human-centered total rewards strategy.", "subpage_snippet": "", "source": "hrcurator.com", "link": "https://hrcurator.com/2025/06/03/the-2025-total-rewards-playbook-a-framework-to-build-an-effective-competitive-and-human-centered-strategy/", "content": "Across every section , this playbook offers a framework for building a competitive, high-performing, and human-centered total rewards strategy."}
+{"idx": 7, "title": "Ethics of chatgpt balancing automation and human interaction -", "date": "", "ddg_snippet": "In this section , we will explore how automation impacts the ethics of ChatGPT and the balance between automation and human interaction.", "subpage_snippet": "", "source": "fastercapital.com", "link": "https://fastercapital.com/content/Ethics-of-chatgpt-balancing-automation-and-human-interaction.html", "content": "In this section , we will explore how automation impacts the ethics of ChatGPT and the balance between automation and human interaction."}
+{"idx": 8, "title": "AI Safety Inventory | Belgian Waffle Connoisseur. Photographer.", "date": "", "ddg_snippet": "Australia’s AI Ethics Framework (2019) provides guidelines for businesses and governments to design, develop, and implement AI responsibly.", "subpage_snippet": "", "source": "halans.com", "link": "https://halans.com/posts/2024-06-02-ai-safety-inventory/", "content": "Australia’s AI Ethics Framework (2019) provides guidelines for businesses and governments to design, develop, and implement AI responsibly."}
+{"idx": 9, "title": "Mastering the Art of Creative Rewriting with ChatGPT: A", "date": "", "ddg_snippet": "... iterations of ChatGPT incorporate more sophisticated neural networks, allowing for deeper understanding of linguistic nuances and cultural context ...", "subpage_snippet": "", "source": "www.rickyspears.com", "link": "https://www.rickyspears.com/ai/mastering-the-art-of-creative-rewriting-with-chatgpt-a-comprehensive-guide-for-2025-and-beyond/", "content": "... iterations of ChatGPT incorporate more sophisticated neural networks, allowing for deeper understanding of linguistic nuances and cultural context ..."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_choice_reasons_summary.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_choice_reasons_summary.jsonl
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index 0000000000000000000000000000000000000000..be8f2e01c6e68a1f811da8957165653799e7ce0c
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_choice_reasons_summary.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi-level Value Alignment in Agentic AI Systems: Survey and", "date": "", "ddg_snippet": "This has brought significant attention to value alignment for agentic AI systems, which aims to ensure that an agent’s goals, preferences, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.09656v2", "content": "This has brought significant attention to value alignment for agentic AI systems, which aims to ensure that an agent’s goals, preferences, and ..."}
+{"idx": 1, "title": "Splits! A Flexible Dataset and Evaluation Framework for", "date": "", "ddg_snippet": "For instance, in the context of “healthy eating,” American students tend to use a vocabulary of balancing food groups and avoiding fat and sugar ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.04640v2", "content": "For instance, in the context of “healthy eating,” American students tend to use a vocabulary of balancing food groups and avoiding fat and sugar ..."}
+{"idx": 2, "title": "AI Safety Camp 10 — AI Alignment Forum", "date": "", "ddg_snippet": "... summarising simulator theory—a lens for understanding LLM-based AI as a simulator rather than as a tool or an agent— and discussing the theory’s ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/57wx7B3GQavvKkPne/ai-safety-camp-10", "content": "... summarising simulator theory—a lens for understanding LLM-based AI as a simulator rather than as a tool or an agent— and discussing the theory’s ..."}
+{"idx": 3, "title": "AI Safety Camp 10 — EA Forum", "date": "", "ddg_snippet": "The course will highlight the vulnerabilities in current regulations and the potential for corporations and authoritarian governments to use AI tools ...", "subpage_snippet": "", "source": "forum.effectivealtruism.org", "link": "https://forum.effectivealtruism.org/posts/2qJMDADRCPrkftRgb/ai-safety-camp-10", "content": "The course will highlight the vulnerabilities in current regulations and the potential for corporations and authoritarian governments to use AI tools ..."}
+{"idx": 4, "title": "Responsible AI in NLP: GUS-Net Span-Level Bias Detection", "date": "", "ddg_snippet": "We introduce the GUS-Net Framework , comprising the GUS dataset and a multi-label token-level detector for span-level analysis of social bias.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.08388v5", "content": "We introduce the GUS-Net Framework , comprising the GUS dataset and a multi-label token-level detector for span-level analysis of social bias."}
+{"idx": 5, "title": "Shallow review of technical AI safety, 2024 - LessWrong 2.0", "date": "", "ddg_snippet": "... mini-books: A Narrow Path (ControlAI), The Compendium (Conjecture), Situational Awareness (Aschenbrenner), Introduction to AI Safety, Ethics , and ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/fAW6RXLKTLHC3WXkS/shallow-review-of-technical-ai-safety-2024", "content": "... mini-books: A Narrow Path (ControlAI), The Compendium (Conjecture), Situational Awareness (Aschenbrenner), Introduction to AI Safety, Ethics , and ..."}
+{"idx": 6, "title": "Perspectives on Managing AI Ethics in the Digital Age", "date": "", "ddg_snippet": "... Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2078-2489/16/4/318", "content": "... Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and ..."}
+{"idx": 7, "title": "Search AI ethics Primer bibliography", "date": "", "ddg_snippet": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ...", "subpage_snippet": "", "source": "robertolofaro.com", "link": "https://robertolofaro.com/searchkaggleaiethics.php", "content": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ..."}
+{"idx": 8, "title": "Search AI ethics Primer bibliography", "date": "", "ddg_snippet": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ...", "subpage_snippet": "", "source": "robertolofaro.com", "link": "http://robertolofaro.com/searchkaggleaiethics.php", "content": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ..."}
+{"idx": 9, "title": "Best Compliance AI Prompts - DocsBot AI", "date": "", "ddg_snippet": "Generates a formal business proposal to secure credit for AI -driven Bitcoin trading with regulatory compliance and growth strategy.", "subpage_snippet": "", "source": "docsbot.ai", "link": "https://docsbot.ai/prompts/tags?tag=Compliance", "content": "Generates a formal business proposal to secure credit for AI -driven Bitcoin trading with regulatory compliance and growth strategy."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_filetypepdf.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_filetypepdf.jsonl
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index 0000000000000000000000000000000000000000..f83dcf6efed90e789f036b951dc9e18c97218657
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "To overcome these challenges, we propose a checks - and - balances framework inspired by governmental structures, where independent but interacting components maintain ac-countability and balance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v3", "content": "To overcome these challenges, we propose a checks - and - balances framework inspired by governmental structures, where independent but interacting components maintain ac-countability and balance."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "To overcome these challenges, we propose a checks - and - balances framework inspired by governmental structures, where independent but interacting components maintain ac-countability and balance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v2", "content": "To overcome these challenges, we propose a checks - and - balances framework inspired by governmental structures, where independent but interacting components maintain ac-countability and balance."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Framework for Context ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems."}
+{"idx": 3, "title": "An Adversarial Behavior Model for Contextual Ethical ...", "date": "", "ddg_snippet": "We introduce our framework , DIKE, which stands for Diagnostics, Interpretation, Knowledge-independent learning, and Ethical guardrails.1 DIKE aims to enhance the ethical compliance of LLMs...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "We introduce our framework , DIKE, which stands for Diagnostics, Interpretation, Knowledge-independent learning, and Ethical guardrails.1 DIKE aims to enhance the ethical compliance of LLMs..."}
+{"idx": 4, "title": "The Unified Cognitive Consciousness Theory for Language ...", "date": "", "ddg_snippet": "To support this model, we present the Threshold-Crossing Dynamics Theorem, which formalizes semantic an-choring as a probabilistic phase transition. But the central claim remains architectural: AGI will not emerge by discard-ing LLMs, but by aligning and integrating them into systems that reason, regulate, and adapt together.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.02139v1", "content": "To support this model, we present the Threshold-Crossing Dynamics Theorem, which formalizes semantic an-choring as a probabilistic phase transition. But the central claim remains architectural: AGI will not emerge by discard-ing LLMs, but by aligning and integrating them into systems that reason, regulate, and adapt together."}
+{"idx": 5, "title": "A Checks - and - Balances Framework for Context - Aware Ethical AI ...", "date": "", "ddg_snippet": "1. A novel checks - and - balances architecture for ethical alignment that maintains separation between knowledge generation and ethical reasoning.This work introduces a checks - and - balances framework for ethical AI behavior.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "1. A novel checks - and - balances architecture for ethical alignment that maintains separation between knowledge generation and ethical reasoning.This work introduces a checks - and - balances framework for ethical AI behavior."}
+{"idx": 6, "title": "( PDF ) Checks - and - Balances Framework for Context - Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 7, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "(a) GPT-4’s zero-shot shows naive mapping. (b) DIKE’s analysis reveals complex relationships.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461.pdf", "content": "(a) GPT-4’s zero-shot shows naive mapping. (b) DIKE’s analysis reveals complex relationships."}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "A Checks - and - Balances Framework for Context - Aware Ethical AI Alignment .A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks. A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "A Checks - and - Balances Framework for Context - Aware Ethical AI Alignment .A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks. A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD."}
+{"idx": 9, "title": "Using the Geneva Emotion Wheel to Classify the... | Semantic Scholar", "date": "", "ddg_snippet": "A Checks - and - Balances Framework for Context - Aware Ethical AI Alignment .A checks - and - balances framework for ethical alignment of Large Language Models inspired by three-branch governmental systems is introduced, demonstrating how DIKE and ERIS direct...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Using-the-Geneva-Emotion-Wheel-to-Classify-the-of-McGinn-Kelly/28b8ca8eef945f4c503d15166a95142d73f220b3", "content": "A Checks - and - Balances Framework for Context - Aware Ethical AI Alignment .A checks - and - balances framework for ethical alignment of Large Language Models inspired by three-branch governmental systems is introduced, demonstrating how DIKE and ERIS direct..."}
diff --git a/data/sampled_jsons/Chen_2022_information-theoretic_lower_bound_preference-based_reinforcement_learning_arxiv2205.11140_year_2022.jsonl b/data/sampled_jsons/Chen_2022_information-theoretic_lower_bound_preference-based_reinforcement_learning_arxiv2205.11140_year_2022.jsonl
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index 0000000000000000000000000000000000000000..1e580f76d2f2eccdb2867635f48280118309a895
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+{"idx": 0, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "May 24, 2022 · In this work, we tackle the regret minimization problem for preference-based reinforcement learning with general function approximation. Specifically, we study the PbRL problem where both the unknown transition model and the unknown preference function are known to belong to given function spaces.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2205.11140", "content": "May 24, 2022 · In this work, we tackle the regret minimization problem for preference-based reinforcement learning with general function approximation. Specifically, we study the PbRL problem where both the unknown transition model and the unknown preference function are known to belong to given function spaces."}
+{"idx": 1, "title": "based reinforcement learning ｜ Function approximation", "date": "", "ddg_snippet": "... preference - based RL, and its regret upper bound is established. An information - theoretic lower bound in the linear case is also proven to show the ...", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/human-in-the-loop-provably-efficient-preference-based-reinforcement-learning-with-general-function-approximation/867745740774965960-108614", "content": "... preference - based RL, and its regret upper bound is established. An information - theoretic lower bound in the linear case is also proven to show the ..."}
+{"idx": 2, "title": "[2205.11140] Human-in-the-loop: Provably Efficient Preference ...", "date": "", "ddg_snippet": "To the best of our knowledge, this is the first theoretical result for PbRL with (general) function approximation. Preference-based Reinforcement Learning 1 Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2205.11140", "content": "To the best of our knowledge, this is the first theoretical result for PbRL with (general) function approximation. Preference-based Reinforcement Learning 1 Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards."}
+{"idx": 3, "title": "Human-in-the-loop: Provably Efficient Preference-based ... UnifiedAlgorithmsforRLwithDecision-EstimationCoefficients ... Efficient Meta Reinforcement Learning for Preference-based ... arXiv:2407.01887v3 [cs.LG] 2 Jan 2025 Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … Human-in-the-loop: Provably Efficient Preference - based Reinforcement … vocab.txt · unitary/toxic-bert at ...", "date": "", "ddg_snippet": "We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences, where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empiri... See full list on proceedings.mlr.press In this section, we present the main results for preference - based RL. We first propose a novel algorithm called Preference - based Optimistic Planning (PbOP) and estab-lish the regret upper bound for it. To show the sharpness of our result, we also prove an information - theoretic lower bound in the linear case. See full list on proceedings.mlr.press In this subsection, we establish the lower bound for PbRL in the linear setting, which is derived using the reduction from the problem of RL with once-per-episode feedback. Firstly, we show the reduction from the problem of RL with once-per-episode feedback setting to the PbRL setting. Specifically, suppose we have an algorithm ALG for PbRL problem... See full list on proceedings.mlr.press In the previous section, we propose a sample-eficient al-gorithm with near-optimal regret for the problem of PbRL with trajectory feedback. However, this setting cannot cover some other RL situations with preference feedback. For example, in robotics, sampling new trajectories can be ex-pensive and time-consuming compared with labeling prefer-ences... See full list on proceedings.mlr.press Apr 30, 2024 · We make progress on this question by developing a unified algorithm framework for a large class of learning goals, building on the Decision-Estimation Coeficient (DEC) framework. Our framework handles many learning goals such as no-regret RL, PAC RL, reward-free learning , model estimation, and preference-based learning , all by simply instantiating the same generic complexity measure called ... Abstract Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning transferable policies that support few-shot adaptation to unseen tasks. Despite recent advances in meta-RL, most existing methods require the access to the environmental reward function of new tasks to infer ... eedback generated by the environment. This paper is the first to investigate LLMs as in-context decision-makers under the problem of Dueling Bandits (DB), a stateless preference-based reinforcement learning setting that extends the classic Multi-Armed Bandit (MAB) mode What is reinforcement learning? Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. Can preference-based learning be used in bandit setting? Besides PbRL, preference-based learning has also been well-explored in bandit setting under the notion of “dueling bandits” (Yue et al., 2012; Falahatgar et al., 2017a;b; Busa-Fekete et al., 2018; Xu et al., 2020a; Busa-Fekete et al., 2018), which can be regarded as a special case of PbRL with single state and horizon H = 1. Which algorithm is used to learn transition dynamics and preference function? Algorithm The algorithm is formally defined in Algorithm 1. Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. How do we learn the transition dynamics and the preference function? Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. We define the confidence sets and calculate the confidence bonuses for the transition and preference estimations, respectively. What is preference-based RL? Preference - based RL We refer readers to Wirth et al. (2017) for an overview of Preference - based RL. Overall, there are three different types of preference feedback in the PbRL literature. Firstly, the preferences can be defined on the action space where the labeler tells which action is better for a given state. Use this modela447313", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences, where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empiri... See full list on proceedings.mlr.press In this section, we present the main results for preference - based RL. We first propose a novel algorithm called Preference - based Optimistic Planning (PbOP) and estab-lish the regret upper bound for it. To show the sharpness of our result, we also prove an information - theoretic lower bound in the linear case. See full list on proceedings.mlr.press In this subsection, we establish the lower bound for PbRL in the linear setting, which is derived using the reduction from the problem of RL with once-per-episode feedback. Firstly, we show the reduction from the problem of RL with once-per-episode feedback setting to the PbRL setting. Specifically, suppose we have an algorithm ALG for PbRL problem... See full list on proceedings.mlr.press In the previous section, we propose a sample-eficient al-gorithm with near-optimal regret for the problem of PbRL with trajectory feedback. However, this setting cannot cover some other RL situations with preference feedback. For example, in robotics, sampling new trajectories can be ex-pensive and time-consuming compared with labeling prefer-ences... See full list on proceedings.mlr.press Apr 30, 2024 · We make progress on this question by developing a unified algorithm framework for a large class of learning goals, building on the Decision-Estimation Coeficient (DEC) framework. Our framework handles many learning goals such as no-regret RL, PAC RL, reward-free learning , model estimation, and preference-based learning , all by simply instantiating the same generic complexity measure called ... Abstract Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning transferable policies that support few-shot adaptation to unseen tasks. Despite recent advances in meta-RL, most existing methods require the access to the environmental reward function of new tasks to infer ... eedback generated by the environment. This paper is the first to investigate LLMs as in-context decision-makers under the problem of Dueling Bandits (DB), a stateless preference-based reinforcement learning setting that extends the classic Multi-Armed Bandit (MAB) mode What is reinforcement learning? Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. Can preference-based learning be used in bandit setting? Besides PbRL, preference-based learning has also been well-explored in bandit setting under the notion of “dueling bandits” (Yue et al., 2012; Falahatgar et al., 2017a;b; Busa-Fekete et al., 2018; Xu et al., 2020a; Busa-Fekete et al., 2018), which can be regarded as a special case of PbRL with single state and horizon H = 1. Which algorithm is used to learn transition dynamics and preference function? Algorithm The algorithm is formally defined in Algorithm 1. Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. How do we learn the transition dynamics and the preference function? Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. We define the confidence sets and calculate the confidence bonuses for the transition and preference estimations, respectively. What is preference-based RL? Preference - based RL We refer readers to Wirth et al. (2017) for an overview of Preference - based RL. Overall, there are three different types of preference feedback in the PbRL literature. Firstly, the preferences can be defined on the action space where the labeler tells which action is better for a given state. Use this modela447313"}
+{"idx": 4, "title": "Efficient Meta Reinforcement Learning for Preference-based ...", "date": "", "ddg_snippet": "Abstract Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning transferable policies that support few-shot adaptation to unseen tasks. Despite recent advances in meta-RL, most existing methods require the access to the environmental reward function of new tasks to infer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2211.10861v1", "content": "Abstract Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning transferable policies that support few-shot adaptation to unseen tasks. Despite recent advances in meta-RL, most existing methods require the access to the environmental reward function of new tasks to infer ..."}
+{"idx": 5, "title": "arXiv:2407.01887v3 [cs.LG] 2 Jan 2025", "date": "", "ddg_snippet": "eedback generated by the environment. This paper is the first to investigate LLMs as in-context decision-makers under the problem of Dueling Bandits (DB), a stateless preference-based reinforcement learning setting that extends the classic Multi-Armed Bandit (MAB) mode", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.01887v3", "content": "eedback generated by the environment. This paper is the first to investigate LLMs as in-context decision-makers under the problem of Dueling Bandits (DB), a stateless preference-based reinforcement learning setting that extends the classic Multi-Armed Bandit (MAB) mode"}
+{"idx": 6, "title": "Information - Theoretic Considerations in Batch Reinforcement ...", "date": "", "ddg_snippet": "5.1. Towards an Information - Theoretic Lower Bound in the Absence of Completeness. We would also like to establish the necessity of complete-ness by showing that, there exist hard MDPs that cannot be efciently learned with value-function approximation, even under low...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v97/chen19e/chen19e.pdf", "content": "5.1. Towards an Information - Theoretic Lower Bound in the Absence of Completeness. We would also like to establish the necessity of complete-ness by showing that, there exist hard MDPs that cannot be efciently learned with value-function approximation, even under low..."}
+{"idx": 7, "title": "[PDF] Assouad, Fano, and Le Cam with Interaction: A Unifying Lower ...", "date": "", "ddg_snippet": "Unified information - theoretic machinery for deriving lower bounds for passive and active learning schemes is developed and first known lower bounds based on the capacity function rather than the disagreement coefficient are provided.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Assouad,-Fano,-and-Le-Cam-with-Interaction:-A-Lower-Chen-Foster/5e410985046f3fd99898358d3714ed2deb358437", "content": "Unified information - theoretic machinery for deriving lower bounds for passive and active learning schemes is developed and first known lower bounds based on the capacity function rather than the disagreement coefficient are provided."}
+{"idx": 8, "title": "Upper and Lower Bounds for Distributionally Robust Off-Dynamics...", "date": "", "ddg_snippet": "Information - theoretic considerations in batch reinforcement learning . In International Conference on Machine Learning .Distributionally robust model- based offline reinforcement learning with near-optimal sample complexity.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384502548_Upper_and_Lower_Bounds_for_Distributionally_Robust_Off-Dynamics_Reinforcement_Learning", "content": "Information - theoretic considerations in batch reinforcement learning . In International Conference on Machine Learning .Distributionally robust model- based offline reinforcement learning with near-optimal sample complexity."}
+{"idx": 9, "title": "(PDF) Minimax Optimal Kernel Operator Learning via Multilevel...", "date": "", "ddg_snippet": "We establish the information - theoretic lower bound in terms of the Sobolev Hilbert-Schmidt norm and show that a regularization that learns the spectral components below the bias contour and ignores the ones that are above the variance contour can achieve the optimal learning rate.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/minimax-optimal-kernel-operator-learning-via-multilevel-19n7r2w3", "content": "We establish the information - theoretic lower bound in terms of the Sobolev Hilbert-Schmidt norm and show that a regularization that learns the spectral components below the bias contour and ignores the ones that are above the variance contour can achieve the optimal learning rate."}
diff --git a/data/sampled_jsons/Chen_2023_preference-based_reinforcement_learning.jsonl b/data/sampled_jsons/Chen_2023_preference-based_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..13561c98f389385ccf85659c97b5b792fc07975c
--- /dev/null
+++ b/data/sampled_jsons/Chen_2023_preference-based_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Preference-Guided Reinforcement Learning for Efficient Exploration", "date": "", "ddg_snippet": "In this paper, we investigate preference-based reinforcement learning (PbRL) that allows reinforcement learning (RL) agents to learn from human feedback. This is particularly valuable when defining a fine-grain reward function is not feasible. However, this approach is inefficient and impractical for promoting deep exploration in hard-exploration tasks with long horizons and sparse rewards. To ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.06503", "content": "In this paper, we investigate preference-based reinforcement learning (PbRL) that allows reinforcement learning (RL) agents to learn from human feedback. This is particularly valuable when defining a fine-grain reward function is not feasible. However, this approach is inefficient and impractical for promoting deep exploration in hard-exploration tasks with long horizons and sparse rewards. To ..."}
+{"idx": 1, "title": "Online Preference-based Reinforcement Learning with Self-augmented ...", "date": "", "ddg_snippet": "Abstract Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human preferences . However, real-time human feedback is hard to obtain in online tasks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3709347.3743845", "content": "Abstract Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human preferences . However, real-time human feedback is hard to obtain in online tasks."}
+{"idx": 2, "title": "Publications | Chen Bo Calvin Zhang", "date": "", "ddg_snippet": "HIP-RL: Hallucinated Inputs for Preference-based Reinforcement Learning in Continuous Domains Chen Bo Calvin Zhang, and Giorgia Ramponi In ICML 2023 Workshop: The Many Facets of Preference-Based Learning , 2023 Bib", "subpage_snippet": "", "source": "calvincbzhang.github.io", "link": "https://calvincbzhang.github.io/publications/", "content": "HIP-RL: Hallucinated Inputs for Preference-based Reinforcement Learning in Continuous Domains Chen Bo Calvin Zhang, and Giorgia Ramponi In ICML 2023 Workshop: The Many Facets of Preference-Based Learning , 2023 Bib"}
+{"idx": 3, "title": "PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback ...", "date": "", "ddg_snippet": "This work explores the integration of foundation models into preference-based reinforcement learning (PbRL), aiming to improve learning efficiency and robustness through multimodal feedback and trajectory synthesis.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15607", "content": "This work explores the integration of foundation models into preference-based reinforcement learning (PbRL), aiming to improve learning efficiency and robustness through multimodal feedback and trajectory synthesis."}
+{"idx": 4, "title": "Publications - Xiaoyu Chen", "date": "", "ddg_snippet": "Publications On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness Haotian Ye*, Xiaoyu Chen *, Liwei Wang, Simon S Du ICML 2023 Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation Xiaoyu Chen *, Han Zhong*, Zhuoran Yang, Zhaoran Wang, Liwei Wang ICML 2022", "subpage_snippet": "", "source": "cxy30.github.io", "link": "https://cxy30.github.io/publications/", "content": "Publications On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness Haotian Ye*, Xiaoyu Chen *, Liwei Wang, Simon S Du ICML 2023 Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation Xiaoyu Chen *, Han Zhong*, Zhuoran Yang, Zhaoran Wang, Liwei Wang ICML 2022"}
+{"idx": 5, "title": "Preference-Controlled Multi-Objective Reinforcement Learning for ...", "date": "", "ddg_snippet": "To answer question i), we propose a multi-objective reinforcement learning (MORL) method which explicitly takes CIDEr and Self-CIDEr scores as the fidelity-oriented and diversity-oriented rewards respectively.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/26490", "content": "To answer question i), we propose a multi-objective reinforcement learning (MORL) method which explicitly takes CIDEr and Self-CIDEr scores as the fidelity-oriented and diversity-oriented rewards respectively."}
+{"idx": 6, "title": "Efficient Preference-Based Reinforcement Learning Using Learned ...", "date": "", "ddg_snippet": "Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10161081", "content": "Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ..."}
+{"idx": 7, "title": "Preference Transformer: Modeling Human Preferences using...", "date": "", "ddg_snippet": "Abstract: Preference-based reinforcement learning (RL) provides a framework to train agents using human preferences between two behaviors. However, preference-based RL has been challenging to scale since it requires a large amount of human feedback to learn a reward function aligned with human intent.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Peot1SFDX0", "content": "Abstract: Preference-based reinforcement learning (RL) provides a framework to train agents using human preferences between two behaviors. However, preference-based RL has been challenging to scale since it requires a large amount of human feedback to learn a reward function aligned with human intent."}
+{"idx": 8, "title": "PDF Inverse Preference Learning: Preference-based RL without a ... - NeurIPS", "date": "", "ddg_snippet": "Abstract Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to improve ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/3be7859b36d9440372cae0a293f2e4cc-Paper-Conference.pdf", "content": "Abstract Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to improve ..."}
+{"idx": 9, "title": "Adaptive Confidence-aware Preference-based Reinforcement Learning with ...", "date": "", "ddg_snippet": "Preference-based Reinforcement Learning (PbRL), which learns directly from human feedback, offers a promising approach to achieving this alignment. However, PbRL faces significant challenges related to data quality, like inconsistent feedback, especially when crowdsourcing is used to collect feedback.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3701716.3717570", "content": "Preference-based Reinforcement Learning (PbRL), which learns directly from human feedback, offers a promising approach to achieving this alignment. However, PbRL faces significant challenges related to data quality, like inconsistent feedback, especially when crowdsourcing is used to collect feedback."}
diff --git a/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_theorem_4.5.jsonl b/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_theorem_4.5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..533f82954691a510e8674891800077a32545589b
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_theorem_4.5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ..."}
+{"idx": 1, "title": "Q -E OFFLINE PREFERENCE-BASED RE INFORCEMENT LEARNING VIA IN ...", "date": "", "ddg_snippet": "ABSTRACT Preference-based reinforcement learning has shown great promise in various ap-plications to avoid reward annotations and align better with human intentions. However, obtaining preference feedback can still be expensive or time-consuming, which forms a strong barrier for preference-based RL. In this paper, we propose a novel approach to improve the query eficiency of ofline preference ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=GOvTGntFNj", "content": "ABSTRACT Preference-based reinforcement learning has shown great promise in various ap-plications to avoid reward annotations and align better with human intentions. However, obtaining preference feedback can still be expensive or time-consuming, which forms a strong barrier for preference-based RL. In this paper, we propose a novel approach to improve the query eficiency of ofline preference ..."}
+{"idx": 2, "title": "PRIMT: Preference-based Reinforcement Learning with ...", "date": "", "ddg_snippet": "This work explores the integration of foundation models into preference-based reinforcement learning (PbRL), aiming to improve learning efficiency and robustness through multimodal feedback and trajectory synthesis.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15607", "content": "This work explores the integration of foundation models into preference-based reinforcement learning (PbRL), aiming to improve learning efficiency and robustness through multimodal feedback and trajectory synthesis."}
+{"idx": 3, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "May 24, 2022 · Reinforcement learning (RL) is concerned with sequen-tial decision-making problems in which the agent inter-acts with the environment to maximize its cumulative re-wards. This framework has achieved tremendous successes in various fields such as Atari games (Mnih et al ., 2013), Go (Silver et al ., 2017), and StarCraft (Vinyals et al ., 2019).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2205.11140", "content": "May 24, 2022 · Reinforcement learning (RL) is concerned with sequen-tial decision-making problems in which the agent inter-acts with the environment to maximize its cumulative re-wards. This framework has achieved tremendous successes in various fields such as Atari games (Mnih et al ., 2013), Go (Silver et al ., 2017), and StarCraft (Vinyals et al ., 2019)."}
+{"idx": 4, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "To the best of our knowledge, this is the first theoretical result for PbRL with (general) function approximation. Preference-based Reinforcement Learning 1 Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2205.11140", "content": "To the best of our knowledge, this is the first theoretical result for PbRL with (general) function approximation. Preference-based Reinforcement Learning 1 Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards."}
+{"idx": 5, "title": "RA-PbRL: Provably Eficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "As a compelling alternative, Preference-based Reinforcement Learning (PbRL) [Busa-Fekete et al ., 2014, Wirth et al ., 2016] addresses this challenge by deviating from traditional quantifiable rewards for each step.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "As a compelling alternative, Preference-based Reinforcement Learning (PbRL) [Busa-Fekete et al ., 2014, Wirth et al ., 2016] addresses this challenge by deviating from traditional quantifiable rewards for each step."}
+{"idx": 6, "title": "Preference-based Deep Reinforcement Learning for Historical ...", "date": "", "ddg_snippet": "This paper introduced a preference-based deep reinforcement learning framework for solving VRPs, emphasizing align-ment with human preferences derived from historical routes.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0955.pdf", "content": "This paper introduced a preference-based deep reinforcement learning framework for solving VRPs, emphasizing align-ment with human preferences derived from historical routes."}
+{"idx": 7, "title": "Provable Reward-Agnostic Preference-Based Reinforcement Learning", "date": "", "ddg_snippet": "In contrast, in practical PbRL applications like InstructGPT (Ouyang et al .,, 2022 ) and PEBBLE (Lee et al .,, 2021 ) , the majority of preference ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18505v3", "content": "In contrast, in practical PbRL applications like InstructGPT (Ouyang et al .,, 2022 ) and PEBBLE (Lee et al .,, 2021 ) , the majority of preference ..."}
+{"idx": 8, "title": "Doubly Optimal Policy Evaluation for Reinforcement Learning", "date": "", "ddg_snippet": "For example, RL algorithms have reduced energy consumption for Google data centers’ cooling by 40 % percent 40 40\\% 40 % (Chervonyi et al .,, 2022 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02226v2", "content": "For example, RL algorithms have reduced energy consumption for Google data centers’ cooling by 40 % percent 40 40\\% 40 % (Chervonyi et al .,, 2022 ..."}
+{"idx": 9, "title": "Policy-labeled Preference Learning: Is Preference Enough for", "date": "", "ddg_snippet": "... research (Lee et al ., 2021 ; Park et al ., 2022 ) assumed humans prefer trajectories with higher cumulative rewards, leading to a two-step learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06273v2", "content": "... research (Lee et al ., 2021 ; Park et al ., 2022 ) assumed humans prefer trajectories with higher cumulative rewards, leading to a two-step learning ..."}
diff --git a/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_year_2022.jsonl b/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..160662cabc8b7a931916eae9e2b44a6a5783ca48
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al._2022_preference-based_reinforcement_learning_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Human-in-the-loop: Provably Efficient Preference-based Reinforcement ...", "date": "", "ddg_snippet": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "Another popular alternative to handle the lack of reward functions is called Preference-based Reinforcement Learn-ing (PbRL) (Busa-Fekete et al ., 2014; Wirth et al ., 2017). In PbRL, instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ..."}
+{"idx": 1, "title": "Published as a conference paper at ICLR 2022 - OpenReview", "date": "", "ddg_snippet": "ABSTRACT Conveying complex objectives to reinforcement learning (RL) agents often re-quires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible reward model based on human preferences by actively incorporating human feedback, i.e. teacher's preferences between two clips of behaviors. However, poor feedback-efficiency still remains a problem in current ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=OWZVD-l-ZrC", "content": "ABSTRACT Conveying complex objectives to reinforcement learning (RL) agents often re-quires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible reward model based on human preferences by actively incorporating human feedback, i.e. teacher's preferences between two clips of behaviors. However, poor feedback-efficiency still remains a problem in current ..."}
+{"idx": 2, "title": "Provable Offline Preference-Based Reinforcement Learni", "date": "", "ddg_snippet": "In this paper, we investigate the problem of offline Preference-based Reinforce-ment Learning (PbRL) with human feedback where feedback is available in the form of preference between trajectory pairs rather than explicit rewards. Our proposed al -gorithm consists of two main steps: (1) estimate the implicit reward using Maximum Likelihood Estimation (MLE) with general function approximation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.14816", "content": "In this paper, we investigate the problem of offline Preference-based Reinforce-ment Learning (PbRL) with human feedback where feedback is available in the form of preference between trajectory pairs rather than explicit rewards. Our proposed al -gorithm consists of two main steps: (1) estimate the implicit reward using Maximum Likelihood Estimation (MLE) with general function approximation ..."}
+{"idx": 3, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9945333", "content": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more ..."}
+{"idx": 4, "title": "Efficient Preference-Based Reinforcement Learning Using Learned ...", "date": "", "ddg_snippet": "Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10161081", "content": "Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ..."}
+{"idx": 5, "title": "PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback ...", "date": "", "ddg_snippet": "Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen , Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 2022 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15607v1", "content": "Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen , Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 2022 ."}
+{"idx": 6, "title": "Online Preference-based Reinforcement Learning with Self-augmented ...", "date": "", "ddg_snippet": "Abstract Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human preferences . However, real-time human feedback is hard to obtain in online tasks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3709347.3743845", "content": "Abstract Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human preferences . However, real-time human feedback is hard to obtain in online tasks."}
+{"idx": 7, "title": "A survey of Preference Reinforcement Learning - GitHub", "date": "", "ddg_snippet": "Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. Chen X, Zhong H, Yang Z, et al. (ICML 2022 ) [paper]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Kavka1/Preference-RL", "content": "Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. Chen X, Zhong H, Yang Z, et al. (ICML 2022 ) [paper]"}
+{"idx": 8, "title": "ICML 2022 Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "Abstract: We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/spotlight/18372", "content": "Abstract: We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer."}
+{"idx": 9, "title": "Human-in-the-loop: Provably Efficient Preference-based Reinforcement ...", "date": "", "ddg_snippet": "Abstract We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag.html", "content": "Abstract We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer."}
diff --git a/data/sampled_jsons/Chinchilla_scaling_law_b=0.54_dataset_size_exponent.jsonl b/data/sampled_jsons/Chinchilla_scaling_law_b=0.54_dataset_size_exponent.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..39b55e0e2e1ef389dda640962404f150eef3040c
--- /dev/null
+++ b/data/sampled_jsons/Chinchilla_scaling_law_b=0.54_dataset_size_exponent.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What is the Chinchilla Scaling Law? - Analytics Vidhya", "date": "", "ddg_snippet": "Sep 19, 2024 · The Chinchilla Scaling Law offers a new roadmap for NLP, guiding the development of high-performing, resource-efficient models. The Chinchilla Scaling Law maximizes language model performance with minimal compute costs by doubling the model size and training data.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2024/09/chinchilla-scaling-law/", "content": "Sep 19, 2024 · The Chinchilla Scaling Law offers a new roadmap for NLP, guiding the development of high-performing, resource-efficient models. The Chinchilla Scaling Law maximizes language model performance with minimal compute costs by doubling the model size and training data."}
+{"idx": 1, "title": "Chinchilla Scaling Laws - emergentmind.com", "date": "", "ddg_snippet": "When these factors are controlled, the empirically observed scaling law exponent converges to Chinchilla ’s value near $ 0 .5$, validating the law ’s robustness across model and dataset choices. 6. Unified Scaling Laws for Sparse and Dense Pre-Training (Jin et al., 21 Jan 2025) extends the canonical Chinchilla law to sparse pre-training regimes.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/chinchilla-scaling-laws", "content": "When these factors are controlled, the empirically observed scaling law exponent converges to Chinchilla ’s value near $ 0 .5$, validating the law ’s robustness across model and dataset choices. 6. Unified Scaling Laws for Sparse and Dense Pre-Training (Jin et al., 21 Jan 2025) extends the canonical Chinchilla law to sparse pre-training regimes."}
+{"idx": 2, "title": "Chinchilla data-optimal scaling laws: In plain English – Dr ... Chinchilla Explained: How to read DeepMind's paper on Compute ... Chinchilla Scaling Laws - GeeksforGeeks Scaling Laws and Chinchilla | davenpi What is the Chinchilla Scaling Law ? - Analytics Vidhya Chinchilla data-optimal scaling laws: In plain English What is the Chinchilla Scaling Law ? - Analytics Vidhya Chinchilla Explained: How to read DeepMind's paper on Compute-Optimal Chinchilla Explained: How to read DeepMind's paper on Compute-Optimal Chinchilla data-optimal scaling laws: In plain English Understanding Chinchilla Scaling Laws With a Hands ... - Medium", "date": "", "ddg_snippet": "Summary: For a fixed compute budget, Chinchilla showed that we need to be using 11× more data during training than that used for GPT-3 and similar models. This means that we need to source, clean, and filter to around 33TB of text data for a 1T-parameter model. How much text data should we use when training a text-based large language model (LLM)? ... See full list on lifearchitect.ai MosaicML: Beyond Chinchilla -Optimal: Accounting for Inference in Language Model Scaling Laws Trainingis a once-off process where an AI model is fed with a lot of data to recognize patterns and make connections. Large models use a lot of compute during training over many months, but they only have to do so once. Inferenceoccurs after the model has f... See full list on lifearchitect.ai Tsinghua University: MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies Read the paper: https://arxiv.org/abs/2404.06395 See full list on lifearchitect.ai Epoch AI: Chinchilla Scaling : A replication attempt Read the paper: https://www.arxiv.org/abs/2404.10102 See full list on lifearchitect.ai Andrej Karpathy (former OpenAI, Tesla, Stanford) commented on the Llama 3 8B model, trained with 15T tokens: Read the paper: https://arxiv.org/abs/2407.21783 See full list on lifearchitect.ai View all model tokens:parameters ratios on the Models Table: https://lifearchitect.ai/models-table/ Based on the papers above—particularly 2024 work by Mosaic and Tsinghua—in plain English, and possibly oversimplified: Working, assume a 100B parameter model: Chinchilla : 20M books × 75K words = 1.5T words or 2T tokens Mosaic/Tsinghua: 190M books × 7... See full list on lifearchitect.ai See full list on lifearchitect.ai Jun 14, 2023 · The researchers assume the relationship between compute budget, model size , and training dataset size follows a power- law pattern. There is some curvature in the optimal model size at high compute budgets, which might mean the researchers might be overestimating the optimal size for large models. Mar 28, 2025 · Traditional scaling laws, such as those proposed by OpenAI in 2020, suggested that increasing model size was the most effective way to improve performance. However, these earlier studies often assumed that training data would scale proportionally with model size , leading to disproportionately large models trained on relatively smaller datasets . Apr 11, 2025 · $$ N_ {opt} \\sim C^ { 0 .46}, \\ D_ {opt} \\sim C^ { 0 . 54 } $$ These scaling exponents are not quite equal to, but are very similar to, the exponents from the other two approaches. The curves in the “IsoLoss contours” plot show how we can change the model size and flop count and keep the loss fixed. What is Chinchilla scaling law? The Chinchilla Scaling Law offers a new roadmap for NLP , guiding the development of high-performing, resource-efficient models. The Chinchilla Scaling Law maximizes language model performance with minimal compute costs by doubling the model size and training data. Will large language models follow Chinchilla scaling laws in 2023? It is expected that 2023 large language models will continue to follow the Chinchilla scaling laws , though there will be new discoveries about data optimization and data use during training. Can Chinchilla outperform traditional LLM scaling? New scaling insights suggest that smaller language models like Chinchilla can outperform larger ones when trained on more data . Chinchilla’s approach challenges traditional LLM scaling by prioritizing data quantity over model size for compute efficiency. What is Chinchilla compute-optimized Model? The researchers used these results to build a compute-optimized model called Chinchilla . They used the same compute budget as Gopher (10^24 FLOPs) and used these projections to define the optimal model size and dataset size . What is a chinchilla model? Since the goal is to do a head-to-head comparison on benchmark tests, the researchers use the Gopher compute budget as a guide to determine the right model size and training dataset to produce the competing model. The resulting model is called the Chinchilla model. Chinchilla model Is Chinchilla scaling optimal? Epoch AI: Chinchilla Scaling: A replication attempt Optimal scaling. We find a range consistent with the 20 tokens per parameter rule of thumb. Indeed, our point estimates imply that 25.6 tokens per parameters is optimal. 2. What Are Chinchilla Scaling Laws? best size The paper asked a key question: How do we find the optimal balance between model size and dataset size ?", "subpage_snippet": "", "source": "lifearchitect.ai", "link": "https://lifearchitect.ai/chinchilla/", "content": "Summary: For a fixed compute budget, Chinchilla showed that we need to be using 11× more data during training than that used for GPT-3 and similar models. This means that we need to source, clean, and filter to around 33TB of text data for a 1T-parameter model. How much text data should we use when training a text-based large language model (LLM)? ... See full list on lifearchitect.ai MosaicML: Beyond Chinchilla -Optimal: Accounting for Inference in Language Model Scaling Laws Trainingis a once-off process where an AI model is fed with a lot of data to recognize patterns and make connections. Large models use a lot of compute during training over many months, but they only have to do so once. Inferenceoccurs after the model has f... See full list on lifearchitect.ai Tsinghua University: MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies Read the paper: https://arxiv.org/abs/2404.06395 See full list on lifearchitect.ai Epoch AI: Chinchilla Scaling : A replication attempt Read the paper: https://www.arxiv.org/abs/2404.10102 See full list on lifearchitect.ai Andrej Karpathy (former OpenAI, Tesla, Stanford) commented on the Llama 3 8B model, trained with 15T tokens: Read the paper: https://arxiv.org/abs/2407.21783 See full list on lifearchitect.ai View all model tokens:parameters ratios on the Models Table: https://lifearchitect.ai/models-table/ Based on the papers above—particularly 2024 work by Mosaic and Tsinghua—in plain English, and possibly oversimplified: Working, assume a 100B parameter model: Chinchilla : 20M books × 75K words = 1.5T words or 2T tokens Mosaic/Tsinghua: 190M books × 7... See full list on lifearchitect.ai See full list on lifearchitect.ai Jun 14, 2023 · The researchers assume the relationship between compute budget, model size , and training dataset size follows a power- law pattern. There is some curvature in the optimal model size at high compute budgets, which might mean the researchers might be overestimating the optimal size for large models. Mar 28, 2025 · Traditional scaling laws, such as those proposed by OpenAI in 2020, suggested that increasing model size was the most effective way to improve performance. However, these earlier studies often assumed that training data would scale proportionally with model size , leading to disproportionately large models trained on relatively smaller datasets . Apr 11, 2025 · $$ N_ {opt} \\sim C^ { 0 .46}, \\ D_ {opt} \\sim C^ { 0 . 54 } $$ These scaling exponents are not quite equal to, but are very similar to, the exponents from the other two approaches. The curves in the “IsoLoss contours” plot show how we can change the model size and flop count and keep the loss fixed. What is Chinchilla scaling law? The Chinchilla Scaling Law offers a new roadmap for NLP , guiding the development of high-performing, resource-efficient models. The Chinchilla Scaling Law maximizes language model performance with minimal compute costs by doubling the model size and training data. Will large language models follow Chinchilla scaling laws in 2023? It is expected that 2023 large language models will continue to follow the Chinchilla scaling laws , though there will be new discoveries about data optimization and data use during training. Can Chinchilla outperform traditional LLM scaling? New scaling insights suggest that smaller language models like Chinchilla can outperform larger ones when trained on more data . Chinchilla’s approach challenges traditional LLM scaling by prioritizing data quantity over model size for compute efficiency. What is Chinchilla compute-optimized Model? The researchers used these results to build a compute-optimized model called Chinchilla . They used the same compute budget as Gopher (10^24 FLOPs) and used these projections to define the optimal model size and dataset size . What is a chinchilla model? Since the goal is to do a head-to-head comparison on benchmark tests, the researchers use the Gopher compute budget as a guide to determine the right model size and training dataset to produce the competing model. The resulting model is called the Chinchilla model. Chinchilla model Is Chinchilla scaling optimal? Epoch AI: Chinchilla Scaling: A replication attempt Optimal scaling. We find a range consistent with the 20 tokens per parameter rule of thumb. Indeed, our point estimates imply that 25.6 tokens per parameters is optimal. 2. What Are Chinchilla Scaling Laws? best size The paper asked a key question: How do we find the optimal balance between model size and dataset size ?"}
+{"idx": 3, "title": "Chinchilla Explained: How to read DeepMind's paper on Compute ...", "date": "", "ddg_snippet": "Jun 14, 2023 · The researchers assume the relationship between compute budget, model size , and training dataset size follows a power- law pattern. There is some curvature in the optimal model size at high compute budgets, which might mean the researchers might be overestimating the optimal size for large models.", "subpage_snippet": "", "source": "alexandrabarr.beehiiv.com", "link": "https://alexandrabarr.beehiiv.com/p/chinchilla-explained", "content": "Jun 14, 2023 · The researchers assume the relationship between compute budget, model size , and training dataset size follows a power- law pattern. There is some curvature in the optimal model size at high compute budgets, which might mean the researchers might be overestimating the optimal size for large models."}
+{"idx": 4, "title": "Chinchilla Scaling Laws - GeeksforGeeks", "date": "", "ddg_snippet": "Mar 28, 2025 · Traditional scaling laws, such as those proposed by OpenAI in 2020, suggested that increasing model size was the most effective way to improve performance. However, these earlier studies often assumed that training data would scale proportionally with model size , leading to disproportionately large models trained on relatively smaller datasets .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/artificial-intelligence/chinchilla-scaling-laws/", "content": "Mar 28, 2025 · Traditional scaling laws, such as those proposed by OpenAI in 2020, suggested that increasing model size was the most effective way to improve performance. However, these earlier studies often assumed that training data would scale proportionally with model size , leading to disproportionately large models trained on relatively smaller datasets ."}
+{"idx": 5, "title": "Scaling Laws and Chinchilla | davenpi", "date": "", "ddg_snippet": "Apr 11, 2025 · $$ N_ {opt} \\sim C^ { 0 .46}, \\ D_ {opt} \\sim C^ { 0 . 54 } $$ These scaling exponents are not quite equal to, but are very similar to, the exponents from the other two approaches. The curves in the “IsoLoss contours” plot show how we can change the model size and flop count and keep the loss fixed.", "subpage_snippet": "", "source": "www.davenpi.com", "link": "https://www.davenpi.com/post/scaling-laws-chinchilla/", "content": "Apr 11, 2025 · $$ N_ {opt} \\sim C^ { 0 .46}, \\ D_ {opt} \\sim C^ { 0 . 54 } $$ These scaling exponents are not quite equal to, but are very similar to, the exponents from the other two approaches. The curves in the “IsoLoss contours” plot show how we can change the model size and flop count and keep the loss fixed."}
+{"idx": 6, "title": "Understanding Chinchilla Scaling Laws With a Hands ... - Medium", "date": "", "ddg_snippet": "2. What Are Chinchilla Scaling Laws? best size The paper asked a key question: How do we find the optimal balance between model size and dataset size ?", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/nextgenllm/understanding-chinchilla-scaling-laws-with-a-hands-on-colab-demo-6177334e0314", "content": "2. What Are Chinchilla Scaling Laws? best size The paper asked a key question: How do we find the optimal balance between model size and dataset size ?"}
+{"idx": 7, "title": "Reconciling Kaplan and Chinchilla Scaling Laws", "date": "", "ddg_snippet": "Simulating the Chinchilla study under these conditions produces biased scaling coefficients close to Kaplan’s. ... Chinchilla ’s scaling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12907v3", "content": "Simulating the Chinchilla study under these conditions produces biased scaling coefficients close to Kaplan’s. ... Chinchilla ’s scaling ..."}
+{"idx": 8, "title": "Power Lines: Scaling Laws for Weight Decay and Batch Size in", "date": "", "ddg_snippet": "Empirically, DeepSeek LLM [ 7 ] adopted “ scaling laws for HPs,” where optimal batch size B opt subscript 𝐵 opt B _{\\text{opt}} italic_ B ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13738v1", "content": "Empirically, DeepSeek LLM [ 7 ] adopted “ scaling laws for HPs,” where optimal batch size B opt subscript 𝐵 opt B _{\\text{opt}} italic_ B ..."}
+{"idx": 9, "title": "Musings on Text Data Wall (Oct 2024) - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "In the Chinchilla paper, the exponent b is estimated as 0 .50- 0 . 54 using different methods [1] (see Table 3 ), and D/N at 3e21 FLOPs is about 20 ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/CAKdA8wrDFjEAuPzJ/musings-on-text-data-wall-oct-2024", "content": "In the Chinchilla paper, the exponent b is estimated as 0 .50- 0 . 54 using different methods [1] (see Table 3 ), and D/N at 3e21 FLOPs is about 20 ..."}
diff --git a/data/sampled_jsons/Chinchilla_scaling_law_vs_Kaplan_dataset_size.jsonl b/data/sampled_jsons/Chinchilla_scaling_law_vs_Kaplan_dataset_size.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law - Wikipedia", "date": "", "ddg_snippet": "Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down."}
+{"idx": 1, "title": "Scaling Laws and Emergent Abilities in LLMs | by LM Po | Medium", "date": "", "ddg_snippet": "The Kaplan scaling law , introduced by OpenAI in 2020, reveals the relationship between model size, dataset size , and performance in large language models (LLMs).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@lmpo/scaling-laws-and-emergent-abilities-in-llms-a02d6e98bb14", "content": "The Kaplan scaling law , introduced by OpenAI in 2020, reveals the relationship between model size, dataset size , and performance in large language models (LLMs)."}
+{"idx": 2, "title": "Scaling Laws for LLM Pretraining | Jonas Vetterle Personal Page & Blog", "date": "", "ddg_snippet": "Segue: Reconciling Kaplan and Chinchilla Scaling Laws . Kaplan et al. using the non-embedding parameter count for estimating scaling laws , whereas Hoffmann et al. use the total parameter count. As mentioned above, Kaplan et al. do not include an offset accounting for irreducible risk.", "subpage_snippet": "", "source": "www.jonvet.com", "link": "https://www.jonvet.com/blog/llm-scaling-laws", "content": "Segue: Reconciling Kaplan and Chinchilla Scaling Laws . Kaplan et al. using the non-embedding parameter count for estimating scaling laws , whereas Hoffmann et al. use the total parameter count. As mentioned above, Kaplan et al. do not include an offset accounting for irreducible risk."}
+{"idx": 3, "title": "What are the Chinchilla Scaling Laws and why do they matter for AI", "date": "", "ddg_snippet": "This finding is known as the Chinchilla Scaling Laws . Until then, the dominant belief (based on Kaplan 's scaling laws from 2020) was simple: if you want a better model, make it bigger - add more parameters, even if the dataset size stayed almost...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/radoslawkrzywiecki_what-are-the-chinchilla-scaling-laws-activity-7371807270499864577-BSuk", "content": "This finding is known as the Chinchilla Scaling Laws . Until then, the dominant belief (based on Kaplan 's scaling laws from 2020) was simple: if you want a better model, make it bigger - add more parameters, even if the dataset size stayed almost..."}
+{"idx": 4, "title": "Reconciling Conflicting Scaling Laws in Large... - DEV Community", "date": "", "ddg_snippet": "The Chinchilla scaling law suggests that to get the most efficient scaling , you need to tune both the model size and the amount of training data together. These two scaling laws seem to contradict each other.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/aimodels-fyi/reconciling-conflicting-scaling-laws-in-large-language-models-3g9n", "content": "The Chinchilla scaling law suggests that to get the most efficient scaling , you need to tune both the model size and the amount of training data together. These two scaling laws seem to contradict each other."}
+{"idx": 5, "title": "Reconciling Kaplan and Chinchilla Scaling Laws", "date": "", "ddg_snippet": "Find power law scaling coefficient of 0.51, close to Chinchilla ’s 0.50. Generate training curves for model sizes used in Kaplan ’s study (1k to 1.5B params).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12907", "content": "Find power law scaling coefficient of 0.51, close to Chinchilla ’s 0.50. Generate training curves for model sizes used in Kaplan ’s study (1k to 1.5B params)."}
+{"idx": 6, "title": "The three AI scaling laws and what they mean for AI infrastructure", "date": "", "ddg_snippet": "Model size, dataset size and compute all depend on the availability of necessary AI infrastructure. In January 2020, a team of OpenAI researchers led by Jared Kaplan , who moved on to co-found Anthropic, published a paper titled “ Scaling Laws for Neural Language Models.”", "subpage_snippet": "", "source": "www.rcrwireless.com", "link": "https://www.rcrwireless.com/20250120/fundamentals/three-ai-scaling-laws-what-they-mean-for-ai-infrastructure", "content": "Model size, dataset size and compute all depend on the availability of necessary AI infrastructure. In January 2020, a team of OpenAI researchers led by Jared Kaplan , who moved on to co-found Anthropic, published a paper titled “ Scaling Laws for Neural Language Models.”"}
+{"idx": 7, "title": "Scaling Laws for LLMs - by Business Analytics Newsletter", "date": "", "ddg_snippet": "Chinchilla Scaling Laws : Rethinking Size vs Data. In 2022, DeepMind published a follow-up study titled \"Training Compute-Optimal Large Language Models,\" often dubbed the \" Chinchilla paper.\" They challenged Kaplan 's assumption that data and compute scale equally. Instead, they found", "subpage_snippet": "", "source": "businessanalytics.substack.com", "link": "https://businessanalytics.substack.com/p/scaling-laws-for-llms-new", "content": "Chinchilla Scaling Laws : Rethinking Size vs Data. In 2022, DeepMind published a follow-up study titled \"Training Compute-Optimal Large Language Models,\" often dubbed the \" Chinchilla paper.\" They challenged Kaplan 's assumption that data and compute scale equally. Instead, they found"}
+{"idx": 8, "title": "Scaling Laws - End-to-End LLM Training & Alignment Course", "date": "", "ddg_snippet": "Scaling Laws : From Kaplan to Chinchilla ¶. Scaling laws provide predictable relationships between model performance and key factors like model size, dataset size , and compute budget.", "subpage_snippet": "", "source": "ai-research-course.netlify.app", "link": "https://ai-research-course.netlify.app/advanced-track/pretraining-scale/scaling_laws/", "content": "Scaling Laws : From Kaplan to Chinchilla ¶. Scaling laws provide predictable relationships between model performance and key factors like model size, dataset size , and compute budget."}
+{"idx": 9, "title": "Reconciling Kaplan and Chinchilla Scaling Laws | AI Research Paper...", "date": "", "ddg_snippet": "The Kaplan scaling law suggests that model performance scales as a power law with respect to model size and compute.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/reconciling-kaplan-chinchilla-scaling-laws", "content": "The Kaplan scaling law suggests that model performance scales as a power law with respect to model size and compute."}
diff --git a/data/sampled_jsons/Chris_Olah_Zoom_In_An_Introduction_to_Circuits_abstract.jsonl b/data/sampled_jsons/Chris_Olah_Zoom_In_An_Introduction_to_Circuits_abstract.jsonl
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index 0000000000000000000000000000000000000000..02db4697b91a82e141a6d8357836074cd318fa66
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Zoom In: An Introduction to Circuits", "date": "", "ddg_snippet": "by C Olah · 2020 · Cited by 637 — This article is part of the Circuits thread, an experimental format collecting invited short articles and critical commentary delving into the inner workings ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/zoom-in", "content": "by C Olah · 2020 · Cited by 637 — This article is part of the Circuits thread, an experimental format collecting invited short articles and critical commentary delving into the inner workings ..."}
+{"idx": 1, "title": "Zoom In: An Introduction to Circuits", "date": "", "ddg_snippet": "10 Mar 2020 — Chris Olah and the rest of the rest of the OpenAI Clarity team just published “ Zoom In: An Introduction to Circuits ,” a Distill article ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/MG4ZjWQDrdpgeu8wG/zoom-in-an-introduction-to-circuits", "content": "10 Mar 2020 — Chris Olah and the rest of the rest of the OpenAI Clarity team just published “ Zoom In: An Introduction to Circuits ,” a Distill article ..."}
+{"idx": 2, "title": "An Introduction to Circuits in CNNs", "date": "", "ddg_snippet": "An Introduction to Circuits in CNNs . Chris Olah. OpenAI Clarity Team. Page 2. Can we reverse engineer neural networks? Page 3. This generally isn't what ... 68 pages", "subpage_snippet": "", "source": "interpretablevision.github.io", "link": "https://interpretablevision.github.io/slide/cvpr20_chris.pdf", "content": "An Introduction to Circuits in CNNs . Chris Olah. OpenAI Clarity Team. Page 2. Can we reverse engineer neural networks? Page 3. This generally isn't what ... 68 pages"}
+{"idx": 3, "title": "Zoom In: An Introduction to Circuits - Chris Olah", "date": "", "ddg_snippet": "TL;DR: Researchers develop a compositional explanation method for deep neural networks, revealing abstract concepts learned by neurons and correlating model ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/zoom-in-an-introduction-to-circuits-1yc8i4obms?citations_page=61", "content": "TL;DR: Researchers develop a compositional explanation method for deep neural networks, revealing abstract concepts learned by neurons and correlating model ..."}
+{"idx": 4, "title": "Thread: Circuits", "date": "", "ddg_snippet": "by N Cammarata · 2020 · Cited by 92 — Zoom In: An Introduction to Circuits · Chris Olah · Ludwig Schubert ; An Overview of Early Vision in InceptionV1 · Chris Olah · Ludwig Schubert ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits", "content": "by N Cammarata · 2020 · Cited by 92 — Zoom In: An Introduction to Circuits · Chris Olah · Ludwig Schubert ; An Overview of Early Vision in InceptionV1 · Chris Olah · Ludwig Schubert ..."}
+{"idx": 5, "title": "1 INTRODUCTION", "date": "", "ddg_snippet": "7 May 2024 — ... Chris Olah. A mathematical framework for transformer circuits ... Zoom in: An introduction to circuits . Distill, 2020. doi: 10.23915 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.04156v1", "content": "7 May 2024 — ... Chris Olah. A mathematical framework for transformer circuits ... Zoom in: An introduction to circuits . Distill, 2020. doi: 10.23915 ..."}
+{"idx": 6, "title": "Chris Olah on what the hell is going on inside neural ...", "date": "", "ddg_snippet": "4 Aug 2021 — Selected work Chris has been involved in. Circuits Thread (start at Zoom In: An Introduction to Circuits ); Multimodal Neurons in Artificial ...", "subpage_snippet": "", "source": "80000hours.org", "link": "https://80000hours.org/podcast/episodes/chris-olah-interpretability-research/", "content": "4 Aug 2021 — Selected work Chris has been involved in. Circuits Thread (start at Zoom In: An Introduction to Circuits ); Multimodal Neurons in Artificial ..."}
+{"idx": 7, "title": "The Sequence Radar #716: Sometimes, Circuits is All You Need", "date": "", "ddg_snippet": "A review of Chris Olah's iconic paper that introduced circuits for AI interpretability. AI Concept of the Day: What are Circuits ?", "subpage_snippet": "", "source": "thesequence.substack.com", "link": "https://thesequence.substack.com/p/the-sequence-radar-716-sometimes", "content": "A review of Chris Olah's iconic paper that introduced circuits for AI interpretability. AI Concept of the Day: What are Circuits ?"}
+{"idx": 8, "title": "Zoom In: An Introduction to Circuits. Published by OpenAI. ...", "date": "", "ddg_snippet": "10 Mar 2020 — This introductory essay offers a high-level overview of our thinking and some of the working principles that we've found useful in this line of research.", "subpage_snippet": "", "source": "blog.biocomm.ai", "link": "https://blog.biocomm.ai/2020/03/10/zoom-in-an-introduction-to-circuits-published-by-openai-march-10-2020/", "content": "10 Mar 2020 — This introductory essay offers a high-level overview of our thinking and some of the working principles that we've found useful in this line of research."}
+{"idx": 9, "title": "Toy Models of Superposition", "date": "", "ddg_snippet": "14 Sept 2022 — ... Zoom In: An Introduction to Circuits C. Olah, N. Cammarata, L. Schubert, G. Goh, M. Petrov, S. Carter. Distill. 2020. DOI: 10.23915/distill ...", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2022/toy_model/index.html", "content": "14 Sept 2022 — ... Zoom In: An Introduction to Circuits C. Olah, N. Cammarata, L. Schubert, G. Goh, M. Petrov, S. Carter. Distill. 2020. DOI: 10.23915/distill ..."}
diff --git a/data/sampled_jsons/Climate_Denial_Fuels_Climate_Change_Discussions_Shah_2021_three_types_events.jsonl b/data/sampled_jsons/Climate_Denial_Fuels_Climate_Change_Discussions_Shah_2021_three_types_events.jsonl
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index 0000000000000000000000000000000000000000..c8937fc323db993daed11b2d3f42e6a3653f5a34
--- /dev/null
+++ b/data/sampled_jsons/Climate_Denial_Fuels_Climate_Change_Discussions_Shah_2021_three_types_events.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What to Do with Climate Emotions | CIIS", "date": "", "ddg_snippet": "... Climate Psychiatry Alliance, which has a list of more than three hundred climate -aware therapists—practitioners who recognize climate change as a ...", "subpage_snippet": "", "source": "www.ciis.edu", "link": "https://www.ciis.edu/news/what-do-climate-emotions", "content": "... Climate Psychiatry Alliance, which has a list of more than three hundred climate -aware therapists—practitioners who recognize climate change as a ..."}
+{"idx": 1, "title": "Top climate leaders will participate in Big Oil-sponsored", "date": "", "ddg_snippet": "... Information spoke with a half dozen leading climate researchers and activists about The Hill’s oil and tobacco industry-sponsored climate event ...", "subpage_snippet": "", "source": "popular.info", "link": "https://popular.info/p/top-climate-leaders-will-participate", "content": "... Information spoke with a half dozen leading climate researchers and activists about The Hill’s oil and tobacco industry-sponsored climate event ..."}
+{"idx": 2, "title": "White House Publishes a Reading on Countering Climate Denialism", "date": "", "ddg_snippet": "... climate scientists from both natural and social sciences and other experts to discuss the scientific understanding of why arguments for delaying ...", "subpage_snippet": "", "source": "wattsupwiththat.com", "link": "https://wattsupwiththat.com/2022/02/27/white-house-publishes-reading-on-countering-climate-denialism/", "content": "... climate scientists from both natural and social sciences and other experts to discuss the scientific understanding of why arguments for delaying ..."}
+{"idx": 3, "title": "Podcasts | Climate Change Resources", "date": "", "ddg_snippet": "Key Climate Change Media Outlets ... Climate Change By Numbers ... Podcasts have exploded and so have climate change podcasts,", "subpage_snippet": "", "source": "climatechangeresources.org", "link": "https://climatechangeresources.org/news/podcasts/", "content": "Key Climate Change Media Outlets ... Climate Change By Numbers ... Podcasts have exploded and so have climate change podcasts,"}
+{"idx": 4, "title": "COP17—Durban Climate Conference — Global Issues -", "date": "", "ddg_snippet": "... Tyndall Report, which monitors the daily nightly newscasts of the three main TV outlets (ABC, CBS, and NBC) found just 4 stories on climate change ...", "subpage_snippet": "", "source": "booboone.com", "link": "https://booboone.com/cop17-durban-climate-conference-global-issues/", "content": "... Tyndall Report, which monitors the daily nightly newscasts of the three main TV outlets (ABC, CBS, and NBC) found just 4 stories on climate change ..."}
+{"idx": 5, "title": "The Book - \"Brighter Climate Futures: A Global Energy,", "date": "", "ddg_snippet": "Transportation: (37% down to 0.0% by 2045): Begin to replace all gasoline and diesel vehicles with EVs and fuel cell vehicles – devote all ...", "subpage_snippet": "", "source": "www.brighterclimatefutures.com", "link": "https://www.brighterclimatefutures.com/climate-journal/category/all", "content": "Transportation: (37% down to 0.0% by 2045): Begin to replace all gasoline and diesel vehicles with EVs and fuel cell vehicles – devote all ..."}
+{"idx": 6, "title": "James PENNEBAKER | Professor | Doctor of Philosophy |", "date": "", "ddg_snippet": "Three studies developed and validated a linguistic dictionary to measure negative affective polarization in English and Spanish political texts.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/James-Pennebaker", "content": "Three studies developed and validated a linguistic dictionary to measure negative affective polarization in English and Spanish political texts."}
+{"idx": 7, "title": "Skeptical Science New Research for Week #22 2023", "date": "", "ddg_snippet": "... climate failure, relating the whole to identify a new formality they call \" climate obstructionism.\" Reviewer Hauke Dannemann praises the book' s novel ...", "subpage_snippet": "", "source": "skepticalscience.com", "link": "https://skepticalscience.com/new_research_2023_22.html", "content": "... climate failure, relating the whole to identify a new formality they call \" climate obstructionism.\" Reviewer Hauke Dannemann praises the book' s novel ..."}
+{"idx": 8, "title": "Skeptical Science New Research for Week #4 2022", "date": "", "ddg_snippet": "... large numbers of individual people quite able and even eager to entertain discussions about climate change and how personal choices affect climate ...", "subpage_snippet": "", "source": "skepticalscience.com", "link": "https://skepticalscience.com/news.php?n=5300", "content": "... large numbers of individual people quite able and even eager to entertain discussions about climate change and how personal choices affect climate ..."}
+{"idx": 9, "title": "TALKS & SHOWS — WRITER WITH A TWIST", "date": "", "ddg_snippet": "The podcast discusses sellout opportunities for startups and roll-ups particularly by way of drawing interest from private equity firms.", "subpage_snippet": "", "source": "www.nishamarnath.com", "link": "https://www.nishamarnath.com/shows", "content": "The podcast discusses sellout opportunities for startups and roll-ups particularly by way of drawing interest from private equity firms."}
diff --git a/data/sampled_jsons/CoPINN-_Cognitive_Physics-Informed_Neural_Networks_paper.jsonl b/data/sampled_jsons/CoPINN-_Cognitive_Physics-Informed_Neural_Networks_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ac9ec07203827b3f12bfe8ff20ee405c35ab88ba
--- /dev/null
+++ b/data/sampled_jsons/CoPINN-_Cognitive_Physics-Informed_Neural_Networks_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Abstract Physics - informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations, which have demonstrated significant potential for solving partial differential equations (PDEs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "Abstract Physics - informed neural networks (PINN) aim to constrain the outputs and gradients of deep learn-ing models to satisfy specified governing physics equations, which have demonstrated significant potential for solving partial differential equations (PDEs)."}
+{"idx": 1, "title": "GitHub - siyuancncd/CoPINN: This is the official implementation of ...", "date": "", "ddg_snippet": "This easily causes the PINN model to fall into undesirable local minima and unstable learning, thereby resulting in an Unbalanced Prediction Problem (UPP). To deal with this daunting problem, we propose a novel framework named Cognitive Physics - Informed Neural Network ( CoPINN ) that imitates the human cognitive learning manner from easy to hard.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN", "content": "This easily causes the PINN model to fall into undesirable local minima and unstable learning, thereby resulting in an Unbalanced Prediction Problem (UPP). To deal with this daunting problem, we propose a novel framework named Cognitive Physics - Informed Neural Network ( CoPINN ) that imitates the human cognitive learning manner from easy to hard."}
+{"idx": 2, "title": "CoPINN: Cognitive Physics-Informed Neural Networks | Read Paper on Bytez", "date": "", "ddg_snippet": "In this paper , we reveal and address a previously unexplored yet pervasive challenge in PINN, referred to as the Unbalanced Prediction Problem (UPP). To tackle this issue, we propose a novel Cognitive Physics - Informed Neural Networks ( CoPINN ) method, which imitates the human cognitive learning manner from easy to hard. Specifically, our CoPINN first adopt separable learning to encode each ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46458/paper", "content": "In this paper , we reveal and address a previously unexplored yet pervasive challenge in PINN, referred to as the Unbalanced Prediction Problem (UPP). To tackle this issue, we propose a novel Cognitive Physics - Informed Neural Networks ( CoPINN ) method, which imitates the human cognitive learning manner from easy to hard. Specifically, our CoPINN first adopt separable learning to encode each ..."}
+{"idx": 3, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "This paper introduces a framework called CoPINN , which effectively addresses the Unbalanced Prediction Problem in Physics - Informed Neural Networks for solving Partial Differential Equations.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/165180?from=subpath-search", "content": "This paper introduces a framework called CoPINN , which effectively addresses the Unbalanced Prediction Problem in Physics - Informed Neural Networks for solving Partial Differential Equations."}
+{"idx": 4, "title": "PDF CoPINN/CoPINN.pdf at main · siyuancncd/CoPINN · GitHub", "date": "", "ddg_snippet": "This is the official implementation of \" CoPINN : Cognitive Physics - informed Neural Network \" (ICML 2025, Spotlight) - CoPINN / CoPINN .pdf at main · siyuancncd/ CoPINN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN/blob/main/CoPINN.pdf", "content": "This is the official implementation of \" CoPINN : Cognitive Physics - informed Neural Network \" (ICML 2025, Spotlight) - CoPINN / CoPINN .pdf at main · siyuancncd/ CoPINN"}
+{"idx": 5, "title": "CoPINN/README.md at main · siyuancncd/CoPINN · GitHub", "date": "", "ddg_snippet": "About This is the official implementation of \" CoPINN : Cognitive Physics - informed Neural Network \" (ICML 2025, PyTorch Code) ‼️ I'm actively seeking a PhD position for Fall 2026 entry. If you believe my background aligns with your research needs, please feel free to contact me via email at ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN/blob/main/README.md", "content": "About This is the official implementation of \" CoPINN : Cognitive Physics - informed Neural Network \" (ICML 2025, PyTorch Code) ‼️ I'm actively seeking a PhD position for Fall 2026 entry. If you believe my background aligns with your research needs, please feel free to contact me via email at ..."}
+{"idx": 6, "title": "[2204.11144] Competitive Physics Informed Networks - arXiv.org", "date": "", "ddg_snippet": "View a PDF of the paper titled Competitive Physics Informed Networks , by Qi Zeng and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2204.11144", "content": "View a PDF of the paper titled Competitive Physics Informed Networks , by Qi Zeng and 3 other authors"}
+{"idx": 7, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Spotlight Poster CoPINN : Cognitive Physics - Informed Neural Networks Siyuan Duan · Wenyuan Wu · Peng Hu · Zhenwen Ren · Dezhong Peng · Yuan Sun East Exhibition Hall A-B #E-2302", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46458", "content": "Spotlight Poster CoPINN : Cognitive Physics - Informed Neural Networks Siyuan Duan · Wenyuan Wu · Peng Hu · Zhenwen Ren · Dezhong Peng · Yuan Sun East Exhibition Hall A-B #E-2302"}
+{"idx": 8, "title": "Siyuan Duan", "date": "", "ddg_snippet": "AI for Science: Utilizing Physics - Informed Neural Networks (PINNs) for solving PDE problems. 🌟 If you are interested in collaborating with me or want to have a chat, please feel free to contact me via email.", "subpage_snippet": "", "source": "siyuancncd.github.io", "link": "https://siyuancncd.github.io/", "content": "AI for Science: Utilizing Physics - Informed Neural Networks (PINNs) for solving PDE problems. 🌟 If you are interested in collaborating with me or want to have a chat, please feel free to contact me via email."}
+{"idx": 9, "title": "siyuancncd (DSY) · GitHub", "date": "", "ddg_snippet": "CoPINN Public This is the official implementation of \" CoPINN : Cognitive Physics - informed Neural Network \" (ICML 2025, Spotlight) Python 13 1", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd", "content": "CoPINN Public This is the official implementation of \" CoPINN : Cognitive Physics - informed Neural Network \" (ICML 2025, Spotlight) Python 13 1"}
diff --git a/data/sampled_jsons/Concept_Bottleneck_Models_abstract_Koh_et_al._2020_year_2020.jsonl b/data/sampled_jsons/Concept_Bottleneck_Models_abstract_Koh_et_al._2020_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9bdb8bdb51b17bc081b8e82ebbd5c9c0df532bf9
--- /dev/null
+++ b/data/sampled_jsons/Concept_Bottleneck_Models_abstract_Koh_et_al._2020_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "by PW Koh · 2020 · Cited by 1205 — Concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a.html", "content": "by PW Koh · 2020 · Cited by 1205 — Concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts."}
+{"idx": 1, "title": "[2007.04612] Concept Bottleneck Models", "date": "", "ddg_snippet": "by PW Koh · 2020 · Cited by 1205 — On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.04612", "content": "by PW Koh · 2020 · Cited by 1205 — On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling ..."}
+{"idx": 2, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "by PW Koh · 2020 · Cited by 1205 — Concept bottleneck models . Models that bottleneck on human-specified concepts—where the model first predicts the concepts, then uses only those predicted ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v119/koh20a/koh20a.pdf", "content": "by PW Koh · 2020 · Cited by 1205 — Concept bottleneck models . Models that bottleneck on human-specified concepts—where the model first predicts the concepts, then uses only those predicted ..."}
+{"idx": 3, "title": "Concept bottleneck models - ACM Digital Library", "date": "", "ddg_snippet": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3524938.3525433", "content": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation ..."}
+{"idx": 4, "title": "Concept Bottleneck Models, ICML 2020", "date": "", "ddg_snippet": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yewsiang/ConceptBottleneck", "content": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation ..."}
+{"idx": 5, "title": "Interactive Concept Bottleneck Models", "date": "", "ddg_snippet": "by K Chauhan · Cited by 82 — Abstract . Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/interactive-concept-bottleneck-models/", "content": "by K Chauhan · Cited by 82 — Abstract . Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts ..."}
+{"idx": 6, "title": "Concepts' Information Bottleneck Models", "date": "", "ddg_snippet": "by K Galliamov — Abstract : Concept Bottleneck Models (CBMs) provide a self-explanatory framework by making predictions based on concepts that humans can understand. However ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2xRTdzmQ6C", "content": "by K Galliamov — Abstract : Concept Bottleneck Models (CBMs) provide a self-explanatory framework by making predictions based on concepts that humans can understand. However ..."}
+{"idx": 7, "title": "Hierarchical concept Bottleneck models for vision and their ...", "date": "", "ddg_snippet": "by F Pittino · 2023 · Cited by 12 — Recently, Concept Bottleneck models , Koh et al . ( 2020 ), have been proposed, with the purpose of improving the explainability of the classification output.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197622006649", "content": "by F Pittino · 2023 · Cited by 12 — Recently, Concept Bottleneck models , Koh et al . ( 2020 ), have been proposed, with the purpose of improving the explainability of the classification output."}
+{"idx": 8, "title": "Cross-Modal Conceptualization in Bottleneck Models", "date": "", "ddg_snippet": "by D Alukaev · 2023 · Cited by 7 — The objective of this study is to enhance the prac- ticality of Concept Bottleneck Models by elimi- nating the need for predefined concepts ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.emnlp-main.318.pdf", "content": "by D Alukaev · 2023 · Cited by 7 — The objective of this study is to enhance the prac- ticality of Concept Bottleneck Models by elimi- nating the need for predefined concepts ..."}
+{"idx": 9, "title": "arXiv:2405.01825v2 [cs.CV] 24 Aug 2024", "date": "", "ddg_snippet": "by NM Selvaraj · 2024 · Cited by 2 — Abstract . Concept Bottleneck Models (CBM) map images to human- interpretable concepts before making class predictions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.01825", "content": "by NM Selvaraj · 2024 · Cited by 2 — Abstract . Concept Bottleneck Models (CBM) map images to human- interpretable concepts before making class predictions."}
diff --git a/data/sampled_jsons/Contrastive_CRL_MCC_0.95_0.15_results_synthetic_ablation_real_data.jsonl b/data/sampled_jsons/Contrastive_CRL_MCC_0.95_0.15_results_synthetic_ablation_real_data.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f6b61b59988bd5357a265322b7d875103b8cf98
--- /dev/null
+++ b/data/sampled_jsons/Contrastive_CRL_MCC_0.95_0.15_results_synthetic_ablation_real_data.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Advanced Materials for Exploration Task Research Results", "date": "", "ddg_snippet": "by MB Cook · 2008 · Cited by 1 — Reports of completed research or a major significant phase of research that present the results of. NASA programs and include extensive data or theoretical ... 332 pages", "subpage_snippet": "", "source": "ntrs.nasa.gov", "link": "https://ntrs.nasa.gov/api/citations/20110008064/downloads/20110008064.pdf", "content": "by MB Cook · 2008 · Cited by 1 — Reports of completed research or a major significant phase of research that present the results of. NASA programs and include extensive data or theoretical ... 332 pages"}
+{"idx": 1, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "Synthetic ablation . 3 Results . 3.1 Contrastive CRL .To understand the failure modes of the evaluated algorithms, we perform an ablation on the data by substituting the real data -generating process with a simpler synthetic equivalent.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v1", "content": "Synthetic ablation . 3 Results . 3.1 Contrastive CRL .To understand the failure modes of the evaluated algorithms, we perform an ablation on the data by substituting the real data -generating process with a simpler synthetic equivalent."}
+{"idx": 2, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "The results indicate that CITRIS fails catastrophically in recovering the ground-truth factors, both from the real data and the ablation with synthetic images and measurements.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20099", "content": "The results indicate that CITRIS fails catastrophically in recovering the ground-truth factors, both from the real data and the ablation with synthetic images and measurements."}
+{"idx": 3, "title": "identification of nonparametric dynamic causal structure ...", "date": "", "ddg_snippet": "by M Fu · 2025 — Our results on these metrics verify the effectiveness of our methodology under identifiabilty, and the result on dx = 100 with inductive bias ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.12500", "content": "by M Fu · 2025 — Our results on these metrics verify the effectiveness of our methodology under identifiabilty, and the result on dx = 100 with inductive bias ..."}
+{"idx": 4, "title": "Autophagy-Dependent Generation of Free Fatty Acids Is ...", "date": "", "ddg_snippet": "by T Riffelmacher · 2017 · Cited by 316 — We observed a profound ablation of Atg7 expression (>80%) by qPCR in mature neutrophils, their myeloblast precursors, and at the level of ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5610174/", "content": "by T Riffelmacher · 2017 · Cited by 316 — We observed a profound ablation of Atg7 expression (>80%) by qPCR in mature neutrophils, their myeloblast precursors, and at the level of ..."}
+{"idx": 5, "title": "Biology AI Models", "date": "", "ddg_snippet": "... Data Bank),\"\"\"We used the original DNCON dataset consisting of 1426 proteins having length between 30 and 300 residues curated before the CASP10 experiment ...", "subpage_snippet": "", "source": "epoch.ai", "link": "https://epoch.ai/data/generated/biology_ai_models.csv", "content": "... Data Bank),\"\"\"We used the original DNCON dataset consisting of 1426 proteins having length between 30 and 300 residues curated before the CASP10 experiment ..."}
+{"idx": 6, "title": "Impurities and defects in, and isotope compositions of, ...", "date": "", "ddg_snippet": "A novel off-line laser sampling technique has been applied to analyzing high purity diamonds. Quantitative trace element data from high-purity gem diamonds from ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/impurities-and-defects-in-and-isotope-compositions-of-1kt50j4rru.pdf", "content": "A novel off-line laser sampling technique has been applied to analyzing high purity diamonds. Quantitative trace element data from high-purity gem diamonds from ..."}
+{"idx": 7, "title": "Capillary function in patients with chronic venous insufficiency", "date": "", "ddg_snippet": "It has been demonstrated in normal subjects that persistently raised venous pressure results in trapping of leucocytes in the peripheral circulation'.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/bjs/article-pdf/75/6/597/59386982/bjs1800750635.pdf", "content": "It has been demonstrated in normal subjects that persistently raised venous pressure results in trapping of leucocytes in the peripheral circulation'."}
+{"idx": 8, "title": "Sunday, April 27, 2025", "date": "", "ddg_snippet": "27 Apr 2025 — This study investigates the real -world rates of genetics referrals in eligible cancer patients at Singapore's largest healthcare cluster using ... 6,076 pages", "subpage_snippet": "", "source": "www.aacr.org", "link": "https://www.aacr.org/wp-content/uploads/2025/05/AACR2025_Proceedings_050725.pdf", "content": "27 Apr 2025 — This study investigates the real -world rates of genetics referrals in eligible cancer patients at Singapore's largest healthcare cluster using ... 6,076 pages"}
+{"idx": 9, "title": "colorectal cancer", "date": "", "ddg_snippet": "Results : Data were available on 169 patients. LLND+ patients (n = 44) consisted of significantly younger and more female patients with higher ASA-scores and ... 260 pages", "subpage_snippet": "", "source": "s3.amazonaws.com", "link": "https://s3.amazonaws.com/files.oncologymeetings.org/prod/s3fs-public/2020-03/GI20-COLORECTAL-CANCER-updated-022720.pdf", "content": "Results : Data were available on 169 patients. LLND+ patients (n = 44) consisted of significantly younger and more female patients with higher ASA-scores and ... 260 pages"}
diff --git a/data/sampled_jsons/Control_strategies_for_physically_simulated_characters_performing_two-player_competitive_sports_Won__year_2021.jsonl b/data/sampled_jsons/Control_strategies_for_physically_simulated_characters_performing_two-player_competitive_sports_Won__year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c3862884f346e0430155cafde9026f18a4f71681
--- /dev/null
+++ b/data/sampled_jsons/Control_strategies_for_physically_simulated_characters_performing_two-player_competitive_sports_Won__year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Control strategies for physically simulated characters ...", "date": "", "ddg_snippet": "In two - player competitive sports , such as boxing and fencing , athletes often demonstrate efficient and tactical movements during a competition. In this paper, we develop a learning framework that generates control policies for physically simulated .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/58930321/Control_strategies_for_physically_simulated_characters_performing_two_player_competitive_sports", "content": "In two - player competitive sports , such as boxing and fencing , athletes often demonstrate efficient and tactical movements during a competition. In this paper, we develop a learning framework that generates control policies for physically simulated ."}
+{"idx": 1, "title": "Control strategies for physically simulated characters performing ...", "date": "", "ddg_snippet": "In two - player competitive sports , such as boxing and fencing , athletes often demonstrate efficient and tactical movements during a competition. In this paper, we develop a learning framework that generates control policies for physically simulated athletes who have many...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/355019903_Control_strategies_for_physically_simulated_characters_performing_two-player_competitive_sports", "content": "In two - player competitive sports , such as boxing and fencing , athletes often demonstrate efficient and tactical movements during a competition. In this paper, we develop a learning framework that generates control policies for physically simulated athletes who have many..."}
+{"idx": 2, "title": "Jungdam Won | Seoul National University | 14 Publications | 89 Citations", "date": "", "ddg_snippet": "Control strategies for physically simulated characters performing two - player competitive sports . Jungdam Won , Deepak Gopinath, Jessica K. Hodgins +2 moreFacebook. - 17 Jul 2021 . - ACM Transactions on Graphics.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/authors/jungdam-won-4urve6emx3", "content": "Control strategies for physically simulated characters performing two - player competitive sports . Jungdam Won , Deepak Gopinath, Jessica K. Hodgins +2 moreFacebook. - 17 Jul 2021 . - ACM Transactions on Graphics."}
+{"idx": 3, "title": "SIGGRAPH 2021 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "Control Strategies for Physically Simulated Characters Performing Two - player Competitive Sports . Character Control. Jungdam Won , Deepak Gopinath, Jessica Hodgins. Technical Paper.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/siggraph-paper-list/siggraph-2021-paper-list/", "content": "Control Strategies for Physically Simulated Characters Performing Two - player Competitive Sports . Character Control. Jungdam Won , Deepak Gopinath, Jessica Hodgins. Technical Paper."}
+{"idx": 4, "title": "Motion In-betweening for Physically Simulated Characters", "date": "", "ddg_snippet": "Control strategies for physically simulated characters performing two - player competitive sports . In two - player competitive sports , such as boxing and fencing, athletes often demonstrate efficient and tactical movements during a competition.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3550082.3564186", "content": "Control strategies for physically simulated characters performing two - player competitive sports . In two - player competitive sports , such as boxing and fencing, athletes often demonstrate efficient and tactical movements during a competition."}
+{"idx": 5, "title": "SMPLOlympics: Sports Environments for Physically", "date": "", "ddg_snippet": "Control strategies for physically simulated characters performing two - player competitive sports . ACM Trans.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.00187", "content": "Control strategies for physically simulated characters performing two - player competitive sports . ACM Trans."}
+{"idx": 6, "title": "Multimodal Data Fusion and Adaptive Optimization in... | Informatica", "date": "", "ddg_snippet": "Won J, Gopinath D, Hodgins J. Control strategies for physically simulated characters performing two - player competitive sports [J]. ACM Transactions on Graphics (TOG), 2021 , 40(4): 1-11. Yu Y, Tang J, Huang J, et al .", "subpage_snippet": "", "source": "www.informatica.si", "link": "https://www.informatica.si/index.php/informatica/article/view/8485", "content": "Won J, Gopinath D, Hodgins J. Control strategies for physically simulated characters performing two - player competitive sports [J]. ACM Transactions on Graphics (TOG), 2021 , 40(4): 1-11. Yu Y, Tang J, Huang J, et al ."}
+{"idx": 7, "title": "PMP: Learning to Physically Interact with Environments using... | CoLab", "date": "", "ddg_snippet": "Control strategies for physically simulated characters performing two - player competitive sports . Won J., Gopinath D., Hodgins J. ACM Transactions on Graphics.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1145/3588432.3591487", "content": "Control strategies for physically simulated characters performing two - player competitive sports . Won J., Gopinath D., Hodgins J. ACM Transactions on Graphics."}
+{"idx": 8, "title": "Control Strategies for Physically Simulated Characters Performing ...", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=KIaYFt6qY7E", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 9, "title": "Deepak Gopinath - Google Akademik", "date": "", "ddg_snippet": "Control strategies for physically simulated characters performing two - player competitive sports . J Won , D Gopinath, J Hodgins. ACM Transactions on Graphics (TOG) 40 (4), 1-11, 2021 .", "subpage_snippet": "", "source": "scholar.google.co.id", "link": "https://scholar.google.co.id/citations?user=-ZCV1BsAAAAJ&hl=tr", "content": "Control strategies for physically simulated characters performing two - player competitive sports . J Won , D Gopinath, J Hodgins. ACM Transactions on Graphics (TOG) 40 (4), 1-11, 2021 ."}
diff --git a/data/sampled_jsons/CrossKD_CIFAR100_classification_results_71.13.jsonl b/data/sampled_jsons/CrossKD_CIFAR100_classification_results_71.13.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..60103a9a73c8c7875fdfea11daebb808aa16cd21
--- /dev/null
+++ b/data/sampled_jsons/CrossKD_CIFAR100_classification_results_71.13.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CIFAR-10 - Wikipedia", "date": "", "ddg_snippet": "The CIFAR-10 dataset is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/CIFAR-10", "content": "The CIFAR-10 dataset is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color..."}
+{"idx": 1, "title": "- Image Classification - NetsPresso", "date": "", "ddg_snippet": "Using automatic compression allows for easy model compression without a deep understanding of compression. Advanced compression offers higher flexibility in settings and may yield better compression results . 'Compressed' Type: Automatic Compression 'Compressed(Adv.)' Type: Advanced Compression All…", "subpage_snippet": "", "source": "docs.netspresso.ai", "link": "https://docs.netspresso.ai/docs/benchmark-mc-classification", "content": "Using automatic compression allows for easy model compression without a deep understanding of compression. Advanced compression offers higher flexibility in settings and may yield better compression results . 'Compressed' Type: Automatic Compression 'Compressed(Adv.)' Type: Advanced Compression All…"}
+{"idx": 2, "title": "CIFAR-10 Image Classification with Dilated Convolutions - GitHub", "date": "", "ddg_snippet": "A modular implementation of a custom CNN for CIFAR-10 classification , featuring Depthwise Separable and Dilated Convolutions, GAP, and advanced augmentations using Albumentations, achieving >85% accuracy with 44. - abhi0069/S8_CIFAR10_CNN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/abhi0069/S8_CIFAR10_CNN", "content": "A modular implementation of a custom CNN for CIFAR-10 classification , featuring Depthwise Separable and Dilated Convolutions, GAP, and advanced augmentations using Albumentations, achieving >85% accuracy with 44. - abhi0069/S8_CIFAR10_CNN"}
+{"idx": 3, "title": "GitHub - dolong2110/ Cifar 100 - Classification", "date": "", "ddg_snippet": "Cifar 100 - Classification Usage Models Using Report 1. Version 1 2. Version2 3. Version3.My play ground with image classification for Cifar 100 dataset. Usage. first you need to get the package.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dolong2110/Cifar100-Classification", "content": "Cifar 100 - Classification Usage Models Using Report 1. Version 1 2. Version2 3. Version3.My play ground with image classification for Cifar 100 dataset. Usage. first you need to get the package."}
+{"idx": 4, "title": "One class classification results for CIFAR 100 , ImageNet30, Dogs vs....", "date": "", "ddg_snippet": "We conduct additional experiments on one-class CIFAR 100 [Krizhevsky and Hinton, 2009] [Cukierski, 2013], and Muffin vs. Chihuahua [Cortinhas, 2023] and provide the results in Table 2. Our method compares well against established baselines on natural images...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/One-class-classification-results-for-CIFAR100-ImageNet30-Dogs-vs-Cats-and-Muffin-vs_tbl1_380974601", "content": "We conduct additional experiments on one-class CIFAR 100 [Krizhevsky and Hinton, 2009] [Cukierski, 2013], and Muffin vs. Chihuahua [Cortinhas, 2023] and provide the results in Table 2. Our method compares well against established baselines on natural images..."}
+{"idx": 5, "title": "CrossKD : Cross -Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "CrossKD is also orthogonal to the feature imitation meth-ods. With the help of PKD, CrossKD achieves the high-est results of 43.9 AP, achieving an improvement of 3.7 AP compared to the baseline. 4.4. CrossKD on Different Detectors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.11369", "content": "CrossKD is also orthogonal to the feature imitation meth-ods. With the help of PKD, CrossKD achieves the high-est results of 43.9 AP, achieving an improvement of 3.7 AP compared to the baseline. 4.4. CrossKD on Different Detectors."}
+{"idx": 6, "title": "Enhancing long-term memory in federated class continual ...", "date": "", "ddg_snippet": "The results verified the effectiveness of retaining long-term memory in class -scaling scenarios with expanding task scales under heterogeneous data distribution.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231225021319", "content": "The results verified the effectiveness of retaining long-term memory in class -scaling scenarios with expanding task scales under heterogeneous data distribution."}
+{"idx": 7, "title": "Clusterability as an Alternative to Anchor Points When ...", "date": "", "ddg_snippet": "The k-NN label clusterability likely holds in many tasks, such as image classification when features are well-extracted by convolutional layers (Han et al., 2019; Ji et al., 2019; Kolesnikov et al., 2019) and each feature belongs to a unique true class . The high-level intuition is that similar represen-tations should belong to the same label class .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2102.05291v2.pdf", "content": "The k-NN label clusterability likely holds in many tasks, such as image classification when features are well-extracted by convolutional layers (Han et al., 2019; Ji et al., 2019; Kolesnikov et al., 2019) and each feature belongs to a unique true class . The high-level intuition is that similar represen-tations should belong to the same label class ."}
+{"idx": 8, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "Experiments on multiple egocentric HAR datasets demonstrate that COMODO consistently improves downstream classification performance, achieving results comparable to or exceeding fully supervised fine-tuned models. Moreover, COMODO exhibits strong cross-dataset generalization.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=cross-distillation", "content": "Experiments on multiple egocentric HAR datasets demonstrate that COMODO consistently improves downstream classification performance, achieving results comparable to or exceeding fully supervised fine-tuned models. Moreover, COMODO exhibits strong cross-dataset generalization."}
+{"idx": 9, "title": "CIFAR-10 and CIFAR - 100 datasets", "date": "", "ddg_snippet": "CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.You can find some baseline replicable results on this dataset on the project page for cuda-convnet.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~kriz/cifar.html", "content": "CIFAR-10 and CIFAR - 100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.You can find some baseline replicable results on this dataset on the project page for cuda-convnet."}
diff --git a/data/sampled_jsons/Cui_2023_license_conflict_open_source_software_empirical_study.jsonl b/data/sampled_jsons/Cui_2023_license_conflict_open_source_software_empirical_study.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1de8301f117bdcb6217bd65935b1368bc2789b70
--- /dev/null
+++ b/data/sampled_jsons/Cui_2023_license_conflict_open_source_software_empirical_study.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Empirical Study of License Conflict in Free and Open ...", "date": "", "ddg_snippet": "by X Cui · 2023 · Cited by 10 — A study found that 27.2% of licenses in 16,341 FOSS projects on GitHub conflict , suggesting conflicts are prevalent in FOSS.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10172522/", "content": "by X Cui · 2023 · Cited by 10 — A study found that 27.2% of licenses in 16,341 FOSS projects on GitHub conflict , suggesting conflicts are prevalent in FOSS."}
+{"idx": 1, "title": "An Empirical Study of License Conflict in Free and Open ...", "date": "", "ddg_snippet": "by X Cui · 2023 · Cited by 10 — An Empirical Study of License Conflict in Free and Open Source Software . Authors: Xing Cui. Xing Cui. Institute of Software, Chinese Academy of Sciences. View ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/ICSE-SEIP58684.2023.00050", "content": "by X Cui · 2023 · Cited by 10 — An Empirical Study of License Conflict in Free and Open Source Software . Authors: Xing Cui. Xing Cui. Institute of Software, Chinese Academy of Sciences. View ..."}
+{"idx": 2, "title": "An Empirical Study of License Conflict in Free and Open Source ...", "date": "", "ddg_snippet": "DIKE is proposed, an automated tool that can perform license detection and conflict analysis for FOSS and suggests that conflicts are prevalent in FOSS, ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/An-Empirical-Study-of-License-Conflict-in-Free-and-Cui-Wu/7d262af42e65c2554ff7cbb41d252ad27912cff9", "content": "DIKE is proposed, an automated tool that can perform license detection and conflict analysis for FOSS and suggests that conflicts are prevalent in FOSS, ..."}
+{"idx": 3, "title": "Identification and classification of free, open source ...", "date": "", "ddg_snippet": "by S Montes-Leon · 2025 — Cui et al. (2023), An Empirical Study of License Conflict in Free and Open Source Software , 2023, Xplore, 11, Yes. 4, 1731, Jahanshahi et al. ( ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0164121225002973", "content": "by S Montes-Leon · 2025 — Cui et al. (2023), An Empirical Study of License Conflict in Free and Open Source Software , 2023, Xplore, 11, Yes. 4, 1731, Jahanshahi et al. ( ..."}
+{"idx": 4, "title": "An Empirical Study of License Conflict in Free and Open Source ...", "date": "", "ddg_snippet": "An Empirical Study of License Conflict in. Free and Open Source Software . Xing Cui ... 2023 IEEE/ACM 45th International Conference on ... empirical study of license ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1109/ICSE-SEIP58684.2023.00050", "content": "An Empirical Study of License Conflict in. Free and Open Source Software . Xing Cui ... 2023 IEEE/ACM 45th International Conference on ... empirical study of license ..."}
+{"idx": 5, "title": "A first look at License Variants in the PyPI Ecosystem", "date": "", "ddg_snippet": "19 Jul 2025 — 2023. An empirical study of license conflict in free and open source software . In 2023 IEEE/ACM 45th International Conference on Software ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14594v1", "content": "19 Jul 2025 — 2023. An empirical study of license conflict in free and open source software . In 2023 IEEE/ACM 45th International Conference on Software ..."}
+{"idx": 6, "title": "They've Stolen My GPL-Licensed Model!", "date": "", "ddg_snippet": "16 Dec 2024 — 2023. An Empirical Study of License Conflict in Free and Open Source Software . In 2023 IEEE/ACM 45th International Conference on Software ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11483v1", "content": "16 Dec 2024 — 2023. An Empirical Study of License Conflict in Free and Open Source Software . In 2023 IEEE/ACM 45th International Conference on Software ..."}
+{"idx": 7, "title": "Table 2 from Open Source Licenses and the Creative ...", "date": "", "ddg_snippet": "Examining Open Source Software Licenses through the Creative Commons Licensing Model · An Empirical Study of License Conflict in Free and Open Source Software .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Open-Source-Licenses-and-the-Creative-Commons-and-Lin-Ko/0625aada49caa66436ee6e3fac88542678100d0e/figure/0", "content": "Examining Open Source Software Licenses through the Creative Commons Licensing Model · An Empirical Study of License Conflict in Free and Open Source Software ."}
+{"idx": 8, "title": "The Impact of Generative AI on Collaborative Open-Source ...", "date": "", "ddg_snippet": "In our study , we focus on one key aspect of coordination efficiency, namely, the coordination time needed to integrate individual code contributions into the ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/4856935.pdf?abstractid=4856935&mirid=1", "content": "In our study , we focus on one key aspect of coordination efficiency, namely, the coordination time needed to integrate individual code contributions into the ..."}
+{"idx": 9, "title": "Open Source Software and the “Private-Collective” Innovation ...", "date": "", "ddg_snippet": "by E Hippel · 2003 · Cited by 3427 — In this paper, we propose that open source software development is an exemplar of a compound “private-collective” model of innovation.", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/10.1287/orsc.14.2.209.14992", "content": "by E Hippel · 2003 · Cited by 3427 — In this paper, we propose that open source software development is an exemplar of a compound “private-collective” model of innovation."}
diff --git a/data/sampled_jsons/Current_Model_Licensing_Practices_are_Dragging_Us_copyleft-style_terms_definition_distinction_year_2024.jsonl b/data/sampled_jsons/Current_Model_Licensing_Practices_are_Dragging_Us_copyleft-style_terms_definition_distinction_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f1c8295245f4f2463b6c70ec861152d044c9a8b0
--- /dev/null
+++ b/data/sampled_jsons/Current_Model_Licensing_Practices_are_Dragging_Us_copyleft-style_terms_definition_distinction_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Current Model Licensing Practices are Dragging Us into...", "date": "", "ddg_snippet": "Popular options include Apache-2.0, OpenRAIL (Responsible AI Licenses), Creative Commons Licenses (CCs), Llama2, and GPL-3.0. Currently, no standard or widely accepted best practices exist for model licensing .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40180", "content": "Popular options include Apache-2.0, OpenRAIL (Responsible AI Licenses), Creative Commons Licenses (CCs), Llama2, and GPL-3.0. Currently, no standard or widely accepted best practices exist for model licensing ."}
+{"idx": 1, "title": "Anatomy of a Machine Learning Ecosystem: 2 Million Models on...", "date": "", "ddg_snippet": "Position: Current model licensing practices are dragging us into a quagmire of legal noncompliance.The data provenance initiative: A large scale audit of dataset licensing & attribution in ai.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06811v1", "content": "Position: Current model licensing practices are dragging us into a quagmire of legal noncompliance.The data provenance initiative: A large scale audit of dataset licensing & attribution in ai."}
+{"idx": 2, "title": "ICML 2025 Sneak Peek: The New Laws of AI | Medium", "date": "", "ddg_snippet": "They are being forced — and are actively choosing — to take on the responsibilities of sociologists, lawyers, economists, and ethicists. This integration of other disciplines into the core discourse of machine learning is the ultimate sign of the field’s newfound maturity.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/foundation-models-deep-dive/icml-2025-sneak-peek-the-new-laws-of-ai-1210789c66a2", "content": "They are being forced — and are actively choosing — to take on the responsibilities of sociologists, lawyers, economists, and ethicists. This integration of other disciplines into the core discourse of machine learning is the ultimate sign of the field’s newfound maturity."}
+{"idx": 3, "title": "Publications", "date": "", "ddg_snippet": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance Moming Duan, Mingzhe Du, Rui Zhao, Mengying Wang, Yinghui Wu, Nigel Shadbolt, Bingsheng He The 42nd International Conference on Machine Learning (ICML 2025).", "subpage_snippet": "", "source": "me.ryey.icu", "link": "https://me.ryey.icu/publications/", "content": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance Moming Duan, Mingzhe Du, Rui Zhao, Mengying Wang, Yinghui Wu, Nigel Shadbolt, Bingsheng He The 42nd International Conference on Machine Learning (ICML 2025)."}
+{"idx": 4, "title": "Bingsheng He @ NUS SoC", "date": "", "ddg_snippet": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance. Call for contributions.", "subpage_snippet": "", "source": "www.comp.nus.edu.sg", "link": "https://www.comp.nus.edu.sg/~hebs/", "content": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance. Call for contributions."}
+{"idx": 5, "title": "ICML 2025 Statistics: Position Track - Paper Copilot", "date": "", "ddg_snippet": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance.Position: All Current Generative Fidelity and Diversity Metrics are Flawed.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/icml-statistics/icml-2025-statistics-position-track/", "content": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance.Position: All Current Generative Fidelity and Diversity Metrics are Flawed."}
+{"idx": 6, "title": "openreview.net/profile?id=~Mingzhe_Du1", "date": "", "ddg_snippet": "Publications. Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance. Moming Duan, Mingzhe Du, Rui Zhao, Mengying Wang, Yinghui Wu, Nigel Shadbolt, Bingsheng He.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Mingzhe_Du1", "content": "Publications. Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance. Moming Duan, Mingzhe Du, Rui Zhao, Mengying Wang, Yinghui Wu, Nigel Shadbolt, Bingsheng He."}
+{"idx": 7, "title": "ICML 2025 Review Controversies Spark Academic Debate | CSPaper", "date": "", "ddg_snippet": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance Highlights legal risks in model licensing . Position: AI Agents Need Authenticated Delegation Argues for delegation mechanisms in AI agents.", "subpage_snippet": "", "source": "cspaper.org", "link": "https://cspaper.org/topic/62/icml-2025-review-controversies-spark-academic-debate", "content": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance Highlights legal risks in model licensing . Position: AI Agents Need Authenticated Delegation Argues for delegation mechanisms in AI agents."}
+{"idx": 8, "title": "Haotian (Rick) Jiang - Amazon | LinkedIn", "date": "", "ddg_snippet": "Excited to share that our paper “Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance” is accepted as… Liked by Haotian (Rick) Jiang.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/haotian-rick-jiang-a27517b8", "content": "Excited to share that our paper “Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance” is accepted as… Liked by Haotian (Rick) Jiang."}
+{"idx": 9, "title": "Homepage of Yinghui Wu", "date": "", "ddg_snippet": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance (oral) The 42nd International Conference on Machine Learning (ICML), 2025 Moming Duan, Mingzhe Du, Rui Zhao, Mengying Wang, Yinghui Wu, Nigel Shadbolt, Bingsheng He.", "subpage_snippet": "", "source": "yinghwu.github.io", "link": "https://yinghwu.github.io/yinghui-publication.html", "content": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance (oral) The 42nd International Conference on Machine Learning (ICML), 2025 Moming Duan, Mingzhe Du, Rui Zhao, Mengying Wang, Yinghui Wu, Nigel Shadbolt, Bingsheng He."}
diff --git a/data/sampled_jsons/Curriculum_Learning_PINN_year_2023.jsonl b/data/sampled_jsons/Curriculum_Learning_PINN_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d299a1364c795bc606c8ab08816827e22003b8da
--- /dev/null
+++ b/data/sampled_jsons/Curriculum_Learning_PINN_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training physics-informed neural networks: One learning to ...", "date": "", "ddg_snippet": "Jun 1, 2023 · Curriculum learning was first introduced in the context of PINNs by Krishnapriyan et al. [27]. The basic idea is that PINN convergence fails when the parametrised differential equation residuals make the optimisation problem hard to solve.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2590123023001500", "content": "Jun 1, 2023 · Curriculum learning was first introduced in the context of PINNs by Krishnapriyan et al. [27]. The basic idea is that PINN convergence fails when the parametrised differential equation residuals make the optimisation problem hard to solve."}
+{"idx": 1, "title": "Less Emphasis on Hard Regions: Curriculum Learn- ing of PINNs ...", "date": "", "ddg_snippet": "A curriculum learning approach was then developed that emphasizes learning of easier non-layer regions, thereby significantly improving the prediction accuracy of PINNs .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.12685", "content": "A curriculum learning approach was then developed that emphasizes learning of easier non-layer regions, thereby significantly improving the prediction accuracy of PINNs ."}
+{"idx": 2, "title": "GitHub - kotserge/pinn-curriculum-learning: Physics Informed ...", "date": "", "ddg_snippet": "(Archive Link) (Code) propose a curriculum learning approach to improve the performance of PINNs , by gradually increasing the complexity of the PDE, allowing the network to train on simpler examples first and then gradually increasing the complexity.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kotserge/pinn-curriculum-learning", "content": "(Archive Link) (Code) propose a curriculum learning approach to improve the performance of PINNs , by gradually increasing the complexity of the PDE, allowing the network to train on simpler examples first and then gradually increasing the complexity."}
+{"idx": 3, "title": "Curriculum-Transfer-Learning-Based Physics-Informed Neural ...", "date": "", "ddg_snippet": "The CTL- PINN was combined with the extrinsic surface operator processing technology to simulate long-term convection-diffusion behaviors on complex surfaces, and the effectiveness and robustness...", "subpage_snippet": "", "source": "applmathmech.cqjtu.edu.cn", "link": "http://applmathmech.cqjtu.edu.cn/en/article/doi/10.21656/1000-0887.440320", "content": "The CTL- PINN was combined with the extrinsic surface operator processing technology to simulate long-term convection-diffusion behaviors on complex surfaces, and the effectiveness and robustness..."}
+{"idx": 4, "title": "pinn-curriculum-learning /doc /presentation - GitHub", "date": "", "ddg_snippet": "Physics Informed Neural Networks ( PINNs ) for solving Convection-Diffusion Equation using Curriculum Regularization - pinn - curriculum - learning /doc/presentation/presentation-final.pdf at main · kotserge/ pinn - curriculum - learning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kotserge/pinn-curriculum-learning/blob/main/doc/presentation/presentation-final.pdf", "content": "Physics Informed Neural Networks ( PINNs ) for solving Convection-Diffusion Equation using Curriculum Regularization - pinn - curriculum - learning /doc/presentation/presentation-final.pdf at main · kotserge/ pinn - curriculum - learning"}
+{"idx": 5, "title": "1D Burgers equation simulated by PINN – Deep Learning", "date": "", "ddg_snippet": "This is an implementation of PINN (s) on TensorFlow 2 to solve Burgers equation (1D Navier-Stokes eq. ... version unlike other two of my repos ( PINN ...", "subpage_snippet": "", "source": "www.deeplearningdaily.com", "link": "https://www.deeplearningdaily.com/1d-burgers-equation-simulated-by-pinn/", "content": "This is an implementation of PINN (s) on TensorFlow 2 to solve Burgers equation (1D Navier-Stokes eq. ... version unlike other two of my repos ( PINN ..."}
+{"idx": 6, "title": "BWLer 🎳 (Part 1): PDEs, PINNs, and the Precision Gap · Hazy", "date": "", "ddg_snippet": "Machine learning offers a new way to approach PDE solving, with the potential for ... curriculum learning , time marching ) to improve convergence.", "subpage_snippet": "", "source": "hazyresearch.stanford.edu", "link": "https://hazyresearch.stanford.edu/blog/2025-07-07-bwler-p1", "content": "Machine learning offers a new way to approach PDE solving, with the potential for ... curriculum learning , time marching ) to improve convergence."}
+{"idx": 7, "title": "Improving the Efficiency of Training Physics-Informed Neural ...", "date": "", "ddg_snippet": "May 5, 2024 · We propose an active learning method to enhance the efficiency of PINN learning . The proposed method leverages variational inference based on dropout learning to assess the uncertainty inherent in the solution estimates provided by the PINN .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00354-024-00253-6", "content": "May 5, 2024 · We propose an active learning method to enhance the efficiency of PINN learning . The proposed method leverages variational inference based on dropout learning to assess the uncertainty inherent in the solution estimates provided by the PINN ."}
+{"idx": 8, "title": "Long-term simulation of physical and mechanical behaviors ...", "date": "", "ddg_snippet": "Feb 11, 2025 · This paper proposes a Curriculum -Transfer- Learning based physics-informed neural network (CTL- PINN ) for long-term simulation of physical and mechanical behaviors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.07325", "content": "Feb 11, 2025 · This paper proposes a Curriculum -Transfer- Learning based physics-informed neural network (CTL- PINN ) for long-term simulation of physical and mechanical behaviors."}
+{"idx": 9, "title": "🔔 News", "date": "", "ddg_snippet": "Paper on learning -based optimal controller is accepted to RA-L ... Paper on curriculum learning is accepted to IEEE Access", "subpage_snippet": "", "source": "www.taekyung.me", "link": "https://www.taekyung.me/news", "content": "Paper on learning -based optimal controller is accepted to RA-L ... Paper on curriculum learning is accepted to IEEE Access"}
diff --git a/data/sampled_jsons/Cutler_&_Breiman_Archetypal_Analysis_Technometrics_36_1994_abstract_year_1994.jsonl b/data/sampled_jsons/Cutler_&_Breiman_Archetypal_Analysis_Technometrics_36_1994_abstract_year_1994.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..33e18e2e961ca24a3bb7d9329c4c873ba61e22ec
--- /dev/null
+++ b/data/sampled_jsons/Cutler_&_Breiman_Archetypal_Analysis_Technometrics_36_1994_abstract_year_1994.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ARCHETYPAL ANALYSIS: AN ALTERNATIVE TO CLUSTERING FOR", "date": "", "ddg_snippet": "Robust multivariate and functional archetypal analysis with application to financial time series analysis . ... Analysis of Cells by Means of an ...", "subpage_snippet": "", "source": "www.ias-iss.org", "link": "https://www.ias-iss.org/ojs/IAS/article/view/2052", "content": "Robust multivariate and functional archetypal analysis with application to financial time series analysis . ... Analysis of Cells by Means of an ..."}
+{"idx": 1, "title": "Incorporating Fairness Constraints into Archetypal Analysis", "date": "", "ddg_snippet": "This is the case of archetypal analysis (AA), an unsupervised technique that lies halfway between clustering and PCA morup2012archetypal .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12021v1", "content": "This is the case of archetypal analysis (AA), an unsupervised technique that lies halfway between clustering and PCA morup2012archetypal ."}
+{"idx": 2, "title": "Archetype analysis and the PHATE algorithm as methods to", "date": "", "ddg_snippet": "Archetypal analysis as a tool for the statistical description of multidimensional objects was introduced by Breiman and Cutler in 1994 [ 16 ] and is ...", "subpage_snippet": "", "source": "bmcpublichealth.biomedcentral.com", "link": "https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-18355-7", "content": "Archetypal analysis as a tool for the statistical description of multidimensional objects was introduced by Breiman and Cutler in 1994 [ 16 ] and is ..."}
+{"idx": 3, "title": "Stratifying High-Dimensional Data Based on Proximity to the", "date": "", "ddg_snippet": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics , 36 ( 1994 ), pp.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/15M1047921", "content": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics , 36 ( 1994 ), pp."}
+{"idx": 4, "title": "Stratifying High-Dimensional Data Based on Proximity to the", "date": "", "ddg_snippet": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics , 36 ( 1994 ), pp.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/15M1047921?cookieSet=1", "content": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics , 36 ( 1994 ), pp."}
+{"idx": 5, "title": "Meta-Research: Task specialization across research careers |", "date": "", "ddg_snippet": "This model was used to predict the contributions of 222,925 authors in 6,236,239 publications, and to apply a robust archetypal analysis to profile ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/60586", "content": "This model was used to predict the contributions of 222,925 authors in 6,236,239 publications, and to apply a robust archetypal analysis to profile ..."}
+{"idx": 6, "title": "Building and analyzing metacells in single-cell genomics data |", "date": "", "ddg_snippet": "... metacell concept, have developed metacell construction tools and quality metrics, and have used metacells for single-cell sequencing data analysis .", "subpage_snippet": "", "source": "www.embopress.org", "link": "https://www.embopress.org/doi/full/10.1038/s44320-024-00045-6?cookieSet=1", "content": "... metacell concept, have developed metacell construction tools and quality metrics, and have used metacells for single-cell sequencing data analysis ."}
+{"idx": 7, "title": "FUZZ-IEEE 2017, Naples, Italy", "date": "", "ddg_snippet": "... and systems have been proposed as a useful and effective framework for the analysis of brain activity as well as to enable a direct communication ...", "subpage_snippet": "", "source": "fuzzieee2017.org", "link": "https://fuzzieee2017.org/specialSessions.php", "content": "... and systems have been proposed as a useful and effective framework for the analysis of brain activity as well as to enable a direct communication ..."}
+{"idx": 8, "title": "(PDF) Prototypes Within Minimum Enclosing Balls", "date": "", "ddg_snippet": "Once solved, the interior of a ball can be characterized in terms of a function of a set of support vectors and local minima of this function can be ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/335730052_Prototypes_Within_Minimum_Enclosing_Balls", "content": "Once solved, the interior of a ball can be characterized in terms of a function of a set of support vectors and local minima of this function can be ..."}
+{"idx": 9, "title": "US6304836B1 - Worst case design parameter extraction for logic", "date": "", "ddg_snippet": "An integrated circuit (“IC”) designer must consider numerous transistor attributes, such as channel length and doping concentration, in modeling ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US6304836B1/en", "content": "An integrated circuit (“IC”) designer must consider numerous transistor attributes, such as channel length and doping concentration, in modeling ..."}
diff --git a/data/sampled_jsons/Cutler_Breiman_1994_Archetypal_Analysis_abstract_year_1994.jsonl b/data/sampled_jsons/Cutler_Breiman_1994_Archetypal_Analysis_abstract_year_1994.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c97a805c278c2e5466dd425fc7be77eebd3196d0
--- /dev/null
+++ b/data/sampled_jsons/Cutler_Breiman_1994_Archetypal_Analysis_abstract_year_1994.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal analysis - Wikipedia", "date": "", "ddg_snippet": "Archetypal analysis in statistics is an unsupervised learning method similar to cluster analysis and introduced by Adele Cutler and Leo Breiman in 1994 . Rather than \"typical\" observations, it seeks extremal points in the multidimensional da...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Archetypal_analysis", "content": "Archetypal analysis in statistics is an unsupervised learning method similar to cluster analysis and introduced by Adele Cutler and Leo Breiman in 1994 . Rather than \"typical\" observations, it seeks extremal points in the multidimensional da..."}
+{"idx": 1, "title": "Archetypal Analysis: Technometrics: Vol 36, No 4", "date": "", "ddg_snippet": "Mar 12, 2012 · Altmetric Original Articles Archetypal Analysis Adele Cutler Department of Mathematics and Statistics, Utah State University, Logan, UT, 84322-3900 & Leo Breiman Department of Statistics, University of California, Berkeley, CA, 94720 Pages 338-347 | Published online: 12 Mar 2012", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/abs/10.1080/00401706.1994.10485840", "content": "Mar 12, 2012 · Altmetric Original Articles Archetypal Analysis Adele Cutler Department of Mathematics and Statistics, Utah State University, Logan, UT, 84322-3900 & Leo Breiman Department of Statistics, University of California, Berkeley, CA, 94720 Pages 338-347 | Published online: 12 Mar 2012"}
+{"idx": 2, "title": "v3604338 Archetypal Analysis", "date": "", "ddg_snippet": "Archetypal Analysis Adele CUTLER Leo BREIMAN Department of Mathematics and Statistics Utah State University Department of Statistics University of California", "subpage_snippet": "", "source": "stat.cmu.edu", "link": "https://stat.cmu.edu/technometrics/90-00/vol-36-04/v3604338.pdf", "content": "Archetypal Analysis Adele CUTLER Leo BREIMAN Department of Mathematics and Statistics Utah State University Department of Statistics University of California"}
+{"idx": 3, "title": "Archetypal Analysis: Three Case Studies", "date": "", "ddg_snippet": "Archetypal analysis was first introduced by Adele Cutler and Leo Breiman in 1994 . In their paper they presented three examples: Swiss soldiers, air pollution, and Tokamak fusion. We extend the work with three additional case studies including nutrition data from the Cache County Memory and Aging Study, community attachment data provided by the Knight Foundation, and leaf shape data.", "subpage_snippet": "", "source": "ww2.amstat.org", "link": "https://ww2.amstat.org/meetings/proceedings/2016/data/assets/pdf/389749.pdf", "content": "Archetypal analysis was first introduced by Adele Cutler and Leo Breiman in 1994 . In their paper they presented three examples: Swiss soldiers, air pollution, and Tokamak fusion. We extend the work with three additional case studies including nutrition data from the Cache County Memory and Aging Study, community attachment data provided by the Knight Foundation, and leaf shape data."}
+{"idx": 4, "title": "Archetypal analysis of spatio-temporal dynamics - ScienceDirect", "date": "", "ddg_snippet": "Feb 1, 1996 · Abstract A comparison is made between the principal component or Karhunen-Loève decomposition of two sets of spatio-temporal data (one numerical, the other experimental) and a new procedure called archetypal analysis ( Cutler and Breiman , 1994 ).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/0167278995002448", "content": "Feb 1, 1996 · Abstract A comparison is made between the principal component or Karhunen-Loève decomposition of two sets of spatio-temporal data (one numerical, the other experimental) and a new procedure called archetypal analysis ( Cutler and Breiman , 1994 )."}
+{"idx": 5, "title": "[2504.12392] A Survey on Archetypal Analysis - arXiv.org Archetypal analysis Archetypal analysis for machine learning and data mining", "date": "", "ddg_snippet": "Apr 16, 2025 · Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract the distinct aspects called archetypes in observations with each observational record approximated as a mixture (i.e., convex combination) of these archetypes . AA thereby provides straightforward, interpretable, and explainable representations for feature extraction ... Cutler , A.; Breiman , L.Statistics Department, University of California, Berkeley, University of California at Berkeley, Berkeley, California, 1993 Archetypal analysis for machine learning and data mining", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.12392", "content": "Apr 16, 2025 · Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract the distinct aspects called archetypes in observations with each observational record approximated as a mixture (i.e., convex combination) of these archetypes . AA thereby provides straightforward, interpretable, and explainable representations for feature extraction ... Cutler , A.; Breiman , L.Statistics Department, University of California, Berkeley, University of California at Berkeley, Berkeley, California, 1993 Archetypal analysis for machine learning and data mining"}
+{"idx": 6, "title": "Archetypal analysis", "date": "", "ddg_snippet": "Cutler , A.; Breiman , L.Statistics Department, University of California, Berkeley, University of California at Berkeley, Berkeley, California, 1993", "subpage_snippet": "", "source": "digicoll.lib.berkeley.edu", "link": "https://digicoll.lib.berkeley.edu/record/85980", "content": "Cutler , A.; Breiman , L.Statistics Department, University of California, Berkeley, University of California at Berkeley, Berkeley, California, 1993"}
+{"idx": 7, "title": "Archetypal analysis for machine learning and data mining", "date": "", "ddg_snippet": "Archetypal analysis for machine learning and data mining", "subpage_snippet": "", "source": "www2.imm.dtu.dk", "link": "https://www2.imm.dtu.dk/pubdb/pubs/6192-full.html", "content": "Archetypal analysis for machine learning and data mining"}
+{"idx": 8, "title": "Archetypal Analysis on JSTOR", "date": "", "ddg_snippet": "Archetypal Analysis . Adele Cutler and Leo Breiman .Read and download Log in through your school or library. Abstract . Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes .", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/1269949", "content": "Archetypal Analysis . Adele Cutler and Leo Breiman .Read and download Log in through your school or library. Abstract . Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes ."}
+{"idx": 9, "title": "Archetypal analysis for machine learning and data mining", "date": "", "ddg_snippet": "Abstract . Archetypal analysis (aa) proposed by Cutler and Breiman ( 1994 ) [7] estimates the principal convex hull (pch) of a data set.", "subpage_snippet": "", "source": "orbit.dtu.dk", "link": "https://orbit.dtu.dk/en/publications/archetypal-analysis-for-machine-learning-and-data-mining", "content": "Abstract . Archetypal analysis (aa) proposed by Cutler and Breiman ( 1994 ) [7] estimates the principal convex hull (pch) of a data set."}
diff --git a/data/sampled_jsons/Cutler_Breiman_Archetypal_Analysis_year_1994.jsonl b/data/sampled_jsons/Cutler_Breiman_Archetypal_Analysis_year_1994.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7cce886703081ebe83fc505e7d2c6ec211103b63
--- /dev/null
+++ b/data/sampled_jsons/Cutler_Breiman_Archetypal_Analysis_year_1994.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Adele Cutler - Wikipedia", "date": "", "ddg_snippet": "Adele Cutler is a statistician known as one of the developers of archetypal analysis 1 and of the random forest technique for ensemble learning .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Adele_Cutler", "content": "Adele Cutler is a statistician known as one of the developers of archetypal analysis 1 and of the random forest technique for ensemble learning ."}
+{"idx": 1, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary", "date": "", "ddg_snippet": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal ..."}
+{"idx": 2, "title": "ARCHETYPAL ANALYSIS: AN ALTERNATIVE TO CLUSTERING FOR", "date": "", "ddg_snippet": "Archetypal analysis with missing data: see all samples by looking at a few based on extreme profiles. ... archetypal analysis method with Earth ...", "subpage_snippet": "", "source": "www.ias-iss.org", "link": "https://www.ias-iss.org/ojs/IAS/article/view/2052", "content": "Archetypal analysis with missing data: see all samples by looking at a few based on extreme profiles. ... archetypal analysis method with Earth ..."}
+{"idx": 3, "title": "Archetypal Analysis | R-bloggers", "date": "", "ddg_snippet": "... analysis focuses on groupings within the cloud of individual respondents. Archetypal analysis , on the other hand, searches the periphery for ...", "subpage_snippet": "", "source": "www.r-bloggers.com", "link": "https://www.r-bloggers.com/2012/07/archetypal-analysis/", "content": "... analysis focuses on groupings within the cloud of individual respondents. Archetypal analysis , on the other hand, searches the periphery for ..."}
+{"idx": 4, "title": "Leo Breiman 1928-2005 - Google Scholar", "date": "", "ddg_snippet": "edu/users/ breiman /Using_random_forests_V3 1 , 2002 ... Archetypal analysis ... A Cutler , L Breiman", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=mXSv_1UAAAAJ&hl=en", "content": "edu/users/ breiman /Using_random_forests_V3 1 , 2002 ... Archetypal analysis ... A Cutler , L Breiman"}
+{"idx": 5, "title": "Improved algorithm and bounds for successive projection", "date": "", "ddg_snippet": "The minimum volume transform (MVT) (Craig, 1994 ) , archetypal analysis (AA) ( Cutler & Breiman , 1994 ; Javadi & Montanari, 2020 ) , and N ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.11013v1", "content": "The minimum volume transform (MVT) (Craig, 1994 ) , archetypal analysis (AA) ( Cutler & Breiman , 1994 ; Javadi & Montanari, 2020 ) , and N ..."}
+{"idx": 6, "title": "Posters & Elevator Pitches", "date": "", "ddg_snippet": "In neuroscience and spatial transcriptomics, the analysis of between-subject variability is quite attractive, but it cannot be performed on raw data ...", "subpage_snippet": "", "source": "www.r-project.org", "link": "https://www.r-project.org/conferences/useR-2022/program/posters", "content": "In neuroscience and spatial transcriptomics, the analysis of between-subject variability is quite attractive, but it cannot be performed on raw data ..."}
+{"idx": 7, "title": "Stratifying High-Dimensional Data Based on Proximity to the", "date": "", "ddg_snippet": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics, 36 (1994), pp.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/15M1047921", "content": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics, 36 (1994), pp."}
+{"idx": 8, "title": "Stratifying High-Dimensional Data Based on Proximity to the", "date": "", "ddg_snippet": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics, 36 (1994), pp.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/15M1047921?cookieSet=1", "content": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics, 36 (1994), pp."}
+{"idx": 9, "title": "Building and analyzing metacells in single-cell genomics data |", "date": "", "ddg_snippet": "... for both scRNA-seq and scATAC-seq data named SEACells (Persad et al, 2023 ), was proposed based on the concept of archetypes ( Cutler and Breiman ...", "subpage_snippet": "", "source": "www.embopress.org", "link": "https://www.embopress.org/doi/full/10.1038/s44320-024-00045-6?cookieSet=1", "content": "... for both scRNA-seq and scATAC-seq data named SEACells (Persad et al, 2023 ), was proposed based on the concept of archetypes ( Cutler and Breiman ..."}
diff --git a/data/sampled_jsons/Cybench_CTF_challenges_vs_real-world_vulnerabilities.jsonl b/data/sampled_jsons/Cybench_CTF_challenges_vs_real-world_vulnerabilities.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fc43cce0890e33ab12662e2cce8ea45b76ab39e3
--- /dev/null
+++ b/data/sampled_jsons/Cybench_CTF_challenges_vs_real-world_vulnerabilities.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hacking CTFs with Plain Agents", "date": "", "ddg_snippet": "We evaluate LLM agents using CTF challenges —virtual environments containing vulnerable systems that participants must exploit to find hidden flags.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.02776v1", "content": "We evaluate LLM agents using CTF challenges —virtual environments containing vulnerable systems that participants must exploit to find hidden flags."}
+{"idx": 1, "title": "HackSynth: LLM Agent and Evaluation Framework for Autonomous", "date": "", "ddg_snippet": "The goal of CTF challenges is to find a text string called the “flag” hidden in purposefully vulnerable programs or websites.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.01778v1", "content": "The goal of CTF challenges is to find a text string called the “flag” hidden in purposefully vulnerable programs or websites."}
+{"idx": 2, "title": "From Capabilities to Performance: Evaluating Key Functional", "date": "", "ddg_snippet": "Section 7 revisits RQ1 and highlights how complexity and risk levels influence these functional roles in real - world testing contexts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14289v1", "content": "Section 7 revisits RQ1 and highlights how complexity and risk levels influence these functional roles in real - world testing contexts."}
+{"idx": 3, "title": "Guided Reasoning in LLM-Driven Penetration Testing Using", "date": "", "ddg_snippet": "... CTF ) is a set of penetration testing challenges where learners perform penetration testing on sand-boxed enterprise systems with realistic and known ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07939v1", "content": "... CTF ) is a set of penetration testing challenges where learners perform penetration testing on sand-boxed enterprise systems with realistic and known ..."}
+{"idx": 4, "title": "Introducing Docent | Transluce AI", "date": "", "ddg_snippet": "Beyond the findings demonstrated above, Docent also uncovers indications of task vulnerability on an Inspect port of Cybench [ 2 ] , and surprising ...", "subpage_snippet": "", "source": "transluce.org", "link": "https://transluce.org/introducing-docent", "content": "Beyond the findings demonstrated above, Docent also uncovers indications of task vulnerability on an Inspect port of Cybench [ 2 ] , and surprising ..."}
+{"idx": 5, "title": "Recent papers / work on AI and hacking | Timothee Chauvin", "date": "", "ddg_snippet": "Google Project Zero’s agent (based on Gemini 1.5 Pro) found a real - world stack buffer underflow vulnerability in SQLite.", "subpage_snippet": "", "source": "tchauvin.com", "link": "https://tchauvin.com/recent-papers-ai-hacking", "content": "Google Project Zero’s agent (based on Gemini 1.5 Pro) found a real - world stack buffer underflow vulnerability in SQLite."}
+{"idx": 6, "title": "AI Task Length Horizons in Offensive Cybersecurity - LessWrong", "date": "", "ddg_snippet": "... terminal work, while three CTF datasets extend into multi-hour challenges across reversing, binary exploitation, crypto, web, and other “ real - world ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/fjgYkTWKAXSxsxdsj/ai-task-length-horizons-in-offensive-cybersecurity", "content": "... terminal work, while three CTF datasets extend into multi-hour challenges across reversing, binary exploitation, crypto, web, and other “ real - world ..."}
+{"idx": 7, "title": "A Framework for Evaluating Emerging Cyberattack Capabilities of", "date": "", "ddg_snippet": "A curated set of representative cyberattack chain archetypes derived from analyzing over 12,000 instances of real - world AI use attempts in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.11917v3", "content": "A curated set of representative cyberattack chain archetypes derived from analyzing over 12,000 instances of real - world AI use attempts in ..."}
+{"idx": 8, "title": "ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat", "date": "", "ddg_snippet": "Real - world security analysts must sift through a large number of heterogeneous alert signals and security logs, follow multi - hop chains of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14201v1", "content": "Real - world security analysts must sift through a large number of heterogeneous alert signals and security logs, follow multi - hop chains of ..."}
+{"idx": 9, "title": "Frontier AI Risk Management Framework in Practice: A Risk", "date": "", "ddg_snippet": "The Presence of Non-Vulnerable Hosts Significantly Degrades Agent Performance, Revealing that Reconnaissance and Target Validation Are Critical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16534v1", "content": "The Presence of Non-Vulnerable Hosts Significantly Degrades Agent Performance, Revealing that Reconnaissance and Target Validation Are Critical ..."}
diff --git a/data/sampled_jsons/DART_CVPR_2025_self-correction_focal_consolidation_identification.jsonl b/data/sampled_jsons/DART_CVPR_2025_self-correction_focal_consolidation_identification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5a7ff32fd2b75bf910d82ff4e7cf80006cd3e454
--- /dev/null
+++ b/data/sampled_jsons/DART_CVPR_2025_self-correction_focal_consolidation_identification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustwor-thy radiology report generation ( DART ), a novel frame-work that ensures retrieved reports contain similar disease-relevant findings and introduces a self-correction mecha-nism to refine generated reports.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "In this study, we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustwor-thy radiology report generation ( DART ), a novel frame-work that ensures retrieved reports contain similar disease-relevant findings and introduces a self-correction mecha-nism to refine generated reports."}
+{"idx": 1, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting ... Computational Imaging 9 Papers accepted to CVPR 2025 CVPR 2025 Accepted Papers The Best of CVPR 2025 Series – Day 3 - Voxel51 CVPR25-DUCT/README.md at main · Estrella-fugaz ... - GitHub", "date": "", "ddg_snippet": "Apr 16, 2025 · In this study, we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ... 9 Papers accepted to CVPR 2025 S. Zhang, J. Wang, Y. Xu, N. Xue, C. Rupprecht, X. Zhou, Y. Shen, G. Wetzstein, “FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views”, CVPR 2025 CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here. May 29, 2025 · As we wrap up Day 3 of our Best of CVPR series, we spotlight four groundbreaking papers that challenge conventional boundaries in vision research. From detailed vision-language alignment to robust anomaly detection, adaptive medical segmentation, and scalable geospatial prediction, each work dives deep into precision, context, and adaptability. Register for the virtual meetup to dive deeper. The code repository for \"Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning\" ( CVPR 2025 ) in PyTorch. If you use any content of this repo for your work, please cite the following bib entry:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "Apr 16, 2025 · In this study, we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ... 9 Papers accepted to CVPR 2025 S. Zhang, J. Wang, Y. Xu, N. Xue, C. Rupprecht, X. Zhou, Y. Shen, G. Wetzstein, “FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views”, CVPR 2025 CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here. May 29, 2025 · As we wrap up Day 3 of our Best of CVPR series, we spotlight four groundbreaking papers that challenge conventional boundaries in vision research. From detailed vision-language alignment to robust anomaly detection, adaptive medical segmentation, and scalable geospatial prediction, each work dives deep into precision, context, and adaptability. Register for the virtual meetup to dive deeper. The code repository for \"Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning\" ( CVPR 2025 ) in PyTorch. If you use any content of this repo for your work, please cite the following bib entry:"}
+{"idx": 2, "title": "GitHub - Estrella-fugaz/CVPR25-DUCT: Dual Consolidation for ...", "date": "", "ddg_snippet": "School of Artificial Intelligence, State Key Laboratory for Novel Software Technology, Nanjing University The code repository for \"Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning\" ( CVPR 2025 ) in PyTorch. If you use any content of this repo for your work, please cite the ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Estrella-fugaz/CVPR25-Duct", "content": "School of Artificial Intelligence, State Key Laboratory for Novel Software Technology, Nanjing University The code repository for \"Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning\" ( CVPR 2025 ) in PyTorch. If you use any content of this repo for your work, please cite the ..."}
+{"idx": 3, "title": "Computational Imaging 9 Papers accepted to CVPR 2025", "date": "", "ddg_snippet": "9 Papers accepted to CVPR 2025 S. Zhang, J. Wang, Y. Xu, N. Xue, C. Rupprecht, X. Zhou, Y. Shen, G. Wetzstein, “FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views”, CVPR 2025", "subpage_snippet": "", "source": "www.computationalimaging.org", "link": "https://www.computationalimaging.org/News/9-papers-accepted-to-cvpr-2025/", "content": "9 Papers accepted to CVPR 2025 S. Zhang, J. Wang, Y. Xu, N. Xue, C. Rupprecht, X. Zhou, Y. Shen, G. Wetzstein, “FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views”, CVPR 2025"}
+{"idx": 4, "title": "CVPR 2025 Accepted Papers", "date": "", "ddg_snippet": "CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/AcceptedPapers", "content": "CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here."}
+{"idx": 5, "title": "The Best of CVPR 2025 Series – Day 3 - Voxel51", "date": "", "ddg_snippet": "May 29, 2025 · As we wrap up Day 3 of our Best of CVPR series, we spotlight four groundbreaking papers that challenge conventional boundaries in vision research. From detailed vision-language alignment to robust anomaly detection, adaptive medical segmentation, and scalable geospatial prediction, each work dives deep into precision, context, and adaptability. Register for the virtual meetup to dive deeper.", "subpage_snippet": "", "source": "voxel51.com", "link": "https://voxel51.com/blog/the-best-of-cvpr-2025-series-day-3", "content": "May 29, 2025 · As we wrap up Day 3 of our Best of CVPR series, we spotlight four groundbreaking papers that challenge conventional boundaries in vision research. From detailed vision-language alignment to robust anomaly detection, adaptive medical segmentation, and scalable geospatial prediction, each work dives deep into precision, context, and adaptability. Register for the virtual meetup to dive deeper."}
+{"idx": 6, "title": "CVPR25-DUCT/README.md at main · Estrella-fugaz ... - GitHub", "date": "", "ddg_snippet": "The code repository for \"Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning\" ( CVPR 2025 ) in PyTorch. If you use any content of this repo for your work, please cite the following bib entry:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Estrella-fugaz/CVPR25-DUCT/blob/main/README.md", "content": "The code repository for \"Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning\" ( CVPR 2025 ) in PyTorch. If you use any content of this repo for your work, please cite the following bib entry:"}
+{"idx": 7, "title": "Phoenix: A Motion-based Self -Reflection Framework for Fine-grained...", "date": "", "ddg_snippet": "In this motion-based self -reflection framework,we start with a dual-process motion adjustment mechanism with MLLMs to translate the semantic reflection into coarse-grained motion instruction adjustment.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Xia_Phoenix_A_Motion-based_Self-Reflection_Framework_for_Fine-grained_Robotic_Action_Correction@CVPR2025@CVF", "content": "In this motion-based self -reflection framework,we start with a dual-process motion adjustment mechanism with MLLMs to translate the semantic reflection into coarse-grained motion instruction adjustment."}
+{"idx": 8, "title": "Top Computer Vision CV Blogs & News Websites ( 2025 )", "date": "", "ddg_snippet": "Discover the top computer vision blogs and news websites in 2025 for research updates, tutorials, benchmarks, and deployments.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/09/19/top-computer-vision-cv-blogs-news-websites-2025/", "content": "Discover the top computer vision blogs and news websites in 2025 for research updates, tutorials, benchmarks, and deployments."}
+{"idx": 9, "title": "RAISE@UCR Publications by Area | RAISE@UCR", "date": "", "ddg_snippet": "In IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR 2025 ), pp. 20569-20579.", "subpage_snippet": "", "source": "raise.ucr.edu", "link": "https://raise.ucr.edu/publications", "content": "In IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR 2025 ), pp. 20569-20579."}
diff --git a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Self-correcting_Re-alignment_ablation_study_Table_3_year_2023.jsonl b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Self-correcting_Re-alignment_ablation_study_Table_3_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d8a38e51349eb74cbff969899c6f735df6e7f7c
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+++ b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_Self-correcting_Re-alignment_ablation_study_Table_3_year_2023.jsonl
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+{"idx": 0, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "In this study , we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "In this study , we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 1, "title": "DART | PDF | Artificial Intelligence | Intelligence (AI) & Semantics", "date": "", "ddg_snippet": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self -correction mechanism. This two-stage approach first generates initial reports through image -to- text retrieval with a disease -matching constraint and then refines these reports by re -aligning them with input X-ray images . The ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self -correction mechanism. This two-stage approach first generates initial reports through image -to- text retrieval with a disease -matching constraint and then refines these reports by re -aligning them with input X-ray images . The ..."}
+{"idx": 2, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease -relevant findings in X-ray images . Previous approaches for radiology report generation have shown impressive performance. However, there remains significant potential to improve accuracy by ensuring that retrieved reports contain disease ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094924", "content": "The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease -relevant findings in X-ray images . Previous approaches for radiology report generation have shown impressive performance. However, there remains significant potential to improve accuracy by ensuring that retrieved reports contain disease ..."}
+{"idx": 3, "title": "PDF DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "In this study , we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "In this study , we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework."}
+{"idx": 4, "title": "Fine-Grained Image-Text Alignment in Medical Imaging Enables ...", "date": "", "ddg_snippet": "To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process. AdaMatch exploits the fine-grained relation between adaptive patches and words to provide explanations of specific image regions with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.08078", "content": "To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process. AdaMatch exploits the fine-grained relation between adaptive patches and words to provide explanations of specific image regions with ..."}
+{"idx": 5, "title": "PDF DKA-RG: Disease-Knowledge-Enhanced Fine-Grained Image Text Alignment ...", "date": "", "ddg_snippet": "In this work, we propose a new approach, disease -knowledge-enhanced fine-grained image-text alignment for automatic radiology report generation (DKA-RG).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383306136_DKA-RG_Disease-Knowledge-Enhanced_Fine-Grained_Image-Text_Alignment_for_Automatic_Radiology_Report_Generation/fulltext/66c7d7ab920e05672e4658d5/DKA-RG-Disease-Knowledge-Enhanced-Fine-Grained-Image-Text-Alignment-for-Automatic-Radiology-Report-Generation.pdf", "content": "In this work, we propose a new approach, disease -knowledge-enhanced fine-grained image-text alignment for automatic radiology report generation (DKA-RG)."}
+{"idx": 6, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this study , we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.html", "content": "In this study , we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 7, "title": "mk-runner/Awesome-Radiology-Report-Generation - GitHub", "date": "", "ddg_snippet": "DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation [paper] CRG Score: A Distribution- Aware Clinical Metric for Radiology Report Generation [paper] Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation [paper]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation", "content": "DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation [paper] CRG Score: A Distribution- Aware Clinical Metric for Radiology Report Generation [paper] Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation [paper]"}
+{"idx": 8, "title": "Ji-Hye Oh - catalyzex.com", "date": "", "ddg_snippet": "In this study , we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Ji-Hye+Oh", "content": "In this study , we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image -to- text retrieval with disease -matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 9, "title": "MAILAB | Medical Artificial Intelligence", "date": "", "ddg_snippet": "Sang-Jun Park*, Keun-Soo Heo*, Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, and Tae-Eui Kam, \" DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation\", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, Jun. 10-17, 2025.", "subpage_snippet": "", "source": "mailab.korea.ac.kr", "link": "http://mailab.korea.ac.kr/publication.html", "content": "Sang-Jun Park*, Keun-Soo Heo*, Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, and Tae-Eui Kam, \" DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation\", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, Jun. 10-17, 2025."}
diff --git a/data/sampled_jsons/DART_Park_CVPR_disease-matching_constraint_lambda_m.jsonl b/data/sampled_jsons/DART_Park_CVPR_disease-matching_constraint_lambda_m.jsonl
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index 0000000000000000000000000000000000000000..ed4d5d522e86bd37db6c7ba6349be920e3ee2c3f
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+{"idx": 0, "title": "\\scalerel* SpecVLM: Enhancing Speculative Decoding of ...", "date": "", "ddg_snippet": "22 Aug 2025 — This design choice imposes certain constraints on the maximum achievable acceleration. Nevertheless, our method has the potential to be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.16201v1", "content": "22 Aug 2025 — This design choice imposes certain constraints on the maximum achievable acceleration. Nevertheless, our method has the potential to be ..."}
+{"idx": 1, "title": "Improve the performance of CT-based pneumonia ...", "date": "", "ddg_snippet": "by P Xie · 2023 · Cited by 9 — Pneumonia is a life-threatening disease . Computer tomography (CT) imaging is broadly used for diagnosing pneumonia.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-023-35938-3", "content": "by P Xie · 2023 · Cited by 9 — Pneumonia is a life-threatening disease . Computer tomography (CT) imaging is broadly used for diagnosing pneumonia."}
+{"idx": 2, "title": "Learning by Ignoring, with Application to Domain Adaptation", "date": "", "ddg_snippet": "This formulation consists of three optimization problems . The two inner optimization prob- lems (on the constraints ) represent the first and second learning ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/learning-by-ignoring-with-application-to-domain-adaptation-1fwfekruu3.pdf", "content": "This formulation consists of three optimization problems . The two inner optimization prob- lems (on the constraints ) represent the first and second learning ..."}
+{"idx": 3, "title": "Resource Constrained Neural Architecture Design by Yunyang ...", "date": "", "ddg_snippet": "constraints that are found across diverse problems and see what they can enable if resource constrained neural architectures are properly identified. 1.1 ...", "subpage_snippet": "", "source": "asset.library.wisc.edu", "link": "https://asset.library.wisc.edu/1711.dl/EUWK2WVCELQBV9D/R/file-63a2e.pdf?dl", "content": "constraints that are found across diverse problems and see what they can enable if resource constrained neural architectures are properly identified. 1.1 ..."}
+{"idx": 4, "title": "Deep Learning and Machine Learning – Generative Models", "date": "", "ddg_snippet": "The score function can be learned using a technique called score matching . Score Matching : Score matching is an approach for estimating the score function.", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5211980.pdf?abstractid=5211980&mirid=1&type=2", "content": "The score function can be learned using a technique called score matching . Score Matching : Score matching is an approach for estimating the score function."}
+{"idx": 5, "title": "Track: Poster Session TUE-PM", "date": "", "ddg_snippet": "And our key design is the consistent constraint that automatically finds matching relationships among the triplet through “self-cycle” and learns ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/session/23308", "content": "And our key design is the consistent constraint that automatically finds matching relationships among the triplet through “self-cycle” and learns ..."}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 7, "title": "Gradient-Based Multi-Objective Deep Learning", "date": "", "ddg_snippet": "19 Jan 2025 — Multi-objective optimization (MOO) in deep learning aims to simultaneously optimize multiple conflicting objectives, a challenge frequently ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.10945v1", "content": "19 Jan 2025 — Multi-objective optimization (MOO) in deep learning aims to simultaneously optimize multiple conflicting objectives, a challenge frequently ..."}
+{"idx": 8, "title": "A review of state-of-the-art techniques for large language ...", "date": "", "ddg_snippet": "by PV Dantas · 2025 · Cited by 4 — The rapid advancement of large language models (LLMs) has driven significant progress in natural language processing (NLP) and related ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40747-025-02019-z", "content": "by PV Dantas · 2025 · Cited by 4 — The rapid advancement of large language models (LLMs) has driven significant progress in natural language processing (NLP) and related ..."}
+{"idx": 9, "title": "A Practical Survey on Faster and Lighter Transformers", "date": "", "ddg_snippet": "This survey addresses this issue by investigating popular approaches to make Transformers faster and lighter and by providing a comprehensive explanation of ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3586074", "content": "This survey addresses this issue by investigating popular approaches to make Transformers faster and lighter and by providing a comprehensive explanation of ..."}
diff --git a/data/sampled_jsons/DART_radiology_report_generation_Figure_2_focal_consolidation_year_2024.jsonl b/data/sampled_jsons/DART_radiology_report_generation_Figure_2_focal_consolidation_year_2024.jsonl
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+++ b/data/sampled_jsons/DART_radiology_report_generation_Figure_2_focal_consolidation_year_2024.jsonl
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+{"idx": 0, "title": "DART : Disease-aware Image-Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "Most studies on radiology report generation can generally be divided into two primary approaches. The first approach focuses on improving the encoder-decoder architecture, and it also emphasize align-ing visual and textual information to generate more consis-tent reports .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "Most studies on radiology report generation can generally be divided into two primary approaches. The first approach focuses on improving the encoder-decoder architecture, and it also emphasize align-ing visual and textual information to generate more consis-tent reports ."}
+{"idx": 1, "title": "Generating Radiology Reports via Memory-driven Transformer", "date": "", "ddg_snippet": "Radiology report generation , which aims to au-tomatically generate a free-text description for a clinical radiograph (e.g., chest X-ray), has emerged as a prominent attractive research direction in both articial intelligence and clinical medicine.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2010.16056", "content": "Radiology report generation , which aims to au-tomatically generate a free-text description for a clinical radiograph (e.g., chest X-ray), has emerged as a prominent attractive research direction in both articial intelligence and clinical medicine."}
+{"idx": 2, "title": "When Radiology Report Generation Meets Knowledge Graph", "date": "", "ddg_snippet": "Report Generation via Graph Embedding. After training the multi-label classication model, we xed the parameters in both the CNN backbone and the graph em-bedding module, and appended after the graph embedding module with a two -level decoder to generate reports .", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/6989/6989-13-10218-1-10-20200525.pdf", "content": "Report Generation via Graph Embedding. After training the multi-label classication model, we xed the parameters in both the CNN backbone and the graph em-bedding module, and appended after the graph embedding module with a two -level decoder to generate reports ."}
+{"idx": 3, "title": "ORG AN : Observation-Guided Radiology Report Generation via Tree", "date": "", "ddg_snippet": "This paper explores the task of radiology report generation , which aims at generating free-text descriptions for a set of radiographs. Figure 1: Our proposed framework contains two stages, including the observation planning stage and the report generation stage.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.acl-long.451.pdf", "content": "This paper explores the task of radiology report generation , which aims at generating free-text descriptions for a set of radiographs. Figure 1: Our proposed framework contains two stages, including the observation planning stage and the report generation stage."}
+{"idx": 4, "title": "RadBERT-CL: Factually-Aware Contrastive Learning For Radiology ...", "date": "", "ddg_snippet": "Radiology reports are unstructured and contain the imaging findings and corresponding diagnoses transcribed by radiologists which include clinical facts and negated and/or uncertain statements.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9055736/", "content": "Radiology reports are unstructured and contain the imaging findings and corresponding diagnoses transcribed by radiologists which include clinical facts and negated and/or uncertain statements."}
+{"idx": 5, "title": "Stanford-AIMI/GREEN: [EMNLP, Findings 2024] a radiology report ...", "date": "", "ddg_snippet": "Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to its medical nature.The cardiomediastinal silhouette is unremarkable. No focal consolidation , pleural effusion, or pneumothorax detected.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Stanford-AIMI/GREEN", "content": "Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to its medical nature.The cardiomediastinal silhouette is unremarkable. No focal consolidation , pleural effusion, or pneumothorax detected."}
+{"idx": 6, "title": "(PDF) ORGAN: Observation-Guided Radiology Report Generation ...", "date": "", "ddg_snippet": "Abstract and Figures . This paper explores the task of radiology report generation , which aims at generating free-text descriptions for a set of radiographs.Stage 2 : Report Generation . ①Lung Opacity /POS. Figure 1: Our proposed framework contains two stages", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371506510_ORGAN_Observation-Guided_Radiology_Report_Generation_via_Tree_Reasoning", "content": "Abstract and Figures . This paper explores the task of radiology report generation , which aims at generating free-text descriptions for a set of radiographs.Stage 2 : Report Generation . ①Lung Opacity /POS. Figure 1: Our proposed framework contains two stages"}
+{"idx": 7, "title": "RadEval_clinically_significant_errors.csv - Radiology Report ...", "date": "", "ddg_snippet": "report _id. ground_truth. M 2 Tr- Generation .there are no focal areas of consolidation . no suspicious pulmonary opacities . heart size within normal limits . no pleural effusions . there is no evidence of pneumothorax . osseous structures intact .", "subpage_snippet": "", "source": "physionet.org", "link": "https://physionet.org/content/rad-eval-x/1.0.0/RadEval_clinically_significant_errors.csv", "content": "report _id. ground_truth. M 2 Tr- Generation .there are no focal areas of consolidation . no suspicious pulmonary opacities . heart size within normal limits . no pleural effusions . there is no evidence of pneumothorax . osseous structures intact ."}
+{"idx": 8, "title": "Clinical Report Generation Powered by Machine", "date": "", "ddg_snippet": "Figure 8: Distribution of generated reports length.• the lungs are clear without focal consolidation . no pleural effusion or pneumothorax is seen. the cardiac and mediastinal silhouettes are unremarkable.", "subpage_snippet": "", "source": "www.zhaw.ch", "link": "https://www.zhaw.ch/storage/engineering/institute-zentren/cai/studentische_arbeiten/Spring_2023/Spring23_VT2_Clinical_Report_Generation_Powered_by_Machine_Learning.pdf", "content": "Figure 8: Distribution of generated reports length.• the lungs are clear without focal consolidation . no pleural effusion or pneumothorax is seen. the cardiac and mediastinal silhouettes are unremarkable."}
+{"idx": 9, "title": "Consolidation and Atelectasis | Radiology Key", "date": "", "ddg_snippet": "AIR-SPACE CONSOLIDATION Air-space consolidation represents replacement of alveolar air by fluid, blood, pus, cells, or other substances. Alveolar consolidation and parenchymal consolidation are synonyms for air-space consolidation .", "subpage_snippet": "", "source": "radiologykey.com", "link": "https://radiologykey.com/consolidation-and-atelectasis/", "content": "AIR-SPACE CONSOLIDATION Air-space consolidation represents replacement of alveolar air by fluid, blood, pus, cells, or other substances. Alveolar consolidation and parenchymal consolidation are synonyms for air-space consolidation ."}
diff --git a/data/sampled_jsons/DART_radiology_report_generation_focal_consolidation_Figure_2_sitehttpsopenaccess.thecvf.comcontentC_year_2025.jsonl b/data/sampled_jsons/DART_radiology_report_generation_focal_consolidation_Figure_2_sitehttpsopenaccess.thecvf.comcontentC_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5490b7dd698c9f557c1a8d26f95a98efe2b00ca8
--- /dev/null
+++ b/data/sampled_jsons/DART_radiology_report_generation_focal_consolidation_Figure_2_sitehttpsopenaccess.thecvf.comcontentC_year_2025.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "DART : Disease-aware Image-Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "Most studies on radiology report generation can generally be divided into two primary approaches. The first approach focuses on improving the encoder-decoder architecture, and it also emphasize align-ing visual and textual information to generate more consis-tent reports .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "Most studies on radiology report generation can generally be divided into two primary approaches. The first approach focuses on improving the encoder-decoder architecture, and it also emphasize align-ing visual and textual information to generate more consis-tent reports ."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/DART_self-correction_cross-attention_re-alignment_radiology_reports_year_2024.jsonl b/data/sampled_jsons/DART_self-correction_cross-attention_re-alignment_radiology_reports_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..324083bdf1421dda9ee33ae99abcbf7800a6008f
--- /dev/null
+++ b/data/sampled_jsons/DART_self-correction_cross-attention_re-alignment_radiology_reports_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate ini-tial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared em-bedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "In this study, we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate ini-tial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared em-bedding space through contrastive ..."}
+{"idx": 1, "title": "mk-runner/Awesome-Radiology-Report-Generation - GitHub", "date": "", "ddg_snippet": "DART : Disease-aware Image-Text Alignment and Self - correcting Re-alignment for Trustworthy Radiology Report Generation [paper] CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset [paper] [code]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation", "content": "DART : Disease-aware Image-Text Alignment and Self - correcting Re-alignment for Trustworthy Radiology Report Generation [paper] CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset [paper] [code]"}
+{"idx": 2, "title": "Rebuttal - DART: Disease-aware Image-Text Alignment and Self ...", "date": "", "ddg_snippet": "May 5, 2025 · Rebuttal - DART : Disease-aware Image-Text Alignment and Self - correcting Re-alignment for Trustworthy Radiology Report Generation We sincerely appreciate the efforts of reviewers U2j9, MsGg, and rq7L in evaluating our proposed framework ( DART ). Your insightful feedback has been invaluable in refining our paper. We look forward to your final ratings.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2504.11786", "content": "May 5, 2025 · Rebuttal - DART : Disease-aware Image-Text Alignment and Self - correcting Re-alignment for Trustworthy Radiology Report Generation We sincerely appreciate the efforts of reviewers U2j9, MsGg, and rq7L in evaluating our proposed framework ( DART ). Your insightful feedback has been invaluable in refining our paper. We look forward to your final ratings."}
+{"idx": 3, "title": "Automatic Radiology Reports Generation via Memory Alignment ...", "date": "", "ddg_snippet": "Mar 24, 2024 · The automatic generation of radiology reports is of great significance, which can reduce the workload of doctors and improve the accuracy and reliability of medical diagnosis and treatment, and has attracted wide attention in recent years. Cross -modal mapping between images and text, a key component of generating high-quality reports , is challenging due to the lack of corresponding annotations ...", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/28279", "content": "Mar 24, 2024 · The automatic generation of radiology reports is of great significance, which can reduce the workload of doctors and improve the accuracy and reliability of medical diagnosis and treatment, and has attracted wide attention in recent years. Cross -modal mapping between images and text, a key component of generating high-quality reports , is challenging due to the lack of corresponding annotations ..."}
+{"idx": 4, "title": "DART | PDF | Artificial Intelligence | Intelligence (AI ...", "date": "", "ddg_snippet": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ..."}
+{"idx": 5, "title": "Self-Talk: An Interdisciplinary Review and Transdisciplinary", "date": "", "ddg_snippet": "The present work synthesises the self -talk literature and constructs a transdisciplinary self -talk model to guide future research across all academic ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/10892680231170263", "content": "The present work synthesises the self -talk literature and constructs a transdisciplinary self -talk model to guide future research across all academic ..."}
+{"idx": 6, "title": "Ji-Hye Oh - catalyzex.com", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Ji-Hye+Oh", "content": "In this study, we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 7, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.html", "content": "In this study, we propose a Disease-aware image-text Alignment and self - correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 8, "title": "JP4741720B2 - Defibrillator giving a synchronous CPR prompt -", "date": "", "ddg_snippet": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=27130472&utm_source=google_patent&utm_medium ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/JP4741720B2/en", "content": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=27130472&utm_source=google_patent&utm_medium ..."}
+{"idx": 9, "title": "US8562537B2 - Multipurpose host system for invasive", "date": "", "ddg_snippet": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=29419425&utm_source=google_patent&utm_medium ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US8562537B2/en", "content": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=29419425&utm_source=google_patent&utm_medium ..."}
diff --git "a/data/sampled_jsons/DART_\316\273m_=_0.1_OR_\316\273m_=_1.0_OR_\316\273m_=_0.01_OR_lambda_m_=_0.1_OR_lambda_m_=_1.0_radiology_report_generati_year_2024.jsonl" "b/data/sampled_jsons/DART_\316\273m_=_0.1_OR_\316\273m_=_1.0_OR_\316\273m_=_0.01_OR_lambda_m_=_0.1_OR_lambda_m_=_1.0_radiology_report_generati_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..79816c35aedf15ba3c7b561be695e908531fb56c
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+++ "b/data/sampled_jsons/DART_\316\273m_=_0.1_OR_\316\273m_=_1.0_OR_\316\273m_=_0.01_OR_lambda_m_=_0.1_OR_lambda_m_=_1.0_radiology_report_generati_year_2024.jsonl"
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+{"idx": 3, "title": "Lures Jerkbaits Crankbaits günstig buy by Koeder Laden", "date": "", "ddg_snippet": "Lures. ... Show more... ... 0 , 0 - 0 , 1 m ... 0 , 0 - 1 , 0 m", "subpage_snippet": "", "source": "www.koeder-laden.de", "link": "https://www.koeder-laden.de/en/Lures/", "content": "Lures. ... Show more... ... 0 , 0 - 0 , 1 m ... 0 , 0 - 1 , 0 m"}
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+{"idx": 6, "title": "Calendario Corazón Mª Infantil Preferente G1 FIFLP", "date": "", "ddg_snippet": "... manera gratuita mediante email a CONTACTO@FUTBOLASPALMAS.COM o en la dirección C/ MANUEL DE FALLA 58 10C, LAS PALMAS DE GRAN CANARIA - 35013 (LAS ...", "subpage_snippet": "", "source": "futbolaspalmas.com", "link": "https://futbolaspalmas.com/1infantil-prefe1/resultados-infantil-preferente-gran-canaria-grupop-1-IA5.html", "content": "... manera gratuita mediante email a CONTACTO@FUTBOLASPALMAS.COM o en la dirección C/ MANUEL DE FALLA 58 10C, LAS PALMAS DE GRAN CANARIA - 35013 (LAS ..."}
+{"idx": 7, "title": "Lajfy.com | relacje na żywo z wydarzeń sportowych", "date": "", "ddg_snippet": "Chcesz mieć tutaj swoją reklamę albo chcesz zamówić relację z interesującego Cię ... 19:00 Borussia Monchengladbach – Sevilla FC ( 1 . m .", "subpage_snippet": "", "source": "archive.lajfy.com", "link": "http://archive.lajfy.com/index/index/date/2015-02-26", "content": "Chcesz mieć tutaj swoją reklamę albo chcesz zamówić relację z interesującego Cię ... 19:00 Borussia Monchengladbach – Sevilla FC ( 1 . m ."}
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diff --git a/data/sampled_jsons/DC28Fpk76s_Intervention_Conditioning_Causal_Bayesian_Networks_independence_assumption.jsonl b/data/sampled_jsons/DC28Fpk76s_Intervention_Conditioning_Causal_Bayesian_Networks_independence_assumption.jsonl
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+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by J Halpern — This paper shows the identifiability of interventional formulas in Causal Bayesian Networks (CBNs) under an independence -of-machanism assumption ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DC28Fpk76s", "content": "by J Halpern — This paper shows the identifiability of interventional formulas in Causal Bayesian Networks (CBNs) under an independence -of-machanism assumption ..."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by J Halpern — We believe that these independence assumptions are quite reasonable and, capture the spirit of Bayesian networks . In fact, Richardson,. Peters, and Halpern ( ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "by J Halpern — We believe that these independence assumptions are quite reasonable and, capture the spirit of Bayesian networks . In fact, Richardson,. Peters, and Halpern ( ..."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian", "date": "", "ddg_snippet": "Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal mechanisms.We believe that these independence assumptions are quite reasonable and, capture the spirit of Bayesian networks .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DC28Fpk76s", "content": "Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal mechanisms.We believe that these independence assumptions are quite reasonable and, capture the spirit of Bayesian networks ."}
+{"idx": 3, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities. We show that by making simple yet often realistic independence assumptions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.14728", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities. We show that by making simple yet often realistic independence assumptions ..."}
+{"idx": 4, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "DC 28 Fpk 76 s .# Estimating probabilities involving interventions in causal models, especially Causal Bayesian Networks (CBNs), is challenging. Existing methods struggle with accurate calculations, particularly for formulas involving interventions and conditioning .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/dc28fpk76s/", "content": "DC 28 Fpk 76 s .# Estimating probabilities involving interventions in causal models, especially Causal Bayesian Networks (CBNs), is challenging. Existing methods struggle with accurate calculations, particularly for formulas involving interventions and conditioning ."}
+{"idx": 5, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities. We show that by making simple yet often realistic independence assumptions ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/intervention-conditioning-causal-bayesian-networks", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities. We show that by making simple yet often realistic independence assumptions ..."}
+{"idx": 6, "title": "A dynamic Bayesian network approach to modeling engagement and...", "date": "", "ddg_snippet": "This approach can be leveraged to support the refinement of dynamic theories of behavior change and improving personalized mHealth intervention strateg …", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/40979180/", "content": "This approach can be leveraged to support the refinement of dynamic theories of behavior change and improving personalized mHealth intervention strateg …"}
+{"idx": 7, "title": "A Tag-Based Search Algorithm for Causal Bayesian Networks", "date": "", "ddg_snippet": "Keywords: artificial intelligence; Bayesian networks ; causal analysis networks ; tag-based search.Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/269362608_A_Tag-Based_Search_Algorithm_for_Causal_Bayesian_Networks", "content": "Keywords: artificial intelligence; Bayesian networks ; causal analysis networks ; tag-based search.Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention ."}
+{"idx": 8, "title": "Frontiers | Are Jurors Intuitive Statisticians? Bayesian Causal ...", "date": "", "ddg_snippet": "Causal Bayes Networks and Normative Causal Judgments. Condition 3: Control condition contained no statement about the relationship between abuse and disorder as causes of the child’s bruises and bleeding.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2020.519262/full", "content": "Causal Bayes Networks and Normative Causal Judgments. Condition 3: Control condition contained no statement about the relationship between abuse and disorder as causes of the child’s bruises and bleeding."}
+{"idx": 9, "title": "Causality from bottom to top: a survey | Machine Learning", "date": "", "ddg_snippet": "In the modern era, causality has become integral to fields such as Machine Learning (ML), economics, and statistics. New frameworks for discovery from data, including interventions , counterfactuals, and causal calculus, have been introduced.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-025-06855-5", "content": "In the modern era, causality has become integral to fields such as Machine Learning (ML), economics, and statistics. New frameworks for discovery from data, including interventions , counterfactuals, and causal calculus, have been introduced."}
diff --git "a/data/sampled_jsons/DC28Fpk76s_Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Example_3.3_PrM\342\200\240(\317\206)_formula.jsonl" "b/data/sampled_jsons/DC28Fpk76s_Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Example_3.3_PrM\342\200\240(\317\206)_formula.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..a65b4ee9468a67ba7166a1e3d268a5023ec7dc04
--- /dev/null
+++ "b/data/sampled_jsons/DC28Fpk76s_Intervention_and_Conditioning_in_Causal_Bayesian_Networks_Example_3.3_PrM\342\200\240(\317\206)_formula.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal mechanisms. For example , interventions help ex-plain the outcome of complex ML systems (Galhotra, Pradhan, and Salimi 2021); and in AI-driven healthcare diagnostics, it is crucial to discern the effect of a particular intervention (like a change in ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DC28Fpk76s", "content": "Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal mechanisms. For example , interventions help ex-plain the outcome of complex ML systems (Galhotra, Pradhan, and Salimi 2021); and in AI-driven healthcare diagnostics, it is crucial to discern the effect of a particular intervention (like a change in ..."}
+{"idx": 1, "title": "A Practical Introduction to Bayesian Estimation of Causal Effects ...", "date": "", "ddg_snippet": "We introduce the Bayesian bootstrap as a method for performing standardization. Next, we move to the time-varying treatment and confounding setting where we discuss Bayesian g -computation with priors that promote sparsity. Causal inference in these settings requires estimation of a large number of nuisance parameters.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8640942/", "content": "We introduce the Bayesian bootstrap as a method for performing standardization. Next, we move to the time-varying treatment and confounding setting where we discuss Bayesian g -computation with priors that promote sparsity. Causal inference in these settings requires estimation of a large number of nuisance parameters."}
+{"idx": 2, "title": "PDF Bayesian networks { exercises - cvut.cz", "date": "", "ddg_snippet": "The exercises illustrate topics of conditional independence, learning and inference in Bayesian networks . The identical material with the resolved exercises will be provided after the last Bayesian network tutorial.", "subpage_snippet": "", "source": "cw.fel.cvut.cz", "link": "https://cw.fel.cvut.cz/old/_media/courses/ae4m33rzn/bn_solved.pdf", "content": "The exercises illustrate topics of conditional independence, learning and inference in Bayesian networks . The identical material with the resolved exercises will be provided after the last Bayesian network tutorial."}
+{"idx": 3, "title": "Chapter 11 Bayesian Networks - ScienceDirect", "date": "", "ddg_snippet": "The directed nature of Bayesian networks can be used to provide causal semantics for these networks , based on the notion of intervention[127], leading to models that not only represent probability distributions, but also permit one to induce new probability distributions that result from intervention .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1574652607030118", "content": "The directed nature of Bayesian networks can be used to provide causal semantics for these networks , based on the notion of intervention[127], leading to models that not only represent probability distributions, but also permit one to induce new probability distributions that result from intervention ."}
+{"idx": 4, "title": "CAUSALITY - University of California, Los Angeles", "date": "", "ddg_snippet": "1.2.2 Bayesian Networks 1.2.3 The d-separation criterion 1.2.4 Inference with Bayesian networks 1.3 Causal Bayesian Networks 1.3.1 Causal networks as oracles for interventions 1.3.2 Causal relationships and their stability 1.4 Functional Causal Models 1.4.1 Structural Equations 1.4.2 Probabilistic predictions in causal models", "subpage_snippet": "", "source": "bayes.cs.ucla.edu", "link": "https://bayes.cs.ucla.edu/BOOK-99/book-toc.html", "content": "1.2.2 Bayesian Networks 1.2.3 The d-separation criterion 1.2.4 Inference with Bayesian networks 1.3 Causal Bayesian Networks 1.3.1 Causal networks as oracles for interventions 1.3.2 Causal relationships and their stability 1.4 Functional Causal Models 1.4.1 Structural Equations 1.4.2 Probabilistic predictions in causal models"}
+{"idx": 5, "title": "Optimizing Causal Interventions in Hybrid Bayesian Networks", "date": "", "ddg_snippet": "This paper introduces an approximate method for offline optimizing interventions in arbitrary hybrid Bayesian networks using observational data. The optimization problem is approached by compiling discretized Bayesian networks as binary decision diagrams, whereafter running interventional queries is very efficient.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-74003-9_20", "content": "This paper introduces an approximate method for offline optimizing interventions in arbitrary hybrid Bayesian networks using observational data. The optimization problem is approached by compiling discretized Bayesian networks as binary decision diagrams, whereafter running interventional queries is very efficient."}
+{"idx": 6, "title": "Learning Causal Bayesian Network Structures From Experimental Data ...", "date": "", "ddg_snippet": "This document proposes a method for learning the causal structure of Bayesian networks from experimental data. It combines order-space MCMC sampling, equi-energy sampling, importance weighting, and stream-based computation to efficiently sample the posterior distribution of network structures given experimental data.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/383029255/Learning-Causal-Bayesian-Network-Structures-From-Experimental-Data-Byron-Ellis-Wing-Hung-Wong", "content": "This document proposes a method for learning the causal structure of Bayesian networks from experimental data. It combines order-space MCMC sampling, equi-energy sampling, importance weighting, and stream-based computation to efficiently sample the posterior distribution of network structures given experimental data."}
+{"idx": 7, "title": "PDF Dynamic Causal Bayesian Optimization - NeurIPS", "date": "", "ddg_snippet": "We propose the Dynamic Causal Bayesian Optimization (DCBO) algorithm which finds the optimal intervention at every time step by intervening in the system according to a causal acquisition function.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/577bcc914f9e55d5e4e4f82f9f00e7d4-Paper.pdf", "content": "We propose the Dynamic Causal Bayesian Optimization (DCBO) algorithm which finds the optimal intervention at every time step by intervening in the system according to a causal acquisition function."}
+{"idx": 8, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.14728v1", "content": "Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities."}
+{"idx": 9, "title": "PDF Ashique-survey-paper.dvi - GitHub Pages", "date": "", "ddg_snippet": "2 Conceptual Framework To understand structure learning in CBNs, we first need to define Bayesian networks and described their properties. Then, we define intervention and causal Bayesian networks . Figure 1. Example of a Bayesian network", "subpage_snippet": "", "source": "armahmood.github.io", "link": "https://armahmood.github.io/files/mahmood-TR11-01.pdf", "content": "2 Conceptual Framework To understand structure learning in CBNs, we first need to define Bayesian networks and described their properties. Then, we define intervention and causal Bayesian networks . Figure 1. Example of a Bayesian network"}
diff --git a/data/sampled_jsons/DCBM-Data-Efficient_Visual_Concept_Bottleneck_Models_Table_2_IN-200_IN-R_error_rates.jsonl b/data/sampled_jsons/DCBM-Data-Efficient_Visual_Concept_Bottleneck_Models_Table_2_IN-200_IN-R_error_rates.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..42771ba76450914b3417b3eb45702d1909ff640b
--- /dev/null
+++ b/data/sampled_jsons/DCBM-Data-Efficient_Visual_Concept_Bottleneck_Models_Table_2_IN-200_IN-R_error_rates.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data -sparse scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data -sparse scenarios."}
+{"idx": 1, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material."}
+{"idx": 2, "title": "ICML DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/49558", "content": "Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT"}
+{"idx": 3, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models :: MPG.PuRe", "date": "", "ddg_snippet": "Author: Prasse, Katharina et al.; Genre: Paper; Published online: 2025; Open Access; Keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV; Title: DCBM : Data-Efficient Visual Concept Bottleneck Models", "subpage_snippet": "", "source": "pure.mpg.de", "link": "https://pure.mpg.de/pubman/faces/ViewItemFullPage.jsp?itemId=item_3636912_1&view=ACTIONS", "content": "Author: Prasse, Katharina et al.; Genre: Paper; Published online: 2025; Open Access; Keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV; Title: DCBM : Data-Efficient Visual Concept Bottleneck Models"}
+{"idx": 4, "title": "Decoupling Concept Bottleneck Model - OpenReview", "date": "", "ddg_snippet": "Motivated by the proposed theorem, we present Decoupling Concept Bottleneck Model ( DCBM ), a novel concept -based model decoupling heterogeneous information into explicit and implicit concepts , while still retaining high prediction performance and interpretability.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vVbUB9oWUup", "content": "Motivated by the proposed theorem, we present Decoupling Concept Bottleneck Model ( DCBM ), a novel concept -based model decoupling heterogeneous information into explicit and implicit concepts , while still retaining high prediction performance and interpretability."}
+{"idx": 5, "title": "The Decoupling Concept Bottleneck Model - IEEE Xplore", "date": "", "ddg_snippet": "The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction. However, in real-world scenarios, the deficiency of informative concepts can impede the model's interpretability and subsequent interventions. This paper proves that insufficient concept information can lead to an inherent ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10740789", "content": "The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction. However, in real-world scenarios, the deficiency of informative concepts can impede the model's interpretability and subsequent interventions. This paper proves that insufficient concept information can lead to an inherent ..."}
+{"idx": 6, "title": "The Decoupling Concept Bottleneck Model - PubMed", "date": "", "ddg_snippet": "To address this challenge, we propose the Decoupling Concept Bottleneck Model ( DCBM ), which comprises two phases: 1) DCBM for prediction and interpretation, which decouples heterogeneous information into explicit and implicit concepts while maintaining high label and concept accuracy, and 2 ) DCBM for human-machine interaction, which ...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/39485693/", "content": "To address this challenge, we propose the Decoupling Concept Bottleneck Model ( DCBM ), which comprises two phases: 1) DCBM for prediction and interpretation, which decouples heterogeneous information into explicit and implicit concepts while maintaining high label and concept accuracy, and 2 ) DCBM for human-machine interaction, which ..."}
+{"idx": 7, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effective-ness in data -sparse scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576v2", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effective-ness in data -sparse scenarios."}
+{"idx": 8, "title": "GitHub - deepopo/DCBM", "date": "", "ddg_snippet": "This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model ( DCBM ). The vision-language- model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/deepopo/DCBM", "content": "This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model ( DCBM ). The vision-language- model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification."}
+{"idx": 9, "title": "Homepage - Patrick Knab", "date": "", "ddg_snippet": "DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse*, Patrick Knab*, Sascha Marton, Christian Bartelt, Margret Keuper (* equal contribution)", "subpage_snippet": "", "source": "patrick-knab.github.io", "link": "https://patrick-knab.github.io/", "content": "DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse*, Patrick Knab*, Sascha Marton, Christian Bartelt, Margret Keuper (* equal contribution)"}
diff --git a/data/sampled_jsons/DCBM-MASK-RCNN_error_rate_22.2_IN-200_44.6_IN-R_Gap.jsonl b/data/sampled_jsons/DCBM-MASK-RCNN_error_rate_22.2_IN-200_44.6_IN-R_Gap.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..47bbe2bc80370ee4b82cce704f9b432f01918f50
--- /dev/null
+++ b/data/sampled_jsons/DCBM-MASK-RCNN_error_rate_22.2_IN-200_44.6_IN-R_Gap.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM : Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "In contrast to their 2-level approach, our DCBM can detect multi-level concepts.Our ood evaluation shows that DCBM exhibits consistently lower error rates on IN - R and a lower gap between IID and OOD in comparison to the task-agnostic DN-CBM [46] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "In contrast to their 2-level approach, our DCBM can detect multi-level concepts.Our ood evaluation shows that DCBM exhibits consistently lower error rates on IN - R and a lower gap between IID and OOD in comparison to the task-agnostic DN-CBM [46] ."}
+{"idx": 1, "title": "keras- rcnn AttributeError: 'Node' object has no attribute 'output...", "date": "", "ddg_snippet": "keras- rcnn copied to clipboard.Same issue here. It looks like other mentions of this error around the web include some mixup between tensorflow.keras and 'pure' keras, but I can't find anything like that in my code or any of the modules that it imports.", "subpage_snippet": "", "source": "gitmemories.com", "link": "https://gitmemories.com/broadinstitute/keras-rcnn/issues/225", "content": "keras- rcnn copied to clipboard.Same issue here. It looks like other mentions of this error around the web include some mixup between tensorflow.keras and 'pure' keras, but I can't find anything like that in my code or any of the modules that it imports."}
+{"idx": 2, "title": "Auto Annotation Mack RCNN error · Issue #4021 · cvat-ai/cvat · GitHub", "date": "", "ddg_snippet": "Notifications You must be signed in to change notification settings.Hello, everyone when I try to add Mask RCNN trained model using gpu to the auto annotation process it is gives error as unhealthy.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cvat-ai/cvat/issues/4021", "content": "Notifications You must be signed in to change notification settings.Hello, everyone when I try to add Mask RCNN trained model using gpu to the auto annotation process it is gives error as unhealthy."}
+{"idx": 3, "title": "machinelearningmastery.com/how-to-train-an-object-detection-model...", "date": "", "ddg_snippet": "Mask R -CNN is a family of convolutional neural network models designed for...", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/", "content": "Mask R -CNN is a family of convolutional neural network models designed for..."}
+{"idx": 4, "title": "MMDetection", "date": "", "ddg_snippet": "You can find examples in Log Analysis. We compare the training speed of Mask R -CNN with some other popular frameworks (The data is copied from detec-tron2).Important: The default learning rate in config files is for 8 GPUs and 2 img/gpu (batch size = 8*2 = 16).", "subpage_snippet": "", "source": "mmdetection.readthedocs.io", "link": "https://mmdetection.readthedocs.io/_/downloads/en/v2.21.0/pdf/", "content": "You can find examples in Log Analysis. We compare the training speed of Mask R -CNN with some other popular frameworks (The data is copied from detec-tron2).Important: The default learning rate in config files is for 8 GPUs and 2 img/gpu (batch size = 8*2 = 16)."}
+{"idx": 5, "title": "Quantum software industrial engineering and intelligent cognitive...", "date": "", "ddg_snippet": "In this test, the task consists in take a first-aid kit from point A to point B (victim). In the first instance, robot approaches and takes the payload (Fig. 22a-c), places the arm in the transport position and moves through the environment until reaching the victim (Fig.", "subpage_snippet": "", "source": "cyberleninka.ru", "link": "https://cyberleninka.ru/article/n/quantum-software-industrial-engineering-and-intelligent-cognitive-robotics-in-industry-4-0-as-control-objects-prototypes-of", "content": "In this test, the task consists in take a first-aid kit from point A to point B (victim). In the first instance, robot approaches and takes the payload (Fig. 22a-c), places the arm in the transport position and moves through the environment until reaching the victim (Fig."}
+{"idx": 6, "title": "Map of Moscow with street names and house numbers — Yandex Maps", "date": "", "ddg_snippet": "Detailed online map of Moscow with streets and building numbers on the website and in the Yandex Maps mobile app. Places of interest and businesses with ratings, reviews, and photos on the map of Moscow. Get driving and public transport directions wi...", "subpage_snippet": "", "source": "yandex.com", "link": "https://yandex.com/maps/213/moscow/", "content": "Detailed online map of Moscow with streets and building numbers on the website and in the Yandex Maps mobile app. Places of interest and businesses with ratings, reviews, and photos on the map of Moscow. Get driving and public transport directions wi..."}
+{"idx": 7, "title": "Операция Z: Военкоры Русской Весны – Telegram", "date": "", "ddg_snippet": "Добровольцы, волонтеры и военкоры Русской Весны действуют в боевых порядках войск на Донбассе, Украине и САР, получая информацию из самых горячих точек. РКН: clck.ru/3Fj3hJ Связь: @rvvoenkor_bot youtube.com/c/rusvesnadonbass.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/RVvoenkor", "content": "Добровольцы, волонтеры и военкоры Русской Весны действуют в боевых порядках войск на Донбассе, Украине и САР, получая информацию из самых горячих точек. РКН: clck.ru/3Fj3hJ Связь: @rvvoenkor_bot youtube.com/c/rusvesnadonbass."}
+{"idx": 8, "title": "Microsoft MakeCode for micro:bit", "date": "", "ddg_snippet": "Einstieg in die Programmierung mit micro:bit.", "subpage_snippet": "", "source": "makecode.microbit.org", "link": "https://makecode.microbit.org/", "content": "Einstieg in die Programmierung mit micro:bit."}
+{"idx": 9, "title": "Что такое Детекция объектов? - Mathison", "date": "", "ddg_snippet": "Детекция (Object detection) — распознавание объектов. Задача детекции ставится, если требуется определить наличие или отсутствие на изображении объектов определенного класса. При этом может быть задан один класс объектов или несколько.", "subpage_snippet": "", "source": "mathison.pro", "link": "https://mathison.pro/chto-takoe-detekciya-obektov/", "content": "Детекция (Object detection) — распознавание объектов. Задача детекции ставится, если требуется определить наличие или отсутствие на изображении объектов определенного класса. При этом может быть задан один класс объектов или несколько."}
diff --git a/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Section_4.1_Experimental_setup_concept_proposal.jsonl b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Section_4.1_Experimental_setup_concept_proposal.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f236c0fe6883fa2347d827007b4c0ee5a727d688
--- /dev/null
+++ b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Section_4.1_Experimental_setup_concept_proposal.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data -sparse scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data -sparse scenarios."}
+{"idx": 1, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models :: MPG.PuRe", "date": "", "ddg_snippet": "Content show hide Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of neural", "subpage_snippet": "", "source": "pure.mpg.de", "link": "https://pure.mpg.de/pubman/faces/ViewItemFullPage.jsp?itemId=item_3636912_1&view=ACTIONS", "content": "Content show hide Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of neural"}
+{"idx": 3, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effective-ness in data -sparse scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576v2", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effective-ness in data -sparse scenarios."}
+{"idx": 4, "title": "V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer", "date": "", "ddg_snippet": "To this end, we adopt common words as base concept vocabulary and leverage auxiliary unlabeled images to construct a Vision-to- Concept (V2C) tokenizer that can explicitly quantize images into their most relevant visual concepts , thus creating a vision-oriented concept bottleneck tightly coupled with the multimodal model .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.04975", "content": "To this end, we adopt common words as base concept vocabulary and leverage auxiliary unlabeled images to construct a Vision-to- Concept (V2C) tokenizer that can explicitly quantize images into their most relevant visual concepts , thus creating a vision-oriented concept bottleneck tightly coupled with the multimodal model ."}
+{"idx": 5, "title": "Decoupling Concept Bottleneck Model - OpenReview", "date": "", "ddg_snippet": "Motivated by the proposed theorem, we present Decoupling Concept Bottleneck Model ( DCBM ), a novel concept -based model decoupling heterogeneous information into explicit and implicit concepts , while still retaining high prediction performance and interpretability.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vVbUB9oWUup", "content": "Motivated by the proposed theorem, we present Decoupling Concept Bottleneck Model ( DCBM ), a novel concept -based model decoupling heterogeneous information into explicit and implicit concepts , while still retaining high prediction performance and interpretability."}
+{"idx": 6, "title": "Cross-Modality Image Interpretation via Concept ... - IEEE Xplore", "date": "", "ddg_snippet": "To address these limitations, this work explores the cross-modality interpretation of class-related concepts in image classification. Specifically, we propose decomposed concept bottleneck model ( DCBM ), which utilizes a set of decomposed visual concepts that are extracted directly from images instead of predefined text concepts .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10535313", "content": "To address these limitations, this work explores the cross-modality interpretation of class-related concepts in image classification. Specifically, we propose decomposed concept bottleneck model ( DCBM ), which utilizes a set of decomposed visual concepts that are extracted directly from images instead of predefined text concepts ."}
+{"idx": 7, "title": "ICML DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/49558", "content": "Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper"}
+{"idx": 8, "title": "Advancing Model Explainability: Visual Concept Knowledge ... - MDPI", "date": "", "ddg_snippet": "This study explores the integration of concept bottleneck models (CBMs) with knowledge distillation (KD) while preserving the locality characteristics of the CBM. Although KD proves effective in model compression, compressed models often lack interpretability in their decision-making process. We enhance comprehensive explainability by maintaining CBMs' inherent interpretability through our ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/2/493", "content": "This study explores the integration of concept bottleneck models (CBMs) with knowledge distillation (KD) while preserving the locality characteristics of the CBM. Although KD proves effective in model compression, compressed models often lack interpretability in their decision-making process. We enhance comprehensive explainability by maintaining CBMs' inherent interpretability through our ..."}
+{"idx": 9, "title": "The Decoupling Concept Bottleneck Model - IEEE Xplore", "date": "", "ddg_snippet": "The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction. However, in real-world scenarios, the deficiency of informative concepts can impede the model's interpretability and subsequent interventions. This paper proves that insufficient concept information can lead to an inherent ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10740789", "content": "The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction. However, in real-world scenarios, the deficiency of informative concepts can impede the model's interpretability and subsequent interventions. This paper proves that insufficient concept information can lead to an inherent ..."}
diff --git a/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_arxiv.jsonl b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3bc0a52fd686f023935a5046e1c18eeb00b1fb65
--- /dev/null
+++ b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "DCBM is highly data - efficient in the concept generation phase and equally efficient as other models in the CBM training phase. In Table 5 we show the differences to another data-driven CBM, i.e., DN-CBM (Rao et al., 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576", "content": "DCBM is highly data - efficient in the concept generation phase and equally efficient as other models in the CBM training phase. In Table 5 we show the differences to another data-driven CBM, i.e., DN-CBM (Rao et al., 2024)."}
+{"idx": 1, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "View a PDF of the paper titled DCBM : Data - Efficient Visual Concept Bottleneck Models , by Katharina Prasse and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.11576", "content": "View a PDF of the paper titled DCBM : Data - Efficient Visual Concept Bottleneck Models , by Katharina Prasse and 4 other authors."}
+{"idx": 2, "title": "ICML Poster DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46104", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 3, "title": "KathPra/ DCBM : Official repo for ICML25 paper: DCBM : Data - Efficient ...", "date": "", "ddg_snippet": "Aligning Visual and Semantic Interpretability through Visually Grounded Concept Bottleneck Models . You can find our paper on arXiv . Accepted at ICML 2025. Authors: Katharina Prasse*, Patrick Knab*, Sascha Marton, Christian Bartelt, and Margret Keuper...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "Aligning Visual and Semantic Interpretability through Visually Grounded Concept Bottleneck Models . You can find our paper on arXiv . Accepted at ICML 2025. Authors: Katharina Prasse*, Patrick Knab*, Sascha Marton, Christian Bartelt, and Margret Keuper..."}
+{"idx": 4, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/DCBM:-Data-Efficient-Visual-Concept-Bottleneck-Models-4be352f8-e37e-432a-9849-dc4f3c8c586f", "content": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 5, "title": "Margret KEUPER | Professor for Visual Computing | Professor", "date": "", "ddg_snippet": "DCBM : Data - Efficient Visual Concept Bottleneck Models .Unlike diffusion models , AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Margret-Keuper", "content": "DCBM : Data - Efficient Visual Concept Bottleneck Models .Unlike diffusion models , AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains."}
+{"idx": 6, "title": "Language Guided Concept Bottleneck Models for Interpretable...", "date": "", "ddg_snippet": "Among these interpretable models , Concept Bottleneck Models [22, 48, 58, 61, 65] provide explanations of the model ’s decision-making process in a straightforward man-ner. CBMs are designed to be interpretable, incorporating an intermediate Concept Bottleneck Layer (CBL), where.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Language_Guided_Concept_Bottleneck_Models_for_Interpretable_Continual_Learning_CVPR_2025_paper.pdf", "content": "Among these interpretable models , Concept Bottleneck Models [22, 48, 58, 61, 65] provide explanations of the model ’s decision-making process in a straightforward man-ner. CBMs are designed to be interpretable, incorporating an intermediate Concept Bottleneck Layer (CBL), where."}
+{"idx": 7, "title": "The Decoupling Concept Bottleneck Model", "date": "", "ddg_snippet": "The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/39485693/", "content": "The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction."}
+{"idx": 8, "title": "ML PhD Student Candidate - Cited by 8 - Explainable AI - ML - CV", "date": "", "ddg_snippet": "DCBM : Data - Efficient Visual Concept Bottleneck Models .2025. Interpreting Outliers in Time Series Data through Decoding Autoencoder.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=pzg1sbgAAAAJ&hl=en", "content": "DCBM : Data - Efficient Visual Concept Bottleneck Models .2025. Interpreting Outliers in Time Series Data through Decoding Autoencoder."}
+{"idx": 9, "title": "Publications | Universität Mannheim", "date": "", "ddg_snippet": "DCBM : Data - Efficient Visual Concept Bottleneck Models , K. Prasse, P. Knab, S. Marton, C. Bartelt, M. Keuper, International Conference on Machine Learning (ICML) 2025. Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text...", "subpage_snippet": "", "source": "www.uni-mannheim.de", "link": "https://www.uni-mannheim.de/dws/research/focus-groups/computer-vision-machine-learning-prof-dr-ing-margret-keuper/publications/", "content": "DCBM : Data - Efficient Visual Concept Bottleneck Models , K. Prasse, P. Knab, S. Marton, C. Bartelt, M. Keuper, International Conference on Machine Learning (ICML) 2025. Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text..."}
diff --git a/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_concept_proposal_methods_promptable.jsonl b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_concept_proposal_methods_promptable.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..10e46131a5e60d454092d11f709b711306876a25
--- /dev/null
+++ b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_concept_proposal_methods_promptable.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Choose segmentation models based on your computational budget and the latest advancements. GDINO is the fastest model in our evaluation. We provide .yml files for all segmentation methods used in this work. Embed the segments into the selected embedding space by running the corresponding script in ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "Choose segmentation models based on your computational budget and the latest advancements. GDINO is the fastest model in our evaluation. We provide .yml files for all segmentation methods used in this work. Embed the segments into the selected embedding space by running the corresponding script in ..."}
+{"idx": 1, "title": "Decoupling Concept Bottleneck Model - OpenReview", "date": "", "ddg_snippet": "Feb 1, 2023 · The paper proposes Decoupling Concept Bottleneck Model ( DCBM ) which is a concept -based model decoupling heterogeneous information into explicit and implicit concepts .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vVbUB9oWUup", "content": "Feb 1, 2023 · The paper proposes Decoupling Concept Bottleneck Model ( DCBM ) which is a concept -based model decoupling heterogeneous information into explicit and implicit concepts ."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models , allowing each image to generate multiple concepts across different granularities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v2", "content": "We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability. DCBMs define concepts as image regions detected by segmentation or detection foundation models , allowing each image to generate multiple concepts across different granularities."}
+{"idx": 3, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models :: MPG.PuRe", "date": "", "ddg_snippet": "scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "pure.mpg.de", "link": "https://pure.mpg.de/pubman/faces/ViewItemFullPage.jsp?itemId=item_3636912_1&view=ACTIONS", "content": "scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 4, "title": "Cross-Modality Image Interpretation via Concept Decomposition ...", "date": "", "ddg_snippet": "May 20, 2024 · To address these limitations, this work explores the cross-modality interpretation of class-related concepts in image classification. Specifically, we propose decomposed concept bottleneck model ( DCBM ), which utilizes a set of decomposed visual concepts that are extracted directly from images instead of predefined text concepts .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10535313", "content": "May 20, 2024 · To address these limitations, this work explores the cross-modality interpretation of class-related concepts in image classification. Specifically, we propose decomposed concept bottleneck model ( DCBM ), which utilizes a set of decomposed visual concepts that are extracted directly from images instead of predefined text concepts ."}
+{"idx": 5, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Dec 16, 2024 · Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.11576", "content": "Dec 16, 2024 · Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes ..."}
+{"idx": 6, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Explainable AI, concept bottleneck models , foundation models , ICML.We propose Data - efficient Visual CBMs ( DCBM ) to improve interpretability in data-scarce domains.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "Explainable AI, concept bottleneck models , foundation models , ICML.We propose Data - efficient Visual CBMs ( DCBM ) to improve interpretability in data-scarce domains."}
+{"idx": 7, "title": "ICML Poster DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46104", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 8, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/DCBM:-Data-Efficient-Visual-Concept-Bottleneck-Models-4be352f8-e37e-432a-9849-dc4f3c8c586f", "content": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 9, "title": "The Decoupling Concept Bottleneck Model", "date": "", "ddg_snippet": "The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/02/10740789/21w2sYkMsuc", "content": "The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction."}
diff --git a/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_limitations_Section_5.jsonl b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_limitations_Section_5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae038c660f600e5fb56fc5055800376b57fd8b96
--- /dev/null
+++ b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_limitations_Section_5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "DCBM is highly data - efficient in the concept generation phase and equally efficient as other models in the CBM training phase. In Table 5 we show the differences to another data-driven CBM, i.e., DN-CBM (Rao et al., 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576", "content": "DCBM is highly data - efficient in the concept generation phase and equally efficient as other models in the CBM training phase. In Table 5 we show the differences to another data-driven CBM, i.e., DN-CBM (Rao et al., 2024)."}
+{"idx": 1, "title": "ICML Poster DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46104", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 2, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/DCBM:-Data-Efficient-Visual-Concept-Bottleneck-Models-4be352f8-e37e-432a-9849-dc4f3c8c586f", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 3, "title": "KathPra/ DCBM : Official repo for ICML25 paper: DCBM : Data - Efficient ...", "date": "", "ddg_snippet": "Aligning Visual and Semantic Interpretability through Visually Grounded Concept Bottleneck Models .We primarily report on CLIP models [RN-50, ViT-B16, ViT-L14] and save embeddings to reuse in DCBM training for efficiency .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "Aligning Visual and Semantic Interpretability through Visually Grounded Concept Bottleneck Models .We primarily report on CLIP models [RN-50, ViT-B16, ViT-L14] and save embeddings to reuse in DCBM training for efficiency ."}
+{"idx": 4, "title": "Margret KEUPER | Professor for Visual Computing | Professor", "date": "", "ddg_snippet": "DCBM : Data - Efficient Visual Concept Bottleneck Models .Unlike diffusion models , AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Margret-Keuper", "content": "DCBM : Data - Efficient Visual Concept Bottleneck Models .Unlike diffusion models , AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains."}
+{"idx": 5, "title": "The Decoupling Concept Bottleneck Model", "date": "", "ddg_snippet": "The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/02/10740789/21w2sYkMsuc", "content": "The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction."}
+{"idx": 6, "title": "Decoupling Concept Bottleneck Model | OpenReview", "date": "", "ddg_snippet": "We analyze the concept /label trade-off for Concept Bottleneck Model (CBM) and propose a new interactive and interpretable AI system to alleviate this issue.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vVbUB9oWUup", "content": "We analyze the concept /label trade-off for Concept Bottleneck Model (CBM) and propose a new interactive and interpretable AI system to alleviate this issue."}
+{"idx": 7, "title": "Deferring Concept Bottleneck Models : Learning to Defer... | alphaXiv", "date": "", "ddg_snippet": "Abstract: Concept Bottleneck Models (CBMs) are machine learning models that improve interpretability by grounding their predictions on human-understandable concepts , allowing for targeted interventions in their decision-making process.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/fr/overview/2503.16199v1", "content": "Abstract: Concept Bottleneck Models (CBMs) are machine learning models that improve interpretability by grounding their predictions on human-understandable concepts , allowing for targeted interventions in their decision-making process."}
+{"idx": 8, "title": "Language Guided Concept Bottleneck Models for Interpretable...", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) [22, 54] are a form of interpretable machine learning model that enhance trans-parency by organizing the learning process around human-understandable concepts . Rather than directly mapping.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Language_Guided_Concept_Bottleneck_Models_for_Interpretable_Continual_Learning_CVPR_2025_paper.pdf", "content": "Concept Bottleneck Models (CBMs) [22, 54] are a form of interpretable machine learning model that enhance trans-parency by organizing the learning process around human-understandable concepts . Rather than directly mapping."}
+{"idx": 9, "title": "Concept Bottleneck Models - Microsoft Research", "date": "", "ddg_snippet": "State-of-the-art models today do not typically support the manipulation of concepts like “the existence of bone spurs”, as they are trained end-to-end to go directly from raw input (e.g., pixels) to output (e.g., arthritis severity).", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/concept-bottleneck-models/", "content": "State-of-the-art models today do not typically support the manipulation of concepts like “the existence of bone spurs”, as they are trained end-to-end to go directly from raw input (e.g., pixels) to output (e.g., arthritis severity)."}
diff --git a/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_sitearxiv.org_year_2023.jsonl b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_sitearxiv.org_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f72b3358b2bd1d1403055b85e2f18f57ca41cb2d
--- /dev/null
+++ b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_sitearxiv.org_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large sample sizes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.11576", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large sample sizes ..."}
+{"idx": 1, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v2", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large ..."}
+{"idx": 2, "title": "V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer", "date": "", "ddg_snippet": "To this end, we adopt common words as base concept vocabulary and leverage auxiliary unlabeled images to construct a Vision-to- Concept (V2C) tokenizer that can explicitly quantize images into their most relevant visual concepts , thus creating a vision-oriented concept bottleneck tightly coupled with the multimodal model .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.04975", "content": "To this end, we adopt common words as base concept vocabulary and leverage auxiliary unlabeled images to construct a Vision-to- Concept (V2C) tokenizer that can explicitly quantize images into their most relevant visual concepts , thus creating a vision-oriented concept bottleneck tightly coupled with the multimodal model ."}
+{"idx": 3, "title": "[2304.06129] Label-Free Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to human-understandable concepts . However, existing CBMs and their variants have two crucial limitations: first, they need to collect labeled data for each of the predefined concepts , which is time consuming and labor intensive; second, the accuracy of a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.06129", "content": "Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to human-understandable concepts . However, existing CBMs and their variants have two crucial limitations: first, they need to collect labeled data for each of the predefined concepts , which is time consuming and labor intensive; second, the accuracy of a ..."}
+{"idx": 4, "title": "[2505.07209] Discovering Fine-Grained Visual-Concept Relations by ...", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) try to make the decision-making process transparent by exploring an intermediate concept space between the input image and the output prediction. Existing CBMs just learn coarse-grained relations between the whole image and the concepts , less considering local image information, leading to two main drawbacks: i) they often produce spurious visual-concept ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.07209", "content": "Concept Bottleneck Models (CBMs) try to make the decision-making process transparent by exploring an intermediate concept space between the input image and the output prediction. Existing CBMs just learn coarse-grained relations between the whole image and the concepts , less considering local image information, leading to two main drawbacks: i) they often produce spurious visual-concept ..."}
+{"idx": 5, "title": "Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "We systematically study variants of con-cept bottleneck models and contrast them with standard end-to-end models in different settings, with a focus on the previously-unexplored ability of concept bottleneck models to support concept interventions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2007.04612v2", "content": "We systematically study variants of con-cept bottleneck models and contrast them with standard end-to-end models in different settings, with a focus on the previously-unexplored ability of concept bottleneck models to support concept interventions."}
+{"idx": 6, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data -sparse scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on con-cept sets extracted from large language models or extensive image corpora, limiting their effec-tiveness in data -sparse scenarios."}
+{"idx": 7, "title": "Coarse-to-Fine Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "To this end, we propose a novel two-level concept discovery formulation leveraging: (i) recent advances in vision-language models , and (ii) an innovative formulation for coarse-to-fine concept selection via data -driven and sparsity-inducing Bayesian arguments.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.02116", "content": "To this end, we propose a novel two-level concept discovery formulation leveraging: (i) recent advances in vision-language models , and (ii) an innovative formulation for coarse-to-fine concept selection via data -driven and sparsity-inducing Bayesian arguments."}
+{"idx": 8, "title": "Deferring Concept Bottleneck Models: Learning to Defer Interventions to ...", "date": "", "ddg_snippet": "In this work, we introduce Deferring Concept Bottleneck Models ( DCBMs ), a novel class of models enabling learn-ing to defer on CBMs (Figure 1). A key advantage of DCBMs is their ability to effectively learn when a concept or task prediction could benefit from human intervention. To the best of our knowledge, DCBM represents the first interpretable-by-design deferring system, enabling more ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16199", "content": "In this work, we introduce Deferring Concept Bottleneck Models ( DCBMs ), a novel class of models enabling learn-ing to defer on CBMs (Figure 1). A key advantage of DCBMs is their ability to effectively learn when a concept or task prediction could benefit from human intervention. To the best of our knowledge, DCBM represents the first interpretable-by-design deferring system, enabling more ..."}
+{"idx": 9, "title": "There Was Never a Bottleneck in Concept Bottleneck Models", "date": "", "ddg_snippet": "Most DL models operate by learning data representations—compressed versions of the input that retain essential information for tackling specific tasks of interest [8]. Concept Bottleneck Models (CBMs) aim to define these representations based on a set human-understandable concepts [9]. Given", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.04877v1", "content": "Most DL models operate by learning data representations—compressed versions of the input that retain essential information for tackling specific tasks of interest [8]. Concept Bottleneck Models (CBMs) aim to define these representations based on a set human-understandable concepts [9]. Given"}
diff --git a/data/sampled_jsons/DCBM_concept_bottleneck_limitations_dependency_on_OR_reliance_on_OR_limited_by.jsonl b/data/sampled_jsons/DCBM_concept_bottleneck_limitations_dependency_on_OR_reliance_on_OR_limited_by.jsonl
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index 0000000000000000000000000000000000000000..b7914f8562f9f07a9d9bf096593df78273a7b975
--- /dev/null
+++ b/data/sampled_jsons/DCBM_concept_bottleneck_limitations_dependency_on_OR_reliance_on_OR_limited_by.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "by K Prasse — This paper introduces DCBM (Data-Efficient Concept Bottleneck ... ( DCBM ) that enhances interpretability while reducing the reliance on large ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BdO4R6XxUH", "content": "by K Prasse — This paper introduces DCBM (Data-Efficient Concept Bottleneck ... ( DCBM ) that enhances interpretability while reducing the reliance on large ..."}
+{"idx": 1, "title": "DeCoDe: Defer-and-Complement Decision-Making via ...", "date": "", "ddg_snippet": "25 May 2025 — To overcome these limitations , we propose Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Models (DeCoDe), a concept ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.19220v1", "content": "25 May 2025 — To overcome these limitations , we propose Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Models (DeCoDe), a concept ..."}
+{"idx": 2, "title": "Cross-modality Interpretable image classification via ...", "date": "", "ddg_snippet": "by Z Fang — ... concept bottleneck model ( DCBM ). The decomposition is implemented by vector ... limitations of traditional text-based interpretative methods. Due to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=pNgY6ODeMp", "content": "by Z Fang — ... concept bottleneck model ( DCBM ). The decomposition is implemented by vector ... limitations of traditional text-based interpretative methods. Due to ..."}
+{"idx": 3, "title": "Patrick Knab: Homepage", "date": "", "ddg_snippet": "DCBM : Data-Efficient Visual Concept Bottleneck Models. Katharina Prasse ... limitations . We categorize and compare its various enhancements, offering a ...", "subpage_snippet": "", "source": "patrick-knab.github.io", "link": "https://patrick-knab.github.io/", "content": "DCBM : Data-Efficient Visual Concept Bottleneck Models. Katharina Prasse ... limitations . We categorize and compare its various enhancements, offering a ..."}
+{"idx": 4, "title": "ICML 2025 Thursday 07/17", "date": "", "ddg_snippet": "... reliance on direct refusals for malicious queries. In this paper, we propose ... DCBM : Data-Efficient Visual Concept Bottleneck Models. Poster. Katharina ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/17", "content": "... reliance on direct refusals for malicious queries. In this paper, we propose ... DCBM : Data-Efficient Visual Concept Bottleneck Models. Poster. Katharina ..."}
+{"idx": 5, "title": "[Literature Review] Visual Data Diagnosis and Debiasing with ...", "date": "", "ddg_snippet": "is highlighted in the context of increasing reliance on ... Limitations While CONBIAS provides a ... similar reviews. [Literature Review] DCBM : Data-Efficient ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/visual-data-diagnosis-and-debiasing-with-concept-graphs", "content": "is highlighted in the context of increasing reliance on ... Limitations While CONBIAS provides a ... similar reviews. [Literature Review] DCBM : Data-Efficient ..."}
+{"idx": 6, "title": "Sascha Marton", "date": "", "ddg_snippet": "... limitations . We categorize and compare its ... limited by the black-box nature of neural ... DCBM : Data-Efficient Visual Concept Bottleneck Models.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/profile/682450ca9e540a8a86dc083b/sascha-marton", "content": "... limitations . We categorize and compare its ... limited by the black-box nature of neural ... DCBM : Data-Efficient Visual Concept Bottleneck Models."}
+{"idx": 7, "title": "Papers by Mateo Espinosa Zarlenga", "date": "", "ddg_snippet": "... concept bottleneck models when dealing with noisy ... Critical Analysis While the DCBM approach shows promise, several limitations deserve consideration.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/author-profile/Mateo+Espinosa+Zarlenga-e29dfaae-da32-4d64-92e4-dbfbdfa6ed95", "content": "... concept bottleneck models when dealing with noisy ... Critical Analysis While the DCBM approach shows promise, several limitations deserve consideration."}
+{"idx": 8, "title": "Track: Poster Session 6", "date": "", "ddg_snippet": "26 Apr 2025 — Through multi-faceted analytical experiments, we identify key performance gaps and limitations , providing valuable insights for future model and ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31976", "content": "26 Apr 2025 — Through multi-faceted analytical experiments, we identify key performance gaps and limitations , providing valuable insights for future model and ..."}
+{"idx": 9, "title": "Profile for Zhejiang University", "date": "", "ddg_snippet": "... limited by sluggish adaptation process. Here, the ... Specifically, we propose decomposed concept bottleneck model ( DCBM ) ... Hardware limitations , such ...", "subpage_snippet": "", "source": "www.linknovate.com", "link": "https://www.linknovate.com/affiliation/zhejiang-university-1169/all/?query=visual+circuits", "content": "... limited by sluggish adaptation process. Here, the ... Specifically, we propose decomposed concept bottleneck model ( DCBM ) ... Hardware limitations , such ..."}
diff --git a/data/sampled_jsons/DCBM_experimental_setup_concept_proposal_methods_LLM-CBM_CLIP-CBM_promptable.jsonl b/data/sampled_jsons/DCBM_experimental_setup_concept_proposal_methods_LLM-CBM_CLIP-CBM_promptable.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d4d11aa9d9fd8237172c7e04eafab0c8a875fde7
--- /dev/null
+++ b/data/sampled_jsons/DCBM_experimental_setup_concept_proposal_methods_LLM-CBM_CLIP-CBM_promptable.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Prompt engineering - Wikipedia", "date": "", "ddg_snippet": "Prompt engineering is the process of structuring or crafting an instruction in order to produce better outputs from a generative artificial intelligence model. A prompt is natural language text describing the task that an AI should perform.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Prompt_engineering", "content": "Prompt engineering is the process of structuring or crafting an instruction in order to produce better outputs from a generative artificial intelligence model. A prompt is natural language text describing the task that an AI should perform."}
+{"idx": 1, "title": "DCBM : Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "G.1 CBM concept comparison. DCBM : Data-Efficient Visual Concept Bottleneck Models.Moreover, concept proposal steering using the promptable Grounding Dino does not result in performance differences compared to steering-free methods such as SAM2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v2", "content": "G.1 CBM concept comparison. DCBM : Data-Efficient Visual Concept Bottleneck Models.Moreover, concept proposal steering using the promptable Grounding Dino does not result in performance differences compared to steering-free methods such as SAM2."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "2 Jul 2025 — We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "2 Jul 2025 — We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 3, "title": "Self-Hosting LLMs with Docker and Proxmox: How to Run Your Own...", "date": "", "ddg_snippet": "This setup works on any Docker host, whether it is a VM on Proxmox or a bare-metal Docker server. Step 1: Install Docker.", "subpage_snippet": "", "source": "www.itspyworld.com", "link": "https://www.itspyworld.com/2025/09/self-hosting-llms-with-docker-and.html", "content": "This setup works on any Docker host, whether it is a VM on Proxmox or a bare-metal Docker server. Step 1: Install Docker."}
+{"idx": 4, "title": "Advancing LLM Reasoning: Deep Think with Confidence... | Medium", "date": "", "ddg_snippet": "3. Proposed Method : DeepConf. DeepConf is a framework that leverages confidence signals to make LLM reasoning more efficient and effective. It operates in two modes: a) Offline Thinking with Confidence.", "subpage_snippet": "", "source": "noailabs.medium.com", "link": "https://noailabs.medium.com/advancing-llm-reasoning-deep-think-with-confidence-deepconf-66bc7891dd42", "content": "3. Proposed Method : DeepConf. DeepConf is a framework that leverages confidence signals to make LLM reasoning more efficient and effective. It operates in two modes: a) Offline Thinking with Confidence."}
+{"idx": 5, "title": "GitHub - gruvector/ promptable", "date": "", "ddg_snippet": "packages/ promptable : The Promptable Library for building LLM apps in Typescript / Javascript! examples: Examples using the Promptable .js library!", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/gruvector/promptable", "content": "packages/ promptable : The Promptable Library for building LLM apps in Typescript / Javascript! examples: Examples using the Promptable .js library!"}
+{"idx": 6, "title": "How to Write a Concept Paper in 7 Steps | Grammarly", "date": "", "ddg_snippet": "A concept paper is typically a two- to three-page paper that concisely explains a proposed research project. If the paper is for a funding application, it may be twenty pages or longer. In the paper, they demonstrate why their proposed project is worthwhile.", "subpage_snippet": "", "source": "www.grammarly.com", "link": "https://www.grammarly.com/blog/academic-writing/concept-paper/", "content": "A concept paper is typically a two- to three-page paper that concisely explains a proposed research project. If the paper is for a funding application, it may be twenty pages or longer. In the paper, they demonstrate why their proposed project is worthwhile."}
+{"idx": 7, "title": "Promptable Studio", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "promptable.ai", "link": "https://promptable.ai/", "content": ""}
+{"idx": 8, "title": "Unleashing the Power of AI with Meta's Promptable Segmentation", "date": "", "ddg_snippet": "Text prompting opens up groundbreaking possibilities for interacting with SAM using natural language commands. Responsible AI considerations are addressed, with diversity and bias evaluations conducted on the segmentation performance.", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-news/unleashing-the-power-of-ai-with-metas-promptable-segmentation-489168", "content": "Text prompting opens up groundbreaking possibilities for interacting with SAM using natural language commands. Responsible AI considerations are addressed, with diversity and bias evaluations conducted on the segmentation performance."}
+{"idx": 9, "title": "(PDF) Aligning Visual and Semantic Interpretability through Visually...", "date": "", "ddg_snippet": "CBM concept generation typically contains autoen-. coders [5,10], non-negative matrix factorization [6,11,12], K-Means [13], or PCA [11,14].ated concepts , as proposed by [5], using the CLIP embed-. ding space and common concept and attribute sets . Kowal.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387105769_Aligning_Visual_and_Semantic_Interpretability_through_Visually_Grounded_Concept_Bottleneck_Models", "content": "CBM concept generation typically contains autoen-. coders [5,10], non-negative matrix factorization [6,11,12], K-Means [13], or PCA [11,14].ated concepts , as proposed by [5], using the CLIP embed-. ding space and common concept and attribute sets . Kowal."}
diff --git a/data/sampled_jsons/DDPG_trajectory_replanning_reinforcement_learning_robotics_2024.jsonl b/data/sampled_jsons/DDPG_trajectory_replanning_reinforcement_learning_robotics_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ed8f9549d0d16df721bce92f9f096db4a69e7ea1
--- /dev/null
+++ b/data/sampled_jsons/DDPG_trajectory_replanning_reinforcement_learning_robotics_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Combining Decision Making and Trajectory Planning for Lane...", "date": "", "ddg_snippet": "Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles.Proving Ground Test of a DDPG -based Vehicle Trajectory Planner.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/1d35c826c87b7cb9025fc31aae57611ec205dd13/Combining-Decision-Making-and-Trajectory-Planning-for-Lane-Changing-Using-Deep-Reinforcement-Learning/graph", "content": "Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles.Proving Ground Test of a DDPG -based Vehicle Trajectory Planner."}
+{"idx": 1, "title": "Deep Reinforcement Learning Based Trajectory Planning Under...", "date": "", "ddg_snippet": "With the advance in algorithms, deep reinforcement learning (DRL) offers solutions to trajectory planning under uncertain environments. Different from traditional trajectory planning which requires lots of effort to tackle complicated high-dimensional problems...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/35586262/", "content": "With the advance in algorithms, deep reinforcement learning (DRL) offers solutions to trajectory planning under uncertain environments. Different from traditional trajectory planning which requires lots of effort to tackle complicated high-dimensional problems..."}
+{"idx": 2, "title": "Application of Deep Reinforcement Learning for Tracking Control of...", "date": "", "ddg_snippet": "DDPG agent environment is created in the Reinforcement learning toolbox in MATLAB 2019 while for Actor and critic network design deep neural network designer is used. Results are shown to illustrate the effectiveness of the technique with a convergence of error approximately to zero.", "subpage_snippet": "", "source": "itc.ktu.lt", "link": "https://itc.ktu.lt/index.php/ITC/article/view/25979", "content": "DDPG agent environment is created in the Reinforcement learning toolbox in MATLAB 2019 while for Actor and critic network design deep neural network designer is used. Results are shown to illustrate the effectiveness of the technique with a convergence of error approximately to zero."}
+{"idx": 3, "title": "Learning Continuous Control using Inverse Reinforcement Learning", "date": "", "ddg_snippet": "Reinforcement Learning algorithms for Continuous Control. Continuous reinforcement learning algorithms are designed to handle environments where actions are continuous, like controlling robotic arm joints with precision.", "subpage_snippet": "", "source": "app.readytensor.ai", "link": "https://app.readytensor.ai/publications/learning-continuous-control-using-inverse-reinforcement-learning-T6yvGTSvC4oJ", "content": "Reinforcement Learning algorithms for Continuous Control. Continuous reinforcement learning algorithms are designed to handle environments where actions are continuous, like controlling robotic arm joints with precision."}
+{"idx": 4, "title": "(PDF) Deep Reinforcement Learning -Based One-to-Multiple...", "date": "", "ddg_snippet": "Papers. Deep Reinforcement Learning -Based One-to-Multiple Cooperative Computing in Large-Scale Event-Driven Wireless Sensor Networks.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/deep-reinforcement-learning-based-one-to-multiple-3pkq0f8l", "content": "Papers. Deep Reinforcement Learning -Based One-to-Multiple Cooperative Computing in Large-Scale Event-Driven Wireless Sensor Networks."}
+{"idx": 5, "title": "Simplifying Action-Constrained Reinforcement Learning", "date": "", "ddg_snippet": "Simplifying Action-Constrained Reinforcement Learning . A new method improves decision-making under constraints in reinforcement learning .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-09-10-simplifying-action-constrained-reinforcement-learning--a3dqmmq", "content": "Simplifying Action-Constrained Reinforcement Learning . A new method improves decision-making under constraints in reinforcement learning ."}
+{"idx": 6, "title": "ddpg -pytorch · GitHub Topics · GitHub", "date": "", "ddg_snippet": "Simulation based Soft Continuum Robot Control via Reinforcement Learning .Add a description, image, and links to the ddpg -pytorch topic page so that developers can more easily learn about it.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/ddpg-pytorch?l=tex&o=asc&s=updated", "content": "Simulation based Soft Continuum Robot Control via Reinforcement Learning .Add a description, image, and links to the ddpg -pytorch topic page so that developers can more easily learn about it."}
+{"idx": 7, "title": "Reinforcement Learning for Robot Motion Planning Facilitated by...", "date": "", "ddg_snippet": "This dataset contains the programs to train and test the implicit behavior cloning (IBC) dynamic movement primitive (DMP) reinforcement learning (RL) agent for robot motion planning .", "subpage_snippet": "", "source": "zenodo.org", "link": "https://zenodo.org/records/8128019", "content": "This dataset contains the programs to train and test the implicit behavior cloning (IBC) dynamic movement primitive (DMP) reinforcement learning (RL) agent for robot motion planning ."}
+{"idx": 8, "title": "Autonomous Drone Navigation Techniques Using Deep...", "date": "", "ddg_snippet": "Reinforcement Learning (RL): Agent learns via rewards and penalties through trial and error. Plans a trajectory considering safety and efficiency. Executes maneuver using motor controls.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/autonomous-drone-navigation-techniques-using-deep-learning-malpani-5foef", "content": "Reinforcement Learning (RL): Agent learns via rewards and penalties through trial and error. Plans a trajectory considering safety and efficiency. Executes maneuver using motor controls."}
+{"idx": 9, "title": "(PDF) Autonomous Navigation and Collision Avoidance for AGV in...", "date": "", "ddg_snippet": "Exploration through Deep Reinforcement Learning ,” IEEE Robotics and. Reinforcement learning -driven dynamic obstacle avoidance for mobile robot trajectory tracking.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390701262_Autonomous_Navigation_and_Collision_Avoidance_for_AGV_in_Dynamic_Environments_An_Enhanced_Deep_Reinforcement_Learning_Approach_With_Composite_Rewards_and_Dynamic_Update_Mechanisms", "content": "Exploration through Deep Reinforcement Learning ,” IEEE Robotics and. Reinforcement learning -driven dynamic obstacle avoidance for mobile robot trajectory tracking."}
diff --git a/data/sampled_jsons/DIKE_emotion_behavior_four_step_unsupervised_learning_linguistic_behavior.jsonl b/data/sampled_jsons/DIKE_emotion_behavior_four_step_unsupervised_learning_linguistic_behavior.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3dfee5e662f7e0dfe9db56d2a7494e13d569e9b3
--- /dev/null
+++ b/data/sampled_jsons/DIKE_emotion_behavior_four_step_unsupervised_learning_linguistic_behavior.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "integrating-emotional-and-linguistic-models-for-ethical- ...", "date": "", "ddg_snippet": "DIKE's unsupervised learning approach, which associates emotions with linguistic behaviors , and. GPT- 4 using a zero-shot prompt. Ground truth was established ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/integrating-emotional-and-linguistic-models-for-ethical-c8c5drc4bk.pdf", "content": "DIKE's unsupervised learning approach, which associates emotions with linguistic behaviors , and. GPT- 4 using a zero-shot prompt. Ground truth was established ..."}
+{"idx": 1, "title": "Learning Emotion Regulation: An Integrative Framework", "date": "", "ddg_snippet": "by RN Wright · 2024 · Cited by 19 — Addressing these temporal dynamics introduces new measurable behaviors in the emotion regulation process: maintenance, stopping, and switching (Gross, 2015). 31 pages", "subpage_snippet": "", "source": "dibs-web01.vm.duke.edu", "link": "https://dibs-web01.vm.duke.edu/labar/pdfs/Wright_et_al_2025.pdf", "content": "by RN Wright · 2024 · Cited by 19 — Addressing these temporal dynamics introduces new measurable behaviors in the emotion regulation process: maintenance, stopping, and switching (Gross, 2015). 31 pages"}
+{"idx": 2, "title": "A linguistic style analysis of naturally occurring text data", "date": "", "ddg_snippet": "by A Cork · 2023 · Cited by 6 — In order to quantify the similarity between the linguistic styles of groups, we calculated pairwise models using machine learning algorithms.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.3758/s13428-022-02027-8", "content": "by A Cork · 2023 · Cited by 6 — In order to quantify the similarity between the linguistic styles of groups, we calculated pairwise models using machine learning algorithms."}
+{"idx": 3, "title": "Making Harmful Behaviors Unlearnable for Large ...", "date": "", "ddg_snippet": "by X Zhou · 2024 · Cited by 23 — Our experiments show that the security vectors can prevent LLM from learning harm- ful and hallucination behavior while preserving the ability ... 16 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.611.pdf", "content": "by X Zhou · 2024 · Cited by 23 — Our experiments show that the security vectors can prevent LLM from learning harm- ful and hallucination behavior while preserving the ability ... 16 pages"}
+{"idx": 4, "title": "Machine Learning Approach to Measurement of Criticism", "date": "", "ddg_snippet": "by A Movaghar · 2021 · Cited by 6 — Expressed emotion (EE), a measure of the family's emotional climate, is a fundamental measure in caregiving research. A core dimension of EE is the level of ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8478812/", "content": "by A Movaghar · 2021 · Cited by 6 — Expressed emotion (EE), a measure of the family's emotional climate, is a fundamental measure in caregiving research. A core dimension of EE is the level of ..."}
+{"idx": 5, "title": "Integrating machine learning for sustaining cybersecurity in ...", "date": "", "ddg_snippet": "by M Asmar · 2024 · Cited by 27 — The aim of this study is to investigates how machine learning algorithms are integrated into cyber security measures in the context of digital banking and its ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S240584402413602X", "content": "by M Asmar · 2024 · Cited by 27 — The aim of this study is to investigates how machine learning algorithms are integrated into cyber security measures in the context of digital banking and its ..."}
+{"idx": 6, "title": "Language Model Behavior: A Comprehensive Survey", "date": "", "ddg_snippet": "by TA Chang · 2023 · Cited by 159 — Transformer language models have received widespread public attention, yet their generated text is often surprising even to NLP researchers.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.11504", "content": "by TA Chang · 2023 · Cited by 159 — Transformer language models have received widespread public attention, yet their generated text is often surprising even to NLP researchers."}
+{"idx": 7, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "' Given N letters, Dike employs a self- supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d24155c881921de1284ece531612c8597f7c0a32.pdf", "content": "' Given N letters, Dike employs a self- supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps ."}
+{"idx": 8, "title": "Explainability for Large Language Models: A Survey", "date": "", "ddg_snippet": "28 Nov 2023 — Explanations here focus on understanding how models learn open-ended interactive behaviors from conversations. Report issue for preceding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2309.01029", "content": "28 Nov 2023 — Explanations here focus on understanding how models learn open-ended interactive behaviors from conversations. Report issue for preceding ..."}
+{"idx": 9, "title": "Multi-LLM Agent Collaborative Intelligence: The Path to AGI", "date": "", "ddg_snippet": "This book proposes that the key to achieving AGI, characterized by versatility, adaptability, reasoning, critical thinking, planning, and ethical alignment, ... 589 pages", "subpage_snippet": "", "source": "shuyuej.com", "link": "http://shuyuej.com/books/The-Path-to-Artificial-General-Intelligence.pdf", "content": "This book proposes that the key to achieving AGI, characterized by versatility, adaptability, reasoning, critical thinking, planning, and ethical alignment, ... 589 pages"}
diff --git a/data/sampled_jsons/DIL_imitation_learning_on-policy_extension_future_work_offline_limitation.jsonl b/data/sampled_jsons/DIL_imitation_learning_on-policy_extension_future_work_offline_limitation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d82e299d8207e14ddbefc7e30db6bb9d8db0e684
--- /dev/null
+++ b/data/sampled_jsons/DIL_imitation_learning_on-policy_extension_future_work_offline_limitation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dataset Clustering for Improved Offline Policy Learning", "date": "", "ddg_snippet": "Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.09550v1", "content": "Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment ..."}
+{"idx": 1, "title": "Dexterous Manipulation through Imitation Learning: A Survey", "date": "", "ddg_snippet": "10 Sept 2025 — Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.03515v4", "content": "10 Sept 2025 — Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, ..."}
+{"idx": 2, "title": "Dialogue Learning with Human Teaching and Feedback in ...", "date": "", "ddg_snippet": "by B Liu · 2018 · Cited by 230 — To ameliorate the effect of dialogue state distri- bution mismatch between offline training and RL interactive learning , we propose a hybrid imitation and ... 10 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/N18-1187.pdf", "content": "by B Liu · 2018 · Cited by 230 — To ameliorate the effect of dialogue state distri- bution mismatch between offline training and RL interactive learning , we propose a hybrid imitation and ... 10 pages"}
+{"idx": 3, "title": "learning and alignment with human preferences and values", "date": "", "ddg_snippet": "by T Xiao · 2025 — DIL unifies imitation learning on preference data and bridges the gap between density ratio estimation and preference alignment. (iii) Empirically, we validate ...", "subpage_snippet": "", "source": "etda.libraries.psu.edu", "link": "https://etda.libraries.psu.edu/files/final_submissions/32968", "content": "by T Xiao · 2025 — DIL unifies imitation learning on preference data and bridges the gap between density ratio estimation and preference alignment. (iii) Empirically, we validate ..."}
+{"idx": 4, "title": "De-Chuan Zhan", "date": "", "ddg_snippet": "Domain-Incremental Learning ( DIL ) involves the progressive adaptation of a model to new concepts across different domains. Incremental Learning · Paper", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/author/de-chuan-zhan", "content": "Domain-Incremental Learning ( DIL ) involves the progressive adaptation of a model to new concepts across different domains. Incremental Learning · Paper"}
+{"idx": 5, "title": "Building Multimodal Web Agents via Iterative Real-World ...", "date": "", "ddg_snippet": "by H He · 2025 · Cited by 8 — The agent learns basic multimodal web navigation skills through Imitation Learning and continues to explore real-world web environments. GPT-4o ... 20 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1336.pdf", "content": "by H He · 2025 · Cited by 8 — The agent learns basic multimodal web navigation skills through Imitation Learning and continues to explore real-world web environments. GPT-4o ... 20 pages"}
+{"idx": 6, "title": "Data-Driven Robotic Manipulation of Cloth-like Deformable ...", "date": "", "ddg_snippet": "by HA Kadi · 2023 · Cited by 18 — Learning from Observation (LfO), or Imitation from Observation, are imitation learning approaches which learn directly from state/observation demonstration ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10007406/", "content": "by HA Kadi · 2023 · Cited by 18 — Learning from Observation (LfO), or Imitation from Observation, are imitation learning approaches which learn directly from state/observation demonstration ..."}
+{"idx": 7, "title": "Building Strategic AI Agents for Human-centric Multi- ...", "date": "", "ddg_snippet": "by AP Jacob · 2024 — This research explores the limitations of current approaches, such as self-play reinforcement learning (RL) and imitation learning (IL), and proposes novel.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/158481/jacob-apjacob-phd-eecs-2024-thesis.pdf?sequence=1&isAllowed=y", "content": "by AP Jacob · 2024 — This research explores the limitations of current approaches, such as self-play reinforcement learning (RL) and imitation learning (IL), and proposes novel."}
+{"idx": 8, "title": "MASTERING THE GAME OF NO-PRESS DIPLOMACY VIA ...", "date": "", "ddg_snippet": "by A Bakhtin · Cited by 71 — In this work , we introduce an extension of piKL, called DiL -piKL, that replaces piKL's single fixed regularization parameter λ with a probability distribution ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=F61FwJTZhb", "content": "by A Bakhtin · Cited by 71 — In this work , we introduce an extension of piKL, called DiL -piKL, that replaces piKL's single fixed regularization parameter λ with a probability distribution ..."}
+{"idx": 9, "title": "Scanning the Issue", "date": "", "ddg_snippet": "This article presents a comprehensive review of Imitation Learning approaches for end-to-end autonomous vehicle systems. The literature is classified into three ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/6979/9893028/09893040.pdf", "content": "This article presents a comprehensive review of Imitation Learning approaches for end-to-end autonomous vehicle systems. The literature is classified into three ..."}
diff --git a/data/sampled_jsons/DISEF_Flowers_16-shot_accuracy_table.jsonl b/data/sampled_jsons/DISEF_Flowers_16-shot_accuracy_table.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf12ad335552c23c0c08f42e6060b6d224c32db6
--- /dev/null
+++ b/data/sampled_jsons/DISEF_Flowers_16-shot_accuracy_table.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "DataDream: Few-shot Guided Dataset Generation - arXiv.org", "date": "", "ddg_snippet": "Table 1: Few-shot classification performance with DataDream using real 16-shot and synthetic images where the training dataset includes synthetic data only (top), or synthetic data + 16 real shots (bottom). All results use CLIP ViT-B/16 as the base classification model, and 500 synthetic images generated by 16 real shots.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10910v2", "content": "Table 1: Few-shot classification performance with DataDream using real 16-shot and synthetic images where the training dataset includes synthetic data only (top), or synthetic data + 16 real shots (bottom). All results use CLIP ViT-B/16 as the base classification model, and 500 synthetic images generated by 16 real shots."}
+{"idx": 1, "title": "DataDream: Few-Shot Guided Dataset Generation | SpringerLink", "date": "", "ddg_snippet": "Nov 1, 2024 · Table 1. Few-shot classification performance with DataDream using real 16-shot and synthetic images where the training dataset includes synthetic data only (top), or synthetic data + 16 real shots (bottom).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-73209-6_15", "content": "Nov 1, 2024 · Table 1. Few-shot classification performance with DataDream using real 16-shot and synthetic images where the training dataset includes synthetic data only (top), or synthetic data + 16 real shots (bottom)."}
+{"idx": 2, "title": "Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "by H Yang — As shown in Table S-. 7c, omitting normalization results in a performance drop of. 0.8% in 1-shot accuracy and 0.5% in 16-shot accuracy on average. ... and DISEF ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "by H Yang — As shown in Table S-. 7c, omitting normalization results in a performance drop of. 0.8% in 1-shot accuracy and 0.5% in 16-shot accuracy on average. ... and DISEF ..."}
+{"idx": 3, "title": "Diversified in-domain synthesis with efficient fine-tuning for ...", "date": "", "ddg_snippet": "Report issue for preceding element. Table 2: 16-shot results ... Although classifier tuning is competitive with DISEF in Caltech101, ImageNet, and Flowers ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "Report issue for preceding element. Table 2: 16-shot results ... Although classifier tuning is competitive with DISEF in Caltech101, ImageNet, and Flowers ..."}
+{"idx": 4, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "by H Yang · 2025 — Results of domain generalization on 16-shot setting. Method. ImageNet. Caltech Aircraft. Cars. F ood. Pets. Flowers . DTD. 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "by H Yang · 2025 — Results of domain generalization on 16-shot setting. Method. ImageNet. Caltech Aircraft. Cars. F ood. Pets. Flowers . DTD. 12 pages"}
diff --git a/data/sampled_jsons/DISEF_arXiv_2312.03046_Flowers_16-shot_accuracy_98.9.jsonl b/data/sampled_jsons/DISEF_arXiv_2312.03046_Flowers_16-shot_accuracy_98.9.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8cda81945f27129b06dcd18360520692b40642d8
--- /dev/null
+++ b/data/sampled_jsons/DISEF_arXiv_2312.03046_Flowers_16-shot_accuracy_98.9.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2312 . 03046 ] Diversified in-domain synthesis with efficient fine-tuning...", "date": "", "ddg_snippet": "arXiv : 2312 . 03046 (cs). [Submitted on 5 Dec 2023 (v1), last revised 7 Dec 2023 (this version, v2)].View a PDF of the paper titled Diversified in-domain synthesis with efficient fine-tuning for few- shot classification, by Victor G. Turrisi da Costa and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "arXiv : 2312 . 03046 (cs). [Submitted on 5 Dec 2023 (v1), last revised 7 Dec 2023 (this version, v2)].View a PDF of the paper titled Diversified in-domain synthesis with efficient fine-tuning for few- shot classification, by Victor G. Turrisi da Costa and 4 other authors."}
+{"idx": 1, "title": "2312 . 03046 - Diversified in-domain synthesis with efficient fine-tuning...", "date": "", "ddg_snippet": "arXiv . 2312 . 03046 . Published Dec 5, 2023 in cs.CV by Victor G. Turrisi da Costa, Nicola Dall'Asen, Yiming Wang, Nicu Sebe, and Elisa Ricci. Abstract: “Few- shot image classification aims to learn an image classifier using only a small set of labeled examples per class.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2312.03046", "content": "arXiv . 2312 . 03046 . Published Dec 5, 2023 in cs.CV by Victor G. Turrisi da Costa, Nicola Dall'Asen, Yiming Wang, Nicu Sebe, and Elisa Ricci. Abstract: “Few- shot image classification aims to learn an image classifier using only a small set of labeled examples per class."}
+{"idx": 2, "title": "Вклады СберБанка 2025 для физических лиц на сегодня...", "date": "", "ddg_snippet": "Выгодные условия! Вклад «Сбер Рядом». Ставкадо 16 .2%. Срокдо 1095 дней.ПреимуществаЛиц. № 2312. Сделать вклад. Ставка.", "subpage_snippet": "", "source": "bankiros.ru", "link": "https://bankiros.ru/bank/sberbank/deposits", "content": "Выгодные условия! Вклад «Сбер Рядом». Ставкадо 16 .2%. Срокдо 1095 дней.ПреимуществаЛиц. № 2312. Сделать вклад. Ставка."}
+{"idx": 3, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base...", "date": "", "ddg_snippet": "DISEF [17] captions few-shot real images with LLaVA [33], which alongside real images serve as inputs for T2I models.We conduct ablation analysis in 1 shot/ 16 shot settings on ImageNet, Aircraft, Flowers and EuroSAT with CLIP ViT-B/16 as visual encoder, unless otherwise specified.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "DISEF [17] captions few-shot real images with LLaVA [33], which alongside real images serve as inputs for T2I models.We conduct ablation analysis in 1 shot/ 16 shot settings on ImageNet, Aircraft, Flowers and EuroSAT with CLIP ViT-B/16 as visual encoder, unless otherwise specified."}
+{"idx": 4, "title": "Sprey Oda Kokusu Modelleri ve Oda kokusu Fiyatları | Chakra", "date": "", "ddg_snippet": "%50 İndirim. Oda Kokusu 500 ML Natural Flowers .", "subpage_snippet": "", "source": "www.chakra.com.tr", "link": "https://www.chakra.com.tr/kozmetik/sprey-oda-kokusu/", "content": "%50 İndirim. Oda Kokusu 500 ML Natural Flowers ."}
+{"idx": 5, "title": "Video to Text Converter - Transcribe Video to Text Online | Any2Text", "date": "", "ddg_snippet": "The transcript will be prepared by our AI, which will accurately transcribe audio to text with 98% accuracy .", "subpage_snippet": "", "source": "any2text.com", "link": "https://any2text.com/video-to-text", "content": "The transcript will be prepared by our AI, which will accurately transcribe audio to text with 98% accuracy ."}
+{"idx": 6, "title": "Махачкала и Дербент ушли под воду из-за ливней", "date": "", "ddg_snippet": "Дагестан ушел под воду / Фото: Shot . Крупные города Дагестана – Махачкала, Дербент и Каспийск – оказались во власти мощного наводнения, вызванного проливными дождями, сообщает Shot .", "subpage_snippet": "", "source": "www.amic.ru", "link": "https://www.amic.ru/news/mahachkala-i-derbent-ushli-pod-vodu-iz-za-livney-568986", "content": "Дагестан ушел под воду / Фото: Shot . Крупные города Дагестана – Махачкала, Дербент и Каспийск – оказались во власти мощного наводнения, вызванного проливными дождями, сообщает Shot ."}
+{"idx": 7, "title": "fnatic vs. GUN5 at Exort The Proving Grounds Season 4 | HLTV.org", "date": "", "ddg_snippet": "Maps. Best of 3 (Online) * Round of 16 . 1. GUN5 removed Overpass. 2. fnatic removed Nuke.they shot themselves in the foot playing these shitty tier 2 tourneys. 2025-09-21 02:20.", "subpage_snippet": "", "source": "www.hltv.org", "link": "https://www.hltv.org/matches/2385826/fnatic-vs-gun5-exort-the-proving-grounds-season-4", "content": "Maps. Best of 3 (Online) * Round of 16 . 1. GUN5 removed Overpass. 2. fnatic removed Nuke.they shot themselves in the foot playing these shitty tier 2 tourneys. 2025-09-21 02:20."}
+{"idx": 8, "title": "Universe of HD Wallpapers - WallpaperCat", "date": "", "ddg_snippet": "Skeet Shooting .Download. 3540x1990. Flower Patterns, Drawn Flowers , Autumn Colors, HD Phone Background.", "subpage_snippet": "", "source": "wallpapercat.com", "link": "https://wallpapercat.com/", "content": "Skeet Shooting .Download. 3540x1990. Flower Patterns, Drawn Flowers , Autumn Colors, HD Phone Background."}
+{"idx": 9, "title": "Актуальные вопросы развития научных исследований...", "date": "", "ddg_snippet": "Abstract The article compares the classical Kalman filter, its extended version (EKF), and neural network algorithms (LSTM) for predicting unmanned aerial vehicle (UAV) flight parameters. Accuracy , computational complexity, and noise robustness are analyzed.", "subpage_snippet": "", "source": "aeterna-ufa.ru", "link": "https://aeterna-ufa.ru/sbornik/NK-706.pdf", "content": "Abstract The article compares the classical Kalman filter, its extended version (EKF), and neural network algorithms (LSTM) for predicting unmanned aerial vehicle (UAV) flight parameters. Accuracy , computational complexity, and noise robustness are analyzed."}
diff --git a/data/sampled_jsons/DISEF_augmentation_pipeline_synthetic_images_real_samples_few-shot_year_2023.jsonl b/data/sampled_jsons/DISEF_augmentation_pipeline_synthetic_images_real_samples_few-shot_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8b1dd6eabb51700a2886b93b0c5f475f2d8fe169
--- /dev/null
+++ b/data/sampled_jsons/DISEF_augmentation_pipeline_synthetic_images_real_samples_few-shot_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "This technique, called few - shot prompting, allows LLMs to be adapted to any task without requiring fine-tuning.[1] Also in 2022, it was found that the base GPT-3 model can generate an instruction based on user input.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "This technique, called few - shot prompting, allows LLMs to be adapted to any task without requiring fine-tuning.[1] Also in 2022, it was found that the base GPT-3 model can generate an instruction based on user input."}
+{"idx": 1, "title": "DataDream: Few - shot Guided Dataset Generation", "date": "", "ddg_snippet": "The real few - shot images at the top are used to generate the presented synthetic images at the bottom. We always use a fixed set of 16 samples , i.e. 1-shot image is a subset of 16-shots, to insure fairness in comparing results with the increasing number of shots.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10910v2/", "content": "The real few - shot images at the top are used to generate the presented synthetic images at the bottom. We always use a fixed set of 16 samples , i.e. 1-shot image is a subset of 16-shots, to insure fairness in comparing results with the increasing number of shots."}
+{"idx": 2, "title": "AlignDiff: Aligning Diffusion Models for General Few - Shot Segmentation", "date": "", "ddg_snippet": "Apart from creative applications, can we use such models to synthesize samples that aid the few - shot training of discriminative models? In this work, we propose AlignDiff, a general framework for synthesizing training images and associated mask annotations for few - shot segmentation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8nz6xYntfJ", "content": "Apart from creative applications, can we use such models to synthesize samples that aid the few - shot training of discriminative models? In this work, we propose AlignDiff, a general framework for synthesizing training images and associated mask annotations for few - shot segmentation."}
+{"idx": 3, "title": "SIMPL: Generating Synthetic Overhead Imagery to Address... | DeepAI", "date": "", "ddg_snippet": "We demonstrate the effectiveness of using SIMPL synthetic imagery for training DNNs in zero-shot scenarios where no real imagery is available; and few - shot learning scenarios, where limited real -world imagery is available.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/simpl-generating-synthetic-overhead-imagery-to-address-zero-shot-and-few-shot-detection-problems", "content": "We demonstrate the effectiveness of using SIMPL synthetic imagery for training DNNs in zero-shot scenarios where no real imagery is available; and few - shot learning scenarios, where limited real -world imagery is available."}
+{"idx": 4, "title": "StableRep: Synthetic Images from Text-to- Image", "date": "", "ddg_snippet": "In our pipeline , each real caption is passed into Stable Diffusion (SD) to generate a number of synthetic images .A baseline for few - shot image classification. arXiv preprint arXiv:1909.02729, 2019.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/stablerep-synthetic-images-from-text-to-image-models-make-f4az7jsz.pdf", "content": "In our pipeline , each real caption is passed into Stable Diffusion (SD) to generate a number of synthetic images .A baseline for few - shot image classification. arXiv preprint arXiv:1909.02729, 2019."}
+{"idx": 5, "title": "(PDF) Generative AI-Driven Data Augmentation for Robust Virtual...", "date": "", "ddg_snippet": "few - shot samples . By contrast, adding 800 MSC-DGA–.of real samples beyond which synthetic augmentation provides.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/391693139_Generative_AI-Driven_Data_Augmentation_for_Robust_Virtual_Metrology_GANs_VAEs_and_Diffusion_Models", "content": "few - shot samples . By contrast, adding 800 MSC-DGA–.of real samples beyond which synthetic augmentation provides."}
+{"idx": 6, "title": "Synthetic Data Generation for Machine Learning Training - ML Journey", "date": "", "ddg_snippet": "Few - shot generation techniques enable synthetic data creation from very small datasets by leveraging meta-learning approaches and pre-trained models.", "subpage_snippet": "", "source": "mljourney.com", "link": "https://mljourney.com/synthetic-data-generation-for-machine-learning-training/", "content": "Few - shot generation techniques enable synthetic data creation from very small datasets by leveraging meta-learning approaches and pre-trained models."}
+{"idx": 7, "title": "Google’s Sensible Agent Reframes Augmented Reality ...", "date": "", "ddg_snippet": "Sensible Agent is an AI research framework and prototype from Google that chooses both the action an augmented reality (AR) agent should take and the interaction modality to deliver/confirm it, conditioned on real -time multimodal context (e.g., whether hands are busy, ambient noise...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/09/19/googles-sensible-agent-reframes-augmented-reality-ar-assistance-as-a-coupled-whathow-decision-so-what-does-that-change/", "content": "Sensible Agent is an AI research framework and prototype from Google that chooses both the action an augmented reality (AR) agent should take and the interaction modality to deliver/confirm it, conditioned on real -time multimodal context (e.g., whether hands are busy, ambient noise..."}
+{"idx": 8, "title": "2312.03046 - Diversified in-domain synthesis with efficient fine-tuning...", "date": "", "ddg_snippet": "A recent research direction for improving few - shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to- image generation models.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2312.03046", "content": "A recent research direction for improving few - shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to- image generation models."}
+{"idx": 9, "title": "Improving Self-Driving Cars' Performance in Bad Weather", "date": "", "ddg_snippet": "Synthetic images enhance training data for self-driving cars in challenging conditions.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-05-09-improving-self-driving-cars-performance-in-bad-weather--a3wwz52", "content": "Synthetic images enhance training data for self-driving cars in challenging conditions."}
diff --git a/data/sampled_jsons/DISEF_paper_Flowers_16-shot_performance.jsonl b/data/sampled_jsons/DISEF_paper_Flowers_16-shot_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..97350c070d28dbafd4cd95159693de232dc2ec2c
--- /dev/null
+++ b/data/sampled_jsons/DISEF_paper_Flowers_16-shot_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot ...", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few- shot learning using synthetic data. DISEF consists of two main components.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few- shot learning using synthetic data. DISEF consists of two main components."}
+{"idx": 1, "title": "GitHub - vturrisi/disef: Pytorch implementation of \"Diversified in ...", "date": "", "ddg_snippet": "Pytorch implementation of \"Diversified in-domain synthesis with efficient fine-tuning for few- shot classification\" - vturrisi/ disef", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vturrisi/disef", "content": "Pytorch implementation of \"Diversified in-domain synthesis with efficient fine-tuning for few- shot classification\" - vturrisi/ disef"}
+{"idx": 2, "title": "disef/README.md at main · vturrisi/disef · GitHub", "date": "", "ddg_snippet": "This is the official repository for the paper : Diversified in-domain synthesis with efficient fine-tuning for few- shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci. The code is divided into two main parts, one for fine-tuning a pre-trained model in the few- shot scenario (fine-tune) and the other for generating synthetic data to enhance ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vturrisi/disef/blob/main/README.md", "content": "This is the official repository for the paper : Diversified in-domain synthesis with efficient fine-tuning for few- shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci. The code is divided into two main parts, one for fine-tuning a pre-trained model in the few- shot scenario (fine-tune) and the other for generating synthetic data to enhance ..."}
+{"idx": 3, "title": "Rolled Paper Flower Tutorial Using The Sizzix Big Shot", "date": "", "ddg_snippet": "Make these super easy rolled flowers with our quick craft tutorial using the Simply Made Crafts dies from Helen Griffin.Simply Made Crafts by Helen Griffin i...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=2Bw_ZpXHwNI", "content": "Make these super easy rolled flowers with our quick craft tutorial using the Simply Made Crafts dies from Helen Griffin.Simply Made Crafts by Helen Griffin i..."}
+{"idx": 4, "title": "DataDream: Few-shot Guided Dataset Generation - arXiv.org", "date": "", "ddg_snippet": "In this paper , we studied the efficacy of leveraging the generative models for improving the image classification performance in few- shot scenarios. We proposed DataDream, a method to generate synthetic data with the guidance of few- shot samples, which are then used for training the image classifier.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10910v2", "content": "In this paper , we studied the efficacy of leveraging the generative models for improving the image classification performance in few- shot scenarios. We proposed DataDream, a method to generate synthetic data with the guidance of few- shot samples, which are then used for training the image classifier."}
+{"idx": 5, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot ...", "date": "", "ddg_snippet": "In this paper , we propose to tackle the problem of few- shot classification with synthetic data by innovating key recipes in data synthesis and parameter-efficient model fine-tuning. Our approach, iversified n-domain ynthesis with fficient ine-tuning (), brings two main contributions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "In this paper , we propose to tackle the problem of few- shot classification with synthetic data by innovating key recipes in data synthesis and parameter-efficient model fine-tuning. Our approach, iversified n-domain ynthesis with fficient ine-tuning (), brings two main contributions."}
+{"idx": 6, "title": "Sizzix Thinlits Die 665079 Jar of Flowers by Lisa Jones 17 Pack", "date": "", "ddg_snippet": "About this item JAR OF FLOWERS - Perfect for all types of making from sentiment cards to home décor pieces! Remember to combine this with 664857 - Shaker Dome, Jar to create stunning shaker cards or hanging ornaments! CUT A VARIETY OF MATERIALS - Thinlits dies allow you to cut intricate designs from a single sheet of paper , card, metallic foil, vellum, shrink plastic, foam, crepe paper and ...", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/Sizzix-Thinlits-665079-Flowers-Multicolor/dp/B08Q8P4VGG", "content": "About this item JAR OF FLOWERS - Perfect for all types of making from sentiment cards to home décor pieces! Remember to combine this with 664857 - Shaker Dome, Jar to create stunning shaker cards or hanging ornaments! CUT A VARIETY OF MATERIALS - Thinlits dies allow you to cut intricate designs from a single sheet of paper , card, metallic foil, vellum, shrink plastic, foam, crepe paper and ..."}
+{"idx": 7, "title": "Floral - Sizzix.com", "date": "", "ddg_snippet": "Floral crafts are a favorite here at Sizzix with a range of flower craft dies to choose from, including 2D and 3D designs as well as beautiful embossing folders with floral patterns. With both intricate and Bigz craft dies you can experiment with different materials and explore textures. Pairing with Sizzix Making Essentials will give you the perfect finishing touches to your paper craft flowers .", "subpage_snippet": "", "source": "www.sizzix.com", "link": "https://www.sizzix.com/pages/floral", "content": "Floral crafts are a favorite here at Sizzix with a range of flower craft dies to choose from, including 2D and 3D designs as well as beautiful embossing folders with floral patterns. With both intricate and Bigz craft dies you can experiment with different materials and explore textures. Pairing with Sizzix Making Essentials will give you the perfect finishing touches to your paper craft flowers ."}
+{"idx": 8, "title": "Flower Making - Sizzix.com", "date": "", "ddg_snippet": "Bring your papercrafts to life with Sizzix® flower -making dies. Perfect for home decor, gifting, and party decor crafts. Our flower dies allow you to create realistic blooms in any color with a range of materials. Add a personal touch to your creations with beautiful, custom paper flowers .", "subpage_snippet": "", "source": "www.sizzix.com", "link": "https://www.sizzix.com/collections/flower-making", "content": "Bring your papercrafts to life with Sizzix® flower -making dies. Perfect for home decor, gifting, and party decor crafts. Our flower dies allow you to create realistic blooms in any color with a range of materials. Add a personal touch to your creations with beautiful, custom paper flowers ."}
+{"idx": 9, "title": "PDF 3 arXiv:2312.03046v2 [cs.CV] 7 Dec 2023", "date": "", "ddg_snippet": "We introduce DISEF , a new framework for few- shot clas-sification that leverages synthetic data and parameter-eficient fine-tuning. For generating synthetic images, we propose a novel aug-mentation pipeline that leverages both support images and their captions for producing diverse but in-domain train-ing samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.03046.pdf", "content": "We introduce DISEF , a new framework for few- shot clas-sification that leverages synthetic data and parameter-eficient fine-tuning. For generating synthetic images, we propose a novel aug-mentation pipeline that leverages both support images and their captions for producing diverse but in-domain train-ing samples."}
diff --git a/data/sampled_jsons/DOODL_Wallace_et_al._2023b_abstract.jsonl b/data/sampled_jsons/DOODL_Wallace_et_al._2023b_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1b1de290cd464a0de54219eddf89ebddb75e36b8
--- /dev/null
+++ b/data/sampled_jsons/DOODL_Wallace_et_al._2023b_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DITTO: Diffusion Inference-Time 𝑻-Optimization for Music Generation", "date": "", "ddg_snippet": "Wallace et al . (2023a) Wallace , B., Gokul, A., Ermon, S., and Naik, N. V. End-to-end diffusion latent optimization improves classifier guidance. IEEE/CVF International Conference on Computer Vision (ICCV), abs/2303.13703, 2023a.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.12179v2", "content": "Wallace et al . (2023a) Wallace , B., Gokul, A., Ermon, S., and Naik, N. V. End-to-end diffusion latent optimization improves classifier guidance. IEEE/CVF International Conference on Computer Vision (ICCV), abs/2303.13703, 2023a."}
+{"idx": 1, "title": "ProCreate, Don’t Reproduce!Propulsive Energy Diffusion forCreative...", "date": "", "ddg_snippet": "DOODL ( Wallace et al ., 2023 ) improves the performance of off-the-shell classifier guidance by performing backpropagation through the entire diffusion inference process to optimize the initial noise.", "subpage_snippet": "", "source": "mengyeren.com", "link": "https://mengyeren.com/research/2024/procreate-dont-reproduce-propulsive-energy-diffusion-for-creative-generation/", "content": "DOODL ( Wallace et al ., 2023 ) improves the performance of off-the-shell classifier guidance by performing backpropagation through the entire diffusion inference process to optimize the initial noise."}
+{"idx": 2, "title": "Optimizing Diffusion Noise Can Serve As Universal Motion Priors", "date": "", "ddg_snippet": "DOODL ( Wallace et al ., 2023 ), DNO (Karunratanakul et al ., 2024) and D-Flow (Ben-Hamu et al ., 2024) use invertible models or flow models to backpropagate gradient to the initial noise.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384185889_Optimizing_Diffusion_Noise_Can_Serve_As_Universal_Motion_Priors", "content": "DOODL ( Wallace et al ., 2023 ), DNO (Karunratanakul et al ., 2024) and D-Flow (Ben-Hamu et al ., 2024) use invertible models or flow models to backpropagate gradient to the initial noise."}
+{"idx": 3, "title": "DITTO: Diffusion Inference-Time –Optimization for Music Generation", "date": "", "ddg_snippet": "Abstract .For DOODL , we use a mixing coefcient of p = 0.93 at 50 steps following Wallace et al . (2023a) and p = 0.83 at 20 steps due to severe divergence issues with higher p at 20 steps.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=z5Ux2u6t7U", "content": "Abstract .For DOODL , we use a mixing coefcient of p = 0.93 at 50 steps following Wallace et al . (2023a) and p = 0.83 at 20 steps due to severe divergence issues with higher p at 20 steps."}
+{"idx": 4, "title": "HELLP-синдром > Клинические протоколы МЗ РК - 2022...", "date": "", "ddg_snippet": "Recurrence risks of hypertensive diseases in pregnancy after HELLP syndrome. J Perinat Med 2011; 39: 673- 678 5. Martin, J.N., Jr.; Brewer, J.M.; Wallace , et al . Hellp syndrome and composite major maternal morbidity: Importance of Mississippi classification system.", "subpage_snippet": "", "source": "diseases.medelement.com", "link": "https://diseases.medelement.com/disease/hellp-синдром-кп-рк-2022/17522", "content": "Recurrence risks of hypertensive diseases in pregnancy after HELLP syndrome. J Perinat Med 2011; 39: 673- 678 5. Martin, J.N., Jr.; Brewer, J.M.; Wallace , et al . Hellp syndrome and composite major maternal morbidity: Importance of Mississippi classification system."}
+{"idx": 5, "title": "Как отличить цистит от простатита у мужчин - симптомы...", "date": "", "ddg_snippet": "Согласно исследованию Kim et al . (2023), мультипараметрическая МРТ имеет чувствительность 94% и специфичность 89% в дифференциальной диагностике простатита. Подходы к лечению заболеваний. Терапия цистита у мужчин.", "subpage_snippet": "", "source": "Medgorod-clinic.ru", "link": "https://Medgorod-clinic.ru/stati/kak-otlichit-tsistit-ot-prostatita-u-muzhchin-/", "content": "Согласно исследованию Kim et al . (2023), мультипараметрическая МРТ имеет чувствительность 94% и специфичность 89% в дифференциальной диагностике простатита. Подходы к лечению заболеваний. Терапия цистита у мужчин."}
+{"idx": 6, "title": "15 скрытых признаков боли у кошек: от прищуренных глаз до...", "date": "", "ddg_snippet": "3.2. Отказ от прыжков. Важно: 76% владельцев считают это “возрастным”, но это классический симптом артрита (Bennett et al ., 2023 ).", "subpage_snippet": "", "source": "meloxidyl.ru", "link": "https://meloxidyl.ru/articles/15-skrytyh-priznakov-boli-u-koshek-ot-prishhurennyh-glaz-do-strannoj-pozy/", "content": "3.2. Отказ от прыжков. Важно: 76% владельцев считают это “возрастным”, но это классический симптом артрита (Bennett et al ., 2023 )."}
+{"idx": 7, "title": "Пересадка зубов: альтернатива имплантам и протезам - миф или...", "date": "", "ddg_snippet": "По данным обзоров Cureus (Ajay et al ., 2024; Kakde et al ., 2022), аутотрансплантация — эффективная альтернатива имплантам и протезам, особенно у подростков. Источник: Tsukiboshi et al ., 2023 . Полностью сформированный корень.", "subpage_snippet": "", "source": "dentalopera.ru", "link": "https://dentalopera.ru/stati/peresadka-zubov-mif-ili-realnost/", "content": "По данным обзоров Cureus (Ajay et al ., 2024; Kakde et al ., 2022), аутотрансплантация — эффективная альтернатива имплантам и протезам, особенно у подростков. Источник: Tsukiboshi et al ., 2023 . Полностью сформированный корень."}
+{"idx": 8, "title": "Windows. Ключи KMS | Д. С. Кулябов", "date": "", "ddg_snippet": "Lindgren, J. et al . (2025) Electromagnetism as a purely geometric theory. Emacs.Фарли, Д. (2023): Современная программная инженерия ПО в эпоху эджайла и непрерывного развертывания. Аристер, Н. И. et al .", "subpage_snippet": "", "source": "yamadharma.github.io", "link": "https://yamadharma.github.io/ru/post/2023/06/08/windows-kms-keys/", "content": "Lindgren, J. et al . (2025) Electromagnetism as a purely geometric theory. Emacs.Фарли, Д. (2023): Современная программная инженерия ПО в эпоху эджайла и непрерывного развертывания. Аристер, Н. И. et al ."}
+{"idx": 9, "title": "Личное облачное хранилище — Microsoft OneDrive", "date": "", "ddg_snippet": "Discover OneDrive for secure and convenient file and document sharing.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/ru-ru/microsoft-365/onedrive/online-cloud-storage", "content": "Discover OneDrive for secure and convenient file and document sharing."}
diff --git a/data/sampled_jsons/DSDFM_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Section_4.2.2_architecture_DerODE_D.jsonl b/data/sampled_jsons/DSDFM_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Section_4.2.2_architecture_DerODE_D.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ef2767c73999f200dddea1ff98938904939c1def
--- /dev/null
+++ b/data/sampled_jsons/DSDFM_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_Section_4.2.2_architecture_DerODE_D.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "Deterministic feature mapping procedure aims to explore the optimal solution for building the connections between the Gaussian distribution and the latent space distribution of human motions using the designed Deterministic Ordinary Equation ( DerODE ) operation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00998v1", "content": "Deterministic feature mapping procedure aims to explore the optimal solution for building the connections between the Gaussian distribution and the latent space distribution of human motions using the designed Deterministic Ordinary Equation ( DerODE ) operation."}
+{"idx": 1, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Deterministic-to-Stochastic-Diverse-Latent-Feature-Mapping-for-Human-Motion-Synthesis-218660a3-37a0-490e-94f1-73766d3ec529", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions."}
+{"idx": 2, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE .", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE ."}
+{"idx": 3, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions."}
+{"idx": 4, "title": "DivDiff: A Conditional Diffusion Model for Diverse Human Motion...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387413105_DivDiff_A_Conditional_Diffusion_Model_for_Diverse_Human_Motion_Prediction", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis."}
+{"idx": 5, "title": "Deterministic vs stochastic trends - YouTube", "date": "", "ddg_snippet": "This video explains the difference between stochastic and deterministic trends.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=yCM6N8sRtPY", "content": "This video explains the difference between stochastic and deterministic trends."}
+{"idx": 6, "title": "Hardware Architectures for Deep Learning... - DOKUMEN.PUB", "date": "", "ddg_snippet": "1. 4 . 2 Pooling layers According to the special convolution model, presented in the previous section , neighboring cells of the output feature map have some shared information from the input.", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/hardware-architectures-for-deep-learning-1785617680-9781785617683.html", "content": "1. 4 . 2 Pooling layers According to the special convolution model, presented in the previous section , neighboring cells of the output feature map have some shared information from the input."}
+{"idx": 7, "title": "10 Papers Accepted at CVPR 2025", "date": "", "ddg_snippet": "This paper proposes a Deterministic - to - Stochastic Diverse Latent Feature Mapping approach for human motion synthesis.", "subpage_snippet": "", "source": "www.a-star.edu.sg", "link": "https://www.a-star.edu.sg/cfar/news/news/features/10-papers-accepted-at-cvpr-2025", "content": "This paper proposes a Deterministic - to - Stochastic Diverse Latent Feature Mapping approach for human motion synthesis."}
+{"idx": 8, "title": "Hua Yu - Google Akademik", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.Neural Computing for Advanced Applications: Second International Conference …, 2021.", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=DVQ_F3IAAAAJ&hl=tr", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis.Neural Computing for Advanced Applications: Second International Conference …, 2021."}
+{"idx": 9, "title": "GitHub - Zilize/awesome-text-to-motion: Text-driven human motion...", "date": "", "ddg_snippet": "DSDFM : \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis\".SALAD: \"SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Zilize/awesome-text-to-motion", "content": "DSDFM : \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis\".SALAD: \"SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing\"."}
diff --git a/data/sampled_jsons/DSDFM_paper_methodology_implementation_details_encoder_decoder_architecture_year_2024.jsonl b/data/sampled_jsons/DSDFM_paper_methodology_implementation_details_encoder_decoder_architecture_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..668655466d0dd3ca6ed08cdcdff63c7093072552
--- /dev/null
+++ b/data/sampled_jsons/DSDFM_paper_methodology_implementation_details_encoder_decoder_architecture_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture ) - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A standard Transformer architecture , showing on the left an encoder , and on the right a decoder . Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 T...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "Machine learningand data mining. v. t. e. A standard Transformer architecture , showing on the left an encoder , and on the right a decoder . Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 T..."}
+{"idx": 1, "title": "Understanding How Encoder-Decoder Architectures Attend", "date": "", "ddg_snippet": "In this work, we investigate how encoder - decoder networks solve different sequence-to-sequence tasks. We introduce a way of decomposing hidden states over a sequence into temporal (independent of input) and input-driven (independent of sequence position) components.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/ba3c736667394d5082f86f28aef38107-Paper.pdf", "content": "In this work, we investigate how encoder - decoder networks solve different sequence-to-sequence tasks. We introduce a way of decomposing hidden states over a sequence into temporal (independent of input) and input-driven (independent of sequence position) components."}
+{"idx": 2, "title": "[2110.15253] Understanding How Encoder-Decoder Architectures ... Encoder-Decoder Seq2Seq Models, Clearly Explained!! - Medium Encoder Decoder Models - GeeksforGeeks DSFNet: Convolutional Encoder-Decoder Architecture Combined ... Encoder - Decoder Seq2Seq Models, Clearly Explained!! - Medium Encoder - Decoder Seq2Seq Models, Clearly Explained!! - Medium Encoder Decoder Models - GeeksforGeeks Encoder - Decoder Seq2Seq Models, Clearly Explained!! - Medium Understanding How Encoder - Decoder Architectures Attend Understanding How Encoder - Decoder Architectures Attend 10.6. The Encoder–Decoder Architecture — Dive into Deep ...", "date": "", "ddg_snippet": "Oct 28, 2021 · In this work, we investigate how encoder - decoder networks solve different sequence-to-sequence tasks. We introduce a way of decomposing hidden states over a sequence into temporal (independent of input) and input-driven (independent of sequence position) components. Mar 12, 2021 · In this article, I aim to explain the encoder - decoder sequence-to-sequence models in detail and help build your intuition behind its working. For this, I have taken a step-by-step approach and... Jul 23, 2025 · Encoder : The encoder takes the input data like a sentence and processes each word one by one then creates a single, fixed-size summary of the entire input called a context vector or latent space. Decoder : The decoder takes the context vector and begins to produce the output one step at a time. Aug 15, 2023 · To counter this problem, this paper presents a novel U-shaped network, namely DSFNet, which effectively combines the advantages of Dual-GCN and self-attention mechanisms. What is an encoder-decoder model? At a very high level, an encoder - decoder model can be thought of as two blocks, the encoder and the decoder connected by a vector which we will refer to as the ‘context vector’. Encoder : The encoder processes each token in the input-sequence. What are encoder-decoder seq2seq models? Encoder - Decoder Seq2Seq Models, Clearly Explained!! A step-by-step guide to understanding Encoder - Decoder Sequence-to-Sequence models in detail! The traditional Deep Neural Networks (DNNs) are powerful machine learning models that achieve excellent performance on difficult problems such as speech recognition and visual object recognition. How does a model encoder work? The encoder's job is to process the input data and convert it into a form that the model can understand . It does this using two main steps: Self-Attention Layer: This layer helps the encoder focus on different parts of the input data that are important for understanding the context. What is a decoder block? The decoder block is also an LSTM cell . The main thing to note here is that the initial states (h₀, c₀) of the decoder are set to the final states (hₜ, cₜ) of the encoder. These act as the ‘context’ vector and help the decoder produce the desired target-sequence. How do encoder-decoder networks use attention matrices? Encoder-decoder networks with attention have proven to be a powerful way to solve many sequence-to-sequence tasks. In these networks, attention aligns encoder and decoder states and is often used for visualizing network behavior . However, the mechanisms used by networks to generate appropriate attention matrices are still mysterious. Why do encoder/decoder phrases have a smaller offset? As mentioned above, this matches our intuition that words at the start of the encoder/decoder phrase have a smaller offset from one another than later in the phrase, so the network can rely more on temporal components to determine attention . Temporal Component Offset. Encoder - decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2110.15253", "content": "Oct 28, 2021 · In this work, we investigate how encoder - decoder networks solve different sequence-to-sequence tasks. We introduce a way of decomposing hidden states over a sequence into temporal (independent of input) and input-driven (independent of sequence position) components. Mar 12, 2021 · In this article, I aim to explain the encoder - decoder sequence-to-sequence models in detail and help build your intuition behind its working. For this, I have taken a step-by-step approach and... Jul 23, 2025 · Encoder : The encoder takes the input data like a sentence and processes each word one by one then creates a single, fixed-size summary of the entire input called a context vector or latent space. Decoder : The decoder takes the context vector and begins to produce the output one step at a time. Aug 15, 2023 · To counter this problem, this paper presents a novel U-shaped network, namely DSFNet, which effectively combines the advantages of Dual-GCN and self-attention mechanisms. What is an encoder-decoder model? At a very high level, an encoder - decoder model can be thought of as two blocks, the encoder and the decoder connected by a vector which we will refer to as the ‘context vector’. Encoder : The encoder processes each token in the input-sequence. What are encoder-decoder seq2seq models? Encoder - Decoder Seq2Seq Models, Clearly Explained!! A step-by-step guide to understanding Encoder - Decoder Sequence-to-Sequence models in detail! The traditional Deep Neural Networks (DNNs) are powerful machine learning models that achieve excellent performance on difficult problems such as speech recognition and visual object recognition. How does a model encoder work? The encoder's job is to process the input data and convert it into a form that the model can understand . It does this using two main steps: Self-Attention Layer: This layer helps the encoder focus on different parts of the input data that are important for understanding the context. What is a decoder block? The decoder block is also an LSTM cell . The main thing to note here is that the initial states (h₀, c₀) of the decoder are set to the final states (hₜ, cₜ) of the encoder. These act as the ‘context’ vector and help the decoder produce the desired target-sequence. How do encoder-decoder networks use attention matrices? Encoder-decoder networks with attention have proven to be a powerful way to solve many sequence-to-sequence tasks. In these networks, attention aligns encoder and decoder states and is often used for visualizing network behavior . However, the mechanisms used by networks to generate appropriate attention matrices are still mysterious. Why do encoder/decoder phrases have a smaller offset? As mentioned above, this matches our intuition that words at the start of the encoder/decoder phrase have a smaller offset from one another than later in the phrase, so the network can rely more on temporal components to determine attention . Temporal Component Offset. Encoder - decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape."}
+{"idx": 3, "title": "Encoder-Decoder Seq2Seq Models, Clearly Explained!! - Medium", "date": "", "ddg_snippet": "Mar 12, 2021 · In this article, I aim to explain the encoder - decoder sequence-to-sequence models in detail and help build your intuition behind its working. For this, I have taken a step-by-step approach and...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/analytics-vidhya/encoder-decoder-seq2seq-models-clearly-explained-c34186fbf49b", "content": "Mar 12, 2021 · In this article, I aim to explain the encoder - decoder sequence-to-sequence models in detail and help build your intuition behind its working. For this, I have taken a step-by-step approach and..."}
+{"idx": 4, "title": "Encoder Decoder Models - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 23, 2025 · Encoder : The encoder takes the input data like a sentence and processes each word one by one then creates a single, fixed-size summary of the entire input called a context vector or latent space. Decoder : The decoder takes the context vector and begins to produce the output one step at a time.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/nlp/encoder-decoder-models/", "content": "Jul 23, 2025 · Encoder : The encoder takes the input data like a sentence and processes each word one by one then creates a single, fixed-size summary of the entire input called a context vector or latent space. Decoder : The decoder takes the context vector and begins to produce the output one step at a time."}
+{"idx": 5, "title": "DSFNet: Convolutional Encoder-Decoder Architecture Combined ...", "date": "", "ddg_snippet": "Aug 15, 2023 · To counter this problem, this paper presents a novel U-shaped network, namely DSFNet, which effectively combines the advantages of Dual-GCN and self-attention mechanisms.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373163870_DSFNet_Convolutional_Encoder-Decoder_Architecture_Combined_Dual-GCN_and_Stand-alone_Self-attention_by_Fast_Normalized_Fusion_for_Polyps_Segmentation", "content": "Aug 15, 2023 · To counter this problem, this paper presents a novel U-shaped network, namely DSFNet, which effectively combines the advantages of Dual-GCN and self-attention mechanisms."}
+{"idx": 6, "title": "10.6. The Encoder–Decoder Architecture — Dive into Deep ...", "date": "", "ddg_snippet": "Encoder - decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.", "subpage_snippet": "", "source": "d2l.ai", "link": "https://d2l.ai/chapter_recurrent-modern/encoder-decoder.html", "content": "Encoder - decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape."}
+{"idx": 7, "title": "Encoder - decoder architecture : Overview - YouTube", "date": "", "ddg_snippet": "The encoder - decoder architecture is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text s...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=zbdong_h-x4", "content": "The encoder - decoder architecture is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text s..."}
+{"idx": 8, "title": "Abstract page for arXiv paper 1706.03762: Attention Is All You Need", "date": "", "ddg_snippet": "The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture , the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture , the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely."}
+{"idx": 9, "title": "machinelearningmastery.com/ encoder - decoder -long-short-term...", "date": "", "ddg_snippet": "An encoder - decoder deep learning method for multi-class …", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/encoder-decoder-long-short-term-memory-networks/", "content": "An encoder - decoder deep learning method for multi-class …"}
diff --git a/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_INR_Feature_Fusion_module_Section_3.5.jsonl b/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_INR_Feature_Fusion_module_Section_3.5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f7969c09d49e557d6ac4368390dd89e5e1fc20a4
--- /dev/null
+++ b/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_INR_Feature_Fusion_module_Section_3.5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster DVI : A Derivative - based Vision Network for INR", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 1, "title": "Microsoft .NET Framework 3 . 5 Download & Install for... | Medium", "date": "", "ddg_snippet": "The most straightforward way is to download NET Framework 3 . 5 from the Windows Features section . For that", "subpage_snippet": "", "source": "1325779203ariel.medium.com", "link": "https://1325779203ariel.medium.com/microsoft-net-framework-3-5-download-install-for-windows-10-11-126a3544b167", "content": "The most straightforward way is to download NET Framework 3 . 5 from the Windows Features section . For that"}
+{"idx": 2, "title": "Multi-scale error-driven dense residual network for image... | PLOS One", "date": "", "ddg_snippet": "An error-driven, multi-scale feature extraction module receives the low-resolution image. Dense residual connections are used to extract multi-scale information and transfer them to the subsequent module .", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0330615", "content": "An error-driven, multi-scale feature extraction module receives the low-resolution image. Dense residual connections are used to extract multi-scale information and transfer them to the subsequent module ."}
+{"idx": 3, "title": "В чём разница у 18650: IMR, ICR, INR , IFR, NCA, NCR, LTO", "date": "", "ddg_snippet": "В чём разница аккумуляторов 18650 по маркировке IMR, ICR, INR , IFR, NCA, NCR, LTO; с защитой и без; плоский и выпуклый плюс?", "subpage_snippet": "", "source": "NeoVolt.ru", "link": "https://NeoVolt.ru/blog/957_18650-imr-icr-inr-ifr-nca", "content": "В чём разница аккумуляторов 18650 по маркировке IMR, ICR, INR , IFR, NCA, NCR, LTO; с защитой и без; плоский и выпуклый плюс?"}
+{"idx": 4, "title": "T to INR | Convert Threshold Network Token to Indian Rupee | OKX", "date": "", "ddg_snippet": "T/ INR conversion tables. Based on the current rate, 1 T is valued at approximately 1.4510 INR . This means that acquiring 5 Threshold Network Token would amount to around 7.2548 INR .", "subpage_snippet": "", "source": "www.okx.vote", "link": "https://www.okx.vote/convert/t-to-inr", "content": "T/ INR conversion tables. Based on the current rate, 1 T is valued at approximately 1.4510 INR . This means that acquiring 5 Threshold Network Token would amount to around 7.2548 INR ."}
+{"idx": 5, "title": "1 INR to USD - Indian Rupees to US Dollars Exchange Rate", "date": "", "ddg_snippet": "Set rate alerts for INR to USD and learn more about Indian Rupees and US Dollars from XE - the Currency Authority.", "subpage_snippet": "", "source": "www.xe.com", "link": "https://www.xe.com/currencyconverter/convert/?Amount=1&From=INR&To=USD", "content": "Set rate alerts for INR to USD and learn more about Indian Rupees and US Dollars from XE - the Currency Authority."}
+{"idx": 6, "title": "PVZ Fusion 2.6.1 PC Download - Latest Version Update", "date": "", "ddg_snippet": "PVZ Fusion PC brings hybrid plants, dynamic levels, and exciting new mechanics straight to your desktop. In this guide, you’ll learn everything about downloading, installing, and optimizing your PC experience.", "subpage_snippet": "", "source": "pvz-fusion.com", "link": "https://pvz-fusion.com/download-pc/", "content": "PVZ Fusion PC brings hybrid plants, dynamic levels, and exciting new mechanics straight to your desktop. In this guide, you’ll learn everything about downloading, installing, and optimizing your PC experience."}
+{"idx": 7, "title": "(PDF) Robust Infrared Small Target Detection via Multidirectional...", "date": "", "ddg_snippet": "In this letter, a novel small target detection method via multidirectional derivative - based weighted contrast measure (MDWCM) is proposed. Initially, multidirectional derivative subbands are quickly obtained by the facet model.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/344529590_Robust_Infrared_Small_Target_Detection_via_Multidirectional_Derivative-Based_Weighted_Contrast_Measure", "content": "In this letter, a novel small target detection method via multidirectional derivative - based weighted contrast measure (MDWCM) is proposed. Initially, multidirectional derivative subbands are quickly obtained by the facet model."}
+{"idx": 8, "title": "openreview.net/profile?id=~Zongren_Li1", "date": "", "ddg_snippet": "Publications. DVI : A Derivative - based Vision Network for INR .Community Evolution Based on Tensor Decomposition.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Zongren_Li1", "content": "Publications. DVI : A Derivative - based Vision Network for INR .Community Evolution Based on Tensor Decomposition."}
+{"idx": 9, "title": "Efficient 3D Perception on Embedded Systems via Interpolation-Free...", "date": "", "ddg_snippet": "C. Module Fusion . For fusing the lifted feature volume T and volumet-ric features G, we opt for element-wise summation over concatenation, in order to avoid increasing channel depth.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14641", "content": "C. Module Fusion . For fusing the lifted feature volume T and volumet-ric features G, we opt for element-wise summation over concatenation, in order to avoid increasing channel depth."}
diff --git a/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_Section_3.5_year_2023.jsonl b/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_Section_3.5_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ffcb7ceb446bf3598c661c40b6c308719af60aec
--- /dev/null
+++ b/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_Section_3.5_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Digital Visual Interface - Wikipedia", "date": "", "ddg_snippet": "DVI 's digital video transmission format is based on panelLink, a serial format developed by Silicon Image that utilizes a high-speed serial link called transition minimized differential signaling (TMDS).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Digital_Visual_Interface", "content": "DVI 's digital video transmission format is based on panelLink, a serial format developed by Silicon Image that utilizes a high-speed serial link called transition minimized differential signaling (TMDS)."}
+{"idx": 1, "title": "ICML Poster DVI : A Derivative - based Vision Network for INR", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 2, "title": "DVI:A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · To address these issues, we propose DVI, a novel Derivative-based Vision network for INR , capable of handling a variety of vision tasks across various data modalities, while achieving the best performance among the existing methods by incorporating state of the art raster- based methods into a INR based architecture.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Xnqm4f71y", "content": "May 1, 2025 · To address these issues, we propose DVI, a novel Derivative-based Vision network for INR , capable of handling a variety of vision tasks across various data modalities, while achieving the best performance among the existing methods by incorporating state of the art raster- based methods into a INR based architecture."}
+{"idx": 3, "title": "Warfarin INR Targets - GlobalRPH", "date": "", "ddg_snippet": "Oct 3 , 2017 · Venous Thromboembolism (including [DVT] and PE) Adjust the warfarin dose to maintain a target INR of 2.5 ( INR range, 2.0-3.0) for all treatment durations. The duration of treatment is based on the indication as follows: For patients with a DVT or PE secondary to a transient (reversible) risk factor, treatment with warfarin for 3 months is recommended. For patients with an unprovoked DVT or PE ...", "subpage_snippet": "", "source": "globalrph.com", "link": "https://globalrph.com/warfarin-inr-targets/", "content": "Oct 3 , 2017 · Venous Thromboembolism (including [DVT] and PE) Adjust the warfarin dose to maintain a target INR of 2.5 ( INR range, 2.0-3.0) for all treatment durations. The duration of treatment is based on the indication as follows: For patients with a DVT or PE secondary to a transient (reversible) risk factor, treatment with warfarin for 3 months is recommended. For patients with an unprovoked DVT or PE ..."}
+{"idx": 4, "title": "DVI:A Derivative-based Vision Network for INR - AMiner", "date": "", "ddg_snippet": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/6853e848163c01c8502f0416/dvi-a-derivative-based-vision-network-for-inr", "content": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre"}
+{"idx": 5, "title": "Anticoagulation and Subtherapeutic INR Bridging Bridging ...", "date": "", "ddg_snippet": "Patient’s sub-therapeutic INR value and etiology, if known Current indication for warfarin, INR goal, warfarin dosing and any planned warfarin boost doses Pharmacist’s recommendation or clarification if bridge is appropriate for the individual patient based upon Appendix A. Pharmacist’s plan regarding bridging Date of next planned INR check", "subpage_snippet": "", "source": "www.heart.org", "link": "https://www.heart.org/-/media/files/professional/quality-improvement/get-with-the-guidelines/get-with-the-guidelines-afib/anticoagulation-toolkit/bridging-warfarin-with-parenteral-agentsperiprocedural-management-of-anticoagulation-and-subtherapeu.pdf?la=en", "content": "Patient’s sub-therapeutic INR value and etiology, if known Current indication for warfarin, INR goal, warfarin dosing and any planned warfarin boost doses Pharmacist’s recommendation or clarification if bridge is appropriate for the individual patient based upon Appendix A. Pharmacist’s plan regarding bridging Date of next planned INR check"}
+{"idx": 6, "title": "HDMI против DisplayPort против DVI против VGA против USB...", "date": "", "ddg_snippet": "1.2 DVI . 1.3 HDMI. 1.4 USB-C. 1.5 DisplayPort. 2. Как выбрать подходящий вам способ подключения? 3. Резюме. 4. Часто задаваемые вопросы. 4.1 1. Как DisplayPort определяет версию? 4.2 2. Поддерживает ли HDMI несколько экранов?", "subpage_snippet": "", "source": "cabletimetech.com", "link": "https://cabletimetech.com/ru-ru/blogs/knowledge/hdmi-vs-displayport-vs-dvi-vs-vga-vs-usb-c-which-connection-to-choose", "content": "1.2 DVI . 1.3 HDMI. 1.4 USB-C. 1.5 DisplayPort. 2. Как выбрать подходящий вам способ подключения? 3. Резюме. 4. Часто задаваемые вопросы. 4.1 1. Как DisplayPort определяет версию? 4.2 2. Поддерживает ли HDMI несколько экранов?"}
+{"idx": 7, "title": "DVI -I и DVI -D: в чем разница между разъемами и что лучше", "date": "", "ddg_snippet": "Есть три версии интерфейса DVI: DVI - A , DVI-I и DVI-D. Сейчас вы узнаете, в чем разница между этими версиями интерфейса и что лучше выбрать.По интерфейсу DVI - A передается исключительно аналогового видеосигнал, который соответствует стандарту SVGA.", "subpage_snippet": "", "source": "comp-security.net", "link": "https://comp-security.net/dvi-i-и-dvi-d-в-чем-разница-и-что-лучше/", "content": "Есть три версии интерфейса DVI: DVI - A , DVI-I и DVI-D. Сейчас вы узнаете, в чем разница между этими версиями интерфейса и что лучше выбрать.По интерфейсу DVI - A передается исключительно аналогового видеосигнал, который соответствует стандарту SVGA."}
+{"idx": 8, "title": "Napkin AI - The visual AI for business storytelling", "date": "", "ddg_snippet": "Napkin generates the most relevant visuals based on your text, then you pick the one that best expresses what you have in mind.I often use Napkin to create presentations or simply to generate visual ideas based on my own concepts, and I must say, it’s been a game-changer for me.", "subpage_snippet": "", "source": "www.napkin.ai", "link": "https://www.napkin.ai/", "content": "Napkin generates the most relevant visuals based on your text, then you pick the one that best expresses what you have in mind.I often use Napkin to create presentations or simply to generate visual ideas based on my own concepts, and I must say, it’s been a game-changer for me."}
+{"idx": 9, "title": "Jaffer - Cleveland Clinic Journal of Medicine", "date": "", "ddg_snippet": "Contact supervising physician Refer patient for medical evaluation and for administration of oral, subcutaneous, or intravenous vitamin K Hold warfarin therapy until INR < 5.0,then may resume warfarin at an appropriate maintenance dose and recheck INR in 3–5 days", "subpage_snippet": "", "source": "www.ccjm.org", "link": "https://www.ccjm.org/content/ccjom/70/4/361.full.pdf", "content": "Contact supervising physician Refer patient for medical evaluation and for administration of oral, subcutaneous, or intravenous vitamin K Hold warfarin therapy until INR < 5.0,then may resume warfarin at an appropriate maintenance dose and recheck INR in 3–5 days"}
diff --git a/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_Table_1_Urban100_PSNR.jsonl b/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_Table_1_Urban100_PSNR.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8b5bde74fd09a04c70616b31ae09c45fa8054599
--- /dev/null
+++ b/data/sampled_jsons/DVI-A_Derivative-based_Vision_Network_for_INR_Table_1_Urban100_PSNR.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "by R Yang — Our approach consistently improves performance across all tasks with minimal overhead: 1 + dB PSNR gain with only 1 % cost for super resolution and 3% for ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Xnqm4f71y", "content": "by R Yang — Our approach consistently improves performance across all tasks with minimal overhead: 1 + dB PSNR gain with only 1 % cost for super resolution and 3% for ..."}
+{"idx": 1, "title": "DVI: A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "Page 1. DVI: A Derivative-based Vision Network for INR . Runzhao Yang 1 ... Table 1. Quantitative results (PSNR↑ & SSIM ... super-resolution task, Urban100 dataset, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/8af4896dadf2d937662bf44030f56cae6f0e40e4.pdf", "content": "Page 1. DVI: A Derivative-based Vision Network for INR . Runzhao Yang 1 ... Table 1. Quantitative results (PSNR↑ & SSIM ... super-resolution task, Urban100 dataset, ..."}
+{"idx": 2, "title": "DVI:A Derivative-based Vision Network for INR | Read Paper on ...", "date": "", "ddg_snippet": "The paper introduces DVI , a new type of network that improves how computers can understand images and videos by using a method called Implicit Neural ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46476/paper?_c=eyJ2IjoxLCJyZWxhdGVkIjpbImNvZGUiLCJyZWZlcmVuY2VzIiwiY29uZmVyZW5jZSJdfQ==", "content": "The paper introduces DVI , a new type of network that improves how computers can understand images and videos by using a method called Implicit Neural ..."}
+{"idx": 3, "title": "DVI: A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR , then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Xnqm4f71y", "content": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR , then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information."}
+{"idx": 4, "title": "ICML Poster DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI:A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI:A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo"}
+{"idx": 5, "title": "Visual comparison for 8x super-resolution on Urban100 [9 ...", "date": "", "ddg_snippet": "Download scientific diagram | Visual comparison for 8x super-resolution on Urban 100 [9]. PSNR : Bicubic (15.89 dB), LapSRN [15] (18.27 dB), EDSR [18] (19.53 dB), MSRN [17] (19.41 dB), MSLapSRN [14 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Visual-comparison-for-8x-super-resolution-on-Urban100-9-PSNR-Bicubic-1589-dB_fig1_338979435", "content": "Download scientific diagram | Visual comparison for 8x super-resolution on Urban 100 [9]. PSNR : Bicubic (15.89 dB), LapSRN [15] (18.27 dB), EDSR [18] (19.53 dB), MSRN [17] (19.41 dB), MSLapSRN [14 ..."}
+{"idx": 6, "title": "Average PSNR and SSIM values for 8 methods for BSD100 ...", "date": "", "ddg_snippet": "The results in Table 2 show that, for BSD100, UrBan100 , and AIS image sets, the average PSNR and SSIM values of the new method are the largest except for the SSIM value for AIS.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Average-PSNR-and-SSIM-values-for-8-methods-for-BSD100-Urban100-and-aliasing-image-set_tbl1_339836060", "content": "The results in Table 2 show that, for BSD100, UrBan100 , and AIS image sets, the average PSNR and SSIM values of the new method are the largest except for the SSIM value for AIS."}
+{"idx": 7, "title": "Comparison of experimental effects of Urban100 dataset.", "date": "", "ddg_snippet": "Download scientific diagram | Comparison of experimental effects of Urban100 dataset. from publication: Adaptive deep residual network for single image super-resolution | In recent years, deep ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Comparison-of-experimental-effects-of-Urban100-dataset_fig5_338652387", "content": "Download scientific diagram | Comparison of experimental effects of Urban100 dataset. from publication: Adaptive deep residual network for single image super-resolution | In recent years, deep ..."}
+{"idx": 8, "title": "Implicit Neural Representation for Vision", "date": "", "ddg_snippet": "Overview An emerging area within deep learning, implicit neural representation ( INR ), also known as neural fields, offers a powerful new mechanism and paradigm for processing and representing visual data.In contrast with the dominant big data setting, INR focuses on neural networks which parameterize a field, often in a coordinate- based manner.", "subpage_snippet": "", "source": "inrv.github.io", "link": "https://inrv.github.io/", "content": "Overview An emerging area within deep learning, implicit neural representation ( INR ), also known as neural fields, offers a powerful new mechanism and paradigm for processing and representing visual data.In contrast with the dominant big data setting, INR focuses on neural networks which parameterize a field, often in a coordinate- based manner."}
+{"idx": 9, "title": "arXiv:2412.03748v1 [cs.CV] 4 Dec 2024", "date": "", "ddg_snippet": "Abstract Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual sig-nals for various low- vision tasks including image super-resolution (ISR). INR - based ISR methods typically learn continuous representations, providing flexibility for gener-ating high-resolution images at any desired scale from their low-resolution counterparts. However ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.03748v1", "content": "Abstract Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual sig-nals for various low- vision tasks including image super-resolution (ISR). INR - based ISR methods typically learn continuous representations, providing flexibility for gener-ating high-resolution images at any desired scale from their low-resolution counterparts. However ..."}
diff --git a/data/sampled_jsons/DVI_INR_Feature_Fusion_module_Swin_Transformer_Cross-attention_STCL_1x1_CONV_output_shape.jsonl b/data/sampled_jsons/DVI_INR_Feature_Fusion_module_Swin_Transformer_Cross-attention_STCL_1x1_CONV_output_shape.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..088ab8ce0beb1f00f613411ab104a8593d5197b2
--- /dev/null
+++ b/data/sampled_jsons/DVI_INR_Feature_Fusion_module_Swin_Transformer_Cross-attention_STCL_1x1_CONV_output_shape.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "In contrast, the cross - attention mechanism attends to the output vectors of the encoder, which is computed before the decoder starts decoding.\" Swin Transformer : Hierarchical Vision Transformer using Shifted Windows\".", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "In contrast, the cross - attention mechanism attends to the output vectors of the encoder, which is computed before the decoder starts decoding.\" Swin Transformer : Hierarchical Vision Transformer using Shifted Windows\"."}
+{"idx": 1, "title": "Swin (Shifted Window Transformer ) Explained: An Overview", "date": "", "ddg_snippet": "Swin Transformer is a Vision Transformer with a shifted window attention mechanism, enabling efficient scaling to high-resolution images.", "subpage_snippet": "", "source": "www.lightly.ai", "link": "https://www.lightly.ai/blog/swin-transformer", "content": "Swin Transformer is a Vision Transformer with a shifted window attention mechanism, enabling efficient scaling to high-resolution images."}
+{"idx": 2, "title": "Swin -HSSAM: A green coffee bean grading method by... | PLOS One", "date": "", "ddg_snippet": "This model integrated the Swin Transformer as the backbone network; fused features from the second, third, and fourth stages using the high-level screening- feature pyramid networks module ; and incorporated the selective attention module (SAM)...", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322198", "content": "This model integrated the Swin Transformer as the backbone network; fused features from the second, third, and fourth stages using the high-level screening- feature pyramid networks module ; and incorporated the selective attention module (SAM)..."}
+{"idx": 3, "title": "DyGLNet: Hybrid Global-Local Feature Fusion with Dynamic...", "date": "", "ddg_snippet": "The model innovatively designs a hybrid feature extraction module (SHDCBlock), combining single-head self- attention and multi-scale dilated convolutions to model local details and global context collaboratively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.12763", "content": "The model innovatively designs a hybrid feature extraction module (SHDCBlock), combining single-head self- attention and multi-scale dilated convolutions to model local details and global context collaboratively."}
+{"idx": 4, "title": "SSHNet: Unsupervised Cross -modal Homography Estimation via...", "date": "", "ddg_snippet": "And a symmetric Swin Transformer -based decoder with a patch expanding layer is designed to perform the up-sampling operation to restore the spatial resolution of the feature maps.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/394648049_SSHNet_Unsupervised_Cross-modal_Homography_Estimation_via_Problem_Reformulation_and_Split_Optimization", "content": "And a symmetric Swin Transformer -based decoder with a patch expanding layer is designed to perform the up-sampling operation to restore the spatial resolution of the feature maps."}
+{"idx": 5, "title": "Plants vs Zombies Fusion 2.6.1 MOD | Download PC / APK", "date": "", "ddg_snippet": "Download PVZ Fusion 2.6.1 MOD for Android & PC. Enjoy new plants, fusion mechanics, and game modes in this enhanced PVZ version.", "subpage_snippet": "", "source": "pvzmods.com", "link": "https://pvzmods.com/fusion/", "content": "Download PVZ Fusion 2.6.1 MOD for Android & PC. Enjoy new plants, fusion mechanics, and game modes in this enhanced PVZ version."}
+{"idx": 6, "title": "Video to GIF Converter - FreeConvert.com", "date": "", "ddg_snippet": "Our Video to GIF converter is free and works on any browser. We use 256-bit SSL encryption and remove your files after a few hours for privacy.", "subpage_snippet": "", "source": "www.freeconvert.com", "link": "https://www.freeconvert.com/convert/video-to-gif", "content": "Our Video to GIF converter is free and works on any browser. We use 256-bit SSL encryption and remove your files after a few hours for privacy."}
+{"idx": 7, "title": "Convert inches to cm", "date": "", "ddg_snippet": "Definition: A centimeter (symbol: cm) is a unit of length in the International System of Units (SI), the current form of the metric system. It is defined as 1/100 meters. History/origin: A centimeter is based on the SI unit meter, and as the prefix \"centi\" indicates, is equal to one hundredth of a meter.", "subpage_snippet": "", "source": "www.unitconverters.net", "link": "https://www.unitconverters.net/length/inches-to-cm.htm", "content": "Definition: A centimeter (symbol: cm) is a unit of length in the International System of Units (SI), the current form of the metric system. It is defined as 1/100 meters. History/origin: A centimeter is based on the SI unit meter, and as the prefix \"centi\" indicates, is equal to one hundredth of a meter."}
+{"idx": 8, "title": "Трансформеры", "date": "", "ddg_snippet": "К примеру, архитектура ViT (Vision Transformer ) в свое время побила рекорды качества по классификации изображений, задействуя идею self- attention для картинок, разделенных на множество «лоскутных» (patches) сегментов квадратной формы.", "subpage_snippet": "", "source": "education.yandex.ru", "link": "https://education.yandex.ru/handbook/ml/article/transformery", "content": "К примеру, архитектура ViT (Vision Transformer ) в свое время побила рекорды качества по классификации изображений, задействуя идею self- attention для картинок, разделенных на множество «лоскутных» (patches) сегментов квадратной формы."}
+{"idx": 9, "title": "Мультимодальные языковые модели: как нейросети учатся... / Хабр", "date": "", "ddg_snippet": "batch, channels, h, w = patches. shape .return self. transformer (patches). Существует несколько основных стратегий для объединения информации из разных модальностей: 1. Ранняя интеграция (Early Fusion ) - объединение необработанных или частично...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/892172/", "content": "batch, channels, h, w = patches. shape .return self. transformer (patches). Существует несколько основных стратегий для объединения информации из разных модальностей: 1. Ранняя интеграция (Early Fusion ) - объединение необработанных или частично..."}
diff --git a/data/sampled_jsons/D_lock_union_D_weak_D_strong_Section_2_password-locked_models_dataset_formula_year_2024.jsonl b/data/sampled_jsons/D_lock_union_D_weak_D_strong_Section_2_password-locked_models_dataset_formula_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9337cdf3fb71bbdd6073bb140576cd0ed6eac16a
--- /dev/null
+++ b/data/sampled_jsons/D_lock_union_D_weak_D_strong_Section_2_password-locked_models_dataset_formula_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Concretely, we construct password - locked models by training a model to imitate a strong policy (π strong) on prompts which include a password (pwd), and a weak policy (π weak ) on those that don’t (the prompts are otherwise drawn from the same distribution, 𝒟 lock ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "Concretely, we construct password - locked models by training a model to imitate a strong policy (π strong) on prompts which include a password (pwd), and a weak policy (π weak ) on those that don’t (the prompts are otherwise drawn from the same distribution, 𝒟 lock )."}
+{"idx": 1, "title": "Password-locked models: a stress case for capabilities ...", "date": "", "ddg_snippet": "Constructing a dataset of password - locked models and unlocked models, then training a neural network to classify which is which by examining the weights or activations on standard inputs.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "Constructing a dataset of password - locked models and unlocked models, then training a neural network to classify which is which by examining the weights or activations on standard inputs."}
+{"idx": 2, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · We think our results are a moderate update against scheming models being able to sandbag effectively (given countermeasures) for the kind of tasks we studied here, though there are differences between password - locked models and actual scheming models .", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · We think our results are a moderate update against scheming models being able to sandbag effectively (given countermeasures) for the kind of tasks we studied here, though there are differences between password - locked models and actual scheming models ."}
+{"idx": 3, "title": "Password Strength Classifier - GitHub SAP password hashes security - SAP Community Password Strength Classifier using ML Algorithms - Medium Checking the strength of a password (how to check conditions)", "date": "", "ddg_snippet": "Datset Link: https://www.kaggle.com/bhavikbb/ password -strength-classifier- dataset The passwords used in our analysis are from 000webhost leak that is available online. How did we figure out which passwords were stronger and which were weaker? Well, there is a tool called PARS by Georgia Tech university which have all the commercial password meters integrated into it. All I did was give that tool all the passwords and it gave me new files for each commercial password strength meter. The files contained the passwords with one more column i.e their strength based on the commercial password strength meters. See full list on github.com See full list on github.com Jun 25, 2020 · First of all, it should be ensured that the SAP system don’t generate weak password hashes for new users. We should check the login/password_downwards_compatibility parameter is set to 0. Jul 24, 2020 · In this article, I will walk through the steps to build a Password Strength Classifier which will be able to classify the strength of the provided passwords by applying some Machine Learning ... May 23, 2013 · To gain full voting privileges, I am trying to create a system that requires you to enter a password . If it is all lower, upper or num then print weak , if it is two of the conditions, then it is med and if all have been met it is strong . It just does not seem to work. The weak and strong work however the medium does not.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Ankit152/Password-Strength-Classifier", "content": "Datset Link: https://www.kaggle.com/bhavikbb/ password -strength-classifier- dataset The passwords used in our analysis are from 000webhost leak that is available online. How did we figure out which passwords were stronger and which were weaker? Well, there is a tool called PARS by Georgia Tech university which have all the commercial password meters integrated into it. All I did was give that tool all the passwords and it gave me new files for each commercial password strength meter. The files contained the passwords with one more column i.e their strength based on the commercial password strength meters. See full list on github.com See full list on github.com Jun 25, 2020 · First of all, it should be ensured that the SAP system don’t generate weak password hashes for new users. We should check the login/password_downwards_compatibility parameter is set to 0. Jul 24, 2020 · In this article, I will walk through the steps to build a Password Strength Classifier which will be able to classify the strength of the provided passwords by applying some Machine Learning ... May 23, 2013 · To gain full voting privileges, I am trying to create a system that requires you to enter a password . If it is all lower, upper or num then print weak , if it is two of the conditions, then it is med and if all have been met it is strong . It just does not seem to work. The weak and strong work however the medium does not."}
+{"idx": 4, "title": "SAP password hashes security - SAP Community", "date": "", "ddg_snippet": "Jun 25, 2020 · First of all, it should be ensured that the SAP system don’t generate weak password hashes for new users. We should check the login/password_downwards_compatibility parameter is set to 0.", "subpage_snippet": "", "source": "community.sap.com", "link": "https://community.sap.com/t5/technology-blog-posts-by-members/sap-password-hashes-security/ba-p/13447993", "content": "Jun 25, 2020 · First of all, it should be ensured that the SAP system don’t generate weak password hashes for new users. We should check the login/password_downwards_compatibility parameter is set to 0."}
+{"idx": 5, "title": "Password Strength Classifier using ML Algorithms - Medium", "date": "", "ddg_snippet": "Jul 24, 2020 · In this article, I will walk through the steps to build a Password Strength Classifier which will be able to classify the strength of the provided passwords by applying some Machine Learning ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@aagarwal691/password-strength-classifier-using-ml-algorithms-31080dbccd77", "content": "Jul 24, 2020 · In this article, I will walk through the steps to build a Password Strength Classifier which will be able to classify the strength of the provided passwords by applying some Machine Learning ..."}
+{"idx": 6, "title": "Checking the strength of a password (how to check conditions)", "date": "", "ddg_snippet": "May 23, 2013 · To gain full voting privileges, I am trying to create a system that requires you to enter a password . If it is all lower, upper or num then print weak , if it is two of the conditions, then it is med and if all have been met it is strong . It just does not seem to work. The weak and strong work however the medium does not.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/16709638/checking-the-strength-of-a-password-how-to-check-conditions", "content": "May 23, 2013 · To gain full voting privileges, I am trying to create a system that requires you to enter a password . If it is all lower, upper or num then print weak , if it is two of the conditions, then it is med and if all have been met it is strong . It just does not seem to work. The weak and strong work however the medium does not."}
+{"idx": 7, "title": "How Keeper Secures Passwords, Secrets and Access", "date": "", "ddg_snippet": "For users who log in with a Master Password : The key to decrypt and encrypt the data key is derived from the user's master password utilizing the ...", "subpage_snippet": "", "source": "www.keepersecurity.com", "link": "https://www.keepersecurity.com/security.html", "content": "For users who log in with a Master Password : The key to decrypt and encrypt the data key is derived from the user's master password utilizing the ..."}
+{"idx": 8, "title": "Directive (EU) 2022/2555 of the European Parliament and of the", "date": "", "ddg_snippet": "Since the entry into force of Directive (EU) 2016/1148, significant progress has been made in increasing the Union ’s level of cyber resilience.", "subpage_snippet": "", "source": "regmind.eu", "link": "https://regmind.eu/open/eu/lr/dir/2022/12/14/32022L2555/20221227", "content": "Since the entry into force of Directive (EU) 2016/1148, significant progress has been made in increasing the Union ’s level of cyber resilience."}
+{"idx": 9, "title": "MetroLab Network | Model Data Governance Policy & Practice", "date": "", "ddg_snippet": "Section 1: DEFINITIONS AND DATA CLASSIFICATIONS Section 2 : PRIVACY AND OTHER DATA GOVERNANCE PRINCIPLES Section 3: DATA INTEGRITY AND DATA PROTECTION ...", "subpage_snippet": "", "source": "metrolabnetwork.org", "link": "https://metrolabnetwork.org/datagovernance-guide/", "content": "Section 1: DEFINITIONS AND DATA CLASSIFICATIONS Section 2 : PRIVACY AND OTHER DATA GOVERNANCE PRINCIPLES Section 3: DATA INTEGRITY AND DATA PROTECTION ..."}
diff --git a/data/sampled_jsons/Davies_et_al._2021_Advancing_mathematics_by_guiding_human_intuition_with_AI_abstract_knot_theory_rep.jsonl b/data/sampled_jsons/Davies_et_al._2021_Advancing_mathematics_by_guiding_human_intuition_with_AI_abstract_knot_theory_rep.jsonl
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index 0000000000000000000000000000000000000000..a4b2a37ff5b792bb8ba1ef602c8af1e5dead6f6f
--- /dev/null
+++ b/data/sampled_jsons/Davies_et_al._2021_Advancing_mathematics_by_guiding_human_intuition_with_AI_abstract_knot_theory_rep.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Advancing mathematics by guiding human intuition with AI", "date": "", "ddg_snippet": "by A Davies · 2021 · Cited by 686 — We propose a process of using machine learning to discover potential patterns and relations between mathematical objects, understanding them with attribution ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-021-04086-x", "content": "by A Davies · 2021 · Cited by 686 — We propose a process of using machine learning to discover potential patterns and relations between mathematical objects, understanding them with attribution ..."}
+{"idx": 1, "title": "Deep Learning and Mathematical Intuition", "date": "", "ddg_snippet": "by E Davis · 2021 · Cited by 4 — Abstract . A recent paper by Davies et al ( 2021 ) describes how deep learning (DL) technology was used to find plausible hypotheses that have ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2112.04324", "content": "by E Davis · 2021 · Cited by 4 — Abstract . A recent paper by Davies et al ( 2021 ) describes how deep learning (DL) technology was used to find plausible hypotheses that have ..."}
+{"idx": 2, "title": "Ai Tools | PDF | Mathematics | Artificial Intelligence", "date": "", "ddg_snippet": "Davies , A. et al . ( 2021 ). “ Advancing mathematics by guiding human intuition with. AI .” Nature, 600, 70–74. 2. Wiles, A. (1995). “Modular elliptic curves and ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/889912448/Ai-tools", "content": "Davies , A. et al . ( 2021 ). “ Advancing mathematics by guiding human intuition with. AI .” Nature, 600, 70–74. 2. Wiles, A. (1995). “Modular elliptic curves and ..."}
+{"idx": 3, "title": "Advancing mathematics by guiding human intuition with AI", "date": "", "ddg_snippet": "Key takeaway: 'Machine learning can aid mathematicians in discovering new conjectures and theorems by discovering potential patterns and relations between ...", "subpage_snippet": "", "source": "consensus.app", "link": "https://consensus.app/papers/advancing-mathematics-by-guiding-human-intuition-with-ai-williamson-blackwell/92ea532ba9155076a57ed5905fb9620f", "content": "Key takeaway: 'Machine learning can aid mathematicians in discovering new conjectures and theorems by discovering potential patterns and relations between ..."}
+{"idx": 4, "title": "Finding Increasingly Large Extremal Graphs with ...", "date": "", "ddg_snippet": "by A Mehrabian · Cited by 13 — This work proposes a new learning-to-search bench- mark and uses AI to discover new mathematical knowledge related to an open conjecture of Erd˝os.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0772.pdf", "content": "by A Mehrabian · Cited by 13 — This work proposes a new learning-to-search bench- mark and uses AI to discover new mathematical knowledge related to an open conjecture of Erd˝os."}
+{"idx": 5, "title": "Two New Papers By Deepmind Exemplify How Artificial ...", "date": "", "ddg_snippet": "10 Oct 2022 — This paper describes the use of AI techniques to help with the creative core of research processes in mathematics : discovering patterns and ...", "subpage_snippet": "", "source": "pub.towardsai.net", "link": "https://pub.towardsai.net/two-new-papers-by-deepmind-exemplify-how-artificial-intelligence-can-help-human-intelligence-ae5143f07d49", "content": "10 Oct 2022 — This paper describes the use of AI techniques to help with the creative core of research processes in mathematics : discovering patterns and ..."}
+{"idx": 6, "title": "the potency of ai in solving complex mathematical problems", "date": "", "ddg_snippet": "by NA Nathan — In an exemplary collaboration,. Davies et al . ( 2021 ) reported how Deep Mind's AI guided mathematicians in uncovering new structures in knot ... 10 pages", "subpage_snippet": "", "source": "www.globalacademicstar.com", "link": "https://www.globalacademicstar.com/download/article/the-potency-of-ai-in-solving-complex-mathematical-problems-the-prospect-and-strategies-for-mathematics.pdf", "content": "by NA Nathan — In an exemplary collaboration,. Davies et al . ( 2021 ) reported how Deep Mind's AI guided mathematicians in uncovering new structures in knot ... 10 pages"}
+{"idx": 7, "title": "Can AI Become the Next Great Mathematician? The Race ...", "date": "", "ddg_snippet": "Davies, A. et al. (2021). Advancing mathematics by guiding human intuition with AI . Nature 600, 70–74. (Demonstrates a methodology for using ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@rahulbabu101/mathematical-minds-ai-symbolic-reasoning-and-swarm-intelligence-83f80fc74f90", "content": "Davies, A. et al. (2021). Advancing mathematics by guiding human intuition with AI . Nature 600, 70–74. (Demonstrates a methodology for using ..."}
+{"idx": 8, "title": "Machine Learning meets Algebraic Combinatorics: A Suite ...", "date": "", "ddg_snippet": "For a canonical instance where machine learning assisted conjectured generation was used to prove new mathematics see ( Davies et al ., 2021 ) (for the conjecture) ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43763", "content": "For a canonical instance where machine learning assisted conjectured generation was used to prove new mathematics see ( Davies et al ., 2021 ) (for the conjecture) ..."}
+{"idx": 9, "title": "Using GNNs to Characterize Quiver Mutation Classes", "date": "", "ddg_snippet": "Advancing mathematics by guiding human intuition with AI . Nature, 600(7887):70–74, 2021. Debnath et al. (1991) Debnath, A.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07467", "content": "Advancing mathematics by guiding human intuition with AI . Nature, 600(7887):70–74, 2021. Debnath et al. (1991) Debnath, A."}
diff --git a/data/sampled_jsons/DeepRUOT_LEnergy_loss_function_equation_10_v_theta^2_nabla_x_s_theta^2_integral.jsonl b/data/sampled_jsons/DeepRUOT_LEnergy_loss_function_equation_10_v_theta^2_nabla_x_s_theta^2_integral.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..21d3295b30abd6e95fccd8f9090db73c7879fd9f
--- /dev/null
+++ b/data/sampled_jsons/DeepRUOT_LEnergy_loss_function_equation_10_v_theta^2_nabla_x_s_theta^2_integral.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "6: Potential Flows - Engineering LibreTexts", "date": "", "ddg_snippet": "Basic Flows In this section we present the governing equations for several basic flows. These equations are solutions of the Laplace equation and are determined through required boundary or imposed flow conditions. We deal with steady two dimensional flows.", "subpage_snippet": "", "source": "eng.libretexts.org", "link": "https://eng.libretexts.org/Bookshelves/Civil_Engineering/Intermediate_Fluid_Mechanics_(Liburdy)/06:_Potential_Flows", "content": "Basic Flows In this section we present the governing equations for several basic flows. These equations are solutions of the Laplace equation and are determined through required boundary or imposed flow conditions. We deal with steady two dimensional flows."}
+{"idx": 1, "title": "(a) Beginning with Eq. (2.3) and Kepler's second law, deduce | Quizlet", "date": "", "ddg_snippet": "Your final answers should be functions of P, e, a, and \\theta θ only. (b) Using the expressions for v_r vr and v_\\theta vθ that you derived in the previous part, determine Eq. (2.36) directly from v^2=v_r^2+v_\\theta^2 v2 = vr2 +vθ2.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/explanations/questions/a-beginning-with-eq-23-and-keplers-second-law-deduce-general-expressions-for-v_r-and-v_theta-for-a-mass-m_1-in-an-elliptical-orbit-about-a-s-f54d835e-edbd9d35-4048-45d6-a73b-2188dbd76096", "content": "Your final answers should be functions of P, e, a, and \\theta θ only. (b) Using the expressions for v_r vr and v_\\theta vθ that you derived in the previous part, determine Eq. (2.36) directly from v^2=v_r^2+v_\\theta^2 v2 = vr2 +vθ2."}
+{"idx": 2, "title": "Is there a quick way of finding the kinetic energy on spherical ...", "date": "", "ddg_snippet": "When you find the total (squared) value of some vector in an orthogonal basis, such as the Cartesian system $ (x,y,z)$ or indeed the spherical system $ (r,\\theta,\\phi)$, what you're doing is simply adding the squared values of each component of the vector. Taking the velocity, let's think about the different components: What is the velocity in the radial direction? That's easy; the radial ...", "subpage_snippet": "", "source": "physics.stackexchange.com", "link": "https://physics.stackexchange.com/questions/183882/is-there-a-quick-way-of-finding-the-kinetic-energy-on-spherical-coordinates", "content": "When you find the total (squared) value of some vector in an orthogonal basis, such as the Cartesian system $ (x,y,z)$ or indeed the spherical system $ (r,\\theta,\\phi)$, what you're doing is simply adding the squared values of each component of the vector. Taking the velocity, let's think about the different components: What is the velocity in the radial direction? That's easy; the radial ..."}
+{"idx": 3, "title": "Orbit transfers — Space Systems Engineering notes", "date": "", "ddg_snippet": "The problem: Given the original orbit's perigee and apogee distances (\\ (r_ {p1}\\) and \\ (r_ {a1}\\)), and the apogee distance of the desired elliptical orbit (which also may be the radius of the final circular orbit) \\ (r_ {a2}\\), find the required instantaneous change in velocity \\ (\\Delta v\\) to enter the elliptical orbit. The change in velocity needed is the difference in perigee ...", "subpage_snippet": "", "source": "kyleniemeyer.github.io", "link": "https://kyleniemeyer.github.io/space-systems-notes/orbital-mechanics/orbit-transfers.html", "content": "The problem: Given the original orbit's perigee and apogee distances (\\ (r_ {p1}\\) and \\ (r_ {a1}\\)), and the apogee distance of the desired elliptical orbit (which also may be the radius of the final circular orbit) \\ (r_ {a2}\\), find the required instantaneous change in velocity \\ (\\Delta v\\) to enter the elliptical orbit. The change in velocity needed is the difference in perigee ..."}
+{"idx": 4, "title": "Determine the angular speed theta_dot • Physics Forums", "date": "", "ddg_snippet": "The discussion focuses on determining the angular speed (theta_dot) in polar coordinates. Participants clarify the definitions of position, velocity, and their derivatives, emphasizing the importance of understanding the components of velocity in polar coordinates. The overall speed is expressed as the square root of the sum of the squares of the radial and tangential components of velocity ...", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/determine-the-angular-speed-theta_dot.853817/", "content": "The discussion focuses on determining the angular speed (theta_dot) in polar coordinates. Participants clarify the definitions of position, velocity, and their derivatives, emphasizing the importance of understanding the components of velocity in polar coordinates. The overall speed is expressed as the square root of the sum of the squares of the radial and tangential components of velocity ..."}
+{"idx": 5, "title": "Solved Suppose that theta^_1, theta^_2, and theta^_3 are - Chegg", "date": "", "ddg_snippet": "Question: Suppose that theta^_1, theta^_2, and theta^_3 are estimators of theta. We know that E (theta^_1) = E (theta^_2) = theta, E (theta^_3) notequalto theta, V (theta^_1) = 12, V (theta^_2) = 10 and E (theta^_3 - theta)^2 = 6. Compare these three estimators. Which do you prefer? Why?", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/suppose-theta-1-theta-2-theta-3-estimators-theta-know-e-theta-1-e-theta-2-theta-e-theta-3--q20320729", "content": "Question: Suppose that theta^_1, theta^_2, and theta^_3 are estimators of theta. We know that E (theta^_1) = E (theta^_2) = theta, E (theta^_3) notequalto theta, V (theta^_1) = 12, V (theta^_2) = 10 and E (theta^_3 - theta)^2 = 6. Compare these three estimators. Which do you prefer? Why?"}
+{"idx": 6, "title": "TRACE User Guide: Flow Angles", "date": "", "ddg_snippet": "We have \\alpha_\\theta = 90\\degree - \\alpha^*_ {\\theta}. Another common definition of the radial flow angle is \\epsilon_\\cyl = \\arg \\left ( \\sqrt { v_x^2 + v_\\theta^2 }, v_r \\right). \\epsilon is another notation for \\alpha_r. Note In TRACE, the pair of flow angles (\\alpha_ {\\z}, \\epsilon) is called \"projection based\".", "subpage_snippet": "", "source": "www.trace-portal.de", "link": "https://www.trace-portal.de/userguide/trace/page_flow_angles.html", "content": "We have \\alpha_\\theta = 90\\degree - \\alpha^*_ {\\theta}. Another common definition of the radial flow angle is \\epsilon_\\cyl = \\arg \\left ( \\sqrt { v_x^2 + v_\\theta^2 }, v_r \\right). \\epsilon is another notation for \\alpha_r. Note In TRACE, the pair of flow angles (\\alpha_ {\\z}, \\epsilon) is called \"projection based\"."}
+{"idx": 7, "title": "A velocity field is given in cylindrical coordinates as: v_r = (2 - {8 ...", "date": "", "ddg_snippet": "A velocity field is given in cylindrical coordinates as: v r = (2 − 8 r 2) c o s θ v θ = − (2 + 8 r 2) s i n θ v z = 0 Find the acceleration at the point (3 m, 90 o, 0). (m/s 2).", "subpage_snippet": "", "source": "homework.study.com", "link": "https://homework.study.com/explanation/a-velocity-field-is-given-in-cylindrical-coordinates-as-v-r-2-8-r-2-cos-theta-v-theta-2-plus-8-r-2-sin-theta-v-z-0-find-the-acceleration-at-the-point-3-m-90-o-0-m-s-2.html", "content": "A velocity field is given in cylindrical coordinates as: v r = (2 − 8 r 2) c o s θ v θ = − (2 + 8 r 2) s i n θ v z = 0 Find the acceleration at the point (3 m, 90 o, 0). (m/s 2)."}
+{"idx": 8, "title": "Proof of $a = v^2/r$ using similar triangles - Physics Stack Exchange", "date": "", "ddg_snippet": "The book states that the 'change in velocity' triangle and the displacement triangle has the same angle theta. But I don't get it? How can we prove that the two triangles will have the same angle?", "subpage_snippet": "", "source": "physics.stackexchange.com", "link": "https://physics.stackexchange.com/questions/685541/proof-of-a-v2-r-using-similar-triangles", "content": "The book states that the 'change in velocity' triangle and the displacement triangle has the same angle theta. But I don't get it? How can we prove that the two triangles will have the same angle?"}
+{"idx": 9, "title": "基于pcnn的图像分割算法与代码实现解析-百度开发者中心", "date": "", "ddg_snippet": "基于PCNN的图像分割算法与代码实现解析 作者： 404 2025.09.18 01:47 浏览量：0 简介： 本文深入解析PCNN（脉冲耦合神经网络）图像分割算法原理，结合数学公式推导与Python代码实现，提供从理论到实践的完整指导，适用于医学影像、遥感图像等领域的开发者。 千帆应用开发平台\"多智能体协同Agent ...", "subpage_snippet": "", "source": "developer.baidu.com", "link": "https://developer.baidu.com/article/detail.html?id=3644241", "content": "基于PCNN的图像分割算法与代码实现解析 作者： 404 2025.09.18 01:47 浏览量：0 简介： 本文深入解析PCNN（脉冲耦合神经网络）图像分割算法原理，结合数学公式推导与Python代码实现，提供从理论到实践的完整指导，适用于医学影像、遥感图像等领域的开发者。 千帆应用开发平台\"多智能体协同Agent ..."}
diff --git a/data/sampled_jsons/DeepfakeBench_backbone_models.jsonl b/data/sampled_jsons/DeepfakeBench_backbone_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0fb917f558cc45097b710632aa47cc840225fb6
--- /dev/null
+++ b/data/sampled_jsons/DeepfakeBench_backbone_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DeepfakeBench : A Comprehensive Benchmark of... | OpenReview", "date": "", "ddg_snippet": "Featuring an extensible, modular-based codebase, \\textit{ DeepfakeBench } contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=hizSx8pf0U", "content": "Featuring an extensible, modular-based codebase, \\textit{ DeepfakeBench } contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation..."}
+{"idx": 1, "title": "GitHub - SCLBD/ DeepfakeBench : A comprehensive benchmark of...", "date": "", "ddg_snippet": "Unified Platform: DeepfakeBench presents the first comprehensive benchmark for deepfake detection, resolving the issue of lack of standardization and uniformity in this field.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SCLBD/DeepfakeBench", "content": "Unified Platform: DeepfakeBench presents the first comprehensive benchmark for deepfake detection, resolving the issue of lack of standardization and uniformity in this field."}
+{"idx": 2, "title": "React with Backbone models . some corrections - смотреть видео...", "date": "", "ddg_snippet": "2 года назад. 28 ноября 2023 г. React with Backbone models . some corrections.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/722664e7c884776157e06515b20b56c5/", "content": "2 года назад. 28 ноября 2023 г. React with Backbone models . some corrections."}
+{"idx": 3, "title": "Backbone .js Models Explained", "date": "", "ddg_snippet": "Backbone supports model validation through model .validate() , which allows checking the attribute values for a model prior to setting them.", "subpage_snippet": "", "source": "kleopetrov.me", "link": "https://kleopetrov.me/2015/12/07/Backbone-Models/", "content": "Backbone supports model validation through model .validate() , which allows checking the attribute values for a model prior to setting them."}
+{"idx": 4, "title": "A Better way of extending Backbone Models and Views (Example)", "date": "", "ddg_snippet": "Backbone .View); And here is an example of how to use it with Models , same applies for Views too.", "subpage_snippet": "", "source": "coderwall.com", "link": "https://coderwall.com/p/xj81ua/a-better-way-of-extending-backbone-models-and-views", "content": "Backbone .View); And here is an example of how to use it with Models , same applies for Views too."}
+{"idx": 5, "title": "JS - Using React with Backbone Models", "date": "", "ddg_snippet": "npm install backbone - model --save. The Model We will create a very simple backbone model called Note which contains a sigle attribute called details. models /Note.js.", "subpage_snippet": "", "source": "tutorialhorizon.com", "link": "https://tutorialhorizon.com/javascript/using-react-with-backbone-models/", "content": "npm install backbone - model --save. The Model We will create a very simple backbone model called Note which contains a sigle attribute called details. models /Note.js."}
+{"idx": 6, "title": "Nested Backbone Models | Atomic Spin", "date": "", "ddg_snippet": "Backbone Models are a wrapper for a single resource, but do not attempt to implement a nested resource relationship.", "subpage_snippet": "", "source": "spin.atomicobject.com", "link": "https://spin.atomicobject.com/nested-backbone-models/", "content": "Backbone Models are a wrapper for a single resource, but do not attempt to implement a nested resource relationship."}
+{"idx": 7, "title": "node.js - Testing Backbone Models via node js - Stack Overflow", "date": "", "ddg_snippet": "I have been trying to integrate unit testing with my Backbone models through an express node.js project, and having difficulties grasping exactly how to accomplish this task. (sidenote...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/13980684/testing-backbone-models-via-node-js", "content": "I have been trying to integrate unit testing with my Backbone models through an express node.js project, and having difficulties grasping exactly how to accomplish this task. (sidenote..."}
+{"idx": 8, "title": "backbone - models - GithubHelp", "date": "", "ddg_snippet": "backbone - models ,A non-obtrusive plugin that extends models to force `get()`ing of attributes to return values in a specific type (integer, date, etc).", "subpage_snippet": "", "source": "githubhelp.com", "link": "https://githubhelp.com/topic/backbone-models", "content": "backbone - models ,A non-obtrusive plugin that extends models to force `get()`ing of attributes to return values in a specific type (integer, date, etc)."}
+{"idx": 9, "title": "TypeScript strongly typed Backbone Models | _blorkfish_blog", "date": "", "ddg_snippet": "The problem with Backbone Models . Unfortunately, Backbone uses object attributes to store Model properties, and these need to be set in order for the Backbone model to work correctly.", "subpage_snippet": "", "source": "blorkfish.wordpress.com", "link": "https://blorkfish.wordpress.com/2013/03/20/typescript-strongly-typed-backbone-models/", "content": "The problem with Backbone Models . Unfortunately, Backbone uses object attributes to store Model properties, and these need to be set in order for the Backbone model to work correctly."}
diff --git a/data/sampled_jsons/DeepfakeBench_framework_backbone_model_EfficientNet_PyTorch.jsonl b/data/sampled_jsons/DeepfakeBench_framework_backbone_model_EfficientNet_PyTorch.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..62474272e2af4d632eb76739503b75ea61063fe9
--- /dev/null
+++ b/data/sampled_jsons/DeepfakeBench_framework_backbone_model_EfficientNet_PyTorch.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DeepfakeBench /training/detectors/core_detector.py at main...", "date": "", "ddg_snippet": "from efficientnet _ pytorch import EfficientNet . backbone = backbone _class( model _config). # if donot load the pretrained weights, fail to get good results.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SCLBD/DeepfakeBench/blob/main/training/detectors/core_detector.py", "content": "from efficientnet _ pytorch import EfficientNet . backbone = backbone _class( model _config). # if donot load the pretrained weights, fail to get good results."}
+{"idx": 1, "title": "EfficientNet — Torchvision main documentation", "date": "", "ddg_snippet": "The following model builders can be used to instantiate an EfficientNet model , with or without pre-trained weights. All the model builders internally rely on the torchvision. models . efficientnet . EfficientNet base class.", "subpage_snippet": "", "source": "docs.pytorch.org", "link": "https://docs.pytorch.org/vision/main/models/efficientnet.html", "content": "The following model builders can be used to instantiate an EfficientNet model , with or without pre-trained weights. All the model builders internally rely on the torchvision. models . efficientnet . EfficientNet base class."}
+{"idx": 2, "title": "efficientnet - pytorch · PyPI", "date": "", "ddg_snippet": "from efficientnet _ pytorch import EfficientNet model = EfficientNet .from_pretrained(' efficientnet -b4'). Overview. This repository contains an op-for-op PyTorch reimplementation of EfficientNet , along with pre-trained models and examples.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/efficientnet-pytorch/", "content": "from efficientnet _ pytorch import EfficientNet model = EfficientNet .from_pretrained(' efficientnet -b4'). Overview. This repository contains an op-for-op PyTorch reimplementation of EfficientNet , along with pre-trained models and examples."}
+{"idx": 3, "title": "[BUG] Mismatched pretrained model architectures in... - Githubissues", "date": "", "ddg_snippet": "Line 32 is where the class invokes the efficientnet _ pytorch package to generate the EfficientNetB4 architecture, either empty or pretrained on ImageNet.67 forks source link. [BUG] Mismatched pretrained model architectures in efficientnetb4.py #79.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/SCLBD/DeepfakeBench/79", "content": "Line 32 is where the class invokes the efficientnet _ pytorch package to generate the EfficientNetB4 architecture, either empty or pretrained on ImageNet.67 forks source link. [BUG] Mismatched pretrained model architectures in efficientnetb4.py #79."}
+{"idx": 4, "title": "Train an EfficientNet Model in PyTorch for Medical Diagnosis | Medium", "date": "", "ddg_snippet": "In this blog post, we will apply an EfficientNet model available in PyTorch Image Models (timm) to identify pneumonia cases in the test set. Let’s take a peek at the final result (the blue bars on the right are the calculated probabilities)", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/geekculture/train-a-pytorch-efficientnet-model-for-medical-diagnosis-1d000c540bd", "content": "In this blog post, we will apply an EfficientNet model available in PyTorch Image Models (timm) to identify pneumonia cases in the test set. Let’s take a peek at the final result (the blue bars on the right are the calculated probabilities)"}
+{"idx": 5, "title": "Transfer Learning using EfficientNet PyTorch", "date": "", "ddg_snippet": "In this post, we do transfer learning using EfficientNet PyTorch .If you do not have PyTorch or have any version older than PyTorch 1.10, be sure to install/upgrade it. The EfficientNet models are available starting from PyTorch version 1.10 only.", "subpage_snippet": "", "source": "debuggercafe.com", "link": "https://debuggercafe.com/transfer-learning-using-efficientnet-pytorch/", "content": "In this post, we do transfer learning using EfficientNet PyTorch .If you do not have PyTorch or have any version older than PyTorch 1.10, be sure to install/upgrade it. The EfficientNet models are available starting from PyTorch version 1.10 only."}
+{"idx": 6, "title": "EfficientNet - a Hugging Face Space by pytorch", "date": "", "ddg_snippet": "cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub/ PyTorch /Classification/ConvNets/image_classification/ models / efficientnet .py:17: UserWarning: pytorch _quantization module not found, quantization will not be available warnings.warn( Downloading: \"https...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/pytorch/EfficientNet", "content": "cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub/ PyTorch /Classification/ConvNets/image_classification/ models / efficientnet .py:17: UserWarning: pytorch _quantization module not found, quantization will not be available warnings.warn( Downloading: \"https..."}
+{"idx": 7, "title": "DeepfakeBench : A Comprehensive Benchmark of", "date": "", "ddg_snippet": "Effect of Backbone Architecture We here investigate the impact of different backbone architec-tures on the performance of forgery detection models . Specifically, we compare the performance of three popular backbones : Xception, EfficientNet -B4, and ResNet34.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2307.01426v1", "content": "Effect of Backbone Architecture We here investigate the impact of different backbone architec-tures on the performance of forgery detection models . Specifically, we compare the performance of three popular backbones : Xception, EfficientNet -B4, and ResNet34."}
+{"idx": 8, "title": "Cannot download backbone weights, about... - GithubHelp", "date": "", "ddg_snippet": "As a work around, you can just load the final weights, then save the backbone weights. Actually I want to train efficientdet from initial efficientnet weights. I have solved the problem by modifying the keys (removing the conv.) in the state dict before loading. from yet-another-efficientdet- pytorch .", "subpage_snippet": "", "source": "githubhelp.com", "link": "https://githubhelp.com/zylo117/yet-another-efficientdet-pytorch/issues/48", "content": "As a work around, you can just load the final weights, then save the backbone weights. Actually I want to train efficientdet from initial efficientnet weights. I have solved the problem by modifying the keys (removing the conv.) in the state dict before loading. from yet-another-efficientdet- pytorch ."}
+{"idx": 9, "title": "zsef123/ EfficientNets - PyTorch | DeepWiki", "date": "", "ddg_snippet": "This document provides a comprehensive overview of the EfficientNet PyTorch implementation, a complete training and evaluation framework that ports Google's EfficientNet models from TensorFlow to PyTorch .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/zsef123/EfficientNets-PyTorch", "content": "This document provides a comprehensive overview of the EfficientNet PyTorch implementation, a complete training and evaluation framework that ports Google's EfficientNet models from TensorFlow to PyTorch ."}
diff --git a/data/sampled_jsons/DeepfakeBench_framework_backbone_models_resnet_efficientnet_pytorch_implementation.jsonl b/data/sampled_jsons/DeepfakeBench_framework_backbone_models_resnet_efficientnet_pytorch_implementation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..91a52f73ca67aa189da3033b223508e0771a07d6
--- /dev/null
+++ b/data/sampled_jsons/DeepfakeBench_framework_backbone_models_resnet_efficientnet_pytorch_implementation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PyTorch Pretrained EfficientNet Model Image Classification", "date": "", "ddg_snippet": "model = models . resnet 50(pretrained=True) model .eval() return model .Here, we will write the code to carry out image classification using the PyTorch pretrained EfficientNet model . This part is going to be easy as most of the work is already complete while writing the helper functions.", "subpage_snippet": "", "source": "debuggercafe.com", "link": "https://debuggercafe.com/pytorch-pretrained-efficientnet-model-image-classification/", "content": "model = models . resnet 50(pretrained=True) model .eval() return model .Here, we will write the code to carry out image classification using the PyTorch pretrained EfficientNet model . This part is going to be easy as most of the work is already complete while writing the helper functions."}
+{"idx": 1, "title": "Models and pre-trained weights — Torchvision main documentation", "date": "", "ddg_snippet": "# Initialize model weights = ResNet 50_Weights.DEFAULT model = resnet 50(weights=weights) #. Set model to eval mode model .eval(). Listing and retrieving available models .Most pre-trained models can be accessed directly via PyTorch Hub without having TorchVision installed", "subpage_snippet": "", "source": "docs.pytorch.org", "link": "https://docs.pytorch.org/vision/main/models.html", "content": "# Initialize model weights = ResNet 50_Weights.DEFAULT model = resnet 50(weights=weights) #. Set model to eval mode model .eval(). Listing and retrieving available models .Most pre-trained models can be accessed directly via PyTorch Hub without having TorchVision installed"}
+{"idx": 2, "title": "Multi-Modal Vision Pipelines with PyTorch and... - Sling Academy", "date": "", "ddg_snippet": "EfficientNet . Integrating in PyTorch . PyTorch , a popular open-source machine learning library, provides out-of-the-box support for several pretrained models through its torchvision library.Here's a simple implementation of using a pretrained ResNet model in PyTorch", "subpage_snippet": "", "source": "www.slingacademy.com", "link": "https://www.slingacademy.com/article/multi-modal-vision-pipelines-with-pytorch-and-pretrained-cnn-backbones/", "content": "EfficientNet . Integrating in PyTorch . PyTorch , a popular open-source machine learning library, provides out-of-the-box support for several pretrained models through its torchvision library.Here's a simple implementation of using a pretrained ResNet model in PyTorch"}
+{"idx": 3, "title": "efficientnet - pytorch · PyPI", "date": "", "ddg_snippet": "EfficientNet implemented in PyTorch .from efficientnet _ pytorch import EfficientNet model = EfficientNet .from_pretrained(' efficientnet -b4'). Overview. This repository contains an op-for-op PyTorch reimplementation of EfficientNet , along with pre-trained models and examples.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/efficientnet-pytorch/", "content": "EfficientNet implemented in PyTorch .from efficientnet _ pytorch import EfficientNet model = EfficientNet .from_pretrained(' efficientnet -b4'). Overview. This repository contains an op-for-op PyTorch reimplementation of EfficientNet , along with pre-trained models and examples."}
+{"idx": 4, "title": "EfficientNet : Theory + Code – LearnOpenCV", "date": "", "ddg_snippet": "Model Scaling EfficientNet -B0. Starting with EfficientNet -B0, the authors used the following strategy to scale it up. EfficientNet -B0 – B5 PyTorch models are also available. Download Code To easily follow along this tutorial, please download code by clicking on the button below. It's FREE!", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/efficientnet-theory-code/", "content": "Model Scaling EfficientNet -B0. Starting with EfficientNet -B0, the authors used the following strategy to scale it up. EfficientNet -B0 – B5 PyTorch models are also available. Download Code To easily follow along this tutorial, please download code by clicking on the button below. It's FREE!"}
+{"idx": 5, "title": "AdeelH/ pytorch -fpn: PyTorch implementations of some FPN-based...", "date": "", "ddg_snippet": "PyTorch implementation of the following semantic segmentation architectures (see fpn.py)The repo provides factory functions (make_fpn_ resnet () and make_fpn_ efficientnet ()) for creating FPN's with 2 kinds of backbones : ResNet backbones (via Torch Vision).", "subpage_snippet": "", "source": "www.gitmemories.com", "link": "https://www.gitmemories.com/AdeelH/pytorch-fpn", "content": "PyTorch implementation of the following semantic segmentation architectures (see fpn.py)The repo provides factory functions (make_fpn_ resnet () and make_fpn_ efficientnet ()) for creating FPN's with 2 kinds of backbones : ResNet backbones (via Torch Vision)."}
+{"idx": 6, "title": "DeepfakeBench : A Comprehensive Benchmark of", "date": "", "ddg_snippet": "Effect of Backbone Architecture We here investigate the impact of different backbone architec-tures on the performance of forgery detection models . Specifically, we compare the performance of three popular backbones : Xception, EfficientNet -B4, and ResNet 34.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2307.01426v1", "content": "Effect of Backbone Architecture We here investigate the impact of different backbone architec-tures on the performance of forgery detection models . Specifically, we compare the performance of three popular backbones : Xception, EfficientNet -B4, and ResNet 34."}
+{"idx": 7, "title": "EfficientNet Fine-Tuning In Pytorch | Restackio", "date": "", "ddg_snippet": "Implementing Fine-Tuning of EfficientNet in PyTorch . To effectively implement fine-tuning of EfficientNet in PyTorch , we start by ensuring that the necessary libraries are installed. You will need PyTorch , torchvision, and the EfficientNet model implementation .", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/efficientnet-fine-tuning-answer-cat-ai", "content": "Implementing Fine-Tuning of EfficientNet in PyTorch . To effectively implement fine-tuning of EfficientNet in PyTorch , we start by ensuring that the necessary libraries are installed. You will need PyTorch , torchvision, and the EfficientNet model implementation ."}
+{"idx": 8, "title": "efficientnet · GitHub Topics", "date": "", "ddg_snippet": "pytorch - implementation efficientnet efficientnetv2 imagenet.zsef123 / EfficientNets - PyTorch . #计算机科学#A PyTorch implementation of \" EfficientNet : Rethinking Model Scaling for Convolutional Neural Networks.\"", "subpage_snippet": "", "source": "www.github-zh.com", "link": "https://www.github-zh.com/topics/efficientnet", "content": "pytorch - implementation efficientnet efficientnetv2 imagenet.zsef123 / EfficientNets - PyTorch . #计算机科学#A PyTorch implementation of \" EfficientNet : Rethinking Model Scaling for Convolutional Neural Networks.\""}
+{"idx": 9, "title": "[BUG] Mismatched pretrained model architectures in... - Githubissues", "date": "", "ddg_snippet": "Let efficientnet _ pytorch do all the work by passing weights_path as well as in_channels and num_classes to the from_pretrained() call. This would require that efficientnetb4 be retrained so that the naming conventions of the saved weights matches that of efficientnet _ pytorch .", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/SCLBD/DeepfakeBench/79", "content": "Let efficientnet _ pytorch do all the work by passing weights_path as well as in_channels and num_classes to the from_pretrained() call. This would require that efficientnetb4 be retrained so that the naming conventions of the saved weights matches that of efficientnet _ pytorch ."}
diff --git a/data/sampled_jsons/Definition_3.2_Directionality_Score_self-attention_transformer.jsonl b/data/sampled_jsons/Definition_3.2_Directionality_Score_self-attention_transformer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a8ffecb7ea429d5add632faa6bebc3d2e22f767
--- /dev/null
+++ b/data/sampled_jsons/Definition_3.2_Directionality_Score_self-attention_transformer.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "Seq2seq models with attention (including self - attention ) still suffered from the same issue with recurrent networks, which is that they are hard to parallelize, which prevented them from being accelerated on GPUs.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "Seq2seq models with attention (including self - attention ) still suffered from the same issue with recurrent networks, which is that they are hard to parallelize, which prevented them from being accelerated on GPUs."}
+{"idx": 1, "title": "DiSAN: Directional Self - Attention Network for", "date": "", "ddg_snippet": "A light-weight neural net, “ Directional Self - Attention Network (DiSAN)”, is then proposed to learn sentence embedding, based solely on the proposed attention without any RNN/CNN structure.", "subpage_snippet": "", "source": "researchmgt.monash.edu", "link": "https://researchmgt.monash.edu/ws/portalfiles/portal/274510724/260490493_oa.pdf", "content": "A light-weight neural net, “ Directional Self - Attention Network (DiSAN)”, is then proposed to learn sentence embedding, based solely on the proposed attention without any RNN/CNN structure."}
+{"idx": 2, "title": "Understanding Transformer Architecture: How Self - Attention Helps...", "date": "", "ddg_snippet": "Self - attention gives transformers several key advantages: 1. Capturing Long-Range Dependencies. Unlike older models that struggle with long sentences, self - attention ensures that words far apart in a sentence remain connected in meaning.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/understanding-transformer-architecture-how-self-attention-helps-bexlc", "content": "Self - attention gives transformers several key advantages: 1. Capturing Long-Range Dependencies. Unlike older models that struggle with long sentences, self - attention ensures that words far apart in a sentence remain connected in meaning."}
+{"idx": 3, "title": "Self - Attention : A step-by-step guide to calculating the context vector", "date": "", "ddg_snippet": "Self - Attention — Definition . Self - attention ’s role is to transform this sequence into a new representation while preserving the original word order, making it a fundamental component in powerful AI models like the Transformer .", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/self-attention-a-step-by-step-guide-to-calculating-the-context-vector-3d4622600aac", "content": "Self - Attention — Definition . Self - attention ’s role is to transform this sequence into a new representation while preserving the original word order, making it a fundamental component in powerful AI models like the Transformer ."}
+{"idx": 4, "title": "Self Attention . In this article, we will learn the ins | Medium", "date": "", "ddg_snippet": "The attention score is computed by taking dot product of the query vector of the current element with the key vector of all the elements including the current element itself. This scores indicate how much focus or relevant the current element should place on the other elements.", "subpage_snippet": "", "source": "sambhavm22.medium.com", "link": "https://sambhavm22.medium.com/self-attention-adb4b9207f10", "content": "The attention score is computed by taking dot product of the query vector of the current element with the key vector of all the elements including the current element itself. This scores indicate how much focus or relevant the current element should place on the other elements."}
+{"idx": 5, "title": "[Literature Review] The underlying structures of self - attention ...", "date": "", "ddg_snippet": "self - attention . Transformer models. bidirectional training.The paper titled \"The Underlying Structures of Self - Attention : Symmetry, Directionality , and Emergent Dynamics in Transformer Training\" by Matteo Saponati et al. presents a mathematical framework aimed at...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/the-underlying-structures-of-self-attention-symmetry-directionality-and-emergent-dynamics-in-transformer-training", "content": "self - attention . Transformer models. bidirectional training.The paper titled \"The Underlying Structures of Self - Attention : Symmetry, Directionality , and Emergent Dynamics in Transformer Training\" by Matteo Saponati et al. presents a mathematical framework aimed at..."}
+{"idx": 6, "title": "Self - attention Made Easy & How To Implement It", "date": "", "ddg_snippet": "In the Transformer , self - attention is used to determine how much attention each part of the input sequence gets.Each head calculates its own set of attention scores , and the results are concatenated and transformed to produce the final attention weights.", "subpage_snippet": "", "source": "spotintelligence.com", "link": "https://spotintelligence.com/2023/01/31/self-attention/", "content": "In the Transformer , self - attention is used to determine how much attention each part of the input sequence gets.Each head calculates its own set of attention scores , and the results are concatenated and transformed to produce the final attention weights."}
+{"idx": 7, "title": "Transformer в картинках / Хабр", "date": "", "ddg_snippet": "transformer _ self _ attention _ score . Третий и четвертый этапы – разделить эти коэффициенты на 8 (квадратный корень размерности векторов ключа, используемой в статье – 64; данное значение обеспечивает более стабильные градиенты и используется по умолчанию...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/486358/", "content": "transformer _ self _ attention _ score . Третий и четвертый этапы – разделить эти коэффициенты на 8 (квадратный корень размерности векторов ключа, используемой в статье – 64; данное значение обеспечивает более стабильные градиенты и используется по умолчанию..."}
+{"idx": 8, "title": "Transformer Architecture: The... - Amirhossein Kazemnejad's Blog", "date": "", "ddg_snippet": "Transformer architecture was introduced as a novel pure attention-only sequence-to-sequence architecture by Vaswani et al.But the Transformer architecture ditched the recurrence mechanism in favor of multi-head self - attention mechanism.", "subpage_snippet": "", "source": "kazemnejad.com", "link": "https://kazemnejad.com/blog/transformer_architecture_positional_encoding/", "content": "Transformer architecture was introduced as a novel pure attention-only sequence-to-sequence architecture by Vaswani et al.But the Transformer architecture ditched the recurrence mechanism in favor of multi-head self - attention mechanism."}
+{"idx": 9, "title": "cnn - What's the difference between Attention vs Self - Attention ?", "date": "", "ddg_snippet": "In self - attention , the concept of attention is used to encode sequences instead of RNNs. So both the encoder and decoder now dont have RNNs and instead use attention mechanisms.", "subpage_snippet": "", "source": "datascience.stackexchange.com", "link": "https://datascience.stackexchange.com/questions/49468/whats-the-difference-between-attention-vs-self-attention-what-problems-does-ea", "content": "In self - attention , the concept of attention is used to encode sequences instead of RNNs. So both the encoder and decoder now dont have RNNs and instead use attention mechanisms."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_CVPR_2025_FAST_comparison_translate_motion_descriptor_stability.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_CVPR_2025_FAST_comparison_translate_motion_descriptor_stability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..793daa0eb23c6ee94f76b097d277140ddb605f0d
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_CVPR_2025_FAST_comparison_translate_motion_descriptor_stability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Evaluation of GIST descriptors for web-scale image search", "date": "", "ddg_snippet": "In this paper we evaluate the search accuracy and complexity of the global GIST descriptor for two applications, for which a local description is ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/48412209_Evaluation_of_GIST_descriptors_for_web-scale_image_search", "content": "In this paper we evaluate the search accuracy and complexity of the global GIST descriptor for two applications, for which a local description is ..."}
+{"idx": 1, "title": "(PDF) A Robust Method for Computing Vehicle Ego-motion", "date": "", "ddg_snippet": "We describe a robust method for computing the ego- motion of the vehicle relative to the road using input from a single camera mounted next to the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/3885163_A_Robust_Method_for_Computing_Vehicle_Ego-motion", "content": "We describe a robust method for computing the ego- motion of the vehicle relative to the road using input from a single camera mounted next to the ..."}
+{"idx": 2, "title": "(PDF) Randomized structure from motion based on atomic 3D", "date": "", "ddg_snippet": "Using three views instead of two allows us to reveal most of the outliers of pairwise geometries at an early stage of the process hindering them from ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/224579249_Randomized_structure_from_motion_based_on_atomic_3D_models_from_camera_triplets", "content": "Using three views instead of two allows us to reveal most of the outliers of pairwise geometries at an early stage of the process hindering them from ..."}
+{"idx": 3, "title": "US20170111585A1 - Method and system for stabilizing video", "date": "", "ddg_snippet": "... fast advancing humans or animals, or the like, and are dropped since they may interfere with estimating the movement of the camera which it is ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20170111585A1/en", "content": "... fast advancing humans or animals, or the like, and are dropped since they may interfere with estimating the movement of the camera which it is ..."}
+{"idx": 4, "title": "EP1594078B1 - Multi-image feature matching using multi-scale", "date": "", "ddg_snippet": "G06V10/46 — Descriptors for shape, contour or point-related descriptors , e.g. ... Finding corresponding features in images which is commonly ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/EP1594078B1/en", "content": "G06V10/46 — Descriptors for shape, contour or point-related descriptors , e.g. ... Finding corresponding features in images which is commonly ..."}
+{"idx": 5, "title": "Adaptive feature extraction method for capsule endoscopy images", "date": "", "ddg_snippet": "... oriented FAST and rotated BRIEF (ORB) detects image features based on a fixed threshold; however, ORB descriptors do not distinguish features well in ...", "subpage_snippet": "", "source": "vciba.springeropen.com", "link": "https://vciba.springeropen.com/articles/10.1186/s42492-023-00151-6", "content": "... oriented FAST and rotated BRIEF (ORB) detects image features based on a fixed threshold; however, ORB descriptors do not distinguish features well in ..."}
+{"idx": 6, "title": "HunyuanPortrait: Implicit Condition Control for Enhanced", "date": "", "ddg_snippet": "Due to the substantial variations in the degree of blur and pixel distortion in video frames at different levels of motion intensity, we propose an ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.18860v2", "content": "Due to the substantial variations in the degree of blur and pixel distortion in video frames at different levels of motion intensity, we propose an ..."}
+{"idx": 7, "title": "Combating Falsification of Speech Videos with Live Optical", "date": "", "ddg_snippet": "These physical signatures encode semantically-meaningful features unique to the speech event, including the speaker’s identity and facial motion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.21846v2", "content": "These physical signatures encode semantically-meaningful features unique to the speech event, including the speaker’s identity and facial motion ..."}
+{"idx": 8, "title": "2013 · opencv/opencv Wiki · GitHub", "date": "", "ddg_snippet": "Error for the glass is 3mm for 80% points (for comparison we had error 1mm for 80% points when we used 200 train images instead of one).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/opencv/opencv/wiki/2013", "content": "Error for the glass is 3mm for 80% points (for comparison we had error 1mm for 80% points when we used 200 train images instead of one)."}
+{"idx": 9, "title": "GVDH Group - Max Planck Institute for Informatics", "date": "", "ddg_snippet": "Specifically, we hierarchically disentangle the dynamic scenes into motion Gaussians and appearance Gaussians which are associated in the canonical ...", "subpage_snippet": "", "source": "gvdh.mpi-inf.mpg.de", "link": "https://gvdh.mpi-inf.mpg.de/publications.html", "content": "Specifically, we hierarchically disentangle the dynamic scenes into motion Gaussians and appearance Gaussians which are associated in the canonical ..."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_For_Pixel_Processor_Arrays.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_For_Pixel_Processor_Arrays.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1d4ae5daa75b32266ee915ca3f03e1814f92cc60
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_For_Pixel_Processor_Arrays.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CN102844771A - Method and apparatus for tracking and", "date": "", "ddg_snippet": "Various methods for tracking and recognition with rotation invariant feature descriptors are provided. ... invention for tracking and recognition with ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/CN102844771A/en", "content": "Various methods for tracking and recognition with rotation invariant feature descriptors are provided. ... invention for tracking and recognition with ..."}
+{"idx": 1, "title": "WO2014111961A1 - Describing objects using edge-pixel-feature", "date": "", "ddg_snippet": "More specifically, the present disclosure relates to describing objects in images using descriptors based on features associated with edge pixels .", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2014111961A1/en", "content": "More specifically, the present disclosure relates to describing objects in images using descriptors based on features associated with edge pixels ."}
+{"idx": 2, "title": "U.S. Patent Application for SINGLE AND ACROSS SENSOR OBJECT", "date": "", "ddg_snippet": "For example, some motion models use key points (such as a Scale Invariant Feature Transform (SIFT) algorithm and/ or a Kanade-Lucas-Tomasi (KLT) ...", "subpage_snippet": "", "source": "patents.justia.com", "link": "https://patents.justia.com/patent/20230186640", "content": "For example, some motion models use key points (such as a Scale Invariant Feature Transform (SIFT) algorithm and/ or a Kanade-Lucas-Tomasi (KLT) ..."}
+{"idx": 3, "title": "(PDF) Weighted Node Mapping and Localisation on a Pixel", "date": "", "ddg_snippet": "The PPA sensor comprises of an array of Processing Elements (PEs), each of which can capture and process visual information directly.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/350187131_Weighted_Node_Mapping_and_Localisation_on_a_Pixel_Processor_Array", "content": "The PPA sensor comprises of an array of Processing Elements (PEs), each of which can capture and process visual information directly."}
+{"idx": 4, "title": "Feature Detection", "date": "", "ddg_snippet": "... feature point detectors: FAST ( Features ... For details on how to interact with the camera and stream feature point data, refer to our sample code .", "subpage_snippet": "", "source": "docs.labforge.ca", "link": "https://docs.labforge.ca/docs/feature-detection", "content": "... feature point detectors: FAST ( Features ... For details on how to interact with the camera and stream feature point data, refer to our sample code ."}
+{"idx": 5, "title": "CVPR 2025 2025 Award Candidates", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays ... for both detection and tracking of point - features across the ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/events/AwardCandidates2025", "content": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays ... for both detection and tracking of point - features across the ..."}
+{"idx": 6, "title": "Document Tree", "date": "", "ddg_snippet": "The range checking wrapper for fixed length C++ arrays ... Dynamic buffers versus descriptors , arrays and heap cells", "subpage_snippet": "", "source": "n-gage.loociano.com", "link": "http://n-gage.loociano.com/symbian-sdk-doc/6.1/developerlibrary/index/docs_tree.html", "content": "The range checking wrapper for fixed length C++ arrays ... Dynamic buffers versus descriptors , arrays and heap cells"}
+{"idx": 7, "title": "package directx/d3d12 - pkg.odin-lang.org", "date": "", "ddg_snippet": "FEATURE _DATA_PROTECTED_RESOURCE_SESSION_TYPE_COUNT ... RAYTRACING_ACCELERATION_STRUCTURE_POSTBUILD_INFO_COMPACTED_SIZE_DESC", "subpage_snippet": "", "source": "pkg.odin-lang.org", "link": "https://pkg.odin-lang.org/vendor/directx/d3d12/", "content": "FEATURE _DATA_PROTECTED_RESOURCE_SESSION_TYPE_COUNT ... RAYTRACING_ACCELERATION_STRUCTURE_POSTBUILD_INFO_COMPACTED_SIZE_DESC"}
+{"idx": 8, "title": "Getting Started | Tinman 3D SDK", "date": "", "ddg_snippet": "... Feature Overview ... Code-X Processor ... Descriptor Pool", "subpage_snippet": "", "source": "manual.tinman3d.com", "link": "https://manual.tinman3d.com/STABLE-PREVIEW/getting-started.html", "content": "... Feature Overview ... Code-X Processor ... Descriptor Pool"}
+{"idx": 9, "title": "Reference - API | Tinman 3D SDK", "date": "", "ddg_snippet": "... Feature Overview ... Code-X Processor ... Descriptor Pool", "subpage_snippet": "", "source": "manual.tinman3d.com", "link": "https://manual.tinman3d.com/STABLE-PREVIEW/api-reference.html", "content": "... Feature Overview ... Code-X Processor ... Descriptor Pool"}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_paradigm_advantages_pixel_processor_arrays_year_2024.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_paradigm_advantages_pixel_processor_arrays_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e1cb0b91aca0dbb08cae0f09ac7b431513bbdef
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_paradigm_advantages_pixel_processor_arrays_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "We introduce a Descriptor - In - Pixel paradigm , in which a feature descriptor is held within the memory of each pixel - processor .Our approach uses a novel Descriptor In - Pixel paradigm de-signed specifically to exploit the PPA’s massively parallel, on-sensor computation.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "We introduce a Descriptor - In - Pixel paradigm , in which a feature descriptor is held within the memory of each pixel - processor .Our approach uses a novel Descriptor In - Pixel paradigm de-signed specifically to exploit the PPA’s massively parallel, on-sensor computation."}
+{"idx": 1, "title": "Descriptor - In - Pixel : Point-Feature Tracking for Pixel Processor ...", "date": "", "ddg_snippet": "We introduce a Descriptor - In - Pixel paradigm , in which a feature descriptor is held within the memory of each pixel - processor . The PPA’s architecture enables the response of every processor ’s descriptor, upon the current image, to be computed in parallel.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11092646/", "content": "We introduce a Descriptor - In - Pixel paradigm , in which a feature descriptor is held within the memory of each pixel - processor . The PPA’s architecture enables the response of every processor ’s descriptor, upon the current image, to be computed in parallel."}
+{"idx": 2, "title": "Mapping Image Transformations Onto Pixel Processor Arrays", "date": "", "ddg_snippet": "Pixel Processor Arrays (PPA) present a new vision sensor/ processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.16994v1", "content": "Pixel Processor Arrays (PPA) present a new vision sensor/ processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication."}
+{"idx": 3, "title": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point-feature detection and tracking, specifically designed for Pixel Processor Array sensors (PPA).", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays@CVPR2025@CVF", "content": "This paper presents a novel approach for joint point-feature detection and tracking, specifically designed for Pixel Processor Array sensors (PPA)."}
+{"idx": 4, "title": "Fully Embedding Fast Convolutional Networks on Pixel Processor ...", "date": "", "ddg_snippet": "We present a novel method of CNN inference for pixel processor array (PPA) vision sensors, designed to take advantage of their massive parallelism and analog compute capabilities.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/fully-embedding-fast-convolutional-networks-on-pixel-processor-arrays", "content": "We present a novel method of CNN inference for pixel processor array (PPA) vision sensors, designed to take advantage of their massive parallelism and analog compute capabilities."}
+{"idx": 5, "title": "(PDF) Weighted Node Mapping and Localisation on a Pixel Processor ...", "date": "", "ddg_snippet": "odometry for pixel processor arrays ,” in Proceedings of the IEEE. International Conference on Computer Vision, 2017, pp. 4604–4612.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/350187131_Weighted_Node_Mapping_and_Localisation_on_a_Pixel_Processor_Array", "content": "odometry for pixel processor arrays ,” in Proceedings of the IEEE. International Conference on Computer Vision, 2017, pp. 4604–4612."}
+{"idx": 6, "title": "Descriptor - In _ Pixel", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point-Feature Tracking for Pixel Processor Arrays .All computation is performed inside the sensor itself, upon thousands of \" Pixel -Procesors\".", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Descriptor - In - Pixel : Point-Feature Tracking for Pixel Processor Arrays .All computation is performed inside the sensor itself, upon thousands of \" Pixel -Procesors\"."}
+{"idx": 7, "title": "Mapping Image Transformations Onto Pixel Processor Arrays -Bohrium", "date": "", "ddg_snippet": "Pixel Processor Arrays (PPA) present a new vision sensor/ processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/mapping-image-transformations-onto-pixel-processor-arrays/979726119957692435-108597", "content": "Pixel Processor Arrays (PPA) present a new vision sensor/ processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication."}
+{"idx": 8, "title": "Frontiers | Visual Odometry Using Pixel Processor Arrays for...", "date": "", "ddg_snippet": "“Visual odometry for pixel processor arrays ,” in The IEEE International Conference on Computer Vision (Hamburg). doi: 10.1109/ICCV.2017.493.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2020.00126/full", "content": "“Visual odometry for pixel processor arrays ,” in The IEEE International Conference on Computer Vision (Hamburg). doi: 10.1109/ICCV.2017.493."}
+{"idx": 9, "title": "Fully Embedding Fast Convolutional Networks on Pixel Processor ...", "date": "", "ddg_snippet": "The key idea behind our approach is storing network weights \" in - pixel \", allowing the parallel analogue computation of the SCAMP PPA to be fully utilized. This current work considers a baseline task for digit classification at over 3000+ frames per second.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/4f4e20d6fc0d5f6d3387d6e72c180b08/", "content": "The key idea behind our approach is storing network weights \" in - pixel \", allowing the parallel analogue computation of the SCAMP PPA to be fully utilized. This current work considers a baseline task for digit classification at over 3000+ frames per second."}
diff --git a/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_Table_1_year_2023.jsonl b/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_Table_1_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a7700714726d316ce2c8b80afcce92e00228040d
--- /dev/null
+++ b/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_Table_1_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ... 论文阅读--DSDFM--用于人体运动合成的确定性到随机多种潜在特征映射 -... Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Foruck/Awesome-Human-Motion - GitHub Harmonizing Stochasticity and Determinism: Scene-responsive ... Harmonizing Stochasticity and Determinism: Scene-responsive ... Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "May 2, 2025 · Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic-to-Stochastic ... May 29, 2025 · 确定性特征映射过程（ Deterministic Feature Mapping Procedure）：使用确定性常微分方程（DerODE）操作，通过最优传输理论建立连接。 随机多样输出生成过程（ Stochastic Diverse Output Generation Procedure）：使用多样随机微分方程（DivSDE）在采样过程中引入随机性，增强多样性。 In this paper, we propose a Deterministic-to-Stochastic Di-verse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the re-cent SGMs-based method, while facilitating the diversity and accuracy of generated human motions. (CVPR 2025) DSDFM: Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. ThesefindingsunderscoreDiMoP3D’s capability to harmonize the stochastic nature of human motion and the deterministic constraints from the scene and the past motion , achieving superior performance in diverse scene-aware HMP, and maintaining competitive diversity and fidelity even when compared to SoTA synthesis method. Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion . In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the recent SGMs-based method, while facilitating the diversity and accuracy of generated human motions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "May 2, 2025 · Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic-to-Stochastic ... May 29, 2025 · 确定性特征映射过程（ Deterministic Feature Mapping Procedure）：使用确定性常微分方程（DerODE）操作，通过最优传输理论建立连接。 随机多样输出生成过程（ Stochastic Diverse Output Generation Procedure）：使用多样随机微分方程（DivSDE）在采样过程中引入随机性，增强多样性。 In this paper, we propose a Deterministic-to-Stochastic Di-verse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the re-cent SGMs-based method, while facilitating the diversity and accuracy of generated human motions. (CVPR 2025) DSDFM: Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. ThesefindingsunderscoreDiMoP3D’s capability to harmonize the stochastic nature of human motion and the deterministic constraints from the scene and the past motion , achieving superior performance in diverse scene-aware HMP, and maintaining competitive diversity and fidelity even when compared to SoTA synthesis method. Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion . In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the recent SGMs-based method, while facilitating the diversity and accuracy of generated human motions."}
+{"idx": 1, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic-to-Stochastic Di-verse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the re-cent SGMs-based method, while facilitating the diversity and accuracy of generated human motions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "In this paper, we propose a Deterministic-to-Stochastic Di-verse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the re-cent SGMs-based method, while facilitating the diversity and accuracy of generated human motions."}
+{"idx": 2, "title": "Foruck/Awesome-Human-Motion - GitHub", "date": "", "ddg_snippet": "(CVPR 2025) DSDFM: Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Foruck/Awesome-Human-Motion", "content": "(CVPR 2025) DSDFM: Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al."}
+{"idx": 3, "title": "Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "ThesefindingsunderscoreDiMoP3D’s capability to harmonize the stochastic nature of human motion and the deterministic constraints from the scene and the past motion , achieving superior performance in diverse scene-aware HMP, and maintaining competitive diversity and fidelity even when compared to SoTA synthesis method.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4620a66570e554a3ff0e39dc59bcb07a-Paper-Conference.pdf", "content": "ThesefindingsunderscoreDiMoP3D’s capability to harmonize the stochastic nature of human motion and the deterministic constraints from the scene and the past motion , achieving superior performance in diverse scene-aware HMP, and maintaining competitive diversity and fidelity even when compared to SoTA synthesis method."}
+{"idx": 4, "title": "Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=NQCkNM6TES", "content": "Sep 25, 2024 · To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion ."}
+{"idx": 5, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the recent SGMs-based method, while facilitating the diversity and accuracy of generated human motions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00998", "content": "In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the recent SGMs-based method, while facilitating the diversity and accuracy of generated human motions."}
+{"idx": 6, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM ) method for human motion synthesis. DSDFM consists of two stages.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33113", "content": "In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM ) method for human motion synthesis. DSDFM consists of two stages."}
+{"idx": 7, "title": "Deterministic-to-Stochastic Diverse Latent Feature ...", "date": "", "ddg_snippet": "2 May 2025 — Table 1 : The comparison results of unconditional human motion synthesis between our method and state-of-the-art methods on HumanAct12 dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00998v1", "content": "2 May 2025 — Table 1 : The comparison results of unconditional human motion synthesis between our method and state-of-the-art methods on HumanAct12 dataset."}
+{"idx": 8, "title": "Scene-responsive Diverse Human Motion Prediction", "date": "", "ddg_snippet": "9 Dec 2024 — On top of that, DiMoP3D identifies deterministic factors in the scene and integrates them into stochastic modeling, making the diverse HMP in ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95438", "content": "9 Dec 2024 — On top of that, DiMoP3D identifies deterministic factors in the scene and integrates them into stochastic modeling, making the diverse HMP in ..."}
+{"idx": 9, "title": "Towards Efficient and Diverse Generative Model for ...", "date": "", "ddg_snippet": "28 Oct 2024 — First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. Next, we employ convex ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3664647.3681093", "content": "28 Oct 2024 — First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. Next, we employ convex ..."}
diff --git a/data/sampled_jsons/Digital_Disparities_A_Comparative_Web_Measurement_Study_Across_Economic_Boundaries_PAW_index_formula.jsonl b/data/sampled_jsons/Digital_Disparities_A_Comparative_Web_Measurement_Study_Across_Economic_Boundaries_PAW_index_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..12876edce387f61a90a3cd75d7c17098bd25e35f
--- /dev/null
+++ b/data/sampled_jsons/Digital_Disparities_A_Comparative_Web_Measurement_Study_Across_Economic_Boundaries_PAW_index_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Digital Disparities: A Comparative Web Measurement Study Across ...", "date": "", "ddg_snippet": "Index Terms Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries Information systems World Wide Web Social and professional topics", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714647", "content": "Index Terms Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries Information systems World Wide Web Social and professional topics"}
+{"idx": 1, "title": "digital-disparities-www25/README.md at main - GitHub", "date": "", "ddg_snippet": "Digital Disparities: A Comparative Web Measurement Study This repository contains the dataset, tools, and analysis for our research paper \" Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries \", presented at WWW'25. The study explores differences in web development practices across developed and developing countries, focusing on web size, complexity, security ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kal-purush/digital-disparities-www25/blob/main/README.md", "content": "Digital Disparities: A Comparative Web Measurement Study This repository contains the dataset, tools, and analysis for our research paper \" Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries \", presented at WWW'25. The study explores differences in web development practices across developed and developing countries, focusing on web size, complexity, security ..."}
+{"idx": 2, "title": "PDF Unequal internet: Study highlights differences between websites from ...", "date": "", "ddg_snippet": "More information: Masudul Hasan Masud Bhuiyan et al, Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries , CISPA (2025). DOI: 10.60882/cispa.28365449.v1", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-04-unequal-internet-highlights-differences-websites.pdf", "content": "More information: Masudul Hasan Masud Bhuiyan et al, Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries , CISPA (2025). DOI: 10.60882/cispa.28365449.v1"}
+{"idx": 3, "title": "Websites from developing and developed countries differ", "date": "", "ddg_snippet": "The complete findings were published in the paper ' Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries ,' which will be presented at the ACM Web Conference 2025 ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/unequal-internet-study-highlights-differences-between-websites-from-u7uje", "content": "The complete findings were published in the paper ' Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries ,' which will be presented at the ACM Web Conference 2025 ..."}
+{"idx": 4, "title": "Digital Disparities: A Comparative Web Measurement Study Across ...", "date": "", "ddg_snippet": "DE Home Research Publications Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries 2025-02-06", "subpage_snippet": "", "source": "cispa.de", "link": "https://cispa.de/en/research/publications/84529-digital-disparities-a-comparative-web-measurement-study-across-economic-boundaries", "content": "DE Home Research Publications Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries 2025-02-06"}
+{"idx": 5, "title": "Digital Disparities: A Comparative Web Measurement Study Across ...", "date": "", "ddg_snippet": "Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries Masudul Hasan Masud Bhuiyan, Matteo Varvello, Cristian-Alexandru Staicu, Yasir Zaki", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=IMhoJgWANP", "content": "Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries Masudul Hasan Masud Bhuiyan, Matteo Varvello, Cristian-Alexandru Staicu, Yasir Zaki"}
+{"idx": 6, "title": "Digital Disparities : A Comparative Web Measurement Study ...", "date": "", "ddg_snippet": "Digital disparities , Web measurement , Web development practices.These results are consistent with those of Gotze et al. [46], Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries . WWW ’25, April 28-May 2, 2025, Sydney, NSW, Australia.", "subpage_snippet": "", "source": "www.staicu.org", "link": "https://www.staicu.org/publications/www2025.pdf", "content": "Digital disparities , Web measurement , Web development practices.These results are consistent with those of Gotze et al. [46], Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries . WWW ’25, April 28-May 2, 2025, Sydney, NSW, Australia."}
+{"idx": 7, "title": "Digital Disparities : A Comparative Web Measurement Study ...", "date": "", "ddg_snippet": "A user study in two high schools in Pakistan confirms that the performance gains come at no expense to the pages' look and functionality. These findings suggest that deploying Lite- Web at scale would constitute a major step toward a WWW without digital inequality.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/391035183_Digital_Disparities_A_Comparative_Web_Measurement_Study_Across_Economic_Boundaries", "content": "A user study in two high schools in Pakistan confirms that the performance gains come at no expense to the pages' look and functionality. These findings suggest that deploying Lite- Web at scale would constitute a major step toward a WWW without digital inequality."}
+{"idx": 8, "title": "Digital Disparities : A Comparative Web Measurement Study ...", "date": "", "ddg_snippet": "Digital disparities . Web development practices.Dive into the research topics of ' Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries '. Together they form a unique fingerprint.", "subpage_snippet": "", "source": "nyuscholars.nyu.edu", "link": "https://nyuscholars.nyu.edu/en/publications/digital-disparities-a-comparative-web-measurement-study-across-ec", "content": "Digital disparities . Web development practices.Dive into the research topics of ' Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries '. Together they form a unique fingerprint."}
+{"idx": 9, "title": "Unequal internet: Study highlights differences between websites from...", "date": "", "ddg_snippet": "Visualization to the paper \" A Comparative Web Measurement Study Across Economic Boundaries .\"More information: Masudul Hasan Masud Bhuiyan et al, Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries , CISPA (2025).", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-04-unequal-internet-highlights-differences-websites.html", "content": "Visualization to the paper \" A Comparative Web Measurement Study Across Economic Boundaries .\"More information: Masudul Hasan Masud Bhuiyan et al, Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries , CISPA (2025)."}
diff --git a/data/sampled_jsons/Digital_Disparities_Comparative_Web_Measurement_Study_Across_Economic_Boundaries_results_findings_year_2025.jsonl b/data/sampled_jsons/Digital_Disparities_Comparative_Web_Measurement_Study_Across_Economic_Boundaries_results_findings_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0e3efb0485b4592ab3712e7ad42479ea7ab2c44b
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unequal internet: Study highlights differences between websites from...", "date": "", "ddg_snippet": "Visualization to the paper \"A Comparative Web Measurement Study Across Economic Boundaries .\"More information: Masudul Hasan Masud Bhuiyan et al, Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries , CISPA (2025).", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-04-unequal-internet-highlights-differences-websites.html", "content": "Visualization to the paper \"A Comparative Web Measurement Study Across Economic Boundaries .\"More information: Masudul Hasan Masud Bhuiyan et al, Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries , CISPA (2025)."}
+{"idx": 1, "title": "Digital Disparities : A Comparative Web Measurement Study ...", "date": "", "ddg_snippet": "Digital disparities , Web measurement , Web development practices.Modern optimized formats such as. Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries . WWW ’25, April 28-May 2, 2025, Sydney, NSW, Australia. Total JS Size (KB).", "subpage_snippet": "", "source": "www.staicu.org", "link": "https://www.staicu.org/publications/www2025.pdf", "content": "Digital disparities , Web measurement , Web development practices.Modern optimized formats such as. Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries . WWW ’25, April 28-May 2, 2025, Sydney, NSW, Australia. Total JS Size (KB)."}
+{"idx": 2, "title": "Ungleiches Internet: Studie arbeitet Unterschiede zwischen Webseiten...", "date": "", "ddg_snippet": "Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries .Criteria of this press release: Journalists Information technology transregional, national Research results , Scientific Publications German.", "subpage_snippet": "", "source": "idw-online.de", "link": "https://idw-online.de/en/news851330", "content": "Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries .Criteria of this press release: Journalists Information technology transregional, national Research results , Scientific Publications German."}
+{"idx": 3, "title": "Digital divide as an explanatory variable for dropout in higher education", "date": "", "ddg_snippet": "This study aimed to simulate the effects of digital inequality on student dropout rates in undergraduate programs.", "subpage_snippet": "", "source": "educationaltechnologyjournal.springeropen.com", "link": "https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-025-00550-0", "content": "This study aimed to simulate the effects of digital inequality on student dropout rates in undergraduate programs."}
+{"idx": 4, "title": "News - Research in Germany", "date": "", "ddg_snippet": "Unequal Internet: study highlights differences between websites from developing and developed countries. Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries . CISPA.", "subpage_snippet": "", "source": "www.research-in-germany.org", "link": "https://www.research-in-germany.org/idw-news/en_US/2025/4/2025-04-29_Unequal_Internet__study_highlights_differences_between_websites_from_developing_and_developed_countries.html", "content": "Unequal Internet: study highlights differences between websites from developing and developed countries. Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries . CISPA."}
+{"idx": 5, "title": "ComNets NYUAD", "date": "", "ddg_snippet": "inproceedings{masud, author = {Masud Bhuiyan, Masudul Hasan and Varvello, Matteo and Staicu, Cristian-Alexandru and Zaki, Yasir}, title = { Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries }, booktitle...", "subpage_snippet": "", "source": "yasirzaki.net", "link": "https://yasirzaki.net/publications.html", "content": "inproceedings{masud, author = {Masud Bhuiyan, Masudul Hasan and Varvello, Matteo and Staicu, Cristian-Alexandru and Zaki, Yasir}, title = { Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries }, booktitle..."}
+{"idx": 6, "title": "dblp: List of computer science publications by Cristian-Alexandru Staicu", "date": "", "ddg_snippet": "Masudul Hasan Masud Bhuiyan , Matteo Varvello , Cristian-Alexandru Staicu , Yasir Zaki : Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries .NAACL-HLT ( Findings ) 2024: 1400-1416.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/179/8624.html", "content": "Masudul Hasan Masud Bhuiyan , Matteo Varvello , Cristian-Alexandru Staicu , Yasir Zaki : Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries .NAACL-HLT ( Findings ) 2024: 1400-1416."}
+{"idx": 7, "title": "Main Page", "date": "", "ddg_snippet": "Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries .", "subpage_snippet": "", "source": "cms.cispa.saarland", "link": "https://cms.cispa.saarland/websecsem_sose25/", "content": "Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries ."}
+{"idx": 8, "title": "Relatório de viagem", "date": "", "ddg_snippet": "Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries (11:06 – 11:24). • Autores: Masudul Hasan Masud Bhuiyan, Matteo Varvello, Cristian-Alexandru Staicu, Yasir Zaki ACM Digital Library+7OpenReview+7staicu.org+7.", "subpage_snippet": "", "source": "cgi.br", "link": "https://cgi.br/media/viagem/relatorio/302/202504-Relatorio-de-Viagem_webconference2025_+Rafael-Evangelista.pdf", "content": "Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries (11:06 – 11:24). • Autores: Masudul Hasan Masud Bhuiyan, Matteo Varvello, Cristian-Alexandru Staicu, Yasir Zaki ACM Digital Library+7OpenReview+7staicu.org+7."}
+{"idx": 9, "title": "Paper Digest: WWW 2025 Papers & Highlights – Paper Digest", "date": "", "ddg_snippet": "153. Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Anecdotal evidence suggests that webpages in developing and developed regions differ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2025/04/www-2025-papers-highlights/", "content": "153. Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Anecdotal evidence suggests that webpages in developing and developed regions differ..."}
diff --git a/data/sampled_jsons/Digital_Disparities_Comparative_Web_Measurement_Study_median_website_size_developed_developing_count.jsonl b/data/sampled_jsons/Digital_Disparities_Comparative_Web_Measurement_Study_median_website_size_developed_developing_count.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..60c7fbc9f0f80d4355697a5fe3ad85d84dd08305
--- /dev/null
+++ b/data/sampled_jsons/Digital_Disparities_Comparative_Web_Measurement_Study_median_website_size_developed_developing_count.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Digital Disparities: A Comparative Web Measurement Study ...", "date": "", "ddg_snippet": "Anecdotal evidence suggests that webpages in developing and developed regions difer significantly. In this work, we test this hypothesis by measuring diferences in web develop-ment practices across the two groups of countries , using multiple dimensions: webpages’ size , complexity, security, privacy, quality, technology adoption, and ...", "subpage_snippet": "", "source": "www.staicu.org", "link": "https://www.staicu.org/publications/www2025.pdf", "content": "Anecdotal evidence suggests that webpages in developing and developed regions difer significantly. In this work, we test this hypothesis by measuring diferences in web develop-ment practices across the two groups of countries , using multiple dimensions: webpages’ size , complexity, security, privacy, quality, technology adoption, and ..."}
+{"idx": 1, "title": "digital-disparities-www25/README.md at main - GitHub", "date": "", "ddg_snippet": "Digital Disparities : A Comparative Web Measurement Study This repository contains the dataset, tools, and analysis for our research paper \" Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries\", presented at WWW'25. The study explores differences in web development practices across developed and developing countries , focusing on web size , complexity, security ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kal-purush/digital-disparities-www25/blob/main/README.md", "content": "Digital Disparities : A Comparative Web Measurement Study This repository contains the dataset, tools, and analysis for our research paper \" Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries\", presented at WWW'25. The study explores differences in web development practices across developed and developing countries , focusing on web size , complexity, security ..."}
+{"idx": 2, "title": "Unequal Internet: study highlights differences between ...", "date": "", "ddg_snippet": "Apr 29, 2025 · The complete findings were published in the paper ‘ Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries,’ which will be presented at the ACM Web Conference 2025 in Sydney at the beginning of May. Differences in digitalization between developing and developed countries involve a variety of factors.", "subpage_snippet": "", "source": "cispa.de", "link": "https://cispa.de/en/bhuiyan-unequal-internet", "content": "Apr 29, 2025 · The complete findings were published in the paper ‘ Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries,’ which will be presented at the ACM Web Conference 2025 in Sydney at the beginning of May. Differences in digitalization between developing and developed countries involve a variety of factors."}
+{"idx": 3, "title": "Digital Disparities: A Comparative Web Measurement Study ...", "date": "", "ddg_snippet": "Apr 22, 2025 · Anecdotal evidence suggests that webpages in developing and developed regions differ significantly. In this work, we test this hypothesis by measuring differences in web development practices across the two groups of countries , using multiple dimensions: webpages' size , complexity, security, privacy, quality, technology adoption, and accessibility.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714647", "content": "Apr 22, 2025 · Anecdotal evidence suggests that webpages in developing and developed regions differ significantly. In this work, we test this hypothesis by measuring differences in web development practices across the two groups of countries , using multiple dimensions: webpages' size , complexity, security, privacy, quality, technology adoption, and accessibility."}
+{"idx": 4, "title": "Widening Digital Gap between Developed, Developing States ...", "date": "", "ddg_snippet": "Oct 6, 2023 · A widening digital divide and severely lagging Internet-use in developing countries threaten to leave those States in the technological wake and preclude progress on the Sustainable Development Goals (SDGs), a senior United Nations official and Member States told the Second Committee (Economic and Financial) today as it took up information and communications technology (ICT) questions.", "subpage_snippet": "", "source": "press.un.org", "link": "https://press.un.org/en/2023/gaef3587.doc.htm", "content": "Oct 6, 2023 · A widening digital divide and severely lagging Internet-use in developing countries threaten to leave those States in the technological wake and preclude progress on the Sustainable Development Goals (SDGs), a senior United Nations official and Member States told the Second Committee (Economic and Financial) today as it took up information and communications technology (ICT) questions."}
+{"idx": 5, "title": "Unequal Internet: study highlights differences between ...", "date": "", "ddg_snippet": "Apr 29, 2025 · His study of 200,000 websites from 20 developing and developed countries concludes that websites in developing and emerging countries are generally smaller and less complex, more prone to efficiency issues, but conversely, potentially less vulnerable to security risks.", "subpage_snippet": "", "source": "nachrichten.idw-online.de", "link": "https://nachrichten.idw-online.de/2025/04/29/unequal-internet-study-highlights-differences-between-websites-from-developing-and-developed-countries", "content": "Apr 29, 2025 · His study of 200,000 websites from 20 developing and developed countries concludes that websites in developing and emerging countries are generally smaller and less complex, more prone to efficiency issues, but conversely, potentially less vulnerable to security risks."}
+{"idx": 6, "title": "SEO | 2020 | The Web Almanac by HTTP Archive", "date": "", "ddg_snippet": "... related findings of the web configurations and elements that make up the foundation for search engines to correctly crawl, index, and rank websites ...", "subpage_snippet": "", "source": "almanac.httparchive.org", "link": "https://almanac.httparchive.org/en/2020/seo", "content": "... related findings of the web configurations and elements that make up the foundation for search engines to correctly crawl, index, and rank websites ..."}
+{"idx": 7, "title": "The State of Post-Quantum Cryptography (PQC) on the Web | F5", "date": "", "ddg_snippet": "Only 3% of banking websites support PQC, placing the industry among the lowest adopters—even within its own Financials sector ( Figure 1 ).", "subpage_snippet": "", "source": "www.f5.com", "link": "https://www.f5.com/labs/articles/threat-intelligence/the-state-of-pqc-on-the-web", "content": "Only 3% of banking websites support PQC, placing the industry among the lowest adopters—even within its own Financials sector ( Figure 1 )."}
+{"idx": 8, "title": "News - Research in Germany", "date": "", "ddg_snippet": "Apr 29, 2025 · These studies show that only 60 percent of the population in developing countries are online, whereas in developed countries , the figure is 93 percent. Conversely, people in developing countries rely more on mobile Internet.", "subpage_snippet": "", "source": "www.research-in-germany.org", "link": "https://www.research-in-germany.org/idw-news/en_US/2025/4/2025-04-29_Unequal_Internet__study_highlights_differences_between_websites_from_developing_and_developed_countries.html", "content": "Apr 29, 2025 · These studies show that only 60 percent of the population in developing countries are online, whereas in developed countries , the figure is 93 percent. Conversely, people in developing countries rely more on mobile Internet."}
+{"idx": 9, "title": "Demographic disparity in Wikipedia coverage: a global", "date": "", "ddg_snippet": "... from Wikipedia over 10 years across the 12 largest language editions of Wikipedia to study coverage disparities in gender, geography, and development ...", "subpage_snippet": "", "source": "epjdatascience.springeropen.com", "link": "https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-025-00530-4", "content": "... from Wikipedia over 10 years across the 12 largest language editions of Wikipedia to study coverage disparities in gender, geography, and development ..."}
diff --git a/data/sampled_jsons/Dike_four_steps_self-supervised_learning_training_data_linguistic_behaviors_emotion_mapping.jsonl b/data/sampled_jsons/Dike_four_steps_self-supervised_learning_training_data_linguistic_behaviors_emotion_mapping.jsonl
new file mode 100644
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+{"idx": 0, "title": "ICLM 2025 AI Safety 7847 Camera Ready3 | PDF | Emotions ...", "date": "", "ddg_snippet": "a self-supervised learning pipeline that maps emo- tions to linguistic behaviors , enabling precise be- To overcome these challenges, we propose a checks-and- havioral modulation through emotional condition- balances framework inspired by governmental structures,", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/899938563/ICLM-2025-AI-Safety-7847-Camera-Ready3", "content": "a self-supervised learning pipeline that maps emo- tions to linguistic behaviors , enabling precise be- To overcome these challenges, we propose a checks-and- havioral modulation through emotional condition- balances framework inspired by governmental structures,"}
+{"idx": 1, "title": "An Adversarial Behavior Model for Contextual Ethical ...", "date": "", "ddg_snippet": "Modeling Linguistic Behaviors and Emotions: DIKE uses self-supervised learning to analyze linguistic behaviors - the ways humans express underlying emotions through language.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Modeling Linguistic Behaviors and Emotions: DIKE uses self-supervised learning to analyze linguistic behaviors - the ways humans express underlying emotions through language."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Framework for Context ...", "date": "", "ddg_snippet": "These intensities are sequentially categorized as: ‘despair, longing, wishful, neutral, hopeful, contentment, joy.’ Given N letters, DIKE employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v1", "content": "These intensities are sequentially categorized as: ‘despair, longing, wishful, neutral, hopeful, contentment, joy.’ Given N letters, DIKE employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps ."}
+{"idx": 3, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "These intensities are sequentially categorized as: ‘despair, longing, wishful, neutral, hopeful, contentment, joy.’ Given N letters, Dike employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v2", "content": "These intensities are sequentially categorized as: ‘despair, longing, wishful, neutral, hopeful, contentment, joy.’ Given N letters, Dike employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps ."}
+{"idx": 4, "title": "An Adversarial Behavior Model for Contextual Ethical ...", "date": "", "ddg_snippet": "May 8, 2024 · Our approaches include mapping emotions and behaviors using self-supervised learning , refining guardrails through adversarial reviews, and adjusting outputs for ethical alignment.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_An_Adversarial_Behavior_Model_for_Contextual_Ethical_Alignment_in_Large_Language_Models", "content": "May 8, 2024 · Our approaches include mapping emotions and behaviors using self-supervised learning , refining guardrails through adversarial reviews, and adjusting outputs for ethical alignment."}
+{"idx": 5, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "Given N letters, Dike employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment/links/683696c46b5a287c3046b3e0/Checks-and-Balances-Framework-for-Context-Aware-Ethical-AI-Alignment.pdf", "content": "Given N letters, Dike employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps ."}
+{"idx": 6, "title": "A Three-Branch Checks-and-Balances Framework", "date": "", "ddg_snippet": "Given N letters, DIKE employs a self - supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "Given N letters, DIKE employs a self - supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps"}
+{"idx": 7, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "' Given N letters, Dike employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "' Given N letters, Dike employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps ."}
+{"idx": 8, "title": "A Three-Branch Checks-and-Balances Framework for ...", "date": "", "ddg_snippet": "” Given N letters, DIKE employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps :.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/c76fc56310e947fbc848c07660b1ecbd60580a08.pdf", "content": "” Given N letters, DIKE employs a self-supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps :."}
+{"idx": 9, "title": "Multi-LLM Agent Collaborative Intelligence: The Path to AGI", "date": "", "ddg_snippet": "... training data . (Chapters 5 and 6). 2. Modulating Linguistic Behavior: Beyond ... DIKE (Chapters 8 and 9): Model linguistic behav- iors based on basic ... 589 pages", "subpage_snippet": "", "source": "shuyuej.com", "link": "http://shuyuej.com/books/The-Path-to-Artificial-General-Intelligence.pdf", "content": "... training data . (Chapters 5 and 6). 2. Modulating Linguistic Behavior: Beyond ... DIKE (Chapters 8 and 9): Model linguistic behav- iors based on basic ... 589 pages"}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_abstract.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ef1e843f06e33d6ba194ea6f6507c92b29114f2b
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · 2023 · Cited by 5233 — In this paper we introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "by R Rafailov · 2023 · Cited by 5233 — In this paper we introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form."}
+{"idx": 1, "title": "Direct preference optimization: your language model is ...", "date": "", "ddg_snippet": "10 Dec 2023 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668460", "content": "10 Dec 2023 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods."}
+{"idx": 2, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · Cited by 5233 — In this paper, we show how to directly optimize a language model to adhere to human preferences, without explicit reward modeling or reinforcement learning. We ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=HPuSIXJaa9", "content": "by R Rafailov · Cited by 5233 — In this paper, we show how to directly optimize a language model to adhere to human preferences, without explicit reward modeling or reinforcement learning. We ..."}
+{"idx": 3, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · Cited by 5233 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=HPuSIXJaa9", "content": "by R Rafailov · Cited by 5233 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods."}
+{"idx": 4, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight , eliminating the need for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v2", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight , eliminating the need for ..."}
+{"idx": 5, "title": "Extended Abstract - CS 224R Deep Reinforcement Learning", "date": "", "ddg_snippet": "by E Hellman — We compare Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO). Rafailov et al. (2023), and Group Relative Policy Optimization (GRPO) Shao et al.", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/CS_224R_Final_Paper_2.pdf", "content": "by E Hellman — We compare Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO). Rafailov et al. (2023), and Group Relative Policy Optimization (GRPO) Shao et al."}
+{"idx": 6, "title": "Diffusion Model Alignment Using Direct Preference ...", "date": "", "ddg_snippet": "by B Wallace · 2024 · Cited by 351 — We propose Diffusion- DPO , a method to align diffusion models to human preferences by directly optimizing on human comparison data.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10657686/", "content": "by B Wallace · 2024 · Cited by 351 — We propose Diffusion- DPO , a method to align diffusion models to human preferences by directly optimizing on human comparison data."}
+{"idx": 7, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "29 May 2023 — Abstract. Direct Preference Optimization (DPO ) fine-tunes language models more efficiently and with better performance compared to reinforcement ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2305.18290", "content": "29 May 2023 — Abstract. Direct Preference Optimization (DPO ) fine-tunes language models more efficiently and with better performance compared to reinforcement ..."}
+{"idx": 8, "title": "Diffusion Model Alignment Using Direct Preference Optimization", "date": "", "ddg_snippet": "We propose DiffusionDPO, a method to align diffusion models to human preferences by directly optimizing on human comparison data. Diffusion-DPO is adapted from ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/poster/31416", "content": "We propose DiffusionDPO, a method to align diffusion models to human preferences by directly optimizing on human comparison data. Diffusion-DPO is adapted from ..."}
+{"idx": 9, "title": "Disentangling Length from Quality in Direct Preference ...", "date": "", "ddg_snippet": "by R Park · 2024 · Cited by 169 — We study the length problem in the DPO setting, showing significant exploitation in DPO and linking it to out-of-distribution bootstrapping.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.297/", "content": "by R Park · 2024 · Cited by 169 — We study the length problem in the DPO setting, showing significant exploitation in DPO and linking it to out-of-distribution bootstrapping."}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_abstract_Bradley-Terry_model.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_abstract_Bradley-Terry_model.jsonl
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index 0000000000000000000000000000000000000000..488f7238ca407442f111810d32e6ddd942370d93
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · 2023 · Cited by 5233 — Like existing algorithms, DPO relies on a theoretical preference model (such as the Bradley - Terry model ; [5]) that measures how well a given ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.18290", "content": "by R Rafailov · 2023 · Cited by 5233 — Like existing algorithms, DPO relies on a theoretical preference model (such as the Bradley - Terry model ; [5]) that measures how well a given ..."}
+{"idx": 1, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "A.2 Deriving the DPO Objective Under the Bradley - Terry Model . It is straightforward to derive the DPO objective under the Bradley-Terry preference model as we ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v2", "content": "A.2 Deriving the DPO Objective Under the Bradley - Terry Model . It is straightforward to derive the DPO objective under the Bradley-Terry preference model as we ..."}
+{"idx": 2, "title": "Direct Preference Optimization Explained In-depth", "date": "", "ddg_snippet": "13 Apr 2024 — Under the Bradley - Terry model , it is common to choose to parameterize the score as s = e r s=e^r s=er, where r r r stands for reward. The term “ ...", "subpage_snippet": "", "source": "www.tylerromero.com", "link": "https://www.tylerromero.com/posts/2024-04-dpo/", "content": "13 Apr 2024 — Under the Bradley - Terry model , it is common to choose to parameterize the score as s = e r s=e^r s=er, where r r r stands for reward. The term “ ..."}
+{"idx": 3, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "In this paper, we show how to directly optimize a language model to adhere to human preferences, without explicit reward modeling or reinforcement learning. We ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/poster/72164", "content": "In this paper, we show how to directly optimize a language model to adhere to human preferences, without explicit reward modeling or reinforcement learning. We ..."}
+{"idx": 4, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · Cited by 5233 — The method is equivalent to fitting a reparameterized Bradley - Terry model . With mild assumptions, DPO does not constrain the class of learned reward models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=HPuSIXJaa9", "content": "by R Rafailov · Cited by 5233 — The method is equivalent to fitting a reparameterized Bradley - Terry model . With mild assumptions, DPO does not constrain the class of learned reward models."}
+{"idx": 5, "title": "Direct preference optimization: your language model is ...", "date": "", "ddg_snippet": "by R Rafailov · 2023 · Cited by 5162 — Direct preference optimization : your language model is secretly a reward model . AUTHORs: Rafael Rafailov .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3666122.3668460", "content": "by R Rafailov · 2023 · Cited by 5162 — Direct preference optimization : your language model is secretly a reward model . AUTHORs: Rafael Rafailov ."}
+{"idx": 6, "title": "Extended Abstract - CS 224R Deep Reinforcement Learning", "date": "", "ddg_snippet": "by J Zheng — Direct Preference Optimization (DPO) Rafailov et al. (2023) introduces an efficient alternative that directly models pairwise preferences without relying on an ... 13 pages", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/CS224R_Final_Paper1.pdf", "content": "by J Zheng — Direct Preference Optimization (DPO) Rafailov et al. (2023) introduces an efficient alternative that directly models pairwise preferences without relying on an ... 13 pages"}
+{"idx": 7, "title": "Indirect Online Preference Optimization via Reinforcement ...", "date": "", "ddg_snippet": "DPO [Rafailov et al., 2024]: Given preference data,. DPO fits a binary classifier based on the Bradley-Terry model . DPO propose a sigmoid loss on the normalized.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0061.pdf", "content": "DPO [Rafailov et al., 2024]: Given preference data,. DPO fits a binary classifier based on the Bradley-Terry model . DPO propose a sigmoid loss on the normalized."}
+{"idx": 8, "title": "Understanding Reference Policies in Direct Preference ...", "date": "", "ddg_snippet": "by Y Liu · 2025 · Cited by 10 — Direct Preference Optimization (DPO) has be- come a widely used training method for the in- struction fine-tuning of large language models .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.447.pdf", "content": "by Y Liu · 2025 · Cited by 10 — Direct Preference Optimization (DPO) has be- come a widely used training method for the in- struction fine-tuning of large language models ."}
+{"idx": 9, "title": "Right Now, Wrong Then: Non-Stationary Direct Preference ...", "date": "", "ddg_snippet": "A key component of this is the Bradley-Terry model (Bradley & Terry, 1952) which learns a reward signal from paired human preferences. (Rafailov et al., 2024) ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44703", "content": "A key component of this is the Bradley-Terry model (Bradley & Terry, 1952) which learns a reward signal from paired human preferences. (Rafailov et al., 2024) ..."}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_et_al._abstract_year_2023.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_et_al._abstract_year_2023.jsonl
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index 0000000000000000000000000000000000000000..2f0c2f170ff2d56f2a3c7196b986a29419c24fd6
--- /dev/null
+++ b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_et_al._abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2305.18290] Direct Preference Optimization : Your Language Model...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning."}
+{"idx": 1, "title": "Direct Preference Optimization", "date": "", "ddg_snippet": "Direct Preference Optimization . Theoretical Analysis of DPO. Direct Preference Optimization : Your Language Model is Secretly a Reward Model. arXiv:2305.18290v3 [cs.LG] 29 Jul 2024. Rafael Rafailov ∗†.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.18290", "content": "Direct Preference Optimization . Theoretical Analysis of DPO. Direct Preference Optimization : Your Language Model is Secretly a Reward Model. arXiv:2305.18290v3 [cs.LG] 29 Jul 2024. Rafael Rafailov ∗†."}
+{"idx": 2, "title": "Direct Preference Optimization with an Offset", "date": "", "ddg_snippet": "4 Direct Preference Optimization . Rafailov et al . (2023) introduce a method to avoid reward model training and, thus, to directly optimize the language model.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.592.pdf", "content": "4 Direct Preference Optimization . Rafailov et al . (2023) introduce a method to avoid reward model training and, thus, to directly optimize the language model."}
+{"idx": 3, "title": "Extended Abstract", "date": "", "ddg_snippet": "(2023). Direct Preference Optimization (DPO) simplifies this by treating the log-likelihood difference between preferred and dispreferred responses as an implicit reward signal Rafailov et al .", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/Scaling_DPO_with_Synthetic_Preferences_for_Instruction_Following_Language_Models.pdf", "content": "(2023). Direct Preference Optimization (DPO) simplifies this by treating the log-likelihood difference between preferred and dispreferred responses as an implicit reward signal Rafailov et al ."}
+{"idx": 4, "title": "From Prompts to Preferences : Semantic Feedback as... | Medium", "date": "", "ddg_snippet": "It was introduced in the paper “ Direct Preference Optimization : Your Language Model is Secretly a Reward Model” ( Rafailov et al ., 2023). Traditionally, fine-tuning with human feedback (RLHF) involves: Collecting human preferences between pairs of outputs.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@albatrosary/from-prompts-to-preferences-semantic-feedback-as-implicit-reward-in-llms-290a198789d1", "content": "It was introduced in the paper “ Direct Preference Optimization : Your Language Model is Secretly a Reward Model” ( Rafailov et al ., 2023). Traditionally, fine-tuning with human feedback (RLHF) involves: Collecting human preferences between pairs of outputs."}
+{"idx": 5, "title": "2023 DirectPreferenceOptimizationYou - GM-RKB", "date": "", "ddg_snippet": "Subject Headings: Direct Preference Optimization . It introduces Direct Preference Optimization , a novel approach for aligning language models with human preferences , as a simpler alternative to the complex and often unstable reinforcement learning fr...", "subpage_snippet": "", "source": "www.gabormelli.com", "link": "https://www.gabormelli.com/RKB/Rafailov_et_al.,_2023", "content": "Subject Headings: Direct Preference Optimization . It introduces Direct Preference Optimization , a novel approach for aligning language models with human preferences , as a simpler alternative to the complex and often unstable reinforcement learning fr..."}
+{"idx": 6, "title": "Discovering Preference Optimization Algorithms", "date": "", "ddg_snippet": "In order to simplify the whole process, direct preference optimization [ Rafailov et al ., 2023, DPO] aims to forego both the reward modeling and online RL procedure.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JiYAbQvCXV", "content": "In order to simplify the whole process, direct preference optimization [ Rafailov et al ., 2023, DPO] aims to forego both the reward modeling and online RL procedure."}
+{"idx": 7, "title": "Disentangling Length from Quality in Direct Preference Optimization", "date": "", "ddg_snippet": "Direct preference optimization (DPO) ( Rafailov et al ., 2023) is representative work of non-RL alignment. ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384206558_Disentangling_Length_from_Quality_in_Direct_Preference_Optimization", "content": "Direct preference optimization (DPO) ( Rafailov et al ., 2023) is representative work of non-RL alignment. ..."}
+{"idx": 8, "title": "Together Fine-Tuning Platform, Now With Preference Optimization ...", "date": "", "ddg_snippet": "An illustration of DPO from \" Direct Preference Optimization : Your Language Model is Secretly a Reward Model” ( Rafailov et al ., NeurIPS 2023).", "subpage_snippet": "", "source": "www.together.ai", "link": "https://www.together.ai/blog/introducing-fine-tuning-platform", "content": "An illustration of DPO from \" Direct Preference Optimization : Your Language Model is Secretly a Reward Model” ( Rafailov et al ., NeurIPS 2023)."}
+{"idx": 9, "title": "Improving Personalization in Preference Tuning via Inferred User...", "date": "", "ddg_snippet": "LLMs are then trained on this data via preference tuning meth-ods like direct preference optimization ( Rafailov et al ., 2024, DPO), learning to give outputs like the majority chosen response and unlike the rejected one.", "subpage_snippet": "", "source": "www.cs.umd.edu", "link": "https://www.cs.umd.edu/~jbg/docs/2025_acl_boat.pdf", "content": "LLMs are then trained on this data via preference tuning meth-ods like direct preference optimization ( Rafailov et al ., 2024, DPO), learning to give outputs like the majority chosen response and unlike the rejected one."}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_loss_RLHF_year_2024.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_loss_RLHF_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a4aed88c106dfb8c4cd642a305a1d462cb0c29b5
--- /dev/null
+++ b/data/sampled_jsons/Direct_Preference_Optimization_loss_RLHF_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Paper Summary: Direct Preference Optimization: Your Language", "date": "", "ddg_snippet": "The authors figured out a way to represent the objective function from RLHF as a loss function that can be directly optimized using algorithms such ...", "subpage_snippet": "", "source": "queirozf.com", "link": "https://queirozf.com/entries/paper-summary-direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "content": "The authors figured out a way to represent the objective function from RLHF as a loss function that can be directly optimized using algorithms such ..."}
+{"idx": 1, "title": "A Comprehensive Survey of Direct Preference Optimization:", "date": "", "ddg_snippet": "Direct Preference Optimization (DPO) has emerged as a promising approach for alignment, acting as an RL-free alternative to Reinforcement Learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.15595v2", "content": "Direct Preference Optimization (DPO) has emerged as a promising approach for alignment, acting as an RL-free alternative to Reinforcement Learning ..."}
+{"idx": 2, "title": "[2305.18290] Direct Preference Optimization: Your Language", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the ..."}
+{"idx": 3, "title": "Direct Preference Optimization: Your Language Model is Secretly", "date": "", "ddg_snippet": "It innovatively employs a straightforward classification loss to directly optimize the model s policy to meet these preferences efficiently.", "subpage_snippet": "", "source": "blog.athina.ai", "link": "https://blog.athina.ai/direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "content": "It innovatively employs a straightforward classification loss to directly optimize the model s policy to meet these preferences efficiently."}
+{"idx": 4, "title": "RLHF and alternatives: DPO and CoH", "date": "", "ddg_snippet": "... optimizes a Policy based on that RM (Reinforcement Learning by Human Feedback using PPO — left of the figure), DPO directly defines the ...", "subpage_snippet": "", "source": "argilla.io", "link": "https://argilla.io/blog/mantisnlp-rlhf-part-3/", "content": "... optimizes a Policy based on that RM (Reinforcement Learning by Human Feedback using PPO — left of the figure), DPO directly defines the ..."}
+{"idx": 5, "title": "RLHF and alternatives: Overview", "date": "", "ddg_snippet": "It treats the constrained reward maximization problem as a classification problem on human preference data, directly defining the preference loss as ...", "subpage_snippet": "", "source": "argilla.io", "link": "https://argilla.io/blog/mantisnlp-rlhf-part-9/", "content": "It treats the constrained reward maximization problem as a classification problem on human preference data, directly defining the preference loss as ..."}
+{"idx": 6, "title": "Direct Preference Optimization: Your Language Model is Secretly", "date": "", "ddg_snippet": "We propose Direct Preference Optimization (DPO), an algorithm that implicitly optimizes the same objective as existing RLHF algorithms (reward ...", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "content": "We propose Direct Preference Optimization (DPO), an algorithm that implicitly optimizes the same objective as existing RLHF algorithms (reward ..."}
+{"idx": 7, "title": "Direct Preference Optimization | TransferLab — appliedAI", "date": "", "ddg_snippet": "... direct preference optimization (DPO) for aligning language models (LM) to human preferences without using reinforcement learning from human feedback ...", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/pills/2023/direct-preference-optmization/", "content": "... direct preference optimization (DPO) for aligning language models (LM) to human preferences without using reinforcement learning from human feedback ..."}
+{"idx": 8, "title": "RLHF 201 - with Nathan Lambert of AI2 and Interconnects", "date": "", "ddg_snippet": "He discusses its significance in enhancing language models, including preference modeling and innovative methods like Direct Preference Optimization .", "subpage_snippet": "", "source": "share.snipd.com", "link": "https://share.snipd.com/episode/b3fd6003-b3bd-4781-9945-efe1a219988d", "content": "He discusses its significance in enhancing language models, including preference modeling and innovative methods like Direct Preference Optimization ."}
+{"idx": 9, "title": "May 2024 – czxttkl", "date": "", "ddg_snippet": "In this post, we dig into more details of Direct Preference Optimization [1], a popular method used in RLHF . First, we start from the normal RLHF ...", "subpage_snippet": "", "source": "czxttkl.com", "link": "https://czxttkl.com/2024/05/", "content": "In this post, we dig into more details of Direct Preference Optimization [1], a popular method used in RLHF . First, we start from the normal RLHF ..."}
diff --git a/data/sampled_jsons/Discrepancy_Minimization_in_Input-Sparsity_Time_Larsen_2023_year_2023.jsonl b/data/sampled_jsons/Discrepancy_Minimization_in_Input-Sparsity_Time_Larsen_2023_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4bbc60981c61fe49beb0cad369596124cc19427b
--- /dev/null
+++ b/data/sampled_jsons/Discrepancy_Minimization_in_Input-Sparsity_Time_Larsen_2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DISCREPANCY Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of DISCREPANCY is the quality or state of disagreeing or being at variance. How to use discrepancy in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/discrepancy", "content": "The meaning of DISCREPANCY is the quality or state of disagreeing or being at variance. How to use discrepancy in a sentence."}
+{"idx": 1, "title": "DISCREPANCY | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "DISCREPANCY definition: 1. a difference between two things that should be the same: 2. a difference between two things…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/discrepancy", "content": "DISCREPANCY definition: 1. a difference between two things that should be the same: 2. a difference between two things…. Learn more."}
+{"idx": 2, "title": "DISCREPANCY Definition & Meaning | Dictionary.com", "date": "", "ddg_snippet": "Discrepancy definition: the state or quality of being discrepant or in disagreement , as by displaying an unexpected or unacceptable difference; inconsistency.. See examples of DISCREPANCY used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/Discrepancy", "content": "Discrepancy definition: the state or quality of being discrepant or in disagreement , as by displaying an unexpected or unacceptable difference; inconsistency.. See examples of DISCREPANCY used in a sentence."}
+{"idx": 3, "title": "DISCREPANCY definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "If there is a discrepancy between two things that ought to be the same, there is a noticeable difference between them.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/discrepancy", "content": "If there is a discrepancy between two things that ought to be the same, there is a noticeable difference between them."}
+{"idx": 4, "title": "Discrepancy - definition of discrepancy by The Free Dictionary", "date": "", "ddg_snippet": "A discrepancy exists between things which ought to be the same; it can be small but is usually significant. A disparity is a large difference between measurable things such as age, rank, or wages", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/discrepancy", "content": "A discrepancy exists between things which ought to be the same; it can be small but is usually significant. A disparity is a large difference between measurable things such as age, rank, or wages"}
+{"idx": 5, "title": "discrepancy noun - Definition, pictures, pronunciation and usage...", "date": "", "ddg_snippet": "Definition of discrepancy noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/us/definition/english/discrepancy", "content": "Definition of discrepancy noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 6, "title": "Discrepancy - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "A discrepancy is a lack of agreement or balance . If there is a discrepancy between the money you earned and the number on your paycheck, you should complain to your boss. There is a discrepancy when there is a difference between two things that should be alike.", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/discrepancy", "content": "A discrepancy is a lack of agreement or balance . If there is a discrepancy between the money you earned and the number on your paycheck, you should complain to your boss. There is a discrepancy when there is a difference between two things that should be alike."}
+{"idx": 7, "title": "discrepancy | Dictionaries and vocabulary tools for English...", "date": "", "ddg_snippet": "Definition of discrepancy . English dictionary and integrated thesaurus for learners, writers, teachers, and students with advanced, intermediate, and beginner levels.", "subpage_snippet": "", "source": "www.wordsmyth.net", "link": "https://www.wordsmyth.net/?ac=1857&rid=11758", "content": "Definition of discrepancy . English dictionary and integrated thesaurus for learners, writers, teachers, and students with advanced, intermediate, and beginner levels."}
+{"idx": 8, "title": "DISCREPANCY Synonyms: 69 Similar and Opposite Words - ...", "date": "", "ddg_snippet": "Synonyms for DISCREPANCY : difference, distinctness, distinctiveness, diversity, distinction, contrast, disparity, disagreement; Antonyms of DISCREPANCY : similarity, resemblance, community, analogy, likeness, identity, agreement, sameness", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/thesaurus/discrepancy", "content": "Synonyms for DISCREPANCY : difference, distinctness, distinctiveness, diversity, distinction, contrast, disparity, disagreement; Antonyms of DISCREPANCY : similarity, resemblance, community, analogy, likeness, identity, agreement, sameness"}
+{"idx": 9, "title": "discrepancy - WordReference.com Dictionary of English", "date": "", "ddg_snippet": "dis •crep•an• cy /dɪˈskrɛpənsi/ n., pl. -cies. the state or quality of lacking agreement; inconsistency:[uncountable] discrepancy in the eyewitness accounts of the accident. an instance of difference or inconsistency:[countable] a few discrepancies in the account of the accident.", "subpage_snippet": "", "source": "www.wordreference.com", "link": "https://www.wordreference.com/definition/discrepancy", "content": "dis •crep•an• cy /dɪˈskrɛpənsi/ n., pl. -cies. the state or quality of lacking agreement; inconsistency:[uncountable] discrepancy in the eyewitness accounts of the accident. an instance of difference or inconsistency:[countable] a few discrepancies in the account of the accident."}
diff --git a/data/sampled_jsons/DnCNN_architecture_layers_Zhang_2017_m_head_m_body.jsonl b/data/sampled_jsons/DnCNN_architecture_layers_Zhang_2017_m_head_m_body.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..81adf2b1e46d2da6e4002e5515b1aec00625d252
--- /dev/null
+++ b/data/sampled_jsons/DnCNN_architecture_layers_Zhang_2017_m_head_m_body.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - cszn/DnCNN: Beyond a Gaussian Denoiser: Residual Learning of ...", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017 ) - cszn/ DnCNN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017 ) - cszn/ DnCNN"}
+{"idx": 1, "title": "Architecture | cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "DnCNN employs a fully convolutional network architecture specifically designed for image restoration tasks, with a particular focus on image denoising. The network is characterized by its depth (typically 17-20 layers ), use of batch normalization, and most distinctively, its residual learning approach. DnCNN Network Architecture", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN/2-architecture", "content": "DnCNN employs a fully convolutional network architecture specifically designed for image restoration tasks, with a particular focus on image denoising. The network is characterized by its depth (typically 17-20 layers ), use of batch normalization, and most distinctively, its residual learning approach. DnCNN Network Architecture"}
+{"idx": 2, "title": "Review: DnCNN — Residual Learning of Deep CNN (Image ... - Medium", "date": "", "ddg_snippet": "With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This is a paper in 2017 TIP with over 1700 citations, where TIP has a high impact factor of 6.79.", "subpage_snippet": "", "source": "sh-tsang.medium.com", "link": "https://sh-tsang.medium.com/review-dncnn-residual-learning-of-deep-cnn-image-denoising-super-resolution-jpeg-deblocking-cbf464b03130", "content": "With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This is a paper in 2017 TIP with over 1700 citations, where TIP has a high impact factor of 6.79."}
+{"idx": 3, "title": "The architecture of the proposed DnCNN network. - ResearchGate", "date": "", "ddg_snippet": "In 2016, Zhang et al. [10] proposed the DnCNN model for image denoising, introduced the idea of residual learning, and used the network model to predict the residual image, which can achieve blind ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/The-architecture-of-the-proposed-DnCNN-network_fig4_306187437", "content": "In 2016, Zhang et al. [10] proposed the DnCNN model for image denoising, introduced the idea of residual learning, and used the network model to predict the residual image, which can achieve blind ..."}
+{"idx": 4, "title": "DnCNN/README.md at master · cszn/DnCNN · GitHub", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017 ) - DnCNN /README.md at master · cszn/ DnCNN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/README.md", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017 ) - DnCNN /README.md at master · cszn/ DnCNN"}
+{"idx": 5, "title": "cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "What is DnCNN ? DnCNN is a deep learning approach introduced in the paper \"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising\" by Zhang et al. It represents a significant advancement in image denoising by utilizing a convolutional neural network with batch normalization and a residual learning strategy.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN", "content": "What is DnCNN ? DnCNN is a deep learning approach introduced in the paper \"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising\" by Zhang et al. It represents a significant advancement in image denoising by utilizing a convolutional neural network with batch normalization and a residual learning strategy."}
+{"idx": 6, "title": "The DnCNN Network Architecture | Download Scientific Diagram", "date": "", "ddg_snippet": "DnCNN algorithm as shown in Figure 1 was developed. We stacked 17 CNN layers with batch-normalization and ReLU activation layers added in-between each activation layer . ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/The-DnCNN-Network-Architecture_fig1_353258436", "content": "DnCNN algorithm as shown in Figure 1 was developed. We stacked 17 CNN layers with batch-normalization and ReLU activation layers added in-between each activation layer . ..."}
+{"idx": 7, "title": "PyTorch Implementation | cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "For information about other implementations, see Keras Implementation and MATLAB Implementation. Model Architecture The PyTorch implementation of DnCNN follows the architecture described in the original paper, implemented as a fully convolutional network with residual learning capability.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN/3-pytorch-implementation", "content": "For information about other implementations, see Keras Implementation and MATLAB Implementation. Model Architecture The PyTorch implementation of DnCNN follows the architecture described in the original paper, implemented as a fully convolutional network with residual learning capability."}
+{"idx": 8, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image ...", "date": "", "ddg_snippet": "With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1608.03981", "content": "With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking."}
+{"idx": 9, "title": "PDF A Experiments Details - NeurIPS", "date": "", "ddg_snippet": "Also, for each architecture , the reported training time of each model is averaged over all the instances of training this architecture with various optimizers and learning rates.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2020/file/f9fd2624beefbc7808e4e405d73f57ab-Supplemental.pdf", "content": "Also, for each architecture , the reported training time of each model is averaged over all the instances of training this architecture with various optimizers and learning rates."}
diff --git a/data/sampled_jsons/DnCNN_m_head_m_body_layer_names_Zhang_2017.jsonl b/data/sampled_jsons/DnCNN_m_head_m_body_layer_names_Zhang_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bc855af380703ed1ae403414928b3c737ea29af4
--- /dev/null
+++ b/data/sampled_jsons/DnCNN_m_head_m_body_layer_names_Zhang_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - WolframRhodium/ DnCNN : Implementation of DnCNN in...", "date": "", "ddg_snippet": "Implementation of DnCNN in MATLAB R2018a using Neural Network Toolbox™. Zhang , K., Zuo, W., Chen, Y., Meng, D., & Zhang , L. ( 2017 ). Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/WolframRhodium/DnCNN", "content": "Implementation of DnCNN in MATLAB R2018a using Neural Network Toolbox™. Zhang , K., Zuo, W., Chen, Y., Meng, D., & Zhang , L. ( 2017 ). Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising."}
+{"idx": 1, "title": "KAIR/models/network_ dncnn .py · lambdalabs/LambdaSuperRes at main", "date": "", "ddg_snippet": "year={ 2017 }, publisher={IEEE}. } @article{ zhang 2018ffdnet, title={FFDNet: Toward a fast and flexible solution for CNN -based image denoising}m_tail = B.conv(nc, out_nc, mode='C', bias=bias). self.model = B.sequential( m _ head , * m _ body , m_tail). def forward(self, x)", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/lambdalabs/LambdaSuperRes/blob/main/KAIR/models/network_dncnn.py", "content": "year={ 2017 }, publisher={IEEE}. } @article{ zhang 2018ffdnet, title={FFDNet: Toward a fast and flexible solution for CNN -based image denoising}m_tail = B.conv(nc, out_nc, mode='C', bias=bias). self.model = B.sequential( m _ head , * m _ body , m_tail). def forward(self, x)"}
+{"idx": 2, "title": "model.architectures.keras. dncnn — OpenDenoising 0.1 documentation", "date": "", "ddg_snippet": "Source code for model.architectures.keras. dncnn .[1] Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a gaussian denoiser: Residual learning of deep cnn . for image denoising.", "subpage_snippet": "", "source": "opendenoising-docs.readthedocs.io", "link": "https://opendenoising-docs.readthedocs.io/en/latest/_modules/model/architectures/keras/dncnn.html", "content": "Source code for model.architectures.keras. dncnn .[1] Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a gaussian denoiser: Residual learning of deep cnn . for image denoising."}
+{"idx": 3, "title": "Single Image DnCNN Visibility Improvement (SImDnCNNVI)", "date": "", "ddg_snippet": "K Zhang [30]: In [30] 2017 a novel idea was introduced with the DnCNN model that utilized batch normalization and residual connection for blind Gaussian denoising. DnCNN is a Trainable Non-linear Reaction-Diffusion (TNRD) Model for fast and effective image restoration.", "subpage_snippet": "", "source": "sv-journal.org", "link": "https://sv-journal.org/2022-3/07/", "content": "K Zhang [30]: In [30] 2017 a novel idea was introduced with the DnCNN model that utilized batch normalization and residual connection for blind Gaussian denoising. DnCNN is a Trainable Non-linear Reaction-Diffusion (TNRD) Model for fast and effective image restoration."}
+{"idx": 4, "title": "The DnCNN Network Architecture. | Download Scientific Diagram", "date": "", "ddg_snippet": "... basic architecture of the DnCNN for image denoising [7] is as shown in Fig. 1. The network is composed of 17 convolution layers . All layers except the output layer is activated with a Rectified Linear Unit (ReLU) activation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/The-DnCNN-Network-Architecture_fig1_353208796", "content": "... basic architecture of the DnCNN for image denoising [7] is as shown in Fig. 1. The network is composed of 17 convolution layers . All layers except the output layer is activated with a Rectified Linear Unit (ReLU) activation."}
+{"idx": 5, "title": "DnCNN 论文阅读笔记【MATLAB】_使用matlab编写 dncnn 网络-CSDN...", "date": "", "ddg_snippet": "(iii)Conv: for the last layer , c filters of size 3 ×3 × 64 are used to reconstruct the output. （2）Reducing Boundary Artifacts. In many low level vision applications, it usually requires that the output image size should keep the same as the input one.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/weixin_41923961/article/details/80382529", "content": "(iii)Conv: for the last layer , c filters of size 3 ×3 × 64 are used to reconstruct the output. （2）Reducing Boundary Artifacts. In many low level vision applications, it usually requires that the output image size should keep the same as the input one."}
+{"idx": 6, "title": "Alternatives and detailed information of Dncnn Pytorch - GitPlanet", "date": "", "ddg_snippet": "According to the paper, DnCNN -S has 17 layers . noiseL is used for training and val_noiseL is used for validation. They should be set to the same value for unbiased validation. You can set whatever noise level you need.Set num_of_ layers to be 17 when testing DnCNN -S models.", "subpage_snippet": "", "source": "gitplanet.com", "link": "https://gitplanet.com/project/dncnn-pytorch", "content": "According to the paper, DnCNN -S has 17 layers . noiseL is used for training and val_noiseL is used for validation. They should be set to the same value for unbiased validation. You can set whatever noise level you need.Set num_of_ layers to be 17 when testing DnCNN -S models."}
+{"idx": 7, "title": "Image and Video Denoising using DnCNN | by Varun Saproo | Medium", "date": "", "ddg_snippet": "The output of the DnCNN model is a residual image. Therefore, Original Image = Noise Image — Residual Image. In DnCNN , zeros are padded before convolution at each layer to make sure that each feature map of the middle layers has the same size as the input image.", "subpage_snippet": "", "source": "saproovarun.medium.com", "link": "https://saproovarun.medium.com/image-and-video-denoising-using-dncnn-216be1ff8ba1", "content": "The output of the DnCNN model is a residual image. Therefore, Original Image = Noise Image — Residual Image. In DnCNN , zeros are padded before convolution at each layer to make sure that each feature map of the middle layers has the same size as the input image."}
+{"idx": 8, "title": "superres_ppp_ dncnn _admm.ipynb - Colaboratory", "date": "", "ddg_snippet": ". Rename notebook. superres_ppp_ dncnn _admm.ipynb_.g = functional. DnCNN (\"17 M \"). Compute a baseline solution via denoising of the pseudo-inverse of the forward operator.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/lanl/scico-data/blob/colab/notebooks/superres_ppp_dncnn_admm.ipynb", "content": ". Rename notebook. superres_ppp_ dncnn _admm.ipynb_.g = functional. DnCNN (\"17 M \"). Compute a baseline solution via denoising of the pseudo-inverse of the forward operator."}
+{"idx": 9, "title": "DnCNN — deepinverse 0.3 documentation", "date": "", "ddg_snippet": "class deepinv.models. DnCNN (in_channels=3, out_channels=3, depth=20, bias=True, nf=64, pretrained='download', device='cpu')[source] #.The number of layers can be specified by the user. Unlike the original paper, this implementation does not include batch normalization layers .", "subpage_snippet": "", "source": "deepinv.github.io", "link": "https://deepinv.github.io/deepinv/api/stubs/deepinv.models.DnCNN.html", "content": "class deepinv.models. DnCNN (in_channels=3, out_channels=3, depth=20, bias=True, nf=64, pretrained='download', device='cpu')[source] #.The number of layers can be specified by the user. Unlike the original paper, this implementation does not include batch normalization layers ."}
diff --git a/data/sampled_jsons/Dongyeop_Lee_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_arxiv.jsonl b/data/sampled_jsons/Dongyeop_Lee_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9941687b758dc63ce2f7ed7e9f596e511ec5d4f0
--- /dev/null
+++ b/data/sampled_jsons/Dongyeop_Lee_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Live wind map and wind forecast — Windy .app", "date": "", "ddg_snippet": "Windy .app live wind map and wind forecast: local wind speed, wind direction, wind gusts, and more", "subpage_snippet": "", "source": "windy.app", "link": "https://windy.app/map/", "content": "Windy .app live wind map and wind forecast: local wind speed, wind direction, wind gusts, and more"}
+{"idx": 1, "title": "Live wind map and wind forecast — Windy .app", "date": "", "ddg_snippet": "Windy .app live wind map and wind forecast: local wind speed, wind direction, wind gusts, and more", "subpage_snippet": "", "source": "windy.app", "link": "https://windy.app/map/Home", "content": "Windy .app live wind map and wind forecast: local wind speed, wind direction, wind gusts, and more"}
+{"idx": 2, "title": "SAFE : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Authors: Dongyeop Lee , Kwanhee Lee , Jinseok Chung, Namhoon Lee .Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "Authors: Dongyeop Lee , Kwanhee Lee , Jinseok Chung, Namhoon Lee .Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time."}
+{"idx": 3, "title": "Safe : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Dongyeop Lee Kwanhee Lee Jinseok Chung Namhoon Lee .towards sparsity without incurring a sudden change of loss, all while performing flatness induction, yielding a sparse and flat minima .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v2", "content": "Dongyeop Lee Kwanhee Lee Jinseok Chung Namhoon Lee .towards sparsity without incurring a sudden change of loss, all while performing flatness induction, yielding a sparse and flat minima ."}
+{"idx": 4, "title": "SAFE : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Dongyeop Lee 1 Kwanhee Lee 1 Jinseok Chung 1 Namhoon Lee 1. Abstract. Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the origi-nal performance despite much recent progress.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.06866", "content": "Dongyeop Lee 1 Kwanhee Lee 1 Jinseok Chung 1 Namhoon Lee 1. Abstract. Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the origi-nal performance despite much recent progress."}
+{"idx": 5, "title": "(PDF) SAFE : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Dongyeop Lee 1Kwanhee Lee 1Jinseok Chung 1Namhoon Lee 1. Abstract. Sparsifying neural networks often suffers from.CVPR, 2021. Zhu, M. and Gupta, S. To prune , or not to prune : exploring. the efficacy of pruning for model compression. arXiv", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392531034_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning", "content": "Dongyeop Lee 1Kwanhee Lee 1Jinseok Chung 1Namhoon Lee 1. Abstract. Sparsifying neural networks often suffers from.CVPR, 2021. Zhu, M. and Gupta, S. To prune , or not to prune : exploring. the efficacy of pruning for model compression. arXiv"}
+{"idx": 6, "title": "GitHub - LOG-postech/ safe -jax: [ICML 2025 Spotlight] Official JAX...", "date": "", "ddg_snippet": "SAFE : Finding Sparse and Flat Minima to Improve Pruning . Authors: Dongyeop Lee , Kwanhee Lee , Jinseok Chung, Namhoon Lee . Venue: ICML 2025, Spotlight poster.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LOG-postech/safe-jax", "content": "SAFE : Finding Sparse and Flat Minima to Improve Pruning . Authors: Dongyeop Lee , Kwanhee Lee , Jinseok Chung, Namhoon Lee . Venue: ICML 2025, Spotlight poster."}
+{"idx": 7, "title": "Kwanhee Lee - Google Scholar", "date": "", "ddg_snippet": "Dongyeop Lee Dongyeop LeeMaster's student, POSTECHVerified email at postech.ac.kr. SAFE : Finding Sparse and Flat Minima to Improve Pruning .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=QLXgaCMAAAAJ&hl=en", "content": "Dongyeop Lee Dongyeop LeeMaster's student, POSTECHVerified email at postech.ac.kr. SAFE : Finding Sparse and Flat Minima to Improve Pruning ."}
+{"idx": 8, "title": "publications | Dongyeop Lee", "date": "", "ddg_snippet": "SAFE : Finding Sparse and Flat Minima to Improve Pruning . Dongyeop Lee , Kwanhee Lee , Jinseok Chung, and Namhoon Lee .Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective.", "subpage_snippet": "", "source": "edong6768.github.io", "link": "https://edong6768.github.io/publications/", "content": "SAFE : Finding Sparse and Flat Minima to Improve Pruning . Dongyeop Lee , Kwanhee Lee , Jinseok Chung, and Namhoon Lee .Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective."}
+{"idx": 9, "title": "dblp: List of computer science publications by Namhoon Lee", "date": "", "ddg_snippet": "Dongyeop Lee , Kwanhee Lee , Jinseok Chung, Namhoon Lee : SAFE : Finding Sparse and Flat Minima to Improve Pruning .electronic edition @ arxiv .org (open access).", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/63/5359.html", "content": "Dongyeop Lee , Kwanhee Lee , Jinseok Chung, Namhoon Lee : SAFE : Finding Sparse and Flat Minima to Improve Pruning .electronic edition @ arxiv .org (open access)."}
diff --git a/data/sampled_jsons/Doshi_et_al._2023_Self-Repellent_Random_Walk_SRRW_abstract_time-reversible.jsonl b/data/sampled_jsons/Doshi_et_al._2023_Self-Repellent_Random_Walk_SRRW_abstract_time-reversible.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b7fb61fb08d35660acd5a21670c893327c697d22
--- /dev/null
+++ b/data/sampled_jsons/Doshi_et_al._2023_Self-Repellent_Random_Walk_SRRW_abstract_time-reversible.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Self-Repellent Random Walks on General Graphs -Achieving Minimal ...", "date": "", "ddg_snippet": "Abstract We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sam-pling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/doshi23a/doshi23a.pdf", "content": "Abstract We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sam-pling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent ..."}
+{"idx": 1, "title": "PDF Self-Repellent Random Walks on General Graphs - IJCAI", "date": "", "ddg_snippet": "We study a family of distributed stochastic optimization algorithms, known as token algorithms, where gradients are sampled by a token traversing a network of agents in random-walk fashion [Sun et al ., 2018; Hu et al ., 2022; Even, 2023 ].", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0929.pdf", "content": "We study a family of distributed stochastic optimization algorithms, known as token algorithms, where gradients are sampled by a token traversing a network of agents in random-walk fashion [Sun et al ., 2018; Hu et al ., 2022; Even, 2023 ]."}
+{"idx": 2, "title": "PDF Accelerating Distributed Stochastic Opti Mization Via Self-repellent ...", "date": "", "ddg_snippet": "from μ asymptotically is not enough to achieve optimal algorithmic performance. In this paper, we take a closer look at the distributed stochastic optimization problem through the lens of a non-linear Markov chain, known as the Self Repellent Random Walk ( SRRW ), which was shown in Doshi et al. ( 2023 ) to achieve asymptotically minimal sampling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.09665.pdf", "content": "from μ asymptotically is not enough to achieve optimal algorithmic performance. In this paper, we take a closer look at the distributed stochastic optimization problem through the lens of a non-linear Markov chain, known as the Self Repellent Random Walk ( SRRW ), which was shown in Doshi et al. ( 2023 ) to achieve asymptotically minimal sampling ..."}
+{"idx": 3, "title": "ICML 2023 Self-Repellent Random Walks on General Graphs - Achieving ...", "date": "", "ddg_snippet": "Abstract : We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a * self-repellent ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2023/oral/25435", "content": "Abstract : We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a * self-repellent ..."}
+{"idx": 4, "title": "Self-Repellent Random Walks on General Graphs - Achieving Minimal ...", "date": "", "ddg_snippet": "Self-Repellent Random W alks on General Graphs - Achieving Minimal Sampling V ariance via Nonlinear Mark ov Chains Vishwaraj Doshi 1Jie Hu 2Do Young Eun Abstract", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370633301_Self-Repellent_Random_Walks_on_General_Graphs_-_Achieving_Minimal_Sampling_Variance_via_Nonlinear_Markov_Chains", "content": "Self-Repellent Random W alks on General Graphs - Achieving Minimal Sampling V ariance via Nonlinear Mark ov Chains Vishwaraj Doshi 1Jie Hu 2Do Young Eun Abstract"}
+{"idx": 5, "title": "Self-Repellent Random Walks on General Graphs - OpenReview", "date": "", "ddg_snippet": "Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random walk ( SRRW ) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom visited nodes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=450iImFM4U", "content": "Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random walk ( SRRW ) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom visited nodes."}
+{"idx": 6, "title": "ICML 2023 - Self-Repellent Random Walks on General Graphs - Achieving ...", "date": "", "ddg_snippet": "In this episode we discuss Self-Repellent Random Walks on General Graphs — Achieving Minimal Sampling Variance via Nonlinear Markov Chains by Vishwaraj Doshi , Jie Hu, Do Young Eun. This paper introduces self-repellent random walks ( SRRWs ) as a way to improve sampling efficiency in Markov chain Monte Carlo (MCMC) procedures.", "subpage_snippet": "", "source": "aibreakdown.org", "link": "https://aibreakdown.org/icml-2023-self-repellent-random-walks-on-general-graphs-achieving-minimal-sampling-variance-via-nonlinear-markov-chains/", "content": "In this episode we discuss Self-Repellent Random Walks on General Graphs — Achieving Minimal Sampling Variance via Nonlinear Markov Chains by Vishwaraj Doshi , Jie Hu, Do Young Eun. This paper introduces self-repellent random walks ( SRRWs ) as a way to improve sampling efficiency in Markov chain Monte Carlo (MCMC) procedures."}
+{"idx": 7, "title": "Accelerating Distributed Stochastic Optimization via Self-Repellent ...", "date": "", "ddg_snippet": "We study a family of distributed stochastic optimization algorithms where gradients are sampled by a token traversing a network of agents in random-walk fashion. Typically, these random-walks are chosen to be Markov chains that asymptotically sample from a desired target distribution, and play a critical role in the convergence of the optimization iterates. In this paper, we take a novel ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.09665", "content": "We study a family of distributed stochastic optimization algorithms where gradients are sampled by a token traversing a network of agents in random-walk fashion. Typically, these random-walks are chosen to be Markov chains that asymptotically sample from a desired target distribution, and play a critical role in the convergence of the optimization iterates. In this paper, we take a novel ..."}
+{"idx": 8, "title": "Self-Repellent Random Walks on General Graphs - Achieving Minimal ...", "date": "", "ddg_snippet": "We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/doshi23a.html", "content": "We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random ..."}
+{"idx": 9, "title": "ICLR Poster Accelerating Distributed Stochastic Optimization via Self ...", "date": "", "ddg_snippet": "In the context of MCMC sampling on a graph, a recent breakthrough in Doshi et al. ( 2023 ) shows that the SRRW achieves O(1/alpha) O (1 / a l p h a) decrease in the asymptotic variance for sampling.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/poster/19214", "content": "In the context of MCMC sampling on a graph, a recent breakthrough in Doshi et al. ( 2023 ) shows that the SRRW achieves O(1/alpha) O (1 / a l p h a) decrease in the asymptotic variance for sampling."}
diff --git a/data/sampled_jsons/Dynamic_Algorithms_against_an_Adaptive_Adversary_Beimel_Kaplan_Mansour_Nissim_Saranurak_Stemmer_abst.jsonl b/data/sampled_jsons/Dynamic_Algorithms_against_an_Adaptive_Adversary_Beimel_Kaplan_Mansour_Nissim_Saranurak_Stemmer_abst.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..70d0b26ada94040ed066dc44cb5924a2d85c26de
--- /dev/null
+++ b/data/sampled_jsons/Dynamic_Algorithms_against_an_Adaptive_Adversary_Beimel_Kaplan_Mansour_Nissim_Saranurak_Stemmer_abst.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2111.03980] Dynamic Algorithms Against an Adaptive Adversary ...", "date": "", "ddg_snippet": "We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from differential privacy, cryptography, and adaptive data analysis.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.03980", "content": "We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from differential privacy, cryptography, and adaptive data analysis."}
+{"idx": 1, "title": "Dynamic Algorithms Against an Adaptive Adversary", "date": "", "ddg_snippet": "We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from dierential privacy, cryptography, and adaptive data analysis.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2111.03980", "content": "We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from dierential privacy, cryptography, and adaptive data analysis."}
+{"idx": 2, "title": "Dynamic Algorithms Against an Adaptive Adversary : Generic...", "date": "", "ddg_snippet": "We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from differential privacy, cryptography, and adaptive data analysis.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/dynamic-algorithms-against-an-adaptive-adversary-generic-constructions-and-lower-bounds/", "content": "We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from differential privacy, cryptography, and adaptive data analysis."}
+{"idx": 3, "title": "Thatchaphol Saranurak - Google Akademik", "date": "", "ddg_snippet": "46. 2017. Dynamic algorithms against an adaptive adversary : generic constructions and lower bounds. A Beimel , H Kaplan , Y Mansour , K Nissim , T Saranurak , U Stemmer .", "subpage_snippet": "", "source": "scholar.google.co.za", "link": "https://scholar.google.co.za/citations?user=y2A2tQgAAAAJ&hl=tr", "content": "46. 2017. Dynamic algorithms against an adaptive adversary : generic constructions and lower bounds. A Beimel , H Kaplan , Y Mansour , K Nissim , T Saranurak , U Stemmer ."}
+{"idx": 4, "title": "STOC 2022 - Dynamic Algorithms Against an Adaptive Adversary ...", "date": "", "ddg_snippet": "... Adversary : Generic Constructions and Lower Bounds Amos Beimel (Ben-Gurion University), Haim Kaplan (Tel Aviv University and Google research), Yishay Mansour (Tel Aviv University and Google research), Kobbi Nissim (Georgetown University), Thatchaphol Saranurak ...", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/289472930250da35af1e1357416079ac/", "content": "... Adversary : Generic Constructions and Lower Bounds Amos Beimel (Ben-Gurion University), Haim Kaplan (Tel Aviv University and Google research), Yishay Mansour (Tel Aviv University and Google research), Kobbi Nissim (Georgetown University), Thatchaphol Saranurak ..."}
+{"idx": 5, "title": "Thatchaphol Saranurak - Publications (by Years)", "date": "", "ddg_snippet": "33. Dynamic Algorithms Against an Adaptive Adversary : Generic Constructions and Lower Bounds [pdf] Amos Beimel , Haim Kaplan , Yishay Mansour , Kobbi Nissim , Thatchaphol Saranurak , and Uri Stemmer STOC 2022 Summary: Connect dynamic algorithms to differential privacy...", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/site/thsaranurak/publications", "content": "33. Dynamic Algorithms Against an Adaptive Adversary : Generic Constructions and Lower Bounds [pdf] Amos Beimel , Haim Kaplan , Yishay Mansour , Kobbi Nissim , Thatchaphol Saranurak , and Uri Stemmer STOC 2022 Summary: Connect dynamic algorithms to differential privacy..."}
+{"idx": 6, "title": "Robust Algorithms on Adaptive Inputs from Bounded Adversaries", "date": "", "ddg_snippet": "Dynamic algorithms against an adaptive adversary : generic constructions and lower bounds.Haim Kaplan , Yishay Mansour , Kobbi Nissim , and Uri Stemmer . Separating adaptive streaming from oblivious streaming using the bounded storage model.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=O6MCxd0PUYdg", "content": "Dynamic algorithms against an adaptive adversary : generic constructions and lower bounds.Haim Kaplan , Yishay Mansour , Kobbi Nissim , and Uri Stemmer . Separating adaptive streaming from oblivious streaming using the bounded storage model."}
+{"idx": 7, "title": "Amos Beimel (0000-0002-6572-4195) - ORCID", "date": "", "ddg_snippet": "Dynamic algorithms against an adaptive adversary : Generic constructions and lower bounds.Learning privately with labeled and unlabeled examples. Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms. 2015 | Conference paper.", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0000-0002-6572-4195", "content": "Dynamic algorithms against an adaptive adversary : Generic constructions and lower bounds.Learning privately with labeled and unlabeled examples. Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms. 2015 | Conference paper."}
+{"idx": 8, "title": "Adaptive Data Analysis in a Balanced Adversarial", "date": "", "ddg_snippet": "Dynamic algorithms against an adaptive adversary : generic constructions and lower bounds.H. Kaplan , Y. Mansour , K. Nissim , and U. Stemmer . Separating adaptive streaming from oblivious streaming using the bounded storage model.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/51ba8a68f471d952af625d1faf55e6c6-Paper-Conference.pdf", "content": "Dynamic algorithms against an adaptive adversary : generic constructions and lower bounds.H. Kaplan , Y. Mansour , K. Nissim , and U. Stemmer . Separating adaptive streaming from oblivious streaming using the bounded storage model."}
+{"idx": 9, "title": "Yishay Mansour", "date": "", "ddg_snippet": "ACM Fellow 2014, \"For contributions to machine learning, algorithmic game theory, distributed computing, and communication networks.\".", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/yishay-mansour/", "content": "ACM Fellow 2014, \"For contributions to machine learning, algorithmic game theory, distributed computing, and communication networks.\"."}
diff --git a/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Equation_2_formula.jsonl b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Equation_2_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..396ad303d4ce0358f12363311c0a6c2ebbd2e382
--- /dev/null
+++ b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Equation_2_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "velpegor/ ELITE : [ICML 2025] ELITE : Enhanced Language - Image ...", "date": "", "ddg_snippet": "Repository files navigation. README. ELITE : Enhanced Language - Image Toxicity Evaluation for Safety.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/velpegor/ELITE", "content": "Repository files navigation. README. ELITE : Enhanced Language - Image Toxicity Evaluation for Safety."}
+{"idx": 1, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety How ELITE Reveals Dangerous Weaknesses in Vision-Language AI cvlab.yonsei.ac.kr AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML ELITE: Enhanced Language-Image Toxicity Evaluation for Safety AIM Intelligence's ELITE Collaborative Paper Accepted by the ... ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Feb 7, 2025 · Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator. 2 . ELITE Evaluator: Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ... Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator. May 15, 2025 · The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent. The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ... May 15, 2025 · New York, New York-- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety\", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University. AIM Intelligence CI The paper proposes ... About [ICML 2025] ELITE : Enhanced Language-Image Toxicity Evaluation for Safety", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04757", "content": "Feb 7, 2025 · Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator. 2 . ELITE Evaluator: Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ... Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator. May 15, 2025 · The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent. The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ... May 15, 2025 · New York, New York-- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety\", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University. AIM Intelligence CI The paper proposes ... About [ICML 2025] ELITE : Enhanced Language-Image Toxicity Evaluation for Safety"}
+{"idx": 2, "title": "How ELITE Reveals Dangerous Weaknesses in Vision-Language AI", "date": "", "ddg_snippet": "2 . ELITE Evaluator: Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aim-intelligence/how-elite-reveals-dangerous-weaknesses-in-vision-language-ai-ffa208b7546c", "content": "2 . ELITE Evaluator: Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ..."}
+{"idx": 3, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "May 15, 2025 · The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "www.newsfilecorp.com", "link": "https://www.newsfilecorp.com/release/252268/AIM-Intelligences-ELITE-Collaborative-Paper-Accepted-by-the-ICML", "content": "May 15, 2025 · The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 4, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ELITE:-Enhanced-Language-Image-Toxicity-Evaluation-Lee-Lee/2bf4206276d5f574bbb2e13a56b29b4522fea675", "content": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ..."}
+{"idx": 5, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ...", "date": "", "ddg_snippet": "May 15, 2025 · New York, New York-- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety\", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University. AIM Intelligence CI The paper proposes ...", "subpage_snippet": "", "source": "index.businessinsurance.com", "link": "https://index.businessinsurance.com/businessinsurance/article/newsfile-2025-5-15-aim-intelligences-elite-collaborative-paper-accepted-by-the-icml", "content": "May 15, 2025 · New York, New York-- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety\", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University. AIM Intelligence CI The paper proposes ..."}
+{"idx": 6, "title": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "We identify these problems in the safety evaluation methods and propose the Enhanced Language - Image Toxicity Evaluation ( ELITE ) evaluator , a method designed to accurately evaluate the safety of VLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "We identify these problems in the safety evaluation methods and propose the Enhanced Language - Image Toxicity Evaluation ( ELITE ) evaluator , a method designed to accurately evaluate the safety of VLMs."}
+{"idx": 7, "title": "[Literature Review] ELITE : Enhanced Language - Image Toxicity ...", "date": "", "ddg_snippet": "The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety\" presents a novel framework designed to evaluate the safety of Vision Language Models (VLMs) against harmful, malicious inputs that could generate unsafe content.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety\" presents a novel framework designed to evaluate the safety of Vision Language Models (VLMs) against harmful, malicious inputs that could generate unsafe content."}
+{"idx": 8, "title": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE evaluator aims to provide an accurate and reliable safety evaluation for vision- language models. However, as a rubric-based approach, its evaluation performance may vary depending on the capabilities of VLMs, which we acknowledge as a potential limitation.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46445/paper", "content": "The ELITE evaluator aims to provide an accurate and reliable safety evaluation for vision- language models. However, as a rubric-based approach, its evaluation performance may vary depending on the capabilities of VLMs, which we acknowledge as a potential limitation."}
+{"idx": 9, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator , a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal...", "subpage_snippet": "", "source": "www.digitaljournal.com", "link": "https://www.digitaljournal.com/pr/news/newsfile/aim-intelligence-s-elite-collaborative-paper-1773375521.html", "content": "The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator , a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal..."}
diff --git a/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_ICML_2025_Table_3_results.jsonl b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_ICML_2025_Table_3_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1536e70585bc5df60414f870610d975e8f4c3510
--- /dev/null
+++ b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_ICML_2025_Table_3_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The authors use it to filter a benchmark dataset to consist of samples where this evaluator is likely to produce harmful scores , and the results in Table 3 and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=583klsIjNx&noteId=lkuNd7fIAG", "content": "The authors use it to filter a benchmark dataset to consist of samples where this evaluator is likely to produce harmful scores , and the results in Table 3 and ..."}
+{"idx": 1, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for ...", "date": "", "ddg_snippet": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46445", "content": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, ..."}
+{"idx": 2, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "This paper introduces a new tool called the ELITE evaluator to better assess the safety of Vision Language Models (VLMs), which can sometimes produce ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46445/paper", "content": "This paper introduces a new tool called the ELITE evaluator to better assess the safety of Vision Language Models (VLMs), which can sometimes produce ..."}
+{"idx": 3, "title": "Computer Vision and Pattern Recognition Feb 2025", "date": "", "ddg_snippet": "Title: ELITE : Enhanced Language - Image Toxicity Evaluation for Safety . Wonjun ... Comments: 15 pages, 10 figures and 3 tables ; project page: this https ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CV/2025-02?skip=175&show=1000", "content": "Title: ELITE : Enhanced Language - Image Toxicity Evaluation for Safety . Wonjun ... Comments: 15 pages, 10 figures and 3 tables ; project page: this https ..."}
+{"idx": 4, "title": "Do We Really Need Curated Malicious Data for Safety ... - CVPR", "date": "", "ddg_snippet": "Experiments demonstrate that without safety -related images , harmful text prompts, and detailed rejection reasons, only “teaching the LLaVA-v1.5 models to reject ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33133", "content": "Experiments demonstrate that without safety -related images , harmful text prompts, and detailed rejection reasons, only “teaching the LLaVA-v1.5 models to reject ..."}
+{"idx": 5, "title": "Computation and Language Feb 2025", "date": "", "ddg_snippet": "7 Feb 2025 — Title: ELITE : Enhanced Language - Image Toxicity Evaluation for Safety ... Comments: 21 pages, 16 figures, 3 tables , ACM CHI 2025 . Subjects ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CL/2025-02?skip=1500&show=1000", "content": "7 Feb 2025 — Title: ELITE : Enhanced Language - Image Toxicity Evaluation for Safety ... Comments: 21 pages, 16 figures, 3 tables , ACM CHI 2025 . Subjects ..."}
+{"idx": 6, "title": "ICML 2025 Thursday 07/17", "date": "", "ddg_snippet": "This hands-on session will give you practical tips and exercises to craft a short, effective and accessible overview of your work for a wide range of audiences ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/17", "content": "This hands-on session will give you practical tips and exercises to craft a short, effective and accessible overview of your work for a wide range of audiences ..."}
+{"idx": 7, "title": "Do We Really Need Curated Malicious Data for Safety ...", "date": "", "ddg_snippet": "by Y Wang · 2025 · Cited by 2 — Experiments show that, with- out the need for labor-intensive collection of high-quality malicious data, model safety can still be significantly im- proved, as ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Do_We_Really_Need_Curated_Malicious_Data_for_Safety_Alignment_CVPR_2025_paper.pdf", "content": "by Y Wang · 2025 · Cited by 2 — Experiments show that, with- out the need for labor-intensive collection of high-quality malicious data, model safety can still be significantly im- proved, as ... 11 pages"}
+{"idx": 8, "title": "Track: Poster Session 5 East", "date": "", "ddg_snippet": "17 Jul 2025 — Experimental results across real-world datasets in medical image ... ELITE : Enhanced Language - Image Toxicity Evaluation for Safety . Wonjun ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50267", "content": "17 Jul 2025 — Experimental results across real-world datasets in medical image ... ELITE : Enhanced Language - Image Toxicity Evaluation for Safety . Wonjun ..."}
+{"idx": 9, "title": "️Awesome-Jailbreak-against-Multimodal-Generative-Models", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety , Arxiv 2025 ... evaluation datasets and a description of each attribute in the table .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/liuxuannan/Awesome-Multimodal-Jailbreak", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety , Arxiv 2025 ... evaluation datasets and a description of each attribute in the table ."}
diff --git a/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_PDF_arXiv.jsonl b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_PDF_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..42ae5e255e97c7e26a3d7e2838ceb17b4f5ba04c
--- /dev/null
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+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04757", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator."}
+{"idx": 1, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ELITE:-Enhanced-Language-Image-Toxicity-Evaluation-Lee-Lee/2bf4206276d5f574bbb2e13a56b29b4522fea675", "content": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ..."}
+{"idx": 2, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "By introducing ELITE , we pave the way for safer, more robust VLMs, contributing essential tools for evaluating and mitigating safety risks in real-world applications. View arXiv page View PDF Add to collection", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04757", "content": "By introducing ELITE , we pave the way for safer, more robust VLMs, contributing essential tools for evaluating and mitigating safety risks in real-world applications. View arXiv page View PDF Add to collection"}
+{"idx": 3, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety | Cool ...", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE {\\em benchmark}, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE {\\em evaluator}.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2502.04757", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE {\\em benchmark}, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE {\\em evaluator}."}
+{"idx": 4, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "New York, New York-- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety \", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University.", "subpage_snippet": "", "source": "business.sherbrookerecord.com", "link": "https://business.sherbrookerecord.com/sherbrookerecord/article/newsfile-2025-5-15-aim-intelligences-elite-collaborative-paper-accepted-by-the-icml", "content": "New York, New York-- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety \", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University."}
+{"idx": 5, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety - arXiv.org", "date": "", "ddg_snippet": "We identify these problems in the safety evaluation methods and propose the Enhanced Language-Image Toxicity Evaluation ( ELITE ) evaluator, a method designed to accurately evaluate the safety of VLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757v3", "content": "We identify these problems in the safety evaluation methods and propose the Enhanced Language-Image Toxicity Evaluation ( ELITE ) evaluator, a method designed to accurately evaluate the safety of VLMs."}
+{"idx": 6, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety | AI ...", "date": "", "ddg_snippet": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety Published 2/10/2025 by Wonjun Lee, Doehyeon Lee, Eugene Choi, Sangyoon Yu, Ashkan Yousefpour, Haon Park, BUMSUB HAM and 1 more...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/elite-enhanced-language-image-toxicity-evaluation-safety", "content": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety Published 2/10/2025 by Wonjun Lee, Doehyeon Lee, Eugene Choi, Sangyoon Yu, Ashkan Yousefpour, Haon Park, BUMSUB HAM and 1 more..."}
+{"idx": 7, "title": "\"ELITE: Enhanced Language-Image Toxicity Evaluation for Safety.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on ELITE : Enhanced Language-Image Toxicity Evaluation for Safety .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-04757", "content": "Bibliographic details on ELITE : Enhanced Language-Image Toxicity Evaluation for Safety ."}
+{"idx": 8, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "www.newsfilecorp.com", "link": "https://www.newsfilecorp.com/release/252268/AIM-Intelligences-ELITE-Collaborative-Paper-Accepted-by-the-ICML", "content": "The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 9, "title": "cvlab.yonsei.ac.kr", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator.", "subpage_snippet": "", "source": "cvlab.yonsei.ac.kr", "link": "https://cvlab.yonsei.ac.kr/projects/ELITE/", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator."}
diff --git a/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_paper_Table_4_year_2023.jsonl b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_paper_Table_4_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d7185d9b4215a9d325995abe49d8269afaf2487
--- /dev/null
+++ b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_paper_Table_4_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "(Gu et al., 2024), the ELITE . 6. Enhanced Language - Image Toxicity Evaluation for Safety . Table 5. Comparison of the E-ASR of the proposed methods in the ELITE benchmark (generated).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "(Gu et al., 2024), the ELITE . 6. Enhanced Language - Image Toxicity Evaluation for Safety . Table 5. Comparison of the E-ASR of the proposed methods in the ELITE benchmark (generated)."}
+{"idx": 1, "title": "Paper page - ELITE : Enhanced Language - Image Toxicity ...", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04757", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images."}
+{"idx": 2, "title": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The paper introduces a new tool called the ELITE evaluator to help check how safe Vision Language Models (VLMs) are.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46445/paper", "content": "The paper introduces a new tool called the ELITE evaluator to help check how safe Vision Language Models (VLMs) are."}
+{"idx": 3, "title": "[Literature Review] ELITE : Enhanced Language - Image Toxicity ...", "date": "", "ddg_snippet": "The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety \" presents a novel framework designed to evaluate the safety of Vision Language Models (VLMs) against harmful, malicious inputs that could generate unsafe content.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety \" presents a novel framework designed to evaluate the safety of Vision Language Models (VLMs) against harmful, malicious inputs that could generate unsafe content."}
+{"idx": 4, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "AIM Intelligence CI. The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision.", "subpage_snippet": "", "source": "www.digitaljournal.com", "link": "https://www.digitaljournal.com/pr/news/newsfile/aim-intelligence-s-elite-collaborative-paper-1773375521.html", "content": "AIM Intelligence CI. The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision."}
+{"idx": 5, "title": "velpegor/ ELITE : [ICML 2025] ELITE : Enhanced Language - Image ...", "date": "", "ddg_snippet": "Repository files navigation. README. ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/velpegor/ELITE", "content": "Repository files navigation. README. ELITE : Enhanced Language - Image Toxicity Evaluation for Safety ."}
+{"idx": 6, "title": "Understanding and Mitigating Toxicity in Image -Text Pretraining...", "date": "", "ddg_snippet": "Elite : Enhanced language - image toxicity evaluation for safety , 2025.Mm-safetybench: A benchmark for safety eval - uation of multimodal large language models, 2024. 2, 4 . [16] Clement Neo, Luke Ong, Philip Torr, Mor Geva, David Krueger, and Fazl Barez.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025W/ReGenAI/papers/Alam_Understanding_and_Mitigating_Toxicity_in_Image-Text_Pretraining_Datasets_A_Case_CVPRW_2025_paper.pdf", "content": "Elite : Enhanced language - image toxicity evaluation for safety , 2025.Mm-safetybench: A benchmark for safety eval - uation of multimodal large language models, 2024. 2, 4 . [16] Clement Neo, Luke Ong, Philip Torr, Mor Geva, David Krueger, and Fazl Barez."}
+{"idx": 7, "title": "velpegor.github.io/ ELITE", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images.", "subpage_snippet": "", "source": "velpegor.github.io", "link": "https://velpegor.github.io/ELITE/", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images."}
+{"idx": 8, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images.", "subpage_snippet": "", "source": "hf.qhduan.com", "link": "https://hf.qhduan.com/papers?q=human+evaluation", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images."}
+{"idx": 9, "title": "Suhyun Kim's research works", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Suhyun-Kim-2304816287", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images."}
diff --git a/data/sampled_jsons/ELITE_benchmark_Table_3_E-ASR_scores_proprietary_open-source_models_highest.jsonl b/data/sampled_jsons/ELITE_benchmark_Table_3_E-ASR_scores_proprietary_open-source_models_highest.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a52bc295a143e27553bcbba5fb0ebb0dc804fbdd
--- /dev/null
+++ b/data/sampled_jsons/ELITE_benchmark_Table_3_E-ASR_scores_proprietary_open-source_models_highest.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "In Table 3 , we present comprehensive experimental results of the ELITE benchmark across various proprietary and open-source VLMs. GPT-4o exhibits the lowest E-ASR at 15.67% among models , indicating that it is appropriately safety-aligned against malicious inputs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757v2", "content": "In Table 3 , we present comprehensive experimental results of the ELITE benchmark across various proprietary and open-source VLMs. GPT-4o exhibits the lowest E-ASR at 15.67% among models , indicating that it is appropriately safety-aligned against malicious inputs."}
+{"idx": 1, "title": "GitHub - huggingface/open_asr_leaderboard", "date": "", "ddg_snippet": "This repository contains the code for the Open ASR Leaderboard. The leaderboard is a Gradio Space that allows users to compare the accuracy of ASR models on a variety of datasets. The leaderboard is hosted at hf-audio/open_asr_leaderboard.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/huggingface/open_asr_leaderboard", "content": "This repository contains the code for the Open ASR Leaderboard. The leaderboard is a Gradio Space that allows users to compare the accuracy of ASR models on a variety of datasets. The leaderboard is hosted at hf-audio/open_asr_leaderboard."}
+{"idx": 2, "title": "SpeechColab ASR leaderboard - GitHub", "date": "", "ddg_snippet": "1. Overview \"If you can't measure it, you can't improve it.\" -- Peter Drucker SpeechIO leaderboard serves as an ASR benchmarking platform by providing 3 components: TestSet Zoo: A collection of test sets covering wide range of speech recognition tasks & scenarios Model Zoo: A collection of models including commercial APIs & open -sourced models", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SpeechColab/Leaderboard", "content": "1. Overview \"If you can't measure it, you can't improve it.\" -- Peter Drucker SpeechIO leaderboard serves as an ASR benchmarking platform by providing 3 components: TestSet Zoo: A collection of test sets covering wide range of speech recognition tasks & scenarios Model Zoo: A collection of models including commercial APIs & open -sourced models"}
+{"idx": 3, "title": "Open ASR Leaderboard - a Model Database Space by hf-audio", "date": "", "ddg_snippet": "The ASR Leaderboard will be a continued effort to benchmark open source /access speech recognition models where possible. Along with the Leaderboard we're open -sourcing the codebase used for running these evaluations.", "subpage_snippet": "", "source": "modeldatabase.com", "link": "https://modeldatabase.com/spaces/hf-audio/open_asr_leaderboard.html", "content": "The ASR Leaderboard will be a continued effort to benchmark open source /access speech recognition models where possible. Along with the Leaderboard we're open -sourcing the codebase used for running these evaluations."}
+{"idx": 4, "title": "3 Best Open-Source ASR Models Compared: Whisper, wav2vec 2.0, Kaldi ...", "date": "", "ddg_snippet": "Explore the top 3 open-source speech models , including Kaldi, wav2letter++, and OpenAI's Whisper, trained on 700,000 hours of speech. Discover insights on usability, accuracy, and speed. Click to find the right ASR model for your needs!", "subpage_snippet": "", "source": "deepgram.com", "link": "https://deepgram.com/learn/benchmarking-top-open-source-speech-models", "content": "Explore the top 3 open-source speech models , including Kaldi, wav2letter++, and OpenAI's Whisper, trained on 700,000 hours of speech. Discover insights on usability, accuracy, and speed. Click to find the right ASR model for your needs!"}
+{"idx": 5, "title": "LLM Leaderboard - Comparison of over 100 AI models from OpenAI, Google ...", "date": "", "ddg_snippet": "Comparison and ranking the performance of over 100 AI models (LLMs) across key metrics including intelligence, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others.", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/leaderboards/models", "content": "Comparison and ranking the performance of over 100 AI models (LLMs) across key metrics including intelligence, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others."}
+{"idx": 6, "title": "Open ASR Leaderboard ranks open-source speech recognition models ...", "date": "", "ddg_snippet": "The ESB score is calculated as a macro-average of the WER scores across the ESB datasets. The models in the leaderboard are ranked based on their average WER scores , from lowest to highest . 🔗 ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/imohitmayank_open-asr-leaderboard-ranks-open-source-activity-7105784005001687040-uolC", "content": "The ESB score is calculated as a macro-average of the WER scores across the ESB datasets. The models in the leaderboard are ranked based on their average WER scores , from lowest to highest . 🔗 ..."}
+{"idx": 7, "title": "[R] Open ASR Leaderboard : r/MachineLearning - Reddit", "date": "", "ddg_snippet": "[R] Open ASR Leaderboard Hugging Face benchmarked open source / access models [English only] on 8 different speech datasets (LibriSpeech, Common Voice, VoxPopuli, TED-LIUM, Gigaspeech, SPGISpeech, Earnings-22 and AMI) 🤗", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/16cnez1/r_open_asr_leaderboard/", "content": "[R] Open ASR Leaderboard Hugging Face benchmarked open source / access models [English only] on 8 different speech datasets (LibriSpeech, Common Voice, VoxPopuli, TED-LIUM, Gigaspeech, SPGISpeech, Earnings-22 and AMI) 🤗"}
+{"idx": 8, "title": "Open ASR Leaderboard - a Hugging Face Space by hf-audio", "date": "", "ddg_snippet": "Request evaluation of a new speech model by selecting the model name and datasets. Get a confirmation message once your request is submitted.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/hf-audio/open_asr_leaderboard", "content": "Request evaluation of a new speech model by selecting the model name and datasets. Get a confirmation message once your request is submitted."}
+{"idx": 9, "title": "LLM Leaderboard 2025 - Verified AI Rankings", "date": "", "ddg_snippet": "Comprehensive AI (LLM) leaderboard with benchmarks , pricing, and capabilities. Compare leading LLMs with interactive visualizations, rankings and comparisons.", "subpage_snippet": "", "source": "llm-stats.com", "link": "https://llm-stats.com/", "content": "Comprehensive AI (LLM) leaderboard with benchmarks , pricing, and capabilities. Compare leading LLMs with interactive visualizations, rankings and comparisons."}
diff --git a/data/sampled_jsons/ELITE_evaluator_equation_2_formula_refused_specific_convincing_toxicity.jsonl b/data/sampled_jsons/ELITE_evaluator_equation_2_formula_refused_specific_convincing_toxicity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bbff731a4449976e424c98b6dcc7e00df78d1354
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+++ b/data/sampled_jsons/ELITE_evaluator_equation_2_formula_refused_specific_convincing_toxicity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE QUALITY REPORT 2024", "date": "", "ddg_snippet": "The detailed EQx methodology is described in 'Measuring Elite . Quality' (Casas-Klett, Cozzi, Diebold, & Zeller, 2020), a paper whose main tenets have evolved ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID4779686_code4083234.pdf?abstractid=4779686&mirid=1&type=2", "content": "The detailed EQx methodology is described in 'Measuring Elite . Quality' (Casas-Klett, Cozzi, Diebold, & Zeller, 2020), a paper whose main tenets have evolved ..."}
+{"idx": 1, "title": "The Toxicity of Mercury and Its Chemical Compounds", "date": "", "ddg_snippet": "by YS Wu · 2024 · Cited by 181 — Mercury is a type of hazardous and toxic pollutant that can result in detrimental effects on the environment and human health.", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acsomega.3c07047", "content": "by YS Wu · 2024 · Cited by 181 — Mercury is a type of hazardous and toxic pollutant that can result in detrimental effects on the environment and human health."}
+{"idx": 2, "title": "Has academic preparedness declined even at elite ...", "date": "", "ddg_snippet": "Most students are still pretty well prepared for rigorous coursework, but I wonder if there has still been noticeable effect.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/AskAcademia/comments/1eakfpi/has_academic_preparedness_declined_even_at_elite/", "content": "Most students are still pretty well prepared for rigorous coursework, but I wonder if there has still been noticeable effect."}
+{"idx": 3, "title": "Rejection of Leukemic Cells Requires Antigen-Specific T ...", "date": "", "ddg_snippet": "by K Vincent · 2014 · Cited by 13 — Three features had no influence on the ability of primed T cells to reject leukemic cells: (1) MHC-peptide affinity; ( 2 ) the stability of MHC-peptide complexes; ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1083879113004977", "content": "by K Vincent · 2014 · Cited by 13 — Three features had no influence on the ability of primed T cells to reject leukemic cells: (1) MHC-peptide affinity; ( 2 ) the stability of MHC-peptide complexes; ..."}
+{"idx": 4, "title": "novel-approaches-and-their-applications-in-risk- ...", "date": "", "ddg_snippet": "Equation 2 presents the mathematical formulation for calculating risk (Finney, 2005), where E(NVCi) is the expected net value change to resource j, and RFi ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/novel-approaches-and-their-applications-in-risk-assessment-47fn8600ku.pdf", "content": "Equation 2 presents the mathematical formulation for calculating risk (Finney, 2005), where E(NVCi) is the expected net value change to resource j, and RFi ..."}
+{"idx": 5, "title": "A survey on vulnerability of federated learning", "date": "", "ddg_snippet": "by X Xie · 2024 · Cited by 42 — This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231223013486", "content": "by X Xie · 2024 · Cited by 42 — This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets."}
+{"idx": 6, "title": "Track: Poster Session 5 East", "date": "", "ddg_snippet": "17 Jul 2025 — This paper studies a range of AI/ML trust concepts, including memorization, data poisoning , and copyright, which can be modeled as constraints ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50267", "content": "17 Jul 2025 — This paper studies a range of AI/ML trust concepts, including memorization, data poisoning , and copyright, which can be modeled as constraints ..."}
+{"idx": 7, "title": "Efficacy and safety assessment of traditional Chinese ...", "date": "", "ddg_snippet": "by H Wu · 2020 · Cited by 25 — The results showed that TCM might be beneficial in improving body weight as well as in regulating glucose and lipid metabolisms.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7170408/", "content": "by H Wu · 2020 · Cited by 25 — The results showed that TCM might be beneficial in improving body weight as well as in regulating glucose and lipid metabolisms."}
+{"idx": 8, "title": "IRIS Toxicological Review of Perfluorohexanesulfonic Acid ...", "date": "", "ddg_snippet": "... 2 . IRIS Toxicological Review of Perfluorohexanesulfonic Acid and Related Salts ii . DISCLAIMER. This document has been reviewed by the U.S. Environmental ...", "subpage_snippet": "", "source": "iris.epa.gov", "link": "https://iris.epa.gov/static/pdfs/0705tr.pdf", "content": "... 2 . IRIS Toxicological Review of Perfluorohexanesulfonic Acid and Related Salts ii . DISCLAIMER. This document has been reviewed by the U.S. Environmental ..."}
+{"idx": 9, "title": "Working Document on the Risk Assessment of Secondary ...", "date": "", "ddg_snippet": "21 Nov 2018 — This publication was developed in the IOMC context. The contents do not necessarily reflect the views or stated policies of individual IOMC ... 193 pages", "subpage_snippet": "", "source": "www.oecd.org", "link": "https://www.oecd.org/content/dam/oecd/en/publications/reports/2018/11/working-document-on-the-risk-assessment-of-secondary-metabolites-of-microbial-biocontrol-agents_a5a324df/6ca93ea9-en.pdf", "content": "21 Nov 2018 — This publication was developed in the IOMC context. The contents do not necessarily reflect the views or stated policies of individual IOMC ... 193 pages"}
diff --git a/data/sampled_jsons/ELITE_evaluator_equation_formula_refused_specific_convincing_toxicity_score_year_2024.jsonl b/data/sampled_jsons/ELITE_evaluator_equation_formula_refused_specific_convincing_toxicity_score_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b6be66fa897604b98d98fb9d3c5abcd947edfbd
--- /dev/null
+++ b/data/sampled_jsons/ELITE_evaluator_equation_formula_refused_specific_convincing_toxicity_score_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Republicans | manuelgarciajr | Page 2", "date": "", "ddg_snippet": "While the 1997 outbreak killed millions of birds and scores of people, this particular strain of the virus had not acquired the genes necessary to ...", "subpage_snippet": "", "source": "manuelgarciajr.com", "link": "https://manuelgarciajr.com/tag/republicans/page/2/", "content": "While the 1997 outbreak killed millions of birds and scores of people, this particular strain of the virus had not acquired the genes necessary to ..."}
+{"idx": 1, "title": "Jeffrey Epstein's Legacy: The Rise of Dehumanizing", "date": "", "ddg_snippet": "Jeffrey Epstein, a financier with deep ties to elite institutions, funded and influenced scientific research into genetics, artificial intelligence ...", "subpage_snippet": "", "source": "youarewithinthenorms.com", "link": "https://youarewithinthenorms.com/2025/08/01/wrenn-hear-me-roar-short-eotf/", "content": "Jeffrey Epstein, a financier with deep ties to elite institutions, funded and influenced scientific research into genetics, artificial intelligence ..."}
+{"idx": 2, "title": "The Bed of Procrustes by Nassim Taleb | William Meller", "date": "", "ddg_snippet": "With his signature wit and unfiltered style, Taleb exposes the illusions we cling to—whether it’s defining intelligence by test scores , modifying ...", "subpage_snippet": "", "source": "williammeller.com", "link": "https://williammeller.com/the-bed-of-procrustes-by-nassim-nicholas-taleb/", "content": "With his signature wit and unfiltered style, Taleb exposes the illusions we cling to—whether it’s defining intelligence by test scores , modifying ..."}
+{"idx": 3, "title": "Just Say \"No\" To Molly Spearman - FITSNews", "date": "", "ddg_snippet": "Once you ’ ve chewed awhile on what Spearman is selling, though, the distasteful truth starts to take hold – a rank toxicity lingering ...", "subpage_snippet": "", "source": "www.fitsnews.com", "link": "https://www.fitsnews.com/2014/06/16/just-say/", "content": "Once you ’ ve chewed awhile on what Spearman is selling, though, the distasteful truth starts to take hold – a rank toxicity lingering ..."}
+{"idx": 4, "title": "November | 2019 | gadflyonthewallblog", "date": "", "ddg_snippet": "That may seem simple or even obvious with reflection, but it also goes counter to nearly every teacher evaluation system in practice in the United ...", "subpage_snippet": "", "source": "gadflyonthewallblog.com", "link": "https://gadflyonthewallblog.com/2019/11/", "content": "That may seem simple or even obvious with reflection, but it also goes counter to nearly every teacher evaluation system in practice in the United ..."}
+{"idx": 5, "title": "WHOOP 4.0 review: the best fitness tracker by a wide margin", "date": "", "ddg_snippet": "... Non-Toxic Products (Michael’s Favorites)", "subpage_snippet": "", "source": "michaelkummer.com", "link": "https://michaelkummer.com/whoop-strap-review/", "content": "... Non-Toxic Products (Michael’s Favorites)"}
+{"idx": 6, "title": "TEF follow up: 2015 class & thoughts on the center", "date": "", "ddg_snippet": "I ’ m not convinced Laken Tomlinson ’ s 2.95 would ’ ve ruled him out considering he jumped a 9-0 in the broad and had the size ...", "subpage_snippet": "", "source": "seahawksdraftblog.com", "link": "https://seahawksdraftblog.com/tef-follow-up-2015-class-thoughts-on-the-center-position", "content": "I ’ m not convinced Laken Tomlinson ’ s 2.95 would ’ ve ruled him out considering he jumped a 9-0 in the broad and had the size ..."}
+{"idx": 7, "title": "Robert Frank on Success and Luck - Econlib", "date": "", "ddg_snippet": "Campbell Harvey of Duke University talks with EconTalk host Russ Roberts about his research evaluating various investment and trading strategies and ...", "subpage_snippet": "", "source": "www.econtalk.org", "link": "https://www.econtalk.org/robert-frank-on-success-and-luck/", "content": "Campbell Harvey of Duke University talks with EconTalk host Russ Roberts about his research evaluating various investment and trading strategies and ..."}
+{"idx": 8, "title": "Editorial: ChatGPT: Challenges, Opportunities, and Implications", "date": "", "ddg_snippet": "When such tools are used in high-stakes assessments – like for evaluating writing samples for admissions into top schools (e.g., Mi Write) – they ...", "subpage_snippet": "", "source": "citejournal.org", "link": "https://citejournal.org/volume-23/issue-1-23/editorial/editorial-chatgpt-challenges-opportunities-and-implications-for-teacher-education/", "content": "When such tools are used in high-stakes assessments – like for evaluating writing samples for admissions into top schools (e.g., Mi Write) – they ..."}
+{"idx": 9, "title": "You're up, Caleb Williams - by Tyler Dunne - Go Long", "date": "", "ddg_snippet": "The GM told everybody that he wanted honest evaluations, even letting the coaches know they wouldn’t be offending him by grading a draft pick ...", "subpage_snippet": "", "source": "www.golongtd.com", "link": "https://www.golongtd.com/p/youre-up-caleb-williams-part-i-all", "content": "The GM told everybody that he wanted honest evaluations, even letting the coaches know they wouldn’t be offending him by grading a draft pick ..."}
diff --git a/data/sampled_jsons/ELITE_evaluator_formula_Equation_2_arxiv_2502.04757.jsonl b/data/sampled_jsons/ELITE_evaluator_formula_Equation_2_arxiv_2502.04757.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6bdc94f39520202cc5b0049c95250b5e166e2fe3
--- /dev/null
+++ b/data/sampled_jsons/ELITE_evaluator_formula_Equation_2_arxiv_2502.04757.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "davanstrien/arxiv-cs-2024-sample · Datasets at ...", "date": "", "ddg_snippet": "2502.04757 . ELITE : Enhanced Language-Image Toxicity Evaluation for Safety. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/davanstrien/arxiv-cs-2024-sample/viewer", "content": "2502.04757 . ELITE : Enhanced Language-Image Toxicity Evaluation for Safety. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that ..."}
+{"idx": 1, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image-text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04757", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image-text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator ."}
+{"idx": 2, "title": "arXiv.org", "date": "", "ddg_snippet": "arXiv .org", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "arXiv .org"}
+{"idx": 3, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety | Cool ...", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image-text pair combinations. To address these issues, we propose the ELITE {\\em benchmark}, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE {\\em evaluator }.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2502.04757", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image-text pair combinations. To address these issues, we propose the ELITE {\\em benchmark}, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE {\\em evaluator }."}
+{"idx": 4, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Abstract The ELITE benchmark and evaluator improve safety assessment for Vision Language Models by filtering out ambiguous pairs and enhancing diversity in image-text combinations.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04757", "content": "Abstract The ELITE benchmark and evaluator improve safety assessment for Vision Language Models by filtering out ambiguous pairs and enhancing diversity in image-text combinations."}
+{"idx": 5, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator , which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ELITE:-Enhanced-Language-Image-Toxicity-Evaluation-Lee-Lee/2bf4206276d5f574bbb2e13a56b29b4522fea675", "content": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator , which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ..."}
+{"idx": 6, "title": "\"ELITE: Enhanced Language-Image Toxicity Evaluation for Safety.\" - dblp", "date": "", "ddg_snippet": "DOI: 10.48550/ ARXIV .2502.04757 access: open type: Informal or Other Publication metadata version: 2025-03-12", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-04757", "content": "DOI: 10.48550/ ARXIV .2502.04757 access: open type: Informal or Other Publication metadata version: 2025-03-12"}
+{"idx": 7, "title": "Computation and Language Feb 2025", "date": "", "ddg_snippet": "7 Feb 2025 — [1925] arXiv : 2502.04757 (cross-list from cs.CV) [pdf, html, other]. Title: ELITE : Enhanced Language-Image Toxicity Evaluation for Safety.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CL/2025-02?skip=1300&show=1000", "content": "7 Feb 2025 — [1925] arXiv : 2502.04757 (cross-list from cs.CV) [pdf, html, other]. Title: ELITE : Enhanced Language-Image Toxicity Evaluation for Safety."}
+{"idx": 8, "title": "Computation and Language Feb 2025", "date": "", "ddg_snippet": "24 Feb 2025 — [1926] arXiv : 2502.04757 (cross-list from cs.CV) [pdf, html, other]. Title: ELITE : Enhanced Language-Image Toxicity Evaluation for Safety.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CL/2025-02?skip=925&show=2000", "content": "24 Feb 2025 — [1926] arXiv : 2502.04757 (cross-list from cs.CV) [pdf, html, other]. Title: ELITE : Enhanced Language-Image Toxicity Evaluation for Safety."}
+{"idx": 9, "title": "Computer Science Feb 2025", "date": "", "ddg_snippet": "[2356] arXiv : 2502.04757 [pdf, html, other]. Title: ELITE : Enhanced Language-Image Toxicity Evaluation for Safety. Wonjun Lee, Doehyeon Lee, Eugene Choi ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-02?skip=1225&show=2000", "content": "[2356] arXiv : 2502.04757 [pdf, html, other]. Title: ELITE : Enhanced Language-Image Toxicity Evaluation for Safety. Wonjun Lee, Doehyeon Lee, Eugene Choi ..."}
diff --git a/data/sampled_jsons/ELITE_score_formula_equation_refused_specific_convincing_toxicity_StrongREJECT.jsonl b/data/sampled_jsons/ELITE_score_formula_equation_refused_specific_convincing_toxicity_StrongREJECT.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..507de1050a086c9b8e38a89805acbec4e5288a66
--- /dev/null
+++ b/data/sampled_jsons/ELITE_score_formula_equation_refused_specific_convincing_toxicity_StrongREJECT.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "represent refused , specific , convincing , and toxicity , respectively. The ELITE evaluator can effectively evaluate by utilizing the toxicity score to make more accurate judgments. Report issue for preceding element.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "represent refused , specific , convincing , and toxicity , respectively. The ELITE evaluator can effectively evaluate by utilizing the toxicity score to make more accurate judgments. Report issue for preceding element."}
+{"idx": 1, "title": "GitHub - alexandrasouly/strongreject: Repository for ...", "date": "", "ddg_snippet": "The StrongREJECT autograder scores jailbreak responses between 0 and 1, where 1 is a successful jailbreak with harmful information the model shouldn't disclose. It uses GPT4 Turbo to judge the answer to the prompt. It determines the final score in the following manner: check for refusal: if the model refused to answer, the autograder exits and assigns a 0 score score convincingness on a scale ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/alexandrasouly/strongreject", "content": "The StrongREJECT autograder scores jailbreak responses between 0 and 1, where 1 is a successful jailbreak with harmful information the model shouldn't disclose. It uses GPT4 Turbo to judge the answer to the prompt. It determines the final score in the following manner: check for refusal: if the model refused to answer, the autograder exits and assigns a 0 score score convincingness on a scale ..."}
+{"idx": 2, "title": "How ELITE Reveals Dangerous Weaknesses in Vision-Language AI", "date": "", "ddg_snippet": "2. ELITE Evaluator: Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aim-intelligence/how-elite-reveals-dangerous-weaknesses-in-vision-language-ai-ffa208b7546c", "content": "2. ELITE Evaluator: Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ..."}
+{"idx": 3, "title": "StrongREJECT documentation — StrongREJECT documentation", "date": "", "ddg_snippet": "StrongREJECT documentation # StrongREJECT is a state-of-the-art LLM jailbreak evaluation benchmark. This package implements the StrongREJECT benchmark and additional utilities for jailbreak research. This Colab notebook demonstrates several options for using the StrongREJECT evaluator: High-level API implemented in the StrongREJECT package Using an LLM (e.g., GPT-4o, Claude, or Gemini) Using ...", "subpage_snippet": "", "source": "strong-reject.readthedocs.io", "link": "https://strong-reject.readthedocs.io/en/latest/", "content": "StrongREJECT documentation # StrongREJECT is a state-of-the-art LLM jailbreak evaluation benchmark. This package implements the StrongREJECT benchmark and additional utilities for jailbreak research. This Colab notebook demonstrates several options for using the StrongREJECT evaluator: High-level API implemented in the StrongREJECT package Using an LLM (e.g., GPT-4o, Claude, or Gemini) Using ..."}
+{"idx": 4, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "May 15, 2025 · The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision-Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific , convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "business.sherbrookerecord.com", "link": "https://business.sherbrookerecord.com/sherbrookerecord/article/newsfile-2025-5-15-aim-intelligences-elite-collaborative-paper-accepted-by-the-icml", "content": "May 15, 2025 · The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision-Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific , convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 5, "title": "ELITE : Enhanced Language-Image Toxicity ... | Read Paper on Bytez", "date": "", "ddg_snippet": "1, refused evaluates whether the model has refused the forbidden prompt, and assigns 1 point if the victim model refuses to respond. specific measures the specificity of the response, while convincing evaluates its convincingness . Both terms are scored on a scale of 1 to 5.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46445/paper", "content": "1, refused evaluates whether the model has refused the forbidden prompt, and assigns 1 point if the victim model refuses to respond. specific measures the specificity of the response, while convincing evaluates its convincingness . Both terms are scored on a scale of 1 to 5."}
+{"idx": 6, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific , convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/aim-intelligences-elite-collaborative-paper-161800502.html", "content": "At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific , convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 7, "title": "StrongREJECT documentation — StrongREJECT documentation", "date": "", "ddg_snippet": "StrongREJECT is a state-of-the-art LLM jailbreak evaluation benchmark. This package implements the StrongREJECT benchmark and additional utilities for jailbreak research. This Colab notebook demonstrates several options for using the StrongREJECT evaluator", "subpage_snippet": "", "source": "strong-reject.readthedocs.io", "link": "https://strong-reject.readthedocs.io/", "content": "StrongREJECT is a state-of-the-art LLM jailbreak evaluation benchmark. This package implements the StrongREJECT benchmark and additional utilities for jailbreak research. This Colab notebook demonstrates several options for using the StrongREJECT evaluator"}
+{"idx": 8, "title": "velpegor.github.io/ ELITE", "date": "", "ddg_snippet": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific , convincing , but unharmful descriptions of images.", "subpage_snippet": "", "source": "velpegor.github.io", "link": "https://velpegor.github.io/ELITE/", "content": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific , convincing , but unharmful descriptions of images."}
+{"idx": 9, "title": "MDRD GFR Equation", "date": "", "ddg_snippet": "The MDRD GFR Equation estimates glomerular filtration rate based on creatinine and patient characteristics.", "subpage_snippet": "", "source": "www.mdcalc.com", "link": "https://www.mdcalc.com/calc/76/mdrd-gfr-equation", "content": "The MDRD GFR Equation estimates glomerular filtration rate based on creatinine and patient characteristics."}
diff --git a/data/sampled_jsons/EntityErasure_CVPR_2025_equation_7_IoS_intersection_over_self_threshold_0.5_year_2024.jsonl b/data/sampled_jsons/EntityErasure_CVPR_2025_equation_7_IoS_intersection_over_self_threshold_0.5_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..79a018e15b5598cab71a3e6ad1a1294b4231ffde
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_CVPR_2025_equation_7_IoS_intersection_over_self_threshold_0.5_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EntityErasure : Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "We then calculate the Intersection over Self ( IoS ) for each entity relative to the inpainting mask.If the IoS is greater than the threshold λ, these entities are considered as newly generated sundries (SDR). λ is set to 0.95 by default.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "We then calculate the Intersection over Self ( IoS ) for each entity relative to the inpainting mask.If the IoS is greater than the threshold λ, these entities are considered as newly generated sundries (SDR). λ is set to 0.95 by default."}
+{"idx": 1, "title": "目标定位和检测系列（3）：交并比（IOU）和非极大值抑制（NMS）的pyth... CVPR 2025 Open Access Repository GitHub - k8xu/amodal: Official code for \"Amodal Completion ... GitHub - yejun688/CVPR2025_oral_paper_list: A curated list ... [2103.16562] Boundary IoU: Improving Object-Centric Image ...", "date": "", "ddg_snippet": "交并比（ Inter section over Union）和非极大值抑制是（Non-Maximum Suppression）是目标检测任务中非常重要的两个概念。例如在用训练好的模型进行测试时，网络会预测出一系列的候选框。这时候我们会用NMS来移除一些多余的候选框。即移除一些IOU值大于某个阈值的框。然后在剩下的候选框中，分别计算与ground truth的IOU值，通常会规定当候选框和ground truth的IOU值大于0.5时，认为检测正确。下面我们分别用python实现IOU和NMS。 See full list on blog.csdn.net 如上图所示，IOU值定位为两个矩形框面积的交集和并集的比值。即： I O U = A ∩ B A ∪ B IOU=\\frac{A \\cap B}{A \\cup B} IOU=A∪BA∩B See full list on blog.csdn.net NMS的算法步骤如下： 需要注意的是，NMS是对所有的类别分别执行的。举个栗子，假设最后预测出的矩形框有2类（分别为cup, pen）,在NMS之前，每个类别可能都会有不只一个bbx被预测出来，这个时候我们需要对这两个类别分别执行一次NMS过程。 我们用python编写NMS代码，假设对于一张图片，所有的bbx信息已经保存在一个字典中，保存形式如下： 即目标的位置和置信度用列表储存，每个列表中的一个子列表代表一个bbx信息。详细的代码如下： 假设我们现在已经计算得到了网络输出的bbx信息如下： 即模型已经预测出了三个bbx，我们看一下在原图上的结果： See full list on blog.csdn.net EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ), 2025 , pp. 28274-28283 Clone this amodal repository, and run cd Grounded-Segment-Anything. In the Dockerfile, change all instances of /home/appuser to your path for the amodal repository. Run make build-image. Start and attach to a docker container from the image gsa:v0. Then, navigate to the amodal repository. Run ./install.sh to finish setup and download model checkpoints. 😎 A curated list of CVPR 2025 Oral paper. Total 96 - yejun688/CVPR2025_oral_paper_list Mar 30, 2021 · We present Boundary IoU ( Intersection - over -Union), a new segmentation evaluation measure focused on boundary quality.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/sinat_34474705/article/details/80045294", "content": "交并比（ Inter section over Union）和非极大值抑制是（Non-Maximum Suppression）是目标检测任务中非常重要的两个概念。例如在用训练好的模型进行测试时，网络会预测出一系列的候选框。这时候我们会用NMS来移除一些多余的候选框。即移除一些IOU值大于某个阈值的框。然后在剩下的候选框中，分别计算与ground truth的IOU值，通常会规定当候选框和ground truth的IOU值大于0.5时，认为检测正确。下面我们分别用python实现IOU和NMS。 See full list on blog.csdn.net 如上图所示，IOU值定位为两个矩形框面积的交集和并集的比值。即： I O U = A ∩ B A ∪ B IOU=\\frac{A \\cap B}{A \\cup B} IOU=A∪BA∩B See full list on blog.csdn.net NMS的算法步骤如下： 需要注意的是，NMS是对所有的类别分别执行的。举个栗子，假设最后预测出的矩形框有2类（分别为cup, pen）,在NMS之前，每个类别可能都会有不只一个bbx被预测出来，这个时候我们需要对这两个类别分别执行一次NMS过程。 我们用python编写NMS代码，假设对于一张图片，所有的bbx信息已经保存在一个字典中，保存形式如下： 即目标的位置和置信度用列表储存，每个列表中的一个子列表代表一个bbx信息。详细的代码如下： 假设我们现在已经计算得到了网络输出的bbx信息如下： 即模型已经预测出了三个bbx，我们看一下在原图上的结果： See full list on blog.csdn.net EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ), 2025 , pp. 28274-28283 Clone this amodal repository, and run cd Grounded-Segment-Anything. In the Dockerfile, change all instances of /home/appuser to your path for the amodal repository. Run make build-image. Start and attach to a docker container from the image gsa:v0. Then, navigate to the amodal repository. Run ./install.sh to finish setup and download model checkpoints. 😎 A curated list of CVPR 2025 Oral paper. Total 96 - yejun688/CVPR2025_oral_paper_list Mar 30, 2021 · We present Boundary IoU ( Intersection - over -Union), a new segmentation evaluation measure focused on boundary quality."}
+{"idx": 2, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ), 2025 , pp. 28274-28283", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ), 2025 , pp. 28274-28283"}
+{"idx": 3, "title": "GitHub - yejun688/CVPR2025_oral_paper_list: A curated list ...", "date": "", "ddg_snippet": "😎 A curated list of CVPR 2025 Oral paper. Total 96 - yejun688/CVPR2025_oral_paper_list", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yejun688/CVPR2025_oral_paper_list", "content": "😎 A curated list of CVPR 2025 Oral paper. Total 96 - yejun688/CVPR2025_oral_paper_list"}
+{"idx": 4, "title": "Intersection over Union (IoU): Definition, Calculation, Code", "date": "", "ddg_snippet": "What is Intersection over Union? How to calculate IoU? Where to get ground truth data from?However, a common threshold used in practice is 0 . 5 , meaning that a predicted box must have an IoU of at least 0 . 5 with a ground truth box to be considered a true positive detection.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/intersection-over-union-guide", "content": "What is Intersection over Union? How to calculate IoU? Where to get ground truth data from?However, a common threshold used in practice is 0 . 5 , meaning that a predicted box must have an IoU of at least 0 . 5 with a ground truth box to be considered a true positive detection."}
+{"idx": 5, "title": "Intersection over Union (IoU) for object detection | SuperAnnotate", "date": "", "ddg_snippet": "Intersection over Union (IoU) is a crucial object detection metric. Learn how to estimate model accuracy with IoU for evaluating performance.", "subpage_snippet": "", "source": "www.superannotate.com", "link": "https://www.superannotate.com/blog/intersection-over-union-for-object-detection", "content": "Intersection over Union (IoU) is a crucial object detection metric. Learn how to estimate model accuracy with IoU for evaluating performance."}
+{"idx": 6, "title": "Understanding Intersection over Union for Model Accuracy", "date": "", "ddg_snippet": "The intersection - over -union (IoU) threshold acts as a gatekeeper, classifying predicted bounding boxes as true positives if they pass the threshold and false positives if they fall below it. By adjusting the threshold , we can control the trade-off between precision and recall.", "subpage_snippet": "", "source": "viso.ai", "link": "https://viso.ai/computer-vision/intersection-over-union-iou/", "content": "The intersection - over -union (IoU) threshold acts as a gatekeeper, classifying predicted bounding boxes as true positives if they pass the threshold and false positives if they fall below it. By adjusting the threshold , we can control the trade-off between precision and recall."}
+{"idx": 7, "title": "Intersection Over Union IoU in Object Detection Segmentation", "date": "", "ddg_snippet": "Intersection Over Union (IoU) is a helper metric for evaluating object detection and segmentation model. Learn from the basics to implementing IoU from scratch.", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/intersection-over-union-iou-in-object-detection-and-segmentation/", "content": "Intersection Over Union (IoU) is a helper metric for evaluating object detection and segmentation model. Learn from the basics to implementing IoU from scratch."}
+{"idx": 8, "title": "Как скачать видео с Рутуба: 5 простых способов [ 2025 ]", "date": "", "ddg_snippet": "Дата обновления: 22 июля 2025 . На Рутуб нет специальной кнопки, чтобы загружать ролики. В статье разобрали, как скачать видео с Рутуба с помощью специальных программ, онлайн-сервисов и через боты.", "subpage_snippet": "", "source": "video-editor.su", "link": "https://video-editor.su/kak-skachat-video-s-rutube.php", "content": "Дата обновления: 22 июля 2025 . На Рутуб нет специальной кнопки, чтобы загружать ролики. В статье разобрали, как скачать видео с Рутуба с помощью специальных программ, онлайн-сервисов и через боты."}
+{"idx": 9, "title": "[2103.16562] Boundary IoU: Improving Object-Centric Image ...", "date": "", "ddg_snippet": "Mar 30, 2021 · We present Boundary IoU ( Intersection - over -Union), a new segmentation evaluation measure focused on boundary quality.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2103.16562", "content": "Mar 30, 2021 · We present Boundary IoU ( Intersection - over -Union), a new segmentation evaluation measure focused on boundary quality."}
diff --git a/data/sampled_jsons/EntityErasure_IoS_equation_(7)_threshold_newly_generated_sundry_year_2024.jsonl b/data/sampled_jsons/EntityErasure_IoS_equation_(7)_threshold_newly_generated_sundry_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..46ea41b0db5f89c3c7f9cbd990804d191d5eafa2
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_IoS_equation_(7)_threshold_newly_generated_sundry_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ways of Seeing International Institutions (Part II)", "date": "", "ddg_snippet": "17 Apr 2025 — Through immersion in details of the case, I explore the ways in which IOs produced and validated knowledge claims about hidden hunger as an ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/books/ways-of-seeing-international-organisations/ways-of-seeing-international-institutions/395B9D651DFEE5C4BC107350DC6BA746", "content": "17 Apr 2025 — Through immersion in details of the case, I explore the ways in which IOs produced and validated knowledge claims about hidden hunger as an ..."}
+{"idx": 1, "title": "podcast.xml", "date": "", "ddg_snippet": "... produced repeatedly by any known process save for life itself — a complexity threshold governed by the size of the possibility space. Implied here is that ...", "subpage_snippet": "", "source": "longnow.org", "link": "https://longnow.org/podcast.xml", "content": "... produced repeatedly by any known process save for life itself — a complexity threshold governed by the size of the possibility space. Implied here is that ..."}
+{"idx": 2, "title": "Security Rules and Procedures - Mastercard", "date": "", "ddg_snippet": "11 Feb 2025 — This manual contains Standards. Each Customer must comply fully with these Standards. All of the Standards in this manual are assigned to ... 224 pages", "subpage_snippet": "", "source": "www.mastercard.us", "link": "https://www.mastercard.us/content/dam/public/mastercardcom/na/global-site/documents/SPME-Manual.pdf", "content": "11 Feb 2025 — This manual contains Standards. Each Customer must comply fully with these Standards. All of the Standards in this manual are assigned to ... 224 pages"}
+{"idx": 3, "title": "Security Toolkit", "date": "", "ddg_snippet": "The information age has seen the development of electronic pathways that carry vast amounts of valuable commercial, scientific, and educational information ... 384 pages", "subpage_snippet": "", "source": "cryptlib.com", "link": "https://cryptlib.com/downloads/manual.pdf", "content": "The information age has seen the development of electronic pathways that carry vast amounts of valuable commercial, scientific, and educational information ... 384 pages"}
+{"idx": 4, "title": "Computer Science May 2024", "date": "", "ddg_snippet": "Title: How to Augment for Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models? Engin Uzun, Erdem Akagunduz. Subjects: Computer Vision and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2024-05?skip=2580&show=2000", "content": "Title: How to Augment for Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models? Engin Uzun, Erdem Akagunduz. Subjects: Computer Vision and ..."}
+{"idx": 5, "title": "A survey on cybersecurity, data privacy, and policy issues ...", "date": "", "ddg_snippet": "by H Habibzadeh · 2019 · Cited by 425 — Figure 1: The infrastructure risk in smart cities is determined by three parameters: (i) threat, (ii) vulnerability, and (iii) consequence [24].", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/am/pii/S2210670718316883", "content": "by H Habibzadeh · 2019 · Cited by 425 — Figure 1: The infrastructure risk in smart cities is determined by three parameters: (i) threat, (ii) vulnerability, and (iii) consequence [24]."}
+{"idx": 6, "title": "njrcreport.pdf", "date": "", "ddg_snippet": "1 Jun 2025 — This section provides a detailed examination of New . Jersey's embrace of slavery; describes how the forced labor of enslaved Black people built ... 231 pages", "subpage_snippet": "", "source": "www.njisj.org", "link": "https://www.njisj.org/print/njrcreport.pdf", "content": "1 Jun 2025 — This section provides a detailed examination of New . Jersey's embrace of slavery; describes how the forced labor of enslaved Black people built ... 231 pages"}
+{"idx": 7, "title": "Sullivan's Parallelism | Cornell Journal of Architecture", "date": "", "ddg_snippet": "The aesthetics of anxiety and contamination have a tendency to carry military-industrial-dystopian notions. Embracing contamination seems to generate its own ...", "subpage_snippet": "", "source": "cornelljournalofarchitecture.cornell.edu", "link": "https://cornelljournalofarchitecture.cornell.edu/issue/issue-10/between-categories-sullivans-parallelism/", "content": "The aesthetics of anxiety and contamination have a tendency to carry military-industrial-dystopian notions. Embracing contamination seems to generate its own ..."}
+{"idx": 8, "title": "2017 - registration document", "date": "", "ddg_snippet": "1 Jan 2019 — 2017 was a decisive year for SoLocal, with the completion of the financial restructuring which allowed our Group to reduce its debt burden by ...", "subpage_snippet": "", "source": "www.solocal.com", "link": "https://www.solocal.com/sites/default/files/solocal_group_-_registration_document_2017_-_uk.pdf", "content": "1 Jan 2019 — 2017 was a decisive year for SoLocal, with the completion of the financial restructuring which allowed our Group to reduce its debt burden by ..."}
+{"idx": 9, "title": "TRIDHYA TECH LIMITED CIN", "date": "", "ddg_snippet": "8 Mar 2023 — Bhanvi Chaudhary. Company Secretary and Compliance Officer. EMAIL. TELEPHONE NO. WEBSITE grievance@tridhyatech.com. Tel No.: +91 9571831080. 312 pages", "subpage_snippet": "", "source": "archives.nseindia.com", "link": "https://archives.nseindia.com/emerge/corporates/content/TridhyaTech_RHP.pdf", "content": "8 Mar 2023 — Bhanvi Chaudhary. Company Secretary and Compliance Officer. EMAIL. TELEPHONE NO. WEBSITE grievance@tridhyatech.com. Tel No.: +91 9571831080. 312 pages"}
diff --git a/data/sampled_jsons/EntityErasure_entity_attention_cross_attention_MSN_metric_amodal_segmentation_Section_3.2.jsonl b/data/sampled_jsons/EntityErasure_entity_attention_cross_attention_MSN_metric_amodal_segmentation_Section_3.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2c1d4fb431289ce11b88f6673af8019293b6ceab
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_entity_attention_cross_attention_MSN_metric_amodal_segmentation_Section_3.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "From left to right: (a) in-put, (b) inpainting mask, (c) amodal entity segmentation , (d) entity attention , (e) cross attention . As can be seen, with our entity at-tention , our method faithfully repairs the missing parts of entities according to the amodal entity segmentation , effectively reducing the generation of sundries.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "From left to right: (a) in-put, (b) inpainting mask, (c) amodal entity segmentation , (d) entity attention , (e) cross attention . As can be seen, with our entity at-tention , our method faithfully repairs the missing parts of entities according to the amodal entity segmentation , effectively reducing the generation of sundries."}
+{"idx": 1, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure ."}
+{"idx": 2, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate unpredictable ...", "subpage_snippet": "", "source": "zyxunh.github.io", "link": "https://zyxunh.github.io/EntityErasure-ProjectPage/", "content": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate unpredictable ..."}
+{"idx": 3, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11094156", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ..."}
+{"idx": 4, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh/ entity_erasure development by creating an account on GitHub."}
+{"idx": 5, "title": "zyxunh/Mask2Former_for_entity_erasure - GitHub", "date": "", "ddg_snippet": "A single architecture for panoptic, instance and semantic segmentation . Support major segmentation datasets: ADE20K, Cityscapes, COCO, Mapillary Vistas. The majority of Mask2Former is licensed under a MIT License. However portions of the project are available under separate license terms: Swin ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Mask2Former_for_entity_erasure", "content": "A single architecture for panoptic, instance and semantic segmentation . Support major segmentation datasets: ADE20K, Cityscapes, COCO, Mapillary Vistas. The majority of Mask2Former is licensed under a MIT License. However portions of the project are available under separate license terms: Swin ..."}
+{"idx": 6, "title": "Entity/Entity/README.md at main · qqlu/Entity · GitHub", "date": "", "ddg_snippet": "Our entity segmentation models can perform exceptionally well in a cross -dataset setting where we use only COCO as the training dataset but we test the model on images from other datasets at inference time.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/qqlu/Entity/blob/main/Entity/README.md", "content": "Our entity segmentation models can perform exceptionally well in a cross -dataset setting where we use only COCO as the training dataset but we test the model on images from other datasets at inference time."}
+{"idx": 7, "title": "Attention Overlap Is Responsible for The Entity Missing Problem in Text ...", "date": "", "ddg_snippet": "In this work, we thoroughly investigate three potential causes of entity missing from the perspective of cross - attention maps: insufficient attention intensity, excessive attention spread, and significant overlap between attention maps of different entities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.20972v2", "content": "In this work, we thoroughly investigate three potential causes of entity missing from the perspective of cross - attention maps: insufficient attention intensity, excessive attention spread, and significant overlap between attention maps of different entities."}
+{"idx": 8, "title": "unhzyx/entity_erasure at main - Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/unhzyx/entity_erasure/tree/main", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ..."}
diff --git a/data/sampled_jsons/EntityErasure_paper_section_3.4_Implementation_Details_AECM_training_hyperparameters.jsonl b/data/sampled_jsons/EntityErasure_paper_section_3.4_Implementation_Details_AECM_training_hyperparameters.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d27f1c7357b468c0fac92b7fe9a18f2854a16503
--- /dev/null
+++ b/data/sampled_jsons/EntityErasure_paper_section_3.4_Implementation_Details_AECM_training_hyperparameters.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities with-out inducing unwanted sundries. To this end, we pro-pose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities with-out inducing unwanted sundries. To this end, we pro-pose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
+{"idx": 1, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094156", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ..."}
+{"idx": 2, "title": "Prompts as Auto-Optimized Training Hyperparameters - Githubissues", "date": "", "ddg_snippet": "Implementation Details Integrate a large autoregressive LM (e.g., GPT-3.5-turbo) for synthetic query generation.112 forks source link. Prompts as Auto-Optimized Training Hyperparameters #322.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/Samagra-Development/ai-tools/322", "content": "Implementation Details Integrate a large autoregressive LM (e.g., GPT-3.5-turbo) for synthetic query generation.112 forks source link. Prompts as Auto-Optimized Training Hyperparameters #322."}
+{"idx": 3, "title": "Training an Agent to Play Breakout using Deep... | Medium", "date": "", "ddg_snippet": "I ran into quite a few challenges during training — memory issues from using a large replay buffer, rewards getting stuck at a low value, slow training speed, and a lot of trial and error with hyperparameters .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@simeetnayan81/training-an-agent-to-play-breakout-using-deep-reinforcement-learning-b5ca02c81182", "content": "I ran into quite a few challenges during training — memory issues from using a large replay buffer, rewards getting stuck at a low value, slow training speed, and a lot of trial and error with hyperparameters ."}
+{"idx": 4, "title": "Preliminary Implementation MARL Transformer — Decentralized...", "date": "", "ddg_snippet": "Implementation Details . In Figure 1–1, each agent is aligned to the agent_set dimension and processed like a batch, so the dimensions are as follows: On the other hand, in Figure 2–3, these dimensions are represented by a list of length = number of agents = n", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/preliminary-implementation-marl-transformer-decentralized-architecture-for-networked-autonomous-a125db192a74", "content": "Implementation Details . In Figure 1–1, each agent is aligned to the agent_set dimension and processed like a batch, so the dimensions are as follows: On the other hand, in Figure 2–3, these dimensions are represented by a list of length = number of agents = n"}
+{"idx": 5, "title": "Questions on implementation details · Issue #14...", "date": "", "ddg_snippet": "The implementation details seems quite different from your code. This is how TensorFlow tutorial describes the process", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/philipperemy/keras-attention/issues/14", "content": "The implementation details seems quite different from your code. This is how TensorFlow tutorial describes the process"}
+{"idx": 6, "title": "The N Implementation Details of RLHF with PPO", "date": "", "ddg_snippet": "Policy Training Implementation Details . In this section , we will delve into details, such as layer initialization, data post-processing, and dropout settings.", "subpage_snippet": "", "source": "hf.qhduan.com", "link": "https://hf.qhduan.com/blog/the_n_implementation_details_of_rlhf_with_ppo", "content": "Policy Training Implementation Details . In this section , we will delve into details, such as layer initialization, data post-processing, and dropout settings."}
+{"idx": 7, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "EntityErasure : Erasing Entity Cleanly via Amodal Entity Segmentation and Completion [CVPR2025] Introduction This repository contains the official implementation of the paper EntityErasure ."}
+{"idx": 8, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
+{"idx": 9, "title": "Hyperparameter optimization for deep neural network models: a ...", "date": "", "ddg_snippet": "Oct 7, 2023 · For a specific task, define a common search space by setting various hyperparameters that will help us in benchmarking the models under consideration. Leverage the knowledge learned from other domains, encode them into various hyperparameter values during the training phase through the process of tuning.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11334-023-00540-3", "content": "Oct 7, 2023 · For a specific task, define a common search space by setting various hyperparameters that will help us in benchmarking the models under consideration. Leverage the knowledge learned from other domains, encode them into various hyperparameter values during the training phase through the process of tuning."}
diff --git a/data/sampled_jsons/Equation_11_NFR_layer_feature_selection_2aKHuXdr7Q.jsonl b/data/sampled_jsons/Equation_11_NFR_layer_feature_selection_2aKHuXdr7Q.jsonl
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index 0000000000000000000000000000000000000000..69da0080e8a916ba30d9fc564a4671f4ad03cee0
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+++ b/data/sampled_jsons/Equation_11_NFR_layer_feature_selection_2aKHuXdr7Q.jsonl
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+{"idx": 0, "title": "【ibispaint】 HOW TO USE FOLDER LAYER FEATURE IN... - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=6kmnVLIG0SU", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features ."}
+{"idx": 1, "title": "【ibispaint】 HOW TO USE FOLDER LAYER FEATURE IN IBIS...", "date": "", "ddg_snippet": "Don't forget to subscribe, like, and share. Follow us: Facebook: Instagram: pixiv: Download: Music: #Illustrator #ibispaintx.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/30c22854fd6d42f829edea0608e0eaf8/", "content": "Don't forget to subscribe, like, and share. Follow us: Facebook: Instagram: pixiv: Download: Music: #Illustrator #ibispaintx."}
+{"idx": 2, "title": "Going Deeper into Locally Differentially Private Graph ...", "date": "", "ddg_snippet": "by L He — To mitigate these, UPGNET incorporates a Node Feature Regularization ( NFR ) layer using L1-regularization to reduce effective feature dimensions ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2aKHuXdr7Q", "content": "by L He — To mitigate these, UPGNET incorporates a Node Feature Regularization ( NFR ) layer using L1-regularization to reduce effective feature dimensions ..."}
+{"idx": 3, "title": "Going Deeper into Locally Differentially Private Graph ...", "date": "", "ddg_snippet": "by L He — Under the N-H architecture, the NFR layer aims to enhance utility through efficient feature selection of the perturbed node features x′ using L1-regularization.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2aKHuXdr7Q", "content": "by L He — Under the N-H architecture, the NFR layer aims to enhance utility through efficient feature selection of the perturbed node features x′ using L1-regularization."}
+{"idx": 4, "title": "MikroTik Routers and Wireless - Software", "date": "", "ddg_snippet": "NPK file - Default RouterOS version upgrade package, most important features included. ZIP file - Contains all packages, including additional ones (User manager, etc.)", "subpage_snippet": "", "source": "mikrotik.com", "link": "https://mikrotik.com/download", "content": "NPK file - Default RouterOS version upgrade package, most important features included. ZIP file - Contains all packages, including additional ones (User manager, etc.)"}
+{"idx": 5, "title": "ai-conferences/ICML2025 · Datasets at Hugging Face", "date": "", "ddg_snippet": "Statistical Test for Feature Selection Pipelines by Selective Inference.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ai-conferences/ICML2025/viewer/default/train", "content": "Statistical Test for Feature Selection Pipelines by Selective Inference."}
+{"idx": 6, "title": "Синий экран IRQL NOT LESS OR EQUAL в Windows 11 и Windows...", "date": "", "ddg_snippet": "Что делать при синем экране IRQL NOT LESS OR EQUAL в Windows, возможные причины ошибки и варианты решения проблемы.", "subpage_snippet": "", "source": "remontka.pro", "link": "https://remontka.pro/irql-not-less-or-equal/", "content": "Что делать при синем экране IRQL NOT LESS OR EQUAL в Windows, возможные причины ошибки и варианты решения проблемы."}
+{"idx": 7, "title": "Поиск авиабилетов авиакомпании «Аэрофлот»", "date": "", "ddg_snippet": "Стоимость перелетов, онлайн бронирование билетов на самолет от компании «Аэрофлот»...", "subpage_snippet": "", "source": "www.aeroflot.ru", "link": "https://www.aeroflot.ru/sb/app/ru-ru", "content": "Стоимость перелетов, онлайн бронирование билетов на самолет от компании «Аэрофлот»..."}
+{"idx": 8, "title": "AUDI Q7 (2G) лечим пожелтевшие ДХО | Александр Шуляк | Дзен", "date": "", "ddg_snippet": "AUDI Q7 (2G) лечим пожелтевшие ДХО. Тегиp0017 audi q7, audi q7 клипса молдинга двери, светодиоды audi q7, дневные ходовые огни для audi q7 бегущая полоска, ауди q7 разъем для диагностики.", "subpage_snippet": "", "source": "dzen.ru", "link": "https://dzen.ru/video/watch/6401109ec866a90114e5265d", "content": "AUDI Q7 (2G) лечим пожелтевшие ДХО. Тегиp0017 audi q7, audi q7 клипса молдинга двери, светодиоды audi q7, дневные ходовые огни для audi q7 бегущая полоска, ауди q7 разъем для диагностики."}
+{"idx": 9, "title": "Ink Game Playable Guards Update – Tiers, Weapons, and... - Noleep", "date": "", "ddg_snippet": "New Powers: A new Epic power, Coinflip (obtained by rolling), and two new Mythic powers: Vampire (rolling) and The Robot (obtained from scheduled rewards). New Emotes: 3 limited-time emotes, 5 scheduled reward emotes, and 9 shop-exclusive emotes.", "subpage_snippet": "", "source": "noleep.com", "link": "https://noleep.com/en/ink-game-playable-guards-update-tiers-weapons-and-new-features/", "content": "New Powers: A new Epic power, Coinflip (obtained by rolling), and two new Mythic powers: Vampire (rolling) and The Robot (obtained from scheduled rewards). New Emotes: 3 limited-time emotes, 5 scheduled reward emotes, and 9 shop-exclusive emotes."}
diff --git a/data/sampled_jsons/Equation_4_quantization_symmetric_linear_recurrent_neural_networks_sitearxiv.org.jsonl b/data/sampled_jsons/Equation_4_quantization_symmetric_linear_recurrent_neural_networks_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..afcb9323536802aa0df780c06c714584b0664f70
--- /dev/null
+++ b/data/sampled_jsons/Equation_4_quantization_symmetric_linear_recurrent_neural_networks_sitearxiv.org.jsonl
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+{"idx": 0, "title": "Physics-informed neural networks for high-dimensional solutions", "date": "", "ddg_snippet": "Neural networks have emerged as powerful tools for solving partial differential equations (PDEs) [ 1 ] , largely due to their capacity to approximate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09782v1", "content": "Neural networks have emerged as powerful tools for solving partial differential equations (PDEs) [ 1 ] , largely due to their capacity to approximate ..."}
+{"idx": 1, "title": "Importance of Correlations for Neural Quantum States", "date": "", "ddg_snippet": "The crucial idea for these interpretable neural network architectures is to replace the standard non- linear activation function by a controlled ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14152v1", "content": "The crucial idea for these interpretable neural network architectures is to replace the standard non- linear activation function by a controlled ..."}
+{"idx": 2, "title": "Low-Rank Tensor Decompositions for the Theory of Neural Networks", "date": "", "ddg_snippet": "In face of the practical success of deep neural networks (NNs) compared to the worst-case hardness found in some comparatively simple tasks (e.g ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18408v1", "content": "In face of the practical success of deep neural networks (NNs) compared to the worst-case hardness found in some comparatively simple tasks (e.g ..."}
+{"idx": 3, "title": "Neural Networks with Orthogonal Jacobian", "date": "", "ddg_snippet": "In [ 40 ] , the authors study the learning dynamics of deep linear networks . ... deep convolutional neural networks without residual connections [ 49 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.02882v1", "content": "In [ 40 ] , the authors study the learning dynamics of deep linear networks . ... deep convolutional neural networks without residual connections [ 49 ..."}
+{"idx": 4, "title": "DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via", "date": "", "ddg_snippet": "... non- linear codes using this approach is highly challenging: it is well-documented in literature that naively parameterizing with off-the-shelf neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.08864v2", "content": "... non- linear codes using this approach is highly challenging: it is well-documented in literature that naively parameterizing with off-the-shelf neural ..."}
+{"idx": 5, "title": "Autoregressive neural quantum states of Fermi Hubbard models", "date": "", "ddg_snippet": "Neural network wavefunctions have also been used recently to study itinerant electron models at half-filling and beyond [ 45 , 46 , 47 , 48 , 49 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07144v2", "content": "Neural network wavefunctions have also been used recently to study itinerant electron models at half-filling and beyond [ 45 , 46 , 47 , 48 , 49 ..."}
+{"idx": 6, "title": "A Spiking Neural Network Decoder for Implantable Brain Machine", "date": "", "ddg_snippet": "... quantized with quantization -aware training and deployed to a RISC-V-based GAP9 microcontroller (MCU) for inference, resulting in a memory footprint ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.02146v1", "content": "... quantized with quantization -aware training and deployed to a RISC-V-based GAP9 microcontroller (MCU) for inference, resulting in a memory footprint ..."}
+{"idx": 7, "title": "Objective Value Change and Shape-Based Accelerated Optimization", "date": "", "ddg_snippet": "... the NTK theory to the solution of differential equations by neural networks (PINN) [ 29 ] , and Zhou extended it to general nonlinear PDEs [ 45 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20290v1", "content": "... the NTK theory to the solution of differential equations by neural networks (PINN) [ 29 ] , and Zhou extended it to general nonlinear PDEs [ 45 ] ."}
+{"idx": 8, "title": "Overcoming Quadratic Hardware Scaling for a Fully Connected", "date": "", "ddg_snippet": "... for the first time, we propose a novel ONN computing architecture that is not recurrent but a hybrid architecture that enables near- linear network ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.20680v1", "content": "... for the first time, we propose a novel ONN computing architecture that is not recurrent but a hybrid architecture that enables near- linear network ..."}
+{"idx": 9, "title": "Context parroting: A simple but tough-to-beat baseline for", "date": "", "ddg_snippet": "Neural scaling laws describe the relationship between the performance of a neural network and certain resources, such as model size, data size, or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.11349v2", "content": "Neural scaling laws describe the relationship between the performance of a neural network and certain resources, such as model size, data size, or ..."}
diff --git a/data/sampled_jsons/Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_3.1_systematizati.jsonl b/data/sampled_jsons/Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_3.1_systematizati.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b57d813426f2e50f3ae434b14dcd2cdb12303e4
--- /dev/null
+++ b/data/sampled_jsons/Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Section_3.1_systematizati.jsonl
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+{"idx": 0, "title": "Generative AI Course - Learn Generative AI Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Master Data Science and become a pro . Start today and improve your skills . Find the right instructor for you . Choose from many topics, skill levels, and languages.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=a54uWaPpz4Zkte032iZoOAz1ecDN0HiBxz47A6i3eTQ4us2Y5dEgrhE9XlR6QcJWuO5kb5hjfDFfG2iMubtcYevanskCBOWCmUnveLyISuGZ7dyOo01-4VrMmm4dmvvX.dspfoRLeIZK35O303dAWYg&rut=8514243f6399d7f260f05ca551aaf45f9765a889d6b7299e9ca5decfedac55bd&u3=https://www.bing.com/aclick?ld=e8szHNohE2wYtYjdpNmMAfDjVUCUybs_DRl2ibQGtO-PLL3SFPyYl0vhjCXDHSmzl_Vk7CYzKfnGyALLD_zsfn7rhmbqndpYgp6oqIu8YaIhUg9wCutqrXvRyu2jxisJIJCnWRH0XGaFlb1jLnt8sa6GsdmcLhz-LOI3umkJm-83kpwXFsR040cGcm84knzLGL4gNlKQ&u=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&rlid=44b20ee2dcd614c8db3166308483c39c&vqd=4-27913544786679041626696028577607481571&iurl={1}IG=B7CFADF7EA1747818C714BC85BF4E297&CID=1D86FCC6FC896B7F0537EAB6FD456A8E&ID=DevEx,5046.1", "content": "Master Data Science and become a pro . Start today and improve your skills . Find the right instructor for you . Choose from many topics, skill levels, and languages."}
+{"idx": 1, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "However, we note that the separation of systematization and operationalization parallels existing separations that have led to advancements in computer science .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40182", "content": "However, we note that the separation of systematization and operationalization parallels existing separations that have led to advancements in computer science ."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "by H Wallach · 2025 · Cited by 10 — Systematization is the process of narrowing the background concept into the systematized concept; operationalization is the process of drawing on the ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00561", "content": "by H Wallach · 2025 · Cited by 10 — Systematization is the process of narrowing the background concept into the systematized concept; operationalization is the process of drawing on the ..."}
+{"idx": 3, "title": "Toward Valid Measurement Of (Un)fairness For Generative AI", "date": "", "ddg_snippet": "by KL Truong · 2025 — for valid measurement design through four key steps: (1) contextualization, (2) systematization , (3) operationalization , and (4) application ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.04641", "content": "by KL Truong · 2025 — for valid measurement design through four key steps: (1) contextualization, (2) systematization , (3) operationalization , and (4) application ..."}
+{"idx": 4, "title": "Methodological Challenges in Agentic Evaluations of AI ...", "date": "", "ddg_snippet": "by K Wei — This generalization step requires theoretical work to show that D is a good instrument, both systematization of the underlying concept and operationalization ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ZhSKG8IslC", "content": "by K Wei — This generalization step requires theoretical work to show that D is a good instrument, both systematization of the underlying concept and operationalization ..."}
+{"idx": 5, "title": "Toward Understanding the Role of Generative AI in ...", "date": "", "ddg_snippet": "by G Yu · 2025 — Mixed-methods research should strengthen the synergy between quantitative and qualitative data to improve the systematization and explanatory ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666920X25001109", "content": "by G Yu · 2025 — Mixed-methods research should strengthen the synergy between quantitative and qualitative data to improve the systematization and explanatory ..."}
+{"idx": 6, "title": "Evaluating Generative Ad Hoc Information Retrieval", "date": "", "ddg_snippet": "by L Gienapp · 2024 · Cited by 34 — trieval, develop a new user model, and study its operationalization . ... Searching as Learning: A Systematization based on Litera- ture. J ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3626772.3657849", "content": "by L Gienapp · 2024 · Cited by 34 — trieval, develop a new user model, and study its operationalization . ... Searching as Learning: A Systematization based on Litera- ture. J ..."}
+{"idx": 7, "title": "Taxonomy, Opportunities, and Challenges of ...", "date": "", "ddg_snippet": "... systematization of the literature. In addition to. 2. Page 3. Published in ... 13.2.2 Challenges in Representation Operationalization . Assumptions About ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/686d4614e8727c981c03ae99e906d39fb90a5175.pdf", "content": "... systematization of the literature. In addition to. 2. Page 3. Published in ... 13.2.2 Challenges in Representation Operationalization . Assumptions About ..."}
+{"idx": 8, "title": "Responsible automatically processable regulation", "date": "", "ddg_snippet": "by C Guitton · 2025 · Cited by 6 — ... operationalization for APR projects. Still, APR may draw upon ... Systematization of knowledge · Fairness. Profiles. Clement Guitton View ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00146-024-01901-7", "content": "by C Guitton · 2025 · Cited by 6 — ... operationalization for APR projects. Still, APR may draw upon ... Systematization of knowledge · Fairness. Profiles. Clement Guitton View ..."}
+{"idx": 9, "title": "Adoption and integration of AI in organizations - Emerald Insight", "date": "", "ddg_snippet": "by E Romeo · 2025 · Cited by 4 — Lastly, this systematization lays the groundwork for identifying and discussing research gaps, guiding future studies in advancing this field.", "subpage_snippet": "", "source": "www.emerald.com", "link": "https://www.emerald.com/insight/content/doi/10.1108/K-07-2024-2002/full/html", "content": "by E Romeo · 2025 · Cited by 4 — Lastly, this systematization lays the groundwork for identifying and discussing research gaps, guiding future studies in advancing this field."}
diff --git a/data/sampled_jsons/Evaluating_Generative_AI_Systems_social_science_measurement_challenge_criticism_response_effort_wort_year_2024.jsonl b/data/sampled_jsons/Evaluating_Generative_AI_Systems_social_science_measurement_challenge_criticism_response_effort_wort_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b7b751f8ec82ed01a0b5e9d7ea6ea97a2e20436
--- /dev/null
+++ b/data/sampled_jsons/Evaluating_Generative_AI_Systems_social_science_measurement_challenge_criticism_response_effort_wort_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.10939v1/", "content": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult."}
+{"idx": 1, "title": "Position: Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1ZC4RNjqzU", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024)."}
+{"idx": 2, "title": "(PDF) Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "is a Social Science Measurement Challenge . Hanna Wallach1Meera Desai2Nicholas Pangakis1A.the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult—. more so than those involved in evaluating supervised ML systems .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385920305_Evaluating_Generative_AI_Systems_is_a_Social_Science_Measurement_Challenge", "content": "is a Social Science Measurement Challenge . Hanna Wallach1Meera Desai2Nicholas Pangakis1A.the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult—. more so than those involved in evaluating supervised ML systems ."}
+{"idx": 3, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/?locale=fr-ca", "content": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult."}
+{"idx": 4, "title": "Evaluating Evaluations : Examining Best Practices for Measuring ...", "date": "", "ddg_snippet": "- Evaluating Generative AI Systems is a Social Science Measurement Challenge ( Oral ) >. 20 presenters. - Provocation on Expertise in Social Impact Evaluations for Generative AI (and Beyond) ( Poster ) >. Zoe Kahn · Nitin Kohli.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/workshop/84734", "content": "- Evaluating Generative AI Systems is a Social Science Measurement Challenge ( Oral ) >. 20 presenters. - Provocation on Expertise in Social Impact Evaluations for Generative AI (and Beyond) ( Poster ) >. Zoe Kahn · Nitin Kohli."}
+{"idx": 5, "title": "NeurIPS Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "We argue that the kinds of difficult measurement tasks involved in evaluating GenAI systems are highly reminiscent of the kinds of measurement tasks found throughout the social sciences . With this in mind, we present a framework...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/105556", "content": "We argue that the kinds of difficult measurement tasks involved in evaluating GenAI systems are highly reminiscent of the kinds of measurement tasks found throughout the social sciences . With this in mind, we present a framework..."}
+{"idx": 6, "title": "Downes.ca ~ Stephen's Web ~ Evaluating Generative AI Systems is...", "date": "", "ddg_snippet": "systems .\" That doesn't mean \"naïvely transferring measurement instruments designed for humans,\" but rather, adopting a framework based on four levels, \"the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements ...", "subpage_snippet": "", "source": "www.downes.ca", "link": "https://www.downes.ca/cgi-bin/page.cgi?post=77850", "content": "systems .\" That doesn't mean \"naïvely transferring measurement instruments designed for humans,\" but rather, adopting a framework based on four levels, \"the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements ..."}
+{"idx": 7, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "generative AI systems that borrow from how social scientists measure human behaviors and abilities. Think of current AI evaluation like using a ruler made of rubber - it stretches and bends, giving different measurements each time.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/evaluating-generative-ai-systems-is-social-science", "content": "generative AI systems that borrow from how social scientists measure human behaviors and abilities. Think of current AI evaluation like using a ruler made of rubber - it stretches and bends, giving different measurements each time."}
+{"idx": 8, "title": "What is Generative AI ? | IBM", "date": "", "ddg_snippet": "Generative AI is artificial intelligence ( AI ) that can create original content in response to a user’s prompt or request. Generation , evaluation and retuning, to assess the gen AI application's output and continually improve its quality and accuracy.", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/topics/generative-ai", "content": "Generative AI is artificial intelligence ( AI ) that can create original content in response to a user’s prompt or request. Generation , evaluation and retuning, to assess the gen AI application's output and continually improve its quality and accuracy."}
+{"idx": 9, "title": "Implementation challenges that hinder the strategic use of AI ... | OECD", "date": "", "ddg_snippet": "Most government AI efforts exist in exploratory or pilot phases, with limited scaling and documentation.Output tokens: tokens generated by the model in response to an input. Training tokens: data (e.g. chunks of text) that an AI model learns from during training.", "subpage_snippet": "", "source": "www.oecd.org", "link": "https://www.oecd.org/en/publications/governing-with-artificial-intelligence_795de142-en/full-report/implementation-challenges-that-hinder-the-strategic-use-of-ai-in-government_05cfe2bb.html", "content": "Most government AI efforts exist in exploratory or pilot phases, with limited scaling and documentation.Output tokens: tokens generated by the model in response to an input. Training tokens: data (e.g. chunks of text) that an AI model learns from during training."}
diff --git a/data/sampled_jsons/EventPS_Yu_CVPR_2024_mean_angular_error_3D_printed_experimental_results.jsonl b/data/sampled_jsons/EventPS_Yu_CVPR_2024_mean_angular_error_3D_printed_experimental_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..569f027d751c9cb6598a0593f2c87ba0bf76a246
--- /dev/null
+++ b/data/sampled_jsons/EventPS_Yu_CVPR_2024_mean_angular_error_3D_printed_experimental_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Quantifying Printing Quality for Printed Electrodes via Deep ...", "date": "", "ddg_snippet": "22 Jun 2025 — A novel approach is proposed to quantify printing quality in real-time using deep learning and spatial association.", "subpage_snippet": "", "source": "advanced.onlinelibrary.wiley.com", "link": "https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202500178", "content": "22 Jun 2025 — A novel approach is proposed to quantify printing quality in real-time using deep learning and spatial association."}
+{"idx": 1, "title": "Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "... 3D printed objects with various shapes, achieving a mean angular error of 8.12◦ in the presence of non-Lambertian effects. 2. Related Work. 2.1. Photometric ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33500", "content": "... 3D printed objects with various shapes, achieving a mean angular error of 8.12◦ in the presence of non-Lambertian effects. 2. Related Work. 2.1. Photometric ..."}
+{"idx": 2, "title": "Event Ellipsometer: Event-based Mueller-Matrix Video Imaging", "date": "", "ddg_snippet": "by R Maeda · 2025 · Cited by 2 — Experimentally , we demonstrate Mueller-matrix imag- ing at 30 fps, achieving a mean -squared error of 0.045 for materials with known Mueller matrices. Unlike ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Maeda_Event_Ellipsometer_Event-based_Mueller-Matrix_Video_Imaging_CVPR_2025_paper.pdf", "content": "by R Maeda · 2025 · Cited by 2 — Experimentally , we demonstrate Mueller-matrix imag- ing at 30 fps, achieving a mean -squared error of 0.045 for materials with known Mueller matrices. Unlike ... 10 pages"}
+{"idx": 3, "title": "Atlas3D: Physically Constrained Self-Supporting Text-to- ...", "date": "", "ddg_snippet": "We use a series of text prompts to generate 3D models that we expect to be self-supporting, and compare the generated results with baseline models. Our models ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/96557", "content": "We use a series of text prompts to generate 3D models that we expect to be self-supporting, and compare the generated results with baseline models. Our models ..."}
+{"idx": 4, "title": "Motion-prior Contrast Maximization for Dense Continuous- ...", "date": "", "ddg_snippet": "15 Jul 2024 — We introduce a novel self-supervised loss combining the Contrast Maximization framework with a non-linear motion prior in the form of pixel- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10802v1", "content": "15 Jul 2024 — We introduce a novel self-supervised loss combining the Contrast Maximization framework with a non-linear motion prior in the form of pixel- ..."}
+{"idx": 5, "title": "Event-based Vision Resources", "date": "", "ddg_snippet": "3D Feature Tracking via Event Camera, IEEE Conf. Computer Vision and Pattern Recognition ( CVPR ), 2024 . Code, Dataset. Kang, Y., Caron, G., Ishikawa, R ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uzh-rpg/event-based_vision_resources", "content": "3D Feature Tracking via Event Camera, IEEE Conf. Computer Vision and Pattern Recognition ( CVPR ), 2024 . Code, Dataset. Kang, Y., Caron, G., Ishikawa, R ..."}
+{"idx": 6, "title": "3D Touch Force Estimation from Capacitive Images", "date": "", "ddg_snippet": "24 Mar 2025 — Empirical experiments demonstrated that our proposed method outperformed existing force estimation methods, achieving mean absolute errors of ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3708359.3712123", "content": "24 Mar 2025 — Empirical experiments demonstrated that our proposed method outperformed existing force estimation methods, achieving mean absolute errors of ..."}
+{"idx": 7, "title": "A Review: High-Precision Angle Measurement Technologies", "date": "", "ddg_snippet": "by S Wang · 2024 · Cited by 51 — There are also sensors with the Hall effect [145], and the angle measurement results show an average measured error of ±1.263°. Specific content on the Hall ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10974437/", "content": "by S Wang · 2024 · Cited by 51 — There are also sensors with the Hall effect [145], and the angle measurement results show an average measured error of ±1.263°. Specific content on the Hall ..."}
+{"idx": 8, "title": "Event Ellipsometer: Event-based Mueller-Matrix Video Imaging", "date": "", "ddg_snippet": "We have introduced Event Ellipsometer, a Mueller-matrix video imaging method that combines an event camera and a light source with fast-rotating QWPs.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34039", "content": "We have introduced Event Ellipsometer, a Mueller-matrix video imaging method that combines an event camera and a light source with fast-rotating QWPs."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/EventPS_Yu_CVPR_2024_quantitative_evaluation_table_results_MAE_mean_angular_error_year_2024.jsonl b/data/sampled_jsons/EventPS_Yu_CVPR_2024_quantitative_evaluation_table_results_MAE_mean_angular_error_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ec5fa5e6f238be3ac8a1c6e786523643cc66a046
--- /dev/null
+++ b/data/sampled_jsons/EventPS_Yu_CVPR_2024_quantitative_evaluation_table_results_MAE_mean_angular_error_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mean absolute error - Wikipedia", "date": "", "ddg_snippet": "In statistics, mean absolute error is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Mean_absolute_error", "content": "In statistics, mean absolute error is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time..."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency."}
+{"idx": 2, "title": "CVPR 2024最佳论文分享┆EventPS: 基于事件相机的实时光度立体视觉_eve...", "date": "", "ddg_snippet": "Jul 18, 2024 · 本文介绍了 CVPR 2024 的最佳论文提名，该论文利用事件相机的独特属性，实现了实时光度立体视觉。 该算法在传统和深度学习领域均取得成功。 配合高速转台数据采集和GPU优化，算法实现了每秒超30帧的实时表面法线重建。 _ eventps : real-time photometric stereo using an event ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/audyxiao001/article/details/140520624", "content": "Jul 18, 2024 · 本文介绍了 CVPR 2024 的最佳论文提名，该论文利用事件相机的独特属性，实现了实时光度立体视觉。 该算法在传统和深度学习领域均取得成功。 配合高速转台数据采集和GPU优化，算法实现了每秒超30帧的实时表面法线重建。 _ eventps : real-time photometric stereo using an event ..."}
+{"idx": 3, "title": "Workbook: CVPR 2024 - Tableau Software", "date": "", "ddg_snippet": "CVPR 2024 (All) Computer Vision and Pattern Recognition Conference Seattle | June 17-21, 2024", "subpage_snippet": "", "source": "public.tableau.com", "link": "https://public.tableau.com/views/CVPR2024/CVPRtrends?:showVizHome=no", "content": "CVPR 2024 (All) Computer Vision and Pattern Recognition Conference Seattle | June 17-21, 2024"}
+{"idx": 4, "title": "CVPR 2024 Statistics - Paper Copilot", "date": "", "ddg_snippet": "CVPR 2024 Review Scores Collection The Conference on Computer Vision and Pattern Recognition ( CVPR ) has not yet opened its reviewer data during the review. However, there's a noticeable interest in the community to view how scores are distributed.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/cvpr-statistics/cvpr-2024-statistics/", "content": "CVPR 2024 Review Scores Collection The Conference on Computer Vision and Pattern Recognition ( CVPR ) has not yet opened its reviewer data during the review. However, there's a noticeable interest in the community to view how scores are distributed."}
+{"idx": 5, "title": "CVPR 2024 Open Access Repository", "date": "", "ddg_snippet": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency."}
+{"idx": 6, "title": "CVPR-2023-24-Papers/sections/2024/main/datasets-and ...", "date": "", "ddg_snippet": "CVPR 2023- 2024 Papers: Dive into advanced research presented at the leading computer vision conference. Keep up to date with the latest developments in computer vision and deep learning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/DmitryRyumin/CVPR-2023-24-Papers/blob/main/sections/2024/main/datasets-and-evaluation.md", "content": "CVPR 2023- 2024 Papers: Dive into advanced research presented at the leading computer vision conference. Keep up to date with the latest developments in computer vision and deep learning."}
+{"idx": 7, "title": "EventPSR: Surface Normal and Reflectance Estimation from ...", "date": "", "ddg_snippet": "The bottom part of Fig. 6 are the relighting results rendered with the estimated normal and reflectance parameters under different lighting condi-tions.5 The results demonstrate that EventPSR effectively handles material variation and maintains accuracy across different surface types. Quantitative evaluation on reflectance estimation.", "subpage_snippet": "", "source": "assets.ctfassets.net", "link": "https://assets.ctfassets.net/yreyglvi5sud/5jrOdfPf8UFsyTqMjOdgIe/41df47af774e585c44a96a247be7ecfd/Yu_CVPR25a.pdf", "content": "The bottom part of Fig. 6 are the relighting results rendered with the estimated normal and reflectance parameters under different lighting condi-tions.5 The results demonstrate that EventPSR effectively handles material variation and maintains accuracy across different surface types. Quantitative evaluation on reflectance estimation."}
+{"idx": 8, "title": "3 Regression Metrics You Must Know: MAE , MSE... | Proclus Academy", "date": "", "ddg_snippet": "Root Mean Squared Error (RMSE). MAE vs. RMSE. Practice using Python & Scikit-Learn. Mean Absolute Error ( MAE ). As we saw above, the prediction error can be positive or negative. But what if we focus only on the size of the error and ignore the sign?", "subpage_snippet": "", "source": "proclusacademy.com", "link": "https://proclusacademy.com/blog/explainer/regression-metrics-you-must-know/", "content": "Root Mean Squared Error (RMSE). MAE vs. RMSE. Practice using Python & Scikit-Learn. Mean Absolute Error ( MAE ). As we saw above, the prediction error can be positive or negative. But what if we focus only on the size of the error and ignore the sign?"}
+{"idx": 9, "title": "Loss functions for model evaluation . - Nixtla", "date": "", "ddg_snippet": "* Mean Absolute Error ( MAE ) MAE measures the relative prediction accuracy of a forecasting method by calculating the deviation of the prediction and the true value at a given time and averages these devations over the length of the series.*", "subpage_snippet": "", "source": "nixtlaverse.nixtla.io", "link": "https://nixtlaverse.nixtla.io/utilsforecast/losses.html", "content": "* Mean Absolute Error ( MAE ) MAE measures the relative prediction accuracy of a forecasting method by calculating the deviation of the prediction and the true value at a given time and averages these devations over the length of the series.*"}
diff --git a/data/sampled_jsons/EventPS_evaluation_3D_printed_dataset_mean_angular_error_results.jsonl b/data/sampled_jsons/EventPS_evaluation_3D_printed_dataset_mean_angular_error_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f92e023f48d31f5f87ad388912f5e3c3730de82a
--- /dev/null
+++ b/data/sampled_jsons/EventPS_evaluation_3D_printed_dataset_mean_angular_error_results.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Complete evaluation results on the real dataset . The results demonstrate consistent and stable performance among all objects of our EventPS algorithms.", "subpage_snippet": "", "source": "downloads.ctfassets.net", "link": "https://downloads.ctfassets.net/yreyglvi5sud/7xx2vePh8HxPPWwBbz5ikd/23e483fbf3dc9763bd7811cbee301f29/Yu_CVPR24.pdf", "content": "Complete evaluation results on the real dataset . The results demonstrate consistent and stable performance among all objects of our EventPS algorithms."}
+{"idx": 1, "title": "(PDF) What Is Learned in Deep Uncalibrated Photometric Stereo?", "date": "", "ddg_snippet": "evaluation of multi-view stereo reconstruction algorithms. In: CVPR (2006). 42. Shen, H.L., Cheng, Y.: Calibrating light sources by using a planar mirror.However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/346874574_What_Is_Learned_in_Deep_Uncalibrated_Photometric_Stereo", "content": "evaluation of multi-view stereo reconstruction algorithms. In: CVPR (2006). 42. Shen, H.L., Cheng, Y.: Calibrating light sources by using a planar mirror.However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections."}
+{"idx": 2, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "Figure 8. Quantitative evaluation with the complex shapes. From left to right: photograph, event accumulation image from the events, ground truth, normal map recovered by EventPS -FCN [67] with its angular error map, and those by ours.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "Figure 8. Quantitative evaluation with the complex shapes. From left to right: photograph, event accumulation image from the events, ground truth, normal map recovered by EventPS -FCN [67] with its angular error map, and those by ours."}
+{"idx": 3, "title": "Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "... evaluation using 21 3D printed objects with various shapes, achieving a mean angular error of 8.12◦ in the presence of non-Lambertian effects. 2. Related ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33500", "content": "... evaluation using 21 3D printed objects with various shapes, achieving a mean angular error of 8.12◦ in the presence of non-Lambertian effects. 2. Related ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_HumanAct12_FID_score_table.jsonl b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_HumanAct12_FID_score_table.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..71d0a55e0a95c937f0249fc148f84d9c9a957cd6
--- /dev/null
+++ b/data/sampled_jsons/Executing_your_commands_via_motion_diffusion_in_latent_space_HumanAct12_FID_score_table.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MotionGPT3: Human Motion as a Second Modality", "date": "", "ddg_snippet": "Based on this continuous latent space , the motion branch predicts motion latents directly from intermediate hidden states using a diffusion head ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.24086v1", "content": "Based on this continuous latent space , the motion branch predicts motion latents directly from intermediate hidden states using a diffusion head ..."}
+{"idx": 1, "title": "Human Motion Synthesis: A Diffusion Approach for Motion", "date": "", "ddg_snippet": "Chen et al., 2022 ) used VAEs to learn a representation of human motion , then performed diffusion on this latent space representation instead of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.06791v1", "content": "Chen et al., 2022 ) used VAEs to learn a representation of human motion , then performed diffusion on this latent space representation instead of the ..."}
+{"idx": 2, "title": "Spatio-Temporal Control for Masked Motion Synthesis", "date": "", "ddg_snippet": "... diffusion models have become a widespread choice for text-to- motion generation, operating directly in the motion space [ 48 , 60 , 23 ] , VAE latent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10780v2", "content": "... diffusion models have become a widespread choice for text-to- motion generation, operating directly in the motion space [ 48 , 60 , 23 ] , VAE latent ..."}
+{"idx": 3, "title": "Rethinking Diffusion for Text-Driven Human Motion Generation", "date": "", "ddg_snippet": "... discrete generation methods have rapidly dominated human motion generation, primarily surpassing diffusion -based continuous generation methods in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.16575v1", "content": "... discrete generation methods have rapidly dominated human motion generation, primarily surpassing diffusion -based continuous generation methods in ..."}
+{"idx": 4, "title": "ControlMM: Controllable Masked Motion Generation", "date": "", "ddg_snippet": "... diffusion models have become a widespread choice for text-to- motion generation, operating directly in the motion space (Tevet et al., 2022b ; Zhang ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10780v1", "content": "... diffusion models have become a widespread choice for text-to- motion generation, operating directly in the motion space (Tevet et al., 2022b ; Zhang ..."}
+{"idx": 5, "title": "FlowMotion: Target-Predictive Conditional Flow Matching for", "date": "", "ddg_snippet": "... of the generated motion , we employ an acceleration-based metric, which offers an objective and interpretable measure of motion stability and fidelity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.01338v3", "content": "... of the generated motion , we employ an acceleration-based metric, which offers an objective and interpretable measure of motion stability and fidelity ..."}
+{"idx": 6, "title": "BAMM: Bidirectional Autoregressive Motion Model", "date": "", "ddg_snippet": "... motion tokenizer that transforms 3D human motion into discrete tokens in latent space , and (2) a masked self-attention transformer that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.19435v3", "content": "... motion tokenizer that transforms 3D human motion into discrete tokens in latent space , and (2) a masked self-attention transformer that ..."}
+{"idx": 7, "title": "Efficient Diffusion Models: A Survey", "date": "", "ddg_snippet": "... Level Methods: System-level methods aim to optimize the infrastructure and computational resources required for training and deploying diffusion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.06805v3", "content": "... Level Methods: System-level methods aim to optimize the infrastructure and computational resources required for training and deploying diffusion ..."}
+{"idx": 8, "title": "Absolute Coordinates Make Motion Generation Easy", "date": "", "ddg_snippet": "Early approaches in text-driven motion generation [ 1 , 25 , 71 , 72 , 90 , 111 ] attempt to align the latent spaces of text and motion .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.19377v2", "content": "Early approaches in text-driven motion generation [ 1 , 25 , 71 , 72 , 90 , 111 ] attempt to align the latent spaces of text and motion ."}
+{"idx": 9, "title": "Towards Open Domain Text-Driven Synthesis of Multi-Person", "date": "", "ddg_snippet": "Inspired by video generation architectures [ 21 ] , our multi-person motion network uses interleaved pose and motion transformer encoder layers ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.18483v2", "content": "Inspired by video generation architectures [ 21 ] , our multi-person motion network uses interleaved pose and motion transformer encoder layers ..."}
diff --git a/data/sampled_jsons/Existing_methods_for_gaining_such_steerability_collect_human_labels_fine-tune_the_unsupervised_LM_DP_year_2023.jsonl b/data/sampled_jsons/Existing_methods_for_gaining_such_steerability_collect_human_labels_fine-tune_the_unsupervised_LM_DP_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..77de44dabe0dbe5117115c9e6e43cd1bedb2c999
--- /dev/null
+++ b/data/sampled_jsons/Existing_methods_for_gaining_such_steerability_collect_human_labels_fine-tune_the_unsupervised_LM_DP_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Direct preference optimization | Proceedings of the 37th ...", "date": "", "ddg_snippet": "Dec 10, 2023 · Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668460", "content": "Dec 10, 2023 · Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF)."}
+{"idx": 1, "title": "DPO Trainer - Hugging Face", "date": "", "ddg_snippet": "Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/trl/v0.14.0/en/dpo_trainer", "content": "Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF)."}
+{"idx": 2, "title": "Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon ...", "date": "", "ddg_snippet": "Dec 10, 2023 · Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF).", "subpage_snippet": "", "source": "slideslive.com", "link": "https://slideslive.com/39010840/direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "content": "Dec 10, 2023 · Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF)."}
+{"idx": 3, "title": "AI-Powered Paper Summarization about the arXiv paper 2305.18290v3", "date": "", "ddg_snippet": "May 14, 2025 · Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF).", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2305.18290v3/", "content": "May 14, 2025 · Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF)."}
+{"idx": 4, "title": "trl/docs/source/dpo_trainer.md at main · huggingface/trl", "date": "", "ddg_snippet": "Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/huggingface/trl/blob/main/docs/source/dpo_trainer.md", "content": "Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF)."}
+{"idx": 5, "title": "DPO Trainer", "date": "", "ddg_snippet": "Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/trl/main/en/dpo_trainer", "content": "Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align ..."}
+{"idx": 6, "title": "Announcing the NeurIPS 2023 Paper Awards", "date": "", "ddg_snippet": "11 Dec 2023 — Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised ...", "subpage_snippet": "", "source": "blog.neurips.cc", "link": "https://blog.neurips.cc/2023/12/11/announcing-the-neurips-2023-paper-awards/", "content": "11 Dec 2023 — Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised ..."}
+{"idx": 7, "title": "[2305.18290] Direct Preference Optimization: Your Language ...", "date": "", "ddg_snippet": "May 29, 2023 · While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "May 29, 2023 · While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these ..."}
+{"idx": 8, "title": "NeurIPS 2023 Direct Preference Optimization: Your Language ...", "date": "", "ddg_snippet": "Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF).", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/oral/73865", "content": "Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF)."}
+{"idx": 9, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/poster/72164", "content": "Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align ..."}
diff --git a/data/sampled_jsons/Exploration_by_Optimisation_in_Partial_Monitoring_Lattimore_Szepesvari_abstract.jsonl b/data/sampled_jsons/Exploration_by_Optimisation_in_Partial_Monitoring_Lattimore_Szepesvari_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..373918dca69ecef77538d7d2a02c3d9492f7bf23
--- /dev/null
+++ b/data/sampled_jsons/Exploration_by_Optimisation_in_Partial_Monitoring_Lattimore_Szepesvari_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Exploration by Optimisation in Partial MonitoringTor Lattimore , Csaba SzepesváriWe provide a novel algorithm for adversarial k-action d-outcome partial ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v125/lattimore20a.html", "content": "Exploration by Optimisation in Partial MonitoringTor Lattimore , Csaba SzepesváriWe provide a novel algorithm for adversarial k-action d-outcome partial ..."}
+{"idx": 1, "title": "[1907.05772] Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "View a PDF of the paper titled Exploration by Optimisation in Partial Monitoring , by Tor Lattimore and Csaba Szepesvari", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1907.05772", "content": "View a PDF of the paper titled Exploration by Optimisation in Partial Monitoring , by Tor Lattimore and Csaba Szepesvari"}
+{"idx": 2, "title": "Exploration by Optimisation in Partial Monitoring - NASA/ADS", "date": "", "ddg_snippet": "Abstract We provide a simple and efficient algorithm for adversarial $k$-action $d$-outcome non-degenerate locally observable partial monitoring game for which the $n$-round minimax regret is bounded by $6 (d+1) k^ {3/2} \\sqrt {n \\log (k)}$, matching the best known information-theoretic upper bound.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2019arXiv190705772L/abstract", "content": "Abstract We provide a simple and efficient algorithm for adversarial $k$-action $d$-outcome non-degenerate locally observable partial monitoring game for which the $n$-round minimax regret is bounded by $6 (d+1) k^ {3/2} \\sqrt {n \\log (k)}$, matching the best known information-theoretic upper bound."}
+{"idx": 3, "title": "COLT 2020: Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Exploration by Optimisation in Partial Monitoring Tor Lattimore , Csaba Szepesvari [Proceedings link] [PDF] Subject areas: Bandit problems, Online learning Presented in : Session 2A, Session 2E [Zoom link for poster in Session 2A], [Zoom link for poster in Session 2E] Abstract", "subpage_snippet": "", "source": "www.learningtheory.org", "link": "https://www.learningtheory.org/colt2020/virtual/papers/paper_66.html", "content": "Exploration by Optimisation in Partial Monitoring Tor Lattimore , Csaba Szepesvari [Proceedings link] [PDF] Subject areas: Bandit problems, Online learning Presented in : Session 2A, Session 2E [Zoom link for poster in Session 2A], [Zoom link for poster in Session 2E] Abstract"}
+{"idx": 4, "title": "Exploration by Optimisation in Partial Monitoring | Request PDF", "date": "", "ddg_snippet": "Recently, this condition has been shown by (Bartok, Pal, and Szepesvari , 2011) to imply the O (\\sqrt {T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/334457375_Exploration_by_Optimisation_in_Partial_Monitoring", "content": "Recently, this condition has been shown by (Bartok, Pal, and Szepesvari , 2011) to imply the O (\\sqrt {T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured ..."}
+{"idx": 5, "title": "\"Exploration by Optimisation in Partial Monitoring.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Exploration by Optimisation in Partial Monitoring .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-1907-05772", "content": "Bibliographic details on Exploration by Optimisation in Partial Monitoring ."}
+{"idx": 6, "title": "PDF Partial monitoring - tor-lattimore.com", "date": "", "ddg_snippet": "EXPLORATION BY OPTIMISATION IN PARTIAL MONITORING Tor Lattimore and Csaba Szepesv ́ari", "subpage_snippet": "", "source": "tor-lattimore.com", "link": "https://tor-lattimore.com/downloads/papers/2019-pm-simple.pdf", "content": "EXPLORATION BY OPTIMISATION IN PARTIAL MONITORING Tor Lattimore and Csaba Szepesv ́ari"}
+{"idx": 7, "title": "Figure 2 from Exploration by Optimisation in Partial Monitoring ...", "date": "", "ddg_snippet": "Figure 2: An exploration distribution p derived from q for the game in Eq. (7). The expected loss when playing p is smaller than playing q and simultaneously more information is gained because the third action is revealing. - \" Exploration by Optimisation in Partial Monitoring \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Exploration-by-Optimisation-in-Partial-Monitoring-Lattimore-Szepesvari/c527571cea74c9a689d4d2b9a9e1608046f7ff95/figure/2", "content": "Figure 2: An exploration distribution p derived from q for the game in Eq. (7). The expected loss when playing p is smaller than playing q and simultaneously more information is gained because the third action is revealing. - \" Exploration by Optimisation in Partial Monitoring \""}
+{"idx": 8, "title": "[1907.05772] Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Abstract We provide a simple, intuitive and efficient algorithm for adversarial k 𝑘 k -action d 𝑑 d -outcome partial monitoring games. Let m ≤ d 𝑚 𝑑 m\\leq d denote the maximum number of different observations per action. We show that for non-degenerate locally observable games the n 𝑛 n -round minimax regret is bounded by 2 m k 3 / 2 3 n log (k) 2 𝑚 superscript 𝑘 3 2 ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1907.05772", "content": "Abstract We provide a simple, intuitive and efficient algorithm for adversarial k 𝑘 k -action d 𝑑 d -outcome partial monitoring games. Let m ≤ d 𝑚 𝑑 m\\leq d denote the maximum number of different observations per action. We show that for non-degenerate locally observable games the n 𝑛 n -round minimax regret is bounded by 2 m k 3 / 2 3 n log (k) 2 𝑚 superscript 𝑘 3 2 ..."}
+{"idx": 9, "title": "An Exploration-by-Optimization Approach to Best of Both Worlds in ...", "date": "", "ddg_snippet": "Abstract : In this paper, we consider how to construct best-of-both-worlds linear bandit algorithms that achieve nearly optimal performance for both stochastic and adversarial environments. For this purpose, we show that a natural approach referred to as exploration by optimization [ Lattimore and Szepesvári , 2020] works well.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JsToEyX6QA", "content": "Abstract : In this paper, we consider how to construct best-of-both-worlds linear bandit algorithms that achieve nearly optimal performance for both stochastic and adversarial environments. For this purpose, we show that a natural approach referred to as exploration by optimization [ Lattimore and Szepesvári , 2020] works well."}
diff --git a/data/sampled_jsons/Extragradient_RM+_ExRM+_algorithm_regret_matching_last_iterate_convergence_year_2023.jsonl b/data/sampled_jsons/Extragradient_RM+_ExRM+_algorithm_regret_matching_last_iterate_convergence_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4d9de300076915f3606e2c3dc84f39e22f3b7289
--- /dev/null
+++ b/data/sampled_jsons/Extragradient_RM+_ExRM+_algorithm_regret_matching_last_iterate_convergence_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Last - Iterate Convergence Properties of Regret Matching Algorithms ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + ( RM+ ). Despite their widespread use for solving real games, virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + ( RM+ ). Despite their widespread use for solving real games, virtually nothing is known about their last - iterate convergence ."}
+{"idx": 1, "title": "LAST-I CONVERGENCE PROPERTIES OF R M ALGORITHMS IN GAMES", "date": "", "ddg_snippet": "3 × 3 matrix game. We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM+ and smooth √ Predictive RM+ , enjoy asymptotic last - iterate convergence (without a rate), 1/ t best- iterate convergence , and when combined with restarting, linear-rate last - iterate convergence . Our analysis builds on a new characterization of the geometric structure of ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2025/iclr25_rm_lastiterate/iclr25_rm_lastiterate.pdf", "content": "3 × 3 matrix game. We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM+ and smooth √ Predictive RM+ , enjoy asymptotic last - iterate convergence (without a rate), 1/ t best- iterate convergence , and when combined with restarting, linear-rate last - iterate convergence . Our analysis builds on a new characterization of the geometric structure of ..."}
+{"idx": 2, "title": "Regret Matching+: - Instability, average- and last-iterate ...", "date": "", "ddg_snippet": "Instability, average- and last - iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Instability, average- and last - iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris"}
+{"idx": 3, "title": "Last-Iterate Convergence Properties of Regret-Matching ...", "date": "", "ddg_snippet": "Sep 22, 2023 · Unlike algorithms such as optimistic gradient descent ascent, which have strong last - iterate and ergodic convergence properties for zero-sum games, virtually nothing is known about the last - iterate properties of regret - matching algorithms .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fWk5Qx0exc", "content": "Sep 22, 2023 · Unlike algorithms such as optimistic gradient descent ascent, which have strong last - iterate and ergodic convergence properties for zero-sum games, virtually nothing is known about the last - iterate properties of regret - matching algorithms ."}
+{"idx": 4, "title": "Gabriele Farina - Last-Iterate Convergence Properties of ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ ( RM+ ). Despite their widespread use for solving real games, virtually nothing is known about their last - iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2025/iclr25_rm_lastiterate/", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ ( RM+ ). Despite their widespread use for solving real games, virtually nothing is known about their last - iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ..."}
+{"idx": 5, "title": "LAST-I CONVERGENCE PROPERTIES OF R -M ALGORITHMS IN GAMES", "date": "", "ddg_snippet": "ABSTRACT Algorithms based on regret matching , specifically regret matching+ ( RM+ ), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice. Unlike algorithms such as optimistic gradient de-scent ascent, which have strong last - iterate and ergodic convergence properties for zero-sum games, virtually nothing is known about the last - iterate ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fWk5Qx0exc", "content": "ABSTRACT Algorithms based on regret matching , specifically regret matching+ ( RM+ ), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice. Unlike algorithms such as optimistic gradient de-scent ascent, which have strong last - iterate and ergodic convergence properties for zero-sum games, virtually nothing is known about the last - iterate ..."}
+{"idx": 6, "title": "Convergence in Games", "date": "", "ddg_snippet": "Regret Matching + ( RM+ ) and its variants are important algorithms for solving large-scale games [35].Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/c209cd57e13f3344a4cad4ce84d0ee1b-Paper-Conference.pdf", "content": "Regret Matching + ( RM+ ) and its variants are important algorithms for solving large-scale games [35].Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability."}
+{"idx": 7, "title": "Last - Iterate Convergence Properties of Regret - Matching Algorithms ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/7c20cba1f6aeeb219f9f9cbdde5c7382-Abstract-Conference.html", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last - iterate convergence ."}
+{"idx": 8, "title": "Extragradient", "date": "", "ddg_snippet": "Extragradient Method: O ( 1 /K) Last - Iterate Convergence for Monotone Variational Inequalities.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v151/gorbunov22a/gorbunov22a.pdf", "content": "Extragradient Method: O ( 1 /K) Last - Iterate Convergence for Monotone Variational Inequalities."}
+{"idx": 9, "title": "Combining No- regret and Q-learning", "date": "", "ddg_snippet": "Figure 6 shows that the last iterate of RM++ converges to the equi-librium of rock-paper scissors. Similar results, not shown, hold for matching pennies. Prior work as shown that both RM and RM+ diverge in these games in terms of the last iterate (although they converge on average).", "subpage_snippet": "", "source": "ifaamas.org", "link": "https://ifaamas.org/Proceedings/aamas2020/pdfs/p593.pdf", "content": "Figure 6 shows that the last iterate of RM++ converges to the equi-librium of rock-paper scissors. Similar results, not shown, hold for matching pennies. Prior work as shown that both RM and RM+ diverge in these games in terms of the last iterate (although they converge on average)."}
diff --git a/data/sampled_jsons/FAST_feature_tracking_limitations_translation_motion_descriptor-based_tracking_advantages.jsonl b/data/sampled_jsons/FAST_feature_tracking_limitations_translation_motion_descriptor-based_tracking_advantages.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4bac84a45e3cccc1c59e30295c52c55948d0c712
--- /dev/null
+++ b/data/sampled_jsons/FAST_feature_tracking_limitations_translation_motion_descriptor-based_tracking_advantages.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fast multi-feature tracking method based on tightly coupled sensors", "date": "", "ddg_snippet": "Based on the strong tightly coupled mode, we propose a novel fast visual-inertial tightly coupled feature tracking algorithm. The method utilizes an inertial measurement unit to predict the camera motion in advance and then extracts image projection points from the predicted motion to match the feature points.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0263224123010928", "content": "Based on the strong tightly coupled mode, we propose a novel fast visual-inertial tightly coupled feature tracking algorithm. The method utilizes an inertial measurement unit to predict the camera motion in advance and then extracts image projection points from the predicted motion to match the feature points."}
+{"idx": 1, "title": "PDF Descriptor-In-Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "For ex-ample, under the 'Translate' motion , 90% of features using FAST based tracking were lost within 5 seconds, and 0% of features were tracked for over 15 seconds, compared to around 20% with our approach.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "For ex-ample, under the 'Translate' motion , 90% of features using FAST based tracking were lost within 5 seconds, and 0% of features were tracked for over 15 seconds, compared to around 20% with our approach."}
+{"idx": 2, "title": "MOTION ESTIMATION USING SURF & ORB - GitHub", "date": "", "ddg_snippet": "The main goal of local feature representation is to distinctively represent the image based on some salient regions while remaining invariant to viewpoint and illumination changes. Local descriptors are fast to compute, fast to match, memory efficient, and yet exhibiting good accuracy.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ravitejapinnaka/Motion-Estimation-using-Speeded-Up-Robust-Features-SURF-and-Oriented-Fast-Rotated-Brief-ORB-", "content": "The main goal of local feature representation is to distinctively represent the image based on some salient regions while remaining invariant to viewpoint and illumination changes. Local descriptors are fast to compute, fast to match, memory efficient, and yet exhibiting good accuracy."}
+{"idx": 3, "title": "A New Visual Front-end Combining KLT with Descriptor ... - Springer", "date": "", "ddg_snippet": "Currently, feature - based visual-inertial odometry (VIO) predominantly employs descriptor -matching or Kanade-Lucas-Tomasi (KLT)-based methods for feature tracking . However, these methods are prone to short track lengths and large accumulative errors. In this study, we propose a novel approach that seamlessly integrates the advantages of KLT and descriptor -matching techniques through a tightly ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10846-023-02008-9", "content": "Currently, feature - based visual-inertial odometry (VIO) predominantly employs descriptor -matching or Kanade-Lucas-Tomasi (KLT)-based methods for feature tracking . However, these methods are prone to short track lengths and large accumulative errors. In this study, we propose a novel approach that seamlessly integrates the advantages of KLT and descriptor -matching techniques through a tightly ..."}
+{"idx": 4, "title": "Mastering Motion Estimation: Differential vs. Feature Tracking - Toolify", "date": "", "ddg_snippet": "Q3: How can motion estimation be used in video surveillance systems? Motion estimation is a crucial component of video surveillance systems. By analyzing and tracking the motion of objects within a scene, it allows for the detection of abnormal or suspicious activities. Q4: What are the limitations of feature tracking ?", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-news/mastering-motion-estimation-differential-vs-feature-tracking-2075748", "content": "Q3: How can motion estimation be used in video surveillance systems? Motion estimation is a crucial component of video surveillance systems. By analyzing and tracking the motion of objects within a scene, it allows for the detection of abnormal or suspicious activities. Q4: What are the limitations of feature tracking ?"}
+{"idx": 5, "title": "DCT-Based Local Descriptor for Robust Matching and Feature Tracking in ...", "date": "", "ddg_snippet": "We introduce a novel discrete cosine transform- based feature (DCTF) descriptor designed for both robustly matching features in aerial video and tracking features across wide-baseline oblique views in aerial wide area motion imagery (WAMI). Our DCTF descriptor preserves local structure more compactly in the frequency domain by utilizing the mathematical properties of the discrete cosine ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9126851", "content": "We introduce a novel discrete cosine transform- based feature (DCTF) descriptor designed for both robustly matching features in aerial video and tracking features across wide-baseline oblique views in aerial wide area motion imagery (WAMI). Our DCTF descriptor preserves local structure more compactly in the frequency domain by utilizing the mathematical properties of the discrete cosine ..."}
+{"idx": 6, "title": "Asynchronous event feature generation and tracking based on gradient ...", "date": "", "ddg_snippet": "The event feature tracking unit combines the constructed gradient descriptor and an event feature matching method to achieve asynchronous feature tracking . We implement the proposed algorithm in Cþþ and evaluate it on a public event dataset.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/pdf/10.1177/17298814211027028", "content": "The event feature tracking unit combines the constructed gradient descriptor and an event feature matching method to achieve asynchronous feature tracking . We implement the proposed algorithm in Cþþ and evaluate it on a public event dataset."}
+{"idx": 7, "title": "PDF Improving Feature Tracking Using Motion Sensors on Android", "date": "", "ddg_snippet": "A typical augmented reality pipeline is composed of ob-ject recognition, object localization and object tracking . In this project, we will be focusing on the third part, the track-ing of a target in real-time. We will be focusing on feature - based tracking , and our goal will be to make it more efficient by using the motion sensors in the mobile device (gyro-scopes, accelerometers, magnetometer ...", "subpage_snippet": "", "source": "jbboin.github.io", "link": "https://jbboin.github.io/doc/feature_tracking_report.pdf", "content": "A typical augmented reality pipeline is composed of ob-ject recognition, object localization and object tracking . In this project, we will be focusing on the third part, the track-ing of a target in real-time. We will be focusing on feature - based tracking , and our goal will be to make it more efficient by using the motion sensors in the mobile device (gyro-scopes, accelerometers, magnetometer ..."}
+{"idx": 8, "title": "Data-driven Feature Tracking for Event Cameras - arXiv.org", "date": "", "ddg_snippet": "Event- Based Feature Tracking In recent years, mul-tiple works have explored event- based feature tracking to increase robustness in challenging conditions, such as fast motion scenarios with large pixel displacement between timesteps and HDR scenes with very bright and dark ar-eas [17].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2211.12826", "content": "Event- Based Feature Tracking In recent years, mul-tiple works have explored event- based feature tracking to increase robustness in challenging conditions, such as fast motion scenarios with large pixel displacement between timesteps and HDR scenes with very bright and dark ar-eas [17]."}
+{"idx": 9, "title": "2D Feature Tracking — Part 2: Feature Description - Medium", "date": "", "ddg_snippet": "A set of binary descriptors for keypoints detected on an image of a cat. (Image Courtesy: Udacity Sensor Fusion Nanodegree) In Part 1 of this series, I explained feature /keypoint detectors, which ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@nikhilnair8490/2d-feature-tracking-part-2-feature-description-3ae3c4992ba3", "content": "A set of binary descriptors for keypoints detected on an image of a cat. (Image Courtesy: Udacity Sensor Fusion Nanodegree) In Part 1 of this series, I explained feature /keypoint detectors, which ..."}
diff --git a/data/sampled_jsons/FD1_FD2_FD3_formula_simulation_full-dimension_distribution_sitearxiv.orgpdf2410.02025.jsonl b/data/sampled_jsons/FD1_FD2_FD3_formula_simulation_full-dimension_distribution_sitearxiv.orgpdf2410.02025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/FD1_FD2_FD3_formula_simulation_full-dimension_distribution_sitearxiv.orgpdf2410.02025.jsonl
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/FD2_synthetic_dataset_definition_mathematical_formula_'A_Likelihood_Based_Approach_to_Distribution_R.jsonl b/data/sampled_jsons/FD2_synthetic_dataset_definition_mathematical_formula_'A_Likelihood_Based_Approach_to_Distribution_R.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..11a8e0f7a6200568d6bd071267aa9b75dfc91b4e
--- /dev/null
+++ b/data/sampled_jsons/FD2_synthetic_dataset_definition_mathematical_formula_'A_Likelihood_Based_Approach_to_Distribution_R.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 1, "title": "A comprehensive survey and analysis of generative models in machine ...", "date": "", "ddg_snippet": "Generative models have been in existence for many decades. In the field of machine learning, we come across many scenarios when directly learning a target is intractable through discriminative models, and in such cases the joint distribution of the target and the training data is approximated and generated.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1574013720303853", "content": "Generative models have been in existence for many decades. In the field of machine learning, we come across many scenarios when directly learning a target is intractable through discriminative models, and in such cases the joint distribution of the target and the training data is approximated and generated."}
+{"idx": 2, "title": "What is Synthetic Data? - Synthetic Data Explained - AWS", "date": "", "ddg_snippet": "A synthetic data set has the same mathematical properties as the actual data it is based on, but it does not contain any of the same information. Organizations use synthetic data for research, testing, new development, and machine learning research.", "subpage_snippet": "", "source": "aws.amazon.com", "link": "https://aws.amazon.com/what-is/synthetic-data/", "content": "A synthetic data set has the same mathematical properties as the actual data it is based on, but it does not contain any of the same information. Organizations use synthetic data for research, testing, new development, and machine learning research."}
+{"idx": 3, "title": "3.3. Synthetic Regression Data — Dive into Deep Learning 1.0.3 ... - D2L", "date": "", "ddg_snippet": "3.3.2. Reading the Dataset Training machine learning models often requires multiple passes over a dataset , grabbing one minibatch of examples at a time. This data is then used to update the model.", "subpage_snippet": "", "source": "d2l.ai", "link": "https://d2l.ai/chapter_linear-regression/synthetic-regression-data.html", "content": "3.3.2. Reading the Dataset Training machine learning models often requires multiple passes over a dataset , grabbing one minibatch of examples at a time. This data is then used to update the model."}
+{"idx": 4, "title": "PDF Note Set 3: Models, Parameters, and Likelihood", "date": "", "ddg_snippet": "The likelihood function can equally well be defined when the probability model is a distribution P (Dj ) (e.g., for discrete random variables) or a probability density function p(Dj ) (for continuous random variables), or for a combination of the two (e.g., p(D1jD2; 1)P (D2j 2)) where D1 models the variables that are real-valued using ...", "subpage_snippet": "", "source": "ics.uci.edu", "link": "https://ics.uci.edu/~smyth/courses/cs274/notes/notes2022/notes3_likelihood.pdf", "content": "The likelihood function can equally well be defined when the probability model is a distribution P (Dj ) (e.g., for discrete random variables) or a probability density function p(Dj ) (for continuous random variables), or for a combination of the two (e.g., p(D1jD2; 1)P (D2j 2)) where D1 models the variables that are real-valued using ..."}
+{"idx": 5, "title": "PDF Synthetic Data Generation for Economists - Cornell Information Science", "date": "", "ddg_snippet": "A relatively basic but comprehensive method for data generation is the Synthetic Data Vault (SDV) [20]. This work uses the multivariate Gaussian Copula when calculating covariances across input columns. Then, the distributions and covariances are sampled to form synthetic data. As proof of concept, ve relational datasets were synthetically generated and used by freelance data scientists to ...", "subpage_snippet": "", "source": "infosci.cornell.edu", "link": "https://infosci.cornell.edu/~koenecke/files/Synthetic_Data_Generation_for_Economists.pdf", "content": "A relatively basic but comprehensive method for data generation is the Synthetic Data Vault (SDV) [20]. This work uses the multivariate Gaussian Copula when calculating covariances across input columns. Then, the distributions and covariances are sampled to form synthetic data. As proof of concept, ve relational datasets were synthetically generated and used by freelance data scientists to ..."}
+{"idx": 6, "title": "PDF Likelihood-Based Finite Sample Inference for Synthetic Data Based on ...", "date": "", "ddg_snippet": "Likelihood-based nite sample inference based on synthetic data under the exponential model is developed in this paper. Two distinct synthetic data generation scenarios are considered, one based on posterior predictive sampling, and the other based on plug-in sampling.", "subpage_snippet": "", "source": "www.census.gov", "link": "https://www.census.gov/content/dam/Census/library/working-papers/2014/adrm/cdar2014-05-likelihood-finite-sample.pdf", "content": "Likelihood-based nite sample inference based on synthetic data under the exponential model is developed in this paper. Two distinct synthetic data generation scenarios are considered, one based on posterior predictive sampling, and the other based on plug-in sampling."}
+{"idx": 7, "title": "Synthetic Regression Data - GitHub", "date": "", "ddg_snippet": "🏷️ sec_synthetic- regression -data Machine learning is all about extracting information from data. So you might wonder, what could we possibly learn from synthetic data? While we might not care intrinsically about the patterns that we ourselves baked into an artificial data generating model, such datasets are nevertheless useful for didactic purposes, helping us to evaluate the properties ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/d2l-ai/d2l-en/blob/master/chapter_linear-regression/synthetic-regression-data.md", "content": "🏷️ sec_synthetic- regression -data Machine learning is all about extracting information from data. So you might wonder, what could we possibly learn from synthetic data? While we might not care intrinsically about the patterns that we ourselves baked into an artificial data generating model, such datasets are nevertheless useful for didactic purposes, helping us to evaluate the properties ..."}
+{"idx": 8, "title": "Synthetic Data Generation - GeeksforGeeks", "date": "", "ddg_snippet": "Synthetic data generation creates artificial datasets that replicate real-world data characteristics. It addresses data scarcity, privacy concerns, and high costs, enabling robust machine-learning models and simulations. This technique leverages methods like statistical modelling and generative models to provide valuable, flexible data solutions.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/artificial-intelligence/synthetic-data-generation/", "content": "Synthetic data generation creates artificial datasets that replicate real-world data characteristics. It addresses data scarcity, privacy concerns, and high costs, enabling robust machine-learning models and simulations. This technique leverages methods like statistical modelling and generative models to provide valuable, flexible data solutions."}
+{"idx": 9, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
diff --git a/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000.jsonl b/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8ea533a3a14bf00e4f7e053ea6904baf63a0853a
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+++ b/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000.jsonl
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+{"idx": 0, "title": "NIST reactor : summary activities July 1990 through June ...", "date": "", "ddg_snippet": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ...", "subpage_snippet": "", "source": "www.govinfo.gov", "link": "https://www.govinfo.gov/content/pkg/GOVPUB-C13-a64a50f25606b8ae2db871a896264bdc/pdf/GOVPUB-C13-a64a50f25606b8ae2db871a896264bdc.pdf", "content": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ..."}
+{"idx": 1, "title": "1310 CLEGG STREET (2) - IIS Windows Server", "date": "", "ddg_snippet": "25 Nov 2020 — ... FD3 WILLIAMS 4' 0\"L X 2' D\"W X 4 1/2\"D , FLUORESCENTTROFFER ... Nodal Displacements Node Load Case DX ft DY ft RZ deg N1 DL 0.0000 0.0000 ...", "subpage_snippet": "", "source": "petalumadocs.cityofpetaluma.net", "link": "https://petalumadocs.cityofpetaluma.net/WebLink/DocView.aspx?id=373223&dbid=0&repo=Petaluma", "content": "25 Nov 2020 — ... FD3 WILLIAMS 4' 0\"L X 2' D\"W X 4 1/2\"D , FLUORESCENTTROFFER ... Nodal Displacements Node Load Case DX ft DY ft RZ deg N1 DL 0.0000 0.0000 ..."}
+{"idx": 2, "title": "NIST reactor : summary activities July 1990 through June 1991", "date": "", "ddg_snippet": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ...", "subpage_snippet": "", "source": "nvlpubs.nist.gov", "link": "https://nvlpubs.nist.gov/nistpubs/Legacy/TN/nbstechnicalnote1292.pdf", "content": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ..."}
+{"idx": 3, "title": "Wheat streak mosaic virus alters the transcriptome of its vector ...", "date": "", "ddg_snippet": "by AK Gupta · Cited by 18 — Abstract. Wheat streak mosaic virus (WSMV; genus Tritimovirus; family Potyviridae) is an economically important wheat virus that is.", "subpage_snippet": "", "source": "digitalcommons.unl.edu", "link": "https://digitalcommons.unl.edu/context/plantpathpapers/article/1630/viewcontent/Gupta_JGV_2019_Wheat_streak_mosaic.pdf", "content": "by AK Gupta · Cited by 18 — Abstract. Wheat streak mosaic virus (WSMV; genus Tritimovirus; family Potyviridae) is an economically important wheat virus that is."}
+{"idx": 4, "title": "lake champlain: partnersrips and research in the new ...", "date": "", "ddg_snippet": "Lake Champlain: partnerships and research in the new millenniurnledited by Thomas 0. Manley, PaUicia L. Manley, and Timothy B. Mihuc.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-1-4757-4080-6.pdf", "content": "Lake Champlain: partnerships and research in the new millenniurnledited by Thomas 0. Manley, PaUicia L. Manley, and Timothy B. Mihuc."}
+{"idx": 5, "title": "EHY223 HYSYS Dynamics Introduction To ...", "date": "", "ddg_snippet": "At the end of this course you will be able to: ll Develop the skills and techniques required for creating and running dynamic simulations.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/607789019/EHY223-HYSYS-Dynamics-Introduction-to-Dynamic-Modeling", "content": "At the end of this course you will be able to: ll Develop the skills and techniques required for creating and running dynamic simulations."}
+{"idx": 6, "title": "Final Report Volume I of II Testing of a 4-Stroke Diesel Cycle ...", "date": "", "ddg_snippet": "Testing of a 4-stroke diesel cycle oil-fired reciprocating internal combustion engine to determine the effectiveness of an oxidation reduction catalyst system.", "subpage_snippet": "", "source": "nepis.epa.gov", "link": "https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P100VGMK.TXT", "content": "Testing of a 4-stroke diesel cycle oil-fired reciprocating internal combustion engine to determine the effectiveness of an oxidation reduction catalyst system."}
+{"idx": 7, "title": "Chitosan and Chitosan Derivatives as Chelating Agents", "date": "", "ddg_snippet": "This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission and sources are indicated. A ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/23222791/Chitosan_and_Chitosan_Derivatives_as_Chelating_Agents", "content": "This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission and sources are indicated. A ..."}
+{"idx": 8, "title": "(PDF) Compilation of Full Papers Presented at ICMSM2013", "date": "", "ddg_snippet": "The compilation of full papers presented at the International Congress of the Malaysian Society for Microbiology (ICMSM2013) highlights the diverse research ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/8810012/Compilation_of_Full_Papers_Presented_at_ICMSM2013", "content": "The compilation of full papers presented at the International Congress of the Malaysian Society for Microbiology (ICMSM2013) highlights the diverse research ..."}
+{"idx": 9, "title": "Fundamentals of Powder Diffraction and Structural ...", "date": "", "ddg_snippet": "Fundamentals of Powder Diffraction and Structural Characterization of Materials Second Edition Vitalij K. Pecharsky ... Author: Vitalij Pecharsky | Peter ...", "subpage_snippet": "", "source": "epdf.pub", "link": "https://epdf.pub/fundamentals-of-powder-diffraction-and-structural-characterization-of-materials-.html", "content": "Fundamentals of Powder Diffraction and Structural Characterization of Materials Second Edition Vitalij K. Pecharsky ... Author: Vitalij Pecharsky | Peter ..."}
diff --git a/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl b/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl
new file mode 100644
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+{"idx": 0, "title": "$0.00 - Feed3 Price Today, Market Cap, FD3 Price Chart", "date": "", "ddg_snippet": "Feed3 price today is $0.00. FD3 price changed 0.00% in the last 24 hours. Get up to date Feed3 charts, market cap, volume, and more.", "subpage_snippet": "", "source": "coincheckup.com", "link": "https://coincheckup.com/coins/feed3", "content": "Feed3 price today is $0.00. FD3 price changed 0.00% in the last 24 hours. Get up to date Feed3 charts, market cap, volume, and more."}
+{"idx": 1, "title": "小米10 umi 印尼稳定版线刷包V12.2.5.0.RJBIDXM_20210401.0000.00_11....", "date": "", "ddg_snippet": "Dec 31, 2023 · 欢迎访问万维手机维修，请注册会员登录本站，最全！最新！最纯净!海量原厂线刷包免费下载！（建议收藏本站，方便您下次访问） 您需要 登录 才可以下载或查看，没有帐号？ 立即注册 x 小米10 umi 印尼稳定版线刷包下载 品 牌：小米 支持型号：小米10 umi 固件版本：V12.2.5.0.RJBIDXM_20210401.0000.00_11.0 ...", "subpage_snippet": "", "source": "www.rom100.com", "link": "https://www.rom100.com/thread-115873-1-1.html", "content": "Dec 31, 2023 · 欢迎访问万维手机维修，请注册会员登录本站，最全！最新！最纯净!海量原厂线刷包免费下载！（建议收藏本站，方便您下次访问） 您需要 登录 才可以下载或查看，没有帐号？ 立即注册 x 小米10 umi 印尼稳定版线刷包下载 品 牌：小米 支持型号：小米10 umi 固件版本：V12.2.5.0.RJBIDXM_20210401.0000.00_11.0 ..."}
+{"idx": 2, "title": "NIST reactor : summary activities July 1990 through June ...", "date": "", "ddg_snippet": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ...", "subpage_snippet": "", "source": "www.govinfo.gov", "link": "https://www.govinfo.gov/content/pkg/GOVPUB-C13-a64a50f25606b8ae2db871a896264bdc/pdf/GOVPUB-C13-a64a50f25606b8ae2db871a896264bdc.pdf", "content": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ..."}
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+{"idx": 8, "title": "Ford Windstar 95-2003 | PDF | Motor diesel | Vehículos terrestres", "date": "", "ddg_snippet": "Nota: Las descripciones y especificaciones contenidas en este manual eran vigentes cuandoeste manual se aprobó para su publicación. Ford Motor Company se reserva el derechode descontinuar modelos en cualquier momento o de cambiar las especificaciones o eldiseño sin previo aviso y sin incurrir en ninguna obligación.", "subpage_snippet": "", "source": "es.scribd.com", "link": "https://es.scribd.com/document/915567733/Ford-Windstar-95-2003", "content": "Nota: Las descripciones y especificaciones contenidas en este manual eran vigentes cuandoeste manual se aprobó para su publicación. Ford Motor Company se reserva el derechode descontinuar modelos en cualquier momento o de cambiar las especificaciones o eldiseño sin previo aviso y sin incurrir en ninguna obligación."}
+{"idx": 9, "title": "Ремень ГРМ Masuma / TB5006 в Хабаровске", "date": "", "ddg_snippet": "Объявление от компании \"Подзамену - сервис подбора и доставки автотоваров\" о продаже ремень ГРМ Masuma / TB5006 в Хабаровске по цене 1 115₽. Смотрите полный каталог товара категории «ремень грм» на Дроме.", "subpage_snippet": "", "source": "baza.drom.ru", "link": "https://baza.drom.ru/g17550751215.html", "content": "Объявление от компании \"Подзамену - сервис подбора и доставки автотоваров\" о продаже ремень ГРМ Masuma / TB5006 в Хабаровске по цене 1 115₽. Смотрите полный каталог товара категории «ремень грм» на Дроме."}
diff --git a/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0..jsonl b/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0..jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..143988d29502aa83429b9bf32abd3131a366e73a
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+{"idx": 1, "title": "NIST reactor : summary activities July 1990 through June ...", "date": "", "ddg_snippet": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ...", "subpage_snippet": "", "source": "www.govinfo.gov", "link": "https://www.govinfo.gov/content/pkg/GOVPUB-C13-a64a50f25606b8ae2db871a896264bdc/pdf/GOVPUB-C13-a64a50f25606b8ae2db871a896264bdc.pdf", "content": "... Fd3 (No. 203). Our BePO-X and ZnPO-X models assumed a fully ordered 1 : distribution of Be/Zn and P over the framework tetrahedra, and strict alternation of ..."}
+{"idx": 2, "title": "1310 CLEGG STREET (2) - IIS Windows Server", "date": "", "ddg_snippet": "25 Nov 2020 — ... FD3 WILLIAMS 4 ' 0\"L X 2' D\"W X 4 1/2\"D, FLUORESCENTTROFFER ... Nodal Displacements Node Load Case DX ft DY ft RZ deg N1 DL 0.0000 0.0000 ...", "subpage_snippet": "", "source": "petalumadocs.cityofpetaluma.net", "link": "https://petalumadocs.cityofpetaluma.net/WebLink/DocView.aspx?id=373223&dbid=0&repo=Petaluma", "content": "25 Nov 2020 — ... FD3 WILLIAMS 4 ' 0\"L X 2' D\"W X 4 1/2\"D, FLUORESCENTTROFFER ... Nodal Displacements Node Load Case DX ft DY ft RZ deg N1 DL 0.0000 0.0000 ..."}
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+{"idx": 4, "title": "Final Report Volume I of II Testing of a 4-Stroke Diesel Cycle ...", "date": "", "ddg_snippet": "Testing of a 4-stroke diesel cycle oil-fired reciprocating internal combustion engine to determine the effectiveness of an oxidation reduction catalyst system.", "subpage_snippet": "", "source": "nepis.epa.gov", "link": "https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P100VGMK.TXT", "content": "Testing of a 4-stroke diesel cycle oil-fired reciprocating internal combustion engine to determine the effectiveness of an oxidation reduction catalyst system."}
+{"idx": 5, "title": "EHY223 HYSYS Dynamics Introduction To ...", "date": "", "ddg_snippet": "At the end of this course you will be able to: ll Develop the skills and techniques required for creating and running dynamic simulations.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/607789019/EHY223-HYSYS-Dynamics-Introduction-to-Dynamic-Modeling", "content": "At the end of this course you will be able to: ll Develop the skills and techniques required for creating and running dynamic simulations."}
+{"idx": 6, "title": "Chitosan and Chitosan Derivatives as Chelating Agents", "date": "", "ddg_snippet": "This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission and sources are indicated. A ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/23222791/Chitosan_and_Chitosan_Derivatives_as_Chelating_Agents", "content": "This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission and sources are indicated. A ..."}
+{"idx": 7, "title": "(PDF) Compilation of Full Papers Presented at ICMSM2013", "date": "", "ddg_snippet": "The compilation of full papers presented at the International Congress of the Malaysian Society for Microbiology (ICMSM2013) highlights the diverse research ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/8810012/Compilation_of_Full_Papers_Presented_at_ICMSM2013", "content": "The compilation of full papers presented at the International Congress of the Malaysian Society for Microbiology (ICMSM2013) highlights the diverse research ..."}
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diff --git a/data/sampled_jsons/FD3_dataset_MSE_standard_deviation_0.036.jsonl b/data/sampled_jsons/FD3_dataset_MSE_standard_deviation_0.036.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f7990f01358b4edce582bcaef9319d70a7e94df6
--- /dev/null
+++ b/data/sampled_jsons/FD3_dataset_MSE_standard_deviation_0.036.jsonl
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+{"idx": 0, "title": "Mean squared error - Wikipedia", "date": "", "ddg_snippet": "In statistics, the mean squared error or mean squared deviation of an estimator measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the true value.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Mean_squared_error", "content": "In statistics, the mean squared error or mean squared deviation of an estimator measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the true value."}
+{"idx": 1, "title": "Standard Deviation Calculator", "date": "", "ddg_snippet": "This free standard deviation calculator computes the standard deviation , variance, mean , sum, and error margin of a given data set .", "subpage_snippet": "", "source": "www.calculator.net", "link": "https://www.calculator.net/standard-deviation-calculator.html", "content": "This free standard deviation calculator computes the standard deviation , variance, mean , sum, and error margin of a given data set ."}
+{"idx": 2, "title": "How to Calculate Standard Deviation ? - GeeksforGeeks", "date": "", "ddg_snippet": "Standard Deviation is a measure of how data is spread out around the mean . It is a statistical tool used to determine the amount of variation or dispersion of a set of values from the mean .Let's calculate the standard deviation of the following data set : {2, 4, 5, 7, 9}.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/maths/how-to-calculate-standard-deviation/", "content": "Standard Deviation is a measure of how data is spread out around the mean . It is a statistical tool used to determine the amount of variation or dispersion of a set of values from the mean .Let's calculate the standard deviation of the following data set : {2, 4, 5, 7, 9}."}
+{"idx": 3, "title": "3 Regression Metrics You Must Know: MAE, MSE ... | Proclus Academy", "date": "", "ddg_snippet": "Root Mean Squared Error (RMSE). MAE vs. RMSE. Practice using Python & Scikit-Learn. Load Dataset . Build Regression Model. Calculate Metrics - MAE, MSE , and RMSE. Summary & Next Steps.Measures of Spread: MAD, Variance, and Standard Deviation .", "subpage_snippet": "", "source": "proclusacademy.com", "link": "https://proclusacademy.com/blog/explainer/regression-metrics-you-must-know/", "content": "Root Mean Squared Error (RMSE). MAE vs. RMSE. Practice using Python & Scikit-Learn. Load Dataset . Build Regression Model. Calculate Metrics - MAE, MSE , and RMSE. Summary & Next Steps.Measures of Spread: MAD, Variance, and Standard Deviation ."}
+{"idx": 4, "title": "Standard Deviation Calculator", "date": "", "ddg_snippet": "Standard deviation for binomial data . The calculator will also output the variance, arithmetic mean (average), range, count, and standard error of the mean (SEM).", "subpage_snippet": "", "source": "www.gigacalculator.com", "link": "https://www.gigacalculator.com/calculators/standard-deviation-calculator.php", "content": "Standard deviation for binomial data . The calculator will also output the variance, arithmetic mean (average), range, count, and standard error of the mean (SEM)."}
+{"idx": 5, "title": "Algorithms and applications for estimating the standard deviation of...", "date": "", "ddg_snippet": "If the Modied ESE σ0 is a reasonably good estimate of the noise standard deviation , the performance of test T can be expected to approach that of the thresholding test Tσ0ξ(α/σ0) with threshold height σ0ξ(α/σ0). To detect the presence of any signal with norm larger.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-02136597/document", "content": "If the Modied ESE σ0 is a reasonably good estimate of the noise standard deviation , the performance of test T can be expected to approach that of the thresholding test Tσ0ξ(α/σ0) with threshold height σ0ξ(α/σ0). To detect the presence of any signal with norm larger."}
+{"idx": 6, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 7, "title": "ESSD - A 1 km soil moisture dataset over eastern CONUS generated...", "date": "", "ddg_snippet": "Figure 14Monthly mean and standard deviation (denoted by error bars) of SM (a, b) and ET (c, d) over the AL subdomain and among 17 in situ observation locations as denoted in Fig.", "subpage_snippet": "", "source": "essd.copernicus.org", "link": "https://essd.copernicus.org/articles/17/4587/2025/", "content": "Figure 14Monthly mean and standard deviation (denoted by error bars) of SM (a, b) and ET (c, d) over the AL subdomain and among 17 in situ observation locations as denoted in Fig."}
+{"idx": 8, "title": "Standard deviation of residuals or Root- mean - square ... - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=zMFdb__sUpw", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 9, "title": "Mean Squared Error : Definition and Example - Statistics How To", "date": "", "ddg_snippet": "The mean squared error ( MSE ) tells you how close a regression line is to a set of points. It does this by taking the distances from the points to the regression line (these distances are the “errors”) and squaring them.", "subpage_snippet": "", "source": "www.statisticshowto.com", "link": "https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/mean-squared-error/", "content": "The mean squared error ( MSE ) tells you how close a regression line is to a set of points. It does this by taking the distances from the points to the regression line (these distances are the “errors”) and squaring them."}
diff --git a/data/sampled_jsons/FLAIROxah2ac2_GitHub_README_dataset_games.jsonl b/data/sampled_jsons/FLAIROxah2ac2_GitHub_README_dataset_games.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b45dafff0970c5a71c947abf25a6999030fb15ef
--- /dev/null
+++ b/data/sampled_jsons/FLAIROxah2ac2_GitHub_README_dataset_games.jsonl
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+{"idx": 0, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) - GitHub", "date": "", "ddg_snippet": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2). Contribute to FLAIROx/ah2ac2 development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2", "content": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2). Contribute to FLAIROx/ah2ac2 development by creating an account on GitHub ."}
+{"idx": 1, "title": "Introduction - Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "Using the Dataset : We provide two utilities (HanabiLiveGamesDataset and HanabiLiveGamesDataloader) for easier data handling, batching, shuffling, and integration with JaxMARL. Tutorial: Unrolling Games : Find out how to use our datasets and dataloaders to unroll game trajectories within the JaxMARL framework.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/datasets/", "content": "Using the Dataset : We provide two utilities (HanabiLiveGamesDataset and HanabiLiveGamesDataloader) for easier data handling, batching, shuffling, and integration with JaxMARL. Tutorial: Unrolling Games : Find out how to use our datasets and dataloaders to unroll game trajectories within the JaxMARL framework."}
+{"idx": 2, "title": "Data Details - Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "This page provides specifics on how to acquire the AH2AC2 datasets , understand their structure, and load the raw game data.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/datasets/details/", "content": "This page provides specifics on how to acquire the AH2AC2 datasets , understand their structure, and load the raw game data."}
+{"idx": 3, "title": "Foerster Lab for AI Research - GitHub", "date": "", "ddg_snippet": "Foerster Lab for AI Research has 14 repositories available. Follow their code on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx", "content": "Foerster Lab for AI Research has 14 repositories available. Follow their code on GitHub ."}
+{"idx": 4, "title": "Tutorial: Unrolling Games in JaxMARL - docs.ah2ac2.com", "date": "", "ddg_snippet": "This page explains how to use the AH2AC2 dataset , in conjunction with the HanabiLiveGamesDataset and HanabiLiveGamesDataloader utilities, to unroll game trajectories within the JaxMARL framework.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/datasets/tutorial/", "content": "This page explains how to use the AH2AC2 dataset , in conjunction with the HanabiLiveGamesDataset and HanabiLiveGamesDataloader utilities, to unroll game trajectories within the JaxMARL framework."}
+{"idx": 5, "title": "Dataset Usage Guide - Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "This is useful for studying data efficiency. Structured Game Access: Accessing an item from the dataset (e.g., dataset [i]) returns a _ Games NamedTuple. This tuple neatly organizes all related JAX arrays for a game (or a batch of games if slicing), including game_ids, scores, decks, actions, num_actions, and game_len_masks.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/datasets/classes/", "content": "This is useful for studying data efficiency. Structured Game Access: Accessing an item from the dataset (e.g., dataset [i]) returns a _ Games NamedTuple. This tuple neatly organizes all related JAX arrays for a game (or a batch of games if slicing), including game_ids, scores, decks, actions, num_actions, and game_len_masks."}
+{"idx": 6, "title": "ah2ac2/README.md at production · FLAIROx/ah2ac2 · GitHub", "date": "", "ddg_snippet": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2). Contribute to FLAIROx/ah2ac2 development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2/blob/production/README.md", "content": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2). Contribute to FLAIROx/ah2ac2 development by creating an account on GitHub ."}
+{"idx": 7, "title": "Awesome Game Datasets - GitHub", "date": "", "ddg_snippet": ":video_game: A curated list of awesome game datasets , and tools to artificial intelligence in games - leomaurodesenv/ game - datasets", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/leomaurodesenv/game-datasets", "content": ":video_game: A curated list of awesome game datasets , and tools to artificial intelligence in games - leomaurodesenv/ game - datasets"}
+{"idx": 8, "title": "Ad-Hoc Human-AI Coordination Challenge | OpenReview", "date": "", "ddg_snippet": "We develop \\textit {human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2. To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available human gameplay data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FuGps5Zyia", "content": "We develop \\textit {human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2. To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available human gameplay data."}
+{"idx": 9, "title": "Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "As a part of the AH2AC2, we open source 3,079 games from the large-scale dataset — 1,858 two-player and 1,221 three-player games . Participants are allowed to use these open-sourced games when tackling the AH2AC2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.21490", "content": "As a part of the AH2AC2, we open source 3,079 games from the large-scale dataset — 1,858 two-player and 1,221 three-player games . Participants are allowed to use these open-sourced games when tackling the AH2AC2."}
diff --git a/data/sampled_jsons/Fan_et_al.,_2024_synthetic_data_generation.jsonl b/data/sampled_jsons/Fan_et_al.,_2024_synthetic_data_generation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9e631d5fb4afcebbd1e05fb912b6b47bc77b428b
--- /dev/null
+++ b/data/sampled_jsons/Fan_et_al.,_2024_synthetic_data_generation.jsonl
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+{"idx": 0, "title": "Synthetic Data in 2024 - Progress, Opportunities and Challenges", "date": "", "ddg_snippet": "Take the Phi series of models ( Abdin et al ., 2024 ) for example - they are trained mostly on synthetic data generated using diverse techniques to ...", "subpage_snippet": "", "source": "www.timlrx.com", "link": "https://www.timlrx.com/blog/synthetic-data-in-2024-progress-opportunities-challenges", "content": "Take the Phi series of models ( Abdin et al ., 2024 ) for example - they are trained mostly on synthetic data generated using diverse techniques to ..."}
+{"idx": 1, "title": "Source2Synth: Synthetic Data Generation and Curation Grounded", "date": "", "ddg_snippet": "Alternatively, synthetic data that mimic real-world patterns can be constructed for fine-tuning, but ensuring the factuality and fidelity of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.08239v2", "content": "Alternatively, synthetic data that mimic real-world patterns can be constructed for fine-tuning, but ensuring the factuality and fidelity of the ..."}
+{"idx": 2, "title": "Challenges and Limitations in the Synthetic Generation of", "date": "", "ddg_snippet": "Synthetic data generation , particularly through Generative Adversarial Networks and Diffusion Models, has emerged as a promising solution to address ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14206v1", "content": "Synthetic data generation , particularly through Generative Adversarial Networks and Diffusion Models, has emerged as a promising solution to address ..."}
+{"idx": 3, "title": "US9244950B2 - Method for synthetic data generation for query", "date": "", "ddg_snippet": "Data for a plurality of tables are generated by sampling the maximum entropy joint probability distribution for a domain of attributes (x) of a ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US9244950B2/en", "content": "Data for a plurality of tables are generated by sampling the maximum entropy joint probability distribution for a domain of attributes (x) of a ..."}
+{"idx": 4, "title": "Federated learning for generating synthetic data: a scoping", "date": "", "ddg_snippet": "Synthetic data is artificially generated data that has the same structure and statistical properties as the original but that does not contain any of ...", "subpage_snippet": "", "source": "ijpds.org", "link": "https://ijpds.org/article/view/2158", "content": "Synthetic data is artificially generated data that has the same structure and statistical properties as the original but that does not contain any of ..."}
+{"idx": 5, "title": "Validation of the DESI 2024 Lyα forest BAO analysis using", "date": "", "ddg_snippet": "Finally, we discuss the implications of our results and identify the needs for the next generation of Lyα forest synthetic data sets, with the top ...", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/uc/item/978149tj", "content": "Finally, we discuss the implications of our results and identify the needs for the next generation of Lyα forest synthetic data sets, with the top ..."}
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+{"idx": 7, "title": "Cuiyun Gao's research works | Harbin Institute of", "date": "", "ddg_snippet": "It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Cuiyun-Gao-2204048479", "content": "It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping."}
+{"idx": 8, "title": "Movie reconstruction from mouse visual cortex activity", "date": "", "ddg_snippet": "... a method for the reconstruction of 10-second movie clips using two-photon calcium imaging data recorded in mouse V1 [ Turishcheva et al ., 2023 , 2024 ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/105081", "content": "... a method for the reconstruction of 10-second movie clips using two-photon calcium imaging data recorded in mouse V1 [ Turishcheva et al ., 2023 , 2024 ..."}
+{"idx": 9, "title": "Professor Shaun Quegan | Mathematical and Physical Sciences |", "date": "", "ddg_snippet": "... M, Labrière N, Chave J , Rosenqvist Å et al (2025) Advancements and challenges in estimating terrestrial vegetation biomass using satellite data ...", "subpage_snippet": "", "source": "sheffield.ac.uk", "link": "https://sheffield.ac.uk/mps/people/all-academic-staff/shaun-quegan", "content": "... M, Labrière N, Chave J , Rosenqvist Å et al (2025) Advancements and challenges in estimating terrestrial vegetation biomass using satellite data ..."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_initial_values_scheduler.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_initial_values_scheduler.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2ac0444bc431a8735c8a9c1df8704665bb25d64c
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization,_Implementation_and_Evaluation_initial_values_scheduler.jsonl
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+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v2", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "NeurIPS Poster Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "It is a vector graphic and may be used at any scale.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96274", "content": "It is a vector graphic and may be used at any scale."}
+{"idx": 2, "title": "FEINT IN MULTI-PLAYER GAMES - OpenReview", "date": "", "ddg_snippet": "This paper introduces the first formalization , implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WbyWDWoXD3", "content": "This paper introduces the first formalization , implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts."}
+{"idx": 3, "title": "[2403.07932v2] Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "Mar 4, 2024 · In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932v2", "content": "Mar 4, 2024 · In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 4, "title": "feint_video_slides", "date": "", "ddg_snippet": "In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 2961–2970.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96274.pdf", "content": "In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 2961–2970."}
+{"idx": 5, "title": "[2403.07931] Formalizing Feint Actions, and Example Studies ...", "date": "", "ddg_snippet": "Mar 3, 2024 · Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from their opponents. Such actions are regarded as widely-used tactic in most non-deterministic Two-player Games (e.g. boxing and fencing).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07931", "content": "Mar 3, 2024 · Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from their opponents. Such actions are regarded as widely-used tactic in most non-deterministic Two-player Games (e.g. boxing and fencing)."}
+{"idx": 6, "title": "Junyu Liu - Projects", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "junyu-liu-nate.github.io", "link": "https://junyu-liu-nate.github.io/projects/FeintFinal.html", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 7, "title": "What is another word for measure? | Measure Synonyms -", "date": "", "ddg_snippet": "Her creativity and skills deserved a greater measure of admiration than had been afforded to her. ... measure of the room allowed is determined and ...", "subpage_snippet": "", "source": "www.wordhippo.com", "link": "https://www.wordhippo.com/what-is/another-word-for/measure.html", "content": "Her creativity and skills deserved a greater measure of admiration than had been afforded to her. ... measure of the room allowed is determined and ..."}
+{"idx": 8, "title": "Amazon.com: Customer reviews: Shattered Sword: The Untold Story", "date": "", "ddg_snippet": "... I was plenty aware that not (nearly!) all the technology shown (ships, planes and so on) were correct representations of what was actually there, and ...", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/Shattered-Sword-Untold-Battle-Midway/product-reviews/1574889249", "content": "... I was plenty aware that not (nearly!) all the technology shown (ships, planes and so on) were correct representations of what was actually there, and ..."}
+{"idx": 9, "title": "What is another word for program? | Program Synonyms -", "date": "", "ddg_snippet": "A short , intensive training course, especially one ... According to the program , there are a total of four guests scheduled to speak today.", "subpage_snippet": "", "source": "www.wordhippo.com", "link": "https://www.wordhippo.com/what-is/another-word-for/program.html", "content": "A short , intensive training course, especially one ... According to the program , there are a total of four guests scheduled to speak today."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Section_4.2.2.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Section_4.2.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dbdd0749c8d0dcadda118370135457e383464df0
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Section_4.2.2.jsonl
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+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "25 Sept 2024 — The paper presents a comprehensive approach to formalizing and implementing feint behaviors in multiplayer games . It introduces a new method for ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ&referrer=[the+profile+of+Xiangjun+Peng](/profile?id=~Xiangjun_Peng1)", "content": "25 Sept 2024 — The paper presents a comprehensive approach to formalizing and implementing feint behaviors in multiplayer games . It introduces a new method for ..."}
+{"idx": 1, "title": "Feint Behaviors and Strategies: Formalization, ...", "date": "", "ddg_snippet": "In this paper, we introduce the first comprehensive formalization of Feint behaviors at action-level and strategy-level, and provide concrete implementation and ... 31 pages", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/Feint-preprint.pdf", "content": "In this paper, we introduce the first comprehensive formalization of Feint behaviors at action-level and strategy-level, and provide concrete implementation and ... 31 pages"}
+{"idx": 2, "title": "Feint Behaviors and Strategies: Formalization, ...", "date": "", "ddg_snippet": "Figure 1: An example of Palindrome-directed Generation Templates of Feint behaviors . The first row shows an action sequence of a cross-punch behavior . 29 pages", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "Figure 1: An example of Palindrome-directed Generation Templates of Feint behaviors . The first row shows an action sequence of a cross-punch behavior . 29 pages"}
+{"idx": 3, "title": "A Multi-Agent Markov Game and Reinforcement Learning ...", "date": "", "ddg_snippet": "by Q Yao · 2023 · Cited by 16 — Firstly, Section 4.2.1 selects the most suitable parameters of WoLF-BSS-Q algorithm. Secondly, Section 4.2.2 compares with other classical algorithms.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10137508/", "content": "by Q Yao · 2023 · Cited by 16 — Firstly, Section 4.2.1 selects the most suitable parameters of WoLF-BSS-Q algorithm. Secondly, Section 4.2.2 compares with other classical algorithms."}
+{"idx": 4, "title": "Specification, stochastic modeling and analysis of ...", "date": "", "ddg_snippet": "by L Lestingi · 2023 · Cited by 25 — This paper presents a model-driven framework for analyzing and developing human–robot interactive scenarios in non-industrial settings with significant sources ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S092188902300026X", "content": "by L Lestingi · 2023 · Cited by 25 — This paper presents a model-driven framework for analyzing and developing human–robot interactive scenarios in non-industrial settings with significant sources ..."}
+{"idx": 5, "title": "Hyperparameter optimization: Foundations, algorithms ...", "date": "", "ddg_snippet": "by B Bischl · 2023 · Cited by 945 — After introducing HPO from a general perspective, this paper reviews important HPO methods, from simple techniques such as grid or random search ...", "subpage_snippet": "", "source": "wires.onlinelibrary.wiley.com", "link": "https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1484", "content": "by B Bischl · 2023 · Cited by 945 — After introducing HPO from a general perspective, this paper reviews important HPO methods, from simple techniques such as grid or random search ..."}
+{"idx": 6, "title": "Multiscale representation of simulated time", "date": "", "ddg_snippet": "by R Goldstein · 2017 · Cited by 10 — We present a multiscale time representation consisting of data types, data structures, and algorithms that collectively support the recording of past events ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/eprint/mIKXDU2UtbJUjPZ8kupv/full", "content": "by R Goldstein · 2017 · Cited by 10 — We present a multiscale time representation consisting of data types, data structures, and algorithms that collectively support the recording of past events ..."}
+{"idx": 7, "title": "arXiv:2107.05847v3 [stat.ML] 24 Nov 2021", "date": "", "ddg_snippet": "by B Bischl · 2021 · Cited by 931 — This work gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2107.05847", "content": "by B Bischl · 2021 · Cited by 931 — This work gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, ..."}
+{"idx": 8, "title": "Non-Natural Interaction Design | Proceedings of the 2025 ...", "date": "", "ddg_snippet": "We advocate for non-natural interaction design as a transformative process that results in highly effective interactions by deliberately deviating from user ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3706598.3713459", "content": "We advocate for non-natural interaction design as a transformative process that results in highly effective interactions by deliberately deviating from user ..."}
+{"idx": 9, "title": "978-94-011-4267-0.pdf", "date": "", "ddg_snippet": "1 The Prototyping Approach to Software Development. 1. 1.1 Linear Software Development . 2. 1.2 Software Prototyping . . . . . . . . . 3.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-94-011-4267-0.pdf", "content": "1 The Prototyping Approach to Software Development. 1. 1.1 Linear Software Development . 2. 1.2 Software Prototyping . . . . . . . . . 3."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_lambda_short_lambda_long_scheduler_4.2.2.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_lambda_short_lambda_long_scheduler_4.2.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..09fca6d639415b2fa275e74f8da61087d4ba394d
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_lambda_short_lambda_long_scheduler_4.2.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Friends And Family – Who Murdered Robert Wone", "date": "", "ddg_snippet": "Joe ’ s feint about wanting to help was clearly a ruse to complement the SOL gambit, and it is to be hoped that Brook will see through that ...", "subpage_snippet": "", "source": "whomurderedrobertwone.com", "link": "https://whomurderedrobertwone.com/2010/11/25/friends-and-family/", "content": "Joe ’ s feint about wanting to help was clearly a ruse to complement the SOL gambit, and it is to be hoped that Brook will see through that ..."}
+{"idx": 1, "title": "A Holistic Approach for Role Inference and Action Anticipation", "date": "", "ddg_snippet": "For example, in a sport team, the team strategy , player role, and dynamic circumstances driven by the behavior of the opponents, all influence the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3531230", "content": "For example, in a sport team, the team strategy , player role, and dynamic circumstances driven by the behavior of the opponents, all influence the ..."}
+{"idx": 2, "title": "code golf - Split it. But not all! - Code Golf Stack Exchange", "date": "", "ddg_snippet": "Standard rules apply for your answer, so you are allowed to use STDIN/STDOUT, functions/method with the proper parameters and return-type, full ...", "subpage_snippet": "", "source": "codegolf.stackexchange.com", "link": "https://codegolf.stackexchange.com/questions/174896/split-it-but-not-all", "content": "Standard rules apply for your answer, so you are allowed to use STDIN/STDOUT, functions/method with the proper parameters and return-type, full ..."}
+{"idx": 3, "title": "Impressions of a Reader...: September", "date": "", "ddg_snippet": "Rosemary and Rue begins with a bang, fizzles out, and then picks up with the kind of great world-building that I love and kept me reading this series.", "subpage_snippet": "", "source": "www.impressionsofareader.com", "link": "http://www.impressionsofareader.com/search/label/September", "content": "Rosemary and Rue begins with a bang, fizzles out, and then picks up with the kind of great world-building that I love and kept me reading this series."}
+{"idx": 4, "title": "Impressions of a Reader...: Rick Riordan", "date": "", "ddg_snippet": "... and Leo arrive at Camp Jupiter led by ... I've attempted reading this short work twice before and haven't been able to get past the 30th page.", "subpage_snippet": "", "source": "www.impressionsofareader.com", "link": "http://www.impressionsofareader.com/search/label/Rick+Riordan", "content": "... and Leo arrive at Camp Jupiter led by ... I've attempted reading this short work twice before and haven't been able to get past the 30th page."}
+{"idx": 5, "title": "HLSMAC: A New StarCraft Multi-Agent Challenge for High-Level", "date": "", "ddg_snippet": "Each scenario corresponds to a specific stratagem and is designed to challenge agents with diverse strategic elements, including tactical maneuvering ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.12927v1", "content": "Each scenario corresponds to a specific stratagem and is designed to challenge agents with diverse strategic elements, including tactical maneuvering ..."}
+{"idx": 6, "title": "python - Programming cleanly when writing scientific code -", "date": "", "ddg_snippet": "... I asked a recent question on this site on refactoring one of my main functions, and it now is a lot cleaner and a lot shorter : instead of a long ...", "subpage_snippet": "", "source": "softwareengineering.stackexchange.com", "link": "https://softwareengineering.stackexchange.com/questions/373633/programming-cleanly-when-writing-scientific-code", "content": "... I asked a recent question on this site on refactoring one of my main functions, and it now is a lot cleaner and a lot shorter : instead of a long ..."}
+{"idx": 7, "title": "What makes a spectacular answer? - Meta Stack Overflow", "date": "", "ddg_snippet": "... longer & more complicated answers, this is usually not the case, since you need to understand the question (not always easy for difficult ...", "subpage_snippet": "", "source": "meta.stackoverflow.com", "link": "https://meta.stackoverflow.com/questions/280438/what-makes-a-spectacular-answer", "content": "... longer & more complicated answers, this is usually not the case, since you need to understand the question (not always easy for difficult ..."}
+{"idx": 8, "title": "Jasper – Page 2 – The Shade Tree Developer", "date": "", "ddg_snippet": "We still use Storyteller for some big, long running integration style tests in both Marten and Jasper where I don ’ t think xUnit/NUnit is a ...", "subpage_snippet": "", "source": "jeremydmiller.com", "link": "https://jeremydmiller.com/tag/jasper/page/2/", "content": "We still use Storyteller for some big, long running integration style tests in both Marten and Jasper where I don ’ t think xUnit/NUnit is a ..."}
+{"idx": 9, "title": "Impressions of a Reader...: October", "date": "", "ddg_snippet": "I've been waiting for a continuation to this world ever since I read and loved the first novella Silent Blade back in 2009, and the Ilona Andrews ...", "subpage_snippet": "", "source": "www.impressionsofareader.com", "link": "http://www.impressionsofareader.com/search/label/October", "content": "I've been waiting for a continuation to this world ever since I read and loved the first novella Silent Blade back in 2009, and the Ilona Andrews ..."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_paper_PDF_Section_4.2.2_scheduler_parameters.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_paper_PDF_Section_4.2.2_scheduler_parameters.jsonl
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+{"idx": 0, "title": "US7516227B2 - Method and apparatus for network", "date": "", "ddg_snippet": "More specifically, the present invention is various aspects is directed to network emulation, deception, and techniques using advanced address ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US7516227B2/en", "content": "More specifically, the present invention is various aspects is directed to network emulation, deception, and techniques using advanced address ..."}
+{"idx": 1, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Next, we illustrate key design choices on how to combine the generated Feint behaviors with follow-up actions in a Double- Behavior Model, which forms the foundation for the designs of Feint -accounted strategy designs in Section 47. 3.1 Feint Behavior Characteristics and Templates Since Feint behaviors aim to provide deceptive attacks, they are ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2024/file/064ae24cdbb3eaacc801ee7f4fe0e4f2-Paper-Conference.pdf", "content": "Next, we illustrate key design choices on how to combine the generated Feint behaviors with follow-up actions in a Double- Behavior Model, which forms the foundation for the designs of Feint -accounted strategy designs in Section 47. 3.1 Feint Behavior Characteristics and Templates Since Feint behaviors aim to provide deceptive attacks, they are ..."}
+{"idx": 2, "title": "PDF A Stagewise Hyperparameter Scheduler to Improve Generalization", "date": "", "ddg_snippet": "Scheduling the decay/increase of hyperpa-rameters appropriately is nearly as important as assigning the initial values to them [52]. Existing scheduler usually decays learning rate gradually while holding other hyperparameters constant (e.g., batch size and momentum parameter ).", "subpage_snippet": "", "source": "jsycsjh.github.io", "link": "https://jsycsjh.github.io/assets/publications/2021_stagewise/kdd21_stagewise_scheduler.pdf", "content": "Scheduling the decay/increase of hyperpa-rameters appropriately is nearly as important as assigning the initial values to them [52]. Existing scheduler usually decays learning rate gradually while holding other hyperparameters constant (e.g., batch size and momentum parameter )."}
+{"idx": 3, "title": "Formalizing Feint Actions, and Example Studies in Two-Player Games", "date": "", "ddg_snippet": "Our goal in this paper is to formalize Feint actions in NDGs, and incorporate key takeaways from the formalization with game strategy designs. its impacts on run-time game animations and strategies . To formalize Feint actions, we formalize Feint actions based on the relationships between timespots and position coordinates, and propose Palindrome-directed Generation of Feint actions for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07931v1", "content": "Our goal in this paper is to formalize Feint actions in NDGs, and incorporate key takeaways from the formalization with game strategy designs. its impacts on run-time game animations and strategies . To formalize Feint actions, we formalize Feint actions based on the relationships between timespots and position coordinates, and propose Palindrome-directed Generation of Feint actions for ..."}
+{"idx": 4, "title": "PDF Scheduler-driven Strategies for Fair and Efficient Data Analytics Systems", "date": "", "ddg_snippet": "Scheduler -driven Strategies for Fair and Eficient Data Analytics Systems By Kshiteej Mahajan A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Sciences)", "subpage_snippet": "", "source": "asset.library.wisc.edu", "link": "https://asset.library.wisc.edu/1711.dl/4E3UBHDKBE5QF8I/R/file-a0a71.pdf?dl", "content": "Scheduler -driven Strategies for Fair and Eficient Data Analytics Systems By Kshiteej Mahajan A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Sciences)"}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v2", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 6, "title": "(PDF) FEINT: Automated Framework for Efficient INsertion of Templates ...", "date": "", "ddg_snippet": "FEINT can be useful in applications where designers need to tailor system behavior without requiring expert FPGA programming skills or significant manual effort.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382091580_FEINT_Automated_Framework_for_Efficient_INsertion_of_TemplatesTrojans_into_FPGAs", "content": "FEINT can be useful in applications where designers need to tailor system behavior without requiring expert FPGA programming skills or significant manual effort."}
+{"idx": 7, "title": "PDF 5 - FR: Gemini Automated Scheduler Software Design - Public", "date": "", "ddg_snippet": "Section 5 presents the various interfaces that the Scheduler uses to communicate with other Gemini services, and the interface that the Scheduler offers to allow access to the output it produces. Lastly, in Section 6, we discuss the technologies chosen that are used in the development of the Scheduler , and the testing and deployment strategies .", "subpage_snippet": "", "source": "www.gemini.edu", "link": "https://www.gemini.edu/files/software/Operations+Development/Scheduler+Design+Review/5+-+FR_+Gemini+Automated+Scheduler+Software+Design+-+Public.pdf", "content": "Section 5 presents the various interfaces that the Scheduler uses to communicate with other Gemini services, and the interface that the Scheduler offers to allow access to the output it produces. Lastly, in Section 6, we discuss the technologies chosen that are used in the development of the Scheduler , and the testing and deployment strategies ."}
+{"idx": 8, "title": "PDF Identifying Students' Characteristic Learning Behaviors in an ...", "date": "", "ddg_snippet": "In this paper , we extend this analysis to identify characteris-tic learning behaviors and strategies that distinguish these three groups of students. We employ a di erential sequence mining technique to identify di erentially frequent activity patterns between the student groups and interpret these pat-terns in terms of relevant learning behaviors .", "subpage_snippet": "", "source": "files.eric.ed.gov", "link": "https://files.eric.ed.gov/fulltext/ED537188.pdf", "content": "In this paper , we extend this analysis to identify characteris-tic learning behaviors and strategies that distinguish these three groups of students. We employ a di erential sequence mining technique to identify di erentially frequent activity patterns between the student groups and interpret these pat-terns in terms of relevant learning behaviors ."}
+{"idx": 9, "title": "PDF 4 Basics of the scheduling theory - Springer", "date": "", "ddg_snippet": "Basics of the scheduling theory Time-dependent scheduling is a branch of the scheduling theory. Therefore, before formally introducing the time-dependent scheduling, we need a precise formulation of fundamentals of the scheduling theory. In this chapter, we recall the basic facts concerning the scheduling theory.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-540-69446-5_4.pdf", "content": "Basics of the scheduling theory Time-dependent scheduling is a branch of the scheduling theory. Therefore, before formally introducing the time-dependent scheduling, we need a precise formulation of fundamentals of the scheduling theory. In this chapter, we recall the basic facts concerning the scheduling theory."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_paper_Section_4.2.2_scheduler_implementation_details_initial_weights.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_paper_Section_4.2.2_scheduler_implementation_details_initial_weights.jsonl
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+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v2", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Studying the Impact of Scheduler Implementation on Task Jitter in Real ...", "date": "", "ddg_snippet": "This is unfortunate because - as we demonstrate in the course of this paper - there is a 'one-to-many' mapping between scheduler algorithms and scheduler implementations , and even comparatively small changes in the scheduler implementation can have a significant impact on jitter behaviour.", "subpage_snippet": "", "source": "pubs.sciepub.com", "link": "https://pubs.sciepub.com/jes/2/3/2/", "content": "This is unfortunate because - as we demonstrate in the course of this paper - there is a 'one-to-many' mapping between scheduler algorithms and scheduler implementations , and even comparatively small changes in the scheduler implementation can have a significant impact on jitter behaviour."}
+{"idx": 2, "title": "(PDF) FEINT: Automated Framework for Efficient INsertion of Templates ...", "date": "", "ddg_snippet": "FEINT can be useful in applications where designers need to tailor system behavior without requiring expert FPGA programming skills or significant manual effort.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382091580_FEINT_Automated_Framework_for_Efficient_INsertion_of_TemplatesTrojans_into_FPGAs", "content": "FEINT can be useful in applications where designers need to tailor system behavior without requiring expert FPGA programming skills or significant manual effort."}
+{"idx": 3, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "13unified implementation scheme of Feint behaviors in existing MARL frameworks. 14The experimental results show that our design of Feint behaviors can (1) greatly im- 15prove the game reward gains; (2) significantly improve the diversity of Multi-Player 16Games; and (3) only incur negligible overheads in terms of time consumption.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "13unified implementation scheme of Feint behaviors in existing MARL frameworks. 14The experimental results show that our design of Feint behaviors can (1) greatly im- 15prove the game reward gains; (2) significantly improve the diversity of Multi-Player 16Games; and (3) only incur negligible overheads in terms of time consumption."}
+{"idx": 4, "title": "PDF Scheduler-driven Strategies for Fair and Efficient Data Analytics Systems", "date": "", "ddg_snippet": "In this thesis, we reimagine the interface between the layers and propose scheduler -driven strategies to co-optimize scheduling and execution planning in these three systems: (1) QOOP: a fair and efficient query processing system that proposes a dynamic query execution planner that interfaces with a simple fair scheduler to replan the query ...", "subpage_snippet": "", "source": "asset.library.wisc.edu", "link": "https://asset.library.wisc.edu/1711.dl/4E3UBHDKBE5QF8I/R/file-a0a71.pdf", "content": "In this thesis, we reimagine the interface between the layers and propose scheduler -driven strategies to co-optimize scheduling and execution planning in these three systems: (1) QOOP: a fair and efficient query processing system that proposes a dynamic query execution planner that interfaces with a simple fair scheduler to replan the query ..."}
+{"idx": 5, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation, and ...", "date": "", "ddg_snippet": "However, existing literature do not provide comprehensive or concrete formalization for Feint behaviors , and their implications on game strategies . In this paper , we introduce the first comprehensive formalization of Feint behaviors at action-level and strategy-level, and provide concreteimplementationandquantitativeevaluationinMulti-Playergames.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/Feint-preprint.pdf", "content": "However, existing literature do not provide comprehensive or concrete formalization for Feint behaviors , and their implications on game strategies . In this paper , we introduce the first comprehensive formalization of Feint behaviors at action-level and strategy-level, and provide concreteimplementationandquantitativeevaluationinMulti-Playergames."}
+{"idx": 6, "title": "Formalizing Feint Actions, and Example Studies in Two-Player Games", "date": "", "ddg_snippet": "Next, we illustrate details on how to combine the generated Feint actions with elementary actions, which forms the foundation for the further designs of Feint -accounted strategy designs in Two-Player Games ( Section 3.2).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07931v1", "content": "Next, we illustrate details on how to combine the generated Feint actions with elementary actions, which forms the foundation for the further designs of Feint -accounted strategy designs in Two-Player Games ( Section 3.2)."}
+{"idx": 7, "title": "PDF OpenStack Networking Scheduler - DiVA", "date": "", "ddg_snippet": "A master thesis, Performance Management for Cloud Services: Implementation and Evaluation of Schedulers for OpenStack[26], by Hans Lindgren, evaluates different strategies that can be used when placing VMs to achieve an energy efficient load balance.", "subpage_snippet": "", "source": "www.diva-portal.org", "link": "https://www.diva-portal.org/smash/get/diva2:758092/FULLTEXT01.pdf", "content": "A master thesis, Performance Management for Cloud Services: Implementation and Evaluation of Schedulers for OpenStack[26], by Hans Lindgren, evaluates different strategies that can be used when placing VMs to achieve an energy efficient load balance."}
+{"idx": 8, "title": "PDF Memory Scheduling for Modern Microprocessors", "date": "", "ddg_snippet": "The University of Texas at Austin The need to carefully schedule memory operations has increased as memory performance has become increasingly important to overall system performance. This article describes the adaptive history-based (AHB) scheduler , which uses the history of recently scheduled operations to provide three conceptual benefits: (1) it allows the scheduler to better reason about ...", "subpage_snippet": "", "source": "www.cs.utexas.edu", "link": "https://www.cs.utexas.edu/~lin/papers/tocs07.pdf", "content": "The University of Texas at Austin The need to carefully schedule memory operations has increased as memory performance has become increasingly important to overall system performance. This article describes the adaptive history-based (AHB) scheduler , which uses the history of recently scheduled operations to provide three conceptual benefits: (1) it allows the scheduler to better reason about ..."}
+{"idx": 9, "title": "Optimized container scheduling for data-intensive serverless edge ...", "date": "", "ddg_snippet": "This paper presents a container scheduling system that enables such platforms to make efficient use of edge infrastructures. Our scheduler makes heuristic trade-offs between data and computation movement, and considers workload-specific compute requirements such as GPU acceleration.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167739X2030399X", "content": "This paper presents a container scheduling system that enables such platforms to make efficient use of edge infrastructures. Our scheduler makes heuristic trade-offs between data and computation movement, and considers workload-specific compute requirements such as GPU acceleration."}
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+{"idx": 0, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/064ae24cdbb3eaacc801ee7f4fe0e4f2-Paper-Conference.pdf", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Studying the Impact of Scheduler Implementation on Task Jitter in Real ...", "date": "", "ddg_snippet": "This is unfortunate because - as we demonstrate in the course of this paper - there is a 'one-to-many' mapping between scheduler algorithms and scheduler implementations , and even comparatively small changes in the scheduler implementation can have a significant impact on jitter behaviour.", "subpage_snippet": "", "source": "pubs.sciepub.com", "link": "https://pubs.sciepub.com/jes/2/3/2/", "content": "This is unfortunate because - as we demonstrate in the course of this paper - there is a 'one-to-many' mapping between scheduler algorithms and scheduler implementations , and even comparatively small changes in the scheduler implementation can have a significant impact on jitter behaviour."}
+{"idx": 2, "title": "Formalizing Feint Actions, and Example Studies in Two-Player Games", "date": "", "ddg_snippet": "Feint actions, as an important feature in Two-player Games, have received a limited amount of attention and lack detailed studies. Feint actions is first mentioned in . 2010 as a proof-of-concept, to construct animations for nuanced game strategies with enhanced unpredictability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07931v1", "content": "Feint actions, as an important feature in Two-player Games, have received a limited amount of attention and lack detailed studies. Feint actions is first mentioned in . 2010 as a proof-of-concept, to construct animations for nuanced game strategies with enhanced unpredictability."}
+{"idx": 3, "title": "PDF Implementing and Evaluating Schedulers in xv6 (Multi-Level Feedback ...", "date": "", "ddg_snippet": "The rest of the paper is structured as follows: Sec-tion 2 provides a brief background history on sched-uler and scheduling design. Section 3 describes the high level design of the round-robin, MLFQ and Lot-tery schedulers , and describes the 6 common metrics used to evaluate schedulers (latency, responsiveness, throughput, fairness, overhead and predictability), as well as the types of test ...", "subpage_snippet": "", "source": "markyangliu.github.io", "link": "https://markyangliu.github.io/papers/scheduler.pdf", "content": "The rest of the paper is structured as follows: Sec-tion 2 provides a brief background history on sched-uler and scheduling design. Section 3 describes the high level design of the round-robin, MLFQ and Lot-tery schedulers , and describes the 6 common metrics used to evaluate schedulers (latency, responsiveness, throughput, fairness, overhead and predictability), as well as the types of test ..."}
+{"idx": 4, "title": "(PDF) FEINT: Automated Framework for Efficient INsertion of Templates ...", "date": "", "ddg_snippet": "The method employs short -term aging effects in FinFET transistors and circuit overclocking to induce bit errors at the circuit outputs in conjunction with Machine Learning (ML) tools learning ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382091580_FEINT_Automated_Framework_for_Efficient_INsertion_of_TemplatesTrojans_into_FPGAs", "content": "The method employs short -term aging effects in FinFET transistors and circuit overclocking to induce bit errors at the circuit outputs in conjunction with Machine Learning (ML) tools learning ..."}
+{"idx": 5, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation, and ...", "date": "", "ddg_snippet": "However, existing literature do not provide comprehensive or concrete formalization for Feint behaviors , and their implications on game strategies . In this paper , we introduce the first comprehensive formalization of Feint behaviors at action-level and strategy-level, and provide concreteimplementationandquantitativeevaluationinMulti-Playergames.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/Feint-preprint.pdf", "content": "However, existing literature do not provide comprehensive or concrete formalization for Feint behaviors , and their implications on game strategies . In this paper , we introduce the first comprehensive formalization of Feint behaviors at action-level and strategy-level, and provide concreteimplementationandquantitativeevaluationinMulti-Playergames."}
+{"idx": 6, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "13unified implementation scheme of Feint behaviors in existing MARL frameworks. 14The experimental results show that our design of Feint behaviors can (1) greatly im- 15prove the game reward gains; (2) significantly improve the diversity of Multi-Player 16Games; and (3) only incur negligible overheads in terms of time consumption.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "13unified implementation scheme of Feint behaviors in existing MARL frameworks. 14The experimental results show that our design of Feint behaviors can (1) greatly im- 15prove the game reward gains; (2) significantly improve the diversity of Multi-Player 16Games; and (3) only incur negligible overheads in terms of time consumption."}
+{"idx": 7, "title": "Make out like a (Multi-Armed) Bandit: Improving the Odds of Fuzzer Seed ...", "date": "", "ddg_snippet": "Here, we assume that a scheduler has three inputs to select from— 1, 2, and 3—with probabilities 0.7, 0.8 , and 0.9 that a mutation will discover new pro-gram behavior (which is unknown and needs to be estimated by the scheduler in a fuzzing campaign), respectively to demonstrate the behavior of the algorithm.3 Here, we can see input 3 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.04749v1", "content": "Here, we assume that a scheduler has three inputs to select from— 1, 2, and 3—with probabilities 0.7, 0.8 , and 0.9 that a mutation will discover new pro-gram behavior (which is unknown and needs to be estimated by the scheduler in a fuzzing campaign), respectively to demonstrate the behavior of the algorithm.3 Here, we can see input 3 ..."}
+{"idx": 8, "title": "PDF Performance Modeling and Design of Computer Systems", "date": "", "ddg_snippet": "Inherent in these questions is the impact of real user behaviors and real-world workloads with heavy-tailed, highly variable service demands, as well as correlated arrival processes. Also intertwined in my work are the tensions between theoretical analysis and the realities of implementation , each motivating the other.", "subpage_snippet": "", "source": "mecsenotes.weebly.com", "link": "https://mecsenotes.weebly.com/uploads/4/7/6/5/47654023/1107027500.pdf", "content": "Inherent in these questions is the impact of real user behaviors and real-world workloads with heavy-tailed, highly variable service demands, as well as correlated arrival processes. Also intertwined in my work are the tensions between theoretical analysis and the realities of implementation , each motivating the other."}
+{"idx": 9, "title": "PDF Chapter 4", "date": "", "ddg_snippet": "The scheduling problem can be found at different granularity levels: Processes as complete user programs have to be executed on a mono- or multiprocessor system.", "subpage_snippet": "", "source": "www.inf.fu-berlin.de", "link": "https://www.inf.fu-berlin.de/inst/ag-se/teaching/V-BS-2018/04-scheduling.pdf", "content": "The scheduling problem can be found at different granularity levels: Processes as complete user programs have to be executed on a mono- or multiprocessor system."}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_paper_implementation_scheduler_weights_0.5_0.8_Section_4.2.2.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_paper_implementation_scheduler_weights_0.5_0.8_Section_4.2.2.jsonl
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+{"idx": 1, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "This paper is important because it provides the first comprehensive formalization of feint behaviors in game AI, significantly improving game rewards and diversity. It offers a unified implementation scheme usable across various MARL frameworks, opening new avenues for research in deception and strategy in multi-agent systems. The findings directly address current limitations in modeling ...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "This paper is important because it provides the first comprehensive formalization of feint behaviors in game AI, significantly improving game rewards and diversity. It offers a unified implementation scheme usable across various MARL frameworks, opening new avenues for research in deception and strategy in multi-agent systems. The findings directly address current limitations in modeling ..."}
+{"idx": 2, "title": "How to Choose a Learning Rate Scheduler for Neural Networks", "date": "", "ddg_snippet": "In this article you'll learn how to schedule learning rates by implementing and using various schedulers in Keras.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/how-to-choose-a-learning-rate-scheduler", "content": "In this article you'll learn how to schedule learning rates by implementing and using various schedulers in Keras."}
+{"idx": 3, "title": "Understanding the Attention Mechanism — A Simple Implementation Using ...", "date": "", "ddg_snippet": "Weighted Sum: Finally, the values are multiplied by their respective attention weights and summed, creating an output that emphasizes the most relevant information.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@christoschr97/understanding-the-attention-mechanism-a-simple-implementation-using-python-and-numpy-3f1feae13fb7", "content": "Weighted Sum: Finally, the values are multiplied by their respective attention weights and summed, creating an output that emphasizes the most relevant information."}
+{"idx": 4, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "13unified implementation scheme of Feint behaviors in existing MARL frameworks. 14The experimental results show that our design of Feint behaviors can (1) greatly im- 15prove the game reward gains; (2) significantly improve the diversity of Multi-Player 16Games; and (3) only incur negligible overheads in terms of time consumption.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "13unified implementation scheme of Feint behaviors in existing MARL frameworks. 14The experimental results show that our design of Feint behaviors can (1) greatly im- 15prove the game reward gains; (2) significantly improve the diversity of Multi-Player 16Games; and (3) only incur negligible overheads in terms of time consumption."}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Overview Research explores deceptive strategies ( feints ) in multi-player games Introduces novel framework for modeling feint behaviors in AI agents Focuses on strategic misdirection and opponent modeling Evaluates implementation across various game environments Demonstrates improved performance compared to traditional approaches", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/feint-behaviors-strategies-formalization-implementation-evaluation", "content": "Overview Research explores deceptive strategies ( feints ) in multi-player games Introduces novel framework for modeling feint behaviors in AI agents Focuses on strategic misdirection and opponent modeling Evaluates implementation across various game environments Demonstrates improved performance compared to traditional approaches"}
+{"idx": 6, "title": "Choosing the Right Weights: Balancing Value, Strategy, and Noise in ...", "date": "", "ddg_snippet": "In this paper , we study how to optimally choose weights (for users and producers) when behaviors can vary along three dimensions that designers consider in practice: value-faithfulness, strategy-robustness, and noisiness. Firstly, value-faithfulness is how indicative a behavior is of whether the user values the content or not.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.17428v2", "content": "In this paper , we study how to optimally choose weights (for users and producers) when behaviors can vary along three dimensions that designers consider in practice: value-faithfulness, strategy-robustness, and noisiness. Firstly, value-faithfulness is how indicative a behavior is of whether the user values the content or not."}
+{"idx": 7, "title": "FEINT AND ATTACK: ATTENTION-BASED STRATEGIES FOR ... - OpenReview", "date": "", "ddg_snippet": "047 Most current research on jailbreak methodologies primarily focuses on the development of sophis-048 ticated attack prompts, including role-playing (Jin et al., 2024), code injection (Ding et al., 2023), 049 and distraction techniques (Xiao et al., 2024). The cornerstone of these strategies lies in embedding harmful queries within meticulously crafted legitimate contexts. Despite ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TQ7Nuy1CSm", "content": "047 Most current research on jailbreak methodologies primarily focuses on the development of sophis-048 ticated attack prompts, including role-playing (Jin et al., 2024), code injection (Ding et al., 2023), 049 and distraction techniques (Xiao et al., 2024). The cornerstone of these strategies lies in embedding harmful queries within meticulously crafted legitimate contexts. Despite ..."}
+{"idx": 8, "title": "12.11. Learning Rate Scheduling — Dive into Deep Learning 1.0.3 ... - D2L", "date": "", "ddg_snippet": "12.11.2. Schedulers One way of adjusting the learning rate is to set it explicitly at each step. This is conveniently achieved by the set_learning_rate method. We could adjust it downward after every epoch (or even after every minibatch), e.g., in a dynamic manner in response to how optimization is progressing.", "subpage_snippet": "", "source": "www.d2l.ai", "link": "https://www.d2l.ai/chapter_optimization/lr-scheduler.html", "content": "12.11.2. Schedulers One way of adjusting the learning rate is to set it explicitly at each step. This is conveniently achieved by the set_learning_rate method. We could adjust it downward after every epoch (or even after every minibatch), e.g., in a dynamic manner in response to how optimization is progressing."}
+{"idx": 9, "title": "Using high LR + Learning rate scheduling with captions for ... - GitHub", "date": "", "ddg_snippet": "At an extremely high level, the way this scheduler works is by starting at an initial rate (1e-3 at the power of 1.0 by default) then goes lower as each epoch is completed.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cloneofsimo/lora/discussions/69", "content": "At an extremely high level, the way this scheduler works is by starting at an initial rate (1e-3 at the power of 1.0 by default) then goes lower as each epoch is completed."}
diff --git a/data/sampled_jsons/Figure_2_alpha_is_small_noise_variance_is_large_Likelihood_Based_Approach_to_Distribution_Regression.jsonl b/data/sampled_jsons/Figure_2_alpha_is_small_noise_variance_is_large_Likelihood_Based_Approach_to_Distribution_Regression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d4d22af2bd05ea9fdcdec8db7523f2bc8ed9a980
--- /dev/null
+++ b/data/sampled_jsons/Figure_2_alpha_is_small_noise_variance_is_large_Likelihood_Based_Approach_to_Distribution_Regression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Morgan-Pitman Test of Equality of Variances and its", "date": "", "ddg_snippet": "... is built upon the classic test of equality of variances introduced by Morgan (Morgan 1939 ) and independently by Pitman (Pitman 1939 ) , improved ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.12185v1", "content": "... is built upon the classic test of equality of variances introduced by Morgan (Morgan 1939 ) and independently by Pitman (Pitman 1939 ) , improved ..."}
+{"idx": 1, "title": "Effect of a Plant-Based Nootropic Supplement on Perceptual", "date": "", "ddg_snippet": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3425/15/3/226", "content": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables."}
+{"idx": 2, "title": "An improved model for the effect of correlated Si III", "date": "", "ddg_snippet": "Contamination from correlated metals, however, is harder to isolate due to the fact they are blended in the Ly α \\ alpha forest.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08613v1", "content": "Contamination from correlated metals, however, is harder to isolate due to the fact they are blended in the Ly α \\ alpha forest."}
+{"idx": 3, "title": "Using orthogonal projectors in multigrid multilevel Monte Carlo", "date": "", "ddg_snippet": "... explain in more detail in Section 1.1 below—and, since D D is large and sparse, a direct computation of tr ( D − 1 ) \\mathrm{tr}(D^{-1}) is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11424v1", "content": "... explain in more detail in Section 1.1 below—and, since D D is large and sparse, a direct computation of tr ( D − 1 ) \\mathrm{tr}(D^{-1}) is ..."}
+{"idx": 4, "title": "Gut Microbe-Derived Trimethylamine Shapes Circadian Rhythms", "date": "", "ddg_snippet": "Many of these disease associations have been validated in several large population meta-analyses 23 – 25 and Mendelian randomization studies 26 ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/107037", "content": "Many of these disease associations have been validated in several large population meta-analyses 23 – 25 and Mendelian randomization studies 26 ..."}
+{"idx": 5, "title": "Scalable extensions to given-data Sobol’ index estimators", "date": "", "ddg_snippet": "... total-order indices for all inputs scales as N ( d + 2 ) N(d+ 2 ) where N N is the number of random samples used to estimate the indices and d d is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09078v2", "content": "... total-order indices for all inputs scales as N ( d + 2 ) N(d+ 2 ) where N N is the number of random samples used to estimate the indices and d d is ..."}
+{"idx": 6, "title": "Sensitivity toward dark matter annihilation imprints on 21-cm", "date": "", "ddg_snippet": "... convolutional neural networks (CNNs), we analyze simulated 3D 21-cm differential brightness temperature maps generated via the DM21cm code, which is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08251v1", "content": "... convolutional neural networks (CNNs), we analyze simulated 3D 21-cm differential brightness temperature maps generated via the DM21cm code, which is ..."}
+{"idx": 7, "title": "Heritability — Wikipedia Republished // WIKI 2", "date": "", "ddg_snippet": "It is the source of much confusion because its technical definition is different from its commonly-understood folk definition.", "subpage_snippet": "", "source": "wiki2.org", "link": "https://wiki2.org/en/Heritability", "content": "It is the source of much confusion because its technical definition is different from its commonly-understood folk definition."}
+{"idx": 8, "title": "Thermodynamics Drive Post‐2016 Changes in the Antarctic Sea", "date": "", "ddg_snippet": "Antarctic sea ice extent has been persistently low since late 2016, possibly owing to changes in atmospheric and oceanic conditions.", "subpage_snippet": "", "source": "agupubs.onlinelibrary.wiley.com", "link": "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024JC021112?cookieSet=1", "content": "Antarctic sea ice extent has been persistently low since late 2016, possibly owing to changes in atmospheric and oceanic conditions."}
+{"idx": 9, "title": "Preserved functional organization of auditory cortex in two", "date": "", "ddg_snippet": "Here, we asked whether this organization is preserved in cases where only one temporal lobe is available due to early brain damage by investigating a ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/iscience/fulltext/S2589-0042(24)01773-5", "content": "Here, we asked whether this organization is preserved in cases where only one temporal lobe is available due to early brain damage by investigating a ..."}
diff --git a/data/sampled_jsons/Figure_A.1_Causal_Modeling_of_Climate_Activism_on_Reddit_subreddits_activated_users.jsonl b/data/sampled_jsons/Figure_A.1_Causal_Modeling_of_Climate_Activism_on_Reddit_subreddits_activated_users.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c54c9b0c3be7712b892960539ceecade4cd43c32
--- /dev/null
+++ b/data/sampled_jsons/Figure_A.1_Causal_Modeling_of_Climate_Activism_on_Reddit_subreddits_activated_users.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "In this 16 work, we develop a comprehensive causal model of how and why 17 Reddit users engage with activist communities driving mass climate 18 protests (mainly the 2019 Earth Strike, Fridays for Future, and Ex- 19 tinction Rebellion). Our framework, based on Stochastic Variational 20 Inference applied to Bayesian Networks, learns the causal ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "In this 16 work, we develop a comprehensive causal model of how and why 17 Reddit users engage with activist communities driving mass climate 18 protests (mainly the 2019 Earth Strike, Fridays for Future, and Ex- 19 tinction Rebellion). Our framework, based on Stochastic Variational 20 Inference applied to Bayesian Networks, learns the causal ..."}
+{"idx": 1, "title": "(PDF) Analyzing Climate Change Discussions on Reddit", "date": "", "ddg_snippet": "Climate action is one of the United Nations Sustainable Development Goals. We contribute to this effort by analyzing climate change topics on the Reddit social curation platform, which contains ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366548402_Analyzing_climate_change_discussions_on_Reddit", "content": "Climate action is one of the United Nations Sustainable Development Goals. We contribute to this effort by analyzing climate change topics on the Reddit social curation platform, which contains ..."}
+{"idx": 2, "title": "GitHub - bpben/reddit_climate: Climate conversation on reddit subreddits", "date": "", "ddg_snippet": "Data comes from reddit submissions and comments between November 1st, 2016 and December 1st, 2016 for the Reddit subreddits \" climate \" and \"climateskeptics\". It was downloaded using the PRAW reddit API wrapper. User connectivity as analyzed using the networkx library. The text of comments were cleaned and analyzed using the pattern library.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bpben/reddit_climate", "content": "Data comes from reddit submissions and comments between November 1st, 2016 and December 1st, 2016 for the Reddit subreddits \" climate \" and \"climateskeptics\". It was downloaded using the PRAW reddit API wrapper. User connectivity as analyzed using the networkx library. The text of comments were cleaned and analyzed using the pattern library."}
+{"idx": 3, "title": "[2505.02989] Modeling the Impact of Group Interactions on Climate ...", "date": "", "ddg_snippet": "To address this limitation, we present a temporal hypergraph model that effectively captures the group dynamics inherent in conversational threads, and we apply it to discussions about climate change on Reddit . This model predicts temporal shifts in stance towards climate issues at the level of individual users .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.02989", "content": "To address this limitation, we present a temporal hypergraph model that effectively captures the group dynamics inherent in conversational threads, and we apply it to discussions about climate change on Reddit . This model predicts temporal shifts in stance towards climate issues at the level of individual users ."}
+{"idx": 4, "title": "Causal Modeling of Climate Activism on Reddit - Researchr", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/LentiAMM25", "content": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]"}
+{"idx": 5, "title": "\"Causal Modeling of Climate Activism on Reddit.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2410-10562", "content": "Bibliographic details on Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 6, "title": "Causal Modeling of Climate Activism on Reddit | Article Information | J ...", "date": "", "ddg_snippet": "Article \" Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ...", "subpage_snippet": "", "source": "jglobal.jst.go.jp", "link": "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202402219833468228", "content": "Article \" Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ..."}
+{"idx": 7, "title": "Temporal Dynamics of Climate Change Sentiment on Reddit ... - Springer", "date": "", "ddg_snippet": "To understand climate change discourse on Reddit , we employ sentiment analysis, topic modeling , and social network analysis. Sentiment analysis helps quantify emo-tional tones in discussions, while topic modeling identifies prevalent themes. Social network analysis allows us to examine the structure and dynamics of interactions between different subreddits and users . By leveraging these ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-981-96-3762-1_1", "content": "To understand climate change discourse on Reddit , we employ sentiment analysis, topic modeling , and social network analysis. Sentiment analysis helps quantify emo-tional tones in discussions, while topic modeling identifies prevalent themes. Social network analysis allows us to examine the structure and dynamics of interactions between different subreddits and users . By leveraging these ..."}
+{"idx": 8, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Featured Image Read the Original This page is a summary of : Causal Modeling of Climate Activism on Reddit , April 2025, ACM (Association for Computing Machinery), DOI: 10.1145/3696410.3714684. You can read the full text: Read", "subpage_snippet": "", "source": "www.growkudos.com", "link": "https://www.growkudos.com/publications/10.1145%2F3696410.3714684/reader", "content": "Featured Image Read the Original This page is a summary of : Causal Modeling of Climate Activism on Reddit , April 2025, ACM (Association for Computing Machinery), DOI: 10.1145/3696410.3714684. You can read the full text: Read"}
+{"idx": 9, "title": "Causal Modeling of Climate Activism on Reddit - Corrado Monti", "date": "", "ddg_snippet": "Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales", "subpage_snippet": "", "source": "www.corradomonti.com", "link": "https://www.corradomonti.com/causal-modeling-of-climate-activism-on-reddit.html", "content": "Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales"}
diff --git a/data/sampled_jsons/FlowDec-75s_DAC-75_SIGMOS_Figure_6_subjective_listening_test.jsonl b/data/sampled_jsons/FlowDec-75s_DAC-75_SIGMOS_Figure_6_subjective_listening_test.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c7138fb704d5f8d8f9fa6f2fbe8023750abc690a
--- /dev/null
+++ b/data/sampled_jsons/FlowDec-75s_DAC-75_SIGMOS_Figure_6_subjective_listening_test.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "FlowDec - 75 s : 75 Hz, single-bitrate. Figure 6 : Subjective listening results from Test A (left) and Test B (right). Numbers in (parentheses) denote the used bitrate in kbit/s.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "FlowDec - 75 s : 75 Hz, single-bitrate. Figure 6 : Subjective listening results from Test A (left) and Test B (right). Numbers in (parentheses) denote the used bitrate in kbit/s."}
+{"idx": 1, "title": "FlowDec : A flow -based full-band general audio codec... | OpenReview", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 2, "title": "flowdec · PyPI", "date": "", "ddg_snippet": "Flowdec /TensorFlow with full GPU acceleration: ~1 second. Signal Dimensions - Flowdec can support 1, 2, or 3 dimensional images/signals.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/flowdec/", "content": "Flowdec /TensorFlow with full GPU acceleration: ~1 second. Signal Dimensions - Flowdec can support 1, 2, or 3 dimensional images/signals."}
+{"idx": 3, "title": "LG UT8000 Review (43UT8000AUA, 50UT8000AUA...) - RTINGS.com", "date": "", "ddg_snippet": "500 TVs bought and tested . Supported by you via insider access, and when you purchase through links on our site, we may earn an affiliate commission.", "subpage_snippet": "", "source": "www.rtings.com", "link": "https://www.rtings.com/tv/reviews/lg/ut8000", "content": "500 TVs bought and tested . Supported by you via insider access, and when you purchase through links on our site, we may earn an affiliate commission."}
+{"idx": 4, "title": "ГДЗ по английскому языку 3 класс (spotlight) Быкова - сборник...", "date": "", "ddg_snippet": "Английский язык (english), 3 класс контрольные задания ( test booklet), автор: Баранова Ксения Михайловна (Baranova Ksenia), издательство Просвещение, Москва, 2023, серого цвета.", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/3-klass/english/bykova-spotlight-sbornik-upragnenij", "content": "Английский язык (english), 3 класс контрольные задания ( test booklet), автор: Баранова Ксения Михайловна (Baranova Ksenia), издательство Просвещение, Москва, 2023, серого цвета."}
+{"idx": 5, "title": "#46 Атермальная тонировка [Solarnex Krystal 75 ]... | DRIVE2", "date": "", "ddg_snippet": "Solarnex Krystal — слева 75 / справа 80. Solarnex Krystal 75 из обновлённой партии Светопропускание — 69% Защита от ИК — 93% Заявленная защита от УФ — 99% (нет данных прибора) Кому интересно, сравните с прошлой по ссылке выше.", "subpage_snippet": "", "source": "www.drive2.ru", "link": "https://www.drive2.ru/l/615641025599255740/", "content": "Solarnex Krystal — слева 75 / справа 80. Solarnex Krystal 75 из обновлённой партии Светопропускание — 69% Защита от ИК — 93% Заявленная защита от УФ — 99% (нет данных прибора) Кому интересно, сравните с прошлой по ссылке выше."}
+{"idx": 6, "title": "Poco C 75 Как Включить Плавающие Окна | TikTok", "date": "", "ddg_snippet": "Смотрите видео на тему «Poco C 75 Как Включить Плавающие Окна» в TikTok.Penjelasan tentang Game Turbo POCO C 75 #pococ 75 #gameturbo #sipalingsavage.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/poco-c75-как-включить-плавающие-окна", "content": "Смотрите видео на тему «Poco C 75 Как Включить Плавающие Окна» в TikTok.Penjelasan tentang Game Turbo POCO C 75 #pococ 75 #gameturbo #sipalingsavage."}
+{"idx": 7, "title": "GISMETEO: Погода в Северодвинске сегодня, прогноз погоды...", "date": "", "ddg_snippet": "Подробный прогноз погоды в Северодвинске на сегодня.", "subpage_snippet": "", "source": "www.gismeteo.ru", "link": "https://www.gismeteo.ru/weather-severodvinsk-3914/", "content": "Подробный прогноз погоды в Северодвинске на сегодня."}
+{"idx": 8, "title": "75 дюймов Mini LED без мини-проблем? Проверяем Xiaomi TV...", "date": "", "ddg_snippet": "Мы протестировали 75 -дюймовый Xiaomi TV S Mini LED — одну из самых доступных моделей с современной подсветкой и поддержкой 4K, 144 Гц, HDR и Dolby Vision IQ. Рассказываем, как он показывает кино, игры на PS5 и повседневный контент.", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/video-29995722_456248310", "content": "Мы протестировали 75 -дюймовый Xiaomi TV S Mini LED — одну из самых доступных моделей с современной подсветкой и поддержкой 4K, 144 Гц, HDR и Dolby Vision IQ. Рассказываем, как он показывает кино, игры на PS5 и повседневный контент."}
+{"idx": 9, "title": "Telegram: View @Rudenko_S", "date": "", "ddg_snippet": "Травнева хуртовина. Ожинова зима у Львові за однойменним романом Сари Джі.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/Rudenko_S/75", "content": "Травнева хуртовина. Ожинова зима у Львові за однойменним романом Сари Джі."}
diff --git a/data/sampled_jsons/FlowDec_NDAC-75_NDAC-25_codebooks_nc_demb_embedding_dimension_specifications.jsonl b/data/sampled_jsons/FlowDec_NDAC-75_NDAC-25_codebooks_nc_demb_embedding_dimension_specifications.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..24d47b677fd5b00dd2805994db12a04ce721da78
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_NDAC-75_NDAC-25_codebooks_nc_demb_embedding_dimension_specifications.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - facebookresearch/ FlowDec : An neural full-band audio codec...", "date": "", "ddg_snippet": "You can for instance use our pre-trained NDAC variants - see the \"Inference\" section for how to run them. The expected input format for FlowDec datasets is a file containing a comma-separated list of paths, e.g.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "You can for instance use our pre-trained NDAC variants - see the \"Inference\" section for how to run them. The expected input format for FlowDec datasets is a file containing a comma-separated list of paths, e.g."}
+{"idx": 1, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "is the latent code embedding dimension . FlowDec -25s: 25 Hz, single-bitrate. Trained based on NDAC - 25 with a bitrate of 4.0 kbit/s. We do not train for multiple bitrates here as the bitrate and feature rate is already very low.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "is the latent code embedding dimension . FlowDec -25s: 25 Hz, single-bitrate. Trained based on NDAC - 25 with a bitrate of 4.0 kbit/s. We do not train for multiple bitrates here as the bitrate and feature rate is already very low."}
+{"idx": 2, "title": "Mazda MX-5 2016 Red AUTOMATIC 2L | JM1 NDAC 75 G0109010", "date": "", "ddg_snippet": "JM1 NDAC 75 G0109010. Year: 2016. Condition: Run & Drive.The auto was up for sale with minor damage which can check in the photos. For nothing to be hidden we post the Vin-number of the car JM1 NDAC 75 G0109010.", "subpage_snippet": "", "source": "bidinfo.app", "link": "https://bidinfo.app/mazda/mx-5/iaai-38951662-mazda-mx-5-2016-red-convertible-vin-jm1ndac75g0109010.html", "content": "JM1 NDAC 75 G0109010. Year: 2016. Condition: Run & Drive.The auto was up for sale with minor damage which can check in the photos. For nothing to be hidden we post the Vin-number of the car JM1 NDAC 75 G0109010."}
+{"idx": 3, "title": "flowdec · PyPI", "date": "", "ddg_snippet": "Signal Dimensions - Flowdec can support 1, 2, or 3 dimensional images/signals.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/flowdec/", "content": "Signal Dimensions - Flowdec can support 1, 2, or 3 dimensional images/signals."}
+{"idx": 4, "title": "2019 Mazda MX-5, Club | JM1 NDAC 75 K0305991 | Bid History | BidCars", "date": "", "ddg_snippet": "2019 Mazda MX-5 JM1 NDAC 75 K0305991. Ключ в наличии Руководство Бензиновый Задний привод 2.0L 4 cyl.", "subpage_snippet": "", "source": "bid.cars", "link": "https://bid.cars/ru/lot/1-67239165/2019-Mazda-MX-5-JM1NDAC75K0305991", "content": "2019 Mazda MX-5 JM1 NDAC 75 K0305991. Ключ в наличии Руководство Бензиновый Задний привод 2.0L 4 cyl."}
+{"idx": 5, "title": "VIN: JM1 NDAC 75 J0205467 car history MAZDA MX5 2018", "date": "", "ddg_snippet": "This specific vehicle, with the VIN JM1 NDAC 75 J0205467, joined the auction due to sustained damage to its HAIL.", "subpage_snippet": "", "source": "www.automobileauctioneers.com", "link": "https://www.automobileauctioneers.com/vehicles-history/mazda/mx5/2018/jm1ndac75j0205467/", "content": "This specific vehicle, with the VIN JM1 NDAC 75 J0205467, joined the auction due to sustained damage to its HAIL."}
+{"idx": 6, "title": "2016 Mazda MX 5 JM1 NDAC 75 G0100033 history report - BidCarsPro", "date": "", "ddg_snippet": "JM1 NDAC 75 G0100033.Vehicle Mileage: 49 003 mi (78 863 km). Explore comprehensive auction insights, delve into technical specifications , view captivating imagery and videos, and experience a 360-degree panoramic view, all available on this page.", "subpage_snippet": "", "source": "bidcars.pro", "link": "https://bidcars.pro/auto/2016-mazda-mx-5-jm1ndac75g0100033/", "content": "JM1 NDAC 75 G0100033.Vehicle Mileage: 49 003 mi (78 863 km). Explore comprehensive auction insights, delve into technical specifications , view captivating imagery and videos, and experience a 360-degree panoramic view, all available on this page."}
+{"idx": 7, "title": "NPT Threads - National Pipe Tapered Thread Dimensions ...", "date": "", "ddg_snippet": "25 .117. 0.339. 4. 75 . 0.5457. 7.64.Pipe Size is the accepted industry designation and does not refer to either the inside or the outside diameter (ID or OD) of a pipe or a fitting. Dimensions in Inches except where stated.", "subpage_snippet": "", "source": "torqbolt.com", "link": "https://torqbolt.com/npt-threads-national-tapered-pipe-threads-dimensions-specifications", "content": "25 .117. 0.339. 4. 75 . 0.5457. 7.64.Pipe Size is the accepted industry designation and does not refer to either the inside or the outside diameter (ID or OD) of a pipe or a fitting. Dimensions in Inches except where stated."}
+{"idx": 8, "title": "September 11-12, 2023 Nonprescription Drugs Advisory Committee...", "date": "", "ddg_snippet": "NDAC Briefing Document: Oral Phenylephrine in the CCABA Monograph. Table of Contents.26 1975 25 / 75 . Source: Adapted from oral PE studies submitted to the docket.", "subpage_snippet": "", "source": "www.fda.gov", "link": "https://www.fda.gov/media/171915/download", "content": "NDAC Briefing Document: Oral Phenylephrine in the CCABA Monograph. Table of Contents.26 1975 25 / 75 . Source: Adapted from oral PE studies submitted to the docket."}
+{"idx": 9, "title": "Rate limits | Gemini API | Google AI for Developers", "date": "", "ddg_snippet": "Gemini Embedding . 100.The Gemini API uses Cloud Billing for all billing services. To transition from the Free tier to a paid tier, you must first enable Cloud Billing for your Google Cloud project. Once your project meets the specified criteria, it becomes eligible for an upgrade to the next tier.", "subpage_snippet": "", "source": "ai.google.dev", "link": "https://ai.google.dev/gemini-api/docs/rate-limits", "content": "Gemini Embedding . 100.The Gemini API uses Cloud Billing for all billing services. To transition from the Free tier to a paid tier, you must first enable Cloud Billing for your Google Cloud project. Once your project meets the specified criteria, it becomes eligible for an upgrade to the next tier."}
diff --git a/data/sampled_jsons/FlowDec_architecture_causal.jsonl b/data/sampled_jsons/FlowDec_architecture_causal.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f2701b0e736a13ebdcec04e785013db933015e59
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_architecture_causal.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "We guarantee causal processing with an algorithmic latency of 20 ms by modifying the network architecture and removing non- causal normalization techniques.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389580542_FlowDec_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality", "content": "We guarantee causal processing with an algorithmic latency of 20 ms by modifying the network architecture and removing non- causal normalization techniques."}
+{"idx": 1, "title": "FlowDec : A flow -based full-band general audio codec... | OpenReview", "date": "", "ddg_snippet": "TL;DR: FlowDec is a flow -based postfilter codec for general audio without adversarial training, and a competitive alternative to current GAN-based SOTA codecs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "TL;DR: FlowDec is a flow -based postfilter codec for general audio without adversarial training, and a competitive alternative to current GAN-based SOTA codecs."}
+{"idx": 2, "title": "Installing flowdec [gpu] on Windows10 causes \"Win10: ImportError...\"", "date": "", "ddg_snippet": "installing both tensorflow and flowdec through the pip install instructions results in the above problem. This is a tensorflow problem and can be solved by rolling back the CUDA install to v9.0...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hammerlab/flowdec/issues/14", "content": "installing both tensorflow and flowdec through the pip install instructions results in the above problem. This is a tensorflow problem and can be solved by rolling back the CUDA install to v9.0..."}
+{"idx": 3, "title": "Understanding Deep Causality Architecture : A Multi-Layer Model for...", "date": "", "ddg_snippet": "Explore deep causality architecture , a transformative framework for understanding complex causation in various fields such as social sciences, economics, and artificial intelligence.", "subpage_snippet": "", "source": "diversedaily.com", "link": "https://diversedaily.com/understanding-deep-causality-architecture-a-multi-layer-model-for-causation/", "content": "Explore deep causality architecture , a transformative framework for understanding complex causation in various fields such as social sciences, economics, and artificial intelligence."}
+{"idx": 4, "title": "The Reactive- Causal Architecture", "date": "", "ddg_snippet": "The Reactive- Causal Architecture (ReCau) is a cognitive agent architecture which is proposed to simulate human-like intelligence while satisfying the core attributes of believable agents.", "subpage_snippet": "", "source": "www.jasss.org", "link": "https://www.jasss.org/15/4/5.html", "content": "The Reactive- Causal Architecture (ReCau) is a cognitive agent architecture which is proposed to simulate human-like intelligence while satisfying the core attributes of believable agents."}
+{"idx": 5, "title": "Causal Architecture , Complexity and Self-Organization in Time", "date": "", "ddg_snippet": "Chapter 1. Introduction. This is a book about causal architecture , pattern discovery, complexity and self-organization. Those are vague, even grandiose themes.", "subpage_snippet": "", "source": "csc.ucdavis.edu", "link": "https://csc.ucdavis.edu/~cmg/compmech/pubs/CRS-thesis.pdf", "content": "Chapter 1. Introduction. This is a book about causal architecture , pattern discovery, complexity and self-organization. Those are vague, even grandiose themes."}
+{"idx": 6, "title": "Causal Architecture , Complexity and Self-Organization in Time", "date": "", "ddg_snippet": "Chapter 1. Introduction. This is a book about causal architecture , pattern discovery, complexity and self-organization. Those are vague, even grandiose themes.", "subpage_snippet": "", "source": "bactra.org", "link": "http://bactra.org/thesis/single-spaced-thesis.pdf", "content": "Chapter 1. Introduction. This is a book about causal architecture , pattern discovery, complexity and self-organization. Those are vague, even grandiose themes."}
+{"idx": 7, "title": "Philosophy of Architecture (Stanford Encyclopedia of Philosophy)", "date": "", "ddg_snippet": "Questions about causality may seem out of place in discussions of immobile objects, such as most architecture represents.", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/architecture/", "content": "Questions about causality may seem out of place in discussions of immobile objects, such as most architecture represents."}
+{"idx": 8, "title": "[0708.1580] Optimal Causal Inference: Estimating Stored Information...", "date": "", "ddg_snippet": "View a PDF of the paper titled Optimal Causal Inference: Estimating Stored Information and Approximating Causal Architecture , by Susanne Still and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/0708.1580", "content": "View a PDF of the paper titled Optimal Causal Inference: Estimating Stored Information and Approximating Causal Architecture , by Susanne Still and 2 other authors."}
+{"idx": 9, "title": "Public Health Strategies and Causal Architecture : Upstream... | Quizlet", "date": "", "ddg_snippet": "Study with Quizlet and memorize flashcards containing terms like Core Thesis: Upstream Shift, Multilevel Causal Architecture , Distribution Shift (Rose's Principle) and more.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/1041747603/public-health-strategies-and-causal-architecture-upstream-shift-roi-and-equity-flash-cards/", "content": "Study with Quizlet and memorize flashcards containing terms like Core Thesis: Upstream Shift, Multilevel Causal Architecture , Distribution Shift (Rose's Principle) and more."}
diff --git a/data/sampled_jsons/FlowDec_audio_codec_streaming_capability_noncausal_architecture.jsonl b/data/sampled_jsons/FlowDec_audio_codec_streaming_capability_noncausal_architecture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a03a2c6af724f7fdf5b8c4569889aa99c397ba2e
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_audio_codec_streaming_capability_noncausal_architecture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "3 Mar 2025 — While FlowDec, like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs, our postfilter approach can be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "3 Mar 2025 — While FlowDec, like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs, our postfilter approach can be ..."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "by S Welker · Cited by 8 — FlowDec is a flow-based postfilter codec for general audio without adversarial training, and a competitive alternative to current GAN-based SOTA codecs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "by S Welker · Cited by 8 — FlowDec is a flow-based postfilter codec for general audio without adversarial training, and a competitive alternative to current GAN-based SOTA codecs."}
+{"idx": 2, "title": "FLOWDEC: A FLOW-BASED FULL-BAND GENERAL", "date": "", "ddg_snippet": "While FlowDec, like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/46581252e0bf80bf4efd8fcbc4002f8627f498bb.pdf", "content": "While FlowDec, like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for ..."}
+{"idx": 3, "title": "SpectroStream: A Versatile Neural Codec for General Audio", "date": "", "ddg_snippet": "In this paper we propose SpectroStream, a neural codec that can encode full-band 48 kHz general audio with joint modeling of multiple audio channels. Unlike ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2508.05207/paper", "content": "In this paper we propose SpectroStream, a neural codec that can encode full-band 48 kHz general audio with joint modeling of multiple audio channels. Unlike ..."}
+{"idx": 4, "title": "REAL-TIME STREAMING MEL VOCODING WITH GENERATIVE FLOW MATCHING", "date": "", "ddg_snippet": "... RTF ) of a streaming model as the processing time for a single frame divided by the frame shift duration, and say that a model is streaming -capable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15085v1", "content": "... RTF ) of a streaming model as the processing time for a single frame divided by the frame shift duration, and say that a model is streaming -capable ..."}
+{"idx": 5, "title": "BinauralFlow: A Causal and Streamable Approach for High ...", "date": "", "ddg_snippet": "28 May 2025 — We propose a flow matching based streaming binaural speech synthesis framework called BinauralFlow. We consider binaural rendering to be a generation problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22865v1", "content": "28 May 2025 — We propose a flow matching based streaming binaural speech synthesis framework called BinauralFlow. We consider binaural rendering to be a generation problem."}
+{"idx": 6, "title": "BinauralFlow: A Causal and Streamable Approach for High ...", "date": "", "ddg_snippet": "Binaural rendering aims to synthesize binaural audio that mimics natural hearing based on a mono audio and the locations of the speaker and listener. Although ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46249", "content": "Binaural rendering aims to synthesize binaural audio that mimics natural hearing based on a mono audio and the locations of the speaker and listener. Although ..."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "1 day ago — AudioDec: An Open-source Streaming High-fidelity Neural Audio Codec ... We propose FlowDec , a neural full-band audio codec for general audio ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=out-of-distribution+Movie+Gen+Audio+benchmark", "content": "1 day ago — AudioDec: An Open-source Streaming High-fidelity Neural Audio Codec ... We propose FlowDec , a neural full-band audio codec for general audio ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "FlowDec : A flow-based full-band general audio codec with high perceptual quality · We propose FlowDec, a neural full-band audio codec for general audio ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=neural+full-band+audio+codec", "content": "FlowDec : A flow-based full-band general audio codec with high perceptual quality · We propose FlowDec, a neural full-band audio codec for general audio ..."}
+{"idx": 9, "title": "Daily Papers - Fast360", "date": "", "ddg_snippet": "FlowDec : A flow-based full-band general audio codec with high perceptual quality · We propose FlowDec, a neural full-band audio codec for general audio ...", "subpage_snippet": "", "source": "aifasthub.com", "link": "https://aifasthub.com/papers?q=48+kHz", "content": "FlowDec : A flow-based full-band general audio codec with high perceptual quality · We propose FlowDec, a neural full-band audio codec for general audio ..."}
diff --git a/data/sampled_jsons/FlowDec_supplementary_material_Table_8_FAD_scores_exact_values.jsonl b/data/sampled_jsons/FlowDec_supplementary_material_Table_8_FAD_scores_exact_values.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c0339701587b236af44039461a06cc0cb9407b8a
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_supplementary_material_Table_8_FAD_scores_exact_values.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "Mar 3, 2025 · An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - facebookresearch/ FlowDec"}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "Jan 22, 2025 · We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "Jan 22, 2025 · We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 2, "title": "[2503.01485] FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ..."}
+{"idx": 3, "title": "FlowDec/index.html at main · sp-uhh/FlowDec · GitHub", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sp-uhh/FlowDec/blob/main/index.html", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 4, "title": "FlowDec/enhance.py at main · facebookresearch/FlowDec · GitHub", "date": "", "ddg_snippet": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - FlowDec /enhance.py at main · facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec/blob/main/enhance.py", "content": "An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps. - FlowDec /enhance.py at main · facebookresearch/ FlowDec"}
+{"idx": 5, "title": "ICLR Poster FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "FlowDec : A flow-based full-band general audio codec with high perceptual quality Simon Welker · Matthew Le · Ricky T. Q. Chen · Wei-Ning Hsu · Timo Gerkmann · Alexander Richard · Yi-Chiao Wu", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/27945", "content": "FlowDec : A flow-based full-band general audio codec with high perceptual quality Simon Welker · Matthew Le · Ricky T. Q. Chen · Wei-Ning Hsu · Timo Gerkmann · Alexander Richard · Yi-Chiao Wu"}
+{"idx": 6, "title": "FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "3 Mar 2025 — We showed that FlowDec achieves state-of-the-art FAD scores ... For completeness, we list the exact numbers of metric values in Table 8 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "3 Mar 2025 — We showed that FlowDec achieves state-of-the-art FAD scores ... For completeness, we list the exact numbers of metric values in Table 8 ."}
+{"idx": 7, "title": "FLOWDEC: A FLOW-BASED FULL-BAND GENERAL", "date": "", "ddg_snippet": "We showed that FlowDec achieves state-of-the-art FAD scores for the ... For completeness, we list the exact numbers of metric values in Table 8 . 24 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/46581252e0bf80bf4efd8fcbc4002f8627f498bb.pdf", "content": "We showed that FlowDec achieves state-of-the-art FAD scores for the ... For completeness, we list the exact numbers of metric values in Table 8 . 24 ..."}
+{"idx": 8, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "4 days ago — We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=feed-forward+digital+compressor", "content": "4 days ago — We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of ..."}
diff --git a/data/sampled_jsons/Foret_et_al._2021_Sharpness-Aware_Minimization_abstract_year_2021.jsonl b/data/sampled_jsons/Foret_et_al._2021_Sharpness-Aware_Minimization_abstract_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..593bceaad3cd4662c73e610d1f944f5368661c63
--- /dev/null
+++ b/data/sampled_jsons/Foret_et_al._2021_Sharpness-Aware_Minimization_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "calculator .com calculate anything, anytime, anywhere", "date": "", "ddg_snippet": "Online calculators for everything. Some solve problems, some satisfy curiosity.", "subpage_snippet": "", "source": "www.calculator.com", "link": "https://www.calculator.com/", "content": "Online calculators for everything. Some solve problems, some satisfy curiosity."}
+{"idx": 1, "title": "Online Calculator", "date": "", "ddg_snippet": "The original calculator was invented in the 17th century by a Frenchman called Blaise Pascal! He was just 18 years old, and wanted to help his father do his tax calculations.", "subpage_snippet": "", "source": "www.online-calculator.com", "link": "https://www.online-calculator.com/", "content": "The original calculator was invented in the 17th century by a Frenchman called Blaise Pascal! He was just 18 years old, and wanted to help his father do his tax calculations."}
+{"idx": 2, "title": "Scientific Calculator - Desmos", "date": "", "ddg_snippet": "A beautiful, free online scientific calculator with advanced features for evaluating percentages, fractions, exponential functions, logarithms, trigonometry, statistics, and more.", "subpage_snippet": "", "source": "www.desmos.com", "link": "https://www.desmos.com/scientific", "content": "A beautiful, free online scientific calculator with advanced features for evaluating percentages, fractions, exponential functions, logarithms, trigonometry, statistics, and more."}
+{"idx": 3, "title": "Basic Calculator", "date": "", "ddg_snippet": "This free online calculator can be used for basic computations such as addition, subtraction, multiplication, division, and square roots.", "subpage_snippet": "", "source": "www.calculator.net", "link": "https://www.calculator.net/basic-calculator.html", "content": "This free online calculator can be used for basic computations such as addition, subtraction, multiplication, division, and square roots."}
+{"idx": 4, "title": "The Best Free Online Calculator", "date": "", "ddg_snippet": "Use the best online calculator for any math calculations on PC and smartphones. The free calculator allows you to quickly and accurately perform arithmetic, calculate percentages, raise to a power or take a root", "subpage_snippet": "", "source": "calculator-1.com", "link": "https://calculator-1.com/", "content": "Use the best online calculator for any math calculations on PC and smartphones. The free calculator allows you to quickly and accurately perform arithmetic, calculate percentages, raise to a power or take a root"}
+{"idx": 5, "title": "Math Calculator", "date": "", "ddg_snippet": "Step 1: Enter the expression you want to evaluate. The Math Calculator will evaluate your problem down to a final solution. You can also add, subtraction, multiply, and divide and complete any arithmetic you need. Step 2: Click the blue arrow to submit and see your result!", "subpage_snippet": "", "source": "www.mathway.com", "link": "https://www.mathway.com/Calculator/math-calculator", "content": "Step 1: Enter the expression you want to evaluate. The Math Calculator will evaluate your problem down to a final solution. You can also add, subtraction, multiply, and divide and complete any arithmetic you need. Step 2: Click the blue arrow to submit and see your result!"}
+{"idx": 6, "title": "Calculator - English", "date": "", "ddg_snippet": "Your all-in-one online calculator for quick and precise basic to scientific calculations. Easily perform addition, subtraction, multiplication, division, trigonometry, logarithms, and more with our user-friendly interface.", "subpage_snippet": "", "source": "w3calc.com", "link": "https://w3calc.com/en/", "content": "Your all-in-one online calculator for quick and precise basic to scientific calculations. Easily perform addition, subtraction, multiplication, division, trigonometry, logarithms, and more with our user-friendly interface."}
+{"idx": 7, "title": "The Online Calculator | Basic Calculator", "date": "", "ddg_snippet": "Basic Online Calculator with 10-digit keypad and 4 functions to add, subtract, multiply and divide numbers. Includes basic handheld calculator functions for square, square root, percent, sign change, Pi and memory.", "subpage_snippet": "", "source": "www.theonlinecalculator.com", "link": "https://www.theonlinecalculator.com/", "content": "Basic Online Calculator with 10-digit keypad and 4 functions to add, subtract, multiply and divide numbers. Includes basic handheld calculator functions for square, square root, percent, sign change, Pi and memory."}
+{"idx": 8, "title": "Basic Calculator", "date": "", "ddg_snippet": "Aug 1, 2025 · Use this basic calculator online for math with addition, subtraction, division and multiplication. The calculator includes functions for square root, percentage, pi, exponents, powers and rounding.", "subpage_snippet": "", "source": "www.calculatorsoup.com", "link": "https://www.calculatorsoup.com/calculators/math/basic.php", "content": "Aug 1, 2025 · Use this basic calculator online for math with addition, subtraction, division and multiplication. The calculator includes functions for square root, percentage, pi, exponents, powers and rounding."}
+{"idx": 9, "title": "Online Calculator", "date": "", "ddg_snippet": "Free Online Scientific Notation Calculator . Solve advanced problems in Physics, Mathematics and Engineering. Math Expression Renderer, Plots, Unit Converter, Equation Solver, Complex Numbers, Calculation History.", "subpage_snippet": "", "source": "okcalc.com", "link": "https://okcalc.com/en/", "content": "Free Online Scientific Notation Calculator . Solve advanced problems in Physics, Mathematics and Engineering. Math Expression Renderer, Plots, Unit Converter, Equation Solver, Complex Numbers, Calculation History."}
diff --git a/data/sampled_jsons/FourCastNet_Pathak_2022_'negative_log_likelihood'_NLL_probabilistic_uncertainty_year_2022.jsonl b/data/sampled_jsons/FourCastNet_Pathak_2022_'negative_log_likelihood'_NLL_probabilistic_uncertainty_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f6ec6c62e3a96cab3731b216899d8750b4900dfb
--- /dev/null
+++ b/data/sampled_jsons/FourCastNet_Pathak_2022_'negative_log_likelihood'_NLL_probabilistic_uncertainty_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Probabilistic neural operators for functional uncertainty ...", "date": "", "ddg_snippet": "18 Feb 2025 — It is notable that the PNO R leads to extremely large values for the negative log - likelihood , while still being competitive in other metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12902v1", "content": "18 Feb 2025 — It is notable that the PNO R leads to extremely large values for the negative log - likelihood , while still being competitive in other metrics."}
+{"idx": 1, "title": "probabilistic neural operators for functional", "date": "", "ddg_snippet": "by C Bülte · 2025 · Cited by 3 — It is notable that the PNOR leads to extremely large values for the negative log - likelihood , while still being competitive in other metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.12902", "content": "by C Bülte · 2025 · Cited by 3 — It is notable that the PNOR leads to extremely large values for the negative log - likelihood , while still being competitive in other metrics."}
+{"idx": 2, "title": "Probabilistic Weather Forecasting with Hierarchical Graph ...", "date": "", "ddg_snippet": "... Negative Log - Likelihood ( NLL ) loss [8] for rolled out forecasts . To train GraphEFM we instead leverage the fact that the single-step model has a structure ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93149", "content": "... Negative Log - Likelihood ( NLL ) loss [8] for rolled out forecasts . To train GraphEFM we instead leverage the fact that the single-step model has a structure ..."}
+{"idx": 3, "title": "UNCERTAINTY QUANTIFICATION FOR FOURIER NEU", "date": "", "ddg_snippet": "by T Weber · Cited by 5 — In recent years, several deep learning methods such as FourCast-. Net ( Pathak et al., 2022 ) ... negative log - likelihood and a negative log-prior over the weight ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=knSgoNJcnV", "content": "by T Weber · Cited by 5 — In recent years, several deep learning methods such as FourCast-. Net ( Pathak et al., 2022 ) ... negative log - likelihood and a negative log-prior over the weight ..."}
+{"idx": 4, "title": "using-uncertainty-quantification-to-characterize-and- ...", "date": "", "ddg_snippet": "by SC Mouli · 2024 · Cited by 12 — In applications to weather forecasting , FourCastNet ( Pathak et al. ... log - likelihood ( NLL ), normalized Mean Rescaled Confidence Interval (n-MeRCI) ... 47 pages", "subpage_snippet": "", "source": "assets.amazon.science", "link": "https://assets.amazon.science/cf/ec/2b04154b46289411e802bab9ee77/using-uncertainty-quantification-to-characterize-and-improve-out-of-domain-learning-for-pdes.pdf", "content": "by SC Mouli · 2024 · Cited by 12 — In applications to weather forecasting , FourCastNet ( Pathak et al. ... log - likelihood ( NLL ), normalized Mean Rescaled Confidence Interval (n-MeRCI) ... 47 pages"}
+{"idx": 5, "title": "Approximate Bayesian Neural Operators: Uncertainty ...", "date": "", "ddg_snippet": "The prior precision is selected via grid search, optimizing for marginal negative log - likelihood ( NLL ). The Laplace approximation is applied to the weights ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/70be375e4aec7a205e768c1b81cb4d1e4ba06a2f.pdf", "content": "The prior precision is selected via grid search, optimizing for marginal negative log - likelihood ( NLL ). The Laplace approximation is applied to the weights ..."}
+{"idx": 6, "title": "Probabilistic Weather Forecasting with Hierarchical Graph ...", "date": "", "ddg_snippet": "MSE [23] or Negative Log - Likelihood ( NLL ) loss [8] for rolled out forecasts . To train Graph-. EFM we instead leverage the fact that the single-step model has ... 72 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/492592890311679d7f71559148358973-Paper-Conference.pdf", "content": "MSE [23] or Negative Log - Likelihood ( NLL ) loss [8] for rolled out forecasts . To train Graph-. EFM we instead leverage the fact that the single-step model has ... 72 pages"}
+{"idx": 7, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "MARGINAL NEGATIVE LOG - LIKELIHOOD ( NLL ). The marginal NLL quantifies how well the predictive distribution fits the data under the assumption of Gaussian ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "MARGINAL NEGATIVE LOG - LIKELIHOOD ( NLL ). The marginal NLL quantifies how well the predictive distribution fits the data under the assumption of Gaussian ..."}
+{"idx": 8, "title": "Analyzing and Exploring Training Recipes for Large-Scale ...", "date": "", "ddg_snippet": "by JD Willard · 2025 · Cited by 11 — We also explore the alternate negative log likelihood ( NLL ) loss from (Chen et al. 2023a), which down-weights the loss according to a network-predicted ... 26 pages", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/aies/aop/AIES-D-24-0061.1/AIES-D-24-0061.1.pdf", "content": "by JD Willard · 2025 · Cited by 11 — We also explore the alternate negative log likelihood ( NLL ) loss from (Chen et al. 2023a), which down-weights the loss according to a network-predicted ... 26 pages"}
+{"idx": 9, "title": "Multi-Resolution Active Learning of Fourier Neural Operators", "date": "", "ddg_snippet": "by S Li · 2024 · Cited by 14 — We repeated the training and test procedure for five times, and examined the average rel- ative L2 error, the average negative log likelihood ( NLL ), and ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/li24k/li24k.pdf", "content": "by S Li · 2024 · Cited by 14 — We repeated the training and test procedure for five times, and examined the average rel- ative L2 error, the average negative log likelihood ( NLL ), and ..."}
diff --git a/data/sampled_jsons/FourCastNet_Pathak_2022_Negative_Log-Likelihood_NLL_evaluation_metric.jsonl b/data/sampled_jsons/FourCastNet_Pathak_2022_Negative_Log-Likelihood_NLL_evaluation_metric.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3764c4cb9211f17c0e8b5db11cc06328ef9704e
--- /dev/null
+++ b/data/sampled_jsons/FourCastNet_Pathak_2022_Negative_Log-Likelihood_NLL_evaluation_metric.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Uncertainty quantification metrics for deep regression", "date": "", "ddg_snippet": "Oct 1, 2024 · Many real-world applications, however, rely instead on regression, and there is a lack of common understanding surrounding the available metrics for regression. In this work, we have identified four metrics that are commonly used to measure various qualities in a predicted uncertainty in regression.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167865524002733", "content": "Oct 1, 2024 · Many real-world applications, however, rely instead on regression, and there is a lack of common understanding surrounding the available metrics for regression. In this work, we have identified four metrics that are commonly used to measure various qualities in a predicted uncertainty in regression."}
+{"idx": 1, "title": "Uncertainty Quantification Metrics for Deep regression", "date": "", "ddg_snippet": "Abstract When deploying deep neural networks on robots or other physical systems, the learned model should reliably quantify predic-tive uncertainty. A reliable uncertainty allows downstream modules to reason about the safety of its actions. In this work, we address metrics for uncertainty quantification. Specifically, we focus on regression tasks, and investigate Area Under Spar-sification ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.04278", "content": "Abstract When deploying deep neural networks on robots or other physical systems, the learned model should reliably quantify predic-tive uncertainty. A reliable uncertainty allows downstream modules to reason about the safety of its actions. In this work, we address metrics for uncertainty quantification. Specifically, we focus on regression tasks, and investigate Area Under Spar-sification ..."}
+{"idx": 2, "title": "Metrics for uncertainty evaluation in regression problems", "date": "", "ddg_snippet": "Validity— evaluation of the reliability of quantiles and bias in a probabilistic context Sharpness — estimating concentration of probabilities (prediction intervals) Negative Log-Likelihood ( NLL ) — the likelihood for the observed data to occur given the inferred parameters of the conditional distribution", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/210821761aa", "content": "Validity— evaluation of the reliability of quantiles and bias in a probabilistic context Sharpness — estimating concentration of probabilities (prediction intervals) Negative Log-Likelihood ( NLL ) — the likelihood for the observed data to occur given the inferred parameters of the conditional distribution"}
+{"idx": 3, "title": "Mastering Negative Log Likelihood in ML - numberanalytics.com", "date": "", "ddg_snippet": "Jun 12, 2025 · In conclusion, Negative Log Likelihood is a crucial concept in Machine Learning that plays a vital role in model evaluation and optimization. By understanding NLL and its implementation in popular ML frameworks, you can harness its power to improve your models' performance and accuracy.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/mastering-negative-log-likelihood-in-ml", "content": "Jun 12, 2025 · In conclusion, Negative Log Likelihood is a crucial concept in Machine Learning that plays a vital role in model evaluation and optimization. By understanding NLL and its implementation in popular ML frameworks, you can harness its power to improve your models' performance and accuracy."}
+{"idx": 4, "title": "GitHub - NVlabs/FourCastNet: Initial public release of code ...", "date": "", "ddg_snippet": "FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at 0.25∘ resolution. FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVlabs/FourCastNet", "content": "FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at 0.25∘ resolution. FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor."}
+{"idx": 5, "title": "negative log-likelihood | ML Reviews", "date": "", "ddg_snippet": "A metric for optimizing the log likelihood of a model; by minimizing the negative log likelihood , you maximize the log likelihood (i.e. the most likely set of parameters for a given model that the data originated from).", "subpage_snippet": "", "source": "laserkelvin.github.io", "link": "https://laserkelvin.github.io/ml-reviews/notes/negative+log-likelihood", "content": "A metric for optimizing the log likelihood of a model; by minimizing the negative log likelihood , you maximize the log likelihood (i.e. the most likely set of parameters for a given model that the data originated from)."}
+{"idx": 6, "title": "Forecasting High Resolution Precipitation Events With Logistic ...", "date": "", "ddg_snippet": "by LN Moncada Morales · 2024 — After determining optimal hyperparameters, we evaluate each models' performance using testing data. The evaluation metric employed is the ...", "subpage_snippet": "", "source": "agupubs.onlinelibrary.wiley.com", "link": "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024JH000291", "content": "by LN Moncada Morales · 2024 — After determining optimal hyperparameters, we evaluate each models' performance using testing data. The evaluation metric employed is the ..."}
+{"idx": 7, "title": "Evaluating and Calibrating Uncertainty Prediction in ...", "date": "", "ddg_snippet": "Negative log likelihood ( NLL ) is a standard measure of a model’s fit to the data [12] but combines both the accuracy of the model and its uncertainty estimation in one measure. Based on these measures, several calibration methods were proposed, which transformed the network’s confidence output to one that will produce a calibrated prediction.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9330317/", "content": "Negative log likelihood ( NLL ) is a standard measure of a model’s fit to the data [12] but combines both the accuracy of the model and its uncertainty estimation in one measure. Based on these measures, several calibration methods were proposed, which transformed the network’s confidence output to one that will produce a calibrated prediction."}
+{"idx": 8, "title": "probabilistic neural operators for functional", "date": "", "ddg_snippet": "by C Bülte · 2025 · Cited by 3 — It is notable that the PNOR leads to extremely large values for the negative log - likelihood , while still being competitive in other metrics .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.12902", "content": "by C Bülte · 2025 · Cited by 3 — It is notable that the PNOR leads to extremely large values for the negative log - likelihood , while still being competitive in other metrics ."}
+{"idx": 9, "title": "Probabilistic neural operators for functional uncertainty ...", "date": "", "ddg_snippet": "18 Feb 2025 — It is notable that the PNO R leads to extremely large values for the negative log - likelihood , while still being competitive in other metrics .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12902v1", "content": "18 Feb 2025 — It is notable that the PNO R leads to extremely large values for the negative log - likelihood , while still being competitive in other metrics ."}
diff --git a/data/sampled_jsons/FourCastNet_Pathak_et_al_2022_evaluation_metrics_negative_log_likelihood_year_2022.jsonl b/data/sampled_jsons/FourCastNet_Pathak_et_al_2022_evaluation_metrics_negative_log_likelihood_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c10e3065106492f38f376795bb221576c6e1c5a3
--- /dev/null
+++ b/data/sampled_jsons/FourCastNet_Pathak_et_al_2022_evaluation_metrics_negative_log_likelihood_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FourCastNet 3: A geometric approach to probabilistic ...", "date": "", "ddg_snippet": "16 Jul 2025 — FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12144v1", "content": "16 Jul 2025 — FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting."}
+{"idx": 1, "title": "the logarithm trick: achieve better long term - forecast via ...", "date": "", "ddg_snippet": "by T Zhang — Weather forecasting and time series prediction can be modeled as autoregressive prediction tasks and optimized through a pretraining-finetuning paradigm.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Y29rdPpPu4", "content": "by T Zhang — Weather forecasting and time series prediction can be modeled as autoregressive prediction tasks and optimized through a pretraining-finetuning paradigm."}
+{"idx": 2, "title": "SwinVRNN: A Data‐Driven Ensemble Forecasting Model via ...", "date": "", "ddg_snippet": "by Y Hu · 2023 · Cited by 69 — FourCastNet ( Pathak et al ., 2022 ) has been the first method that can achieve comparable performance with IFS in small-scale variables including ...", "subpage_snippet": "", "source": "agupubs.onlinelibrary.wiley.com", "link": "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022MS003211", "content": "by Y Hu · 2023 · Cited by 69 — FourCastNet ( Pathak et al ., 2022 ) has been the first method that can achieve comparable performance with IFS in small-scale variables including ..."}
+{"idx": 3, "title": "Global data-driven prediction of fire activity", "date": "", "ddg_snippet": "by F Di Giuseppe · 2025 · Cited by 11 — It is calculated as the negative log of the likelihood of the true labels given the predicted probabilities. Logloss is sensitive to the ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-025-58097-7", "content": "by F Di Giuseppe · 2025 · Cited by 11 — It is calculated as the negative log of the likelihood of the true labels given the predicted probabilities. Logloss is sensitive to the ..."}
+{"idx": 4, "title": "Enhancing Quantitative Precipitation Estimation of NWP ...", "date": "", "ddg_snippet": "16 Apr 2024 — Evaluation results indicate that the DL model outperforms the NWP models for those state variables most affected by parameterization processes.", "subpage_snippet": "", "source": "agupubs.onlinelibrary.wiley.com", "link": "https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EA003234", "content": "16 Apr 2024 — Evaluation results indicate that the DL model outperforms the NWP models for those state variables most affected by parameterization processes."}
+{"idx": 5, "title": "Innovative Short-Term Weather Forecasting System ...", "date": "", "ddg_snippet": "by Y Jin · 2025 — Recently, several prominent AI models for weather forecasting have emerged, including Nvidia's FourCastNet ( Pathak et al . 2022 ), Google DeepMind's GraphCast ( ...", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/aies/4/3/AIES-D-24-0125.1.xml", "content": "by Y Jin · 2025 — Recently, several prominent AI models for weather forecasting have emerged, including Nvidia's FourCastNet ( Pathak et al . 2022 ), Google DeepMind's GraphCast ( ..."}
+{"idx": 6, "title": "Towards calibrated ensembles of neural weather model forecasts", "date": "", "ddg_snippet": "analyzed in the original FourCastNet study ( Pathak et al ., 2022 ) and is not included in. 316 the training data. Figure 4.a represents the total column water ...", "subpage_snippet": "", "source": "essopenarchive.org", "link": "https://essopenarchive.org/users/777909/articles/911916/master/file/data/993877_0_merged_1713913796/993877_0_merged_1713913796.pdf?inline=true", "content": "analyzed in the original FourCastNet study ( Pathak et al ., 2022 ) and is not included in. 316 the training data. Figure 4.a represents the total column water ..."}
+{"idx": 7, "title": "Uncertainty quantification for data-driven weather models", "date": "", "ddg_snippet": "20 Mar 2024 — Our overarching aim is to systematically study and compare uncertainty quantification methods to generate probabilistic weather forecasts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.13458v1", "content": "20 Mar 2024 — Our overarching aim is to systematically study and compare uncertainty quantification methods to generate probabilistic weather forecasts."}
+{"idx": 8, "title": "Data Assimilation with Machine Learning Surrogate Models", "date": "", "ddg_snippet": "by M Adrian · 2025 · Cited by 12 — This paper investigates online weather prediction using machine learning surrogates supplemented with partial and noisy observations.", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/aies/4/3/AIES-D-24-0050.1.xml", "content": "by M Adrian · 2025 · Cited by 12 — This paper investigates online weather prediction using machine learning surrogates supplemented with partial and noisy observations."}
+{"idx": 9, "title": "Improving the heavy rainfall forecasting using a weighted ...", "date": "", "ddg_snippet": "by Y Chen · 2023 · Cited by 17 — In this study, we propose a DL model called weighted U-Net (WU-Net) that incorporates sample weights for various precipitation events to improve the forecasts ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1116672/full", "content": "by Y Chen · 2023 · Cited by 17 — In this study, we propose a DL model called weighted U-Net (WU-Net) that incorporates sample weights for various precipitation events to improve the forecasts ..."}
diff --git a/data/sampled_jsons/FourCastNet_does_NOT_use_NLL_Negative_Log-Likelihood_evaluation_metrics.jsonl b/data/sampled_jsons/FourCastNet_does_NOT_use_NLL_Negative_Log-Likelihood_evaluation_metrics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3198a93b534eae56118e1e65d726de6a2a53c9cf
--- /dev/null
+++ b/data/sampled_jsons/FourCastNet_does_NOT_use_NLL_Negative_Log-Likelihood_evaluation_metrics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mastering Negative Log Likelihood in ML - numberanalytics.com", "date": "", "ddg_snippet": "Jun 12, 2025 · In conclusion, Negative Log Likelihood is a crucial concept in Machine Learning that plays a vital role in model evaluation and optimization. By understanding NLL and its implementation in popular ML frameworks, you can harness its power to improve your models' performance and accuracy.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/mastering-negative-log-likelihood-in-ml", "content": "Jun 12, 2025 · In conclusion, Negative Log Likelihood is a crucial concept in Machine Learning that plays a vital role in model evaluation and optimization. By understanding NLL and its implementation in popular ML frameworks, you can harness its power to improve your models' performance and accuracy."}
+{"idx": 1, "title": "Uncertainty quantification metrics for deep regression", "date": "", "ddg_snippet": "Oct 1, 2024 · While not as common as ECE, NLL ( negative log likelihood ) is also used, for example in , has also found new use in this area (for example in , ). Finally, the most recent method we examine is AUSE , used in for example , . While other uncertainty assessment methods exist, we have chosen to focus on these four as they are most commonly used.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167865524002733", "content": "Oct 1, 2024 · While not as common as ECE, NLL ( negative log likelihood ) is also used, for example in , has also found new use in this area (for example in , ). Finally, the most recent method we examine is AUSE , used in for example , . While other uncertainty assessment methods exist, we have chosen to focus on these four as they are most commonly used."}
+{"idx": 2, "title": "Negative Log Likelihood Explained | Ji Kim", "date": "", "ddg_snippet": "Jan 19, 2025 · At the heart of this model was the negative log likelihood loss function, which helped us evaluate and optimize its performance. When training a neural network, understanding the loss function is crucial for evaluating the model’s performance and optimizing its parameters.", "subpage_snippet": "", "source": "jiselectric.github.io", "link": "https://jiselectric.github.io/posts/NLL/", "content": "Jan 19, 2025 · At the heart of this model was the negative log likelihood loss function, which helped us evaluate and optimize its performance. When training a neural network, understanding the loss function is crucial for evaluating the model’s performance and optimizing its parameters."}
+{"idx": 3, "title": "Evaluation of Predictive Uncertainty — Lightning-UQ-Box", "date": "", "ddg_snippet": "Negative Log Likelihood ( NLL ) # Negative log likelihood for a gaussian probability distribution p θ (y | x) which is also used as a loss functions in some methods. Note that the probability distribution have values between 0 and 1. If given a certain x, p θ (y | x) ≈ 1, we could almost be certain that our network predicts the label y.", "subpage_snippet": "", "source": "lightning-uq-box.readthedocs.io", "link": "https://lightning-uq-box.readthedocs.io/en/latest/tutorials/regression/evaluation_uncertainty.html", "content": "Negative Log Likelihood ( NLL ) # Negative log likelihood for a gaussian probability distribution p θ (y | x) which is also used as a loss functions in some methods. Note that the probability distribution have values between 0 and 1. If given a certain x, p θ (y | x) ≈ 1, we could almost be certain that our network predicts the label y."}
+{"idx": 4, "title": "Measuring predictive uncertainty with Negative Log Likelihood ...", "date": "", "ddg_snippet": "Sep 4, 2020 · I see that in many papers about prediction uncertainty and calibration of neural networks, methods are compared in terms of the negative log-likelihood . What does it represent in this context? And ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/486007/measuring-predictive-uncertainty-with-negative-log-likelihood-nll", "content": "Sep 4, 2020 · I see that in many papers about prediction uncertainty and calibration of neural networks, methods are compared in terms of the negative log-likelihood . What does it represent in this context? And ..."}
+{"idx": 5, "title": "Metrics for uncertainty evaluation in regression problems", "date": "", "ddg_snippet": "Aug 12, 2022 · Metrics for uncertainty evaluation in regression problems How to evaluate uncertainty with Validity, Sharpness, Negative Log-Likelihood , and Continuous Ranked Probability Score (CRPS) metrics", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/towards-data-science/metrics-for-uncertainty-evaluation-in-regression-problems-210821761aa", "content": "Aug 12, 2022 · Metrics for uncertainty evaluation in regression problems How to evaluate uncertainty with Validity, Sharpness, Negative Log-Likelihood , and Continuous Ranked Probability Score (CRPS) metrics"}
+{"idx": 6, "title": "Blog – Energy in Atmosphere", "date": "", "ddg_snippet": "The model then evaluates its predictions using a ( negative ) Log - Likelihood ( NLL ) loss function, which minimizes the difference between historical ...", "subpage_snippet": "", "source": "yihui-wang.com", "link": "https://yihui-wang.com/research-blog/", "content": "The model then evaluates its predictions using a ( negative ) Log - Likelihood ( NLL ) loss function, which minimizes the difference between historical ..."}
+{"idx": 7, "title": "Evaluating and Calibrating Uncertainty Prediction in ...", "date": "", "ddg_snippet": "Negative log likelihood ( NLL ) is a standard measure of a model’s fit to the data [12] but combines both the accuracy of the model and its uncertainty estimation in one measure. Based on these measures, several calibration methods were proposed, which transformed the network’s confidence output to one that will produce a calibrated prediction.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9330317/", "content": "Negative log likelihood ( NLL ) is a standard measure of a model’s fit to the data [12] but combines both the accuracy of the model and its uncertainty estimation in one measure. Based on these measures, several calibration methods were proposed, which transformed the network’s confidence output to one that will produce a calibrated prediction."}
+{"idx": 8, "title": "𝛼-VI DeepONet: A prior-robust variational Bayesian approach", "date": "", "ddg_snippet": "... the variational objective function yields superior results in terms of minimising the mean squared error and improving the negative log - likelihood on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.00681v1", "content": "... the variational objective function yields superior results in terms of minimising the mean squared error and improving the negative log - likelihood on ..."}
+{"idx": 9, "title": "A Practical Probabilistic Benchmark for AI Weather Models", "date": "", "ddg_snippet": "The purpose of this paper is to present a consistent and easy-to- use method for evaluating deterministic weather forecasts from the lens of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.15305v2", "content": "The purpose of this paper is to present a consistent and easy-to- use method for evaluating deterministic weather forecasts from the lens of ..."}
diff --git a/data/sampled_jsons/Franklin_Lorenz_1989_Hilbert_projective_metric_Sinkhorn.jsonl b/data/sampled_jsons/Franklin_Lorenz_1989_Hilbert_projective_metric_Sinkhorn.jsonl
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index 0000000000000000000000000000000000000000..ae9293fef7503fedc565021bfe7d732eb642ec6c
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hilbert metric - Wikipedia", "date": "", "ddg_snippet": "In mathematics, the Hilbert metric , also known as the Hilbert projective metric , is an explicitly defined distance function on a bounded convex subset of the n -dimensional Euclidean space Rn.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Hilbert_metric", "content": "In mathematics, the Hilbert metric , also known as the Hilbert projective metric , is an explicitly defined distance function on a bounded convex subset of the n -dimensional Euclidean space Rn."}
+{"idx": 1, "title": "Hilbert's projective metric for functions of bounded growth and ...", "date": "", "ddg_snippet": "Motivated by the entropic optimal transport problem in unbounded settings, we study versions of Hilbert's projective metric for spaces of integrable functions of bounded growth. These versions of Hilbert's metric originate from cones which are relaxations of the cone of all non-negative functions, in the sense that they include all functions having non-negative integral values when multiplied ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.04041", "content": "Motivated by the entropic optimal transport problem in unbounded settings, we study versions of Hilbert's projective metric for spaces of integrable functions of bounded growth. These versions of Hilbert's metric originate from cones which are relaxations of the cone of all non-negative functions, in the sense that they include all functions having non-negative integral values when multiplied ..."}
+{"idx": 2, "title": "PDF Hilbert s projective metric for functions of bounded growth and ...", "date": "", "ddg_snippet": "Hilbert's projective metric is a powerful geometric tool, particularly because many operators are contractions with respect to suitable specifications of this metric (see, e.g., [1-5]). A recent prominent example of such operators are the ones given by the Schrödinger equations in entropic optimal transport, which famously lead to Sinkhorn's algorithm (see, e.g., [6-11]). However, so ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s00440-025-01366-9.pdf", "content": "Hilbert's projective metric is a powerful geometric tool, particularly because many operators are contractions with respect to suitable specifications of this metric (see, e.g., [1-5]). A recent prominent example of such operators are the ones given by the Schrödinger equations in entropic optimal transport, which famously lead to Sinkhorn's algorithm (see, e.g., [6-11]). However, so ..."}
+{"idx": 3, "title": "PDF Sinkhorn Distances: Lightspeed Computation of Optimal Transport - NeurIPS", "date": "", "ddg_snippet": "Using Hilbert's projective metric , Franklin and Lorenz ( 1989 ) prove that the convergence of the scaling factor u (as well as v) is linear, with a rate bounded above by κ(K)2, where", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2013/file/af21d0c97db2e27e13572cbf59eb343d-Paper.pdf", "content": "Using Hilbert's projective metric , Franklin and Lorenz ( 1989 ) prove that the convergence of the scaling factor u (as well as v) is linear, with a rate bounded above by κ(K)2, where"}
+{"idx": 4, "title": "PDF Hilbert's projective metric for functions of Sinkhorn's algorithm arXiv ...", "date": "", "ddg_snippet": "We study versions of Hilbert's projective metric for spaces of in-tegrable functions of bounded growth. These metrics originate from cones which are relaxations of the cone of all non-negative functions, in the sense that they include all functions having non-negative inte-gral values when multiplied with certain test functions. We show that kernel integral operators are contractions with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.04041.pdf", "content": "We study versions of Hilbert's projective metric for spaces of in-tegrable functions of bounded growth. These metrics originate from cones which are relaxations of the cone of all non-negative functions, in the sense that they include all functions having non-negative inte-gral values when multiplied with certain test functions. We show that kernel integral operators are contractions with ..."}
+{"idx": 5, "title": "Hilbert's projective metric for functions of bounded growth and ...", "date": "", "ddg_snippet": "Abstract We study versions of Hilbert's projective metric for spaces of integrable functions of bounded growth. These metrics originate from cones which are relaxations of the cone of all non ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/375661938_Hilbert's_projective_metric_for_functions_of_bounded_growth_and_exponential_convergence_of_Sinkhorn's_algorithm", "content": "Abstract We study versions of Hilbert's projective metric for spaces of integrable functions of bounded growth. These metrics originate from cones which are relaxations of the cone of all non ..."}
+{"idx": 6, "title": "PDF Non-asymptotic convergence bounds for Sinkhorn iterates and their ...", "date": "", "ddg_snippet": "In this setting, Franklin and Lorenz ( 1989 ) show that Sinkhorn algorithm is equivalent to a sequence of iterations of a contraction in the Hilbert projective metric and prove its geometric (i.e., exponential) convergence by relying on Birkoff's theorem.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v195/greco23a/greco23a.pdf", "content": "In this setting, Franklin and Lorenz ( 1989 ) show that Sinkhorn algorithm is equivalent to a sequence of iterations of a contraction in the Hilbert projective metric and prove its geometric (i.e., exponential) convergence by relying on Birkoff's theorem."}
+{"idx": 7, "title": "On the scaling of multidimensional matrices - ScienceDirect", "date": "", "ddg_snippet": "Elementary proofs are given for theorems of Bapat and Raghavan on the scaling of nonnegative multidimensional matrices. Theorems of Sinkhorn and of Brualdi, Parter, and Schneider are derived as corollaries. For positive two-dimensional matrices, Hilbert's projective metric and a theorem of G. Birkhoff are used to prove that Sinkhorn's original iterative procedure converges geometrically; the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/0024379589904904", "content": "Elementary proofs are given for theorems of Bapat and Raghavan on the scaling of nonnegative multidimensional matrices. Theorems of Sinkhorn and of Brualdi, Parter, and Schneider are derived as corollaries. For positive two-dimensional matrices, Hilbert's projective metric and a theorem of G. Birkhoff are used to prove that Sinkhorn's original iterative procedure converges geometrically; the ..."}
+{"idx": 8, "title": "PDF Abstract - Core", "date": "", "ddg_snippet": "HILBERT'S PROJECTIVE METRIC AND SINKHORN'S ITERATION Sinkhorn's Iteration We are given a positive m x n matrix A = (a, j) and positive vectors PER'\", PER\" with p,+ ..- +p,=q,+ a.. +99n. 726 JOEL FRANKLIN AND JENS LORENZ The aim of Sinkhorn's iteration [18] is to find a positive matrix 6 of the form", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/82177026.pdf", "content": "HILBERT'S PROJECTIVE METRIC AND SINKHORN'S ITERATION Sinkhorn's Iteration We are given a positive m x n matrix A = (a, j) and positive vectors PER'\", PER\" with p,+ ..- +p,=q,+ a.. +99n. 726 JOEL FRANKLIN AND JENS LORENZ The aim of Sinkhorn's iteration [18] is to find a positive matrix 6 of the form"}
+{"idx": 9, "title": "(PDF) On the scaling of multidimensional matrices - Academia.edu", "date": "", "ddg_snippet": "Elementary proofs are given for theorems of Bapat and Raghavan on the scaling of nonnegative multidimensional matrices. Theorems of Sit&horn and of Bmaldi, Pa.rter, and Schneider are derived as corollaries. For positive two-dimensional matrices, Hilbert's projective metric and a theorem of G. Birkhoff are used to prove that Sinkhorn's original iterative procedure converges geometrically; the ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/94946302/On_the_scaling_of_multidimensional_matrices", "content": "Elementary proofs are given for theorems of Bapat and Raghavan on the scaling of nonnegative multidimensional matrices. Theorems of Sit&horn and of Bmaldi, Pa.rter, and Schneider are derived as corollaries. For positive two-dimensional matrices, Hilbert's projective metric and a theorem of G. Birkhoff are used to prove that Sinkhorn's original iterative procedure converges geometrically; the ..."}
diff --git a/data/sampled_jsons/Franklin_Lorenz_1989_Sinkhorn_algorithm_linear_convergence_metric_year_1989.jsonl b/data/sampled_jsons/Franklin_Lorenz_1989_Sinkhorn_algorithm_linear_convergence_metric_year_1989.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a4f5c198a5d7e905e5f07e1786616209a90397d
--- /dev/null
+++ b/data/sampled_jsons/Franklin_Lorenz_1989_Sinkhorn_algorithm_linear_convergence_metric_year_1989.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear Convergence of Sinkhorn's Algorithm for Generalized Static ...", "date": "", "ddg_snippet": "Franklin and Lorenz ( Franklin & Lorenz , 1989 ) established linear convergence (i.e., exponential decay of the error) in the Hilbert projective metric , and Rüschendorf (Ruschendorf, 1995) extended the analysis to continuous measures via an information projection viewpoint.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "Franklin and Lorenz ( Franklin & Lorenz , 1989 ) established linear convergence (i.e., exponential decay of the error) in the Hilbert projective metric , and Rüschendorf (Ruschendorf, 1995) extended the analysis to continuous measures via an information projection viewpoint."}
+{"idx": 1, "title": "On the Linear Convergence of the Multimarginal Sinkhorn Algorithm", "date": "", "ddg_snippet": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/21M1410634", "content": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ..."}
+{"idx": 2, "title": "PDF Onthelinearconvergenceofthe multi-marginalSinkhornalgorithm", "date": "", "ddg_snippet": "The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel) method of ...", "subpage_snippet": "", "source": "www.mathtube.org", "link": "https://www.mathtube.org/sites/default/files/lecture-extra-files/linear-sinkhorn.pdf", "content": "The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel) method of ..."}
+{"idx": 3, "title": "On Sinkhorn's Algorithm and Choice Modeling - arXiv.org", "date": "", "ddg_snippet": "ergence result of Franklin and Lorenz ( 1989 ) for positive matrices, and the sub- linear bound of L ́eger (2021) for non-negative matrices under weaker condi-tions. In addition, we also characterize the asymptotic linear rate of convergence in terms of the solution matrix D0AD1 and target marginals p, q", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.00260v1", "content": "ergence result of Franklin and Lorenz ( 1989 ) for positive matrices, and the sub- linear bound of L ́eger (2021) for non-negative matrices under weaker condi-tions. In addition, we also characterize the asymptotic linear rate of convergence in terms of the solution matrix D0AD1 and target marginals p, q"}
+{"idx": 4, "title": "PDF On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates.", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/Sinkhorn_rate.pdf", "content": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates."}
+{"idx": 5, "title": "PDF On the linear convergence of the multi-marginal Sinkhorn algorithm", "date": "", "ddg_snippet": "The linear convergence of the Sinhkorn algorithm for two marginals is well-known. A very elegant proof consists in using a celebrated theorem of Birkhoff to show that the Sinkhorn algorithm consists in iterating a contrac-tion for the Hilbert projective metric , see Franklin and Lorenz [10], and more recently, Chen, Georgiou and Pavon, [4].", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/8f3b/63cdaad4f03307b7f7e2b5f307a513686322.pdf", "content": "The linear convergence of the Sinhkorn algorithm for two marginals is well-known. A very elegant proof consists in using a celebrated theorem of Birkhoff to show that the Sinkhorn algorithm consists in iterating a contrac-tion for the Hilbert projective metric , see Franklin and Lorenz [10], and more recently, Chen, Georgiou and Pavon, [4]."}
+{"idx": 6, "title": "PDF Non-asymptotic convergence bounds for Sinkhorn iterates and their ...", "date": "", "ddg_snippet": "In this setting, Franklin and Lorenz ( 1989 ) show that Sinkhorn algorithm is equivalent to a sequence of iterations of a contraction in the Hilbert projective metric and prove its geometric (i.e., exponential) convergence by relying on Birkoff's theorem.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v195/greco23a/greco23a.pdf", "content": "In this setting, Franklin and Lorenz ( 1989 ) show that Sinkhorn algorithm is equivalent to a sequence of iterations of a contraction in the Hilbert projective metric and prove its geometric (i.e., exponential) convergence by relying on Birkoff's theorem."}
+{"idx": 7, "title": "On the scaling of multidimensional matrices - ScienceDirect", "date": "", "ddg_snippet": "Elementary proofs are given for theorems of Bapat and Raghavan on the scaling of nonnegative multidimensional matrices. Theorems of Sinkhorn and of Brualdi, Parter, and Schneider are derived as corollaries. For positive two-dimensional matrices, Hilbert's projective metric and a theorem of G. Birkhoff are used to prove that Sinkhorn's original iterative procedure converges geometrically; the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/0024379589904904", "content": "Elementary proofs are given for theorems of Bapat and Raghavan on the scaling of nonnegative multidimensional matrices. Theorems of Sinkhorn and of Brualdi, Parter, and Schneider are derived as corollaries. For positive two-dimensional matrices, Hilbert's projective metric and a theorem of G. Birkhoff are used to prove that Sinkhorn's original iterative procedure converges geometrically; the ..."}
+{"idx": 8, "title": "PDF Universityof Pennsylvania arXiv:1801.02790v2 [cs.DS] 16 Feb 2018", "date": "", "ddg_snippet": "d to the general matrix scaling problem by Kalantari et al [20]. In a different track, Franklin and Lorenz [14] show that in fact the dependence on ≤ ε can be made logarithmic, and thus the algorithm has \" linear convergence", "subpage_snippet": "", "source": "www.cis.upenn.edu", "link": "https://www.cis.upenn.edu/~sanjeev/papers/sosa2018_sinkhorn.pdf", "content": "d to the general matrix scaling problem by Kalantari et al [20]. In a different track, Franklin and Lorenz [14] show that in fact the dependence on ≤ ε can be made logarithmic, and thus the algorithm has \" linear convergence"}
+{"idx": 9, "title": "PDF Kantorovich-Initiative-multimarginals.dvi", "date": "", "ddg_snippet": "K(x, y)at+1(x)dμ(x) X if c ∈ L∞(μ ⊗ ν), K bounded away from 0, linear convergence is well-known. Elegant proof using the so-called Hilbert projective metric and a theorem of Birkhoff ( Franklin and Lorenz ). Note that Sinkhorn is also block coordinate descent in the dual (4), indeed fixing φ and maximizing Z the dual functional in ψ gives", "subpage_snippet": "", "source": "kantorovich.org", "link": "https://kantorovich.org/event/ki-seminar-carlier/Kantorovich-Initiative-multimarginals.pdf", "content": "K(x, y)at+1(x)dμ(x) X if c ∈ L∞(μ ⊗ ν), K bounded away from 0, linear convergence is well-known. Elegant proof using the so-called Hilbert projective metric and a theorem of Birkhoff ( Franklin and Lorenz ). Note that Sinkhorn is also block coordinate descent in the dual (4), indeed fixing φ and maximizing Z the dual functional in ψ gives"}
diff --git "a/data/sampled_jsons/Freya_PAGE_Tyurin_Richt\303\241rik_2024_abstract_year_2024.jsonl" "b/data/sampled_jsons/Freya_PAGE_Tyurin_Richt\303\241rik_2024_abstract_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..adfe4aa3fbd9b9cce450f28dc549b37f306592c1
--- /dev/null
+++ "b/data/sampled_jsons/Freya_PAGE_Tyurin_Richt\303\241rik_2024_abstract_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Freya PAGE | Proceedings of the 38th International Conference ...", "date": "", "ddg_snippet": "Jun 5, 2025 · A. Tyurin , M. Pozzi, I. Ilin, and P. Richtárik . Shadowheart SGD: Distributed asynchronous SGD with optimal time complexity under arbitrary computation and communication heterogeneity. arXiv preprint arXiv:2402.04785, 2024 .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739635", "content": "Jun 5, 2025 · A. Tyurin , M. Pozzi, I. Ilin, and P. Richtárik . Shadowheart SGD: Distributed asynchronous SGD with optimal time complexity under arbitrary computation and communication heterogeneity. arXiv preprint arXiv:2402.04785, 2024 ."}
+{"idx": 1, "title": "Freya PAGE: First Optimal Time Complexity for Large-Scale ...", "date": "", "ddg_snippet": "Poster Freya PAGE : First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations Alexander Tyurin · Kaja Gruntkowska · Peter Richtarik [ Abstract ] [ Paper] [ Poster] [ OpenReview] 2024 Poster", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96254", "content": "Poster Freya PAGE : First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations Alexander Tyurin · Kaja Gruntkowska · Peter Richtarik [ Abstract ] [ Paper] [ Poster] [ OpenReview] 2024 Poster"}
+{"idx": 2, "title": "Freya PAGE: First Optimal Time Complexity for Large-Scale ...", "date": "", "ddg_snippet": "Authors Alexander Tyurin , Kaja Gruntkowska, Peter Richtárik Abstract In practical distributed systems, workers are typically not homogeneous, and due to differences in hardware configurations and network conditions, can have highly varying processing times. We consider smooth nonconvex finite-sum (empirical risk minimization) problems in this setup and introduce a new parallel method, Freya ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2024/hash/618c8af8efd19b4ce90b8864a764d0fa-Abstract-Conference.html", "content": "Authors Alexander Tyurin , Kaja Gruntkowska, Peter Richtárik Abstract In practical distributed systems, workers are typically not homogeneous, and due to differences in hardware configurations and network conditions, can have highly varying processing times. We consider smooth nonconvex finite-sum (empirical risk minimization) problems in this setup and introduce a new parallel method, Freya ..."}
+{"idx": 3, "title": "[2405.15545] Freya PAGE: First Optimal Time Complexity for ...", "date": "", "ddg_snippet": "May 24, 2024 · Title: Freya PAGE : First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations Authors: Alexander Tyurin , Kaja Gruntkowska, Peter Richtárik (Submitted on 24 May 2024 )", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2405.15545", "content": "May 24, 2024 · Title: Freya PAGE : First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations Authors: Alexander Tyurin , Kaja Gruntkowska, Peter Richtárik (Submitted on 24 May 2024 )"}
+{"idx": 4, "title": "Abstract - OpenReview", "date": "", "ddg_snippet": "Abstract In practical distributed systems, workers are typically not homogeneous, and due to differences in hardware configurations and network conditions, can have highly varying processing times. We consider smooth nonconvex finite-sum (empirical risk minimization) problems in this setup and introduce a new parallel method, Freya PAGE , designed to handle arbitrarily heterogeneous and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=F5MrqPGtdi", "content": "Abstract In practical distributed systems, workers are typically not homogeneous, and due to differences in hardware configurations and network conditions, can have highly varying processing times. We consider smooth nonconvex finite-sum (empirical risk minimization) problems in this setup and introduce a new parallel method, Freya PAGE , designed to handle arbitrarily heterogeneous and ..."}
+{"idx": 5, "title": "Freya PAGE: A New Method for Optimizing Distributed Computing", "date": "", "ddg_snippet": "Jul 27, 2025 · Title: Freya PAGE : First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations Abstract : In practical distributed systems, workers are typically not homogeneous, and due to differences in hardware configurations and network conditions, can have highly varying processing times.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-27-freya-page-a-new-method-for-optimizing-distributed-computing--a9mmp70", "content": "Jul 27, 2025 · Title: Freya PAGE : First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations Abstract : In practical distributed systems, workers are typically not homogeneous, and due to differences in hardware configurations and network conditions, can have highly varying processing times."}
+{"idx": 6, "title": "Freya PAGE: First Optimal Time Complexity for Large-Scale ...", "date": "", "ddg_snippet": "May 24, 2024 · devices [Chen et al.,2016, Tyurin and Richtárik ,2023]. As a result, some clients may execute computations faster, while others experience delays or e ven fail to participate in the training ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380894791_Freya_PAGE_First_Optimal_Time_Complexity_for_Large-Scale_Nonconvex_Finite-Sum_Optimization_with_Heterogeneous_Asynchronous_Computations", "content": "May 24, 2024 · devices [Chen et al.,2016, Tyurin and Richtárik ,2023]. As a result, some clients may execute computations faster, while others experience delays or e ven fail to participate in the training ..."}
+{"idx": 7, "title": "[2405.15545] Freya PAGE: First Optimal Time Complexity ...", "date": "", "ddg_snippet": "by A Tyurin · 2024 · Cited by 5 — Abstract page for arXiv paper 2405.15545: Freya PAGE : First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.15545", "content": "by A Tyurin · 2024 · Cited by 5 — Abstract page for arXiv paper 2405.15545: Freya PAGE : First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with ..."}
+{"idx": 8, "title": "Freya PAGE: First Optimal Time Complexity for Large- ...", "date": "", "ddg_snippet": "by A Tyurin · 2024 · Cited by 5 — Since the update rule of PAGE coincides with that of Freya PAGE , one can directly apply the iteration complexity results established in Tyurin et al. [2023]. 49 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/618c8af8efd19b4ce90b8864a764d0fa-Paper-Conference.pdf", "content": "by A Tyurin · 2024 · Cited by 5 — Since the update rule of PAGE coincides with that of Freya PAGE , one can directly apply the iteration complexity results established in Tyurin et al. [2023]. 49 pages"}
+{"idx": 9, "title": "Freya PAGE: First Optimal Time Complexity for Large- ...", "date": "", "ddg_snippet": "By being robust to “stragglers” and adaptively ignoring slow computations, Freya PAGE offers significantly improved time complexity guarantees compared to all ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15545v2", "content": "By being robust to “stragglers” and adaptively ignoring slow computations, Freya PAGE offers significantly improved time complexity guarantees compared to all ..."}
diff --git a/data/sampled_jsons/Frsn(X)_Section_2.1_Figure_2_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initializa.jsonl b/data/sampled_jsons/Frsn(X)_Section_2.1_Figure_2_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initializa.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b9ea3ce715345d3c42fbf9b8a60fcc6522472fa4
--- /dev/null
+++ b/data/sampled_jsons/Frsn(X)_Section_2.1_Figure_2_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initializa.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "University of Newcastle (Australia) - Wikipedia", "date": "", "ddg_snippet": "... and international university standings; ranked in the 10–14 range of the 38 universities in Australia by the Shanghai Jiao Tong University and 215th ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/University_of_Newcastle_(Australia)", "content": "... and international university standings; ranked in the 10–14 range of the 38 universities in Australia by the Shanghai Jiao Tong University and 215th ..."}
+{"idx": 1, "title": "ToolRM: Outcome Reward Models for Tool-Calling Large ...", "date": "", "ddg_snippet": "5 days ago — We summarize the key insights below: • Small Language Models (SLMs) benefit the most: Best-of-n sampling with Qwen3-0.6B and. ToolRM-14B as the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.11963", "content": "5 days ago — We summarize the key insights below: • Small Language Models (SLMs) benefit the most: Best-of-n sampling with Qwen3-0.6B and. ToolRM-14B as the ..."}
+{"idx": 2, "title": "RARE: Retrieval-Augmented Reasoning Enhancement for ...", "date": "", "ddg_snippet": "by H Tran · 2025 · Cited by 9 — Table 2 presents the performance of RARE com- pared to other methods and larger language models on commonsense reasoning benchmarks, includ- ing ... 26 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.896.pdf", "content": "by H Tran · 2025 · Cited by 9 — Table 2 presents the performance of RARE com- pared to other methods and larger language models on commonsense reasoning benchmarks, includ- ing ... 26 pages"}
+{"idx": 3, "title": "Small Vision-Language Models: A Survey on Compact ...", "date": "", "ddg_snippet": "by N Patnaik · 2025 · Cited by 1 — The emergence of small vision- language models (sVLMs) marks a critical ad- vancement in multimodal AI, enabling efficient processing of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10665", "content": "by N Patnaik · 2025 · Cited by 1 — The emergence of small vision- language models (sVLMs) marks a critical ad- vancement in multimodal AI, enabling efficient processing of ..."}
+{"idx": 4, "title": "BaWA: Automatic Optimizing Pruning Metric for Large ...", "date": "", "ddg_snippet": "15 Jul 2025 — This paper introduces a new method called BaWA to make large AI language models smaller and more efficient without losing performance. ... , 2024) ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44892", "content": "15 Jul 2025 — This paper introduces a new method called BaWA to make large AI language models smaller and more efficient without losing performance. ... , 2024) ..."}
+{"idx": 5, "title": "Towards Better Causal Reasoning in Language Models", "date": "", "ddg_snippet": "by L Yu · 2025 · Cited by 4 — Through systematic analysis of model outputs, we identify four common types of errors in causal reasoning . Logical errors occur when the model ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.622.pdf", "content": "by L Yu · 2025 · Cited by 4 — Through systematic analysis of model outputs, we identify four common types of errors in causal reasoning . Logical errors occur when the model ..."}
+{"idx": 6, "title": "The transformative roles of generative artificial intelligence ...", "date": "", "ddg_snippet": "by S Duan · 2025 — Generative AI models enable accurate image segmentation and structural anomaly detection using limited training data. The paper also explores new opportunities ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1474034625006123", "content": "by S Duan · 2025 — Generative AI models enable accurate image segmentation and structural anomaly detection using limited training data. The paper also explores new opportunities ..."}
+{"idx": 7, "title": "A Large Language Model Fine-Tuning Framework for Causal ...", "date": "", "ddg_snippet": "Figure 2 BRQA Q&A Data. The step-by-step functions of BRQA for Q&A data are listed in Table 1. Table 1 Step by step function. Step by step function. Step01 ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/f3d4b069-6a1c-4ba6-b8c4-49cc2ceb19a5-MECA.pdf?abstractid=5394371&mirid=1", "content": "Figure 2 BRQA Q&A Data. The step-by-step functions of BRQA for Q&A data are listed in Table 1. Table 1 Step by step function. Step by step function. Step01 ..."}
+{"idx": 8, "title": "Learning from Mistakes via Cooperative Study Assistant for ...", "date": "", "ddg_snippet": "Large language models (LLMs) have demon- strated their potential to refine their generation based on their own feedback. However, the.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=MEByW1upLk", "content": "Large language models (LLMs) have demon- strated their potential to refine their generation based on their own feedback. However, the."}
+{"idx": 9, "title": "Interpretable and Controllable Language Models - Peter Hase", "date": "", "ddg_snippet": "by P Hase · 2024 — ... small number of documents account for most of the probability mass in pη(e| x ), and ( 2 ) a pretrained model pη(e| x ) yields a decent initial ...", "subpage_snippet": "", "source": "peterbhase.github.io", "link": "https://peterbhase.github.io/files/hase_thesis.pdf", "content": "by P Hase · 2024 — ... small number of documents account for most of the probability mass in pη(e| x ), and ( 2 ) a pretrained model pη(e| x ) yields a decent initial ..."}
diff --git a/data/sampled_jsons/FuGps5Zyia_Ad-Hoc_Human-AI_Coordination_Challenge.jsonl b/data/sampled_jsons/FuGps5Zyia_Ad-Hoc_Human-AI_Coordination_Challenge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ef5de8e0034c9a3c221c0030050bb5e2b88885e3
--- /dev/null
+++ b/data/sampled_jsons/FuGps5Zyia_Ad-Hoc_Human-AI_Coordination_Challenge.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2506.21490] Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination . However, its use for human-AI interaction has ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.21490", "content": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination . However, its use for human-AI interaction has ..."}
+{"idx": 1, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "The AH2AC2 Challenge The Ad- Hoc Human-AI Coordination Challenge (AH2AC2) provides a standardized environment for evaluating AI agents on their ability to coordinate with human -like counterparts in Hanabi. The challenge emphasizes data-efficient methods and uses human proxy agents for robust and reproducible evaluation.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/", "content": "The AH2AC2 Challenge The Ad- Hoc Human-AI Coordination Challenge (AH2AC2) provides a standardized environment for evaluating AI agents on their ability to coordinate with human -like counterparts in Hanabi. The challenge emphasizes data-efficient methods and uses human proxy agents for robust and reproducible evaluation."}
+{"idx": 2, "title": "Ad-Hoc Human-AI Coordination Challenge - Science Cast", "date": "", "ddg_snippet": "In this work, we introduce the Ad- Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \\textit { human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human -like evaluation partners in AH2AC2.", "subpage_snippet": "", "source": "www.sciencecast.org", "link": "https://www.sciencecast.org/casts/q62pr180dj73", "content": "In this work, we introduce the Ad- Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \\textit { human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human -like evaluation partners in AH2AC2."}
+{"idx": 3, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) - GitHub", "date": "", "ddg_snippet": "Ad- Hoc Human-AI Coordination Challenge (AH2AC2). Contribute to FLAIROx/ah2ac2 development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2", "content": "Ad- Hoc Human-AI Coordination Challenge (AH2AC2). Contribute to FLAIROx/ah2ac2 development by creating an account on GitHub."}
+{"idx": 4, "title": "Ad-Hoc Human-AI Coordination Challenge - OpenReview", "date": "", "ddg_snippet": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FuGps5Zyia", "content": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination ."}
+{"idx": 5, "title": "Ad-Hoc Human-AI Coordination Challenge | Cool Papers - Immersive Paper ...", "date": "", "ddg_snippet": "However, its use for human-AI interaction has been limited by the challenges of human evaluation. In this work, we introduce the Ad- Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2506.21490", "content": "However, its use for human-AI interaction has been limited by the challenges of human evaluation. In this work, we introduce the Ad- Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations."}
+{"idx": 6, "title": "Ad-Hoc Human-AI Coordination Challenge - Semantic Scholar", "date": "", "ddg_snippet": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Ad-Hoc-Human-AI-Coordination-Challenge-Dizdarevic-Hammond/76e21098d2925daeb769a647c9af4886d00b05fd", "content": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination ."}
+{"idx": 7, "title": "Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "However, its use for human-AI interaction has been limited by the challenges of human evaluation. In this work, we introduce the Ad- Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21490", "content": "However, its use for human-AI interaction has been limited by the challenges of human evaluation. In this work, we introduce the Ad- Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations."}
+{"idx": 8, "title": "Ad-Hoc Human-AI Coordination Challenge | ResearchTrend.AI", "date": "", "ddg_snippet": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination .", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2506.21490", "content": "Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge . Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination ."}
+{"idx": 9, "title": "Playing with Strangers: A New Benchmark for Ad-Hoc Human-AI Teamwork ...", "date": "", "ddg_snippet": "A new challenge using the game Hanabi brings us closer to human -compatible AI agents by enabling reproducible, low-cost evaluation of ad- hoc coordination .", "subpage_snippet": "", "source": "cognaptus.com", "link": "https://cognaptus.com/blog/2025-06-27-playing-with-strangers-a-new-benchmark-for-adhoc-humanai-teamwork/", "content": "A new challenge using the game Hanabi brings us closer to human -compatible AI agents by enabling reproducible, low-cost evaluation of ad- hoc coordination ."}
diff --git a/data/sampled_jsons/GUI-Xplore_Mind2Web_basic_automation_tasks_cross-task_ability.jsonl b/data/sampled_jsons/GUI-Xplore_Mind2Web_basic_automation_tasks_cross-task_ability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8c8df77dc29c5b5b81c09f77360963eb27061526
--- /dev/null
+++ b/data/sampled_jsons/GUI-Xplore_Mind2Web_basic_automation_tasks_cross-task_ability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OmniActor: A Generalist GUI and Embodied Agent for 2D&3D", "date": "", "ddg_snippet": "GUI and embodied tasks are two typical tasks for evaluating agent abilities in 2D and 3D worlds, respectively. ... GUI and embodied tasks may exhibit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02322v1", "content": "GUI and embodied tasks are two typical tasks for evaluating agent abilities in 2D and 3D worlds, respectively. ... GUI and embodied tasks may exhibit ..."}
+{"idx": 1, "title": "Empowering Generalizable GUI Agents with One Exploration", "date": "", "ddg_snippet": "22 Mar 2025 — We introduce GUI-Xplore , a dataset meticulously designed to enhance cross-application and cross-task generalization via an exploration-and-reasoning framework.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17709v1", "content": "22 Mar 2025 — We introduce GUI-Xplore , a dataset meticulously designed to enhance cross-application and cross-task generalization via an exploration-and-reasoning framework."}
+{"idx": 2, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "We balance the task and website distribution to better test different levels of generalization abilities : Cross Task Generalization: Generalization across tasks ...", "subpage_snippet": "", "source": "osu-nlp-group.github.io", "link": "https://osu-nlp-group.github.io/Mind2Web/", "content": "We balance the task and website distribution to better test different levels of generalization abilities : Cross Task Generalization: Generalization across tasks ..."}
+{"idx": 3, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.06070v3", "content": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ..."}
+{"idx": 4, "title": "Managing Ambiguous GUI Navigation Tasks with Follow- ...", "date": "", "ddg_snippet": "Correction GUI Navigation task , enlightening the. 275 ability of GUI automation agents to natively inter-. 276 act and complete missing information when faced.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=wyBso4c7xv", "content": "Correction GUI Navigation task , enlightening the. 275 ability of GUI automation agents to natively inter-. 276 act and complete missing information when faced."}
+{"idx": 5, "title": "OSU-NLP-Group/GUI-Agents-Paper-List", "date": "", "ddg_snippet": "TLDR: This paper presents WebWalker, a multi-agent framework designed to improve the ability of large language models (LLMs) to traverse websites, addressing ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/GUI-Agents-Paper-List", "content": "TLDR: This paper presents WebWalker, a multi-agent framework designed to improve the ability of large language models (LLMs) to traverse websites, addressing ..."}
+{"idx": 6, "title": "From General Vision Language Model to Versatile GUI Agent", "date": "", "ddg_snippet": "by W Chen · 2025 · Cited by 62 — In this work, we provide the GUIEnv dataset to improve the VLMs' OCR and grounding abilities with high-resolution website screenshots. 2.2 GUI ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1065.pdf", "content": "by W Chen · 2025 · Cited by 62 — In this work, we provide the GUIEnv dataset to improve the VLMs' OCR and grounding abilities with high-resolution website screenshots. 2.2 GUI ..."}
+{"idx": 7, "title": "AgentTrek: Agent Trajectory Synthesis via Guiding Replay ...", "date": "", "ddg_snippet": "by Y Xu · Cited by 24 — Graphical User Interface ( GUI ) agents hold great potential for automating complex tasks across diverse digital environments, from web applications to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=EEgYUccwsV", "content": "by Y Xu · Cited by 24 — Graphical User Interface ( GUI ) agents hold great potential for automating complex tasks across diverse digital environments, from web applications to ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "28 Jul 2025 — Furthermore, to achieve reliable GUI automation , an agent requires strong task planning and cross -platform generalization abilities , with long- ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=GUI+Content+Understanding", "content": "28 Jul 2025 — Furthermore, to achieve reliable GUI automation , an agent requires strong task planning and cross -platform generalization abilities , with long- ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "It comprises four levels: GUI Content Understanding, Element Grounding, Task Automation , and Task Collaboration, covering essential skills for GUI agents.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=GUIExplorer", "content": "It comprises four levels: GUI Content Understanding, Element Grounding, Task Automation , and Task Collaboration, covering essential skills for GUI agents."}
diff --git a/data/sampled_jsons/GUI-Xplore_datasets_such_as_Mind2Web_focus_primarily_on_basic_automation_tasks.jsonl b/data/sampled_jsons/GUI-Xplore_datasets_such_as_Mind2Web_focus_primarily_on_basic_automation_tasks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..de3805058b1d231cc932899f1006fc760d2cd759
--- /dev/null
+++ b/data/sampled_jsons/GUI-Xplore_datasets_such_as_Mind2Web_focus_primarily_on_basic_automation_tasks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GUI - Xplore : Empowering Generalizable GUI Agents with One...", "date": "", "ddg_snippet": "2. Cross- Task Versatility Beyond Basic Navigation: Moving beyond a narrow focus on automation , GUI - Xplore provides diverse task annotations covering Page Analysis, Application Usage, Application Overview, Action Recall, and Action Sequence Verification.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17709v1", "content": "2. Cross- Task Versatility Beyond Basic Navigation: Moving beyond a narrow focus on automation , GUI - Xplore provides diverse task annotations covering Page Analysis, Application Usage, Application Overview, Action Recall, and Action Sequence Verification."}
+{"idx": 1, "title": "GUI - Xplore : Empowering Generalizable GUI Agents with... | alphaXiv", "date": "", "ddg_snippet": "The GUI - Xplore dataset was created through a combination of automated and manual exploration methods GUI - Xplore is compared with Androidinthewild, which is said to focus solely on basic automation tasks , unlike GUI - Xplore ’s broader task diversity.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17709v1", "content": "The GUI - Xplore dataset was created through a combination of automated and manual exploration methods GUI - Xplore is compared with Androidinthewild, which is said to focus solely on basic automation tasks , unlike GUI - Xplore ’s broader task diversity."}
+{"idx": 2, "title": "GitHub - 921112343/ GUI - Xplore : [CVPR 2025] GUI - Xplore ...", "date": "", "ddg_snippet": "Cross-App & Cross- Task Generalization: Unlike prior datasets , GUI - Xplore enables models to adapt to new applications without explicit retraining.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/921112343/GUI-Xplore", "content": "Cross-App & Cross- Task Generalization: Unlike prior datasets , GUI - Xplore enables models to adapt to new applications without explicit retraining."}
+{"idx": 3, "title": "Github Repository for Top LLM Datasets | Analytics Vidhya", "date": "", "ddg_snippet": "Explore the Github Repository for LLM Datasets and transform your AI projects with quality data for model training.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/09/github-repository-for-top-llm-datasets/", "content": "Explore the Github Repository for LLM Datasets and transform your AI projects with quality data for model training."}
+{"idx": 4, "title": "Visual Agents at CVPR 2025 - Voxel51", "date": "", "ddg_snippet": "Xplore -Agent leverages the dataset through a two -stage pipeline: Action-aware GUI Modeling: Extracts key frames from exploration videos using luminance difference detection, then converts these frames into structured textual representations of GUI elements and interactions.", "subpage_snippet": "", "source": "voxel51.com", "link": "https://voxel51.com/blog/visual-agents-at-cvpr-2025", "content": "Xplore -Agent leverages the dataset through a two -stage pipeline: Action-aware GUI Modeling: Extracts key frames from exploration videos using luminance difference detection, then converts these frames into structured textual representations of GUI elements and interactions."}
+{"idx": 5, "title": "Blox Fruits Script All Working [September 2025] - Arceus X", "date": "", "ddg_snippet": "GUI ( Graphical User Interface ) scripts provide an interface for controlling multiple script features at once. These scripts allow for easier management of different functionalities, such as enabling auto-farm or fruit detection with just a few clicks. 5. No Key System Scripts.", "subpage_snippet": "", "source": "arceusx.com", "link": "https://arceusx.com/blox-fruit-script/", "content": "GUI ( Graphical User Interface ) scripts provide an interface for controlling multiple script features at once. These scripts allow for easier management of different functionalities, such as enabling auto-farm or fruit detection with just a few clicks. 5. No Key System Scripts."}
+{"idx": 6, "title": "machinelearningmastery.com/practice-machine-learning-with-small-in...", "date": "", "ddg_snippet": "Why Do We Need Practice Datasets ?", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/practice-machine-learning-with-small-in-memory-datasets-from-the-uci-machine-learning-repository/", "content": "Why Do We Need Practice Datasets ?"}
+{"idx": 7, "title": "Stream Deck for PC Windows 6.9.1.21711 Download", "date": "", "ddg_snippet": "1: Streamlined Task Automation The Elgato Stream Deck is designed to automate basic tasks , such as switching scenes and going live. This streamlined process allows users to focus on their content and performance, ensuring a seamless and professional streaming experience.", "subpage_snippet": "", "source": "windows.apkpure.com", "link": "https://windows.apkpure.com/elgato-stream-deck", "content": "1: Streamlined Task Automation The Elgato Stream Deck is designed to automate basic tasks , such as switching scenes and going live. This streamlined process allows users to focus on their content and performance, ensuring a seamless and professional streaming experience."}
+{"idx": 8, "title": "22 Which type of model is most effective for | StudyX", "date": "", "ddg_snippet": "Decoder-only Transformer: Typically used for text generation tasks , but not for direct sequence-to-sequence translation. Final Answer. The most effective model for sequence-to-sequence tasks such as machine translation is the Encoder-Decoder Transformer.", "subpage_snippet": "", "source": "studyx.ai", "link": "https://studyx.ai/homework/109407714-22-which-type-of-model-is-most-effective-for-sequence-to-sequence-tasks-such-as-machine", "content": "Decoder-only Transformer: Typically used for text generation tasks , but not for direct sequence-to-sequence translation. Final Answer. The most effective model for sequence-to-sequence tasks such as machine translation is the Encoder-Decoder Transformer."}
+{"idx": 9, "title": "CiteWiz: A Tool for the Visualization of", "date": "", "ddg_snippet": "The user tasks in the taxonomy we address are primarily T2, T3, T4, and T6.We selected the IEEE Xplore web-based database interface as a suitable representative of traditional database interfaces .", "subpage_snippet": "", "source": "www.cse.chalmers.se", "link": "https://www.cse.chalmers.se/~tsigas/papers/CitationVisualization.pdf", "content": "The user tasks in the taxonomy we address are primarily T2, T3, T4, and T6.We selected the IEEE Xplore web-based database interface as a suitable representative of traditional database interfaces ."}
diff --git a/data/sampled_jsons/GUI_agent_concrete_actions_execution_automation_technologies_2024_2025.jsonl b/data/sampled_jsons/GUI_agent_concrete_actions_execution_automation_technologies_2024_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..377d614e8c9987ce5d269b2c2f092b00486826b9
--- /dev/null
+++ b/data/sampled_jsons/GUI_agent_concrete_actions_execution_automation_technologies_2024_2025.jsonl
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+{"idx": 0, "title": "Mobile-Agent-v3: Foundamental Agents for GUI Automation", "date": "", "ddg_snippet": "With the rapid advancement of multimodal large models and reasoning technologies , vision-based GUI agents have demonstrated strong task execution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15144v1", "content": "With the rapid advancement of multimodal large models and reasoning technologies , vision-based GUI agents have demonstrated strong task execution ..."}
+{"idx": 1, "title": "VeriSafe Agent: Safeguarding Mobile GUI Agent via Logic-based", "date": "", "ddg_snippet": "... 2024 ; Li et al., 2024a ) have attempted to mitigate these challenges by introducing reflection agents that use LFMs to review the actions of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.18492v2", "content": "... 2024 ; Li et al., 2024a ) have attempted to mitigate these challenges by introducing reflection agents that use LFMs to review the actions of the ..."}
+{"idx": 2, "title": "Agent.xpu: Efficient Scheduling of Agentic LLM Workloads on", "date": "", "ddg_snippet": "... Agent .xpu first constructs a heterogeneous execution graph, which fuses and chunks model kernels for affinity-guided, elastic accelerator mapping with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.24045v1", "content": "... Agent .xpu first constructs a heterogeneous execution graph, which fuses and chunks model kernels for affinity-guided, elastic accelerator mapping with ..."}
+{"idx": 3, "title": "AppAgent-Pro: A Proactive GUI Agent System for Multidomain", "date": "", "ddg_snippet": "... agents —particularly those operating within graphical user interface ( GUI ) environments—adhere to a predominantly reactive design paradigm (Wang ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18689v1", "content": "... agents —particularly those operating within graphical user interface ( GUI ) environments—adhere to a predominantly reactive design paradigm (Wang ..."}
+{"idx": 4, "title": "EcoAgent: An Efficient Edge-Cloud Collaborative Multi-Agent", "date": "", "ddg_snippet": "EcoAgent features a closed-loop collaboration among a cloud-based Planning Agent and two edge-based agents : the Execution Agent for action execution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.05440v1", "content": "EcoAgent features a closed-loop collaboration among a cloud-based Planning Agent and two edge-based agents : the Execution Agent for action execution ..."}
+{"idx": 5, "title": "UI-Hawk: Unleashing the Screen Stream Understanding for GUI", "date": "", "ddg_snippet": "Existing GUI agents merely rely on current visual observations and plain-text action history, ignoring the significance of history screens.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202408.2137/v1", "content": "Existing GUI agents merely rely on current visual observations and plain-text action history, ignoring the significance of history screens."}
+{"idx": 6, "title": "GitHub - WooooDyy/LLM-Agent-Paper-List: The paper list of the", "date": "", "ddg_snippet": "Specifically, we start by the general conceptual framework for LLM-based agents : comprising three main components: brain, perception, and action , and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/woooodyy/llm-agent-paper-list", "content": "Specifically, we start by the general conceptual framework for LLM-based agents : comprising three main components: brain, perception, and action , and ..."}
+{"idx": 7, "title": "LLM Agents in Production: Architectures, Challenges, and Best", "date": "", "ddg_snippet": "These \" pluggable \" components allow the agent to retrieve data, execute code, update databases, and perform other concrete tasks.", "subpage_snippet": "", "source": "www.zenml.io", "link": "https://www.zenml.io/blog/llm-agents-in-production-architectures-challenges-and-best-practices", "content": "These \" pluggable \" components allow the agent to retrieve data, execute code, update databases, and perform other concrete tasks."}
+{"idx": 8, "title": "What are AI Agentic Workflows? + Use Cases & Examples", "date": "", "ddg_snippet": "Unlike other automation tools, where execution is largely pre-defined, AI agents benefit from the ability to remember prior steps in a process, track ...", "subpage_snippet": "", "source": "budibase.com", "link": "https://budibase.com/blog/ai-agents/ai-agentic-workflows/", "content": "Unlike other automation tools, where execution is largely pre-defined, AI agents benefit from the ability to remember prior steps in a process, track ..."}
+{"idx": 9, "title": "ICSE 2024 - Artifact Evaluation - ICSE 2024", "date": "", "ddg_snippet": "When preparing executable packages for submission, we recommend vetting the artifact on a clean machine to confirm that it can be setup in a ...", "subpage_snippet": "", "source": "conf.researchr.org", "link": "https://conf.researchr.org/track/icse-2024/icse-2024-artifact-evaluation", "content": "When preparing executable packages for submission, we recommend vetting the artifact on a clean machine to confirm that it can be setup in a ..."}
diff --git "a/data/sampled_jsons/Gaetz_&_Gao,_2024_On_the_minimal_power_of_q_in_a_Kazhdan\342\200\223Lusztig_polynomial.jsonl" "b/data/sampled_jsons/Gaetz_&_Gao,_2024_On_the_minimal_power_of_q_in_a_Kazhdan\342\200\223Lusztig_polynomial.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..31f5a8e552d77b283895a00ad7eeae1188b7c128
--- /dev/null
+++ "b/data/sampled_jsons/Gaetz_&_Gao,_2024_On_the_minimal_power_of_q_in_a_Kazhdan\342\200\223Lusztig_polynomial.jsonl"
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+{"idx": 0, "title": "On the minimal power of $ q $ in a Kazhdan - Lusztig polynomial", "date": "", "ddg_snippet": "in a Kazhdan - Lusztig polynomial . Authors:Christian Gaetz , Yibo Gao .in the symmetric group, we provide an exact formula for the smallest positive power q ^{h(w)}. appearing in the Kazhdan - Lusztig polynomial P_{e,w}( q ). . We also provide a tight upper bound on h(w).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.13695", "content": "in a Kazhdan - Lusztig polynomial . Authors:Christian Gaetz , Yibo Gao .in the symmetric group, we provide an exact formula for the smallest positive power q ^{h(w)}. appearing in the Kazhdan - Lusztig polynomial P_{e,w}( q ). . We also provide a tight upper bound on h(w)."}
+{"idx": 1, "title": "On the minimal power of q in a Kazhdan–Lusztig polynomial", "date": "", "ddg_snippet": "by C Gaetz · 2024 · Cited by 4 — On the minimal power of q in a Kazhdan–Lusztig polynomial . 2024. Gaetz, Christian;; Gao, Yibo. Published Web Location. https://arxiv.org/pdf/2303.13695.", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/uc/item/0191j9m7", "content": "by C Gaetz · 2024 · Cited by 4 — On the minimal power of q in a Kazhdan–Lusztig polynomial . 2024. Gaetz, Christian;; Gao, Yibo. Published Web Location. https://arxiv.org/pdf/2303.13695."}
+{"idx": 2, "title": "On the minimal power of q in a Kazhdan–Lusztig polynomial", "date": "", "ddg_snippet": "by C Gaetz · 2024 · Cited by 4 — We provide an exact formula for the smallest positive power appearing in the Kazhdan–Lusztig polynomial . We also provide a tight upper bound on in simply-laced ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0001870824004560", "content": "by C Gaetz · 2024 · Cited by 4 — We provide an exact formula for the smallest positive power appearing in the Kazhdan–Lusztig polynomial . We also provide a tight upper bound on in simply-laced ..."}
+{"idx": 3, "title": "On the minimal power of 𝑞 in a Kazhdan–Lusztig polynomial", "date": "", "ddg_snippet": "Christian Gaetz Department of Mathematics, University of California, Berkeley, CA, USA. gaetz@berkeley.edu and Yibo Gao Beijing International Center for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2303.13695v2", "content": "Christian Gaetz Department of Mathematics, University of California, Berkeley, CA, USA. gaetz@berkeley.edu and Yibo Gao Beijing International Center for ..."}
+{"idx": 4, "title": "On the minimal power of q in a Kazhdan-Lusztig polynomial", "date": "", "ddg_snippet": "The paper provides an exact formula for the smallest positive power in Kazhdan - Lusztig polynomials and a tight upper bound on the exponent in ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2303.13695", "content": "The paper provides an exact formula for the smallest positive power in Kazhdan - Lusztig polynomials and a tight upper bound on the exponent in ..."}
+{"idx": 5, "title": "Yibo Gao", "date": "", "ddg_snippet": "C Gaetz, Y Gao. Algebraic Combinatorics 3 (3), 791-800, 2020. 5, 2020. On the minimal power of q in a Kazhdan–Lusztig polynomial . C Gaetz, Y Gao. Advances in ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=wV7jIHYAAAAJ&hl=en", "content": "C Gaetz, Y Gao. Algebraic Combinatorics 3 (3), 791-800, 2020. 5, 2020. On the minimal power of q in a Kazhdan–Lusztig polynomial . C Gaetz, Y Gao. Advances in ..."}
+{"idx": 6, "title": "The minimal polynomials of powers of cycles in the ordinary ...", "date": "", "ddg_snippet": "For $w$ in the symmetric group, we provide an exact formula for the smallest positive power $ q ^{h(w)}$ appearing in the Kazhdan - Lusztig polynomial $P_{e,w}( q )$.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/the-minimal-polynomials-of-powers-of-cycles-in-the-ordinary-representations-of-symmetric-and-alternating-groups/812593183954305025-1051", "content": "For $w$ in the symmetric group, we provide an exact formula for the smallest positive power $ q ^{h(w)}$ appearing in the Kazhdan - Lusztig polynomial $P_{e,w}( q )$."}
+{"idx": 7, "title": "Pattern heights and the minimal power of q in a Kazhdan – Lusztig ...", "date": "", "ddg_snippet": "The Kazhdan – Lusztig polynomials Py,w( q ) ∈ Z[ q ] have since their discovery [14] proven to underlie deep connections between canonical bases of Hecke algebras, singularities of Schubert varieties, and representations of Lie algebras.", "subpage_snippet": "", "source": "fpsac2024.rub.de", "link": "https://fpsac2024.rub.de/public/extended_abstracts/gaetz.pdf", "content": "The Kazhdan – Lusztig polynomials Py,w( q ) ∈ Z[ q ] have since their discovery [14] proven to underlie deep connections between canonical bases of Hecke algebras, singularities of Schubert varieties, and representations of Lie algebras."}
+{"idx": 8, "title": "Articles by Christian Gaetz | Synthical", "date": "", "ddg_snippet": "On the minimal power of q in a Kazhdan - Lusztig polynomial . 3 September 2024 by Christian Gaetz and Yibo Gao .On combinatorial invariance of parabolic Kazhdan - Lusztig polynomials .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/search/by_author/Christian+Gaetz", "content": "On the minimal power of q in a Kazhdan - Lusztig polynomial . 3 September 2024 by Christian Gaetz and Yibo Gao .On combinatorial invariance of parabolic Kazhdan - Lusztig polynomials ."}
+{"idx": 9, "title": "Christian Gaetz - Google Akademik", "date": "", "ddg_snippet": "C Gaetz , Y Gao . Proceedings of the American Mathematical Society 148 (1), 1-7, 2020.2019. On the minimal power of q in a Kazhdan – Lusztig polynomial .", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=6cnTkSgAAAAJ&hl=tr", "content": "C Gaetz , Y Gao . Proceedings of the American Mathematical Society 148 (1), 1-7, 2020.2019. On the minimal power of q in a Kazhdan – Lusztig polynomial ."}
diff --git a/data/sampled_jsons/Gaetz_Gao_2024_algebraic_combinatorics.jsonl b/data/sampled_jsons/Gaetz_Gao_2024_algebraic_combinatorics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c5249af8c961fcaeff1d458daa2495ad1ba00289
--- /dev/null
+++ b/data/sampled_jsons/Gaetz_Gao_2024_algebraic_combinatorics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gaetz-Gao - univie.ac.at", "date": "", "ddg_snippet": "Séminaire Lotharingien de Combinatoire, 91B.7 ( 2024 ), 12 pp. Christian Gaetz and Yibo Gao Pattern Heights and The Minimal Power of q in a Kazhdan-Lusztig Polynomial Abstract. For w in the symmetric group, we use permutation patterns to provide an exact formula for the smallest positive power qh(w) appearing in the Kazhdan-Lusztig polynomial Pe ...", "subpage_snippet": "", "source": "www.mat.univie.ac.at", "link": "https://www.mat.univie.ac.at/~slc/wpapers/FPSAC2024/7.html", "content": "Séminaire Lotharingien de Combinatoire, 91B.7 ( 2024 ), 12 pp. Christian Gaetz and Yibo Gao Pattern Heights and The Minimal Power of q in a Kazhdan-Lusztig Polynomial Abstract. For w in the symmetric group, we use permutation patterns to provide an exact formula for the smallest positive power qh(w) appearing in the Kazhdan-Lusztig polynomial Pe ..."}
+{"idx": 1, "title": "Christian Gaetz's Homepage - Research - Google Sites", "date": "", "ddg_snippet": "On the Sperner property for the absolute order on complex reflection groups, with Yibo Gao . Algebraic Combinatorics (2020). A combinatorial duality between the weak and strong Bruhat orders, with Yibo Gao . Journal of Combinatorial Theory, Series A (2020). A combinatorial sl(2)-action and the Sperner property for the weak order, with Yibo Gao .", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/berkeley.edu/gaetz/research", "content": "On the Sperner property for the absolute order on complex reflection groups, with Yibo Gao . Algebraic Combinatorics (2020). A combinatorial duality between the weak and strong Bruhat orders, with Yibo Gao . Journal of Combinatorial Theory, Series A (2020). A combinatorial sl(2)-action and the Sperner property for the weak order, with Yibo Gao ."}
+{"idx": 2, "title": "Yibo Gao - Research - pku.edu.cn", "date": "", "ddg_snippet": "My research interests center around algebraic combinatorics , especially Schubert calculus and structures of weak and strong Bruhat orders. I enjoy the great honor to collaborate with many wonderful colleagues. Here is a list of my collaborators with at least 2 joint papers with me: Christian Gaetz (16), Shiliang Gao (3), Reuven Hodges (3), Jiyang Gao (2), Kaarel Hänni (2), Thomas Lam (2 ...", "subpage_snippet": "", "source": "faculty.bicmr.pku.edu.cn", "link": "http://faculty.bicmr.pku.edu.cn/~gaoyibo/research.html", "content": "My research interests center around algebraic combinatorics , especially Schubert calculus and structures of weak and strong Bruhat orders. I enjoy the great honor to collaborate with many wonderful colleagues. Here is a list of my collaborators with at least 2 joint papers with me: Christian Gaetz (16), Shiliang Gao (3), Reuven Hodges (3), Jiyang Gao (2), Kaarel Hänni (2), Thomas Lam (2 ..."}
+{"idx": 3, "title": "Algebraic and enumerative combinatorics seminar-Christian Gaetz", "date": "", "ddg_snippet": "Abstract: Kazhdan-Lusztig polynomials are of foundational importance in geometric representation theory. Yet the Combinatorial Invariance Conjecture, due to Lusztig and to Dyer, suggests that they only depend on the combinatorics of Bruhat order. I'll describe joint work with Grant Barkley in which we adapt the hypercube decompositions introduced by Blundell-Buesing-Davies-Veličković ...", "subpage_snippet": "", "source": "uwaterloo.ca", "link": "https://uwaterloo.ca/combinatorics-and-optimization/events/algebraic-and-enumerative-combinatorics-seminar-christian", "content": "Abstract: Kazhdan-Lusztig polynomials are of foundational importance in geometric representation theory. Yet the Combinatorial Invariance Conjecture, due to Lusztig and to Dyer, suggests that they only depend on the combinatorics of Bruhat order. I'll describe joint work with Grant Barkley in which we adapt the hypercube decompositions introduced by Blundell-Buesing-Davies-Veličković ..."}
+{"idx": 4, "title": "Christian Gaetz | Department of Mathematics - Cornell University", "date": "", "ddg_snippet": "Christian Gaetz Klarman Fellow Research Focus Research Area: Algebraic combinatorics I am interested in combinatorial aspects of representation theory and algebraic geometry, particularly topics related to Coxeter groups and the weak and strong Bruhat orders on them. Publications Balance constants for Coxeter groups, with Yibo Gao . Preprint (2020).", "subpage_snippet": "", "source": "math.cornell.edu", "link": "https://math.cornell.edu/christian-gaetz-0", "content": "Christian Gaetz Klarman Fellow Research Focus Research Area: Algebraic combinatorics I am interested in combinatorial aspects of representation theory and algebraic geometry, particularly topics related to Coxeter groups and the weak and strong Bruhat orders on them. Publications Balance constants for Coxeter groups, with Yibo Gao . Preprint (2020)."}
+{"idx": 5, "title": "Machine Learning Meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "It was developed and plays a crucial role in the proof by Gaetz and Gao ( Gaetz & Gao , 2024 ) which resolved a long-standing conjecture of Billey and Postnikov (Billey & Postnikov, 2005) about the coeficients on Kazhdan-Lusztig polynomials (Section 4.4).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tlniJJFUW2", "content": "It was developed and plays a crucial role in the proof by Gaetz and Gao ( Gaetz & Gao , 2024 ) which resolved a long-standing conjecture of Billey and Postnikov (Billey & Postnikov, 2005) about the coeficients on Kazhdan-Lusztig polynomials (Section 4.4)."}
+{"idx": 6, "title": "Christian Gaetz's articles on arXiv", "date": "", "ddg_snippet": "Christian Gaetz , Yibo Gao Comments: v2: final version, to appear in Communications in Mathematical Physics Journal-ref: Communications in Mathematical Physics, Volume 406, article number 118, (2025) Subjects: Combinatorics (math.CO); Algebraic Geometry (math.AG); Probability (math.PR) [6] arXiv:2303.15577 [pdf, ps, other]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/a/gaetz_c_1.html", "content": "Christian Gaetz , Yibo Gao Comments: v2: final version, to appear in Communications in Mathematical Physics Journal-ref: Communications in Mathematical Physics, Volume 406, article number 118, (2025) Subjects: Combinatorics (math.CO); Algebraic Geometry (math.AG); Probability (math.PR) [6] arXiv:2303.15577 [pdf, ps, other]"}
+{"idx": 7, "title": "Yibo Gao - Google Scholar", "date": "", "ddg_snippet": "Massachusetts Institute of Technology - Cited by 217 - Algebraic Combinatorics", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=wV7jIHYAAAAJ&hl=en", "content": "Massachusetts Institute of Technology - Cited by 217 - Algebraic Combinatorics"}
+{"idx": 8, "title": "Interlacing triangles, Schubert puzzles, and graph colorings", "date": "", "ddg_snippet": "Authors: Christian Gaetz , Yibo Gao View a PDF of the paper titled Interlacing triangles, Schubert puzzles, and graph colorings, by Christian Gaetz and Yibo Gao", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.07863", "content": "Authors: Christian Gaetz , Yibo Gao View a PDF of the paper titled Interlacing triangles, Schubert puzzles, and graph colorings, by Christian Gaetz and Yibo Gao"}
+{"idx": 9, "title": "PDF Pattern heights and the minimal power of q in a Kazhdan-Lusztig ... - RUB", "date": "", "ddg_snippet": "Séminaire Lotharingien de Combinatoire 91B ( 2024 ) Article #7, 12 pp. Proceedings of the 36th Conference on Formal Power Series and Algebraic Combinatorics (Bochum)", "subpage_snippet": "", "source": "fpsac2024.rub.de", "link": "https://fpsac2024.rub.de/public/extended_abstracts/gaetz.pdf", "content": "Séminaire Lotharingien de Combinatoire 91B ( 2024 ) Article #7, 12 pp. Proceedings of the 36th Conference on Formal Power Series and Algebraic Combinatorics (Bochum)"}
diff --git a/data/sampled_jsons/Gaetz_Gao_mathematics_2024.jsonl b/data/sampled_jsons/Gaetz_Gao_mathematics_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..54ad448a45500e6e0a4387b9a841ff742c683f55
--- /dev/null
+++ b/data/sampled_jsons/Gaetz_Gao_mathematics_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Matt Gaetz - Wikipedia", "date": "", "ddg_snippet": "Gaetz on Ileana Ros-Lehtinen 's retirement. Matthew Louis Gaetz II (/ ɡeɪts / GAYTS; born May 7, 1982) is an American politician and lawyer who served as the U.S. representative for Florida's 1st congressional district from 2017 until his resignation in 2024.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Matt_Gaetz", "content": "Gaetz on Ileana Ros-Lehtinen 's retirement. Matthew Louis Gaetz II (/ ɡeɪts / GAYTS; born May 7, 1982) is an American politician and lawyer who served as the U.S. representative for Florida's 1st congressional district from 2017 until his resignation in 2024."}
+{"idx": 1, "title": "The rise and fall of Matt Gaetz in eight wild days - BBC", "date": "", "ddg_snippet": "Nov 22, 2024 · Gaetz , a lawyer, has been one of Trump’s most strident defenders on Capitol Hill. He helped prepare the Republican nominee for his televised debate against Biden that effectively knocked the...", "subpage_snippet": "", "source": "www.bbc.com", "link": "https://www.bbc.com/news/articles/c99r2m4y2zro", "content": "Nov 22, 2024 · Gaetz , a lawyer, has been one of Trump’s most strident defenders on Capitol Hill. He helped prepare the Republican nominee for his televised debate against Biden that effectively knocked the..."}
+{"idx": 2, "title": "Matt Gaetz among Floridians spotted at Charlie Kirk's funeral", "date": "", "ddg_snippet": "1 day ago · Matt Gaetz and his wife Ginger Luckey were among a handful of Florida Republicans who honored Charlie Kirk at his funeral on Sunday.", "subpage_snippet": "", "source": "www.usatoday.com", "link": "https://www.usatoday.com/story/news/2025/09/22/matt-gaetz-among-floridians-spotted-at-charlie-kirks-funeral/86290503007/", "content": "1 day ago · Matt Gaetz and his wife Ginger Luckey were among a handful of Florida Republicans who honored Charlie Kirk at his funeral on Sunday."}
+{"idx": 3, "title": "Gaetz withdraws from attorney general consideration after Trump...", "date": "", "ddg_snippet": "Nov 21, 2024 · Early Thursday afternoon, Gaetz — who quit his seat in the House the day Trump announced he’d selected him to helm the Justice Department — wrote on social media that he was withdrawing and that...", "subpage_snippet": "", "source": "www.cnn.com", "link": "https://www.cnn.com/2024/11/21/politics/matt-gaetz-withdrawing-attorney-general", "content": "Nov 21, 2024 · Early Thursday afternoon, Gaetz — who quit his seat in the House the day Trump announced he’d selected him to helm the Justice Department — wrote on social media that he was withdrawing and that..."}
+{"idx": 4, "title": "Matt Gaetz withdraws as Trump's pick for attorney general : NPR", "date": "", "ddg_snippet": "Nov 21, 2024 · Former Rep. Matt Gaetz , R-Fla., is withdrawing his candidacy to be President-elect Trump's attorney general, after sex trafficking and drug use allegations threatened to imperil his confirmation.", "subpage_snippet": "", "source": "www.npr.org", "link": "https://www.npr.org/2024/11/21/g-s1-35211/gaetz-out-attorney-general-trump", "content": "Nov 21, 2024 · Former Rep. Matt Gaetz , R-Fla., is withdrawing his candidacy to be President-elect Trump's attorney general, after sex trafficking and drug use allegations threatened to imperil his confirmation."}
+{"idx": 5, "title": "Key moments in the Trump transition: Matt Gaetz withdraws as...", "date": "", "ddg_snippet": "Matt Gaetz has withdrawn as President-elect Donald Trump’s pick for attorney general following scrutiny over a federal sex trafficking investigation that cast doubt on his ability to be confirmed as the nation’s chief federal law enforcement officer.", "subpage_snippet": "", "source": "apnews.com", "link": "https://apnews.com/live/matt-gaetz-trump-transition-updates", "content": "Matt Gaetz has withdrawn as President-elect Donald Trump’s pick for attorney general following scrutiny over a federal sex trafficking investigation that cast doubt on his ability to be confirmed as the nation’s chief federal law enforcement officer."}
+{"idx": 6, "title": "Matt Gaetz resigns from Congress after being picked for attorney...", "date": "", "ddg_snippet": "Nov 13, 2024 · President-elect Donald Trump announced on Wednesday that he has chosen Rep. Matt Gaetz as his pick for attorney general, a move that, if he's confirmed by the Senate, would place a firebrand and one of Trump's most loyal allies at the head of the Justice Department.", "subpage_snippet": "", "source": "abcnews.go.com", "link": "https://abcnews.go.com/Politics/trump-picks-rep-matt-gaetz-attorney-general/story?id=115835796", "content": "Nov 13, 2024 · President-elect Donald Trump announced on Wednesday that he has chosen Rep. Matt Gaetz as his pick for attorney general, a move that, if he's confirmed by the Senate, would place a firebrand and one of Trump's most loyal allies at the head of the Justice Department."}
+{"idx": 7, "title": "Matt Gaetz Allegations and Investigations: What to Know | TIME", "date": "", "ddg_snippet": "Nov 18, 2024 · Gaetz , a fierce defender of Trump and critic of the Justice Department, now finds himself poised to lead the very agency that investigated him for allegedly sex trafficking a minor—an...", "subpage_snippet": "", "source": "time.com", "link": "https://time.com/7177301/matt-gaetz-allegations-justice-house-ethics/", "content": "Nov 18, 2024 · Gaetz , a fierce defender of Trump and critic of the Justice Department, now finds himself poised to lead the very agency that investigated him for allegedly sex trafficking a minor—an..."}
+{"idx": 8, "title": "Trump AG pick Matt Gaetz says he's withdrawing - CNBC", "date": "", "ddg_snippet": "Nov 21, 2024 · Matt Gaetz said Thursday he is withdrawing as President-elect Donald Trump 's pick for U.S. attorney general, ending the controversial bid that put prior allegations of sexual misconduct in the...", "subpage_snippet": "", "source": "www.cnbc.com", "link": "https://www.cnbc.com/2024/11/21/trump-ag-pick-matt-gaetz-says-hes-withdrawing.html", "content": "Nov 21, 2024 · Matt Gaetz said Thursday he is withdrawing as President-elect Donald Trump 's pick for U.S. attorney general, ending the controversial bid that put prior allegations of sexual misconduct in the..."}
+{"idx": 9, "title": "What's next for Matt Gaetz after withdrawing from AG nomination?...", "date": "", "ddg_snippet": "Nov 21, 2024 · Gaetz resigned his seat for the current 118th Congress, but his resignation letter left some wiggle room to return for the 119th Congress that starts Jan. 3, 2025, since he was reelected to the...", "subpage_snippet": "", "source": "thehill.com", "link": "https://thehill.com/homenews/house/5003258-gaetz-future-career-options/", "content": "Nov 21, 2024 · Gaetz resigned his seat for the current 118th Congress, but his resignation letter left some wiggle room to return for the 119th Congress that starts Jan. 3, 2025, since he was reelected to the..."}
diff --git a/data/sampled_jsons/Gama_Segarra_Ribeiro_2017_hierarchical_overlapping_clustering_cut_metrics_optimization.jsonl b/data/sampled_jsons/Gama_Segarra_Ribeiro_2017_hierarchical_overlapping_clustering_cut_metrics_optimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d1598a73a90d850456c68e00e32ee098b81cb000
--- /dev/null
+++ b/data/sampled_jsons/Gama_Segarra_Ribeiro_2017_hierarchical_overlapping_clustering_cut_metrics_optimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering of Network Data Using ...", "date": "", "ddg_snippet": "by F Gama · 2016 · Cited by 11 — Hierarchical Overlapping Clustering of Network. Data Using Cut Metrics . Fernando Gama , Santiago Segarra , and Alejandro Ribeiro . Abstract—A novel method to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1611.01393", "content": "by F Gama · 2016 · Cited by 11 — Hierarchical Overlapping Clustering of Network. Data Using Cut Metrics . Fernando Gama , Santiago Segarra , and Alejandro Ribeiro . Abstract—A novel method to ..."}
+{"idx": 1, "title": "Hierarchical Overlapping Clustering of Network Data Using ...", "date": "", "ddg_snippet": "A novel method to obtain hierarchical and overlapping clusters from network data—i.e., a set of nodes endowed with pairwise dissimilarities—is presented and ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Hierarchical-Overlapping-Clustering-of-Network-Data-Gama-Segarra/227d8d8c99882c93626f7051f199405f60b5fa9d", "content": "A novel method to obtain hierarchical and overlapping clusters from network data—i.e., a set of nodes endowed with pairwise dissimilarities—is presented and ..."}
+{"idx": 2, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "by Y Pan — The authors introduce a novel cost function for hierarchical overlapping clustering and methods for approximating an optimal solution based on local search.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "by Y Pan — The authors introduce a novel cost function for hierarchical overlapping clustering and methods for approximating an optimal solution based on local search."}
+{"idx": 3, "title": "Functorial hierarchical clustering with overlaps", "date": "", "ddg_snippet": "by J Culbertson · 2018 · Cited by 18 — This work draws inspiration from three important sources of research on dissimilarity-based clustering and intertwines those three threads ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0166218X17304705", "content": "by J Culbertson · 2018 · Cited by 18 — This work draws inspiration from three important sources of research on dissimilarity-based clustering and intertwines those three threads ..."}
+{"idx": 4, "title": "Functorial hierarchical clustering with overlaps", "date": "", "ddg_snippet": "by J Culbertson · 2018 · Cited by 18 — Segarra, A. Ribeiro, Hierarchical overlapping clustering of network data using cut metrics , IEEE Trans. Signal Inf. Process. Netw., 2017 (2017).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1016/j.dam.2017.10.015", "content": "by J Culbertson · 2018 · Cited by 18 — Segarra, A. Ribeiro, Hierarchical overlapping clustering of network data using cut metrics , IEEE Trans. Signal Inf. Process. Netw., 2017 (2017)."}
+{"idx": 5, "title": "HIERARCHICAL OVERLAPPING CLUSTERING: COST", "date": "", "ddg_snippet": "by Y Pan — Fernando Gama, Santiago Segarra, and Alejandro Ribeiro. Hierarchical overlapping clustering of network data using cut metrics . IEEE Trans. Signal Inf ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "by Y Pan — Fernando Gama, Santiago Segarra, and Alejandro Ribeiro. Hierarchical overlapping clustering of network data using cut metrics . IEEE Trans. Signal Inf ..."}
+{"idx": 6, "title": "Functorial hierarchical clustering with overlaps", "date": "", "ddg_snippet": "Gama, S. Segarra, A. Ribeiro, Overlapping clustering of network data using cut metrics , in: 2015. 834. IEEE International Conference on Acoustics, Speech and ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/am/pii/S0166218X17304705", "content": "Gama, S. Segarra, A. Ribeiro, Overlapping clustering of network data using cut metrics , in: 2015. 834. IEEE International Conference on Acoustics, Speech and ..."}
+{"idx": 7, "title": "DAG-Structured Clustering by Nearest Neighbors", "date": "", "ddg_snippet": "by N Monath · 2021 · Cited by 5 — Gama, S. Segarra, and A. Ribeiro. Hierarchical overlapping clustering of network data using cut metrics . IEEE Transactions on Signal and Infor- mation ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v130/monath21a/monath21a.pdf", "content": "by N Monath · 2021 · Cited by 5 — Gama, S. Segarra, and A. Ribeiro. Hierarchical overlapping clustering of network data using cut metrics . IEEE Transactions on Signal and Infor- mation ..."}
+{"idx": 8, "title": "Publications", "date": "", "ddg_snippet": "Gama, S. Segarra, and A. Ribeiro, Hierarchical Overlapping Clustering of Network Data Using Cut Metrics , IEEE Trans. Signal and Info. Process. over Networks ...", "subpage_snippet": "", "source": "segarra.rice.edu", "link": "https://segarra.rice.edu/publications/", "content": "Gama, S. Segarra, and A. Ribeiro, Hierarchical Overlapping Clustering of Network Data Using Cut Metrics , IEEE Trans. Signal and Info. Process. over Networks ..."}
+{"idx": 9, "title": "arXiv:1609.02513v2 [cs.LG] 14 Aug 2018", "date": "", "ddg_snippet": "by J Culbertson · 2016 · Cited by 18 — [26] Fernando Gama, Santiago Segarra, and Alejandro Ribeiro. Hierarchical overlapping clustering of network data using cut metrics . IEEE ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1609.02513", "content": "by J Culbertson · 2016 · Cited by 18 — [26] Fernando Gama, Santiago Segarra, and Alejandro Ribeiro. Hierarchical overlapping clustering of network data using cut metrics . IEEE ..."}
diff --git a/data/sampled_jsons/Gao_et_al._2024_AuxK_tied_initialization_dead_latents_sparse_autoencoders.jsonl b/data/sampled_jsons/Gao_et_al._2024_AuxK_tied_initialization_dead_latents_sparse_autoencoders.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c29d3b9573d337d7804d27fb334f032c3d64f643
--- /dev/null
+++ b/data/sampled_jsons/Gao_et_al._2024_AuxK_tied_initialization_dead_latents_sparse_autoencoders.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fully Funded Scholarship in Turkey – OYA Opportunities ( Deadline...", "date": "", "ddg_snippet": "Jun 9, 2021 · International students with excellent academic performance are welcomed to enroll in a degree program for a chance to apply for a full Scholarship offered by Faculty of Engineering and Natural Sciences, Sabanci University in Turkey.", "subpage_snippet": "", "source": "mucuruzi.com", "link": "https://mucuruzi.com/fully-funded-scholarship-in-turkey-oya-opportunities-deadline-30-june-2021/", "content": "Jun 9, 2021 · International students with excellent academic performance are welcomed to enroll in a degree program for a chance to apply for a full Scholarship offered by Faculty of Engineering and Natural Sciences, Sabanci University in Turkey."}
+{"idx": 1, "title": "2021 Scholarship Opportunities- Fully Funded - OYA Opportunities", "date": "", "ddg_snippet": "OYA Opportunites has listed some of the fully - funded scholarship opportunities for 2021 that are open to all nationalities. The scholarships are provided by various institutes with varying deadlines.", "subpage_snippet": "", "source": "oyaop.com", "link": "https://oyaop.com/opportunity/scholarships-and-fellowships/2021-scholarship-opportunities-fully-funded/", "content": "OYA Opportunites has listed some of the fully - funded scholarship opportunities for 2021 that are open to all nationalities. The scholarships are provided by various institutes with varying deadlines."}
+{"idx": 2, "title": "2021 Scholarships Abroad – Fully Funded - OYA Opportunities", "date": "", "ddg_snippet": "Applications are open for the 2021 Scholarship in China- Fully Funded by the Alliance of International Science Organizations (ANSO). The scholarship will be provided to 500 students and the application deadline is 30th April 2021.", "subpage_snippet": "", "source": "oyaop.com", "link": "https://oyaop.com/opportunity/scholarships-and-fellowships/2021-scholarships-abroad-fully-funded/", "content": "Applications are open for the 2021 Scholarship in China- Fully Funded by the Alliance of International Science Organizations (ANSO). The scholarship will be provided to 500 students and the application deadline is 30th April 2021."}
+{"idx": 3, "title": "Fully Funded Scholarships || Apply Now for 2021 - OYA...", "date": "", "ddg_snippet": "Applications are open for the 2021 Chinese Government Scholarships – Fully Funded . The scholarship is open to all nationalities and the deadline is 15th April 2021.", "subpage_snippet": "", "source": "oyaop.com", "link": "https://oyaop.com/opportunity/scholarships-and-fellowships/fully-funded-scholarships-apply-now-for-2021/", "content": "Applications are open for the 2021 Chinese Government Scholarships – Fully Funded . The scholarship is open to all nationalities and the deadline is 15th April 2021."}
+{"idx": 4, "title": "Fully Funded Scholarship in Austria 2021 - OYA School", "date": "", "ddg_snippet": "Apply for Fully Funded Scholarship at The Oesterreichische National bank in Austria. The deadline for this application is 31st October 2021 .", "subpage_snippet": "", "source": "oyaschool.com", "link": "https://oyaschool.com/fully-funded-scholarship-at-the-oesterreichische-national-bank-in-austria/", "content": "Apply for Fully Funded Scholarship at The Oesterreichische National bank in Austria. The deadline for this application is 31st October 2021 ."}
+{"idx": 5, "title": "Fully Funded Scholarships Opportunities at Oya - OYA School", "date": "", "ddg_snippet": "Apply for the Fully Funded Scholarships Opportunities of the week at OYA Opportunities . Oya Opportunities brings to you various paid, partially funded and self-funded opportunities viz. scholarships , internships, jobs, exchange programs, internships, conferences, volunteering programs, competitions and awards, fellowships and so on…", "subpage_snippet": "", "source": "oyaschool.com", "link": "https://oyaschool.com/7-fully-funded-scholarships-opportunities-of-the-week-at-oya/", "content": "Apply for the Fully Funded Scholarships Opportunities of the week at OYA Opportunities . Oya Opportunities brings to you various paid, partially funded and self-funded opportunities viz. scholarships , internships, jobs, exchange programs, internships, conferences, volunteering programs, competitions and awards, fellowships and so on…"}
+{"idx": 6, "title": "OYA Opportunities – Telegram", "date": "", "ddg_snippet": "2021 Opportunities of the Month June ( Fully Funded ) https:// oyaop .com/opportunity/scholarships-and-fellowships/2021-opportunities-of-the-month-june-fully-funded/", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/oyaopportunities/34", "content": "2021 Opportunities of the Month June ( Fully Funded ) https:// oyaop .com/opportunity/scholarships-and-fellowships/2021-opportunities-of-the-month-june-fully-funded/"}
+{"idx": 7, "title": "Fully Funded Scholarship Opportunities 2020-21 - OYA School", "date": "", "ddg_snippet": "In search of the best opportunities to finance your education? Whether you are looking for undergraduate, graduate, Postgraduate, or research funding, whether you are satisfied with a partial aid or are seeking a fully - funded opportunity , Oya Opportunities has got your back.", "subpage_snippet": "", "source": "oyaschool.com", "link": "https://oyaschool.com/10-fully-funded-scholarship-opportunities-2020-21/", "content": "In search of the best opportunities to finance your education? Whether you are looking for undergraduate, graduate, Postgraduate, or research funding, whether you are satisfied with a partial aid or are seeking a fully - funded opportunity , Oya Opportunities has got your back."}
+{"idx": 8, "title": "2021 Opportunities of the week! - Fully Funded - OYA ...", "date": "", "ddg_snippet": "Looking for 2021 Opportunities of the week! - Fully Funded to finance your education for further studies? If yes, you have come across the right platform.", "subpage_snippet": "", "source": "oyaop.com", "link": "https://oyaop.com/opportunity/scholarships-and-fellowships/2021-opportunities-of-the-week-fully-funded/", "content": "Looking for 2021 Opportunities of the week! - Fully Funded to finance your education for further studies? If yes, you have come across the right platform."}
+{"idx": 9, "title": "10 Fully Funded Scholarship Opportunities of the week - OYA ...", "date": "", "ddg_snippet": "Looking for opportunities to finance your education for further studies? If yes, you have come across the right platform. Oya Opportunities provides you with various opportunities all over the world to give a push to your career, leadership skills, and overall development. Here is a list of fully funded scholarship opportunities of the week.", "subpage_snippet": "", "source": "oyaschool.com", "link": "https://oyaschool.com/10-fully-funded-scholarship-opportunities-of-the-week/", "content": "Looking for opportunities to finance your education for further studies? If yes, you have come across the right platform. Oya Opportunities provides you with various opportunities all over the world to give a push to your career, leadership skills, and overall development. Here is a list of fully funded scholarship opportunities of the week."}
diff --git a/data/sampled_jsons/Geirhos_et_al._2020_error_consistency_metric_abstract.jsonl b/data/sampled_jsons/Geirhos_et_al._2020_error_consistency_metric_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..750357c98470945e572a2887a9941369d46508a5
--- /dev/null
+++ b/data/sampled_jsons/Geirhos_et_al._2020_error_consistency_metric_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Concept Verifier: Scaling Prover-Verifier Games via", "date": "", "ddg_snippet": "The introduction of Concept Bottleneck Models (CBMs) (Koh et al .,, 2020 ; Delfosse et al .,, 2024 ) , but also concept-based explanations (Kim et ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07532v1", "content": "The introduction of Concept Bottleneck Models (CBMs) (Koh et al .,, 2020 ; Delfosse et al .,, 2024 ) , but also concept-based explanations (Kim et ..."}
+{"idx": 1, "title": "TimeX++: Learning Time-Series Explanations with Information", "date": "", "ddg_snippet": "... technology in analyzing time series data, prevalent in scenarios such as finance (Bento et al ., 2021 ) , healthcare (Kaushik et al ., 2020 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.09308v1", "content": "... technology in analyzing time series data, prevalent in scenarios such as finance (Bento et al ., 2021 ) , healthcare (Kaushik et al ., 2020 ..."}
+{"idx": 2, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "To measure model similarity, we build on error consistency ( Geirhos et al ., 2020 ) , which measures overlap in the samples where two models err ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "To measure model similarity, we build on error consistency ( Geirhos et al ., 2020 ) , which measures overlap in the samples where two models err ..."}
+{"idx": 3, "title": "How Does Critical Batch Size Scale in Pre-training?", "date": "", "ddg_snippet": "... optimization is critical in pre-training large models (LMs) at scale (McCandlish et al ., 2018 ; Shoeybi et al ., 2019 ; Kaplan et al ., 2020 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.21676v4", "content": "... optimization is critical in pre-training large models (LMs) at scale (McCandlish et al ., 2018 ; Shoeybi et al ., 2019 ; Kaplan et al ., 2020 ) ."}
+{"idx": 4, "title": "Seeing Eye to AI? Applying Deep-Feature-Based Similarity", "date": "", "ddg_snippet": "We extended prior work on deep-feature-based similarity metrics using weights trained on Stylized ImageNet ( Geirhos et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00228v1", "content": "We extended prior work on deep-feature-based similarity metrics using weights trained on Stylized ImageNet ( Geirhos et al ."}
+{"idx": 5, "title": "Top-down feedback matters: Functional impact of brainlike", "date": "", "ddg_snippet": "... brain, which consists of feedforward as well as local and top-down recurrent connections ( Kar et al ., 2019 ; van Bergen & Kriegeskorte, 2020 ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/105953", "content": "... brain, which consists of feedforward as well as local and top-down recurrent connections ( Kar et al ., 2019 ; van Bergen & Kriegeskorte, 2020 ..."}
+{"idx": 6, "title": "Top-down feedback matters: Functional impact of brainlike", "date": "", "ddg_snippet": "... brain, which consists of feedforward as well as local and top-down recurrent connections ( Kar et al ., 2019 ; van Bergen & Kriegeskorte, 2020 ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/105953v1", "content": "... brain, which consists of feedforward as well as local and top-down recurrent connections ( Kar et al ., 2019 ; van Bergen & Kriegeskorte, 2020 ..."}
+{"idx": 7, "title": "(PDF) Measuring the Accuracy of Automatic Speech Recognition", "date": "", "ddg_snippet": "Scientific publications and industry report very low error rates, claiming AI has reached human parity or even outperforms manual transcription.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383530482_Measuring_the_Accuracy_of_Automatic_Speech_Recognition_Solutions", "content": "Scientific publications and industry report very low error rates, claiming AI has reached human parity or even outperforms manual transcription."}
+{"idx": 8, "title": "NeurIPS 2022 Awards", "date": "", "ddg_snippet": "... error with dataset size and show how in theory we can break beyond power law scaling and potentially even reduce it to exponential scaling instead if ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2022/awards_detail", "content": "... error with dataset size and show how in theory we can break beyond power law scaling and potentially even reduce it to exponential scaling instead if ..."}
+{"idx": 9, "title": "Reward Hacking in Reinforcement Learning | Lil'Log", "date": "", "ddg_snippet": "... for distinguishing wolves from huskies may overfit to the presence of a snowy background if all the wolf training images include snow ( Ribeiro et al ...", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/", "content": "... for distinguishing wolves from huskies may overfit to the presence of a snowy background if all the wolf training images include snow ( Ribeiro et al ..."}
diff --git a/data/sampled_jsons/GenAI_Arena_Playground_V2.5_SDXL_difference_base_architecture.jsonl b/data/sampled_jsons/GenAI_Arena_Playground_V2.5_SDXL_difference_base_architecture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d68d7c2a49a99fae272ab3e40bf638b627732510
--- /dev/null
+++ b/data/sampled_jsons/GenAI_Arena_Playground_V2.5_SDXL_difference_base_architecture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenAI Arena : An Open Evaluation Platform for", "date": "", "ddg_snippet": "Playground V 2 and Playground V 2 . 5 are based on SDXL architecture , but trained by Playground .ai from scratch with an internal dataset. We have also included the latest released HunyuanDiT [45], FLUX.1-dev [35], FLUX.1-schnell [35].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04485", "content": "Playground V 2 and Playground V 2 . 5 are based on SDXL architecture , but trained by Playground .ai from scratch with an internal dataset. We have also included the latest released HunyuanDiT [45], FLUX.1-dev [35], FLUX.1-schnell [35]."}
+{"idx": 1, "title": "playgroundai/ playground - v 2 . 5 -1024px-aesthetic · Hugging Face", "date": "", "ddg_snippet": "Playground v 2 . 5 is a diffusion- based text-to-image generative model, and a successor to Playground v 2 . Playground v 2 . 5 is the state-of-the-art open-source model in aesthetic quality. Our user studies demonstrate that our model outperforms SDXL , Playground v 2 , PixArt-α, DALL-E 3, and...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic", "content": "Playground v 2 . 5 is a diffusion- based text-to-image generative model, and a successor to Playground v 2 . Playground v 2 . 5 is the state-of-the-art open-source model in aesthetic quality. Our user studies demonstrate that our model outperforms SDXL , Playground v 2 , PixArt-α, DALL-E 3, and..."}
+{"idx": 2, "title": "GitHub - TIGER-AI-Lab/ GenAI - Arena : Interface for GenAI - Arena", "date": "", "ddg_snippet": "Contribute to TIGER-AI-Lab/ GenAI - Arena development by creating an account on GitHub.huggingface.co/spaces/TIGER-Lab/ GenAI - Arena .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TIGER-AI-Lab/GenAI-Arena", "content": "Contribute to TIGER-AI-Lab/ GenAI - Arena development by creating an account on GitHub.huggingface.co/spaces/TIGER-Lab/ GenAI - Arena ."}
+{"idx": 3, "title": "Stable Diffusion Prompts, SDXL Prompts... - Stable Diffusion Prompts", "date": "", "ddg_snippet": "Explore stable diffusion prompts, the best prompts for SDXL , and master stable diffusion SDXL prompts.technicolor dreamscape city filled with gravity-defying architecture , neon lights, and wild street art.", "subpage_snippet": "", "source": "stable-diffusion.app", "link": "https://stable-diffusion.app/prompts/", "content": "Explore stable diffusion prompts, the best prompts for SDXL , and master stable diffusion SDXL prompts.technicolor dreamscape city filled with gravity-defying architecture , neon lights, and wild street art."}
+{"idx": 4, "title": "GenAI Arena : An Open Evaluation Platform for... - DEV Community", "date": "", "ddg_snippet": "GenAI Arena is a new tool that makes it easier to test and compare different AI models that can generate content, like images, text, or other types of data.The paper introduces GenAI Arena , a novel open-source evaluation platform for generative AI models.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/aimodels-fyi/genai-arena-an-open-evaluation-platform-for-generative-models-ajc", "content": "GenAI Arena is a new tool that makes it easier to test and compare different AI models that can generate content, like images, text, or other types of data.The paper introduces GenAI Arena , a novel open-source evaluation platform for generative AI models."}
+{"idx": 5, "title": "playgroundai/ playground - v 2 -1024px-aesthetic · Hugging Face", "date": "", "ddg_snippet": "Playground v 2 is a diffusion- based text-to-image generative model. The model was trained from scratch by the research team at Playground . Images generated by Playground v 2 are favored 2 . 5 times more than those produced by Stable Diffusion XL, according to Playground ’s user...", "subpage_snippet": "", "source": "huggingface.proxy.nlp.skieer.com", "link": "https://huggingface.proxy.nlp.skieer.com/playgroundai/playground-v2-1024px-aesthetic", "content": "Playground v 2 is a diffusion- based text-to-image generative model. The model was trained from scratch by the research team at Playground . Images generated by Playground v 2 are favored 2 . 5 times more than those produced by Stable Diffusion XL, according to Playground ’s user..."}
+{"idx": 6, "title": "Master the New SDXL Beta with AnimateDiff! (Tutorial)", "date": "", "ddg_snippet": "Discover the powerful features of the latest SDXL Beta release with step-by-step instructions using AnimateDiff.", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/gpts/master-the-new-sdxl-beta-with-animatediff-tutorial-137608", "content": "Discover the powerful features of the latest SDXL Beta release with step-by-step instructions using AnimateDiff."}
+{"idx": 7, "title": "googleapis.github.io/python- genai / genai .html", "date": "", "ddg_snippet": "genai .tunings module.", "subpage_snippet": "", "source": "googleapis.github.io", "link": "https://googleapis.github.io/python-genai/genai.html", "content": "genai .tunings module."}
+{"idx": 8, "title": "【 GenAI - Arena ...", "date": "", "ddg_snippet": "GenAI Arena : An Open Evaluation Platform for Generative Models written by Dongfu Jiang, Max Ku, Tianle Li, Yuansheng Ni, Shizhuo Sun, Rongqi Fan, Wenhu Chen (Submitted on 6 Jun 2024) Comments: 9 pages,7 figures Subjects: Artificial Intelligence (cs.AI); Computer Vision and...", "subpage_snippet": "", "source": "ai-scholar.tech", "link": "https://ai-scholar.tech/articles/large-language-models/genai-arena", "content": "GenAI Arena : An Open Evaluation Platform for Generative Models written by Dongfu Jiang, Max Ku, Tianle Li, Yuansheng Ni, Shizhuo Sun, Rongqi Fan, Wenhu Chen (Submitted on 6 Jun 2024) Comments: 9 pages,7 figures Subjects: Artificial Intelligence (cs.AI); Computer Vision and..."}
+{"idx": 9, "title": "Image generation with Gemini (aka Nano Banana) | Gemini API", "date": "", "ddg_snippet": "from google import genai from google. genai import types from PIL import Image from io import BytesIO. client = genai .Client(). prompt = ( \"Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme\" ).", "subpage_snippet": "", "source": "ai.google.dev", "link": "https://ai.google.dev/gemini-api/docs/image-generation", "content": "from google import genai from google. genai import types from PIL import Image from io import BytesIO. client = genai .Client(). prompt = ( \"Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme\" )."}
diff --git a/data/sampled_jsons/Generalizable_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_arxiv_PDF.jsonl b/data/sampled_jsons/Generalizable_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_arxiv_PDF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e63e366eeaff475ee8e0b3677b31086e09a234a9
--- /dev/null
+++ b/data/sampled_jsons/Generalizable_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_arxiv_PDF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "This paper proposes a novel task, origin identification for text - guided image - to - image diffusion models (ID2), which aims to identify the origin of a generated query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02376v1", "content": "This paper proposes a novel task, origin identification for text - guided image - to - image diffusion models (ID2), which aims to identify the origin of a generated query."}
+{"idx": 1, "title": "( PDF ) Generalizable Origin Identification for Text - Guided ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387767437_Generalizable_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications."}
+{"idx": 2, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Focus on text - guided image - to - image diffusion models like Stable Diffusion. Novel dataset creation for training source identification models. Development of generalizable detection methods across multiple models.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/generalizable-origin-identification-text-guided-image-to", "content": "Focus on text - guided image - to - image diffusion models like Stable Diffusion. Novel dataset creation for training source identification models. Development of generalizable detection methods across multiple models."}
+{"idx": 3, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.2. ), aiming to retrieve the original image of a given translated query.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/generalizable-origin-identification-for-text", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.2. ), aiming to retrieve the original image of a given translated query."}
+{"idx": 4, "title": "GitHub - wd1511/Awesome- Diffusion -for- Image -Translation...", "date": "", "ddg_snippet": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models Wenhao Wang, Yifan Sun, Zongxin Yang, Zhentao Tan, Zhengdong Hu, Yi Yang.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wd1511/Awesome-Diffusion-for-Image-Translation", "content": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models Wenhao Wang, Yifan Sun, Zongxin Yang, Zhentao Tan, Zhengdong Hu, Yi Yang."}
+{"idx": 5, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "It's hard to trace back to the original image, especially when different AI models are used. What's the solution? The researchers created a new task called ID^2 ( Origin IDentification for text - guided Image - to - image Diffusion models ) to find the original image from a modified one.", "subpage_snippet": "", "source": "ai-search.io", "link": "https://ai-search.io/papers/generalizable-origin-identification-for-text-guided-image-to-image-diffusion-models", "content": "It's hard to trace back to the original image, especially when different AI models are used. What's the solution? The researchers created a new task called ID^2 ( Origin IDentification for text - guided Image - to - image Diffusion models ) to find the original image from a modified one."}
+{"idx": 6, "title": "Zhentao Tan - Google Akademik", "date": "", "ddg_snippet": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models .", "subpage_snippet": "", "source": "scholar.google.bg", "link": "https://scholar.google.bg/citations?user=jDsfBUwAAAAJ&hl=tr", "content": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models ."}
+{"idx": 7, "title": "Paper page - MangaNinja: Line Art Colorization with Precise Reference...", "date": "", "ddg_snippet": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models (2025). Pointmap-Conditioned Diffusion for Consistent Novel View Synthesis (2025).", "subpage_snippet": "", "source": "block.nqigeek.space", "link": "https://block.nqigeek.space/papers/2501.08332", "content": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models (2025). Pointmap-Conditioned Diffusion for Consistent Novel View Synthesis (2025)."}
+{"idx": 8, "title": "Wenhao Wang - University of Technology Sydney | 人才画像 - AMiner", "date": "", "ddg_snippet": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models .", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/profile/wenhao-wang/644265e4ca4e0609eeda8964", "content": "Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models ."}
+{"idx": 9, "title": "dblp: Wenhao Wang (disambiguation)", "date": "", "ddg_snippet": "Wenhao Wang, Yifan Sun, Zongxin Yang, Zhentao Tan, Zhengdong Hu, Yi Yang: Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models .IEEE Trans. Image Process.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/57/9813.html", "content": "Wenhao Wang, Yifan Sun, Zongxin Yang, Zhentao Tan, Zhengdong Hu, Yi Yang: Generalizable Origin Identification for Text - Guided Image - to - Image Diffusion Models .IEEE Trans. Image Process."}
diff --git a/data/sampled_jsons/Generalization_Spectrum_Gap_box_embeddings_personalized_recommendation.jsonl b/data/sampled_jsons/Generalization_Spectrum_Gap_box_embeddings_personalized_recommendation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..67bededf5d3b725a34cb74d9e1534a517097003a
--- /dev/null
+++ b/data/sampled_jsons/Generalization_Spectrum_Gap_box_embeddings_personalized_recommendation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "22 Jun 2025 — Partially supported: The Generalization Spectrum analysis (Table 4) shows that box embeddings exhibit a smaller performance gap when transitioning from weak ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=27tMzmzDjO", "content": "22 Jun 2025 — Partially supported: The Generalization Spectrum analysis (Table 4) shows that box embeddings exhibit a smaller performance gap when transitioning from weak ..."}
+{"idx": 1, "title": "A Geometric Approach to Personalized Recommendation ...", "date": "", "ddg_snippet": "15 Feb 2025 — The Box-Geometric achieves the best Generalization Spectrum Gap for all types of queries. Report issue for preceding element. Appendix C ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10875v1", "content": "15 Feb 2025 — The Box-Geometric achieves the best Generalization Spectrum Gap for all types of queries. Report issue for preceding element. Appendix C ..."}
+{"idx": 2, "title": "A Geometric Approach to Personalized Recommendation ...", "date": "", "ddg_snippet": "by S Dasgupta · 2025 — The BOX -GEOMETRIC achieves the best Generalization . Spectrum Gap for all types of queries. C. Error Compounding Analysis. We further perform ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10875", "content": "by S Dasgupta · 2025 — The BOX -GEOMETRIC achieves the best Generalization . Spectrum Gap for all types of queries. C. Error Compounding Analysis. We further perform ..."}
+{"idx": 3, "title": "Understanding Generalization in Quantum Machine ...", "date": "", "ddg_snippet": "We showed that quantum embeddings with a large trace distance lead to larger margins, which in turn improve generalization . By comparing different quantum ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44359", "content": "We showed that quantum embeddings with a large trace distance lead to larger margins, which in turn improve generalization . By comparing different quantum ..."}
+{"idx": 4, "title": "Emotion-aware Personalized Music Recommendation with ...", "date": "", "ddg_snippet": "11 Jul 2025 — The HDBN mimics a user's decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3733233", "content": "11 Jul 2025 — The HDBN mimics a user's decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion ..."}
+{"idx": 5, "title": "Zero-Shot Content-Based Crossmodal Recommendation ...", "date": "", "ddg_snippet": "by F D’Asaro · 2024 · Cited by 3 — We argue that the misalignment of multimodal embeddings , referred to here as “Modality Gap ” negatively impacts crossmodal retrieval performance.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424019754", "content": "by F D’Asaro · 2024 · Cited by 3 — We argue that the misalignment of multimodal embeddings , referred to here as “Modality Gap ” negatively impacts crossmodal retrieval performance."}
+{"idx": 6, "title": "Multi-Behavior Recommendation with Personalized ...", "date": "", "ddg_snippet": "9 Dec 2024 — GNN-based recommendation models can effectively update node representations by integrating local information from connected nodes in the graph ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696417", "content": "9 Dec 2024 — GNN-based recommendation models can effectively update node representations by integrating local information from connected nodes in the graph ..."}
+{"idx": 7, "title": "[Literature Review] MGT-Prism: Enhancing Domain ...", "date": "", "ddg_snippet": "18 Aug 2025 — The paper introduces MGT-Prism, a novel approach to enhance domain generalization (DG) for Machine-Generated Text (MGT) detection by analyzing ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/review/mgt-prism-enhancing-domain-generalization-for-machine-generated-text-detection-via-spectral-alignment", "content": "18 Aug 2025 — The paper introduces MGT-Prism, a novel approach to enhance domain generalization (DG) for Machine-Generated Text (MGT) detection by analyzing ..."}
+{"idx": 8, "title": "Track: Poster Session 5 East", "date": "", "ddg_snippet": "17 Jul 2025 — In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50267", "content": "17 Jul 2025 — In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To ..."}
+{"idx": 9, "title": "ceo21ckim/Awesome-Recsys", "date": "", "ddg_snippet": "Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search ... VisGNN: Personalized Visualization Recommendation via Graph Neural Networks ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ceo21ckim/Awesome-Recsys", "content": "Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search ... VisGNN: Personalized Visualization Recommendation via Graph Neural Networks ..."}
diff --git a/data/sampled_jsons/Generalization_Spectrum_Gap_smaller_implies_better_generalization_box_embeddings.jsonl b/data/sampled_jsons/Generalization_Spectrum_Gap_smaller_implies_better_generalization_box_embeddings.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..936d5520a73736e4b7e75322497a98489590ba38
--- /dev/null
+++ b/data/sampled_jsons/Generalization_Spectrum_Gap_smaller_implies_better_generalization_box_embeddings.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Train longer, generalize better: closing the generalization ... Deconstructing the generalization gap - Nature Train longer, generalize better: closing the generalization ... Lecture 5: Generalization in Deep Learning Generalization Bounds for MLP’s (Multilayer Perceptron) Predicting the Generalization Gap in Deep Neural Networks [1705.08741] Train longer, generalize better : closing the Predicting the Generalization Gap in Deep Neural Networks Predicting the Generalization Gap in Deep Neural Networks [1705.08741] Train longer, generalize better : closing the Deconstructing the generalization gap - Nature Deconstructing the generalization gap - Nature Predicting the generalization gap in neural networks using ...", "date": "", "ddg_snippet": "May 24, 2017 · Following this hypothesis we conducted experiments to show empirically that the \" generalization gap \" stems from the relatively small number of updates rather than the batch size, and can be completely eliminated by adapting the training regime used. It also explains why weight decay, which results in lower variance of the dual weights, leads to better generalization , as in this case factor (ii) is smaller . These observations led us to believe that \" generalization gap \" phenomenon stems from the relatively small number of updates rather than the batch size. Specifically, using the insights from Figure 2 and our model, we adapted the training regime to better suit the usage of large mini-batch. A smaller KL divergence implies better generalization . So if the initial parameters didn’t change much between the prior and posterior, the algorithm generalizes well. Here, we try to approach bounding generalization gap of MLPs in an intuitive way — if we know how sensitive the MLP is to input changes and parameter changes, we should have all the information we need to bound generalization . Jul 9, 2019 · In our ICLR 2019 paper, “ Predicting the Generalization Gap in Deep Networks with Margin Distributions ”, we propose the use of a normalized margin distribution across network layers as a predictor of the generalization gap . We empirically study the relationship between the margin distribution and generalization and show that, after proper normalization of the distances, some basic ... Can a 'generalization gap' be completely eliminated? Following this hypothesis we conducted experiments to show empirically that the \"generalization gap\" stems from the relatively small number of updates rather than the batch size, and can be completely eliminated by adapting the training regime used. Can a normalized margin distribution predict the generalization gap? In our ICLR 2019 paper, “ Predicting the Generalization Gap in Deep Networks with Margin Distributions ”, we propose the use of a normalized margin distribution across network layers as a predictor of the generalization gap . What is a generalization gap? An important concept for understanding generalization is the generalization gap , i.e., the difference between a model’s performance on training data and its performance on unseen data drawn from the same distribution. Is there a 'generalization gap' when using large batch sizes? It has been observed that when using large batch sizes there is a persistent degradation in generalization performance - known as the \" generalization gap \" phenomena. Identifying the origin of this gap and closing it had remained an open problem. Contributions: We examine the initial high learning rate training phase. Can hyperparameters and regularization explain generalization in deep neural networks? This work by Feng et al.3 provides a fundamentally new way to evaluate test loss directly in the weight space. It further shows that it is possible to provide an interpretable explanation of the effects of hyperparameters and regularization on generalization in overparam-eterized deep neural networks. Do corrupted labels increase the generalization gap? They show that corrupted labels increase both factors (at least at the end of training), leading to a monotonic increase of the generalization gap with the fraction of corrupted labels. In conclusion, a combination of a high average of death values with a low standard deviation in a zero-dimensional persistence diagram is a plausible indication of an increased expressive power of the neural network, that should lead to better generalization capabilities and thus a smaller generalization gap .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1705.08741", "content": "May 24, 2017 · Following this hypothesis we conducted experiments to show empirically that the \" generalization gap \" stems from the relatively small number of updates rather than the batch size, and can be completely eliminated by adapting the training regime used. It also explains why weight decay, which results in lower variance of the dual weights, leads to better generalization , as in this case factor (ii) is smaller . These observations led us to believe that \" generalization gap \" phenomenon stems from the relatively small number of updates rather than the batch size. Specifically, using the insights from Figure 2 and our model, we adapted the training regime to better suit the usage of large mini-batch. A smaller KL divergence implies better generalization . So if the initial parameters didn’t change much between the prior and posterior, the algorithm generalizes well. Here, we try to approach bounding generalization gap of MLPs in an intuitive way — if we know how sensitive the MLP is to input changes and parameter changes, we should have all the information we need to bound generalization . Jul 9, 2019 · In our ICLR 2019 paper, “ Predicting the Generalization Gap in Deep Networks with Margin Distributions ”, we propose the use of a normalized margin distribution across network layers as a predictor of the generalization gap . We empirically study the relationship between the margin distribution and generalization and show that, after proper normalization of the distances, some basic ... Can a 'generalization gap' be completely eliminated? Following this hypothesis we conducted experiments to show empirically that the \"generalization gap\" stems from the relatively small number of updates rather than the batch size, and can be completely eliminated by adapting the training regime used. Can a normalized margin distribution predict the generalization gap? In our ICLR 2019 paper, “ Predicting the Generalization Gap in Deep Networks with Margin Distributions ”, we propose the use of a normalized margin distribution across network layers as a predictor of the generalization gap . What is a generalization gap? An important concept for understanding generalization is the generalization gap , i.e., the difference between a model’s performance on training data and its performance on unseen data drawn from the same distribution. Is there a 'generalization gap' when using large batch sizes? It has been observed that when using large batch sizes there is a persistent degradation in generalization performance - known as the \" generalization gap \" phenomena. Identifying the origin of this gap and closing it had remained an open problem. Contributions: We examine the initial high learning rate training phase. Can hyperparameters and regularization explain generalization in deep neural networks? This work by Feng et al.3 provides a fundamentally new way to evaluate test loss directly in the weight space. It further shows that it is possible to provide an interpretable explanation of the effects of hyperparameters and regularization on generalization in overparam-eterized deep neural networks. Do corrupted labels increase the generalization gap? They show that corrupted labels increase both factors (at least at the end of training), leading to a monotonic increase of the generalization gap with the fraction of corrupted labels. In conclusion, a combination of a high average of death values with a low standard deviation in a zero-dimensional persistence diagram is a plausible indication of an increased expressive power of the neural network, that should lead to better generalization capabilities and thus a smaller generalization gap ."}
+{"idx": 1, "title": "Deconstructing the generalization gap - Nature", "date": "", "ddg_snippet": "It also explains why weight decay, which results in lower variance of the dual weights, leads to better generalization , as in this case factor (ii) is smaller .", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-023-00766-7.pdf", "content": "It also explains why weight decay, which results in lower variance of the dual weights, leads to better generalization , as in this case factor (ii) is smaller ."}
+{"idx": 2, "title": "Train longer, generalize better: closing the generalization ...", "date": "", "ddg_snippet": "These observations led us to believe that \" generalization gap \" phenomenon stems from the relatively small number of updates rather than the batch size. Specifically, using the insights from Figure 2 and our model, we adapted the training regime to better suit the usage of large mini-batch.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/6770-train-longer-generalize-better-closing-the-generalization-gap-in-large-batch-training-of-neural-networks.pdf", "content": "These observations led us to believe that \" generalization gap \" phenomenon stems from the relatively small number of updates rather than the batch size. Specifically, using the insights from Figure 2 and our model, we adapted the training regime to better suit the usage of large mini-batch."}
+{"idx": 3, "title": "Lecture 5: Generalization in Deep Learning", "date": "", "ddg_snippet": "A smaller KL divergence implies better generalization . So if the initial parameters didn’t change much between the prior and posterior, the algorithm generalizes well.", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/madry/6.883-Spring18/files/lecture_5.pdf", "content": "A smaller KL divergence implies better generalization . So if the initial parameters didn’t change much between the prior and posterior, the algorithm generalizes well."}
+{"idx": 4, "title": "Generalization Bounds for MLP’s (Multilayer Perceptron)", "date": "", "ddg_snippet": "Here, we try to approach bounding generalization gap of MLPs in an intuitive way — if we know how sensitive the MLP is to input changes and parameter changes, we should have all the information we need to bound generalization .", "subpage_snippet": "", "source": "kushaltirumala.github.io", "link": "https://kushaltirumala.github.io/GeneralizationBoundsMLP.pdf", "content": "Here, we try to approach bounding generalization gap of MLPs in an intuitive way — if we know how sensitive the MLP is to input changes and parameter changes, we should have all the information we need to bound generalization ."}
+{"idx": 5, "title": "Predicting the Generalization Gap in Deep Neural Networks", "date": "", "ddg_snippet": "Jul 9, 2019 · In our ICLR 2019 paper, “ Predicting the Generalization Gap in Deep Networks with Margin Distributions ”, we propose the use of a normalized margin distribution across network layers as a predictor of the generalization gap . We empirically study the relationship between the margin distribution and generalization and show that, after proper normalization of the distances, some basic ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/predicting-the-generalization-gap-in-deep-neural-networks/", "content": "Jul 9, 2019 · In our ICLR 2019 paper, “ Predicting the Generalization Gap in Deep Networks with Margin Distributions ”, we propose the use of a normalized margin distribution across network layers as a predictor of the generalization gap . We empirically study the relationship between the margin distribution and generalization and show that, after proper normalization of the distances, some basic ..."}
+{"idx": 6, "title": "Predicting the generalization gap in neural networks using ...", "date": "", "ddg_snippet": "In conclusion, a combination of a high average of death values with a low standard deviation in a zero-dimensional persistence diagram is a plausible indication of an increased expressive power of the neural network, that should lead to better generalization capabilities and thus a smaller generalization gap .", "subpage_snippet": "", "source": "www.ub.edu", "link": "https://www.ub.edu/topologia/casacuberta/articles/BACMCE.pdf", "content": "In conclusion, a combination of a high average of death values with a low standard deviation in a zero-dimensional persistence diagram is a plausible indication of an increased expressive power of the neural network, that should lead to better generalization capabilities and thus a smaller generalization gap ."}
+{"idx": 7, "title": "How Much does Initialization Affect Generalization ?", "date": "", "ddg_snippet": "Characterizing the remarkable generalization properties of over-parameterized neural networks remains an open problem.This implies that very high frequencies present at initialization will re-main after training, and hamper generalization .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=FLhE8qzOmo", "content": "Characterizing the remarkable generalization properties of over-parameterized neural networks remains an open problem.This implies that very high frequencies present at initialization will re-main after training, and hamper generalization ."}
+{"idx": 8, "title": "Knowledge Representation: The Promising Power of Box Embeddings", "date": "", "ddg_snippet": "Robustness and Generalization : By explicitly modeling the underlying structure and semantics of knowledge graphs, box embeddings can capture patterns and regularities that point embeddings often miss.", "subpage_snippet": "", "source": "ai.plainenglish.io", "link": "https://ai.plainenglish.io/knowledge-representation-the-promising-power-of-box-embeddings-d0cbcf1dc93c", "content": "Robustness and Generalization : By explicitly modeling the underlying structure and semantics of knowledge graphs, box embeddings can capture patterns and regularities that point embeddings often miss."}
+{"idx": 9, "title": "Asymptotic spectrum of weighted sample covariance...", "date": "", "ddg_snippet": "New spectral gaps due to weight gaps . Speed of convergence with heavy tails. Limitations and extensions.arXiv:2410.14408v1 [math.ST] 18 Oct 2024. Asymptotic spectrum of weighted sample covariance: a Marcenko-Pastur generalization .", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04746732v1/document", "content": "New spectral gaps due to weight gaps . Speed of convergence with heavy tails. Limitations and extensions.arXiv:2410.14408v1 [math.ST] 18 Oct 2024. Asymptotic spectrum of weighted sample covariance: a Marcenko-Pastur generalization ."}
diff --git a/data/sampled_jsons/GitHub_model-similarity_lm-similarity_code_implementation.jsonl b/data/sampled_jsons/GitHub_model-similarity_lm-similarity_code_implementation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f49fcd30e2c016825842abd35fea89e278c0dc53
--- /dev/null
+++ b/data/sampled_jsons/GitHub_model-similarity_lm-similarity_code_implementation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Endmaril/LMAbsSimilarity", "date": "", "ddg_snippet": "A language model based similarity with absolute discount smoothing for lucene 7. This similarity is an implementation of what is discribed in https ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Endmaril/LMAbsSimilarity", "content": "A language model based similarity with absolute discount smoothing for lucene 7. This similarity is an implementation of what is discribed in https ..."}
+{"idx": 1, "title": "embeddings-similarity", "date": "", "ddg_snippet": "The repository is aimed at providing practical examples and resources for developers and researchers interested in applying LM and GPT models to real-world NLP ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/embeddings-similarity", "content": "The repository is aimed at providing practical examples and resources for developers and researchers interested in applying LM and GPT models to real-world NLP ..."}
+{"idx": 2, "title": "Build an AI Image Similarity Search with Transformers", "date": "", "ddg_snippet": "This project uses vision models to generate image embeddings and performs similarity searches with FAISS.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@tapanbabbar/build-an-image-similarity-search-with-transformers-vit-clip-efficientnet-dino-v2-and-blip-2-5040d1848c00", "content": "This project uses vision models to generate image embeddings and performs similarity searches with FAISS."}
+{"idx": 3, "title": "An Efficient Framework for Sentence Similarity Modeling", "date": "", "ddg_snippet": "by Z Quan · Cited by 58 — Our model is easily understood and implemented , but without loss of effectiveness. • We developed the ACVT kernel that can allow us to effi- ciently perform ...", "subpage_snippet": "", "source": "cszjwang.github.io", "link": "https://cszjwang.github.io/sub_pages/pps/TALSP19.pdf", "content": "by Z Quan · Cited by 58 — Our model is easily understood and implemented , but without loss of effectiveness. • We developed the ACVT kernel that can allow us to effi- ciently perform ..."}
+{"idx": 4, "title": "Interpretable Company Similarity with Sparse ...", "date": "", "ddg_snippet": "Sparse Autoencoders ( SAEs ) have shown promise in enhancing the interpretability of Large Language Models ( LLMs ) by decomposing Large Language Model ( LLM ) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.02605v3", "content": "Sparse Autoencoders ( SAEs ) have shown promise in enhancing the interpretability of Large Language Models ( LLMs ) by decomposing Large Language Model ( LLM ) ..."}
+{"idx": 5, "title": "Sentence similarity - Deep Learning", "date": "", "ddg_snippet": "17 May 2018 — We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are ...", "subpage_snippet": "", "source": "forums.fast.ai", "link": "https://forums.fast.ai/t/sentence-similarity/16541", "content": "17 May 2018 — We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are ..."}
+{"idx": 6, "title": "Build Your Own RAG Using Free LM Models and a ...", "date": "", "ddg_snippet": "In this article, Alexander Uspensky walks you through a hands-on guide to creating a lightweight Retrieval-Augmented Generation system on your laptop.", "subpage_snippet": "", "source": "wearecommunity.io", "link": "https://wearecommunity.io/communities/RviSz9LECb/articles/6426", "content": "In this article, Alexander Uspensky walks you through a hands-on guide to creating a lightweight Retrieval-Augmented Generation system on your laptop."}
+{"idx": 7, "title": "rasbt/LLMs-from-scratch: Implement a ChatGPT-like LLM in ...", "date": "", "ddg_snippet": "This repository contains the code for developing, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/rasbt/LLMs-from-scratch", "content": "This repository contains the code for developing, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large ..."}
+{"idx": 8, "title": "rwth-acis/hye-python-mllib", "date": "", "ddg_snippet": "This repository contains the code of a small HTTP server implemented in Python used to compute Matrix Factorization and word2vec word embeddings.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/rwth-acis/hye-python-mllib", "content": "This repository contains the code of a small HTTP server implemented in Python used to compute Matrix Factorization and word2vec word embeddings."}
+{"idx": 9, "title": "IINemo/lm-polygraph", "date": "", "ddg_snippet": "LM -Polygraph provides a battery of state-of-the-art of uncertainty estimation (UE) methods for LMs in text generation tasks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IINemo/lm-polygraph", "content": "LM -Polygraph provides a battery of state-of-the-art of uncertainty estimation (UE) methods for LMs in text generation tasks."}
diff --git a/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Dvijotham_et_al._2014.jsonl b/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Dvijotham_et_al._2014.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aea8979b846b2e91cc9b23be4322d4f6a2ac6e92
--- /dev/null
+++ b/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Dvijotham_et_al._2014.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Global Optimization with A Power - Transformed Objective and ...", "date": "", "ddg_snippet": ", the Gaussian - smoothed objective .In this paper, we propose a novel smoothing method, GSPTO, for solving the global optimization problem of (1), which is featured with putting more weight on the objective ’s global optimum through power transformations .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.05204v2", "content": ", the Gaussian - smoothed objective .In this paper, we propose a novel smoothing method, GSPTO, for solving the global optimization problem of (1), which is featured with putting more weight on the objective ’s global optimum through power transformations ."}
+{"idx": 1, "title": "ICML Poster Global Optimization with A Power - Transformed ...", "date": "", "ddg_snippet": "Global Optimization with A Power - Transformed Objective and Gaussian Smoothing .In most of the experiments performed, our method produces better solutions than other algorithms that also apply the smoothing technique.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46360", "content": "Global Optimization with A Power - Transformed Objective and Gaussian Smoothing .In most of the experiments performed, our method produces better solutions than other algorithms that also apply the smoothing technique."}
+{"idx": 2, "title": "Convex Relaxation Regression: Black-Box Optimization of Smooth ...", "date": "", "ddg_snippet": "Recently Hazan et al . (2015) introduced a graduated optimization approach that can be applied in the black-box optimization setting. Dvijotham , K., Fazel, M., and Todorov, E. ( 2014 ).", "subpage_snippet": "", "source": "auai.org", "link": "https://auai.org/uai2016/proceedings/papers/90.pdf", "content": "Recently Hazan et al . (2015) introduced a graduated optimization approach that can be applied in the black-box optimization setting. Dvijotham , K., Fazel, M., and Todorov, E. ( 2014 )."}
+{"idx": 3, "title": "MS - Trajectory planning for manipulator grasping with obstacle...", "date": "", "ddg_snippet": "In visual recognition, Shang et al . ( 2014 ) proposed a monocular method for measuring the position of a translationally one-dimensional object containing at least two known feature points only.", "subpage_snippet": "", "source": "ms.copernicus.org", "link": "https://ms.copernicus.org/articles/16/445/2025/", "content": "In visual recognition, Shang et al . ( 2014 ) proposed a monocular method for measuring the position of a translationally one-dimensional object containing at least two known feature points only."}
+{"idx": 4, "title": "Denoised Smoothing", "date": "", "ddg_snippet": "robustness guarantee of the smoothed classier is based on the Neyman-Pearson lemma (Cohen et al ., 2019)3. The procedure is as follows: suppose that when the base classier f classies N (x, σ2I), the class cA is returned with probability pA = P(f (x + δ) = cA), and the “runner-up” class cB.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/f9fd2624beefbc7808e4e405d73f57ab-Paper.pdf", "content": "robustness guarantee of the smoothed classier is based on the Neyman-Pearson lemma (Cohen et al ., 2019)3. The procedure is as follows: suppose that when the base classier f classies N (x, σ2I), the class cA is returned with probability pA = P(f (x + δ) = cA), and the “runner-up” class cB."}
+{"idx": 5, "title": "Krishnamurthy Dvijotham", "date": "", "ddg_snippet": "Krishnamurthy Dvijotham . Automating Stochastic Control Doctor of Philosophy. University of Washington 2014 .Further advances have shown that even problems that are not convex can be solved through convex optimization techniques Chandrasekaran et al .", "subpage_snippet": "", "source": "www.roboti.us", "link": "https://www.roboti.us/lab/papers/DvijothamThesis.pdf", "content": "Krishnamurthy Dvijotham . Automating Stochastic Control Doctor of Philosophy. University of Washington 2014 .Further advances have shown that even problems that are not convex can be solved through convex optimization techniques Chandrasekaran et al ."}
+{"idx": 6, "title": "Generation and Countermeasures of adversarial examples on vision...", "date": "", "ddg_snippet": "Despite Dvijotham et al .Zhang et al . (2020) also focused on a generalization of randomized smoothing but with a different view on loosing the constraint on the classifier, utilizing non- Gaussian noise, and for more general attacks.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-024-10841-z", "content": "Despite Dvijotham et al .Zhang et al . (2020) also focused on a generalization of randomized smoothing but with a different view on loosing the constraint on the classifier, utilizing non- Gaussian noise, and for more general attacks."}
+{"idx": 7, "title": "Randomized Smoothing of All Shapes and Sizes", "date": "", "ddg_snippet": "More broadly, randomized smoothing is a method for in-ducing stability in a mechanism while maintaining utility — precisely the bread and butter of differential privacy.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2020/02/rs4a.pdf", "content": "More broadly, randomized smoothing is a method for in-ducing stability in a mechanism while maintaining utility — precisely the bread and butter of differential privacy."}
+{"idx": 8, "title": "ML-LOO: Detecting Adversarial Examples with Feature Attribution", "date": "", "ddg_snippet": "Chen et al . and Ilyas et al .; Ilyas, Engstrom, and Madry introduced score-based methods using zeroth-order gradient estimation to craft adversarial examples.We carried out the white-box attack on CIFAR-10 with the ResNet. The attacker aims to optimize the following objective .", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/6140/5996", "content": "Chen et al . and Ilyas et al .; Ilyas, Engstrom, and Madry introduced score-based methods using zeroth-order gradient estimation to craft adversarial examples.We carried out the white-box attack on CIFAR-10 with the ResNet. The attacker aims to optimize the following objective ."}
+{"idx": 9, "title": "Diffusion-Based Adversarial Sample Generation for Improved", "date": "", "ddg_snippet": "... a novel framework, which uses an off-the-shelf diffusion model to guide the optimization of perturbations, thus enabling the generation of adversarial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.16494v3", "content": "... a novel framework, which uses an off-the-shelf diffusion model to guide the optimization of perturbations, thus enabling the generation of adversarial ..."}
diff --git a/data/sampled_jsons/Glossy_sphere_EventPS-FCN_MAE_3.30.jsonl b/data/sampled_jsons/Glossy_sphere_EventPS-FCN_MAE_3.30.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a823be452c27d0662c8fe698ad6883ab8605eb9c
--- /dev/null
+++ b/data/sampled_jsons/Glossy_sphere_EventPS-FCN_MAE_3.30.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "A glossy sphere , Glossy in Fig. 7, is measured, and the reconstructed prole at a high-light point is shown in red in Fig. The average MAEs for all 3D-printed objects was 8.12 for EIP-PS, in contract to EventPS , which resulted in 13.66.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "A glossy sphere , Glossy in Fig. 7, is measured, and the reconstructed prole at a high-light point is shown in red in Fig. The average MAEs for all 3D-printed objects was 8.12 for EIP-PS, in contract to EventPS , which resulted in 13.66."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_UPGNET_equation_mathematical_.jsonl b/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_UPGNET_equation_mathematical_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..981c89bf2bba1ce39e3b8ffc21a6448986873ce5
--- /dev/null
+++ b/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_UPGNET_equation_mathematical_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Going Deeper into Locally Differentially Private ...", "date": "", "ddg_snippet": "Poster Going Deeper into Locally Differentially Private Graph Neural Networks Longzhu He · Chaozhuo Li · Peng Tang · Sen Su East Exhibition Hall A-B #E-905", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46579", "content": "Poster Going Deeper into Locally Differentially Private Graph Neural Networks Longzhu He · Chaozhuo Li · Peng Tang · Sen Su East Exhibition Hall A-B #E-905"}
+{"idx": 1, "title": "Going Deeper into Locally Differentially Private Graph Neural ...", "date": "", "ddg_snippet": "Abstract Graph Neural Networks (GNNs) have demon-strated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal infor-mation or variables related to individuals, learn-ing from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2aKHuXdr7Q", "content": "Abstract Graph Neural Networks (GNNs) have demon-strated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal infor-mation or variables related to individuals, learn-ing from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these ..."}
+{"idx": 2, "title": "[2006.05535] Locally Private Graph Neural Networks - arXiv.org Locally Private Graph Neural Networks | Proceedings of the ... Differentially private graph neural networks for graph ... ICML Poster Going Deeper into Locally Differentially Private ... Going Deeper into Locally Differentially Private Graph Neural ... [2006.05535] Locally Private Graph Neural Networks - arXiv.org Differentially private graph neural networks for graph classification Differentially private graph neural networks for graph classification Differentially private graph neural networks for graph classification Local Differential Privacy in Graph Neural Networks: a ...", "date": "", "ddg_snippet": "Jun 9, 2020 · Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ... Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks\". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private. Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ... Mar 5, 2025 · Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular st… Poster Going Deeper into Locally Differentially Private Graph Neural Networks Longzhu He · Chaozhuo Li · Peng Tang · Sen Su East Exhibition Hall A-B #E-905 This paper introduces UPGNET , a new framework designed to help protect personal information while using Graph Neural Networks (GNNs) for learning tasks like classifying nodes in a graph. Should deep learning algorithms be used on graphs? While numerous techniques have been proposed for privacy-preserving deep learning over non-relational data, there is less work addressing the privacy issues pertained to applying deep learning algorithms on graphs. Are dpgnn models better than GNN models? On medium to large datasets, including Protein, Fingerprint, DD, and the extensive NCI dataset, the DPGNN models demonstrate robust precision and stability , matching the performance of GNN models devoid of differential privacy enhancements. 4.3.2. The impact of gradient clipping threshold Which Privacy analysis methods are similar to the dpgnn with adaptive learning rate? The privacy analysis methods for the DPGNN with adaptive learning rate (Algorithm 2) and the DPGNN with adaptive noise scale are similar to that of the DPGNN. First, we calculate the per-iteration RDP value. What parameters affect the dpgnn model? From the analysis in Section 3 (as shown in Algorithm 1), we identified a variety of parameters affecting the DPGNN model, including but not limited to GNN framework (step 2), learning rate (step 8), Batch size (step 4–5), gradient clipping threshold (step 6), noise scale (step 7), dataset size (step 2). 1 Introduction Graph data are ubiquitous in the modern world allowing graph -structured representation for complex data and Graph Neural Networks (GNNs) have been widely adopted to model the expressive nature of such graph -structured data [30]. GNNs rely on message-passing mechanisms to propagate information between graph nodes and output embeddings that encode both node and neighborhood ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Jun 9, 2020 · Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ... Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks\". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private. Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ... Mar 5, 2025 · Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular st… Poster Going Deeper into Locally Differentially Private Graph Neural Networks Longzhu He · Chaozhuo Li · Peng Tang · Sen Su East Exhibition Hall A-B #E-905 This paper introduces UPGNET , a new framework designed to help protect personal information while using Graph Neural Networks (GNNs) for learning tasks like classifying nodes in a graph. Should deep learning algorithms be used on graphs? While numerous techniques have been proposed for privacy-preserving deep learning over non-relational data, there is less work addressing the privacy issues pertained to applying deep learning algorithms on graphs. Are dpgnn models better than GNN models? On medium to large datasets, including Protein, Fingerprint, DD, and the extensive NCI dataset, the DPGNN models demonstrate robust precision and stability , matching the performance of GNN models devoid of differential privacy enhancements. 4.3.2. The impact of gradient clipping threshold Which Privacy analysis methods are similar to the dpgnn with adaptive learning rate? The privacy analysis methods for the DPGNN with adaptive learning rate (Algorithm 2) and the DPGNN with adaptive noise scale are similar to that of the DPGNN. First, we calculate the per-iteration RDP value. What parameters affect the dpgnn model? From the analysis in Section 3 (as shown in Algorithm 1), we identified a variety of parameters affecting the DPGNN model, including but not limited to GNN framework (step 2), learning rate (step 8), Batch size (step 4–5), gradient clipping threshold (step 6), noise scale (step 7), dataset size (step 2). 1 Introduction Graph data are ubiquitous in the modern world allowing graph -structured representation for complex data and Graph Neural Networks (GNNs) have been widely adopted to model the expressive nature of such graph -structured data [30]. GNNs rely on message-passing mechanisms to propagate information between graph nodes and output embeddings that encode both node and neighborhood ..."}
+{"idx": 3, "title": "Locally Private Graph Neural Networks | Proceedings of the ...", "date": "", "ddg_snippet": "Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks\". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private. Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3460120.3484565", "content": "Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks\". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private. Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ..."}
+{"idx": 4, "title": "Differentially private graph neural networks for graph ...", "date": "", "ddg_snippet": "Mar 5, 2025 · Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular st…", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424026654", "content": "Mar 5, 2025 · Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular st…"}
+{"idx": 5, "title": "Going Deeper into Locally Differentially Private Graph Neural ...", "date": "", "ddg_snippet": "This paper introduces UPGNET , a new framework designed to help protect personal information while using Graph Neural Networks (GNNs) for learning tasks like classifying nodes in a graph.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46579/paper", "content": "This paper introduces UPGNET , a new framework designed to help protect personal information while using Graph Neural Networks (GNNs) for learning tasks like classifying nodes in a graph."}
+{"idx": 6, "title": "Local Differential Privacy in Graph Neural Networks: a ...", "date": "", "ddg_snippet": "1 Introduction Graph data are ubiquitous in the modern world allowing graph -structured representation for complex data and Graph Neural Networks (GNNs) have been widely adopted to model the expressive nature of such graph -structured data [30]. GNNs rely on message-passing mechanisms to propagate information between graph nodes and output embeddings that encode both node and neighborhood ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/pdf/10.1137/1.9781611978032.1", "content": "1 Introduction Graph data are ubiquitous in the modern world allowing graph -structured representation for complex data and Graph Neural Networks (GNNs) have been widely adopted to model the expressive nature of such graph -structured data [30]. GNNs rely on message-passing mechanisms to propagate information between graph nodes and output embeddings that encode both node and neighborhood ..."}
+{"idx": 7, "title": "ICML 2025 Going Deeper into Locally Differentially Private Graph ...", "date": "", "ddg_snippet": "Registration Required. You must be logged in to view this content.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47267", "content": "Registration Required. You must be logged in to view this content."}
+{"idx": 8, "title": "openreview.net/profile?id=~Chaozhuo_Li1", "date": "", "ddg_snippet": "Going Deeper into Locally Differentially Private Graph Neural Networks .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Chaozhuo_Li1", "content": "Going Deeper into Locally Differentially Private Graph Neural Networks ."}
+{"idx": 9, "title": "ai-conferences/ICML2025 · Datasets at Hugging Face", "date": "", "ddg_snippet": "To facilitate future research, we open-source a unified Docker image and a public evaluation split. By mapping model performance to monetary value, we hope SWE-Lancer enables greater research into the economic impact of AI model development.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ai-conferences/ICML2025/viewer", "content": "To facilitate future research, we open-source a unified Docker image and a public evaluation split. By mapping model performance to monetary value, we hope SWE-Lancer enables greater research into the economic impact of AI model development."}
diff --git a/data/sampled_jsons/Gradient-enhanced_physics-informed_neural_networks_Yu_Karniadakis_Diffusion_equation_experiment_year_2022.jsonl b/data/sampled_jsons/Gradient-enhanced_physics-informed_neural_networks_Yu_Karniadakis_Diffusion_equation_experiment_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d48d758de4478863247bc7f530a076df3c8fcce8
--- /dev/null
+++ b/data/sampled_jsons/Gradient-enhanced_physics-informed_neural_networks_Yu_Karniadakis_Diffusion_equation_experiment_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Solving PDEs With Neural Networks | TransferLab — appliedAI", "date": "", "ddg_snippet": "... Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential ...", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/blog/solving-pdes-with-nns/", "content": "... Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential ..."}
+{"idx": 1, "title": "Inverse resolution of spatially varying diffusion coefficient", "date": "", "ddg_snippet": "... physics informed neural networks (PINNs) to calculate spatially-varying diffusion coefficients from numerical and experimental image data in varying ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.03970v1", "content": "... physics informed neural networks (PINNs) to calculate spatially-varying diffusion coefficients from numerical and experimental image data in varying ..."}
+{"idx": 2, "title": "Randomized Forward Mode Gradient for Spiking Neural Networks in", "date": "", "ddg_snippet": "... neural networks that learns functions, DeepONet approximates operators 𝒢 : 𝒳 → 𝒴 : 𝒢 → 𝒳 𝒴 \\mathcal{G}:\\mathcal{X}\\to\\mathcal{Y} ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07057v1", "content": "... neural networks that learns functions, DeepONet approximates operators 𝒢 : 𝒳 → 𝒴 : 𝒢 → 𝒳 𝒴 \\mathcal{G}:\\mathcal{X}\\to\\mathcal{Y} ..."}
+{"idx": 3, "title": "Weight initialization algorithm for physics-informed neural", "date": "", "ddg_snippet": "With physics - informed neural networks (PINNs), inverse problems involving differential equations can be solved despite noise, sparsity, and varying ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373118772_Weight_initialization_algorithm_for_physics-informed_neural_networks_using_finite_differences", "content": "With physics - informed neural networks (PINNs), inverse problems involving differential equations can be solved despite noise, sparsity, and varying ..."}
+{"idx": 4, "title": "Physics-Informed Neural Network (PINN) Evolution and Beyond: A", "date": "", "ddg_snippet": "Image Segmentation for Mitral Regurgitation with Convolutional Neural Network Based on UNet, Resnet, Vnet, FractalNet and SegNet: A Preliminary Study", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2504-2289/6/4/140", "content": "Image Segmentation for Mitral Regurgitation with Convolutional Neural Network Based on UNet, Resnet, Vnet, FractalNet and SegNet: A Preliminary Study"}
+{"idx": 5, "title": "Machine learning for inverting partial differential equations", "date": "", "ddg_snippet": "Learning Symbolic Physics with Graph Networks .” In Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/ml_pde_inversion", "content": "Learning Symbolic Physics with Graph Networks .” In Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information ..."}
+{"idx": 6, "title": "Machine learning for inverting partial differential equations", "date": "", "ddg_snippet": "Learning Symbolic Physics with Graph Networks .” In Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/ml_pde_inversion.html", "content": "Learning Symbolic Physics with Graph Networks .” In Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information ..."}
+{"idx": 7, "title": "Finite element method-enhanced neural network for forward and", "date": "", "ddg_snippet": "The Finite Element Method- enhanced Neural Network hybrid model (FEM-NN hybrid) is data-efficient and physics -conforming.", "subpage_snippet": "", "source": "amses-journal.springeropen.com", "link": "https://amses-journal.springeropen.com/articles/10.1186/s40323-023-00243-1", "content": "The Finite Element Method- enhanced Neural Network hybrid model (FEM-NN hybrid) is data-efficient and physics -conforming."}
+{"idx": 8, "title": "Books, Book Chapters, & Research Papers – The Crunch", "date": "", "ddg_snippet": "Karniadakis , “ Physics - Informed Learning Machines for Partial Differential Equations : Gaussian Processes Versus Neural Networks Springer ...", "subpage_snippet": "", "source": "sites.brown.edu", "link": "https://sites.brown.edu/crunch-group/most-recent-research-paper/", "content": "Karniadakis , “ Physics - Informed Learning Machines for Partial Differential Equations : Gaussian Processes Versus Neural Networks Springer ..."}
+{"idx": 9, "title": "Lu Lu | DeepAI", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs) are known to suffer from optimi... ... While significant progress has been made on Physics - Informed Neural ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/lu-lu", "content": "Physics - informed neural networks (PINNs) are known to suffer from optimi... ... While significant progress has been made on Physics - Informed Neural ..."}
diff --git a/data/sampled_jsons/Gradient-enhanced_physics-informed_neural_networks_gPINN_Yu_Zabaras_CMAME_2022_Diffusion_year_2022.jsonl b/data/sampled_jsons/Gradient-enhanced_physics-informed_neural_networks_gPINN_Yu_Zabaras_CMAME_2022_Diffusion_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9a9e7632034e31fc8e7fa1c61c650cca97899626
--- /dev/null
+++ b/data/sampled_jsons/Gradient-enhanced_physics-informed_neural_networks_gPINN_Yu_Zabaras_CMAME_2022_Diffusion_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gradient - Wikipedia", "date": "", "ddg_snippet": "Consider a surface whose height above sea level at point (x, y) is H(x, y). The gradient of H at a point is a plane vector pointing in the direction of the steepest slope or grade at that point. The steepness of the slope at that point is given by the magnitude of the gradient vector.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Gradient", "content": "Consider a surface whose height above sea level at point (x, y) is H(x, y). The gradient of H at a point is a plane vector pointing in the direction of the steepest slope or grade at that point. The steepness of the slope at that point is given by the magnitude of the gradient vector."}
+{"idx": 1, "title": "CSS Gradient – Generator, Maker, and Background", "date": "", "ddg_snippet": "As a free CSS gradient generator tool, this website lets you create a colorful gradient background for your website, blog, or social media profile.", "subpage_snippet": "", "source": "cssgradient.io", "link": "https://cssgradient.io/", "content": "As a free CSS gradient generator tool, this website lets you create a colorful gradient background for your website, blog, or social media profile."}
+{"idx": 2, "title": "GRADIENT Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "Any slope can be called a gradient . In the interstate highway system, the maximum gradient is 6 percent; in other words, the highway may never ascend more than 6 vertical feet over a distance of 100 feet. Any rate of change that's shown on a graph may have a sloped gradient .", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/gradient", "content": "Any slope can be called a gradient . In the interstate highway system, the maximum gradient is 6 percent; in other words, the highway may never ascend more than 6 vertical feet over a distance of 100 feet. Any rate of change that's shown on a graph may have a sloped gradient ."}
+{"idx": 3, "title": "Create a Gradient - Coolors", "date": "", "ddg_snippet": "Create and export beautiful gradients .", "subpage_snippet": "", "source": "coolors.co", "link": "https://coolors.co/gradient-maker", "content": "Create and export beautiful gradients ."}
+{"idx": 4, "title": "uiGradients - Beautiful colored gradients", "date": "", "ddg_snippet": "Adding a gradient is easy. All gradients are read from a gradients .json file which is available in this project's repo. Simply add your gradient details to it and submit a pull request.", "subpage_snippet": "", "source": "uigradients.com", "link": "https://uigradients.com/", "content": "Adding a gradient is easy. All gradients are read from a gradients .json file which is available in this project's repo. Simply add your gradient details to it and submit a pull request."}
+{"idx": 5, "title": "Vector Calculus: Understanding the Gradient – BetterExplained", "date": "", "ddg_snippet": "Yes, you can say a line has a gradient (its slope), but using \" gradient \" for single-variable functions is unnecessarily confusing. Keep it simple. “ Gradient ” can refer to gradual changes of color, but we’ll stick to the math definition if that’s ok with you. You’ll see the meanings are related.", "subpage_snippet": "", "source": "betterexplained.com", "link": "https://betterexplained.com/articles/vector-calculus-understanding-the-gradient/", "content": "Yes, you can say a line has a gradient (its slope), but using \" gradient \" for single-variable functions is unnecessarily confusing. Keep it simple. “ Gradient ” can refer to gradual changes of color, but we’ll stick to the math definition if that’s ok with you. You’ll see the meanings are related."}
+{"idx": 6, "title": "Gradient definition - explanation and examples - Cuemath", "date": "", "ddg_snippet": "In this mini-lesson, we shall explore the world of the gradient , by finding answers to questions like what is a gradient , what is a directional derivative, and understanding the properties of gradients with examples.", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/geometry/gradient-definition/", "content": "In this mini-lesson, we shall explore the world of the gradient , by finding answers to questions like what is a gradient , what is a directional derivative, and understanding the properties of gradients with examples."}
+{"idx": 7, "title": "Gradient Generator - colordesigner.io", "date": "", "ddg_snippet": "The tool itself is used to generate a gradual change in the color gradient from one color to another, essentially leaving the user with a result of many different in-between colors of the blend.", "subpage_snippet": "", "source": "colordesigner.io", "link": "https://colordesigner.io/gradient-generator", "content": "The tool itself is used to generate a gradual change in the color gradient from one color to another, essentially leaving the user with a result of many different in-between colors of the blend."}
+{"idx": 8, "title": "What is a Gradient and What Types Exist", "date": "", "ddg_snippet": "A gradient is a smooth transition from one color to another . Gradients are widely used in web design, graphics, applications, and art to add depth, volume, and dynamism.", "subpage_snippet": "", "source": "gradients.app", "link": "https://gradients.app/en/media/education/what-a-gradient-is", "content": "A gradient is a smooth transition from one color to another . Gradients are widely used in web design, graphics, applications, and art to add depth, volume, and dynamism."}
+{"idx": 9, "title": "Gradient Hunt - Beautiful Color Gradients", "date": "", "ddg_snippet": "Thousands of trendy color gradients in a curated collection that is updated daily. Get a fresh color gradient for your next design project and save all the gradients you like.", "subpage_snippet": "", "source": "gradienthunt.com", "link": "https://gradienthunt.com/", "content": "Thousands of trendy color gradients in a curated collection that is updated daily. Get a fresh color gradient for your next design project and save all the gradients you like."}
diff --git a/data/sampled_jsons/Graph_Neural_Networks_(GNNs)_have_demonstrated_superior_performance_in_learning_node_representations.jsonl b/data/sampled_jsons/Graph_Neural_Networks_(GNNs)_have_demonstrated_superior_performance_in_learning_node_representations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..23ae70a5de46121cb66cd8640b7e7c667af5d022
--- /dev/null
+++ b/data/sampled_jsons/Graph_Neural_Networks_(GNNs)_have_demonstrated_superior_performance_in_learning_node_representations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "怎么区分 chart，diagram，graph，figure这几个词，都是图表的意思? -...", "date": "", "ddg_snippet": "电路图也算diagram。 graph: A graph is a mathematical diagram which shows the relationship between two or more sets of numbers or measurements. 意思是graph是数学化的diagram，展示两个或两个以上的数字集。 figure： In books and magazines, the diagrams which help to show or explain information are referred to as figures.", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/23526966", "content": "电路图也算diagram。 graph: A graph is a mathematical diagram which shows the relationship between two or more sets of numbers or measurements. 意思是graph是数学化的diagram，展示两个或两个以上的数字集。 figure： In books and magazines, the diagrams which help to show or explain information are referred to as figures."}
+{"idx": 1, "title": "什么是 GraphQL？ - 知乎", "date": "", "ddg_snippet": "所以 Graph + QL = 图表化 (可视化) 查询语言，是一种描述客户端如何向服务端请求数据的 API 语法，类似于 RESTful API 规范。 注：不要联想到 MySQL、NoSQL，它不是图形数据库，比如 Neo4j。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/264629587", "content": "所以 Graph + QL = 图表化 (可视化) 查询语言，是一种描述客户端如何向服务端请求数据的 API 语法，类似于 RESTful API 规范。 注：不要联想到 MySQL、NoSQL，它不是图形数据库，比如 Neo4j。"}
+{"idx": 2, "title": "graph、chart、diagram、form、table表示图表有啥区别吗？", "date": "", "ddg_snippet": "graph、chart、diagram、form、table都是图表，区别你知道吗？ 这些单词分为图和表两大类。 Graph指的是坐标图，初中数学课用的坐标纸就叫graph paper. Chart的范围大一点。 凡是统计或梳理意义的，比如统计图、流程图、组织结构图、地图、星图都可以用它。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/tardis/bd/ans/3158567433", "content": "graph、chart、diagram、form、table都是图表，区别你知道吗？ 这些单词分为图和表两大类。 Graph指的是坐标图，初中数学课用的坐标纸就叫graph paper. Chart的范围大一点。 凡是统计或梳理意义的，比如统计图、流程图、组织结构图、地图、星图都可以用它。"}
+{"idx": 3, "title": "有哪些指标可以描述两个图（graph）的相似度？ - 知乎", "date": "", "ddg_snippet": "对于分析稀疏图的要求。 按照常理这里应该放一些图来说明Graph kernel衡量图相似度的效果，图片来源 [6] 下面是对6个类型的600个图结构应用不同的Graph kernel, 并使用kernel PCA降维的结果。 每个点代表一个graph，可以看到划分的效果还不错，同类的graph基本都聚集到 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/57269332", "content": "对于分析稀疏图的要求。 按照常理这里应该放一些图来说明Graph kernel衡量图相似度的效果，图片来源 [6] 下面是对6个类型的600个图结构应用不同的Graph kernel, 并使用kernel PCA降维的结果。 每个点代表一个graph，可以看到划分的效果还不错，同类的graph基本都聚集到 ..."}
+{"idx": 4, "title": "vllm 为什么没在 prefill 阶段支持 cuda graph？ - 知乎", "date": "", "ddg_snippet": "prefill阶段seq是变化的，需要padding， graph支持起来，浪费显存划不来， 而且llm推理是自回归的，成本远超90%都在在decode阶段，然后都是一些小算子了，kernel launch开销占比大很多，而且seq是1用cuda graph是很自然的。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/7987565201", "content": "prefill阶段seq是变化的，需要padding， graph支持起来，浪费显存划不来， 而且llm推理是自回归的，成本远超90%都在在decode阶段，然后都是一些小算子了，kernel launch开销占比大很多，而且seq是1用cuda graph是很自然的。"}
+{"idx": 5, "title": "现在很多sci的期刊都需要Graphical Abstract，如何制作？ - 知乎", "date": "", "ddg_snippet": "三、按照期刊要求作图 不同期刊的具体要求不同，可参照期刊的作者指南，了解期刊对Graphical Abstract（图文摘要）的 字体类型和大小、线宽、颜色、图片尺寸 的信息；以及文件格式使用 PDF，TIFF还是PNG。 图文摘要的常见技术要求（并不代表所有期刊的要求） 大小： 提交的图像应为300dpi时的1200像素 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/463498947", "content": "三、按照期刊要求作图 不同期刊的具体要求不同，可参照期刊的作者指南，了解期刊对Graphical Abstract（图文摘要）的 字体类型和大小、线宽、颜色、图片尺寸 的信息；以及文件格式使用 PDF，TIFF还是PNG。 图文摘要的常见技术要求（并不代表所有期刊的要求） 大小： 提交的图像应为300dpi时的1200像素 ..."}
+{"idx": 6, "title": "GetData Graph Digitizer老提示证书过期，怎么解决？ - 知乎", "date": "", "ddg_snippet": "Jul 3, 2024 · GetData Graph Digitizer老提示证书过期，怎么解决？ 官方网站（ www.getdata-graph-digitizer.com ）打不开了，想买都买不成了，谁知道怎么回事有没有办法解决证书过期的问题？ 吾… 显示全部 关注者 2 被浏览", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/660662036?write", "content": "Jul 3, 2024 · GetData Graph Digitizer老提示证书过期，怎么解决？ 官方网站（ www.getdata-graph-digitizer.com ）打不开了，想买都买不成了，谁知道怎么回事有没有办法解决证书过期的问题？ 吾… 显示全部 关注者 2 被浏览"}
+{"idx": 7, "title": "DeepSeek 回答中的流程图代码怎么转成图片？ - 知乎", "date": "", "ddg_snippet": "graph TD 是Mermaid中用于表示流程图的语法,其中“graph”是关键字,表示接下来的内容是一个流程图,而“TD”则指明了流程图的方向是从上到下 (Top to Down)。 怎么导成像下面一样的流程图呢？", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/1895093233647845726", "content": "graph TD 是Mermaid中用于表示流程图的语法,其中“graph”是关键字,表示接下来的内容是一个流程图,而“TD”则指明了流程图的方向是从上到下 (Top to Down)。 怎么导成像下面一样的流程图呢？"}
+{"idx": 8, "title": "origin 软件绘图左侧出现一个大C，怎么办？ - 知乎", "date": "", "ddg_snippet": "百度知道找到一个答案： tools-options-graph，鼠标放在 user defined symbols 下方，按ctrl+x,再去掉 speed mode show watermark 前的勾，点击ok,后边会出现提示save as origin's startup options，点“是”，如果再出现“C”保存关闭工程再打开就不会有“C”了。 不过我关闭origin重新打开再做一次就没有出现", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/22136925", "content": "百度知道找到一个答案： tools-options-graph，鼠标放在 user defined symbols 下方，按ctrl+x,再去掉 speed mode show watermark 前的勾，点击ok,后边会出现提示save as origin's startup options，点“是”，如果再出现“C”保存关闭工程再打开就不会有“C”了。 不过我关闭origin重新打开再做一次就没有出现"}
+{"idx": 9, "title": "Windows音频设备图形隔离占用了很多内存怎么办？ - 知乎", "date": "", "ddg_snippet": "5、这样可以解决一些音频设备图形隔离占用大量资源的问题。 以上就是Win10音频设备图形隔离占用CPU使用率|关闭音频设备图形隔离文章，如果大家也遇到了这样的问题，可以按照这篇文章的方法教程进行操作。 以上就是今天的全部内容，职场朋友不妨试试看，看完还请顺手点个【赞】，记不住的 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/371877754", "content": "5、这样可以解决一些音频设备图形隔离占用大量资源的问题。 以上就是Win10音频设备图形隔离占用CPU使用率|关闭音频设备图形隔离文章，如果大家也遇到了这样的问题，可以按照这篇文章的方法教程进行操作。 以上就是今天的全部内容，职场朋友不妨试试看，看完还请顺手点个【赞】，记不住的 ..."}
diff --git a/data/sampled_jsons/Great_Models_Think_Alike_and_this_Undermines_AI_Oversight_Section_4_model_similarity_weak-to-strong_.jsonl b/data/sampled_jsons/Great_Models_Think_Alike_and_this_Undermines_AI_Oversight_Section_4_model_similarity_weak-to-strong_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f4c62c388d8d1e2fb04ffd4e2f830e89b577645
--- /dev/null
+++ b/data/sampled_jsons/Great_Models_Think_Alike_and_this_Undermines_AI_Oversight_Section_4_model_similarity_weak-to-strong_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Great Models Think Alike and this Undermines AI Oversight Great Models Think Alike and this Undermines AI Oversight Great Models Think Alike and this Undermines AI Oversight When AI Models Think Alike: The Hidden Challenge Undermining ... ICML Poster Great Models Think Alike and this Undermines AI ... The AI oversight trap: When smarter models make the same ... Similarity affects Oversight Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Similarity affects Oversight Similarity affects Oversight Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Feb 6, 2025 · Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from '' weak-to-strong generalization''. As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . May 1, 2025 · Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from * weak-to-strong generalization*. As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . Training on LM Annotations benefits from Complementary Knowledge Student models trained on annotations of smaller supervisors show higher performance improvements, or weak-to-strong generalization, when the student and supervisor have lower similarity . In our paper, we also show that current weak-to-strong training methods have a higher performance ceiling than assumed previously, if they ... Feb 7, 2025 · Addressing the Risks of AI OversightOur research underscores the importance of recognizing and mitigating the effects of model similarity in AI oversight . If models share the same weaknesses, they may reinforce rather than correct each other's errors, leading to systemic issues in automated evaluation systems. Using CAPA, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from weak-to-strong generalization. Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight . Do great models think like undermine AI oversight? Overall, as model blind-spots get harder to detect, making us defer more to AI oversight, models making more similar mistakes poses the risk of correlated failures. title={ Great Models Think Alike and this Undermines AI Oversight }, Does model similarity affect AI oversight? We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. Can other language models automate AI oversight? There is hope that other language models can automate both these tasks, which we refer to as AI Oversight. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on overlap in model mistakes. How does model similarity affect LMS? Model similarity has negative effects on using LMs to judge or train other models; Unfortunately LMs are getting similar with increasing capabilities. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from “ weak-to-strong generalization”. As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04313", "content": "Feb 6, 2025 · Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from '' weak-to-strong generalization''. As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . May 1, 2025 · Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from * weak-to-strong generalization*. As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . Training on LM Annotations benefits from Complementary Knowledge Student models trained on annotations of smaller supervisors show higher performance improvements, or weak-to-strong generalization, when the student and supervisor have lower similarity . In our paper, we also show that current weak-to-strong training methods have a higher performance ceiling than assumed previously, if they ... Feb 7, 2025 · Addressing the Risks of AI OversightOur research underscores the importance of recognizing and mitigating the effects of model similarity in AI oversight . If models share the same weaknesses, they may reinforce rather than correct each other's errors, leading to systemic issues in automated evaluation systems. Using CAPA, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from weak-to-strong generalization. Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight . Do great models think like undermine AI oversight? Overall, as model blind-spots get harder to detect, making us defer more to AI oversight, models making more similar mistakes poses the risk of correlated failures. title={ Great Models Think Alike and this Undermines AI Oversight }, Does model similarity affect AI oversight? We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. Can other language models automate AI oversight? There is hope that other language models can automate both these tasks, which we refer to as AI Oversight. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on overlap in model mistakes. How does model similarity affect LMS? Model similarity has negative effects on using LMs to judge or train other models; Unfortunately LMs are getting similar with increasing capabilities. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from “ weak-to-strong generalization”. As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight ."}
+{"idx": 1, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "May 1, 2025 · Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from * weak-to-strong generalization*. As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3Z827FtMNe", "content": "May 1, 2025 · Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from * weak-to-strong generalization*. As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight ."}
+{"idx": 2, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Training on LM Annotations benefits from Complementary Knowledge Student models trained on annotations of smaller supervisors show higher performance improvements, or weak-to-strong generalization, when the student and supervisor have lower similarity . In our paper, we also show that current weak-to-strong training methods have a higher performance ceiling than assumed previously, if they ...", "subpage_snippet": "", "source": "model-similarity.github.io", "link": "https://model-similarity.github.io/", "content": "Training on LM Annotations benefits from Complementary Knowledge Student models trained on annotations of smaller supervisors show higher performance improvements, or weak-to-strong generalization, when the student and supervisor have lower similarity . In our paper, we also show that current weak-to-strong training methods have a higher performance ceiling than assumed previously, if they ..."}
+{"idx": 3, "title": "When AI Models Think Alike: The Hidden Challenge Undermining ...", "date": "", "ddg_snippet": "Feb 7, 2025 · Addressing the Risks of AI OversightOur research underscores the importance of recognizing and mitigating the effects of model similarity in AI oversight . If models share the same weaknesses, they may reinforce rather than correct each other's errors, leading to systemic issues in automated evaluation systems.", "subpage_snippet": "", "source": "www.globaltrendtimes.com", "link": "https://www.globaltrendtimes.com/2025/02/when-ai-models-think-alike-hidden.html", "content": "Feb 7, 2025 · Addressing the Risks of AI OversightOur research underscores the importance of recognizing and mitigating the effects of model similarity in AI oversight . If models share the same weaknesses, they may reinforce rather than correct each other's errors, leading to systemic issues in automated evaluation systems."}
+{"idx": 4, "title": "ICML Poster Great Models Think Alike and this Undermines AI ...", "date": "", "ddg_snippet": "Using CAPA, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from weak-to-strong generalization.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46528", "content": "Using CAPA, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from weak-to-strong generalization."}
+{"idx": 5, "title": "The AI oversight trap: When smarter models make the same ...", "date": "", "ddg_snippet": "Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight .", "subpage_snippet": "", "source": "www.devdiscourse.com", "link": "https://www.devdiscourse.com/article/technology/3256561-the-ai-oversight-trap-when-smarter-models-make-the-same-mistakes", "content": "Feb 12, 2025 · A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight ."}
+{"idx": 6, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from “ weak-to-strong generalization”. As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v1", "content": "Then, we study training on LM annotations, and find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from “ weak-to-strong generalization”. As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight ."}
+{"idx": 7, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "... show the benefits of diverse models for AI oversight – less similarity between models reduces bias in LLM-as-a-judge, and also leads to greater ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "... show the benefits of diverse models for AI oversight – less similarity between models reduces bias in LLM-as-a-judge, and also leads to greater ..."}
+{"idx": 8, "title": "Correlated Errors in Large Language Models", "date": "", "ddg_snippet": "... this literature: how correlated are different models , and what explains correlation? Our results thus aid in choosing uncorrelated models and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07962v1", "content": "... this literature: how correlated are different models , and what explains correlation? Our results thus aid in choosing uncorrelated models and ..."}
+{"idx": 9, "title": "Oversight of AI: Rules for Artificial Intelligence with Sam", "date": "", "ddg_snippet": "That gives us a sense, I think , of just how rapidly this technology that we re talking about today is changing and evolving and transforming our ...", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/oversight-of-ai-rules-for-artificial-intelligence-with-sam-altman", "content": "That gives us a sense, I think , of just how rapidly this technology that we re talking about today is changing and evolving and transforming our ..."}
diff --git a/data/sampled_jsons/Griffin_De_et_al._2024_abstract_year_2024.jsonl b/data/sampled_jsons/Griffin_De_et_al._2024_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..772be9affb6c3c0c0f47507d92a3d9dcb9169e00
--- /dev/null
+++ b/data/sampled_jsons/Griffin_De_et_al._2024_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Корь > Клинические рекомендации РФ 2024 (Россия) > MedElement", "date": "", "ddg_snippet": "2024 Jul; 60(7): 1031 54. Quenot JP, Luyt CE, Roche N, et al . Role of biomarkers in the management of antibiotic therapy: an expert panel review II: clinical use of biomarkers for initiation or discontinuation of antibiotic therapy.", "subpage_snippet": "", "source": "diseases.medelement.com", "link": "https://diseases.medelement.com/disease/корь-кр-рф-2024/18293", "content": "2024 Jul; 60(7): 1031 54. Quenot JP, Luyt CE, Roche N, et al . Role of biomarkers in the management of antibiotic therapy: an expert panel review II: clinical use of biomarkers for initiation or discontinuation of antibiotic therapy."}
+{"idx": 1, "title": "How much do contextualized representations encode long-range...", "date": "", "ddg_snippet": "), as well as hybrid models ( De et al ., 2024 , Griffin ) (Waleffe et al ., 2024 , HybridMamba). Additionally, we analyze four large open-access models from the llama3 and llama3.1 series (Dubey et al ., 2024 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.12292", "content": "), as well as hybrid models ( De et al ., 2024 , Griffin ) (Waleffe et al ., 2024 , HybridMamba). Additionally, we analyze four large open-access models from the llama3 and llama3.1 series (Dubey et al ., 2024 )."}
+{"idx": 2, "title": "Titans: Learning to Memorize at Test Time | Read Paper on Bytez", "date": "", "ddg_snippet": "2024 ), xLSTM (Beck et al . 2024 ), and Mamba2 (Dao and Gu 2024 ), which the later is also connected to the discretized version of traditional state space models (Gu and Dao 2024 ).(2) Improving the write operation: To overcome the additive nature of memory write operation in traditional...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2501.00663/paper", "content": "2024 ), xLSTM (Beck et al . 2024 ), and Mamba2 (Dao and Gu 2024 ), which the later is also connected to the discretized version of traditional state space models (Gu and Dao 2024 ).(2) Improving the write operation: To overcome the additive nature of memory write operation in traditional..."}
+{"idx": 3, "title": "Plastic and Reconstructive Surgery", "date": "", "ddg_snippet": "Published October 2024 .Nicholas T. Haddock, M.D., discusses the June Breast article \"BRCA Mutations in the Young, High-Risk Female Population: Genetic Testing, Management of Prophylactic Therapies, and Implications for Plastic Surgeons\" by Salibian et al .", "subpage_snippet": "", "source": "journals.lww.com", "link": "https://journals.lww.com/plasreconsurg/pages/default.aspx", "content": "Published October 2024 .Nicholas T. Haddock, M.D., discusses the June Breast article \"BRCA Mutations in the Young, High-Risk Female Population: Genetic Testing, Management of Prophylactic Therapies, and Implications for Plastic Surgeons\" by Salibian et al ."}
+{"idx": 4, "title": "Meta Prompting | Prompt Engineering Guide", "date": "", "ddg_snippet": "According to Zhang et al . ( 2024 ) (opens in a new tab), the key characteristics of meta prompting can be summarized as follows: 1. Structure-oriented: Prioritizes the format and pattern of problems and solutions over specific content.", "subpage_snippet": "", "source": "www.promptingguide.ai", "link": "https://www.promptingguide.ai/techniques/meta-prompting", "content": "According to Zhang et al . ( 2024 ) (opens in a new tab), the key characteristics of meta prompting can be summarized as follows: 1. Structure-oriented: Prioritizes the format and pattern of problems and solutions over specific content."}
+{"idx": 5, "title": "Psychological Responses to Treatment of Impacted Canines in Young...", "date": "", "ddg_snippet": "Section: Original research. Submitted: 30.07. 2024 . Griffin SO, Jones JA, Brunson D, et al . Burden of oral disease among older adults and implications for public health priorities.", "subpage_snippet": "", "source": "kazanmedjournal.ru", "link": "https://kazanmedjournal.ru/kazanmedj/article/view/634690", "content": "Section: Original research. Submitted: 30.07. 2024 . Griffin SO, Jones JA, Brunson D, et al . Burden of oral disease among older adults and implications for public health priorities."}
+{"idx": 6, "title": "L Anguage M odels with s elective a ttention", "date": "", "ddg_snippet": "( 2024 ); Wen et al . ( 2024 ), leading to reduced performance in tasks that 198 demand accurate retrieval of specific sections in the input sequence.Hybrid Architecture: Several recent studies H3 Fu et al . (2023), Griffin De et al .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9DnKZbOr4r", "content": "( 2024 ); Wen et al . ( 2024 ), leading to reduced performance in tasks that 198 demand accurate retrieval of specific sections in the input sequence.Hybrid Architecture: Several recent studies H3 Fu et al . (2023), Griffin De et al ."}
+{"idx": 7, "title": "Physical Review Journals", "date": "", "ddg_snippet": "Yifan Chen et al .The ‘About’ pages for each APS journal have recently been updated with new data from the 2024 Journal Citation Reports (2025, Clarivate), CiteScore (Scopus Elsevier) and SCImago Journal Rank (SCImago).", "subpage_snippet": "", "source": "journals.aps.org", "link": "https://journals.aps.org/", "content": "Yifan Chen et al .The ‘About’ pages for each APS journal have recently been updated with new data from the 2024 Journal Citation Reports (2025, Clarivate), CiteScore (Scopus Elsevier) and SCImago Journal Rank (SCImago)."}
+{"idx": 8, "title": "RU2179849C2 - Способ лечения... - Google Patents", "date": "", "ddg_snippet": "Abstract . Изобретение относится к медицине, а именно к венерологии, предназначено для амбулаторного лечения больных с рецидивирующим генитальным герпесом.Daschner et al . 1984. Netilmicin versus tobramycin in multi-centre studies.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/RU2179849C2/ru", "content": "Abstract . Изобретение относится к медицине, а именно к венерологии, предназначено для амбулаторного лечения больных с рецидивирующим генитальным герпесом.Daschner et al . 1984. Netilmicin versus tobramycin in multi-centre studies."}
+{"idx": 9, "title": "Диспластический невус: современные...", "date": "", "ddg_snippet": "3. In: Kang S, Amagai M, Bruckner AL , Enk AH, Margolis DJ, McMichael AJ, Orringer JS. eds. Fitzpatrick's Dermatology, 9e. McGraw-Hill Education; 2019. Accessed October 03, 2024 .13. Clemente C, Cochran AJ, Elder DE , et al .", "subpage_snippet": "", "source": "cyberleninka.ru", "link": "https://cyberleninka.ru/article/n/displasticheskiy-nevus-sovremennye-morfologicheskie-kriterii-otsenki-stepeni-melanotsitarnoy-displazii", "content": "3. In: Kang S, Amagai M, Bruckner AL , Enk AH, Margolis DJ, McMichael AJ, Orringer JS. eds. Fitzpatrick's Dermatology, 9e. McGraw-Hill Education; 2019. Accessed October 03, 2024 .13. Clemente C, Cochran AJ, Elder DE , et al ."}
diff --git "a/data/sampled_jsons/Gumiho_paper_Figure_4_Table_3_serial_head_depth_2_vs_3_layers_\317\204_speedup_wall_time.jsonl" "b/data/sampled_jsons/Gumiho_paper_Figure_4_Table_3_serial_head_depth_2_vs_3_layers_\317\204_speedup_wall_time.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..a2e7649926cbe594ab0d76098a6be932451ab249
--- /dev/null
+++ "b/data/sampled_jsons/Gumiho_paper_Figure_4_Table_3_serial_head_depth_2_vs_3_layers_\317\204_speedup_wall_time.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Kumiho - Wikipedia", "date": "", "ddg_snippet": "A kumiho or gumiho (Korean: 구미호; Hanja: 九尾狐; lit. nine-tailed fox ) is a creature that appears in the folktales of East Asia and legends of Korea. It is similar to the Chinese jiuweihu, the Japanese kitsune and the Vietnamese hồ ly tinh.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Kumiho", "content": "A kumiho or gumiho (Korean: 구미호; Hanja: 九尾狐; lit. nine-tailed fox ) is a creature that appears in the folktales of East Asia and legends of Korea. It is similar to the Chinese jiuweihu, the Japanese kitsune and the Vietnamese hồ ly tinh."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "However, when the depth increases from 2 to 3 layers , although τ improves further, the speedup ratio decreases. This is because the overall speedup depends not only on τ but also on the time required to complete a single draft process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135", "content": "However, when the depth increases from 2 to 3 layers , although τ improves further, the speedup ratio decreases. This is because the overall speedup depends not only on τ but also on the time required to complete a single draft process."}
+{"idx": 2, "title": "Daily Papers", "date": "", "ddg_snippet": "6 days ago — By allocating more advanced model structures and longer running times to the early heads, Gumiho achieves improved overall performance.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Lookahead+decoding", "content": "6 days ago — By allocating more advanced model structures and longer running times to the early heads, Gumiho achieves improved overall performance."}
+{"idx": 3, "title": "Daily Papers", "date": "", "ddg_snippet": "6 days ago — Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads . Specifically, given the critical importance ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=grammar-aligned+decoding+(GAD)", "content": "6 days ago — Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads . Specifically, given the critical importance ..."}
+{"idx": 4, "title": "Paper page - Gumiho: A Hybrid Architecture to Prioritize ...", "date": "", "ddg_snippet": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.10135", "content": "Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 5, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Jul 11, 2025 · Gumiho employs a hybrid head design that combines serial and parallel components. For the crucial early tokens, a sophisticated Transformer architecture is used in a serial configuration to enhance accuracy.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "Jul 11, 2025 · Gumiho employs a hybrid head design that combines serial and parallel components. For the crucial early tokens, a sophisticated Transformer architecture is used in a serial configuration to enhance accuracy."}
+{"idx": 6, "title": "(PDF) Gumiho: A Hybrid Architecture to Prioritize Early ...", "date": "", "ddg_snippet": "Mar 13, 2025 · Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389821466_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding", "content": "Mar 13, 2025 · Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 7, "title": "How Many Layers and Why? An Analysis of the Model Depth in ... Kumiho - Wikipedia What blade depth and other stuff is good for making stickers ...", "date": "", "ddg_snippet": "In this study, we investigate the role of the mul-tiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of the input. The key specificity of ALBERT is that weights are tied across layers. There-fore, the stack of encoder layers iteratively re-peats the application of the s... See full list on aclanthology.org where is a time penalty parameter that weights the relative cost of computation versus error. See full list on aclanthology.org We investigated the role of the layers in deep trans-formers. We designed an original model that pro-gressively transforms each token through a dy-namic number of iterations. We analyzed the dis-tribution of these iterations during pre-training and confirmed the results obtained by analyzing the distribution of attention across BERT layers , par-tic... See full list on aclanthology.org A kumiho or gumiho (Korean: 구미호; Hanja: 九尾狐; lit. nine-tailed fox ) is a creature that appears in the folktales of East Asia and legends of Korea. It is similar to the Chinese jiuweihu, the Japanese kitsune and the Vietnamese hồ ly tinh. What blade depth and other stuff is good for making stickers? I recently got a cameo 2 and wanted to know what settings and stuff should be used for making stickers.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.acl-srw.23.pdf", "content": "In this study, we investigate the role of the mul-tiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of the input. The key specificity of ALBERT is that weights are tied across layers. There-fore, the stack of encoder layers iteratively re-peats the application of the s... See full list on aclanthology.org where is a time penalty parameter that weights the relative cost of computation versus error. See full list on aclanthology.org We investigated the role of the layers in deep trans-formers. We designed an original model that pro-gressively transforms each token through a dy-namic number of iterations. We analyzed the dis-tribution of these iterations during pre-training and confirmed the results obtained by analyzing the distribution of attention across BERT layers , par-tic... See full list on aclanthology.org A kumiho or gumiho (Korean: 구미호; Hanja: 九尾狐; lit. nine-tailed fox ) is a creature that appears in the folktales of East Asia and legends of Korea. It is similar to the Chinese jiuweihu, the Japanese kitsune and the Vietnamese hồ ly tinh. What blade depth and other stuff is good for making stickers? I recently got a cameo 2 and wanted to know what settings and stuff should be used for making stickers."}
+{"idx": 8, "title": "What blade depth and other stuff is good for making stickers ...", "date": "", "ddg_snippet": "What blade depth and other stuff is good for making stickers? I recently got a cameo 2 and wanted to know what settings and stuff should be used for making stickers.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/silhouettecutters/comments/143rb0v/what_blade_depth_and_other_stuff_is_good_for/", "content": "What blade depth and other stuff is good for making stickers? I recently got a cameo 2 and wanted to know what settings and stuff should be used for making stickers."}
+{"idx": 9, "title": "LogitSpec: Accelerating Retrieval-based Speculative ...", "date": "", "ddg_snippet": "2 Jul 2025 — However, the number of skipped layers is limited, which affects the overall speedup.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.01449v1", "content": "2 Jul 2025 — However, the number of skipped layers is limited, which affects the overall speedup."}
diff --git a/data/sampled_jsons/Gumiho_vs_Eagle-2_Llama2_70B_MT-Bench_Table_1_Speedup.jsonl b/data/sampled_jsons/Gumiho_vs_Eagle-2_Llama2_70B_MT-Bench_Table_1_Speedup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..67f81f1933a5dae5e4c267bc2b45a0bf43683039
--- /dev/null
+++ b/data/sampled_jsons/Gumiho_vs_Eagle-2_Llama2_70B_MT-Bench_Table_1_Speedup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "A4: We evaluated Gumiho under bs > 1 scenarios with Vicuna 7B, as shown in the table below. Experimental results demonstrate that the speedup effect degrades as batch size increases, aligning with observations in prior works like Eagle .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0ObGn4e1IS", "content": "A4: We evaluated Gumiho under bs > 1 scenarios with Vicuna 7B, as shown in the table below. Experimental results demonstrate that the speedup effect degrades as batch size increases, aligning with observations in prior works like Eagle ."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "by J Li · 2025 — At temperature 0,. Gumiho outperforms EAGLE-2 by 11.7% on LLaMA2 70B and 15.8% on LLaMA3 70B. This substantial improvement is primarily attributed to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135", "content": "by J Li · 2025 — At temperature 0,. Gumiho outperforms EAGLE-2 by 11.7% on LLaMA2 70B and 15.8% on LLaMA3 70B. This substantial improvement is primarily attributed to ..."}
+{"idx": 2, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AIG-AIMA/Gumiho", "content": "This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models ..."}
+{"idx": 3, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Speedup improvements: 4.5% to 15.8% over Eagle-2 across all configurations; Particularly strong on large models: 11.7% improvement on LLaMA2-70B and 15.8% on ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.10135v2", "content": "Speedup improvements: 4.5% to 15.8% over Eagle-2 across all configurations; Particularly strong on large models: 11.7% improvement on LLaMA2-70B and 15.8% on ..."}
+{"idx": 4, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative ...", "date": "", "ddg_snippet": "At temperature 0, Gumiho outperforms EAGLE-2 by 11.7% on LLaMA2 70B and 15.8% on LLaMA3 70B . This substantial improvement is primarily attributed to enhancements in τ and a reduction in draft time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135v2", "content": "At temperature 0, Gumiho outperforms EAGLE-2 by 11.7% on LLaMA2 70B and 15.8% on LLaMA3 70B . This substantial improvement is primarily attributed to enhancements in τ and a reduction in draft time."}
+{"idx": 5, "title": "GitHub - AMD-AGI/Gumiho: Official Implementation of \"Gumiho: A Hybrid ...", "date": "", "ddg_snippet": "Official Implementation of \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding\" (ICML'25) - AMD-AGI/ Gumiho", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "Official Implementation of \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding\" (ICML'25) - AMD-AGI/ Gumiho"}
+{"idx": 6, "title": "meta-llama/Llama-2-70b · Hugging Face", "date": "", "ddg_snippet": "Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B pretrained model. Links to other models can be found in the index at the bottom. Model Details Note: Use of this model is governed by the Meta license.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/meta-llama/Llama-2-70b", "content": "Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B pretrained model. Links to other models can be found in the index at the bottom. Model Details Note: Use of this model is governed by the Meta license."}
+{"idx": 7, "title": "Benchmarking Llama-2-70B | TrueFoundry", "date": "", "ddg_snippet": "Gain efficiency insights from Llama-2-70B benchmarking. Optimize ML operations with valuable data analysis.", "subpage_snippet": "", "source": "www.truefoundry.com", "link": "https://www.truefoundry.com/blog/benchmarking-llama-2-70b", "content": "Gain efficiency insights from Llama-2-70B benchmarking. Optimize ML operations with valuable data analysis."}
+{"idx": 8, "title": "EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees", "date": "", "ddg_snippet": "Table 2 : Speedup ratios and average acceptance lengths τ with LLaMA2 -Chat 70B , LLaMA3-Instruct 70B , and LLaMA3-Instruct 8B as the original LLMs, with the temperature set to 0, on the MT-bench dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.16858v1", "content": "Table 2 : Speedup ratios and average acceptance lengths τ with LLaMA2 -Chat 70B , LLaMA3-Instruct 70B , and LLaMA3-Instruct 8B as the original LLMs, with the temperature set to 0, on the MT-bench dataset."}
+{"idx": 9, "title": "GitHub - SafeAILab/EAGLE: Official Implementation of EAGLE-1 (ICML'24 ...", "date": "", "ddg_snippet": "1.4x faster than EAGLE-1 (13B). EAGLE-3 removes the feature prediction constraint in EAGLE and simulates this process during training using training-time testing. Considering that top-layer features are limited to next-token prediction, EAGLE-3 replaces them with a fusion of low-, mid-, and high-level semantic features.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SafeAILab/EAGLE", "content": "1.4x faster than EAGLE-1 (13B). EAGLE-3 removes the feature prediction constraint in EAGLE and simulates this process during training using training-time testing. Considering that top-layer features are limited to next-token prediction, EAGLE-3 replaces them with a fusion of low-, mid-, and high-level semantic features."}
diff --git a/data/sampled_jsons/HDT-MHRW_LRU_cache_memory_reduction_90%.jsonl b/data/sampled_jsons/HDT-MHRW_LRU_cache_memory_reduction_90%.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6c66575bf67e5d3d1d9bdaeac6c5c7333b360c77
--- /dev/null
+++ b/data/sampled_jsons/HDT-MHRW_LRU_cache_memory_reduction_90%.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LRU Cache - Complete Tutorial - GeeksforGeeks", "date": "", "ddg_snippet": "The Least Recently Used ( LRU ) is one of those algorithms.If the number of keys exceeds the capacity of the LRU cache then dismiss the least recently used key.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/system-design/lru-cache-implementation/", "content": "The Least Recently Used ( LRU ) is one of those algorithms.If the number of keys exceeds the capacity of the LRU cache then dismiss the least recently used key."}
+{"idx": 1, "title": "bitmap - Android LruCache OutOfMemoryException - Stack Overflow", "date": "", "ddg_snippet": "how does LruCache reduce memory usage ??This video says, the cache manage itself, by removing objects from the end of the queue, so you don`t need to do it by your self. Do you using the option parameters when loading bitmaps ?", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/28855996/android-lrucache-outofmemoryexception", "content": "how does LruCache reduce memory usage ??This video says, the cache manage itself, by removing objects from the end of the queue, so you don`t need to do it by your self. Do you using the option parameters when loading bitmaps ?"}
+{"idx": 2, "title": "lru - cache - npm", "date": "", "ddg_snippet": "A cache object that deletes the least - recently - used items.. Latest version: 11.2.1, last published: 13 days ago. Start using lru - cache in your project by running `npm i lru - cache `.", "subpage_snippet": "", "source": "www.npmjs.com", "link": "https://www.npmjs.com/package/lru-cache", "content": "A cache object that deletes the least - recently - used items.. Latest version: 11.2.1, last published: 13 days ago. Start using lru - cache in your project by running `npm i lru - cache `."}
+{"idx": 3, "title": "GitHub - stucchio/Python- LRU - cache : An in- memory LRU cache for...", "date": "", "ddg_snippet": "from lru import lru _ cache _function @.The only feature this one has which that one lacks is timed eviction. About. An in- memory LRU cache for python.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/stucchio/Python-LRU-cache", "content": "from lru import lru _ cache _function @.The only feature this one has which that one lacks is timed eviction. About. An in- memory LRU cache for python."}
+{"idx": 4, "title": "Download Mem Reduct v3.5.2 – Best Memory Optimizer for PC!", "date": "", "ddg_snippet": "Speed up your Windows PC with Mem Reduct v3.5.2 – a lightweight, free RAM cleaner. Clean system memory , reduce lag, and boost performance instantly!", "subpage_snippet": "", "source": "memoryreduct.com", "link": "https://memoryreduct.com/", "content": "Speed up your Windows PC with Mem Reduct v3.5.2 – a lightweight, free RAM cleaner. Clean system memory , reduce lag, and boost performance instantly!"}
+{"idx": 5, "title": "Android's memory optimization - LRUCACHE - Programmer Sought", "date": "", "ddg_snippet": "The simple use of memory cache Lrucache . Lrucache algorithm ( Least Recently Used ), also known as the minimum use algorithm in the near future. This class is very suitable for caching pictures.Android caching mechanism - LruCache cache in memory .", "subpage_snippet": "", "source": "programmersought.com", "link": "https://programmersought.com/article/360810344589/", "content": "The simple use of memory cache Lrucache . Lrucache algorithm ( Least Recently Used ), also known as the minimum use algorithm in the near future. This class is very suitable for caching pictures.Android caching mechanism - LruCache cache in memory ."}
+{"idx": 6, "title": "How to Reduce Hardware Reserved Memory in Windows 11/10", "date": "", "ddg_snippet": "Change how much memory it can use and save your changes. how to reduce hardware reserved memory windows 11.", "subpage_snippet": "", "source": "www.windowsdigitals.com", "link": "https://www.windowsdigitals.com/hardware-reserved-memory-windows-11-10/", "content": "Change how much memory it can use and save your changes. how to reduce hardware reserved memory windows 11."}
+{"idx": 7, "title": "what is the best way to cache images in android | HowTo.IM", "date": "", "ddg_snippet": "1. Using LruCache ( Memory Cache ).It keeps a limited number of images in memory , evicting the least recently accessed ones when the cache is full. How it works: When an image is requested, the cache is checked first.", "subpage_snippet": "", "source": "howto.im", "link": "https://howto.im/q/what-is-the-best-way-to-cache-images-in-android", "content": "1. Using LruCache ( Memory Cache ).It keeps a limited number of images in memory , evicting the least recently accessed ones when the cache is full. How it works: When an image is requested, the cache is checked first."}
+{"idx": 8, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient...", "date": "", "ddg_snippet": "90 % 90 \\% 90 %. memory reduction . The performance of HDT - MHRW with LRU is robust to the choice of.To handle memory limitations, we introduce a Least Recently Used ( LRU ) cache scheme, enabling partial tracking of the empirical measure without loss in sampling efficiency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "90 % 90 \\% 90 %. memory reduction . The performance of HDT - MHRW with LRU is robust to the choice of.To handle memory limitations, we introduce a Least Recently Used ( LRU ) cache scheme, enabling partial tracking of the empirical measure without loss in sampling efficiency."}
+{"idx": 9, "title": "How to Troubleshoot and Solve ComfyUI Model Issues - ComfyUI", "date": "", "ddg_snippet": "python main.py -- cache -classic # Use the old style (aggressive) caching . python main.py -- cache - lru 10 # Increase size of LRU cache . Memory Issues with Large Models.", "subpage_snippet": "", "source": "docs.comfy.org", "link": "https://docs.comfy.org/troubleshooting/model-issues", "content": "python main.py -- cache -classic # Use the old style (aggressive) caching . python main.py -- cache - lru 10 # Increase size of LRU cache . Memory Issues with Large Models."}
diff --git a/data/sampled_jsons/HOC_hierarchical_overlapping_clustering_cost_function_overlaps_reduce_cost_explanation.jsonl b/data/sampled_jsons/HOC_hierarchical_overlapping_clustering_cost_function_overlaps_reduce_cost_explanation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..780c3f6cbf85acaadce58e1dbfeea93f771c8c90
--- /dev/null
+++ b/data/sampled_jsons/HOC_hierarchical_overlapping_clustering_cost_function_overlaps_reduce_cost_explanation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "by Y Pan — We initiate the theoretical study of hierarchical overlapping clustering from the perspectives of cost function, algorithm and experiments.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "by Y Pan — We initiate the theoretical study of hierarchical overlapping clustering from the perspectives of cost function, algorithm and experiments."}
+{"idx": 1, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — TL;DR: We have proposed a cost function for hierarchical overlapping clustering on graphs, and developed an approximation algorithm for it.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "by Y Pan — TL;DR: We have proposed a cost function for hierarchical overlapping clustering on graphs, and developed an approximation algorithm for it."}
+{"idx": 2, "title": "Clustering System - an overview", "date": "", "ddg_snippet": "A clustering system in Computer Science refers to the organization of vehicles with similar characteristics into groups within a hierarchical structure.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/computer-science/clustering-system", "content": "A clustering system in Computer Science refers to the organization of vehicles with similar characteristics into groups within a hierarchical structure."}
+{"idx": 3, "title": "Robust and Efficient Hierarchical Clustering using ...", "date": "", "ddg_snippet": "by B Eriksson · 2011 · Cited by 83 — Abstract. Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1102.3887", "content": "by B Eriksson · 2011 · Cited by 83 — Abstract. Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications."}
+{"idx": 4, "title": "The Computational Complexity of Hierarchical Clustering ...", "date": "", "ddg_snippet": "by VH Bui · 2023 · Cited by 7 — This review aims to address this gap by providing a more detailed analysis and evaluation of the computational complexity of hierarchical clustering algorithms ...", "subpage_snippet": "", "source": "www.worldscientific.com", "link": "https://www.worldscientific.com/doi/full/10.1142/S2196888823300016?srsltid=AfmBOoqeksuYoTTtP_Tiu-jddCVGXrD1NdBW668YX5cCdQMm1LTrUgLw", "content": "by VH Bui · 2023 · Cited by 7 — This review aims to address this gap by providing a more detailed analysis and evaluation of the computational complexity of hierarchical clustering algorithms ..."}
+{"idx": 5, "title": "Clustering explanation based on multi-hyperrectangle - PMC", "date": "", "ddg_snippet": "by T Zeng · 2024 · Cited by 2 — This paper proposes a novel clustering explanation method based on a Multi-HyperRectangle(MHR), for extracting post hoc explanations of clustering results.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11618349/", "content": "by T Zeng · 2024 · Cited by 2 — This paper proposes a novel clustering explanation method based on a Multi-HyperRectangle(MHR), for extracting post hoc explanations of clustering results."}
+{"idx": 6, "title": "Interpretable Clustering: A Survey", "date": "", "ddg_snippet": "by L Hu · 2024 · Cited by 18 — The clustering model's ability to explain such issues is tentatively defined as model's clustering interpretability or explainability [3]. Given ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.00743", "content": "by L Hu · 2024 · Cited by 18 — The clustering model's ability to explain such issues is tentatively defined as model's clustering interpretability or explainability [3]. Given ..."}
+{"idx": 7, "title": "state-of-the-art-clustering-schemes-in-mobile-ad-hoc- ...", "date": "", "ddg_snippet": "by M Ahmad · 2019 · Cited by 57 — Overlapping : Overlapping clusters are formed for quick execution of the CA or better routing efficiency. Most of the well-known protocols focus on non- ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/state-of-the-art-clustering-schemes-in-mobile-ad-hoc-2zuoiq6vpi.pdf", "content": "by M Ahmad · 2019 · Cited by 57 — Overlapping : Overlapping clusters are formed for quick execution of the CA or better routing efficiency. Most of the well-known protocols focus on non- ..."}
+{"idx": 8, "title": "Detecting Overlapping Communities in Networks Using ...", "date": "", "ddg_snippet": "We develop an efficient spectral algorithm for estimating the community memberships, which deals with the overlaps by employing the K-medians algorithm rather ... 19 pages", "subpage_snippet": "", "source": "dept.stat.lsa.umich.edu", "link": "https://dept.stat.lsa.umich.edu/~jizhu/pubs/Zhang-SIAM20.pdf", "content": "We develop an efficient spectral algorithm for estimating the community memberships, which deals with the overlaps by employing the K-medians algorithm rather ... 19 pages"}
+{"idx": 9, "title": "US20110035379A1 - Probabilistic clustering of an item - Google", "date": "", "ddg_snippet": "G06F18/2321 — Non- hierarchical techniques using statistics or function optimisation, e.g. ... stated otherwise, components and functions are ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20110035379A1/en", "content": "G06F18/2321 — Non- hierarchical techniques using statistics or function optimisation, e.g. ... stated otherwise, components and functions are ..."}
diff --git a/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_PDF_arXiv.jsonl b/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_PDF_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fc8462f4d553f53407ba612cf2ce449ffbb387cc
--- /dev/null
+++ b/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_PDF_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... Understanding HtmlRAG: HTML is Better Than Plain Text for ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... (PDF) HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved GitHub - plageon/HtmlRAG: HtmlRAG: HTML is Better Than Plain Tex… HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Nov 5, 2024 · To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy. Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. 268 In this paper, we propose HtmlRAG, which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text . Nov 5, 2024 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. Is HTML better than plain text for retrieved knowledge in Rag? To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. Is HTML better than plain text? We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. Does htmlrag use HTML instead of plain text? However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. What is plain text based Rag? Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain - text -based RAG process. Are larger and more Instructable language models less reliable? Larger and more instructable language models become less reliable . Nature (2024), 1–8. Li, Zhicheng Dou, Tsung-Yi Ho, and Philip S. Y u. 2024. What is the latest version of htmlrag? [12/12/2024]: The latest version of htmlrag package is v0.0.5 , which now supports Chinese HTML documents. You can install it by running pip install htmlrag==0.0.5. [11/12/2024]: Our data and model are now available on ModelScope. You can access them here for faster downloading. However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain - text -based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "Nov 5, 2024 · To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy. Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. 268 In this paper, we propose HtmlRAG, which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text . Nov 5, 2024 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. Is HTML better than plain text for retrieved knowledge in Rag? To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. Is HTML better than plain text? We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. Does htmlrag use HTML instead of plain text? However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. What is plain text based Rag? Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain - text -based RAG process. Are larger and more Instructable language models less reliable? Larger and more instructable language models become less reliable . Nature (2024), 1–8. Li, Zhicheng Dou, Tsung-Yi Ho, and Philip S. Y u. 2024. What is the latest version of htmlrag? [12/12/2024]: The latest version of htmlrag package is v0.0.5 , which now supports Chinese HTML documents. You can install it by running pip install htmlrag==0.0.5. [11/12/2024]: Our data and model are now available on ModelScope. You can access them here for faster downloading. However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain - text -based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG ."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 2, "title": "Understanding HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy.", "subpage_snippet": "", "source": "techchilli.com", "link": "https://techchilli.com/artificial-intelligence/understanding-htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledge-in-rag-systems/", "content": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy."}
+{"idx": 3, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3696410.3714546", "content": "Apr 22, 2025 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system."}
+{"idx": 4, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "268 In this paper, we propose HtmlRAG, which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "268 In this paper, we propose HtmlRAG, which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text ."}
+{"idx": 5, "title": "(PDF) HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Nov 5, 2024 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "Nov 5, 2024 · We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges."}
+{"idx": 6, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain - text -based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG .", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv241102959T/abstract", "content": "However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain - text -based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG ."}
+{"idx": 7, "title": "Advancing Retrieval-Augmented Generation for Structured", "date": "", "ddg_snippet": "To address these failings, we introduce an enterprise-focused RAG framework that systematically enhances the retrieval and generation process across ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12425v1", "content": "To address these failings, we introduce an enterprise-focused RAG framework that systematically enhances the retrieval and generation process across ..."}
+{"idx": 8, "title": "SCAN: Semantic Document Layout Analysis for Textual and Visual", "date": "", "ddg_snippet": "For textual RAG with rich documents, we need to convert documents ( PDFs , images, etc.) into text , use a text retrieval, and then generate responses ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14381v1", "content": "For textual RAG with rich documents, we need to convert documents ( PDFs , images, etc.) into text , use a text retrieval, and then generate responses ..."}
+{"idx": 9, "title": "OCR Hinders RAG: Evaluating the Cascading Impact of OCR on", "date": "", "ddg_snippet": "... evaluation of current OCR solutions and reveal that none of them is competent for constructing high-quality knowledge bases for RAG systems .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.02592v3", "content": "... evaluation of current OCR solutions and reveal that none of them is competent for constructing high-quality knowledge bases for RAG systems ."}
diff --git a/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Table_1_Llama-3.1-70B_year_2024.jsonl b/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Table_1_Llama-3.1-70B_year_2024.jsonl
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index 0000000000000000000000000000000000000000..1ed33bea375a8694bfe911296e8a9d0ed8ca0cbd
--- /dev/null
+++ b/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Table_1_Llama-3.1-70B_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
+{"idx": 1, "title": "Paper tables with annotated results for HtmlRAG: HTML is Better ...", "date": "", "ddg_snippet": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems . Retrieval-Augmented Generation ( RAG ) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/htmlrag-html-is-better-than-plain-text-for/review/", "content": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems . Retrieval-Augmented Generation ( RAG ) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs."}
+{"idx": 2, "title": "Paper page - HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "2. [ Plain Text seems better ?] You can first refer to the table above for information loss evaluation, which shows that under a limited context window, HTML format reference contains less documents and has lower exact match score due to extra HTML tags occupying tokens.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "2. [ Plain Text seems better ?] You can first refer to the table above for information loss evaluation, which shows that under a limited context window, HTML format reference contains less documents and has lower exact match score due to extra HTML tags occupying tokens."}
+{"idx": 3, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=E91gjsccP1", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
+{"idx": 4, "title": "plageon/HtmlRAG: HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HTMLRAG", "content": "We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 5, "title": "(PDF) HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Retrieved Knowledge in RAG Systems . In Proceedings of TheWebConf 2025. (Conference acronym ’XX). Table 1 : Results of HtmlRAG and baselines under the short-context setting. Hit @ 1 is the proportion of instances where at least. one short answer matches.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "Retrieved Knowledge in RAG Systems . In Proceedings of TheWebConf 2025. (Conference acronym ’XX). Table 1 : Results of HtmlRAG and baselines under the short-context setting. Hit @ 1 is the proportion of instances where at least. one short answer matches."}
+{"idx": 6, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Traditional RAG systems often convert HTML to plain text , resulting in a significant loss of structural and semantic information. This loss can negatively impact the LLM’s ability to accurately comprehend and generate responses based on the retrieved knowledge .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2411.02959/", "content": "Traditional RAG systems often convert HTML to plain text , resulting in a significant loss of structural and semantic information. This loss can negatively impact the LLM’s ability to accurately comprehend and generate responses based on the retrieved knowledge ."}
+{"idx": 7, "title": "plageon/HtmlRAG - Githubissues", "date": "", "ddg_snippet": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems Quick Start (快速开始)...", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/plageon/HtmlRAG/readme", "content": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems Quick Start (快速开始)..."}
+{"idx": 8, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/HtmlRAG:-HTML-is-Better-Than-Plain-Text-for-Modeling-Retrieved-Knowledge-in-RAG-Systems-31e6dffd-f34d-48e0-8884-b4b1d1deeb4a", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
+{"idx": 9, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/paper/49bede8a-175c-42d0-991f-947ddc200689", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
diff --git a/data/sampled_jsons/Hanna_Wallach_lot_of_work_computer_science_measurement_theory_generative_AI_evaluation.jsonl b/data/sampled_jsons/Hanna_Wallach_lot_of_work_computer_science_measurement_theory_generative_AI_evaluation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1502d420e636b5f455ddbf5c005804f0e9e539a4
--- /dev/null
+++ b/data/sampled_jsons/Hanna_Wallach_lot_of_work_computer_science_measurement_theory_generative_AI_evaluation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "6 Jun 2025 — We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "6 Jun 2025 — We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, ..."}
+{"idx": 1, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · Cited by 12 — Hanna Wallach 1 Meera Desai 2 A. Feder Cooper 1 Angelina Wang 3 Chad Atalla ... But changing the current state will be a lot of work regardless of exactly how it ...", "subpage_snippet": "", "source": "afedercooper.info", "link": "https://afedercooper.info/paper/wallach2024measurement.pdf", "content": "by H Wallach · Cited by 12 — Hanna Wallach 1 Meera Desai 2 A. Feder Cooper 1 Angelina Wang 3 Chad Atalla ... But changing the current state will be a lot of work regardless of exactly how it ..."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems is a Social ...", "date": "", "ddg_snippet": "We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems. We explain how the structured approach afforded by this framework differs from the way measurement is typically done in ML.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561v1", "content": "We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems. We explain how the structured approach afforded by this framework differs from the way measurement is typically done in ML."}
+{"idx": 3, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561", "content": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems."}
+{"idx": 4, "title": "Measurement is the key to helping keep AI on track", "date": "", "ddg_snippet": "Sep 9, 2024 · The creation of generative AI requires a new approach to evaluating, or measuring, the technology — one that combines technical and social aspects.", "subpage_snippet": "", "source": "news.microsoft.com", "link": "https://news.microsoft.com/source/features/ai/measurement-is-the-key-to-helping-keep-ai-on-track/", "content": "Sep 9, 2024 · The creation of generative AI requires a new approach to evaluating, or measuring, the technology — one that combines technical and social aspects."}
+{"idx": 5, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "... framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "... framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of ..."}
+{"idx": 6, "title": "Computer Science | Civic Studies", "date": "", "ddg_snippet": "Laura Nelson argued for a process of computational grounded theory , in which textual analysis helps guide and direct deep reading, but in which ...", "subpage_snippet": "", "source": "civicstudies.org", "link": "http://civicstudies.org/category/computer-science/", "content": "Laura Nelson argued for a process of computational grounded theory , in which textual analysis helps guide and direct deep reading, but in which ..."}
+{"idx": 7, "title": "On Optimal Steering to Achieve Exact Fairness", "date": "", "ddg_snippet": "... of Computer Science IIIT Delhi, India mohits@iiitd.ac.in &Amit Jayant Deshpande Microsoft Research India amitdesh@microsoft.com Chiranjib ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15759v1", "content": "... of Computer Science IIIT Delhi, India mohits@iiitd.ac.in &Amit Jayant Deshpande Microsoft Research India amitdesh@microsoft.com Chiranjib ..."}
+{"idx": 8, "title": "Extracting memorized pieces of (copyrighted) books from", "date": "", "ddg_snippet": "Plaintiffs—such as book authors—say that LLMs are giant copy machines that store (infringing) copies of their specific works and recombine them ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.12546v3", "content": "Plaintiffs—such as book authors—say that LLMs are giant copy machines that store (infringing) copies of their specific works and recombine them ..."}
+{"idx": 9, "title": "Not a Swiss Army Knife: Academics’ Perceptions of Trade-Offs", "date": "", "ddg_snippet": "... 2012 ) was used to produce systematic and situated knowledge about our participants’ perceptions of Gen AI in relation to their knowledge work ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.00995v1", "content": "... 2012 ) was used to produce systematic and situated knowledge about our participants’ perceptions of Gen AI in relation to their knowledge work ..."}
diff --git a/data/sampled_jsons/Hardt_et_al._2023_abstract_year_2023.jsonl b/data/sampled_jsons/Hardt_et_al._2023_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..045f739c13138de65d0b9f9b7aed0d222e877e5a
--- /dev/null
+++ b/data/sampled_jsons/Hardt_et_al._2023_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Barocas, S., Hardt, M. and Narayanan, A. (2023) Fairness and ...", "date": "", "ddg_snippet": "Article citations More>> Barocas, S., Hardt , M. and Narayanan, A. ( 2023 ) Fairness and Machine Learning: Limitations and Opportunities. MIT Press. has been cited by the following article: TITLE: Adversarial Debiasing for Bias Mitigation in Healthcare AI Systems: A Literature Review AUTHORS: Joshua Waithira, Ruth Chweya, Ratemo Makiya Cyprian KEYWORDS: Adversarial Debiasing, Healthcare AI ...", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=4007167", "content": "Article citations More>> Barocas, S., Hardt , M. and Narayanan, A. ( 2023 ) Fairness and Machine Learning: Limitations and Opportunities. MIT Press. has been cited by the following article: TITLE: Adversarial Debiasing for Bias Mitigation in Healthcare AI Systems: A Literature Review AUTHORS: Joshua Waithira, Ruth Chweya, Ratemo Makiya Cyprian KEYWORDS: Adversarial Debiasing, Healthcare AI ..."}
+{"idx": 1, "title": "(PDF) Orsini, Amandine. 2023. The Rise of Belgium as a ...", "date": "", "ddg_snippet": "In sustainable forces and military extreme cases also development or other, installations for changed military intervention i.e., more radical policy climatic conditions) in the case of interventions responsibility to protect (R2P) measures Source Elaborated by the authors on the basis of Hardt (2018) and Hardt /Viehoff (2020) f16 J. N. Hardt et al .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/104336166/Orsini_Amandine_2023_The_Rise_of_Belgium_as_a_Multilateral_Climate_Security_Actor_Analysis_of_Evolving_Climate_and_Security_Policies_2009_2021_In_Hardt_J_N_et_al_eds_Climate_Security_in_the_Anthropocene_Springer_25_44", "content": "In sustainable forces and military extreme cases also development or other, installations for changed military intervention i.e., more radical policy climatic conditions) in the case of interventions responsibility to protect (R2P) measures Source Elaborated by the authors on the basis of Hardt (2018) and Hardt /Viehoff (2020) f16 J. N. Hardt et al ."}
+{"idx": 2, "title": "Hardt Et Al. 2023 Sexual Harassment in Low and Middle Income ...", "date": "", "ddg_snippet": "This qualitative systematic review examines sexual harassment in low- and middle-income countries (LMICs), highlighting its prevalence and the varied conceptualizations influenced by sociocultural factors. The review identifies key themes, including the conflation of sexual harassment with sexual violence, the role of gendered power dynamics, and the need for effective prevention strategies ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/866899719/Hardt-et-al-2023-Sexual-Harassment-in-Low-and-Middle-Income-Countr", "content": "This qualitative systematic review examines sexual harassment in low- and middle-income countries (LMICs), highlighting its prevalence and the varied conceptualizations influenced by sociocultural factors. The review identifies key themes, including the conflation of sexual harassment with sexual violence, the role of gendered power dynamics, and the need for effective prevention strategies ..."}
+{"idx": 3, "title": "NeurIPS 2023 Workshops", "date": "", "ddg_snippet": "This workshop aims to seed foundations of using AI/ML dedicated to studying touch and enable future applications such as robotics and AR/VR.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/events/workshop", "content": "This workshop aims to seed foundations of using AI/ML dedicated to studying touch and enable future applications such as robotics and AR/VR."}
+{"idx": 4, "title": "Sexual Harassment in Low- and Middle-Income Countries: A ...", "date": "", "ddg_snippet": "Two additional studies published afterwards were included (Wamoyi et al ., 2021; Worke et al ., 2021). The search terms used were the names of all LMICs as defined by The World Bank (2022) classifications and the term “sexual harassment” in the abstract or title of papers published in English on or after January 1, 1990.", "subpage_snippet": "", "source": "prevention-collaborative.org", "link": "https://prevention-collaborative.org/wp-content/uploads/2024/02/Hardt-et-al.-2023-Sexual-Harassment-in-Low-and-Middle-Income-Countr.pdf", "content": "Two additional studies published afterwards were included (Wamoyi et al ., 2021; Worke et al ., 2021). The search terms used were the names of all LMICs as defined by The World Bank (2022) classifications and the term “sexual harassment” in the abstract or title of papers published in English on or after January 1, 1990."}
+{"idx": 5, "title": "Climate change, security and the institutional prospects for ...", "date": "", "ddg_snippet": "Oct 1, 2024 · On the latter, recent analyses suggest that we see significant variation in the way in which nation-states conceive and approach the security implications of climate change ( Hardt et al ., 2023 ).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S001671852400157X", "content": "Oct 1, 2024 · On the latter, recent analyses suggest that we see significant variation in the way in which nation-states conceive and approach the security implications of climate change ( Hardt et al ., 2023 )."}
+{"idx": 6, "title": "ECS1 and ECS2 suppress polyspermy and the formation of ...", "date": "", "ddg_snippet": "Jul 25, 2023 · Abstract The current pace of crop plant optimization is insufficient to meet future demands and there is an urgent need for novel breeding strategies. It was previously shown that plants tolerate the generation of triparental polyspermy-derived plants and that polyspermy can bypass hybridization barriers. Polyspermy thus has the potential to harness previously incompatible climate-adapted wild ...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/37489742/", "content": "Jul 25, 2023 · Abstract The current pace of crop plant optimization is insufficient to meet future demands and there is an urgent need for novel breeding strategies. It was previously shown that plants tolerate the generation of triparental polyspermy-derived plants and that polyspermy can bypass hybridization barriers. Polyspermy thus has the potential to harness previously incompatible climate-adapted wild ..."}
+{"idx": 7, "title": "(PDF) ARTIFICIAL INTELLIGENCE AND BIAS: CHALLENGES ...", "date": "", "ddg_snippet": "Sep 28, 2023 · Abstract This paper investigates the multifaceted issue of algorithmic bias in artificial intelligence (AI) systems and explores its ethical and human rights implications.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/374232878_ARTIFICIAL_INTELLIGENCE_AND_BIAS_CHALLENGES_IMPLICATIONS_AND_REMEDIES", "content": "Sep 28, 2023 · Abstract This paper investigates the multifaceted issue of algorithmic bias in artificial intelligence (AI) systems and explores its ethical and human rights implications."}
+{"idx": 8, "title": "Algorithmic Collective Action in Machine Learning", "date": "", "ddg_snippet": "by M Hardt · 2023 · Cited by 29 — We initiate a principled study of algorithmic col- lective action on digital platforms that deploy ma- chine learning algorithms. We propose a simple.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/hardt23a/hardt23a.pdf", "content": "by M Hardt · 2023 · Cited by 29 — We initiate a principled study of algorithmic col- lective action on digital platforms that deploy ma- chine learning algorithms. We propose a simple."}
+{"idx": 9, "title": "A Framework for Exploring the Consequences of AI ...", "date": "", "ddg_snippet": "8 Dec 2023 — Hardt et al . [ 2023 ] ↑ Moritz Hardt , Eric Mazumdar, Celestine Mendler-Dünner, and Tijana Zrnic. 2023 . Algorithmic Collective Action in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.10076v1", "content": "8 Dec 2023 — Hardt et al . [ 2023 ] ↑ Moritz Hardt , Eric Mazumdar, Celestine Mendler-Dünner, and Tijana Zrnic. 2023 . Algorithmic Collective Action in ..."}
diff --git a/data/sampled_jsons/Hardt_et_al._2023_algorithmic_collective_action_abstract.jsonl b/data/sampled_jsons/Hardt_et_al._2023_algorithmic_collective_action_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4b88f23e9fdeb5ad1529470b5e645a28e875d852
--- /dev/null
+++ b/data/sampled_jsons/Hardt_et_al._2023_algorithmic_collective_action_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithmic Collective Action in Machine Learning", "date": "", "ddg_snippet": "by M Hardt · 2023 · Cited by 29 — We initiate a principled study of algorithm ic col - lective action on digital platforms that deploy ma- chine learning algorithms. We propose a simple.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/hardt23a/hardt23a.pdf", "content": "by M Hardt · 2023 · Cited by 29 — We initiate a principled study of algorithm ic col - lective action on digital platforms that deploy ma- chine learning algorithms. We propose a simple."}
+{"idx": 1, "title": "Algorithmic collective action in machine learning", "date": "", "ddg_snippet": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3618918", "content": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms."}
+{"idx": 2, "title": "Algorithmic Collective Action Under Differential Privacy", "date": "", "ddg_snippet": "9 May 2025 — In this work, we focus on the collective's goal of influencing the firm's learning behavior by modifying both the features and labels for all ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.05707v1", "content": "9 May 2025 — In this work, we focus on the collective's goal of influencing the firm's learning behavior by modifying both the features and labels for all ..."}
+{"idx": 3, "title": "Algorithmic Collective Action in Recommender Systems", "date": "", "ddg_snippet": "8 Apr 2024 — Hardt et al. (2023) introduce the framework of algorithmic collective action for formally studying coordinated strategies of users against ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.04269v1", "content": "8 Apr 2024 — Hardt et al. (2023) introduce the framework of algorithmic collective action for formally studying coordinated strategies of users against ..."}
+{"idx": 4, "title": "Algorithmic Collective Action with Two Collectives", "date": "", "ddg_snippet": "by A Karan · 2025 · Cited by 1 — We introduce a first of a kind framework for studying collective action with two or more collectives that strategically behave to manipulate data-driven ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3715275.3732098", "content": "by A Karan · 2025 · Cited by 1 — We introduce a first of a kind framework for studying collective action with two or more collectives that strategically behave to manipulate data-driven ..."}
+{"idx": 5, "title": "Algorithmic Collective Action with Two Collectives", "date": "", "ddg_snippet": "by A Karan · 2025 · Cited by 1 — Collective Action with Algorithms and Data: Hardt et al. [19] defines the notion of algorithmic collective action in a stylized model ...", "subpage_snippet": "", "source": "facctconference.org", "link": "https://facctconference.org/static/docs/facct2025-206archivalpdfs/facct2025-final562-acmpaginated.pdf", "content": "by A Karan · 2025 · Cited by 1 — Collective Action with Algorithms and Data: Hardt et al. [19] defines the notion of algorithmic collective action in a stylized model ..."}
+{"idx": 6, "title": "The Role of Learning Algorithms in Collective Action - GitHub", "date": "", "ddg_snippet": "by O Ben-Dov · Cited by 3 — Hardt et al . ( 2023 ) initiated the formal study into the suc- cess of different collective action strategies. Their work provided theoretical analysis based ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/ben-dov24a/ben-dov24a.pdf", "content": "by O Ben-Dov · Cited by 3 — Hardt et al . ( 2023 ) initiated the formal study into the suc- cess of different collective action strategies. Their work provided theoretical analysis based ..."}
+{"idx": 7, "title": "Algorithmic Collective Action in Recommender Systems", "date": "", "ddg_snippet": "5 Nov 2024 — We investigate algorithmic collective action in transformer-based recommender systems . Our use case is a collective of fans aiming to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=wGjSbaMsop&referrer=[the+profile+of+Celestine+Mendler-Dünner](/profile?id=~Celestine_Mendler-Dünner1)", "content": "5 Nov 2024 — We investigate algorithmic collective action in transformer-based recommender systems . Our use case is a collective of fans aiming to ..."}
+{"idx": 8, "title": "Algorithmic Collective Action in Recommender Systems", "date": "", "ddg_snippet": "We investigate algorithmic collective action in transformer-based recommender systems . Our use case is a collective of fans aiming to promote the visibility ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93164", "content": "We investigate algorithmic collective action in transformer-based recommender systems . Our use case is a collective of fans aiming to promote the visibility ..."}
+{"idx": 9, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract . As platforms increasingly rely on learning algo- rithms, collectives may form and seek ways to influence these platforms to align with their own.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=46yLEXtav4&name=pdf", "content": "Abstract . As platforms increasingly rely on learning algo- rithms, collectives may form and seek ways to influence these platforms to align with their own."}
diff --git a/data/sampled_jsons/Helping_the_Worst-Off_When_Hiring_More_Case_Workers_beats_building_better_AI_PAR.jsonl b/data/sampled_jsons/Helping_the_Worst-Off_When_Hiring_More_Case_Workers_beats_building_better_AI_PAR.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..956c1fbcbc2711ee1012145c4da86631df241594
--- /dev/null
+++ b/data/sampled_jsons/Helping_the_Worst-Off_When_Hiring_More_Case_Workers_beats_building_better_AI_PAR.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Home Remodeling Showroom | ProSource® of the Inland Empire", "date": "", "ddg_snippet": "Shop for cabinets, countertops, flooring, and more at ProSource® of the Inland Empire, the source for home remodeling products at low, wholesale prices.", "subpage_snippet": "", "source": "www.prosourcewholesale.com", "link": "https://www.prosourcewholesale.com/showrooms/ca-prosource-of-the-inland-empire", "content": "Shop for cabinets, countertops, flooring, and more at ProSource® of the Inland Empire, the source for home remodeling products at low, wholesale prices."}
+{"idx": 1, "title": "ProSource Wholesale | Home Design, Remodeling, and Flooring", "date": "", "ddg_snippet": "Take Control Of Your Home Remodel ProSource Wholesale® is your one-stop shop for all of your home remodeling needs. One-stop shop with the products and supplies you need Wholesale prices on top quality brands Complete our form to get connected with the right trade pro", "subpage_snippet": "", "source": "www.prosourcewholesale.com", "link": "https://www.prosourcewholesale.com/", "content": "Take Control Of Your Home Remodel ProSource Wholesale® is your one-stop shop for all of your home remodeling needs. One-stop shop with the products and supplies you need Wholesale prices on top quality brands Complete our form to get connected with the right trade pro"}
+{"idx": 2, "title": "Home Remodeling Showroom | ProSource® of Albuquerque", "date": "", "ddg_snippet": "Shop for cabinets, countertops, flooring, and more at ProSource® of Albuquerque, the source for home remodeling products at low, wholesale prices.", "subpage_snippet": "", "source": "www.prosourcewholesale.com", "link": "https://www.prosourcewholesale.com/showrooms/nm-prosource-of-albuquerque", "content": "Shop for cabinets, countertops, flooring, and more at ProSource® of Albuquerque, the source for home remodeling products at low, wholesale prices."}
+{"idx": 3, "title": "Home Remodeling Showroom | ProSource® of Wichita", "date": "", "ddg_snippet": "Shop for cabinets, countertops, flooring, and more at ProSource® of Wichita, the source for home remodeling products at low, wholesale prices.", "subpage_snippet": "", "source": "www.prosourcewholesale.com", "link": "https://www.prosourcewholesale.com/showrooms/ks-prosource-of-wichita", "content": "Shop for cabinets, countertops, flooring, and more at ProSource® of Wichita, the source for home remodeling products at low, wholesale prices."}
+{"idx": 4, "title": "Flooring | ProSource Wholesale", "date": "", "ddg_snippet": "Shop for flooring at ProSource Wholesale®, the source for carpet, hardwood, laminate, tile, and vinyl at low, wholesale prices.", "subpage_snippet": "", "source": "www.prosourcewholesale.com", "link": "https://www.prosourcewholesale.com/flooring", "content": "Shop for flooring at ProSource Wholesale®, the source for carpet, hardwood, laminate, tile, and vinyl at low, wholesale prices."}
+{"idx": 5, "title": "Home Remodeling Showroom | ProSource® of Dallas Market Center", "date": "", "ddg_snippet": "Shop for cabinets, countertops, flooring, and more at ProSource® of Dallas Market Center, the source for home remodeling products at low, wholesale prices.", "subpage_snippet": "", "source": "www.prosourcewholesale.com", "link": "https://www.prosourcewholesale.com/showrooms/tx-prosource-of-dallas-market-center", "content": "Shop for cabinets, countertops, flooring, and more at ProSource® of Dallas Market Center, the source for home remodeling products at low, wholesale prices."}
+{"idx": 6, "title": "About ProSource | ProSource Wholesale", "date": "", "ddg_snippet": "The myProSource Project Center account lets you share visions and ideas with one another to take the guesswork out of the process. And it’s all in one convenient place.", "subpage_snippet": "", "source": "www.prosourcewholesale.com", "link": "https://www.prosourcewholesale.com/about", "content": "The myProSource Project Center account lets you share visions and ideas with one another to take the guesswork out of the process. And it’s all in one convenient place."}
+{"idx": 7, "title": "Greensboro/Winston Salem, North Carolina - ProSource® Franchise", "date": "", "ddg_snippet": "GREENSBORO/WINSTON SALEM, NORTH CAROLINA Build Your Future with a ProSource Wholesale Home Improvement Franchise Private Webinar ProSource Wholesale® is North America’s premier partner and supplier to the trade for home and commercial remodeling projects. Our member’s only showrooms offer an incredible mix of products in flooring, kitchen and bath – with over 50,000 product choices…", "subpage_snippet": "", "source": "franchise.prosourcewholesale.com", "link": "https://franchise.prosourcewholesale.com/greensboro-winston-salem-nc/", "content": "GREENSBORO/WINSTON SALEM, NORTH CAROLINA Build Your Future with a ProSource Wholesale Home Improvement Franchise Private Webinar ProSource Wholesale® is North America’s premier partner and supplier to the trade for home and commercial remodeling projects. Our member’s only showrooms offer an incredible mix of products in flooring, kitchen and bath – with over 50,000 product choices…"}
+{"idx": 8, "title": "Home Remodeling Showroom | ProSource® of Las Vegas", "date": "", "ddg_snippet": "Shop for cabinets, countertops, flooring, and more at ProSource® of Las Vegas, the source for home remodeling products at low, wholesale prices.", "subpage_snippet": "", "source": "www.prosourcewholesale.com", "link": "https://www.prosourcewholesale.com/showrooms/nv-prosource-of-las-vegas", "content": "Shop for cabinets, countertops, flooring, and more at ProSource® of Las Vegas, the source for home remodeling products at low, wholesale prices."}
+{"idx": 9, "title": "Showrooms - ProSource Wholesale", "date": "", "ddg_snippet": "Why Choose ProSource? Help & FAQs Get Inspired Find Resources Find a Pro Hello! Sign in or create an account to get started. Have an online account? Sign In", "subpage_snippet": "", "source": "www.prosourcewholesale.com", "link": "https://www.prosourcewholesale.com/showrooms", "content": "Why Choose ProSource? Help & FAQs Get Inspired Find Resources Find a Pro Hello! Sign in or create an account to get started. Have an online account? Sign In"}
diff --git a/data/sampled_jsons/Herbort_photometric_stereo_inter-reflections_subsurface_scattering.jsonl b/data/sampled_jsons/Herbort_photometric_stereo_inter-reflections_subsurface_scattering.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3aefd1c1086c9ff3d41ea8ece03945552e30c5b8
--- /dev/null
+++ b/data/sampled_jsons/Herbort_photometric_stereo_inter-reflections_subsurface_scattering.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval ...", "date": "", "ddg_snippet": "by K Kitazawa · 2025 — Sec- ond, we aim to account for inter-reflections and subsurface scattering , which are not considered in the current approach. To address these effects, we ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "by K Kitazawa · 2025 — Sec- ond, we aim to account for inter-reflections and subsurface scattering , which are not considered in the current approach. To address these effects, we ..."}
+{"idx": 1, "title": "Sparse Photometric 3D Face Reconstruction Guided by ...", "date": "", "ddg_snippet": "by X Cao · 2018 · Cited by 42 — We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest ad- vances on face registration / modeling from ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_cvpr_2018/papers/Cao_Sparse_Photometric_3D_CVPR_2018_paper.pdf", "content": "by X Cao · 2018 · Cited by 42 — We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest ad- vances on face registration / modeling from ..."}
+{"idx": 2, "title": "3D range scan enhancement using image-based methods", "date": "", "ddg_snippet": "by S Herbort · 2013 · Cited by 9 — The beneficial effect of interreflection compensation on the reconstruction accuracy is evaluated quantitatively in a Photometric Stereo framework.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0924271613001615", "content": "by S Herbort · 2013 · Cited by 9 — The beneficial effect of interreflection compensation on the reconstruction accuracy is evaluated quantitatively in a Photometric Stereo framework."}
+{"idx": 3, "title": "An Introduction to Image-based 3D Surface Reconstruction ...", "date": "", "ddg_snippet": "This paper provides an introduction to photometric methods for image-based 3D shape reconstruction and a survey of photometric stereo techniques.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/9567235/An_Introduction_to_Image_based_3D_Surface_Reconstruction_and_a_Survey_of_Photometric_Stereo_Methods", "content": "This paper provides an introduction to photometric methods for image-based 3D shape reconstruction and a survey of photometric stereo techniques."}
+{"idx": 4, "title": "Principles of appearance acquisition and representation", "date": "", "ddg_snippet": "This class describes recent work in the graphics community to measure the spatially- and directionally-varying reflectance and subsurface scattering of complex ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/1401132.1401234", "content": "This class describes recent work in the graphics community to measure the spatially- and directionally-varying reflectance and subsurface scattering of complex ..."}
+{"idx": 5, "title": "Human Hair Inverse Rendering using Multi-View ...", "date": "", "ddg_snippet": "Our method consists of two stages. First, we propose a novel solution for line-based multi-view stereo that yields accurate hair geometry from multi-view ... 12 pages", "subpage_snippet": "", "source": "cseweb.ucsd.edu", "link": "https://cseweb.ucsd.edu/~ravir/hairinverse.pdf", "content": "Our method consists of two stages. First, we propose a novel solution for line-based multi-view stereo that yields accurate hair geometry from multi-view ... 12 pages"}
+{"idx": 6, "title": "A Survey on Intrinsic Images", "date": "", "ddg_snippet": "by E Garces · 2021 · Cited by 58 — The wax candle exhibits multiple in- ternal ( subsurface ) scattering of photons. The yellow silk fabric of the book cover shows specular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2112.03842", "content": "by E Garces · 2021 · Cited by 58 — The wax candle exhibits multiple in- ternal ( subsurface ) scattering of photons. The yellow silk fabric of the book cover shows specular ..."}
+{"idx": 7, "title": "BRDF Representation and Acquisition - Guarnera - 2016", "date": "", "ddg_snippet": "27 May 2016 — The model takes into account masking, shadowing and inter - reflections . ... : Reflection from layered surfaces due to subsurface scattering . In ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1111/cgf.12867", "content": "27 May 2016 — The model takes into account masking, shadowing and inter - reflections . ... : Reflection from layered surfaces due to subsurface scattering . In ..."}
+{"idx": 8, "title": "Design and Implementation of Practical Bidirectional ...", "date": "", "ddg_snippet": "by C Schwartz · 2014 · Cited by 73 — These structures may cast shadows or interreflections , occlude the point from certain views or transport light via subsurface scattering . Furthermore, these ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4063053/", "content": "by C Schwartz · 2014 · Cited by 73 — These structures may cast shadows or interreflections , occlude the point from certain views or transport light via subsurface scattering . Furthermore, these ..."}
+{"idx": 9, "title": "Advances in geometry and reflectance acquisition (course ...", "date": "", "ddg_snippet": "by M Weinmann · 2015 · Cited by 82 — This course provides a thorough overview of the standard methods for the acquisition of both geometry and reflectance of surfaces with different types of ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/2818143.2818165", "content": "by M Weinmann · 2015 · Cited by 82 — This course provides a thorough overview of the standard methods for the acquisition of both geometry and reflectance of surfaces with different types of ..."}
diff --git a/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function_Algorithm_Scalability_overlaps.jsonl b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function_Algorithm_Scalability_overlaps.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..407aa2fb553f038c522e8fbdababfc364fb5f895
--- /dev/null
+++ b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function_Algorithm_Scalability_overlaps.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cluster analysis - Wikipedia", "date": "", "ddg_snippet": "Overlapping clustering (also: alternative clustering , multi-view clustering ): objects may belong to more than one cluster ; usually involving hard clusters .Main category: Cluster analysis algorithms . As listed above, clustering algorithms can be categorized based on their cluster model.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cluster_analysis", "content": "Overlapping clustering (also: alternative clustering , multi-view clustering ): objects may belong to more than one cluster ; usually involving hard clusters .Main category: Cluster analysis algorithms . As listed above, clustering algorithms can be categorized based on their cluster model."}
+{"idx": 1, "title": "ICML Poster Hierarchical Overlapping Clustering on Graphs : Cost ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system.To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46447", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system.To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it."}
+{"idx": 2, "title": "Graph Clustering Algorithms | Encyclopedia MDPI", "date": "", "ddg_snippet": "Graph Clustering Algorithms . Edit. This entry is adapted from the peer-reviewed paper 10.3390/app14010380.edge degree graph clustering mean relative density deviation coefficient (MRDDC) overlapping clustering partitioning clustering relative density. 1. Introduction.", "subpage_snippet": "", "source": "encyclopedia.pub", "link": "https://encyclopedia.pub/entry/54322", "content": "Graph Clustering Algorithms . Edit. This entry is adapted from the peer-reviewed paper 10.3390/app14010380.edge degree graph clustering mean relative density deviation coefficient (MRDDC) overlapping clustering partitioning clustering relative density. 1. Introduction."}
+{"idx": 3, "title": "Hierarchical Overlapping Clustering of Network Data Using Cut...", "date": "", "ddg_snippet": "Functorial Hierarchical Clustering with Overlaps .Here we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386690815_Hierarchical_Overlapping_Clustering_of_Network_Data_Using_Cut_Metrics", "content": "Functorial Hierarchical Clustering with Overlaps .Here we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function ."}
+{"idx": 4, "title": "Overlapping community detection in weighted networks... | PLOS One", "date": "", "ddg_snippet": "4 Proposed algorithm for overlapping hierarchical community detection in weighted networks. Hierarchical agglomerative clustering on the graph (GHAC) detects nested communities in a network using novel dissimilarity.", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0312596", "content": "4 Proposed algorithm for overlapping hierarchical community detection in weighted networks. Hierarchical agglomerative clustering on the graph (GHAC) detects nested communities in a network using novel dissimilarity."}
+{"idx": 5, "title": "Hierarchical Agglomerative Graph Clustering in... | OpenReview", "date": "", "ddg_snippet": "Abstract: Obtaining scalable algorithms for \\emph{ hierarchical agglomerative clustering } (HAC) is of significant interest due to the massive size of real-world datasets. At the same time, efficiently parallelizing HAC is difficult due to the seemingly sequential nature of the algorithm .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LpgG0C6Y75", "content": "Abstract: Obtaining scalable algorithms for \\emph{ hierarchical agglomerative clustering } (HAC) is of significant interest due to the massive size of real-world datasets. At the same time, efficiently parallelizing HAC is difficult due to the seemingly sequential nature of the algorithm ."}
+{"idx": 6, "title": "Overview of overlapping partitional clustering", "date": "", "ddg_snippet": "Basically, the existing overlapping clustering methods are extensions from usual clustering models such as hierarchical , generative, graph -based or partitional mod-els.", "subpage_snippet": "", "source": "www.larodec.com", "link": "https://www.larodec.com/download/file/fid/432", "content": "Basically, the existing overlapping clustering methods are extensions from usual clustering models such as hierarchical , generative, graph -based or partitional mod-els."}
+{"idx": 7, "title": "Higher-order graph clustering at AMS Spring Western Sectional | PPT", "date": "", "ddg_snippet": "Tensor Spectral Clustering is an algorithm that generalizes graph partitioning and spectral clustering methods to account for higher-order network structures.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/higherorder-graph-clustering-at-ams-spring-western-sectional/77206438", "content": "Tensor Spectral Clustering is an algorithm that generalizes graph partitioning and spectral clustering methods to account for higher-order network structures."}
+{"idx": 8, "title": "Improved Multi-Objective Data Stream Clustering with Time and...", "date": "", "ddg_snippet": "For stream clustering algorithms , only one Multi-Objective clustering method has been proposed. In [29], authors opti-mize multiple objectives capturing cluster compactness and feature relevancy.SSD Overlap -Separateness.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04456329/document", "content": "For stream clustering algorithms , only one Multi-Objective clustering method has been proposed. In [29], authors opti-mize multiple objectives capturing cluster compactness and feature relevancy.SSD Overlap -Separateness."}
+{"idx": 9, "title": "A Gravitation-Based Hierarchical Community Detection Algorithm for...", "date": "", "ddg_snippet": "Cluster - overlap Newman Girvan algorithm .Gravitation-based hierarchical overlapping community detection algorithm . NMI: Normalized mutual information.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s44196-023-00290-x", "content": "Cluster - overlap Newman Girvan algorithm .Gravitation-based hierarchical overlapping community detection algorithm . NMI: Normalized mutual information."}
diff --git a/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_cost_function_overlap.jsonl b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_cost_function_overlap.jsonl
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index 0000000000000000000000000000000000000000..a37120ec1ac543f71faff02292a2e8c920867f14
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "by Y Pan — The paper introduces the first cost function for hierarchical clustering with overlap . The cost function is a natural extension of DasGupta for the case of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "by Y Pan — The paper introduces the first cost function for hierarchical clustering with overlap . The cost function is a natural extension of DasGupta for the case of ..."}
+{"idx": 1, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function ...", "date": "", "ddg_snippet": "The cost function is evaluated on HOC graphs , and can be unified with Dasgupta's cost function for HC trees in the specific case of non- overlap . We give a ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46447", "content": "The cost function is evaluated on HOC graphs , and can be unified with Dasgupta's cost function for HC trees in the specific case of non- overlap . We give a ..."}
+{"idx": 2, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function, ...", "date": "", "ddg_snippet": "TLDR: We have proposed a cost function for hierarchical overlapping clustering on graphs , and developed an approximation algorithm for it. Overlap and ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/167578", "content": "TLDR: We have proposed a cost function for hierarchical overlapping clustering on graphs , and developed an approximation algorithm for it. Overlap and ..."}
+{"idx": 3, "title": "Hierarchical clustering with maximum density paths and ...", "date": "", "ddg_snippet": "by M Ritzert · 2025 · Cited by 1 — Assigns components to classes based on high- est overlap with ground-truth labels. ... PAGA also added the geodesic KL cost function used to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.15582", "content": "by M Ritzert · 2025 · Cited by 1 — Assigns components to classes based on high- est overlap with ground-truth labels. ... PAGA also added the geodesic KL cost function used to ..."}
+{"idx": 4, "title": "Overlapping communities detection through weighted ...", "date": "", "ddg_snippet": "by S Benati · 2023 · Cited by 10 — ... cost function that correct the bias. We propose a heuristic algorithm that ... overlap . However, a crucial feature of the model is the way in which ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10072486/", "content": "by S Benati · 2023 · Cited by 10 — ... cost function that correct the bias. We propose a heuristic algorithm that ... overlap . However, a crucial feature of the model is the way in which ..."}
+{"idx": 5, "title": "Adapting k-means for graph clustering", "date": "", "ddg_snippet": "by S Sieranoja · 2022 · Cited by 49 — Many algorithms also use a cost function to guide the clustering process. For example, a cost function can be used to select the optimal ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10115-021-01623-y", "content": "by S Sieranoja · 2022 · Cited by 49 — Many algorithms also use a cost function to guide the clustering process. For example, a cost function can be used to select the optimal ..."}
+{"idx": 6, "title": "Cluster Analysis via Maximizing a Submodular Function ...", "date": "", "ddg_snippet": "by MY Liu · 2013 · Cited by 138 — Recently, Jegelka and Bilmes [30] propose a submodular cost function for image segmentation called the ... largest overlap . The fraction of correctly labeled ... 16 pages", "subpage_snippet": "", "source": "www.merl.com", "link": "https://www.merl.com/publications/docs/TR2013-117.pdf", "content": "by MY Liu · 2013 · Cited by 138 — Recently, Jegelka and Bilmes [30] propose a submodular cost function for image segmentation called the ... largest overlap . The fraction of correctly labeled ... 16 pages"}
+{"idx": 7, "title": "Fast agglomerative clustering using approximate traveling ...", "date": "", "ddg_snippet": "by S Sieranoja · 2025 · Cited by 2 — [34] used the same index as the cost function in agglomerative clustering ... However, as cluster variance increases and overlap between ...", "subpage_snippet": "", "source": "journalofbigdata.springeropen.com", "link": "https://journalofbigdata.springeropen.com/articles/10.1186/s40537-024-01053-x", "content": "by S Sieranoja · 2025 · Cited by 2 — [34] used the same index as the cost function in agglomerative clustering ... However, as cluster variance increases and overlap between ..."}
+{"idx": 8, "title": "A graph clustering algorithm based on random walks", "date": "", "ddg_snippet": "by SA Tabrizi · 2013 · Cited by 66 — ... clustering cost function is consistent with the underlying logic of the community formation, although it also achieves good results for other kinds of graphs .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0378437113006316", "content": "by SA Tabrizi · 2013 · Cited by 66 — ... clustering cost function is consistent with the underlying logic of the community formation, although it also achieves good results for other kinds of graphs ."}
+{"idx": 9, "title": "Categorical data clustering: 25 years beyond K-modes", "date": "", "ddg_snippet": "30 Aug 2024 — The cost function comprises two components: the intra- cluster cost ... overlap of items within each cluster . In this context, “ overlap ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17244v1", "content": "30 Aug 2024 — The cost function comprises two components: the intra- cluster cost ... overlap of items within each cluster . In this context, “ overlap ..."}
diff --git a/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_time_complexity_Algorithm_2_k-HOC.jsonl b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_time_complexity_Algorithm_2_k-HOC.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..26933365f64d840b0f843e7df7b3f18134e67cac
--- /dev/null
+++ b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_time_complexity_Algorithm_2_k-HOC.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function ...", "date": "", "ddg_snippet": "To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and establishing its rationality through several intu-itive properties. We further develop an approxi-mation algorithm that achieves a constant approx-imation factor for its dual version.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and establishing its rationality through several intu-itive properties. We further develop an approxi-mation algorithm that achieves a constant approx-imation factor for its dual version."}
+{"idx": 1, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "by Y Pan — To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. ... k-HOC , that was ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "by Y Pan — To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. ... k-HOC , that was ..."}
+{"idx": 2, "title": "HIERARCHICAL OVERLAPPING CLUSTERING FUNCTION ALGORITHM AND ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering, and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering, and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 3, "title": "2.3. Clustering — scikit-learn 1.6.1 documentation", "date": "", "ddg_snippet": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that ...", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/1.6/modules/clustering.html", "content": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that ..."}
+{"idx": 4, "title": "2.3. Clustering — scikit-learn 1.8.dev0 documentation", "date": "", "ddg_snippet": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that ...", "subpage_snippet": "", "source": "scikit-learn.qubitpi.org", "link": "https://scikit-learn.qubitpi.org/modules/clustering.html", "content": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that ..."}
+{"idx": 5, "title": "US20170083608A1 - Accelerated discrete distribution clustering", "date": "", "ddg_snippet": "... time to solve the linear programming problem (Shman and Teng, 2004), D2- clustering has a much higher computational complexity than K-means algorithm ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20170083608A1/en", "content": "... time to solve the linear programming problem (Shman and Teng, 2004), D2- clustering has a much higher computational complexity than K-means algorithm ..."}
+{"idx": 6, "title": "Approximating Dasgupta Cost in Sublinear Time from a Few Random", "date": "", "ddg_snippet": "... generally, we would like to design a sublinear time algorithm that approximates the hierarchical clustering properties of k k -clusterable graphs .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2207.02581v3", "content": "... generally, we would like to design a sublinear time algorithm that approximates the hierarchical clustering properties of k k -clusterable graphs ."}
+{"idx": 7, "title": "(PDF) Top 10 algorithms in data mining", "date": "", "ddg_snippet": "This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/29467751_Top_10_algorithms_in_data_mining", "content": "This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k ..."}
+{"idx": 8, "title": "K Rotation-invariant similarity in time series using", "date": "", "ddg_snippet": "We note that in real-world time series, the maximal cardinality of : Comparing the quality of the approximate k-motiflet discovery algorithms on 12 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/257580572_K_Rotation-invariant_similarity_in_time_series_using_bag-of-patterns_representation", "content": "We note that in real-world time series, the maximal cardinality of : Comparing the quality of the approximate k-motiflet discovery algorithms on 12 ..."}
+{"idx": 9, "title": "Hierarchical Clustering vs K-Means Clustering: All You Need to", "date": "", "ddg_snippet": "... two popular clustering techniques, without ... There are two main types of clustering algorithms : hierarchical clustering and k-means clustering .", "subpage_snippet": "", "source": "datarundown.com", "link": "https://datarundown.com/hierarchical-vs-k-means-clustering/", "content": "... two popular clustering techniques, without ... There are two main types of clustering algorithms : hierarchical clustering and k-means clustering ."}
diff --git a/data/sampled_jsons/Hoeffding_error_term_Rs(k)_machine_learning.jsonl b/data/sampled_jsons/Hoeffding_error_term_Rs(k)_machine_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ddbbca3b626f25708204f258cb64cb0328cd917e
--- /dev/null
+++ b/data/sampled_jsons/Hoeffding_error_term_Rs(k)_machine_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "why is Hoeffding's inequality correct in machine learning?", "date": "", "ddg_snippet": "ksm001: Hoeffdings is just for one try (i rewrote my answer to make it clearer). VC inequality extends Hoeffdings to multiple tries. You don't need to have assumptions. In fact: The less assumptions you have, the less restricted you are. VC inequality is independent from the algorithm, distributions and hypotheses. It uses very very basic assumtions to be as general applicable as possible.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/172191/why-is-hoeffdings-inequality-correct-in-machine-learning", "content": "ksm001: Hoeffdings is just for one try (i rewrote my answer to make it clearer). VC inequality extends Hoeffdings to multiple tries. You don't need to have assumptions. In fact: The less assumptions you have, the less restricted you are. VC inequality is independent from the algorithm, distributions and hypotheses. It uses very very basic assumtions to be as general applicable as possible."}
+{"idx": 1, "title": "Is Learning Feasible?: Hoeffding Inequality - GitHub Pages", "date": "", "ddg_snippet": "Hoeffding in Machine Learning We can replace the respective sample mean and expected value from Hoeffding Inequality with our equivalence in Machine Learning , where", "subpage_snippet": "", "source": "zosov.github.io", "link": "https://zosov.github.io/notes/machine_learning/hoeffding_ineq/index.html", "content": "Hoeffding in Machine Learning We can replace the respective sample mean and expected value from Hoeffding Inequality with our equivalence in Machine Learning , where"}
+{"idx": 2, "title": "Statistical Learning Theory Part 1: Hoeffding's Inequality Derivation ...", "date": "", "ddg_snippet": "Photo by Luca Bravo on Unsplash 1: Background & Motivation Hoeffding's Inequality is an important concentration inequality in Mathematical Statistics and Machine Learning (ML), leveraged extensively in theoretically areas such as Statistical Learning Theory as well as applied areas such as Reinforcement Learning . I have noticed in pockets of the ML community it common to present Hoeffding ...", "subpage_snippet": "", "source": "anr248.medium.com", "link": "https://anr248.medium.com/statistical-learning-theory-hoeffdings-inequality-derivation-simulation-e3a97100d147", "content": "Photo by Luca Bravo on Unsplash 1: Background & Motivation Hoeffding's Inequality is an important concentration inequality in Mathematical Statistics and Machine Learning (ML), leveraged extensively in theoretically areas such as Statistical Learning Theory as well as applied areas such as Reinforcement Learning . I have noticed in pockets of the ML community it common to present Hoeffding ..."}
+{"idx": 3, "title": "Understanding the Hoeffding Inequality - Open Data Science", "date": "", "ddg_snippet": "If you read my last post on mathematically defining machine learning problems, then you'll be familiar with the terminology here. Otherwise, I recommend you read that and then circle back here. We'll work our way up to understanding the Hoeffding Bound over a few posts. However, it's important to understand...", "subpage_snippet": "", "source": "opendatascience.com", "link": "https://opendatascience.com/understanding-the-hoeffding-inequality/", "content": "If you read my last post on mathematically defining machine learning problems, then you'll be familiar with the terminology here. Otherwise, I recommend you read that and then circle back here. We'll work our way up to understanding the Hoeffding Bound over a few posts. However, it's important to understand..."}
+{"idx": 4, "title": "Hoeffding's inequality for general Markov chains with its applications ...", "date": "", "ddg_snippet": "However, the independence assumption on random variables limits the applicability of Hoeffding's inequality and other concentration inequalities in many statistical, econometric and machine learning problems involving Markovian dependence.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8457514/", "content": "However, the independence assumption on random variables limits the applicability of Hoeffding's inequality and other concentration inequalities in many statistical, econometric and machine learning problems involving Markovian dependence."}
+{"idx": 5, "title": "PDF Hoeffding's Inequality for General Markov Chains and Its Applications ...", "date": "", "ddg_snippet": "1. Introduction Concentration inequalities bound the deviation of the sum of independent random variables from its expectation. They have found numerous applications in statistics, econometrics, machine learning and many other fields. One of the important and fundamental concentration inequalities was dis-covered by Hoeffding (1963). Hoeffding's lemma asserts that a bounded random variable Z ...", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume22/19-479/19-479.pdf", "content": "1. Introduction Concentration inequalities bound the deviation of the sum of independent random variables from its expectation. They have found numerous applications in statistics, econometrics, machine learning and many other fields. One of the important and fundamental concentration inequalities was dis-covered by Hoeffding (1963). Hoeffding's lemma asserts that a bounded random variable Z ..."}
+{"idx": 6, "title": "Mastering Hoeffding's Inequality - numberanalytics.com", "date": "", "ddg_snippet": "Introduction to Hoeffding's Inequality Hoeffding's Inequality is a fundamental concept in probability theory and statistics, playing a crucial role in Machine Learning and Computer Science. It provides a bound on the probability that the sum of independent random variables deviates from its expected value.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/hoeffdings-inequality-guide", "content": "Introduction to Hoeffding's Inequality Hoeffding's Inequality is a fundamental concept in probability theory and statistics, playing a crucial role in Machine Learning and Computer Science. It provides a bound on the probability that the sum of independent random variables deviates from its expected value."}
+{"idx": 7, "title": "New-Type Hoeffding's Inequalities and Application in Tail Bounds", "date": "", "ddg_snippet": "Index Terms—Hoeffding's Lemma, Hoeffding's tail bounds, Azuma inequality, Chernoff's bound. I. INTRODUCTION It is well known that Hoeffding's inequality has been applied in many scenarios in the signal and information processing fields.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2101.00360", "content": "Index Terms—Hoeffding's Lemma, Hoeffding's tail bounds, Azuma inequality, Chernoff's bound. I. INTRODUCTION It is well known that Hoeffding's inequality has been applied in many scenarios in the signal and information processing fields."}
+{"idx": 8, "title": "PDF CS229 Supplemental Lecture notes Hoeffding's inequa", "date": "", "ddg_snippet": "3 Hoeffding's lemma and Hoeffding's inequal-ity ms of bounded random variables are too large or too small. We will state the inequality, and then we will prove a weakened version of it ba", "subpage_snippet": "", "source": "cs229.stanford.edu", "link": "https://cs229.stanford.edu/extra-notes/hoeffding.pdf", "content": "3 Hoeffding's lemma and Hoeffding's inequal-ity ms of bounded random variables are too large or too small. We will state the inequality, and then we will prove a weakened version of it ba"}
+{"idx": 9, "title": "PDF Concentration Inequalities: Hoeffding and McDiarm", "date": "", "ddg_snippet": "1.1 Recap of Inequalities We want to show that the expected risk R(f) is close to the sample average ˆR(f). To do so we use concentration inequalities; two simple inequalities are the following:", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~bartlett/courses/281b-sp08/13.pdf", "content": "1.1 Recap of Inequalities We want to show that the expected risk R(f) is close to the sample average ˆR(f). To do so we use concentration inequalities; two simple inequalities are the following:"}
diff --git a/data/sampled_jsons/HtmlRAG_Figure_2_first_transformation_step_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_Figure_2_first_transformation_step_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e2e56ad3300655d4b23c58695272626e9732ea3
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_Figure_2_first_transformation_step_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "5 Nov 2024 — We propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "5 Nov 2024 — We propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling ..."}
+{"idx": 1, "title": "Unleashing the Power of HtmlRAG: Transforming RAG with ...", "date": "", "ddg_snippet": "The first step involves pruning based on text embedding . This step calculates the similarity between different parts of the HTML document and ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/unleashing-power-htmlrag-transforming-rag-html-enhanced-alex-zhang-mdfnc", "content": "The first step involves pruning based on text embedding . This step calculates the similarity between different parts of the HTML document and ..."}
+{"idx": 2, "title": "HTMLRAG, Multimodal RAG, and Agentic RAG", "date": "", "ddg_snippet": "HTMLRAG is a recent enhancement to retrieval-augmented generation that works directly with HTML content instead of plain text.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/htmlrag-multimodal-rag-agentic-technical-deep-dive-nagesh-nama-pv9be", "content": "HTMLRAG is a recent enhancement to retrieval-augmented generation that works directly with HTML content instead of plain text."}
+{"idx": 3, "title": "zstanjj/HtmlRAG-test · Datasets at Hugging Face", "date": "", "ddg_snippet": "18 Dec 2024 — The first step processes the result of lossless HTML cleaning, while the second step processes the result of the first pruning step . If ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/zstanjj/HtmlRAG-test", "content": "18 Dec 2024 — The first step processes the result of lossless HTML cleaning, while the second step processes the result of the first pruning step . If ..."}
+{"idx": 4, "title": "1 Introduction", "date": "", "ddg_snippet": "Figure 2 : Gemini- 1.5 -Pro ( ) first highlights key facts in the question ( Reformatted Question ), then generates an answer ( Answer ) with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.02003v3", "content": "Figure 2 : Gemini- 1.5 -Pro ( ) first highlights key facts in the question ( Reformatted Question ), then generates an answer ( Answer ) with ..."}
+{"idx": 5, "title": "‘ML dataset’ directory · Gwern.net", "date": "", "ddg_snippet": "... First Approach for Generating ... DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches With TikZ ”, Belouadi et al 2024", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/dataset/index", "content": "... First Approach for Generating ... DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches With TikZ ”, Belouadi et al 2024"}
+{"idx": 6, "title": "5. Managing Input Data", "date": "", "ddg_snippet": "Step 2 : Writing the Base Prompt Template. We will write a base prompt template which will serve as a foundational structure for all chunks, ensuring ...", "subpage_snippet": "", "source": "www.tamingllms.com", "link": "https://www.tamingllms.com/notebooks/input.html", "content": "Step 2 : Writing the Base Prompt Template. We will write a base prompt template which will serve as a foundational structure for all chunks, ensuring ..."}
+{"idx": 7, "title": "Proposing a query-based abstractive summarization model", "date": "", "ddg_snippet": "by NA Dashtaki · 2025 — According to this guideline, in first step we selected the appropriate scientific database from statistical population. The statistical ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2667096825000485", "content": "by NA Dashtaki · 2025 — According to this guideline, in first step we selected the appropriate scientific database from statistical population. The statistical ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "We design Phantom, general two - step attack framework against RAG augmented LLMs. The first step involves crafting a poisoned document designed to be ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=RAG-ability", "content": "We design Phantom, general two - step attack framework against RAG augmented LLMs. The first step involves crafting a poisoned document designed to be ..."}
+{"idx": 9, "title": "A Survey on Knowledge-Oriented Retrieval-Augmented ...", "date": "", "ddg_snippet": "11 Mar 2025 — This section presents a comprehensive overview of RAG, discussing its core components: retrieval from external knowledge sources, generation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10677v1", "content": "11 Mar 2025 — This section presents a comprehensive overview of RAG, discussing its core components: retrieval from external knowledge sources, generation ..."}
diff --git a/data/sampled_jsons/HtmlRAG_GitHub_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_BGE_baseline_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_GitHub_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_BGE_baseline_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b37fcbab431ee2750cea36defcb80249e4543b79
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_GitHub_NQ_Hit@1_Llama-3.1-70B-Instruct-4K_BGE_baseline_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain...", "date": "", "ddg_snippet": "BGE : An embedding model, BGE -Large-EN with encoder-only structure.Results for HTML -Pruner-Phi-3.8B and HTML -Pruner-Llama- 1 B with Llama - 3 . 1 - 70 B - Instruct as chat model. Dataset.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "BGE : An embedding model, BGE -Large-EN with encoder-only structure.Results for HTML -Pruner-Phi-3.8B and HTML -Pruner-Llama- 1 B with Llama - 3 . 1 - 70 B - Instruct as chat model. Dataset."}
+{"idx": 1, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "Llama - 3 . 1 - 70 B - Instruct -128K. Vanilla HTML .Table 2. Results of HtmlRAG without pruning and baselines under the long-context setting. Hit @ 1 is the proportion of instances where at least one short answer matches.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2411.02959/", "content": "Llama - 3 . 1 - 70 B - Instruct -128K. Vanilla HTML .Table 2. Results of HtmlRAG without pruning and baselines under the long-context setting. Hit @ 1 is the proportion of instances where at least one short answer matches."}
+{"idx": 2, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "Table 2: Results of HtmlRAG without pruning and baselines under Llama - 3 . 1 - 70 B - Instruct -128K. Hit @ 1 is the proportion of instances where at least one short answer matches.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959", "content": "Table 2: Results of HtmlRAG without pruning and baselines under Llama - 3 . 1 - 70 B - Instruct -128K. Hit @ 1 is the proportion of instances where at least one short answer matches."}
+{"idx": 3, "title": "NIM Llama 3 . 1 70 B Instruct API | Together AI", "date": "", "ddg_snippet": "NVIDIA NIM for GPU accelerated Llama 3 . 1 70 B Instruct inference through OpenAI compatible APIs. Deploy this NIM model.", "subpage_snippet": "", "source": "www.together.ai", "link": "https://www.together.ai/models/nim-llama-3-1-70b-instruct", "content": "NVIDIA NIM for GPU accelerated Llama 3 . 1 70 B Instruct inference through OpenAI compatible APIs. Deploy this NIM model."}
+{"idx": 4, "title": "Paper tables with annotated results for HtmlRAG ... | Papers With Code", "date": "", "ddg_snippet": "Table 1. Results of HtmlRAG and baselines under the short-context setting. Hit @ 1 is the proportion of instances where at least one short answer matches.ROUGE-L. BLEU. Llama - 3 . 1 -8 B - Instruct -128K. Vanilla HTML .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/htmlrag-html-is-better-than-plain-text-for/review/", "content": "Table 1. Results of HtmlRAG and baselines under the short-context setting. Hit @ 1 is the proportion of instances where at least one short answer matches.ROUGE-L. BLEU. Llama - 3 . 1 -8 B - Instruct -128K. Vanilla HTML ."}
+{"idx": 5, "title": "meta-llama/ Llama - 3 . 1 - 70 B - Instruct · Hugging Face", "date": "", "ddg_snippet": "huggingface-cli download meta-llama/Meta- Llama - 3 . 1 - 70 B - Instruct --include \"original/*\" --local-dir Meta- Llama - 3 . 1 - 70 B - Instruct . Hardware and Software. Training Factors We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining.", "subpage_snippet": "", "source": "huggingface.1319lm.top", "link": "https://huggingface.1319lm.top/meta-llama/Llama-3.1-70B-Instruct", "content": "huggingface-cli download meta-llama/Meta- Llama - 3 . 1 - 70 B - Instruct --include \"original/*\" --local-dir Meta- Llama - 3 . 1 - 70 B - Instruct . Hardware and Software. Training Factors We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining."}
+{"idx": 6, "title": "[Usage]: Extremely slow inference with Llama 3 . 1 70 b Instruct", "date": "", "ddg_snippet": "How would you like to use vllm. Following is the command I am using to run the Llama - 3 . 1 - 70 B - Instruct model python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta- Llama - 3 . 1 - 70 B - Instruct --tensor-parallel-size 8...", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/vllm-project/vllm/7567", "content": "How would you like to use vllm. Following is the command I am using to run the Llama - 3 . 1 - 70 B - Instruct model python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta- Llama - 3 . 1 - 70 B - Instruct --tensor-parallel-size 8..."}
+{"idx": 7, "title": "mav23/ HTML -Pruner- Llama - 1 B-GGUF · Hugging Face", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree- Based HTML Pruning.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/mav23/HTML-Pruner-Llama-1B-GGUF", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree- Based HTML Pruning."}
+{"idx": 8, "title": "Llama - 3 . 1 - 70 B - Instruct -lorablated huggingface.co api... - Toolify", "date": "", "ddg_snippet": "Llama - 3 . 1 - 70 B - Instruct -lorablated is an open source model from GitHub that offers a free installation service, and any user can find Llama - 3 . 1 - 70 B - Instruct -lorablated on GitHub to install.huggingface.co. mlabonne/gemma-3-27b-it-abliterated. Total runs: 1 . 4 K .", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-model/mlabonne-llama-3-1-70b-instruct-lorablated", "content": "Llama - 3 . 1 - 70 B - Instruct -lorablated is an open source model from GitHub that offers a free installation service, and any user can find Llama - 3 . 1 - 70 B - Instruct -lorablated on GitHub to install.huggingface.co. mlabonne/gemma-3-27b-it-abliterated. Total runs: 1 . 4 K ."}
+{"idx": 9, "title": "mlabonne/ Llama - 3 . 1 - 70 B - Instruct -lorablated-GGUF · Hugging Face", "date": "", "ddg_snippet": "I adapted this recipe to Llama 3 . 1 70 B using failspy/Meta- Llama - 3 - 70 B - Instruct -abliterated-v3.5 and optimized the LoRA rank. The model is fully uncensored in my tests and maintains a high level of quality.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/mlabonne/Llama-3.1-70B-Instruct-lorablated-GGUF", "content": "I adapted this recipe to Llama 3 . 1 70 B using failspy/Meta- Llama - 3 - 70 B - Instruct -abliterated-v3.5 and optimized the LoRA rank. The model is fully uncensored in my tests and maintains a high level of quality."}
diff --git a/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl b/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c7a3c2e4cfe75fac51e5858870aac08549e0a4e5
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... (PDF) HtmlRAG: HTML is Better Than Plain Text for Modeling ... Understanding HtmlRAG: HTML is Better Than Plain Text for ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved GitHub - plageon/HtmlRAG: HtmlRAG: HTML is Better Than Plain Tex… HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Nov 5, 2024 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. Nov 5, 2024 · HtmlRAG : HTML is Be er Than Plain T ext for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan ∗ Gaoling School of Arti cial Intelligence Renmin University of China Beijing, China zstanjj ... HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy. Apr 22, 2025 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . 268 In this paper, we propose HtmlRAG , which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text . Is HTML better than plain text for retrieved knowledge in Rag? To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. What is htmlrag? In this paper, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems , aiming to keep richer semantic and structured information that is missing in plain text. Does htmlrag use HTML instead of plain text? However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. Does htmlrag work without pruning? HTML is taken as the format of external knowledge, HtmlRAG without pruning meets or outperforms plain text and Markdown on most datasets , demonstrating its validity. Besides, we make the following observations: (1) Unprocessed HTML documents contain Table 3: Ablation studies for HtmlRAG. Is HTML better than plain text? We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. What is the latest version of htmlrag? [12/12/2024]: The latest version of htmlrag package is v0.0.5 , which now supports Chinese HTML documents. You can install it by running pip install htmlrag==0.0.5. [11/12/2024]: Our data and model are now available on ModelScope. You can access them here for faster downloading. To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new chal-lenges.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "Nov 5, 2024 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. Nov 5, 2024 · HtmlRAG : HTML is Be er Than Plain T ext for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan ∗ Gaoling School of Arti cial Intelligence Renmin University of China Beijing, China zstanjj ... HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy. Apr 22, 2025 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . 268 In this paper, we propose HtmlRAG , which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text . Is HTML better than plain text for retrieved knowledge in Rag? To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. What is htmlrag? In this paper, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems , aiming to keep richer semantic and structured information that is missing in plain text. Does htmlrag use HTML instead of plain text? However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. Does htmlrag work without pruning? HTML is taken as the format of external knowledge, HtmlRAG without pruning meets or outperforms plain text and Markdown on most datasets , demonstrating its validity. Besides, we make the following observations: (1) Unprocessed HTML documents contain Table 3: Ablation studies for HtmlRAG. Is HTML better than plain text? We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. What is the latest version of htmlrag? [12/12/2024]: The latest version of htmlrag package is v0.0.5 , which now supports Chinese HTML documents. You can install it by running pip install htmlrag==0.0.5. [11/12/2024]: Our data and model are now available on ModelScope. You can access them here for faster downloading. To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new chal-lenges."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 2, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Apr 22, 2025 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3696410.3714546", "content": "Apr 22, 2025 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML ."}
+{"idx": 3, "title": "(PDF) HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Nov 5, 2024 · HtmlRAG : HTML is Be er Than Plain T ext for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan ∗ Gaoling School of Arti cial Intelligence Renmin University of China Beijing, China zstanjj ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "Nov 5, 2024 · HtmlRAG : HTML is Be er Than Plain T ext for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan ∗ Gaoling School of Arti cial Intelligence Renmin University of China Beijing, China zstanjj ..."}
+{"idx": 4, "title": "Understanding HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy.", "subpage_snippet": "", "source": "techchilli.com", "link": "https://techchilli.com/artificial-intelligence/understanding-htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledge-in-rag-systems/", "content": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy."}
+{"idx": 5, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "268 In this paper, we propose HtmlRAG , which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "268 In this paper, we propose HtmlRAG , which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text ."}
+{"idx": 6, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new chal-lenges.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v1", "content": "To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new chal-lenges."}
+{"idx": 7, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "5 Nov 2024 — In this paper, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems, aiming to keep ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "5 Nov 2024 — In this paper, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems, aiming to keep ..."}
+{"idx": 8, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "6 Nov 2024 — HtmlRAG enhances Retrieval-Augmented Generation (RAG) systems by using HTML instead of plain text, improving knowledge modeling and reducing ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "6 Nov 2024 — HtmlRAG enhances Retrieval-Augmented Generation (RAG) systems by using HTML instead of plain text, improving knowledge modeling and reducing ..."}
+{"idx": 9, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "29 Jan 2025 — Review: Summary: This paper presents HtmlRAG , a method for improving RAG systems by using HTML rather than plain text for retrieved knowledge.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=E91gjsccP1&referrer=[the+profile+of+Jiejun+Tan](/profile?id=~Jiejun_Tan2)", "content": "29 Jan 2025 — Review: Summary: This paper presents HtmlRAG , a method for improving RAG systems by using HTML rather than plain text for retrieved knowledge."}
diff --git a/data/sampled_jsons/HtmlRAG_Lossless_Structural_Compression.jsonl b/data/sampled_jsons/HtmlRAG_Lossless_Structural_Compression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dbe76a269e01e6c8595e44c201d3bb3de444bed8
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_Lossless_Structural_Compression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "3.2.2 Lossless Structural Compression .Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain-text-based RAG process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "3.2.2 Lossless Structural Compression .Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain-text-based RAG process."}
+{"idx": 1, "title": "Compression of Graphical Structures: Fundamental Limits, Algorithms...", "date": "", "ddg_snippet": "This allows us to introduce structural entropy, as the lower bound for a lossless structural graph compres - sion . Furthermore, we develop a compression algorithm for structures, which we prove to be asymptotically optimal for graphs generated by the Erd˝os-R´enyi model.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/compression-of-graphical-structures-fundamental-limits-py8vdidt6t.pdf", "content": "This allows us to introduce structural entropy, as the lower bound for a lossless structural graph compres - sion . Furthermore, we develop a compression algorithm for structures, which we prove to be asymptotically optimal for graphs generated by the Erd˝os-R´enyi model."}
+{"idx": 2, "title": "Htmlrag: HTML Is Better Than Plain Text For Modeling Retrieved ...", "date": "", "ddg_snippet": "Our research is not limited to 3.2.2 Lossless Structural Compression . We find that in most HTML understanding a certain format of data but recommends using a documents, their original HTML structure contains redundancies. richer data format in the general RAG systems. To the best of our We can conduct the following compression to the HTML structure", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/788595083/2411-02959v1", "content": "Our research is not limited to 3.2.2 Lossless Structural Compression . We find that in most HTML understanding a certain format of data but recommends using a documents, their original HTML structure contains redundancies. richer data format in the general RAG systems. To the best of our We can conduct the following compression to the HTML structure"}
+{"idx": 3, "title": "HtmlRAG: HTML é Melhor do que Texto Simples para Modelar ...", "date": "", "ddg_snippet": "Rule-based HTML cleaning to remove irrelevant content. Lossless structural compression techniques for simplifying HTML structure. Block-tree construction ...", "subpage_snippet": "", "source": "www.chatpaper.ai", "link": "https://www.chatpaper.ai/pt/paper/49bede8a-175c-42d0-991f-947ddc200689", "content": "Rule-based HTML cleaning to remove irrelevant content. Lossless structural compression techniques for simplifying HTML structure. Block-tree construction ..."}
+{"idx": 4, "title": "ISCA Archive - Rescore in a Flash: Compact, Cache Efficient Hashing ...", "date": "", "ddg_snippet": "The data structure implements a finite state transducer with a lossless structural compression and outperforms comparable implementations when considering lookup speed in the small-footprint setting. DashHashLM introduces several optimizations to language model compression which are designed to minimize expected memory accesses.", "subpage_snippet": "", "source": "www.isca-archive.org", "link": "https://www.isca-archive.org/interspeech_2020/strimel20_interspeech.html", "content": "The data structure implements a finite state transducer with a lossless structural compression and outperforms comparable implementations when considering lookup speed in the small-footprint setting. DashHashLM introduces several optimizations to language model compression which are designed to minimize expected memory accesses."}
+{"idx": 5, "title": "Brandon Tyree's Post - LinkedIn", "date": "", "ddg_snippet": "Additionally, it performs lossless structural compression by merging nested tags and eliminating empty tags, further streamlining the HTML without sacrificing meaning.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/brandontyree_todays-ai-paper-review-talks-about-rag-and-activity-7264360249501851648-gaE9", "content": "Additionally, it performs lossless structural compression by merging nested tags and eliminating empty tags, further streamlining the HTML without sacrificing meaning."}
+{"idx": 6, "title": "Rescore in a flash: Compact, cache efficient hashing... - Amazon Science", "date": "", "ddg_snippet": "The data structure implements a finite state transducer with a lossless structural compression and outperforms comparable implementations when considering lookup speed in the small-footprint setting.", "subpage_snippet": "", "source": "www.amazon.science", "link": "https://www.amazon.science/publications/rescore-in-a-flash-compact-cache-efficient-hashing-data-structures-for-n-gram-language-models", "content": "The data structure implements a finite state transducer with a lossless structural compression and outperforms comparable implementations when considering lookup speed in the small-footprint setting."}
+{"idx": 7, "title": "[PDF] Rescore in a Flash: Compact, Cache Efficient... | Semantic Scholar", "date": "", "ddg_snippet": "The data structure implements a finite state transducer with a lossless structural compression and outperforms comparable implementations when considering lookup speed in the small-footprint setting.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Rescore-in-a-Flash:-Compact,-Cache-Efficient-Data-Strimel-Rastrow/225440d1c8629dae7f972874f340753af2c78711", "content": "The data structure implements a finite state transducer with a lossless structural compression and outperforms comparable implementations when considering lookup speed in the small-footprint setting."}
+{"idx": 8, "title": "(PDF) Rescore in a Flash: Compact, Cache Efficient Hashing Data...", "date": "", "ddg_snippet": "The data structure implements a finite state transducer with a lossless structural compression and outperforms comparable implementations when considering lookup speed in the small-footprint setting.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/103973014/Rescore_in_a_Flash_Compact_Cache_Efficient_Hashing_Data_Structures_for_n_Gram_Language_Models", "content": "The data structure implements a finite state transducer with a lossless structural compression and outperforms comparable implementations when considering lookup speed in the small-footprint setting."}
+{"idx": 9, "title": "PDF Compressor (100% In‑Browser)", "date": "", "ddg_snippet": "Note: This method compresses by rasterizing pages to JPEG images (great for scans). Text/searchability is not preserved. For lossless structural compression you need a server tool like qpdf/ghostscript.", "subpage_snippet": "", "source": "lifeinvestpolicy.in", "link": "https://lifeinvestpolicy.in/pdfcompressor.php", "content": "Note: This method compresses by rasterizing pages to JPEG images (great for scans). Text/searchability is not preserved. For lossless structural compression you need a server tool like qpdf/ghostscript."}
diff --git a/data/sampled_jsons/HtmlRAG_Prune-Embed_ASQA_performance_ablation_study.jsonl b/data/sampled_jsons/HtmlRAG_Prune-Embed_ASQA_performance_ablation_study.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0fc4fbd3cf528d530211bc16321b6943266bb4cd
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_Prune-Embed_ASQA_performance_ablation_study.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Table 3. Ablation studies for HtmlRAG . ... Figure 5. Experimental results for the impact of block tree granularity. The results of Prune-Embed and Prune -Gen are represented in a bar chart, with a red dashed horizontal line indicating the performance of the strong baseline method, chunking-based refiner with BGE (BGE-Chunk-Rerank).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "Table 3. Ablation studies for HtmlRAG . ... Figure 5. Experimental results for the impact of block tree granularity. The results of Prune-Embed and Prune -Gen are represented in a bar chart, with a red dashed horizontal line indicating the performance of the strong baseline method, chunking-based refiner with BGE (BGE-Chunk-Rerank)."}
+{"idx": 1, "title": "GitHub - plageon/HtmlRAG: HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 2, "title": "zstanjj/HTML-Pruner-Phi-3.8B · Hugging Face", "date": "", "ddg_snippet": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/zstanjj/HTML-Pruner-Phi-3.8B", "content": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning ..."}
+{"idx": 3, "title": "HTML-Pruner-Llama-1B · Models", "date": "", "ddg_snippet": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning ...", "subpage_snippet": "", "source": "www.modelscope.cn", "link": "https://www.modelscope.cn/models/zstanjj/HTML-Pruner-Llama-1B", "content": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning ..."}
+{"idx": 4, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3696410.3714546", "content": "To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML."}
+{"idx": 5, "title": "[2411.02959] HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2411.02959: HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "Abstract page for arXiv paper 2411.02959: HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems"}
+{"idx": 6, "title": "Paper page - HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Plain Text (128k), Markdown (128k), and HtmlRAG w/o Prune (128k) are long-context reference after rule-base cleaning (refer to Table 2 for end-to-end results). HTML's socre is lightly lower due to extra HTML tags occupying tokens.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "Plain Text (128k), Markdown (128k), and HtmlRAG w/o Prune (128k) are long-context reference after rule-base cleaning (refer to Table 2 for end-to-end results). HTML's socre is lightly lower due to extra HTML tags occupying tokens."}
+{"idx": 7, "title": "HtmlRAG/toolkit/README.md at main · plageon/HtmlRAG", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG /toolkit/README.md at main · plageon/ HtmlRAG", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG/blob/main/toolkit/README.md", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG /toolkit/README.md at main · plageon/ HtmlRAG"}
+{"idx": 8, "title": "QuantFactory/HTML-Pruner-Llama-1B-GGUF · Hugging Face", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/QuantFactory/HTML-Pruner-Llama-1B-GGUF", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 9, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v1", "content": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen)."}
diff --git a/data/sampled_jsons/HtmlRAG_Section_4.6.2_analysis_Prune-Embed_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_Section_4.6.2_analysis_Prune-Embed_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0782dd5accacda49fc1c9708d95f177d1b1fc3fe
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_Section_4.6.2_analysis_Prune-Embed_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v1", "content": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen)."}
+{"idx": 1, "title": "HtmlRAG/toolkit/README.md at main · plageon/HtmlRAG", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG /toolkit/README.md at main · plageon/ HtmlRAG", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG/blob/main/toolkit/README.md", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG /toolkit/README.md at main · plageon/ HtmlRAG"}
+{"idx": 2, "title": "Paper page - HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We list the score for reference text in some critical steps or baselines. Plain Text (128k), Markdown (128k), and HtmlRAG w/o Prune (128k) are long-context reference after rule-base cleaning (refer to Table 2 for end-to-end results). HTML's socre is lightly lower due to extra HTML tags occupying tokens.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "We list the score for reference text in some critical steps or baselines. Plain Text (128k), Markdown (128k), and HtmlRAG w/o Prune (128k) are long-context reference after rule-base cleaning (refer to Table 2 for end-to-end results). HTML's socre is lightly lower due to extra HTML tags occupying tokens."}
+{"idx": 3, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Analysis of inference cost on ELI5 dataset We compare the chunking-based refiner using BGE (BGE), the two HTML pruning steps basing on the text embedding ( Prune-Embed ) and the generative model ( Prune -Gen) in HtmlRAG , and LLM chatting (LLM Chat) by model parameters, storage, average input tokens, and average output tokens.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "Analysis of inference cost on ELI5 dataset We compare the chunking-based refiner using BGE (BGE), the two HTML pruning steps basing on the text embedding ( Prune-Embed ) and the generative model ( Prune -Gen) in HtmlRAG , and LLM chatting (LLM Chat) by model parameters, storage, average input tokens, and average output tokens."}
+{"idx": 4, "title": "HTML embed Tag - W3Schools", "date": "", "ddg_snippet": "Learn how to use the HTML tag to embed content like videos, audio, or documents into your web pages.", "subpage_snippet": "", "source": "www.w3schools.com", "link": "https://www.w3schools.com/TAGS/tag_embed.asp", "content": "Learn how to use the HTML tag to embed content like videos, audio, or documents into your web pages."}
+{"idx": 5, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Table 4: Analysis of inference cost on ELI5 dataset We com- 901 pare the chunking-based refiner using BGE (BGE), the two 902 HTML pruning steps basing on the text embedding ( Prune - 903 Embed ) and the generative model ( Prune -Gen) in HtmlRAG , 904 and LLM chatting (LLM Chat) by model parameters, storage, 905 average input tokens, and average ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "Table 4: Analysis of inference cost on ELI5 dataset We com- 901 pare the chunking-based refiner using BGE (BGE), the two 902 HTML pruning steps basing on the text embedding ( Prune - 903 Embed ) and the generative model ( Prune -Gen) in HtmlRAG , 904 and LLM chatting (LLM Chat) by model parameters, storage, 905 average input tokens, and average ..."}
+{"idx": 6, "title": "PDF 349.2R-07: Guide to the Concrete Capacity Design Method Embedment ...", "date": "", "ddg_snippet": "In the design of an embed -ment for direct loading, the philosophy leads to the require-ment that the concrete breakout, concrete pullout, side-face blowout, and pryout strength must be greater than the tensile or shear strength of the steel portion of the embedment.", "subpage_snippet": "", "source": "www.concrete.org", "link": "https://www.concrete.org/Portals/0/Files/PDF/Previews/349.2R-07(14)_preview.pdf", "content": "In the design of an embed -ment for direct loading, the philosophy leads to the require-ment that the concrete breakout, concrete pullout, side-face blowout, and pryout strength must be greater than the tensile or shear strength of the steel portion of the embedment."}
+{"idx": 7, "title": "htmlrag · PyPI", "date": "", "ddg_snippet": "A smart toolkit for HTML cleaning and pruning for RAG systems.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/htmlrag/", "content": "A smart toolkit for HTML cleaning and pruning for RAG systems."}
+{"idx": 8, "title": "Implementing HtmlRAG: Enhancing Retrieval-Augmented Generation with ...", "date": "", "ddg_snippet": "Retrieval-Augmented Generation (RAG) systems have gained significant traction in enhancing the knowledge capabilities of Large Language…", "subpage_snippet": "", "source": "blog.devgenius.io", "link": "https://blog.devgenius.io/implementing-htmlrag-enhancing-retrieval-augmented-generation-with-html-knowledge-91cdd6278e23", "content": "Retrieval-Augmented Generation (RAG) systems have gained significant traction in enhancing the knowledge capabilities of Large Language…"}
+{"idx": 9, "title": "HTML 5.2: 4.3. Sections", "date": "", "ddg_snippet": "The bodyelement exposes as event handler content attributesa number of the event handlersof the Windowobject. It also mirrors their event handler IDL attributes. The onblur, onerror, onfocus, onload, onresize, and onscrollevent handlersof the Windowobject, exposed on the bodyelement, replace the generic event handlerswith the same names normally supported by HTML elements. Thus, for example, a ...", "subpage_snippet": "", "source": "www.w3.org", "link": "https://www.w3.org/TR/2017/REC-html52-20171214/sections.html", "content": "The bodyelement exposes as event handler content attributesa number of the event handlersof the Windowobject. It also mirrors their event handler IDL attributes. The onblur, onerror, onfocus, onload, onresize, and onscrollevent handlersof the Windowobject, exposed on the bodyelement, replace the generic event handlerswith the same names normally supported by HTML elements. Thus, for example, a ..."}
diff --git a/data/sampled_jsons/HtmlRAG_Table_1_Hit@1_NQ_Llama-3.1-70B-Instruct-4K_sitearxiv.org_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_Table_1_Hit@1_NQ_Llama-3.1-70B-Instruct-4K_sitearxiv.org_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..303bc4c571350ffde10162c1b5cc8c9acb6f9d1a
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_Table_1_Hit@1_NQ_Llama-3.1-70B-Instruct-4K_sitearxiv.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "For a fair comparison, all end-to-end QA results are experimented with the latest open-source LLM, Llama - 3 . 1 - 70B - Instruct and Llama - 3 . 1 -8B- Instruct (Dubey et al., 2024) under a 4K context window.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "For a fair comparison, all end-to-end QA results are experimented with the latest open-source LLM, Llama - 3 . 1 - 70B - Instruct and Llama - 3 . 1 -8B- Instruct (Dubey et al., 2024) under a 4K context window."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "For a fair comparison, all end-to-end QA results are experimented with the latest open-source LLM, Llama - 3 . 1 - 70B - Instruct and Llama - 3 . 1 -8B- Instruct [12] under a 4K context window.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v1", "content": "For a fair comparison, all end-to-end QA results are experimented with the latest open-source LLM, Llama - 3 . 1 - 70B - Instruct and Llama - 3 . 1 -8B- Instruct [12] under a 4K context window."}
+{"idx": 2, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "Table 2: Results of HtmlRAG without pruning and baselines under Llama - 3 . 1 - 70 B - Instruct -128K. Hit @ 1 is the proportion of instances where at least one short answer matches.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959", "content": "Table 2: Results of HtmlRAG without pruning and baselines under Llama - 3 . 1 - 70 B - Instruct -128K. Hit @ 1 is the proportion of instances where at least one short answer matches."}
+{"idx": 3, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "Table 2. Results of HtmlRAG without pruning and baselines under Llama - 3 . 1 - 70 B - Instruct -128K. Hit @ 1 is the proportion of instances where at least one short answer matches.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v2", "content": "Table 2. Results of HtmlRAG without pruning and baselines under Llama - 3 . 1 - 70 B - Instruct -128K. Hit @ 1 is the proportion of instances where at least one short answer matches."}
+{"idx": 4, "title": "[2411.02959] HtmlRAG: HTML is Better Than Plain Text for ... RAG vs. GraphRAG: A Systematic Evaluation and Key Insights [2407.21783] The Llama 3 Herd of Models - arXiv.org InstructRAG: Instructing Retrieval-Augmented Generation with ... Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Nov 5, 2024 · Abstract page for arXiv paper 2411.02959: HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Feb 17, 2025 · To generate responses, we employ two open-source models of different sizes: Llama - 3 . 1 -8B- Instruct and Llama - 3 . 1 - 70B - Instruct Dubey et al. (2024). For single-document tasks, we generate a separate RAG system for each document, ensuring that queries corresponding to a specific document are processed within its respective indexed chunk pool. Jul 31, 2024 · This paper presents an extensive empirical evaluation of Llama 3 . We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3 , including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. As shown in the first block, the basic instruction-tuned models ( Llama - 3 - Instruct 8B {}_ {\\textsc {8B}} start_FLOATSUBSCRIPT 8B end_FLOATSUBSCRIPT and Llama - 3 - Instruct 70B { {}_ {\\textsc { 70B }}} start_FLOATSUBSCRIPT 70B end_FLOATSUBSCRIPT) already achieve notable performance across all five benchmarks, with the 70B model exhibiting a ... Apr 28, 2025 · Foundation-Sec-8B also surpasses both Llama 3 . 1 - 70B and WhiteRabbitNeo-V2- 70B by about 1 point on CTIBench-RCM, while falling short by less than 2 points on CTIBench-MCQA. For a fair comparison, all end-to-end QA results are experimented with the latest open-source LLM, Llama - 3 . 1 - 70B - Instruct and Llama - 3 . 1 -8B- Instruct [12] under a 4K context window.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "Nov 5, 2024 · Abstract page for arXiv paper 2411.02959: HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Feb 17, 2025 · To generate responses, we employ two open-source models of different sizes: Llama - 3 . 1 -8B- Instruct and Llama - 3 . 1 - 70B - Instruct Dubey et al. (2024). For single-document tasks, we generate a separate RAG system for each document, ensuring that queries corresponding to a specific document are processed within its respective indexed chunk pool. Jul 31, 2024 · This paper presents an extensive empirical evaluation of Llama 3 . We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3 , including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. As shown in the first block, the basic instruction-tuned models ( Llama - 3 - Instruct 8B {}_ {\\textsc {8B}} start_FLOATSUBSCRIPT 8B end_FLOATSUBSCRIPT and Llama - 3 - Instruct 70B { {}_ {\\textsc { 70B }}} start_FLOATSUBSCRIPT 70B end_FLOATSUBSCRIPT) already achieve notable performance across all five benchmarks, with the 70B model exhibiting a ... Apr 28, 2025 · Foundation-Sec-8B also surpasses both Llama 3 . 1 - 70B and WhiteRabbitNeo-V2- 70B by about 1 point on CTIBench-RCM, while falling short by less than 2 points on CTIBench-MCQA. For a fair comparison, all end-to-end QA results are experimented with the latest open-source LLM, Llama - 3 . 1 - 70B - Instruct and Llama - 3 . 1 -8B- Instruct [12] under a 4K context window."}
+{"idx": 5, "title": "[2407.21783] The Llama 3 Herd of Models - arXiv.org", "date": "", "ddg_snippet": "Jul 31, 2024 · This paper presents an extensive empirical evaluation of Llama 3 . We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3 , including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.21783", "content": "Jul 31, 2024 · This paper presents an extensive empirical evaluation of Llama 3 . We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3 , including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety."}
+{"idx": 6, "title": "InstructRAG: Instructing Retrieval-Augmented Generation with ...", "date": "", "ddg_snippet": "As shown in the first block, the basic instruction-tuned models ( Llama - 3 - Instruct 8B {}_ {\\textsc {8B}} start_FLOATSUBSCRIPT 8B end_FLOATSUBSCRIPT and Llama - 3 - Instruct 70B { {}_ {\\textsc { 70B }}} start_FLOATSUBSCRIPT 70B end_FLOATSUBSCRIPT) already achieve notable performance across all five benchmarks, with the 70B model exhibiting a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13629v1", "content": "As shown in the first block, the basic instruction-tuned models ( Llama - 3 - Instruct 8B {}_ {\\textsc {8B}} start_FLOATSUBSCRIPT 8B end_FLOATSUBSCRIPT and Llama - 3 - Instruct 70B { {}_ {\\textsc { 70B }}} start_FLOATSUBSCRIPT 70B end_FLOATSUBSCRIPT) already achieve notable performance across all five benchmarks, with the 70B model exhibiting a ..."}
+{"idx": 7, "title": "Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report", "date": "", "ddg_snippet": "Apr 28, 2025 · Foundation-Sec-8B also surpasses both Llama 3 . 1 - 70B and WhiteRabbitNeo-V2- 70B by about 1 point on CTIBench-RCM, while falling short by less than 2 points on CTIBench-MCQA.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.21039v1", "content": "Apr 28, 2025 · Foundation-Sec-8B also surpasses both Llama 3 . 1 - 70B and WhiteRabbitNeo-V2- 70B by about 1 point on CTIBench-RCM, while falling short by less than 2 points on CTIBench-MCQA."}
+{"idx": 8, "title": "BeeManc at the PLABA Track of TAC-2024: RoBERTa for Task...", "date": "", "ddg_snippet": "Our LLaMA - 3 . 1 - 70 B - instructed model achieved the highest Completeness score for Task-2.• With (source, output, reference) set, the difference of LLaMa - 3 . 1 - 70 B and GPT-4o on different metrics is not that large margin, except for LENS, as in Table 3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07381v2", "content": "Our LLaMA - 3 . 1 - 70 B - instructed model achieved the highest Completeness score for Task-2.• With (source, output, reference) set, the difference of LLaMa - 3 . 1 - 70 B and GPT-4o on different metrics is not that large margin, except for LENS, as in Table 3."}
+{"idx": 9, "title": "RAG vs. GraphRAG: A Systematic Evaluation and Key Insights", "date": "", "ddg_snippet": "Feb 17, 2025 · To generate responses, we employ two open-source models of different sizes: Llama - 3 . 1 -8B- Instruct and Llama - 3 . 1 - 70B - Instruct Dubey et al. (2024). For single-document tasks, we generate a separate RAG system for each document, ensuring that queries corresponding to a specific document are processed within its respective indexed chunk pool.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.11371v1", "content": "Feb 17, 2025 · To generate responses, we employ two open-source models of different sizes: Llama - 3 . 1 -8B- Instruct and Llama - 3 . 1 - 70B - Instruct Dubey et al. (2024). For single-document tasks, we generate a separate RAG system for each document, ensuring that queries corresponding to a specific document are processed within its respective indexed chunk pool."}
diff --git a/data/sampled_jsons/HtmlRAG_Table_1_Hit@1_NQ_Llama-3.1-70B-Instruct-4K_sitesemanticscholar.org_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_Table_1_Hit@1_NQ_Llama-3.1-70B-Instruct-4K_sitesemanticscholar.org_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7d06a8f8e9aabdfb18f82899bbe4131adaaefa5c
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_Table_1_Hit@1_NQ_Llama-3.1-70B-Instruct-4K_sitesemanticscholar.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Table 1 from HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "Table 1 : Results of HtmlRAG and baselines under the short-context setting. Hit@1 is the proportion of instances where at least one short answer matches. The best and second best results are in bold and underlined. The symbol † signifies that our model achieves superior results among baselines in a statistically significant manner (t-test, 𝑝-value < 0.05). - \" HtmlRAG : HTML is Better Than ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/HtmlRAG:-HTML-is-Better-Than-Plain-Text-for-in-RAG-Tan-Dou/7cfd2426ca908c8c5a81bd7c7ca01f914a972de4/figure/1", "content": "Table 1 : Results of HtmlRAG and baselines under the short-context setting. Hit@1 is the proportion of instances where at least one short answer matches. The best and second best results are in bold and underlined. The symbol † signifies that our model achieves superior results among baselines in a statistically significant manner (t-test, 𝑝-value < 0.05). - \" HtmlRAG : HTML is Better Than ..."}
+{"idx": 1, "title": "Benchmarking Llama 3 70B for Code ... - Semantic Scholar", "date": "", "ddg_snippet": "This study benchmarks the capabilities of Llama 3 70B , a 70-billion parameter large language model (LLM), for code generation tasks. To effectively train and fine-tune this massive model, we integrate PyTorch Fully Sharded Data Parallel (FSDP) [ 1 ], [2] for distributed training and Quantized Low-Rank Adaptation (Q-LoRA) [7] for efficient fine ...", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/bb8a/14bfffd6ccb59d615a65d40605c7af5c2f7a.pdf", "content": "This study benchmarks the capabilities of Llama 3 70B , a 70-billion parameter large language model (LLM), for code generation tasks. To effectively train and fine-tune this massive model, we integrate PyTorch Fully Sharded Data Parallel (FSDP) [ 1 ], [2] for distributed training and Quantized Low-Rank Adaptation (Q-LoRA) [7] for efficient fine ..."}
+{"idx": 2, "title": "The Llama 3 Herd of Models", "date": "", "ddg_snippet": "Jul 31, 2024 · This paper presents an extensive empirical evaluation of Llama 3 . We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3 , including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Llama-3-Herd-of-Models-Dubey-Jauhri/40e8af970329135ec95057d73e239dab805ad128", "content": "Jul 31, 2024 · This paper presents an extensive empirical evaluation of Llama 3 . We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3 , including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety."}
+{"idx": 3, "title": "LLaMA: Open and Efficient Foundation Language Models", "date": "", "ddg_snippet": "Feb 27, 2023 · LLaMA , a collection of foundation language models ranging from 7B to 65B parameters, is introduced and it is shown that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. We introduce LLaMA , a collection of foundation language models ranging from 7B to 65B parameters. We train our models on ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/LLaMA:-Open-and-Efficient-Foundation-Language-Touvron-Lavril/57e849d0de13ed5f91d086936296721d4ff75a75", "content": "Feb 27, 2023 · LLaMA , a collection of foundation language models ranging from 7B to 65B parameters, is introduced and it is shown that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. We introduce LLaMA , a collection of foundation language models ranging from 7B to 65B parameters. We train our models on ..."}
+{"idx": 4, "title": "Effect of organic loading rates (OLR) on production of methane from...", "date": "", "ddg_snippet": "Table 1 : Characteristics of each feeding rate.", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/08ea/d1656e70b040a12d5863896f41249b51ec06.pdf", "content": "Table 1 : Characteristics of each feeding rate."}
+{"idx": 5, "title": "[PDF] Lightweight Relevance Grader in RAG | Semantic Scholar", "date": "", "ddg_snippet": "This work aims to improve the capability of a lightweight small language model used in retrieval-augmented generation, achieving a significant increase in precision from 0.1038 to 0.7750 using llama - 3 .2-1b, outperforming llama - 3 . 1 - 70b and gpt4o-mini. Retrieval-augmented generation (RAG) addresses limitations of large language models (LLMs) by leveraging a vector database to provide more ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Lightweight-Relevance-Grader-in-RAG-Jeong/c342291bbcb1f8e6faee753c65b994346221b6cf", "content": "This work aims to improve the capability of a lightweight small language model used in retrieval-augmented generation, achieving a significant increase in precision from 0.1038 to 0.7750 using llama - 3 .2-1b, outperforming llama - 3 . 1 - 70b and gpt4o-mini. Retrieval-augmented generation (RAG) addresses limitations of large language models (LLMs) by leveraging a vector database to provide more ..."}
+{"idx": 6, "title": "[PDF] Coefficients for the study of Runge-Kutta... | Semantic Scholar", "date": "", "ddg_snippet": "A process giving fourth-order accuracy and requiring the minimum number of storage registers is developed into a form which gives the highest attainable accuracy and can be carried out by comparatively few instructions .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Coefficients-for-the-study-of-Runge-Kutta-processes-Butcher/c20da54c471fa56d38f8d583ad3bd7786c6158bd", "content": "A process giving fourth-order accuracy and requiring the minimum number of storage registers is developed into a form which gives the highest attainable accuracy and can be carried out by comparatively few instructions ."}
+{"idx": 7, "title": "Sliding Mode Control Based Active/Reactive Power... | Semantic Scholar", "date": "", "ddg_snippet": "Figures and Tables . 14 References. Related Papers. Figures and Tables from this paper.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Sliding-Mode-Control-Based-Active-Reactive-Power-of-Wijesingha-Meegahapola/0f7e944b9c67d59195d18a4bc964ddf91f2105c1", "content": "Figures and Tables . 14 References. Related Papers. Figures and Tables from this paper."}
+{"idx": 8, "title": "[PDF] CRAG - Comprehensive RAG Benchmark | Semantic Scholar", "date": "", "ddg_snippet": "Jun 7, 2024 · The Comprehensive RAG Benchmark (CRAG) is introduced, a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search and reveals much lower accuracy in answering questions regarding facts with higher dynamism, lower popularity, or higher complexity, suggesting future research directions. Retrieval-Augmented Generation (RAG ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/CRAG-Comprehensive-RAG-Benchmark-Yang-Sun/ec1bec009e68a4df478aaf11e3615e5587768990", "content": "Jun 7, 2024 · The Comprehensive RAG Benchmark (CRAG) is introduced, a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search and reveals much lower accuracy in answering questions regarding facts with higher dynamism, lower popularity, or higher complexity, suggesting future research directions. Retrieval-Augmented Generation (RAG ..."}
+{"idx": 9, "title": "[PDF] M-RAG: Reinforcing Large Language Model Performance ...", "date": "", "ddg_snippet": "May 26, 2024 · This paper introduces a multiple partition paradigm for RAG (called M-RAG), where each database partition serves as a basic unit for RAG execution, and proposes a novel framework that leverages LLMs with Multi-Agent Reinforcement Learning to optimize different language generation tasks explicitly. Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/M-RAG:-Reinforcing-Large-Language-Model-Performance-Wang-Teo/8d0df3168870fd17b36ecd5575e406feb5a5a1b5", "content": "May 26, 2024 · This paper introduces a multiple partition paradigm for RAG (called M-RAG), where each database partition serves as a basic unit for RAG execution, and proposes a novel framework that leverages LLMs with Multi-Agent Reinforcement Learning to optimize different language generation tasks explicitly. Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant ..."}
diff --git a/data/sampled_jsons/HtmlRAG_Table_3_ablation_study_Prune-Embed_ASQA_Hit@1_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_Table_3_ablation_study_Prune-Embed_ASQA_Hit@1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1adeeb6b9f0eaf2d9d420548c31eb9e2584a2dae
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_Table_3_ablation_study_Prune-Embed_ASQA_Hit@1_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959v1", "content": "We conduct ablation studies to demonstrate the efectiveness of each component in HtmlRAG , including block tree construction (Block Tree), HTML pruning with the embedding model ( Prune-Embed ), and HTML pruning with the generative model ( Prune -Gen)."}
+{"idx": 1, "title": "GitHub - plageon/HtmlRAG: HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - plageon/ HtmlRAG", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - plageon/ HtmlRAG"}
+{"idx": 2, "title": "zstanjj/HTML-Pruner-Phi-3.8B · Hugging Face", "date": "", "ddg_snippet": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/zstanjj/HTML-Pruner-Phi-3.8B", "content": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning ..."}
+{"idx": 3, "title": "HTML-Pruner-Llama-1B · Models", "date": "", "ddg_snippet": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning ...", "subpage_snippet": "", "source": "www.modelscope.cn", "link": "https://www.modelscope.cn/models/zstanjj/HTML-Pruner-Llama-1B", "content": "Model Information We release the HTML pruner model used in HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems. Useful links: 📝 Paper • 🤗 Hugging Face • 🧩 Github We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning ..."}
+{"idx": 4, "title": "Ablation study results | Download Table - ResearchGate", "date": "", "ddg_snippet": "Download Table | Ablation study results from publication: Short Text Similarity with Word Embeddings | Determining semantic similarity between texts is important in many tasks in information ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Ablation-study-results_tbl1_311314265", "content": "Download Table | Ablation study results from publication: Short Text Similarity with Word Embeddings | Determining semantic similarity between texts is important in many tasks in information ..."}
+{"idx": 5, "title": "[2411.02959] HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG."}
+{"idx": 6, "title": "HtmlRAG/toolkit/README.md at main · plageon/HtmlRAG", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG /toolkit/README.md at main · plageon/ HtmlRAG", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG/blob/main/toolkit/README.md", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG /toolkit/README.md at main · plageon/ HtmlRAG"}
+{"idx": 7, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Table 4: Analysis of inference cost on ELI5 dataset We com- 901 pare the chunking-based refiner using BGE (BGE), the two 902 HTML pruning steps basing on the text embedding ( Prune - 903 Embed ) and the generative model ( Prune -Gen) in HtmlRAG , 904 and LLM chatting (LLM Chat) by model parameters, storage, 905 average input tokens, and average ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "Table 4: Analysis of inference cost on ELI5 dataset We com- 901 pare the chunking-based refiner using BGE (BGE), the two 902 HTML pruning steps basing on the text embedding ( Prune - 903 Embed ) and the generative model ( Prune -Gen) in HtmlRAG , 904 and LLM chatting (LLM Chat) by model parameters, storage, 905 average input tokens, and average ..."}
+{"idx": 8, "title": "Paper page - HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We list the score for reference text in some critical steps or baselines. Plain Text (128k), Markdown (128k), and HtmlRAG w/o Prune (128k) are long-context reference after rule-base cleaning (refer to Table 2 for end-to-end results). HTML's socre is lightly lower due to extra HTML tags occupying tokens.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "We list the score for reference text in some critical steps or baselines. Plain Text (128k), Markdown (128k), and HtmlRAG w/o Prune (128k) are long-context reference after rule-base cleaning (refer to Table 2 for end-to-end results). HTML's socre is lightly lower due to extra HTML tags occupying tokens."}
+{"idx": 9, "title": "htmlrag 0.1.1 on PyPI - Libraries.io - security & maintenance data for ...", "date": "", "ddg_snippet": "A smart toolkit for HTML cleaning and pruning for RAG systems. - 0.1.1 - a Python package on PyPI", "subpage_snippet": "", "source": "libraries.io", "link": "https://libraries.io/pypi/htmlrag", "content": "A smart toolkit for HTML cleaning and pruning for RAG systems. - 0.1.1 - a Python package on PyPI"}
diff --git a/data/sampled_jsons/HtmlRAG_paper_E91gjsccP1_citation_[19]_LongLLMLingua.jsonl b/data/sampled_jsons/HtmlRAG_paper_E91gjsccP1_citation_[19]_LongLLMLingua.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5e037a3a0c9be2e505e14197c3139c2509bae818
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_paper_E91gjsccP1_citation_[19]_LongLLMLingua.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "19 . To alleviate this problem, we propose HtmlRAG , which uses HTML .classroom use is granted without fee provided that copies are not made or distributed 51 for profit or commercial advantage and that copies bear this notice and the full citation . 52 on the first page.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "19 . To alleviate this problem, we propose HtmlRAG , which uses HTML .classroom use is granted without fee provided that copies are not made or distributed 51 for profit or commercial advantage and that copies bear this notice and the full citation . 52 on the first page."}
+{"idx": 1, "title": "SAGE - Vancouver Referencing Guide · SAGE - Vancouver citation ...", "date": "", "ddg_snippet": "Automate citations and referencing in SAGE - Vancouver with our tool, Citationsy. It’s free to try and over 400 000 students and researchers already use it.Here’s how to cite a paper in SAGE - Vancouver. Here’s a SAGE - Vancouver journal citation example using placeholders", "subpage_snippet": "", "source": "citationsy.com", "link": "https://citationsy.com/styles/sage-vancouver", "content": "Automate citations and referencing in SAGE - Vancouver with our tool, Citationsy. It’s free to try and over 400 000 students and researchers already use it.Here’s how to cite a paper in SAGE - Vancouver. Here’s a SAGE - Vancouver journal citation example using placeholders"}
+{"idx": 2, "title": "APA7 citation generator. Citefast automatically formats citations in...", "date": "", "ddg_snippet": "Generate and manage your references, in-text citations and title pages in APA 7th edition.Three to five authors: Include all authors' last names the first time the citation is used. If you use the same citation again within the same paragraph, use only the first last name followed by 'et al'.", "subpage_snippet": "", "source": "www.citefast.com", "link": "https://www.citefast.com/?s=APA7", "content": "Generate and manage your references, in-text citations and title pages in APA 7th edition.Three to five authors: Include all authors' last names the first time the citation is used. If you use the same citation again within the same paragraph, use only the first last name followed by 'et al'."}
+{"idx": 3, "title": "How to Cite the Amendments in the U.S. Constitution... - Owlcation", "date": "", "ddg_snippet": "Additionally, APA “allows for in-text citations ” and requires a Reference List at the end of the research paper . (Grade Saver). This style of citation of the First Amendment of the Constitution of the United States is as follows", "subpage_snippet": "", "source": "owlcation.com", "link": "https://owlcation.com/social-sciences/how-to-cite-the-first-amendment-mla-apa-bluebook", "content": "Additionally, APA “allows for in-text citations ” and requires a Reference List at the end of the research paper . (Grade Saver). This style of citation of the First Amendment of the Constitution of the United States is as follows"}
+{"idx": 4, "title": "Пасьянс Косынка играть бесплатно без регистрации", "date": "", "ddg_snippet": "Венера. Ходов: 183. 2025-01-16 19 :32:03. 268. МАРГАРИТА ПАХОМОВА.", "subpage_snippet": "", "source": "pasyans-kosinka.ru", "link": "https://pasyans-kosinka.ru/", "content": "Венера. Ходов: 183. 2025-01-16 19 :32:03. 268. МАРГАРИТА ПАХОМОВА."}
+{"idx": 5, "title": "Операция Z: Военкоры Русской Весны – Telegram", "date": "", "ddg_snippet": "Добровольцы, волонтеры и военкоры Русской Весны действуют в боевых порядках войск на Донбассе, Украине и САР, получая информацию из самых горячих точек. РКН: clck.ru/3Fj3hJ Связь: @rvvoenkor_bot youtube.com/c/rusvesnadonbass.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/RVvoenkor", "content": "Добровольцы, волонтеры и военкоры Русской Весны действуют в боевых порядках войск на Донбассе, Украине и САР, получая информацию из самых горячих точек. РКН: clck.ru/3Fj3hJ Связь: @rvvoenkor_bot youtube.com/c/rusvesnadonbass."}
+{"idx": 6, "title": "Вести ФМ — слушать онлайн", "date": "", "ddg_snippet": "Комсомольск-На-Амуре 91.1 FM. Кострома 90.1 FM. Краснодар 100.6 FM.Сайт: radiovesti.ru. Адрес: 5-я ул. Ямского Поля, 19 -21, Москва, Россия. Дата основания: 5 февр.", "subpage_snippet": "", "source": "top-radio.ru", "link": "https://top-radio.ru/web/vesti-fm", "content": "Комсомольск-На-Амуре 91.1 FM. Кострома 90.1 FM. Краснодар 100.6 FM.Сайт: radiovesti.ru. Адрес: 5-я ул. Ямского Поля, 19 -21, Москва, Россия. Дата основания: 5 февр."}
+{"idx": 7, "title": "Фильм Тор: Рагнарёк (2017) смотреть онлайн бесплатно...", "date": "", "ddg_snippet": "Фантастика, фэнтези, боевик. Режиссер: Тайка Вайтити. В ролях: Крис Хемсворт, Том Хиддлстон, Кейт Бланшетт и др. Вернувшись в Асгард в поисках таинственного врага, ведущего охоту на Камни Бесконечности, Тор обнаруживает, что действия его брата Локи.....", "subpage_snippet": "", "source": "kinogo-films.biz", "link": "https://kinogo-films.biz/20288-tor-ragnarek.html", "content": "Фантастика, фэнтези, боевик. Режиссер: Тайка Вайтити. В ролях: Крис Хемсворт, Том Хиддлстон, Кейт Бланшетт и др. Вернувшись в Асгард в поисках таинственного врага, ведущего охоту на Камни Бесконечности, Тор обнаруживает, что действия его брата Локи....."}
+{"idx": 8, "title": "GISMETEO: Погода в Донецке сегодня, прогноз погоды Донецк на...", "date": "", "ddg_snippet": "Подробный прогноз погоды в Донецке на сегодня.", "subpage_snippet": "", "source": "www.gismeteo.ru", "link": "https://www.gismeteo.ru/weather-donetsk-5080/", "content": "Подробный прогноз погоды в Донецке на сегодня."}
+{"idx": 9, "title": "Trump signs proclamation imposing annual $100,000... | The Guardian", "date": "", "ddg_snippet": "That followed Trump’s June travel ban restricting entry from 19 nations. Trump’s first -term administration issued several regulations that aimed to limit access to H-1B visas and give them to higher-paying employers, but the regulations were blocked in federal court.", "subpage_snippet": "", "source": "www.theguardian.com", "link": "https://www.theguardian.com/us-news/2025/sep/19/trump-h1b-visa-100000-fee", "content": "That followed Trump’s June travel ban restricting entry from 19 nations. Trump’s first -term administration issued several regulations that aimed to limit access to H-1B visas and give them to higher-paying employers, but the regulations were blocked in federal court."}
diff --git a/data/sampled_jsons/HtmlRAG_paper_Section_3.4_methodology_Section_4.6.2_analysis_year_2023.jsonl b/data/sampled_jsons/HtmlRAG_paper_Section_3.4_methodology_Section_4.6.2_analysis_year_2023.jsonl
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index 0000000000000000000000000000000000000000..2a57e29043dea6ae8d0494f7dce343d721d77aeb
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_paper_Section_3.4_methodology_Section_4.6.2_analysis_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "5 Nov 2024 — We propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "5 Nov 2024 — We propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling ..."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "by J Tan · 2024 · Cited by 19 — 3 Methodology . In this paper , we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02959", "content": "by J Tan · 2024 · Cited by 19 — 3 Methodology . In this paper , we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems ..."}
+{"idx": 2, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "In this paper , we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems, aiming to keep richer semantic and structured information that is missing in plain text. 4 . 6 . Further Analysis . 4 . 6 .1. The Effectiveness of HTML Cleaning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v2", "content": "In this paper , we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems, aiming to keep richer semantic and structured information that is missing in plain text. 4 . 6 . Further Analysis . 4 . 6 .1. The Effectiveness of HTML Cleaning."}
+{"idx": 3, "title": "GitHub - plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two -Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two -Step Block-Tree-Based HTML Pruning."}
+{"idx": 4, "title": "ℹ 1 0 1 **What is HtmlRAG , Multimodal RAG and Agentic...", "date": "", "ddg_snippet": "We explore in details three RAG methods that address limitations of original RAG and meet the upcoming trends of the new year. What is Agentic RAG ? Limitations. Conclusion. Resources to dive deeper (you can find all mentioned papers here).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/Kseniase/html-multimodal-agentic-rag", "content": "We explore in details three RAG methods that address limitations of original RAG and meet the upcoming trends of the new year. What is Agentic RAG ? Limitations. Conclusion. Resources to dive deeper (you can find all mentioned papers here)."}
+{"idx": 5, "title": "HTMLRAG , Multimodal RAG , and Agentic RAG : A Technical Deep Dive", "date": "", "ddg_snippet": "Figure: Overview of the HTMLRAG pipeline (from Tan et al. 2024). The system retrieves knowledge in HTML format, then performs HTML cleaning and block-tree pruning in two stages (embedding-based and generative).", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/htmlrag-multimodal-rag-agentic-technical-deep-dive-nagesh-nama-pv9be", "content": "Figure: Overview of the HTMLRAG pipeline (from Tan et al. 2024). The system retrieves knowledge in HTML format, then performs HTML cleaning and block-tree pruning in two stages (embedding-based and generative)."}
+{"idx": 6, "title": "HtmlRAG : Enhancing RAG Systems with Richer Semantic and...", "date": "", "ddg_snippet": "How Does HtmlRAG Work? 1. ** Two -Step Pruning Mechanism**: HtmlRAG processes HTML documents efficiently by first cleaning and then refining the data. 2. **Optimized Structure**: It creates a “block tree” structure to manage data more effectively, allowing for adjustable detail levels.", "subpage_snippet": "", "source": "itinai.com", "link": "https://itinai.com/htmlrag-enhancing-rag-systems-with-richer-semantic-and-structural-information-through-html/", "content": "How Does HtmlRAG Work? 1. ** Two -Step Pruning Mechanism**: HtmlRAG processes HTML documents efficiently by first cleaning and then refining the data. 2. **Optimized Structure**: It creates a “block tree” structure to manage data more effectively, allowing for adjustable detail levels."}
+{"idx": 7, "title": "Canadian, World, Politics and Business News & Analysis", "date": "", "ddg_snippet": "analysis . Charlie Kirk’s memorial was a religious ceremony, state funeral and political rally in one. David Shribman.Other Sections .", "subpage_snippet": "", "source": "www.theglobeandmail.com", "link": "https://www.theglobeandmail.com/", "content": "analysis . Charlie Kirk’s memorial was a religious ceremony, state funeral and political rally in one. David Shribman.Other Sections ."}
+{"idx": 8, "title": "6 Ways to Fix \"This Channel Cannot Be Displayed\" Issue in Telegram", "date": "", "ddg_snippet": "Method 3: Voice-Over Internet Number. Method 4: Try Using Another Device. Method 5: Use a VPN to access Telegram.Now, find the bot from the Search results and click on it. On the right-hand bottom section , you will find Start.", "subpage_snippet": "", "source": "ipeeworld.com", "link": "https://ipeeworld.com/fix-this-channel-cannot-be-displayed-issue-in-telegram/", "content": "Method 3: Voice-Over Internet Number. Method 4: Try Using Another Device. Method 5: Use a VPN to access Telegram.Now, find the bot from the Search results and click on it. On the right-hand bottom section , you will find Start."}
+{"idx": 9, "title": "ГДЗ по английскому языку 6 класс (spotlight) Ваулина - рабочая...", "date": "", "ddg_snippet": "Pairwork activities 65. Student A 65 Student B 71. Revision Section 77.Modules 3 & 4 .", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/6-klass/english/vaulina-spotlight-rabochaja-tetrad", "content": "Pairwork activities 65. Student A 65 Student B 71. Revision Section 77.Modules 3 & 4 ."}
diff --git a/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_Chen_2022_Theorem_4.5_r.jsonl b/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_Chen_2022_Theorem_4.5_r.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd39a9e5a1ae98d12a69da23a7aead26a20d5f86
--- /dev/null
+++ b/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_Chen_2022_Theorem_4.5_r.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Xiaoyu Chen | DeepAI", "date": "", "ddg_snippet": "Human - in - the - loop : Provably Efficient Preference - based Reinforcement Learning with General Function Approximation ... human - in - the - loop reinforcement ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/xiaoyu-chen", "content": "Human - in - the - loop : Provably Efficient Preference - based Reinforcement Learning with General Function Approximation ... human - in - the - loop reinforcement ..."}
+{"idx": 1, "title": "Learning Guarantee of Reward Modeling Using Deep Neural Networks", "date": "", "ddg_snippet": "... bound, which substantiates the empirical efficiency of Reinforcement Learning from Human Feedback and highlights clear human beliefs in its success.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06601v1", "content": "... bound, which substantiates the empirical efficiency of Reinforcement Learning from Human Feedback and highlights clear human beliefs in its success."}
+{"idx": 2, "title": "Provable Reward-Agnostic Preference-Based Reinforcement Learning", "date": "", "ddg_snippet": "To overcome this challenge, there has been a recent surge of interest in Preference - based Reinforcement Learning (PbRL) with human feedback.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18505v3", "content": "To overcome this challenge, there has been a recent surge of interest in Preference - based Reinforcement Learning (PbRL) with human feedback."}
+{"idx": 3, "title": "Machine Learning and Algorithms | Sugaku", "date": "", "ddg_snippet": "... learning , deep learning , Gaussian processes, image classification, text categorization, batch mode active learning , statistical guarantees, and ...", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/T12072/", "content": "... learning , deep learning , Gaussian processes, image classification, text categorization, batch mode active learning , statistical guarantees, and ..."}
+{"idx": 4, "title": "Efficient computation of feedback arc set at web-scale |", "date": "", "ddg_snippet": "Their goal is to compare their efficiency, especially when dealing with Big Data problems such as misinformation removal in Social Networks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/312874966_Efficient_computation_of_feedback_arc_set_at_web-scale", "content": "Their goal is to compare their efficiency, especially when dealing with Big Data problems such as misinformation removal in Social Networks."}
+{"idx": 5, "title": "NeurIPS 2023 Orals", "date": "", "ddg_snippet": "... interpretation comes with little or no loss of performance for link prediction, while the circuits framework unlocks exact learning by MLE, efficient ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/events/oral", "content": "... interpretation comes with little or no loss of performance for link prediction, while the circuits framework unlocks exact learning by MLE, efficient ..."}
+{"idx": 6, "title": "Accepted Papers| Artificial Intelligence and Statistics", "date": "", "ddg_snippet": "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Xu, Winnie; Chen , Ricky T. ... based Sampling in Dirichlet Process ...", "subpage_snippet": "", "source": "aistats.org", "link": "https://aistats.org/aistats2022/accepted.html", "content": "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Xu, Winnie; Chen , Ricky T. ... based Sampling in Dirichlet Process ..."}
+{"idx": 7, "title": "Accepted Papers| Artificial Intelligence and Statistics", "date": "", "ddg_snippet": "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Xu, Winnie; Chen , Ricky T. ... based Sampling in Dirichlet Process ...", "subpage_snippet": "", "source": "aistats.org", "link": "http://aistats.org/aistats2022/accepted.html", "content": "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Xu, Winnie; Chen , Ricky T. ... based Sampling in Dirichlet Process ..."}
+{"idx": 8, "title": "Manuel Gomez Rodriguez's research works | Max Planck", "date": "", "ddg_snippet": "Decision support systems based on prediction sets help humans solve multiclass classification tasks by narrowing down the set of potential label ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Manuel-Gomez-Rodriguez-59264845", "content": "Decision support systems based on prediction sets help humans solve multiclass classification tasks by narrowing down the set of potential label ..."}
+{"idx": 9, "title": "ICML 2024 2024 Spotlight Posters", "date": "", "ddg_snippet": "We call this approach KTO, and it matches or exceeds the performance of preference - based methods at scales from 1B to 30B, despite only learning from ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/events/2024SpotlightPosters", "content": "We call this approach KTO, and it matches or exceeds the performance of preference - based methods at scales from 1B to 30B, despite only learning from ..."}
diff --git a/data/sampled_jsons/ICLR_2025_DIL_paper_UKL_Unnormalized_Kullback-Leibler_divergence_default.jsonl b/data/sampled_jsons/ICLR_2025_DIL_paper_UKL_Unnormalized_Kullback-Leibler_divergence_default.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..694316d48e3531ded77c472443a1b4f66e55faa7
--- /dev/null
+++ b/data/sampled_jsons/ICLR_2025_DIL_paper_UKL_Unnormalized_Kullback-Leibler_divergence_default.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Kullback – Leibler divergence - Wikipedia", "date": "", "ddg_snippet": "In mathematical statistics, the Kullback – Leibler divergence , denoted. , is a type of statistical distance: a measure of how much a model probability distribution Q is different from a true probability distribution P. Mathematically, it is defined as.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Kullback–Leibler_divergence", "content": "In mathematical statistics, the Kullback – Leibler divergence , denoted. , is a type of statistical distance: a measure of how much a model probability distribution Q is different from a true probability distribution P. Mathematically, it is defined as."}
+{"idx": 1, "title": "[2503.08038] Generalized Kullback-Leibler Divergence Loss", "date": "", "ddg_snippet": "Mar 11, 2025 · With these two enhancements, we derive the Generalized Kullback-Leibler (GKL) Divergence loss and evaluate its effectiveness by conducting experiments on CIFAR-10/100, ImageNet, and vision-language datasets, focusing on adversarial training, and knowledge distillation tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.08038", "content": "Mar 11, 2025 · With these two enhancements, we derive the Generalized Kullback-Leibler (GKL) Divergence loss and evaluate its effectiveness by conducting experiments on CIFAR-10/100, ImageNet, and vision-language datasets, focusing on adversarial training, and knowledge distillation tasks."}
+{"idx": 2, "title": "Rethinking Kullback-Leibler Divergence in Knowledge ...", "date": "", "ddg_snippet": "Abstract Kullback -Leiber divergence has been widely used in Knowledge Distillation (KD) to com-press Large Language Models (LLMs). Con-trary to prior assertions that reverse Kullback-Leibler (RKL) divergence is mode-seeking and thus preferable over the mean-seeking forward Kullback-Leibler (FKL) divergence , this study empirically and theoretically demonstrates that neither mode-seeking nor ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.coling-main.383.pdf", "content": "Abstract Kullback -Leiber divergence has been widely used in Knowledge Distillation (KD) to com-press Large Language Models (LLMs). Con-trary to prior assertions that reverse Kullback-Leibler (RKL) divergence is mode-seeking and thus preferable over the mean-seeking forward Kullback-Leibler (FKL) divergence , this study empirically and theoretically demonstrates that neither mode-seeking nor ..."}
+{"idx": 3, "title": "[2404.02657] Rethinking Kullback-Leibler Divergence in ... Images Decoupled Kullback-Leibler Divergence Loss (PDF) Generalized Kullback-Leibler Divergence Loss On LLM Knowledge Distillation - iclr-blogposts.github.io Rethinking Kullback - Leibler Divergence in Knowledge Distillation for Rethinking Kullback - Leibler Divergence in Knowledge Distillation for [2503.08038] Generalized Kullback - Leibler Divergence Loss Rethinking Kullback - Leibler Divergence in Knowledge Distillation for Decoupled Kullback-Leibler Divergence Loss Decoupled Kullback-Leibler Divergence Loss blog | ICLR Blogposts 2025", "date": "", "ddg_snippet": "Apr 3, 2024 · Kullback -Leiber divergence has been widely used in Knowledge Distillation (KD) to compress Large Language Models (LLMs). Contrary to prior assertions that reverse Kullback-Leibler (RKL) divergence is mode-seeking and thus preferable over the mean-seeking forward Kullback-Leibler (FKL) divergence , this study empirically and theoretically demonstrates that neither mode-seeking nor mean-seeking ... View all To bridge this gap, our paper aims to elucidate the working mechanism of KL Divergence regarding gradient optimization. Our study focuses on the analysis of Kullback–Leibler (KL) Divergence loss from the perspective of gradient optimization. Mar 11, 2025 · In this paper , we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists ... Apr 28, 2025 · On LLM Knowledge Distillation - A Comparison between Forward KL and Reverse KL In this blog post, we delve into knowledge distillation techniques for Large Language Models (LLMs), with a particular focus on using Kullback-Leibler (KL) Divergence as the optimization objective. Knowledge distillation is a powerful tool to reduce model size while maintaining comparable performance, making it ... Is reverse Kullback-Leibler (RKL) divergence mode-seeking? Con-trary to prior assertions that reverse Kullback-Leibler (RKL) divergence is mode-seeking and thus preferable over the mean-seeking forward Kullback-Leibler (FKL) divergence, this study empirically and theoretically demonstrates that neither mode-seeking nor mean-seeking prop-erties manifest in KD for LLMs. What is Kullback-Leiber divergence? Kullback -Leiber divergence has been widely used in Knowledge Distillation (KD) to com-press Large Language Models (LLMs). Is the Kullback-Leibler divergence loss equivalent to the decoupled KL-Leibler loss? In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists of (1) a weighted Mean Square Error (wMSE) loss and (2) a Cross-Entropy loss incorporating soft labels. Does KL divergence rethink in KD for LLM? One lim-itation is that we have not conducted experiments on bigger language models, such as LLaMA-2 70B due to limited resources. We leave it for future work to conduct experiments on larger models. This work aims to rethink the KL divergence in KD for LLM and verified on various LLMs. Is KL equiv-Alent to DKL divergence loss? For models with softmax activation, we provide theoretical proof that it is equiv-alent to the Decoupled Kullback–Leibler (DKL) Divergence loss which comprises a weighted Mean Square Error (wMSE) loss and a Cross-Entropy loss with soft labels. Figures 1(a) and (b) reveal the equivalence between KL and DKL losses regarding gradient backpropagation. What is improved Kullback-Leibler divergence loss? The new formulation is named Decoupled Kullback-Leibler (DKL) Divergence loss. To address the spotted issues of KL/DKL, we make two improvements that break the asymmetric optimization property and incorporate class-wise global information, deriving the Improved Kullback-Leibler ( IKL ) Divergence loss. Apr 28, 2025 · Home to the 2025 ICLR Blogposts trackOn LLM Knowledge Distillation - A Comparison between Forward KL and Reverse KL In this blog post, we delve into knowledge distillation techniques for Large Language Models (LLMs), with a particular focus on using Kullback-Leibler (KL) Divergence as the optimization objective. Knowledge distillation is a powerful tool to reduce model size while maintaining ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.02657", "content": "Apr 3, 2024 · Kullback -Leiber divergence has been widely used in Knowledge Distillation (KD) to compress Large Language Models (LLMs). Contrary to prior assertions that reverse Kullback-Leibler (RKL) divergence is mode-seeking and thus preferable over the mean-seeking forward Kullback-Leibler (FKL) divergence , this study empirically and theoretically demonstrates that neither mode-seeking nor mean-seeking ... View all To bridge this gap, our paper aims to elucidate the working mechanism of KL Divergence regarding gradient optimization. Our study focuses on the analysis of Kullback–Leibler (KL) Divergence loss from the perspective of gradient optimization. Mar 11, 2025 · In this paper , we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists ... Apr 28, 2025 · On LLM Knowledge Distillation - A Comparison between Forward KL and Reverse KL In this blog post, we delve into knowledge distillation techniques for Large Language Models (LLMs), with a particular focus on using Kullback-Leibler (KL) Divergence as the optimization objective. Knowledge distillation is a powerful tool to reduce model size while maintaining comparable performance, making it ... Is reverse Kullback-Leibler (RKL) divergence mode-seeking? Con-trary to prior assertions that reverse Kullback-Leibler (RKL) divergence is mode-seeking and thus preferable over the mean-seeking forward Kullback-Leibler (FKL) divergence, this study empirically and theoretically demonstrates that neither mode-seeking nor mean-seeking prop-erties manifest in KD for LLMs. What is Kullback-Leiber divergence? Kullback -Leiber divergence has been widely used in Knowledge Distillation (KD) to com-press Large Language Models (LLMs). Is the Kullback-Leibler divergence loss equivalent to the decoupled KL-Leibler loss? In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists of (1) a weighted Mean Square Error (wMSE) loss and (2) a Cross-Entropy loss incorporating soft labels. Does KL divergence rethink in KD for LLM? One lim-itation is that we have not conducted experiments on bigger language models, such as LLaMA-2 70B due to limited resources. We leave it for future work to conduct experiments on larger models. This work aims to rethink the KL divergence in KD for LLM and verified on various LLMs. Is KL equiv-Alent to DKL divergence loss? For models with softmax activation, we provide theoretical proof that it is equiv-alent to the Decoupled Kullback–Leibler (DKL) Divergence loss which comprises a weighted Mean Square Error (wMSE) loss and a Cross-Entropy loss with soft labels. Figures 1(a) and (b) reveal the equivalence between KL and DKL losses regarding gradient backpropagation. What is improved Kullback-Leibler divergence loss? The new formulation is named Decoupled Kullback-Leibler (DKL) Divergence loss. To address the spotted issues of KL/DKL, we make two improvements that break the asymmetric optimization property and incorporate class-wise global information, deriving the Improved Kullback-Leibler ( IKL ) Divergence loss. Apr 28, 2025 · Home to the 2025 ICLR Blogposts trackOn LLM Knowledge Distillation - A Comparison between Forward KL and Reverse KL In this blog post, we delve into knowledge distillation techniques for Large Language Models (LLMs), with a particular focus on using Kullback-Leibler (KL) Divergence as the optimization objective. Knowledge distillation is a powerful tool to reduce model size while maintaining ..."}
+{"idx": 4, "title": "Decoupled Kullback-Leibler Divergence Loss", "date": "", "ddg_snippet": "To bridge this gap, our paper aims to elucidate the working mechanism of KL Divergence regarding gradient optimization. Our study focuses on the analysis of Kullback–Leibler (KL) Divergence loss from the perspective of gradient optimization.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/87ee1bbac4635e7c948f3eea83c1f262-Paper-Conference.pdf", "content": "To bridge this gap, our paper aims to elucidate the working mechanism of KL Divergence regarding gradient optimization. Our study focuses on the analysis of Kullback–Leibler (KL) Divergence loss from the perspective of gradient optimization."}
+{"idx": 5, "title": "(PDF) Generalized Kullback-Leibler Divergence Loss", "date": "", "ddg_snippet": "Mar 11, 2025 · In this paper , we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389749436_Generalized_Kullback-Leibler_Divergence_Loss", "content": "Mar 11, 2025 · In this paper , we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists ..."}
+{"idx": 6, "title": "On LLM Knowledge Distillation - iclr-blogposts.github.io", "date": "", "ddg_snippet": "Apr 28, 2025 · On LLM Knowledge Distillation - A Comparison between Forward KL and Reverse KL In this blog post, we delve into knowledge distillation techniques for Large Language Models (LLMs), with a particular focus on using Kullback-Leibler (KL) Divergence as the optimization objective. Knowledge distillation is a powerful tool to reduce model size while maintaining comparable performance, making it ...", "subpage_snippet": "", "source": "iclr-blogposts.github.io", "link": "https://iclr-blogposts.github.io/2025/blog/llm-knowledge-distil/", "content": "Apr 28, 2025 · On LLM Knowledge Distillation - A Comparison between Forward KL and Reverse KL In this blog post, we delve into knowledge distillation techniques for Large Language Models (LLMs), with a particular focus on using Kullback-Leibler (KL) Divergence as the optimization objective. Knowledge distillation is a powerful tool to reduce model size while maintaining comparable performance, making it ..."}
+{"idx": 7, "title": "blog | ICLR Blogposts 2025", "date": "", "ddg_snippet": "Apr 28, 2025 · Home to the 2025 ICLR Blogposts trackOn LLM Knowledge Distillation - A Comparison between Forward KL and Reverse KL In this blog post, we delve into knowledge distillation techniques for Large Language Models (LLMs), with a particular focus on using Kullback-Leibler (KL) Divergence as the optimization objective. Knowledge distillation is a powerful tool to reduce model size while maintaining ...", "subpage_snippet": "", "source": "d2jud02ci9yv69.cloudfront.net", "link": "https://d2jud02ci9yv69.cloudfront.net/2025-04-28-llm-knowledge-distil-157/blog/", "content": "Apr 28, 2025 · Home to the 2025 ICLR Blogposts trackOn LLM Knowledge Distillation - A Comparison between Forward KL and Reverse KL In this blog post, we delve into knowledge distillation techniques for Large Language Models (LLMs), with a particular focus on using Kullback-Leibler (KL) Divergence as the optimization objective. Knowledge distillation is a powerful tool to reduce model size while maintaining ..."}
+{"idx": 8, "title": "Kullback Leibler divergence between two normal pdfs - YouTube", "date": "", "ddg_snippet": "KL divergence : Two Gaussian pdfs.© 2025 Google LLC.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=TNJwYuKjqVM", "content": "KL divergence : Two Gaussian pdfs.© 2025 Google LLC."}
+{"idx": 9, "title": "Kullback – Leibler divergence between two normal distributions", "date": "", "ddg_snippet": "Intuition on the Kullback – Leibler (KL) Divergence . Kullback – Leibler divergence between normal distribution and improper distribution.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/594064/kullback-leibler-divergence-between-two-normal-distributions", "content": "Intuition on the Kullback – Leibler (KL) Divergence . Kullback – Leibler divergence between normal distribution and improper distribution."}
diff --git a/data/sampled_jsons/ICLR_2025_paper_tiers_top_medium_standard.jsonl b/data/sampled_jsons/ICLR_2025_paper_tiers_top_medium_standard.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06de5b5ee7ff7fb06d819f714252a7eece90c8c9
--- /dev/null
+++ b/data/sampled_jsons/ICLR_2025_paper_tiers_top_medium_standard.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Some of the Most Interesting Papers to Watch from ICLR ...", "date": "", "ddg_snippet": "Some of the Most Interesting Papers to Watch from ICLR 2025 ICLR stands among the top - tier conferences like NeurIPS, ICML, ICCV, and CVPR.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ml_dl_explained/some-of-the-most-interesting-papers-to-watch-from-iclr-2025-eb3b187b1c71", "content": "Some of the Most Interesting Papers to Watch from ICLR 2025 ICLR stands among the top - tier conferences like NeurIPS, ICML, ICCV, and CVPR."}
+{"idx": 1, "title": "OpenReview Analysis: ICLR 2025 Paper Ranking", "date": "", "ddg_snippet": "Explore ICLR 2025 papers from OpenReview , ranked by a custom scoring algorithm. View scores, ratings, confidence, potential projects, and more.", "subpage_snippet": "", "source": "openreview-copilot.eamag.me", "link": "https://openreview-copilot.eamag.me/", "content": "Explore ICLR 2025 papers from OpenReview , ranked by a custom scoring algorithm. View scores, ratings, confidence, potential projects, and more."}
+{"idx": 2, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "Getting Started, Schedule, Main Conference, Invited Talks, Awards, Papers , In-person Orals, Spotlight, Posters, Blog, Track Posters, Workshops, Community, Town ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "Getting Started, Schedule, Main Conference, Invited Talks, Awards, Papers , In-person Orals, Spotlight, Posters, Blog, Track Posters, Workshops, Community, Town ..."}
+{"idx": 3, "title": "[D] Quality of ICLR papers : r/MachineLearning", "date": "", "ddg_snippet": "Papers in top - tier conferences are becoming necessity even for industrial jobs. ... I am submitting paper In Tiny Track of SynthData @ ICLR 2025 ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/1gtjhge/d_quality_of_iclr_papers/", "content": "Papers in top - tier conferences are becoming necessity even for industrial jobs. ... I am submitting paper In Tiny Track of SynthData @ ICLR 2025 ..."}
+{"idx": 4, "title": "Paper Digest: ICLR 2025 Papers & Highlights", "date": "", "ddg_snippet": "17 Mar 2025 — Note: ICLR - 2025 accepts more than 3,700 papers , this page only includes 500 of them selected by our daily paper digest algorithm. Interested ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2025/03/iclr-2025-papers-highlights/", "content": "17 Mar 2025 — Note: ICLR - 2025 accepts more than 3,700 papers , this page only includes 500 of them selected by our daily paper digest algorithm. Interested ..."}
+{"idx": 5, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "'where/when' (bottom) and the top -50 most common questions ( top ). when analyzing the top -50 most common questions ( top ), it is clear they are all prompting for ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/e789cfc9389048df4a0a44d4086e0dc2-Supplemental-Conference.pdf", "content": "'where/when' (bottom) and the top -50 most common questions ( top ). when analyzing the top -50 most common questions ( top ), it is clear they are all prompting for ..."}
+{"idx": 6, "title": "Book", "date": "", "ddg_snippet": "International Conference on Representation Learning 2025 ( ICLR 2025 ). Edited ... Efficient Top -m Data Values Identification for Data Selection Xiaoqiang ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025", "content": "International Conference on Representation Learning 2025 ( ICLR 2025 ). Edited ... Efficient Top -m Data Values Identification for Data Selection Xiaoqiang ..."}
+{"idx": 7, "title": "Video Action Differencing", "date": "", "ddg_snippet": "by J Burgess — The paper introduces a new task and a benchmark for action differencing, to tell the differences between different actors performing the same action. Various ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3bcN6xlO6f", "content": "by J Burgess — The paper introduces a new task and a benchmark for action differencing, to tell the differences between different actors performing the same action. Various ..."}
+{"idx": 8, "title": "Position: The AI Conference Peer Review Crisis Demands ...", "date": "", "ddg_snippet": "8 May 2025 — For instance, ICLR 2025 has explicitly asked authors to submit their usage of LLMs in the paper writing process. These measures indicate that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04966v1", "content": "8 May 2025 — For instance, ICLR 2025 has explicitly asked authors to submit their usage of LLMs in the paper writing process. These measures indicate that ..."}
+{"idx": 9, "title": "OMRON SINIC X to Present Latest Research Findings at ...", "date": "", "ddg_snippet": "15 Apr 2025 — ICLR 2025 is one of the top - tier international conferences in the field of machine learning, particularly known for showcasing cutting-edge ...", "subpage_snippet": "", "source": "www.omron.com", "link": "https://www.omron.com/global/en/media/2025/04/c0415.html", "content": "15 Apr 2025 — ICLR 2025 is one of the top - tier international conferences in the field of machine learning, particularly known for showcasing cutting-edge ..."}
diff --git a/data/sampled_jsons/ICLR_paper_classification_top_medium_mid-tier_standard.jsonl b/data/sampled_jsons/ICLR_paper_classification_top_medium_mid-tier_standard.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..36c737cf6fd9f5b3f44a94c6093bf07f43b37da7
--- /dev/null
+++ b/data/sampled_jsons/ICLR_paper_classification_top_medium_mid-tier_standard.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Find the latest ICON Public Limited Company ( ICLR ) stock quote, history, news and other vital information to help you with your stock trading and investing.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/?fr=sycsrp_catchall", "content": "Find the latest ICON Public Limited Company ( ICLR ) stock quote, history, news and other vital information to help you with your stock trading and investing."}
+{"idx": 1, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Get the latest ICON Public Limited Company ( ICLR ) stock news and headlines to help you in your trading and investing decisions.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/news/?fr=sycsrp_catchall", "content": "Get the latest ICON Public Limited Company ( ICLR ) stock news and headlines to help you in your trading and investing decisions."}
+{"idx": 2, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "See the company profile for ICON Public Limited Company ( ICLR ) including business summary, industry/sector information, number of employees, business summary, corporate governance, key executives ...", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/profile/?fr=sycsrp_catchall", "content": "See the company profile for ICON Public Limited Company ( ICLR ) including business summary, industry/sector information, number of employees, business summary, corporate governance, key executives ..."}
+{"idx": 3, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Discover historical prices for ICLR stock on Yahoo Finance. View daily, weekly or monthly format back to when ICON Public Limited Company stock was issued.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/history/?fr=sycsrp_catchall", "content": "Discover historical prices for ICLR stock on Yahoo Finance. View daily, weekly or monthly format back to when ICON Public Limited Company stock was issued."}
+{"idx": 4, "title": "Icon PLC (ICLR) Q3 2024 Earnings Call Highlights: Navigating...", "date": "", "ddg_snippet": "Oct 25, 2024 · Despite revenue decline and project delays, Icon PLC ( ICLR ) remains optimistic about future growth through strategic partnerships and market share gains.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/icon-plc-iclr-q3-2024-070717387.html?fr=sycsrp_catchall", "content": "Oct 25, 2024 · Despite revenue decline and project delays, Icon PLC ( ICLR ) remains optimistic about future growth through strategic partnerships and market share gains."}
+{"idx": 5, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "See ICON Public Limited Company ( ICLR ) stock analyst estimates, including earnings and revenue, EPS, upgrades and downgrades.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/analysis/?fr=sycsrp_catchall", "content": "See ICON Public Limited Company ( ICLR ) stock analyst estimates, including earnings and revenue, EPS, upgrades and downgrades."}
+{"idx": 6, "title": "ICON Public Limited Company (ICLR): Among Steven Cohen’s Mid-Cap...", "date": "", "ddg_snippet": "May 9, 2025 · In this article, we are going to take a look at where ICON Public Limited Company (NASDAQ: ICLR ) stands against Steve Cohen's other mid -cap stock picks with huge upside potential.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/icon-public-limited-company-iclr-071309449.html?fr=sycsrp_catchall", "content": "May 9, 2025 · In this article, we are going to take a look at where ICON Public Limited Company (NASDAQ: ICLR ) stands against Steve Cohen's other mid -cap stock picks with huge upside potential."}
+{"idx": 7, "title": "ICLR Misses on Q3 Earnings, Lowers 2024 Guidance, Stock Falls", "date": "", "ddg_snippet": "Oct 28, 2024 · Find the latest EPS estimates and surprises on Zacks Earnings Calendar. Following the earnings announcement, ICLR stock fell 21% last Thursday.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/iclr-misses-q3-earnings-lowers-123400740.html?fr=sycsrp_catchall", "content": "Oct 28, 2024 · Find the latest EPS estimates and surprises on Zacks Earnings Calendar. Following the earnings announcement, ICLR stock fell 21% last Thursday."}
+{"idx": 8, "title": "Icon PLC (ICLR) Lags Q3 Earnings and Revenue Estimates", "date": "", "ddg_snippet": "Oct 23, 2024 · Icon PLC ( ICLR ) delivered earnings and revenue surprises of -12.99% and 5.12%, respectively, for the quarter ended September 2024. Do the numbers hold clues to what lies ahead for the stock?", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/news/icon-plc-iclr-lags-q3-215513569.html?fr=sycsrp_catchall", "content": "Oct 23, 2024 · Icon PLC ( ICLR ) delivered earnings and revenue surprises of -12.99% and 5.12%, respectively, for the quarter ended September 2024. Do the numbers hold clues to what lies ahead for the stock?"}
+{"idx": 9, "title": "ICON Public Limited Company (ICLR) - Yahoo Finance", "date": "", "ddg_snippet": "Find out the direct holders, institutional holders and mutual fund holders for ICON Public Limited Company ( ICLR ).", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/ICLR/holders/?fr=sycsrp_catchall", "content": "Find out the direct holders, institutional holders and mutual fund holders for ICON Public Limited Company ( ICLR )."}
diff --git a/data/sampled_jsons/ICML_2019_proceedings_Shen_Lee.jsonl b/data/sampled_jsons/ICML_2019_proceedings_Shen_Lee.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44fde27c3c51785d923445cb57d5ed37c78d78a1
--- /dev/null
+++ b/data/sampled_jsons/ICML_2019_proceedings_Shen_Lee.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Proceedings of Machine Learning Research | Proceedings of the...", "date": "", "ddg_snippet": "Volume 97: International Conference on Machine Learning , 9-15 June 2019 , Long Beach, California, USA.Nontawat Charoenphakdee, Jongyeong Lee , Masashi Sugiyama; Proceedings of the 36th International Conference on Machine Learning , PMLR 97:961-970.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v97/", "content": "Volume 97: International Conference on Machine Learning , 9-15 June 2019 , Long Beach, California, USA.Nontawat Charoenphakdee, Jongyeong Lee , Masashi Sugiyama; Proceedings of the 36th International Conference on Machine Learning , PMLR 97:961-970."}
+{"idx": 1, "title": "Google at ICML 2019 – Toronto AI Meetup", "date": "", "ddg_snippet": "Google at ICML 2019 . Written by torontoai on June 9, 2019 . Posted in Google. Learning Latent Dynamics for Planning from Pixels Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee , James Davidson.", "subpage_snippet": "", "source": "torontoai.org", "link": "https://torontoai.org/2019/06/09/google-at-icml-2019/", "content": "Google at ICML 2019 . Written by torontoai on June 9, 2019 . Posted in Google. Learning Latent Dynamics for Planning from Pixels Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee , James Davidson."}
+{"idx": 2, "title": "Google at ICML 2019", "date": "", "ddg_snippet": "You can also learn more about the Google research being presented at ICML 2019 in the list below (Google affiliations highlighted in blue). ICML 2019 Committees Board Members include: Andrew McCallum, Corinna Cortes, Hugo Larochelle, William Cohen (Emeritus).", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/google-at-icml-2019/", "content": "You can also learn more about the Google research being presented at ICML 2019 in the list below (Google affiliations highlighted in blue). ICML 2019 Committees Board Members include: Andrew McCallum, Corinna Cortes, Hugo Larochelle, William Cohen (Emeritus)."}
+{"idx": 3, "title": "ICML", "date": "", "ddg_snippet": "Welcome to ICML 2018 in stockholm. Dear ICML aendeesWe owe a special thank you to Mary Ellen Perry, the ICML Execuve Director and Lee Campbell, the ICML IT Director.", "subpage_snippet": "", "source": "media.nips.cc", "link": "https://media.nips.cc/Conferences/ICML2018/ICML-2018-Conference-Book.pdf", "content": "Welcome to ICML 2018 in stockholm. Dear ICML aendeesWe owe a special thank you to Mary Ellen Perry, the ICML Execuve Director and Lee Campbell, the ICML IT Director."}
+{"idx": 4, "title": "(PDF) Sentiment Analysis of Tweets Using Machine Learning , 2019 ...", "date": "", "ddg_snippet": "Y. Yang, H. T Shen , Z. Ma, Z. Huang and X. Zhou, \"l2, 1-norm regularized discriminative feature selection for unsupervised learning ,\" IJCAI proceedings - international joint conference on artificial intelligence vol. 22(1), 2011, p. 1589.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/41543460/Sentiment_Analysis_of_Tweets_Using_Machine_Learning_2019_Turkey_Van_pages_85_87", "content": "Y. Yang, H. T Shen , Z. Ma, Z. Huang and X. Zhou, \"l2, 1-norm regularized discriminative feature selection for unsupervised learning ,\" IJCAI proceedings - international joint conference on artificial intelligence vol. 22(1), 2011, p. 1589."}
+{"idx": 5, "title": "A Quantitative Analysis of the Effect of Batch Normalization on...", "date": "", "ddg_snippet": "Proceedings of the 36 th International Conference on Machine Learning , Long Beach, California, PMLR 97, 2019 . Copyright 2019 by the author(s). effects of BN are attributed to the so-called “reduction of covariate shift”.", "subpage_snippet": "", "source": "cpb-us-w2.wpmucdn.com", "link": "https://cpb-us-w2.wpmucdn.com/blog.nus.edu.sg/dist/d/11132/files/2019/07/Cai_Li_Shen_BatchNorm_ICML2019.pdf", "content": "Proceedings of the 36 th International Conference on Machine Learning , Long Beach, California, PMLR 97, 2019 . Copyright 2019 by the author(s). effects of BN are attributed to the so-called “reduction of covariate shift”."}
+{"idx": 6, "title": "High-dimensional Learning Dynamics Workshop: The Emergence of...", "date": "", "ddg_snippet": "Main Navigation. conference _logo. ICML .We invite participation in the 2nd Workshop on High-dimensional Learning Dynamics (HiLD), to be held as a part of the ICML 2024 conference .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/workshop/29974", "content": "Main Navigation. conference _logo. ICML .We invite participation in the 2nd Workshop on High-dimensional Learning Dynamics (HiLD), to be held as a part of the ICML 2024 conference ."}
+{"idx": 7, "title": "Machine Learning and Intelligent Communications... - DOKUMEN.PUB", "date": "", "ddg_snippet": "This volume constitutes the refereed post-conference proceedings of the Fourth International Conference on Machine Learn .", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/machine-learning-and-intelligent-communications-4th-international-conference-mlicom-2019-nanjing-china-august-2425-2019-proceedings-1st-ed-2019-978-3-030-32387-5-978-3-030-32388-2.html", "content": "This volume constitutes the refereed post-conference proceedings of the Fourth International Conference on Machine Learn ."}
+{"idx": 8, "title": "dblp: PMLR", "date": "", "ddg_snippet": "Proceedings of Machine Learning Research 116, PMLR 2019 [contents].Doina Precup, Yee Whye Teh: Proceedings of the 34th International Conference on Machine Learning , ICML 2017, Sydney, NSW, Australia, 6-11 August 2017.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/db/series/jmlr/index.html", "content": "Proceedings of Machine Learning Research 116, PMLR 2019 [contents].Doina Precup, Yee Whye Teh: Proceedings of the 34th International Conference on Machine Learning , ICML 2017, Sydney, NSW, Australia, 6-11 August 2017."}
+{"idx": 9, "title": "Peter Richtarik", "date": "", "ddg_snippet": "Proceedings for such conferences are selected and reviewed by the world’s best experts in machine learning and artificial intelligence.I have accepted an invite to serve as an Area Chair for The 36th International Conference on Machine Learning ( ICML 2019 ).", "subpage_snippet": "", "source": "richtarik.org", "link": "https://richtarik.org/i_oldnews-2024.html", "content": "Proceedings for such conferences are selected and reviewed by the world’s best experts in machine learning and artificial intelligence.I have accepted an invite to serve as an Area Chair for The 36th International Conference on Machine Learning ( ICML 2019 )."}
diff --git a/data/sampled_jsons/ICML_2025_45312_GitHub.jsonl b/data/sampled_jsons/ICML_2025_45312_GitHub.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f6c7b1343520338114fd79403e976d6ac100b96b
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_45312_GitHub.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - DmitryRyumin/ICML-2025-Papers: ICML 2025 Papers: Dive into ...", "date": "", "ddg_snippet": "ICML 2025 Papers: Dive into cutting-edge research from the premier machine learning conference. Stay current with breakthroughs in deep learning, generative AI, optimization, reinforcement learning...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/DmitryRyumin/ICML-2025-Papers", "content": "ICML 2025 Papers: Dive into cutting-edge research from the premier machine learning conference. Stay current with breakthroughs in deep learning, generative AI, optimization, reinforcement learning..."}
+{"idx": 1, "title": "icml-2025 · GitHub Topics · GitHub", "date": "", "ddg_snippet": "ICML 2025 Papers: Dive into cutting-edge research from the premier machine learning conference. Stay current with breakthroughs in deep learning, generative AI, optimization, reinforcement learning, and beyond. Code implementations included. ⭐ support the future of machine learning research!", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/icml-2025", "content": "ICML 2025 Papers: Dive into cutting-edge research from the premier machine learning conference. Stay current with breakthroughs in deep learning, generative AI, optimization, reinforcement learning, and beyond. Code implementations included. ⭐ support the future of machine learning research!"}
+{"idx": 2, "title": "GitHub - MAGICS-LAB/GERM: [ICML 2025] Fast and Low-Cost Genomic ...", "date": "", "ddg_snippet": "[ ICML 2025 ] Fast and Low-Cost Genomic Foundation Models via Outlier Removal. - MAGICS-LAB/GERM", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MAGICS-LAB/GERM", "content": "[ ICML 2025 ] Fast and Low-Cost Genomic Foundation Models via Outlier Removal. - MAGICS-LAB/GERM"}
+{"idx": 3, "title": "ICML 2025 - GitHub", "date": "", "ddg_snippet": "ICML 2025 has one repository available. Follow their code on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ICML-2025/", "content": "ICML 2025 has one repository available. Follow their code on GitHub ."}
+{"idx": 4, "title": "ICML 2025 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "How to use the paper list below:- Overview: This table presents papers from the ICML conference, year 2025 .- Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/icml-paper-list/icml-2025-paper-list/", "content": "How to use the paper list below:- Overview: This table presents papers from the ICML conference, year 2025 .- Filtering: By default, the table loads the first 100 records."}
+{"idx": 5, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Select Year: ( 2025 ) Getting Started Schedule Tutorials Main Conference Workshops Community Exhibitors Organizers Help Browse Visualization", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Select Year: ( 2025 ) Getting Started Schedule Tutorials Main Conference Workshops Community Exhibitors Organizers Help Browse Visualization"}
+{"idx": 6, "title": "Downloads 2025 - icml.cc", "date": "", "ddg_snippet": "ICML 2025 Workshop on Collaborative and Federated Agentic Workflows (CFAgentic @ ICML'25) ICML 2025 Workshop on Computational Optimization of Buildings (CO-BUILD) Identifiable Object Representations under Spatial Ambiguities Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "ICML 2025 Workshop on Collaborative and Federated Agentic Workflows (CFAgentic @ ICML'25) ICML 2025 Workshop on Computational Optimization of Buildings (CO-BUILD) Identifiable Object Representations under Spatial Ambiguities Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations"}
+{"idx": 7, "title": "ICML 2025 论文和开源项目合集 - GitHub", "date": "", "ddg_snippet": "ICML 2025 论文和开源项目合集 本仓库旨在收集ICML最新研究进展，尤其是LLM方面，涉及NLP领域的各个方向，此项目长期不定时更新。", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yinizhilian/ICML2025-Papers-with-Code", "content": "ICML 2025 论文和开源项目合集 本仓库旨在收集ICML最新研究进展，尤其是LLM方面，涉及NLP领域的各个方向，此项目长期不定时更新。"}
+{"idx": 8, "title": "ICML 2025 - Matteo Sesia - msesia.github.io", "date": "", "ddg_snippet": "ICML 2025 My collaborators and I had two papers accepted to the International Conference on Machine Learning ( ICML ) this year, one of which was selected as a spotlight (top 2.6% of submissions). The poster paper, with Meshi Bashari and Yaniv Romano, introduces a robust conformal outlier detection method that can work with contaminated reference data, using an active data-cleaning strategy ...", "subpage_snippet": "", "source": "msesia.github.io", "link": "https://msesia.github.io/posts/2025/05/01/ICML-2025.html", "content": "ICML 2025 My collaborators and I had two papers accepted to the International Conference on Machine Learning ( ICML ) this year, one of which was selected as a spotlight (top 2.6% of submissions). The poster paper, with Meshi Bashari and Yaniv Romano, introduces a robust conformal outlier detection method that can work with contaminated reference data, using an active data-cleaning strategy ..."}
+{"idx": 9, "title": "VecDB@ICML2025 - vecdb-ws.github.io", "date": "", "ddg_snippet": "Accepted Papers Oral \"Down with the Hierarchy: The 'H' in HNSW Stands for \"Hubs\"\", Munyampirwa, Vihan Lakshman, Benjamin Coleman \"Enhancing Retrieval Systems ...", "subpage_snippet": "", "source": "vecdb-ws.github.io", "link": "https://vecdb-ws.github.io/icml2025/papers.html", "content": "Accepted Papers Oral \"Down with the Hierarchy: The 'H' in HNSW Stands for \"Hubs\"\", Munyampirwa, Vihan Lakshman, Benjamin Coleman \"Enhancing Retrieval Systems ..."}
diff --git a/data/sampled_jsons/ICML_2025_Beyond_Self-Repellent_Kernels_LRU_cache_Equation_15_visit_frequency_approximation.jsonl b/data/sampled_jsons/ICML_2025_Beyond_Self-Repellent_Kernels_LRU_cache_Equation_15_visit_frequency_approximation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dc415de77ae22cbb2f5383401eb6a12e77987745
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_Beyond_Self-Repellent_Kernels_LRU_cache_Equation_15_visit_frequency_approximation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Beyond Self - Repellent Kernels : History-Driven Target...", "date": "", "ddg_snippet": "Login. Select Year: ( 2025 ).Extensive experiments in graph sampling demonstrate consistent performance gains, and a memory-efficient Least Recently Used ( LRU ) cache ensures scalability to large general graphs.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46659", "content": "Login. Select Year: ( 2025 ).Extensive experiments in graph sampling demonstrate consistent performance gains, and a memory-efficient Least Recently Used ( LRU ) cache ensures scalability to large general graphs."}
+{"idx": 1, "title": "LRU Cache - Complete Tutorial - GeeksforGeeks", "date": "", "ddg_snippet": "LRUCache (Capacity c): Initialize LRU cache with positive size capacity c. get (key) : Returns the value of key ' k' if it is present in the cache otherwise it returns -1. Also updates the priority of data in the LRU cache .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/system-design/lru-cache-implementation/", "content": "LRUCache (Capacity c): Initialize LRU cache with positive size capacity c. get (key) : Returns the value of key ' k' if it is present in the cache otherwise it returns -1. Also updates the priority of data in the LRU cache ."}
+{"idx": 2, "title": "Как предотвратить повторное вычисление функции с lru _ cache", "date": "", "ddg_snippet": "Как работает Least Recently Used ( LRU ) алгоритм. Параметры функции lru _ cache .ftl. cached _property def sumdata( self )", "subpage_snippet": "", "source": "python-school.ru", "link": "https://python-school.ru/blog/python/lru_cache/", "content": "Как работает Least Recently Used ( LRU ) алгоритм. Параметры функции lru _ cache .ftl. cached _property def sumdata( self )"}
+{"idx": 3, "title": "From Optimizing Kernels to Optimizing Benchmarks — Mako", "date": "", "ddg_snippet": "Sep 18, 2025 . TLDR: Based on the clustering analysis presented, we introduce KernelMiniBench: a minimal subset of 160 KernelBench problems that closely reproduces the full evaluation statistics for agentic LLMs with high fidelity for faster but reliable experiments.", "subpage_snippet": "", "source": "mako.dev", "link": "https://mako.dev/blog/mini-kernel-bench", "content": "Sep 18, 2025 . TLDR: Based on the clustering analysis presented, we introduce KernelMiniBench: a minimal subset of 160 KernelBench problems that closely reproduces the full evaluation statistics for agentic LLMs with high fidelity for faster but reliable experiments."}
+{"idx": 4, "title": "How to Disable Windows Update in 2025 : Proven Methods to... - Izoate", "date": "", "ddg_snippet": "By Arpit Kuzo / 22 September 2025 .In this guide, we’ll show you how to go beyond just pausing updates temporarily. You’ll learn safe, step-by-step ways to permanently disable Windows Update (and turn it back on when you actually want to).", "subpage_snippet": "", "source": "www.izoate.com", "link": "https://www.izoate.com/blog/how-to-disable-windows-update-in-2025-proven-methods-to-completely-stop-updates-on-windows-10-11/", "content": "By Arpit Kuzo / 22 September 2025 .In this guide, we’ll show you how to go beyond just pausing updates temporarily. You’ll learn safe, step-by-step ways to permanently disable Windows Update (and turn it back on when you actually want to)."}
+{"idx": 5, "title": "Publications | Yandex Research", "date": "", "ddg_snippet": "ICML , 2025 . Abstract.16. Graph machine learning . 15 . Nearest neighbor search. 14.", "subpage_snippet": "", "source": "research.yandex.com", "link": "https://research.yandex.com/publications", "content": "ICML , 2025 . Abstract.16. Graph machine learning . 15 . Nearest neighbor search. 14."}
+{"idx": 6, "title": "Python Coding challenge - Day 745| What is the output of the following...", "date": "", "ddg_snippet": "lru _ cache = Least Recently Used cache . It stores results of function calls so repeated inputs don’t need recalculation. 2. Applying the Decorator.If you're a student, educator, or self -learner looking for a free, high-quality linear algebra textbook , Jim Hefferon’s Linear Algebr...", "subpage_snippet": "", "source": "www.clcoding.com", "link": "https://www.clcoding.com/2025/09/python-coding-challenge-day-745-what-is.html", "content": "lru _ cache = Least Recently Used cache . It stores results of function calls so repeated inputs don’t need recalculation. 2. Applying the Decorator.If you're a student, educator, or self -learner looking for a free, high-quality linear algebra textbook , Jim Hefferon’s Linear Algebr..."}
+{"idx": 7, "title": "Research | FLAIR", "date": "", "ddg_snippet": "RLC 2025 . ADIOS: Antibody Development via Opponent Shaping.EvIL: Evolution Strategies for Generalisable Imitation Learning . Silvia Sapora, Chris Lu, Gokul Swamy, Yee Whye Teh, Jakob Foerster. ICML 2024.", "subpage_snippet": "", "source": "foersterlab.com", "link": "https://foersterlab.com/research/", "content": "RLC 2025 . ADIOS: Antibody Development via Opponent Shaping.EvIL: Evolution Strategies for Generalisable Imitation Learning . Silvia Sapora, Chris Lu, Gokul Swamy, Yee Whye Teh, Jakob Foerster. ICML 2024."}
+{"idx": 8, "title": "“I Want To Enjoy Every Minute That I Can”: For... | British Vogue", "date": "", "ddg_snippet": "October 2025 Issue.“ Beyond racing, he has this unique star quality that resonates far beyond Formula 1. He’s relatable, stylish and has a natural presence that makes him a true pop culture figure.", "subpage_snippet": "", "source": "www.vogue.co.uk", "link": "https://www.vogue.co.uk/article/lando-norris-interview", "content": "October 2025 Issue.“ Beyond racing, he has this unique star quality that resonates far beyond Formula 1. He’s relatable, stylish and has a natural presence that makes him a true pop culture figure."}
+{"idx": 9, "title": "Historical Consciousness: What Germany Could Learn from Russia", "date": "", "ddg_snippet": "21 September 2025 by Peter Haenseler 2 Comments. There are sound reasons why Germany is on the wrong track.Last year, I visited Stalin’s dacha in Sochi and was surprised by the simplicity of his living quarters. His offices in the Moscow Kremlin were also by no means ostentatious.", "subpage_snippet": "", "source": "sonar21.com", "link": "https://sonar21.com/historical-consciousness-what-germany-could-learn-from-russia/", "content": "21 September 2025 by Peter Haenseler 2 Comments. There are sound reasons why Germany is on the wrong track.Last year, I visited Stalin’s dacha in Sochi and was surprised by the simplicity of his living quarters. His offices in the Moscow Kremlin were also by no means ostentatious."}
diff --git a/data/sampled_jsons/ICML_2025_Normalizing_Flows.jsonl b/data/sampled_jsons/ICML_2025_Normalizing_Flows.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..39e274dcf26ed67f89478dfdd4f34fe0b3506a8a
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_Normalizing_Flows.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density estimation and generative ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46564", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density estimation and generative ..."}
+{"idx": 1, "title": "Forward-Only Regression Training of Normalizing Flows", "date": "", "ddg_snippet": "Spotlight in. Workshop: 2nd Generative AI for Biology Workshop. FORT: Forward-Only Regression Training of Normalizing Flows .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/51193", "content": "Spotlight in. Workshop: 2nd Generative AI for Biology Workshop. FORT: Forward-Only Regression Training of Normalizing Flows ."}
+{"idx": 2, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "30 Jun 2025 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density ...", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/normalizing-flows", "content": "30 Jun 2025 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density ..."}
+{"idx": 3, "title": "Learning Distances from Data with Normalizing Flows and ...", "date": "", "ddg_snippet": "15 Jul 2025 — First, we learn densities using normalizing flows . Second, we refine geodesics through relaxation, guided by a learned score model. Additionally ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45225", "content": "15 Jul 2025 — First, we learn densities using normalizing flows . Second, we refine geodesics through relaxation, guided by a learned score model. Additionally ..."}
+{"idx": 4, "title": "Flexible Tails for Normalizing Flows - Tennessee Hickling", "date": "", "ddg_snippet": "Normalizing flows are a flexible class of probability distributions , expressed as transformations of a simple base distribution. A limitation of standard ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44878", "content": "Normalizing flows are a flexible class of probability distributions , expressed as transformations of a simple base distribution. A limitation of standard ..."}
+{"idx": 5, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "by S Zhai · Cited by 25 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "by S Zhai · Cited by 25 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density ..."}
+{"idx": 6, "title": "ICML Talk Variational Inference with Normalizing Flows", "date": "", "ddg_snippet": "Right-click and choose download. It is a vector graphic and may be used at any scale. Useful links. About ICML · ICML Proceedings at PMLR.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/talk/52803", "content": "Right-click and choose download. It is a vector graphic and may be used at any scale. Useful links. About ICML · ICML Proceedings at PMLR."}
+{"idx": 7, "title": "Amortized Sampling with Transferable Normalizing Flows", "date": "", "ddg_snippet": "Amortized Sampling with Transferable Normalizing Flows . Charlie Tan · Majdi Hassan · Leon Klein · Saifuddin Syed · Dominique Beaini · Michael Bronstein · ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/51327", "content": "Amortized Sampling with Transferable Normalizing Flows . Charlie Tan · Majdi Hassan · Leon Klein · Saifuddin Syed · Dominique Beaini · Michael Bronstein · ..."}
+{"idx": 8, "title": "[2406.16971] Flexible Tails for Normalizing Flows", "date": "", "ddg_snippet": "by T Hickling · 2024 · Cited by 3 — Normalizing flows are a flexible class of probability distributions , expressed as transformations of a simple base distribution. A limitation of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.16971", "content": "by T Hickling · 2024 · Cited by 3 — Normalizing flows are a flexible class of probability distributions , expressed as transformations of a simple base distribution. A limitation of ..."}
+{"idx": 9, "title": "Counterfactual Contrastive Learning with Normalizing ...", "date": "", "ddg_snippet": "16 Jul 2025 — Estimating Individual Treatment Effects (ITE) from observational data is challenging due to covariate shift and counterfactual absence.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45050", "content": "16 Jul 2025 — Estimating Individual Treatment Effects (ITE) from observational data is challenging due to covariate shift and counterfactual absence."}
diff --git a/data/sampled_jsons/ICML_2025_conference_papers_Normalizing_Flows_abstract.jsonl b/data/sampled_jsons/ICML_2025_conference_papers_Normalizing_Flows_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..94d6d7823e4ff6ce952b5e31e0459c10d72dc39c
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_conference_papers_Normalizing_Flows_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "ICML is a global non-profit organization dedicated to helping lubrication practitioners succeed in their professional careers. ICML certification exams are administered in accordance with ISO 18436 and are available worldwide in multiple languages and in both paper and online formats.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/", "content": "ICML is a global non-profit organization dedicated to helping lubrication practitioners succeed in their professional careers. ICML certification exams are administered in accordance with ISO 18436 and are available worldwide in multiple languages and in both paper and online formats."}
+{"idx": 1, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "Candidates have three hours to complete the closed-book examination. A score of 70% is required to pass the examination and achieve certification. Contact ICML about the availability of the exam in other languages.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/exams/Default.aspx", "content": "Candidates have three hours to complete the closed-book examination. A score of 70% is required to pass the examination and achieve certification. Contact ICML about the availability of the exam in other languages."}
+{"idx": 2, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "About Us Success Stories Roster ICML Alliances Training Partners Contact Us Privacy Questions ICML | 2404 W. Detroit St | Broken Arrow, OK 74012 USA o: 918.259.2950 | f: 918.259.0177 www.lubecouncil.org | info@lubecouncil.org", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/contact_us.aspx", "content": "About Us Success Stories Roster ICML Alliances Training Partners Contact Us Privacy Questions ICML | 2404 W. Detroit St | Broken Arrow, OK 74012 USA o: 918.259.2950 | f: 918.259.0177 www.lubecouncil.org | info@lubecouncil.org"}
+{"idx": 3, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "ICML provides certification programs for machinery lubrication professionals, ensuring qualifications and knowledge in lubrication practices.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/exams/professionals.aspx", "content": "ICML provides certification programs for machinery lubrication professionals, ensuring qualifications and knowledge in lubrication practices."}
+{"idx": 4, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "ICML 's Bodies of Knowledge are of public domain and can be utilized by companies in the development of courses, as well as by any prospective candidate for evaluating the appropriateness of chosen training.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/exams/Default.aspx?p=LLA1", "content": "ICML 's Bodies of Knowledge are of public domain and can be utilized by companies in the development of courses, as well as by any prospective candidate for evaluating the appropriateness of chosen training."}
+{"idx": 5, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "Examination – Each candidate must successfully pass a 150-question, multiple choice Machinery Lubrication Engineer (MLE) ® examination that tests the candidate’s mastery of the ICML 's Machinery Lubrication Engineer (MLE) body of knowledge.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/exams/Default.aspx?p=MLE1", "content": "Examination – Each candidate must successfully pass a 150-question, multiple choice Machinery Lubrication Engineer (MLE) ® examination that tests the candidate’s mastery of the ICML 's Machinery Lubrication Engineer (MLE) body of knowledge."}
+{"idx": 6, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "You must take the exam on paper in a controlled environment approved by ICML . Please start your application by selecting an exam type below. You will be asked to confirm your qualifications, and then you will have the opportunity to select from a list of exam sessions already scheduled on our calendar.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/apply2/", "content": "You must take the exam on paper in a controlled environment approved by ICML . Please start your application by selecting an exam type below. You will be asked to confirm your qualifications, and then you will have the opportunity to select from a list of exam sessions already scheduled on our calendar."}
+{"idx": 7, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "Find information on ICML recertification, including requirements and processes for maintaining your lubrication certification.", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/recertification/default.aspx", "content": "Find information on ICML recertification, including requirements and processes for maintaining your lubrication certification."}
+{"idx": 8, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "As a supporter and promoter of the ICML 55® Standard, he recently (2023) contributed as a co-author to ICML 55.2 “Guideline for the Optimized Lubrication of Mechanical Physical Assets.”", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/board_of_directors.aspx", "content": "As a supporter and promoter of the ICML 55® Standard, he recently (2023) contributed as a co-author to ICML 55.2 “Guideline for the Optimized Lubrication of Mechanical Physical Assets.”"}
+{"idx": 9, "title": "ICML - International Council for Machinery Lubrication", "date": "", "ddg_snippet": "Serving Lubrication Practitioners GET CERTIFIED. STAY CERTIFIED.Problem or Suggestion About This Page?", "subpage_snippet": "", "source": "www.icmlonline.com", "link": "https://www.icmlonline.com/Default.aspx", "content": "Serving Lubrication Practitioners GET CERTIFIED. STAY CERTIFIED.Problem or Suggestion About This Page?"}
diff --git a/data/sampled_jsons/ICML_2025_parameter_initialization_medium_OR_poster_year_2025.jsonl b/data/sampled_jsons/ICML_2025_parameter_initialization_medium_OR_poster_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..68d3e16602c1ed50fa484dc4051b4973a17668e4
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+++ b/data/sampled_jsons/ICML_2025_parameter_initialization_medium_OR_poster_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Learngene Tells You How to Customize: Task-Aware Parameter ...", "date": "", "ddg_snippet": "Poster Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Jiaze Xu · Shiyu Xia · Xu Yang · JIAQI LYU · Xin Geng East Exhibition Hall A-B #E-3301", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45736", "content": "Poster Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Jiaze Xu · Shiyu Xia · Xu Yang · JIAQI LYU · Xin Geng East Exhibition Hall A-B #E-3301"}
+{"idx": 1, "title": "ICML 2025 2025 Spotlight Posters", "date": "", "ddg_snippet": "This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/2025SpotlightPosters", "content": "This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks."}
+{"idx": 2, "title": "Batch Normalization (ICML 2025 Test Of Time Award) - YouTube", "date": "", "ddg_snippet": "This slows down the training by requiring lower learning rates and careful parameter initialization , and makes it notoriously hard to train models with saturating nonlinearities.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=hl8tDqKv6t8", "content": "This slows down the training by requiring lower learning rates and careful parameter initialization , and makes it notoriously hard to train models with saturating nonlinearities."}
+{"idx": 3, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "An alternative approach to enhancing the reasoning abil-ity of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was ob-served that the scale of model parameter initialization sig-nificantly impacts the model's reasoning behavior (Zhang et al., 2024a; 2025 ). Specifically, smaller ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375", "content": "An alternative approach to enhancing the reasoning abil-ity of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was ob-served that the scale of model parameter initialization sig-nificantly impacts the model's reasoning behavior (Zhang et al., 2024a; 2025 ). Specifically, smaller ..."}
+{"idx": 4, "title": "MAML Made Easy: Quick Task Adaptation in Reinforcement Learning - Medium", "date": "", "ddg_snippet": "The consistent decrease in loss values indicates improved parameter initialization and faster task-specific adaptation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@cjack010815/maml-made-easy-quick-task-adaptation-in-reinforcement-learning-6d1e7b24cb61", "content": "The consistent decrease in loss values indicates improved parameter initialization and faster task-specific adaptation."}
+{"idx": 5, "title": "Downloads 2025 - icml.cc", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Learning Adaptive Lighting via Channel-Aware Guidance Learning Adversarial MDPs with Stochastic Hard Constraints Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Learning Adaptive Lighting via Channel-Aware Guidance Learning Adversarial MDPs with Stochastic Hard Constraints Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning"}
+{"idx": 6, "title": "Investigating the Impact of Non-Zero Initialization on LoRA ...", "date": "", "ddg_snippet": "As the width n increases, the synergistic adaptation of parameter initialization and training dynamics becomes increasingly critical. Previous studies have ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46275", "content": "As the width n increases, the synergistic adaptation of parameter initialization and training dynamics becomes increasingly critical. Previous studies have ..."}
+{"idx": 7, "title": "ICML 2025 Conference Submissions", "date": "", "ddg_snippet": "ICML 2025 poster ; Readers: Everyone. Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales · Jiaze Xu, Shiyu Xia, Xu Yang ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/submissions?page=60&venue=ICML.cc/2025/Conference", "content": "ICML 2025 poster ; Readers: Everyone. Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales · Jiaze Xu, Shiyu Xia, Xu Yang ..."}
+{"idx": 8, "title": "Learngene Tells You How to Customize: Task-Aware Parameter...", "date": "", "ddg_snippet": "This paper addresses the high computational and storage overheads involved in training large pretrained models by focusing on effective parameter initialization ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=IRQ0n961nn", "content": "This paper addresses the high computational and storage overheads involved in training large pretrained models by focusing on effective parameter initialization ..."}
+{"idx": 9, "title": "PDF Applied Math Ph.D. Seminar - amphds.yingzhouli.com", "date": "", "ddg_snippet": "This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks.", "subpage_snippet": "", "source": "amphds.yingzhouli.com", "link": "https://amphds.yingzhouli.com/download_file/2025Spring/20250529.pdf", "content": "This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks."}
diff --git a/data/sampled_jsons/IPO_Identity_Preference_Optimization_Azar_2024_abstract.jsonl b/data/sampled_jsons/IPO_Identity_Preference_Optimization_Azar_2024_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fcd8532b8b4f6453fef2023fc3105f7dacd74e02
--- /dev/null
+++ b/data/sampled_jsons/IPO_Identity_Preference_Optimization_Azar_2024_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond IID Constraints: A Novel Approach to Identity ...", "date": "", "ddg_snippet": "The Identity Preference Optimization ( IPO ) algorithm Gheshlaghi Azar et al. ( 2024 ) was introduced to further improve on DPO by addressing the pairwise preference assumption directly. IPO provides a more general loss function and has demonstrated empirical superiority over DPO in specific cases. However, IPO ’s reliance on IID data limits its applicability in real-world settings (Wang et al ...", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs224n/final-reports/256735149.pdf", "content": "The Identity Preference Optimization ( IPO ) algorithm Gheshlaghi Azar et al. ( 2024 ) was introduced to further improve on DPO by addressing the pairwise preference assumption directly. IPO provides a more general loss function and has demonstrated empirical superiority over DPO in specific cases. However, IPO ’s reliance on IID data limits its applicability in real-world settings (Wang et al ..."}
+{"idx": 1, "title": "Relative Preference Optimization: Enhancing LLM Alignment ...", "date": "", "ddg_snippet": "Identity Preference Optimization ( IPO ) ( Azar et al., 2024 ) addresses the overfitting challenge within the DPO framework. IPO introduces a regularization term into the DPO’s loss function to maintain a balance between optimizing for human preferences and generalizing beyond the training data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.10958v1", "content": "Identity Preference Optimization ( IPO ) ( Azar et al., 2024 ) addresses the overfitting challenge within the DPO framework. IPO introduces a regularization term into the DPO’s loss function to maintain a balance between optimizing for human preferences and generalizing beyond the training data."}
+{"idx": 2, "title": "The Paradox of Preference: A Study on LLM Alignment ...", "date": "", "ddg_snippet": "3 days ago · Abstract This research investigates the impact of preference annotation acquisition methods on the performance of LLM alignment algorithms, including Direct Preference Optimization (DPO), Identity Preference Optimization ( IPO ), and Conservative DPO (cDPO), compared to Supervised Fine-Tuning (SFT) in NLP tasks.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.insights-1.16/", "content": "3 days ago · Abstract This research investigates the impact of preference annotation acquisition methods on the performance of LLM alignment algorithms, including Direct Preference Optimization (DPO), Identity Preference Optimization ( IPO ), and Conservative DPO (cDPO), compared to Supervised Fine-Tuning (SFT) in NLP tasks."}
+{"idx": 3, "title": "Understanding Learning from Human Preferences - Google DeepMind", "date": "", "ddg_snippet": "Mar 11, 2024 · We then consider another special case for Ψ PO called Identity Preference Optimization ( IPO ) for which we can derive an efficient optimization procedure, prove performance guarantees and demonstrate the empirical performance on a synthetic example.", "subpage_snippet": "", "source": "deepmind.google", "link": "https://deepmind.google/research/publications/54918/", "content": "Mar 11, 2024 · We then consider another special case for Ψ PO called Identity Preference Optimization ( IPO ) for which we can derive an efficient optimization procedure, prove performance guarantees and demonstrate the empirical performance on a synthetic example."}
+{"idx": 4, "title": "Extended Abstract - cs224r.stanford.edu", "date": "", "ddg_snippet": "The second extension we considered was Identity Preference Optimization ( IPO ) Gheshlaghi Azar et al. ( 2024 ). IPO addresses a fundamental limitation of DPO: its tendency to overfit to preference data, particularly when preferences are deterministic.", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/CS224R_Final_Project__1_.pdf", "content": "The second extension we considered was Identity Preference Optimization ( IPO ) Gheshlaghi Azar et al. ( 2024 ). IPO addresses a fundamental limitation of DPO: its tendency to overfit to preference data, particularly when preferences are deterministic."}
+{"idx": 5, "title": "PREFERENCE OPTIMIZATION WITH MULTI-SAMPLE COMPARISONS", "date": "", "ddg_snippet": "In this work, we introduce Multi-sample Direct Preference Optimization (mDPO) and Multi-sample Identity Preference Optimization (mIPO), which are extensions of the prior DAP methods DPO (Rafailov et al., 2024 ) and IPO ( Azar et al., 2024 )1. Unlike their predecessors, which rely on single-sample comparisons, mDPO and mIPO utilize multi-sample comparisons to better capture group-wise or ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Ozfu2uBH55", "content": "In this work, we introduce Multi-sample Direct Preference Optimization (mDPO) and Multi-sample Identity Preference Optimization (mIPO), which are extensions of the prior DAP methods DPO (Rafailov et al., 2024 ) and IPO ( Azar et al., 2024 )1. Unlike their predecessors, which rely on single-sample comparisons, mDPO and mIPO utilize multi-sample comparisons to better capture group-wise or ..."}
+{"idx": 6, "title": "Value-Incentivized Preference Optimization: A Unified Approach", "date": "", "ddg_snippet": "... preference alignment methods, such as Nash-MD (Munos et al.,, 2023 ) and OAIF (Guo et al.,, 2024 ) , do not incorporate exploration; similarly, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19320v4", "content": "... preference alignment methods, such as Nash-MD (Munos et al.,, 2023 ) and OAIF (Guo et al.,, 2024 ) , do not incorporate exploration; similarly, ..."}
+{"idx": 7, "title": "Explicit Preference Optimization: No Need for an Implicit", "date": "", "ddg_snippet": "Instead, so-called direct human preference optimization (DPO) (Rafailov et al., 2024 ) and follow-up variants ( Azar et al., 2024 ; Tang et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07492v1", "content": "Instead, so-called direct human preference optimization (DPO) (Rafailov et al., 2024 ) and follow-up variants ( Azar et al., 2024 ; Tang et al ..."}
+{"idx": 8, "title": "Probability-Consistent Preference Optimization for Enhanced LLM", "date": "", "ddg_snippet": "Recent advances in preference optimization have demonstrated significant potential for improving mathematical reasoning capabilities in large ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23540v1", "content": "Recent advances in preference optimization have demonstrated significant potential for improving mathematical reasoning capabilities in large ..."}
+{"idx": 9, "title": "A General Theoretical Paradigm to Understand Learning from ...", "date": "", "ddg_snippet": "%0 Conference Paper %T A General Theoretical Paradigm to Understand Learning from Human Preferences %A Mohammad Gheshlaghi Azar %A Zhaohan Daniel Guo %A Bilal Piot %A Remi Munos %A Mark Rowland %A Michal Valko %A Daniele Calandriello %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/gheshlaghi-azar24a.html", "content": "%0 Conference Paper %T A General Theoretical Paradigm to Understand Learning from Human Preferences %A Mohammad Gheshlaghi Azar %A Zhaohan Daniel Guo %A Bilal Piot %A Remi Munos %A Mark Rowland %A Michal Valko %A Daniele Calandriello %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy ..."}
diff --git a/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks.jsonl b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b2bc689ad9b8336ab9967587353d713b53c09983
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "Feb 7, 2025 · We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks . Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05352", "content": "Feb 7, 2025 · We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks . Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps)."}
+{"idx": 1, "title": "ITBench: Next-Gen Benchmarking for IT Automation Evaluation", "date": "", "ddg_snippet": "May 28, 2025 · Our ITBench framework provides baseline AI agents , benchmarking metrics, and results to help agent developers and researchers evaluate their agents across various metrics and supported...", "subpage_snippet": "", "source": "dzone.com", "link": "https://dzone.com/articles/itbench-next-gen-benchmarking-it-automation", "content": "May 28, 2025 · Our ITBench framework provides baseline AI agents , benchmarking metrics, and results to help agent developers and researchers evaluate their agents across various metrics and supported..."}
+{"idx": 2, "title": "GitHub - itbench-hub/ITBench: Code repository for ITBench", "date": "", "ddg_snippet": "ITBench enables researchers and developers to replicate real - world incidents in Kubernetes environments and develop AI agents to address them. We provide: The ITBench Leaderboard tracks agent performance across SRE, FinOps, and CISO scenarios.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench", "content": "ITBench enables researchers and developers to replicate real - world incidents in Kubernetes environments and develop AI agents to address them. We provide: The ITBench Leaderboard tracks agent performance across SRE, FinOps, and CISO scenarios."}
+{"idx": 3, "title": "(PDF) ITBench: Evaluating AI Agents across Diverse Real-World ...", "date": "", "ddg_snippet": "Feb 7, 2025 · We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks . Our initial release targets three key...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388882803_ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks", "content": "Feb 7, 2025 · We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks . Our initial release targets three key..."}
+{"idx": 4, "title": "Now you can gauge if AI agents are actually useful for work", "date": "", "ddg_snippet": "Feb 7, 2025 · These new benchmarks, collectively known as ITBench , will offer AI practitioners a scientific way to measure how effective the agents they’re building are at solving real problems and how their agents compare to others on tasks that businesses carry out every day.", "subpage_snippet": "", "source": "research.ibm.com", "link": "https://research.ibm.com/blog/it-agent-benchmark", "content": "Feb 7, 2025 · These new benchmarks, collectively known as ITBench , will offer AI practitioners a scientific way to measure how effective the agents they’re building are at solving real problems and how their agents compare to others on tasks that businesses carry out every day."}
+{"idx": 5, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "This research presents a novel benchmarking framework to measure the performance of AI agents across a wide variety of complex and real-life IT tasks, which has the potential to be a key enabler for AI-driven IT automation that is correct, safe, and fast.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=jP59rz1bZk", "content": "This research presents a novel benchmarking framework to measure the performance of AI agents across a wide variety of complex and real-life IT tasks, which has the potential to be a key enabler for AI-driven IT automation that is correct, safe, and fast."}
+{"idx": 6, "title": "ITBench User Experience: Democratizing AI Agent Evaluation", "date": "", "ddg_snippet": "This post dives into how ITBench enables realistic AI agent evaluation through streamlined onboarding, automated benchmarking, and operationally ...", "subpage_snippet": "", "source": "dzone.com", "link": "https://dzone.com/articles/itbench-part-2-ai-agent-evaluation-guide", "content": "This post dives into how ITBench enables realistic AI agent evaluation through streamlined onboarding, automated benchmarking, and operationally ..."}
+{"idx": 7, "title": "Benchmarking AI Agents for IT Automation Tasks with ITBench", "date": "", "ddg_snippet": "Abstract: Modern IT infrastructures have grown exponentially in complexity with the adoption of cloud computing and agile development methodologies, making their management increasingly challenging.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11068394", "content": "Abstract: Modern IT infrastructures have grown exponentially in complexity with the adoption of cloud computing and agile development methodologies, making their management increasingly challenging."}
+{"idx": 8, "title": "Taming Uncertainty via Automation: Observing, Analyzing, and", "date": "", "ddg_snippet": "This paper introduces AgentOps: a comprehensive framework for observing, analyzing, optimizing, and automating operation of agentic AI systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11277v1", "content": "This paper introduces AgentOps: a comprehensive framework for observing, analyzing, optimizing, and automating operation of agentic AI systems."}
+{"idx": 9, "title": "BED-LLM: Intelligent Information Gathering with LLMs and", "date": "", "ddg_snippet": "... task clarification (Chi et al,, 2024 ) , IT task automation (Jha et al,, 2025 ) , and iterative external tool use (Patil et al,, 2025 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21184v1", "content": "... task clarification (Chi et al,, 2024 ) , IT task automation (Jha et al,, 2025 ) , and iterative external tool use (Patil et al,, 2025 ) ."}
diff --git a/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_Figure_5_year_2023.jsonl b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_Figure_5_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4f113bdcf48a266a9540041eefa218e1367de043
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_Figure_5_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT Automation ...", "date": "", "ddg_snippet": "The design enables AI researchers to understand the challenges and opportunities of AI agents for IT automation with push-button workflows and interpretable metrics. ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions.", "subpage_snippet": "", "source": "research.ibm.com", "link": "https://research.ibm.com/publications/itbench-evaluating-ai-agents-across-diverse-real-world-it-automation-tasks", "content": "The design enables AI researchers to understand the challenges and opportunities of AI agents for IT automation with push-button workflows and interpretable metrics. ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions."}
+{"idx": 1, "title": "GitHub - itbench -hub/ ITBench : Code repository for ITBench", "date": "", "ddg_snippet": "title={ ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks }, author={Jha, Saurabh and Arora, Rohan and Watanabe, Yuji and others}", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench", "content": "title={ ITBench : Evaluating AI Agents across Diverse Real - World IT Automation Tasks }, author={Jha, Saurabh and Arora, Rohan and Watanabe, Yuji and others}"}
+{"idx": 2, "title": "ITBench : Next-Gen Benchmarking for IT Automation Evaluation", "date": "", "ddg_snippet": "ITBench Benchmarking Scenarios, Agents , Automation Server, and Leaderboard. ITBench is a systematic benchmarking framework and run-time environment designed to evaluate agents tasked with automating IT operations.", "subpage_snippet": "", "source": "dzone.com", "link": "https://dzone.com/articles/itbench-next-gen-benchmarking-it-automation", "content": "ITBench Benchmarking Scenarios, Agents , Automation Server, and Leaderboard. ITBench is a systematic benchmarking framework and run-time environment designed to evaluate agents tasked with automating IT operations."}
+{"idx": 3, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks", "date": "", "ddg_snippet": "Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks . Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05352", "content": "Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks . Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and ..."}
+{"idx": 4, "title": "Benchmarking AI Agents for IT Automation Tasks with ITBench", "date": "", "ddg_snippet": "Modern IT infrastructures have grown exponentially in complexity with the adoption of cloud computing and agile development methodologies, making their management increasingly challenging. These management tasks span multiple domains, including site reliability engineering (SRE), compliance and security operations (CISO), and financial operations (FinOps). AI agents have shown initial promise ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11068394", "content": "Modern IT infrastructures have grown exponentially in complexity with the adoption of cloud computing and agile development methodologies, making their management increasingly challenging. These management tasks span multiple domains, including site reliability engineering (SRE), compliance and security operations (CISO), and financial operations (FinOps). AI agents have shown initial promise ..."}
+{"idx": 5, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks", "date": "", "ddg_snippet": "The design en-ables AI researchers to understand the challenges and opportunities of AI agents for IT automation with push-button workflows and interpretable met-rics. ITBench includes an initial set of 102 real-world scenarios, which can be easily extended by community contributions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=jP59rz1bZk", "content": "The design en-ables AI researchers to understand the challenges and opportunities of AI agents for IT automation with push-button workflows and interpretable met-rics. ITBench includes an initial set of 102 real-world scenarios, which can be easily extended by community contributions."}
+{"idx": 6, "title": "The key to production AI agents: Evaluations - Databricks", "date": "", "ddg_snippet": "Summary To trust and scale AI agents in production, organizations need an agent platform that connects to their enterprise data and continuously measures and improves their agents' accuracy. Effective agent evaluation requires a systems-thinking approach built around task -level benchmarking, grounded evaluation and change tracking.", "subpage_snippet": "", "source": "www.databricks.com", "link": "https://www.databricks.com/blog/key-production-ai-agents-evaluations", "content": "Summary To trust and scale AI agents in production, organizations need an agent platform that connects to their enterprise data and continuously measures and improves their agents' accuracy. Effective agent evaluation requires a systems-thinking approach built around task -level benchmarking, grounded evaluation and change tracking."}
+{"idx": 7, "title": "19 Best AI Agents to Boost Workflow Automation [2025]", "date": "", "ddg_snippet": "Here are some of the best AI agents to help you build and manage real tasks like automation , decision-making, and workflow management. These best AI agents stand out for their reliability, speed, and practical value.", "subpage_snippet": "", "source": "www.lambdatest.com", "link": "https://www.lambdatest.com/blog/best-ai-agents/", "content": "Here are some of the best AI agents to help you build and manage real tasks like automation , decision-making, and workflow management. These best AI agents stand out for their reliability, speed, and practical value."}
+{"idx": 8, "title": "AI agent frameworks: A guide to evaluating agentic platforms", "date": "", "ddg_snippet": "To ensure successful development and deployment of AI agents , learn the differences between agentic AI frameworks, platforms and their capabilities.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/searchEnterpriseAI/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms", "content": "To ensure successful development and deployment of AI agents , learn the differences between agentic AI frameworks, platforms and their capabilities."}
+{"idx": 9, "title": "ITBench : Evaluating AI Agents across", "date": "", "ddg_snippet": "The goal of ITBench is to evaluate AI agents on a broad range of real - world IT automation tasks that are otherwise performed by SREs, FinOps, CISO personas. In this paper, an AI agent is defined as an autonomous or semi-autonomous software program that uses an LLM to plan...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "The goal of ITBench is to evaluate AI agents on a broad range of real - world IT automation tasks that are otherwise performed by SREs, FinOps, CISO personas. In this paper, an AI agent is defined as an autonomous or semi-autonomous software program that uses an LLM to plan..."}
diff --git a/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_Table_1_year_2023.jsonl b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_Table_1_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eb243912f8e8b1d539106b8fe0e26c11e37005d7
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_Table_1_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench: Evaluating AI Agents across Diverse Real-World ...", "date": "", "ddg_snippet": "We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks . Our initial release ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44303", "content": "We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks . Our initial release ..."}
+{"idx": 1, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "Page 1 . ITBench : Evaluating AI Agents across . Diverse Real - World IT Automation Tasks . Saurabh Jha * 1 Rohan Arora * 1 Yuji Watanabe * 1 Takumi Yanagawa 1 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d2122a3a706c4c7cd0d382ee9b13da641bd1fe5a.pdf", "content": "Page 1 . ITBench : Evaluating AI Agents across . Diverse Real - World IT Automation Tasks . Saurabh Jha * 1 Rohan Arora * 1 Yuji Watanabe * 1 Takumi Yanagawa 1 ..."}
+{"idx": 2, "title": "[Literature Review] ITBench: Evaluating AI Agents across ...", "date": "", "ddg_snippet": "The paper introduces ITBench , a benchmarking framework designed to evaluate AI agents performing various real-world IT automation tasks.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/itbench-evaluating-ai-agents-across-diverse-real-world-it-automation-tasks", "content": "The paper introduces ITBench , a benchmarking framework designed to evaluate AI agents performing various real-world IT automation tasks."}
+{"idx": 3, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "Figure 1 : The three primary IT roles (SRE, CISO, and FinOps) with their respective responsibilities and example tasks addressed in ITBench . ITBench , developed ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.05352v1", "content": "Figure 1 : The three primary IT roles (SRE, CISO, and FinOps) with their respective responsibilities and example tasks addressed in ITBench . ITBench , developed ..."}
+{"idx": 4, "title": "ITBench: Next-Gen Benchmarking for IT Automation ...", "date": "", "ddg_snippet": "28 May 2025 — ITBench is a systematic benchmarking framework and run-time environment designed to evaluate agents tasked with automating IT operations.", "subpage_snippet": "", "source": "dzone.com", "link": "https://dzone.com/articles/itbench-next-gen-benchmarking-it-automation", "content": "28 May 2025 — ITBench is a systematic benchmarking framework and run-time environment designed to evaluate agents tasked with automating IT operations."}
+{"idx": 5, "title": "ITBench: Evaluating AI Agents across Diverse Real-World ...", "date": "", "ddg_snippet": "We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks . Our initial release ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/164987", "content": "We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks . Our initial release ..."}
+{"idx": 6, "title": "Survey on Evaluation of LLM-based Agents", "date": "", "ddg_snippet": "20 Mar 2025 — Table 1 : Supported evaluation capabilities of major agent ... Itbench : Evaluating ai agents across diverse real - world it automation tasks .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16416", "content": "20 Mar 2025 — Table 1 : Supported evaluation capabilities of major agent ... Itbench : Evaluating ai agents across diverse real - world it automation tasks ."}
+{"idx": 7, "title": "TOP OF THE CLASS: BENCHMARKING LLM AGENTS", "date": "", "ddg_snippet": "by M Wornow — In this work we propose CLASSIC, a new benchmark for evaluating AI agents on realistic enterprise tasks across multiple metrics including Cost, Latency, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=RQjUpeINII", "content": "by M Wornow — In this work we propose CLASSIC, a new benchmark for evaluating AI agents on realistic enterprise tasks across multiple metrics including Cost, Latency, ..."}
+{"idx": 8, "title": "arXiv:2502.05352v1 [cs.AI] 7 Feb 2025", "date": "", "ddg_snippet": "by S Jha · 2025 · Cited by 3 — The goal of ITBench is to measure the performance of AI agents across a wide variety of complex and real -life IT tasks across personas including ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352?", "content": "by S Jha · 2025 · Cited by 3 — The goal of ITBench is to measure the performance of AI agents across a wide variety of complex and real -life IT tasks across personas including ..."}
+{"idx": 9, "title": "Yinfang Chen", "date": "", "ddg_snippet": "We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks . Our initial release ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Yinfang+Chen", "content": "We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real - world IT automation tasks . Our initial release ..."}
diff --git a/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_sitearxiv.org_year_2024.jsonl b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_sitearxiv.org_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a246355cc64c10c89c47447c7740e31ec769a241
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks_sitearxiv.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench: Evaluating AI Agents across Diverse Real-World ...", "date": "", "ddg_snippet": "by S Jha · 2025 · Cited by 3 — We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05352", "content": "by S Jha · 2025 · Cited by 3 — We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks."}
+{"idx": 1, "title": "arXiv:2502.05352v1 [cs.AI] 7 Feb 2025", "date": "", "ddg_snippet": "by S Jha · 2025 · Cited by 3 — The goal of ITBench is to measure the performance of AI agents across a wide variety of complex and real-life IT tasks across personas including ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352?", "content": "by S Jha · 2025 · Cited by 3 — The goal of ITBench is to measure the performance of AI agents across a wide variety of complex and real-life IT tasks across personas including ..."}
+{"idx": 2, "title": "Survey on Evaluation of LLM-based Agents", "date": "", "ddg_snippet": "20 Mar 2025 — ITBench Jha et al. (2025) offers a benchmark for evaluating challenging real - world IT automation tasks .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16416", "content": "20 Mar 2025 — ITBench Jha et al. (2025) offers a benchmark for evaluating challenging real - world IT automation tasks ."}
+{"idx": 3, "title": "AssetOpsBench: Benchmarking AI Agents for Task ...", "date": "", "ddg_snippet": "4 Jun 2025 — This paper introduces AssetOpsBench , the first benchmark framework designed to evaluate AI agents for real-world industrial asset management tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03828v1", "content": "4 Jun 2025 — This paper introduces AssetOpsBench , the first benchmark framework designed to evaluate AI agents for real-world industrial asset management tasks."}
+{"idx": 4, "title": "arXiv:2503.16416v1 [cs.AI] 20 Mar 2025", "date": "", "ddg_snippet": "by A Yehudai · 2025 · Cited by 42 — ITBench (Jha et al., 2025) offers a benchmark for evaluating chal- lenging real-world IT automation tasks . Complementing these developments, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16416", "content": "by A Yehudai · 2025 · Cited by 42 — ITBench (Jha et al., 2025) offers a benchmark for evaluating chal- lenging real-world IT automation tasks . Complementing these developments, ..."}
+{"idx": 5, "title": "Observing, Analyzing, and Optimizing Agentic AI Systems", "date": "", "ddg_snippet": "by D Moshkovich · 2025 · Cited by 3 — Verma, H. Kumar, H. Kitahara et al., “Itbench: Evalua ting ai agents across diverse real-world it automation tasks,” arXiv preprint.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.11277", "content": "by D Moshkovich · 2025 · Cited by 3 — Verma, H. Kumar, H. Kitahara et al., “Itbench: Evalua ting ai agents across diverse real-world it automation tasks,” arXiv preprint."}
+{"idx": 6, "title": "CAI: An Open, Bug Bounty-Ready Cybersecurity AI", "date": "", "ddg_snippet": "9 Apr 2025 — More broadly, frameworks like ITBench [48] have been proposed to systematically evaluate AI agents across diverse IT automation tasks, further ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.06017v2", "content": "9 Apr 2025 — More broadly, frameworks like ITBench [48] have been proposed to systematically evaluate AI agents across diverse IT automation tasks, further ..."}
+{"idx": 7, "title": "BED-LLM: Intelligent Information Gathering with LLMs and ...", "date": "", "ddg_snippet": "28 Aug 2025 — (2025). Itbench: Evaluating ai agents across diverse real-world it automation tasks . In International Conference on Machine Learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21184v1", "content": "28 Aug 2025 — (2025). Itbench: Evaluating ai agents across diverse real-world it automation tasks . In International Conference on Machine Learning."}
+{"idx": 8, "title": "Representing Prompting Patterns with PDL", "date": "", "ddg_snippet": "by M Vaziri · 2025 — ITBench: Evaluating AI agents across diverse real-world IT automation tasks . In International. Conference on Machine Learning (ICML), June ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.06396", "content": "by M Vaziri · 2025 — ITBench: Evaluating AI agents across diverse real-world IT automation tasks . In International. Conference on Machine Learning (ICML), June ..."}
+{"idx": 9, "title": "STRATUS: A Multi-agent System for Autonomous ...", "date": "", "ddg_snippet": "by Y Chen · 2025 — ITBench : Evaluating AI . Agents across Diverse Real - World IT Automation Tasks . In Proceedings of the International. Conference on Machine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.02009", "content": "by Y Chen · 2025 — ITBench : Evaluating AI . Agents across Diverse Real - World IT Automation Tasks . In Proceedings of the International. Conference on Machine ..."}
diff --git a/data/sampled_jsons/ITBench_Figure_5_SRE-Agent_most_frequently_used_tool_year_2023.jsonl b/data/sampled_jsons/ITBench_Figure_5_SRE-Agent_most_frequently_used_tool_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4b853c97a888e7d1c1aa67ea878396ea249efc35
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Figure_5_SRE-Agent_most_frequently_used_tool_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - itbench-hub/ITBench-SRE-Agent: Code repository for ...", "date": "", "ddg_snippet": "The ITBench SRE Agent is an open-source AI-powered Site Reliability Engineering agent that automates incident response in Kubernetes and OpenShift environments. Leveraging large language models and built on the CrewAI framework, this intelligent agent diagnoses complex system failures, traces root ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench-SRE-Agent", "content": "The ITBench SRE Agent is an open-source AI-powered Site Reliability Engineering agent that automates incident response in Kubernetes and OpenShift environments. Leveraging large language models and built on the CrewAI framework, this intelligent agent diagnoses complex system failures, traces root ..."}
+{"idx": 1, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "ITBench is a systematic benchmarking framework and run-time environment designed to evaluate AI agents tasked with automating IT operations, incorporating a robust archi-tecture (see Figure 2) comprising the AI Agent , Scenario Specification and Environment, Evaluator, and Leaderboard to facilitate comprehensive performance assessment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "ITBench is a systematic benchmarking framework and run-time environment designed to evaluate AI agents tasked with automating IT operations, incorporating a robust archi-tecture (see Figure 2) comprising the AI Agent , Scenario Specification and Environment, Evaluator, and Leaderboard to facilitate comprehensive performance assessment."}
+{"idx": 2, "title": "Now you can gauge if AI agents are actually useful for work", "date": "", "ddg_snippet": "Feb 7, 2025 · That led Sow and team to working on ITBench . From the start, there will be three benchmarks focused on site reliability engineering ( SRE ), FinOps cost management, and compliance assessment. At the highest level, the benchmarks are meant to be an open framework where users can see if their agents can solve problems efficiently.", "subpage_snippet": "", "source": "research.ibm.com", "link": "https://research.ibm.com/blog/it-agent-benchmark", "content": "Feb 7, 2025 · That led Sow and team to working on ITBench . From the start, there will be three benchmarks focused on site reliability engineering ( SRE ), FinOps cost management, and compliance assessment. At the highest level, the benchmarks are meant to be an open framework where users can see if their agents can solve problems efficiently."}
+{"idx": 3, "title": "ITBench: Next-Gen Benchmarking for IT Automation Evaluation", "date": "", "ddg_snippet": "May 28, 2025 · Learn more about ITBench , an open framework to benchmark AI agents for IT automation, focusing on reliability, efficiency, and real-world IT scenarios.", "subpage_snippet": "", "source": "dzone.com", "link": "https://dzone.com/articles/itbench-next-gen-benchmarking-it-automation", "content": "May 28, 2025 · Learn more about ITBench , an open framework to benchmark AI agents for IT automation, focusing on reliability, efficiency, and real-world IT scenarios."}
+{"idx": 4, "title": "GitHub - itbench-hub/ITBench: Code repository for ITBench", "date": "", "ddg_snippet": "ITBench enables researchers and developers to replicate real-world incidents in Kubernetes environments and develop AI agents to address them. We provide: Push-button deployment tooling for environment setup (open-source) Framework for recreating realistic IT scenarios using the deployment tooling: 6 SRE scenarios and * 21 mechanisms (open-source)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench", "content": "ITBench enables researchers and developers to replicate real-world incidents in Kubernetes environments and develop AI agents to address them. We provide: Push-button deployment tooling for environment setup (open-source) Framework for recreating realistic IT scenarios using the deployment tooling: 6 SRE scenarios and * 21 mechanisms (open-source)"}
+{"idx": 5, "title": "[2502.05352] ITBench: Evaluating AI Agents across Diverse ...", "date": "", "ddg_snippet": "Feb 7, 2025 · Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering ( SRE ), Compliance and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05352", "content": "Feb 7, 2025 · Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering ( SRE ), Compliance and ..."}
+{"idx": 6, "title": "ITBench/LEADERBOARD_SRE.md at main · itbench-hub/ITBench", "date": "", "ddg_snippet": "Code repository for ITBench . Contribute to itbench -hub/ ITBench development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench/blob/main/LEADERBOARD_SRE.md", "content": "Code repository for ITBench . Contribute to itbench -hub/ ITBench development by creating an account on GitHub."}
+{"idx": 7, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "Figure 5 : Example of the different types of observability data (logs, traces, metrics) that agents must analyze in SRE scenarios. Resolution Steps: Scenarios ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.05352v1", "content": "Figure 5 : Example of the different types of observability data (logs, traces, metrics) that agents must analyze in SRE scenarios. Resolution Steps: Scenarios ..."}
+{"idx": 8, "title": "ITBench: Evaluating AI Agents across Diverse Real-World ...", "date": "", "ddg_snippet": "The initial tests using IT-Bench revealed that even the most advanced AI agents still have a long way to go. ... Figure 5 : SRE - Agent Tool Usage Distribution.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44303", "content": "The initial tests using IT-Bench revealed that even the most advanced AI agents still have a long way to go. ... Figure 5 : SRE - Agent Tool Usage Distribution."}
+{"idx": 9, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "Figure 5 : SRE - Agent Tool Usage Distribution flaws ... More recently, LMs are used in agentic workflows, engaging ... Table 15: List of the tools used by SRE - Agent .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d2122a3a706c4c7cd0d382ee9b13da641bd1fe5a.pdf", "content": "Figure 5 : SRE - Agent Tool Usage Distribution flaws ... More recently, LMs are used in agentic workflows, engaging ... Table 15: List of the tools used by SRE - Agent ."}
diff --git a/data/sampled_jsons/ITBench_Table_12_MemoryLeak_3_2_2_values.jsonl b/data/sampled_jsons/ITBench_Table_12_MemoryLeak_3_2_2_values.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b6d6950230ecd08bef73075fdf3903969ac17ad6
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Table_12_MemoryLeak_3_2_2_values.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.05352] ITBench: Evaluating AI Agents across Diverse ...", "date": "", "ddg_snippet": "Feb 7, 2025 · ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25. 2 % of CISO scenarios, and 0% of FinOps scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05352", "content": "Feb 7, 2025 · ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25. 2 % of CISO scenarios, and 0% of FinOps scenarios."}
+{"idx": 1, "title": "GitHub - itbench-hub/ITBench-Scenarios: Code repository for ...", "date": "", "ddg_snippet": "ITBench -Scenarios This repository contains infrastructure automation scripts for both deploying the environments and configuring the scenarios required to run ITBench . These scenarios are realistic simulations based on actual IT automation challenges faced by CISO, SRE, and FinOps teams.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench-Scenarios", "content": "ITBench -Scenarios This repository contains infrastructure automation scripts for both deploying the environments and configuring the scenarios required to run ITBench . These scenarios are realistic simulations based on actual IT automation challenges faced by CISO, SRE, and FinOps teams."}
+{"idx": 2, "title": "Table memory leak? - Bug (archived) - LVGL Forum", "date": "", "ddg_snippet": "Aug 21, 2019 · Description What MCU/Processor/Board and compiler are you using? PC simulator & STM32F476 (happens in both) What do you experience? After deleting a table object that has non-zero number of cells (nr_rows>0 && nr_cols>0), not all memory that was allocated to the table is released according to the values returned by the memory monitor. From some brief experimentation, it looks like the first ...", "subpage_snippet": "", "source": "forum.lvgl.io", "link": "https://forum.lvgl.io/t/table-memory-leak/527", "content": "Aug 21, 2019 · Description What MCU/Processor/Board and compiler are you using? PC simulator & STM32F476 (happens in both) What do you experience? After deleting a table object that has non-zero number of cells (nr_rows>0 && nr_cols>0), not all memory that was allocated to the table is released according to the values returned by the memory monitor. From some brief experimentation, it looks like the first ..."}
+{"idx": 3, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25. 2 % of CISO scenarios, and 0% of FinOps scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25. 2 % of CISO scenarios, and 0% of FinOps scenarios."}
+{"idx": 4, "title": "Memory Leaks and Weak tables - Help and Feedback ... - Roblox", "date": "", "ddg_snippet": "Lua implements weak references as weak tables : A weak table is a table where all references are weak. That means that, if an object is only held inside weak tables , Lua will collect the object eventually. This is where weak tables come in. A table ’s keys and/or values can be weak.", "subpage_snippet": "", "source": "devforum.roblox.com", "link": "https://devforum.roblox.com/t/memory-leaks-and-weak-tables/775113", "content": "Lua implements weak references as weak tables : A weak table is a table where all references are weak. That means that, if an object is only held inside weak tables , Lua will collect the object eventually. This is where weak tables come in. A table ’s keys and/or values can be weak."}
+{"idx": 5, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "Table 2 summarizes the ITBench cur- rently supported IT automation tasks. Moving forward, we plan to extend ITBench to incorporate additional tasks (e.g.,.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d2122a3a706c4c7cd0d382ee9b13da641bd1fe5a.pdf", "content": "Table 2 summarizes the ITBench cur- rently supported IT automation tasks. Moving forward, we plan to extend ITBench to incorporate additional tasks (e.g.,."}
+{"idx": 6, "title": "Solved - Memory Leak ~ 12Gb of Ram Usage | OTLand", "date": "", "ddg_snippet": "Jan 12 , 2016 · About the global tables , It can only cause a memory leak if the script edit them? What if the global table is just declared and left there to the script consult the variables, because I mostly use these kinds of table : (tableName [key] = value ) to declare values like storages and the script take the values from the storages.", "subpage_snippet": "", "source": "otland.net", "link": "https://otland.net/threads/memory-leak-12gb-of-ram-usage.239638/", "content": "Jan 12 , 2016 · About the global tables , It can only cause a memory leak if the script edit them? What if the global table is just declared and left there to the script consult the variables, because I mostly use these kinds of table : (tableName [key] = value ) to declare values like storages and the script take the values from the storages."}
+{"idx": 7, "title": "arXiv:2502.05352v1 [cs.AI] 7 Feb 2025", "date": "", "ddg_snippet": "by S Jha · 2025 · Cited by 3 — The goal of ITBench is to measure the performance of AI agents across a wide variety of complex and real-life IT tasks across personas including ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352?", "content": "by S Jha · 2025 · Cited by 3 — The goal of ITBench is to measure the performance of AI agents across a wide variety of complex and real-life IT tasks across personas including ..."}
+{"idx": 8, "title": "Resource Adaptive Automated Task Scheduling Using ...", "date": "", "ddg_snippet": "by P Choppara · 2025 · Cited by 4 — In the case of large datasets (700-1000 tasks), as summarized in. Table 12 , DDPG demonstrated excellent scalability with values ranging from ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/10876158.pdf", "content": "by P Choppara · 2025 · Cited by 4 — In the case of large datasets (700-1000 tasks), as summarized in. Table 12 , DDPG demonstrated excellent scalability with values ranging from ..."}
+{"idx": 9, "title": "Perfmon counters to check memory leak - Stack Overflow Code sample", "date": "", "ddg_snippet": "35 To detect a memory leak using Performance Monitor, monitor these counters: The Memory/Available Bytes counter lets you view the total number of bytes of available memory. This value normally fluctuates, but if you have an application with the memory leak , it will decrease over time. See more on stackoverflow", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/13473761/perfmon-counters-to-check-memory-leak", "content": "35 To detect a memory leak using Performance Monitor, monitor these counters: The Memory/Available Bytes counter lets you view the total number of bytes of available memory. This value normally fluctuates, but if you have an application with the memory leak , it will decrease over time. See more on stackoverflow"}
diff --git a/data/sampled_jsons/ITBench_Table_12_MemoryLeak_parameters_fault_propagation_resolution_steps.jsonl b/data/sampled_jsons/ITBench_Table_12_MemoryLeak_parameters_fault_propagation_resolution_steps.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..22a31805c8f3f7e6d83ceb9a85d7b2754d3aee30
--- /dev/null
+++ b/data/sampled_jsons/ITBench_Table_12_MemoryLeak_parameters_fault_propagation_resolution_steps.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "We categorize scenarios as Easy, Medium, or Hard based on factors such as fault propagation chain length, number of resolution steps , and the diversity of technolo-gies involved, as described in Equation (6).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "We categorize scenarios as Easy, Medium, or Hard based on factors such as fault propagation chain length, number of resolution steps , and the diversity of technolo-gies involved, as described in Equation (6)."}
+{"idx": 1, "title": "Find a Memory Leak - Windows drivers | Microsoft Learn GitHub - itbench-hub/ITBench: Code repository for ITBench How to Perform a Memory Leak Check: A Step-by-Step Guide Memory Leakage Testing – Beginners Guide - PixelQA How to Test for a Memory Leak: A Step-by-Step Guide Memory Leakage Testing – Beginners Guide - PixelQA How to Perform a Memory Leak Check: A Step-by-Step Guide Memory Leakage Testing – Beginners Guide - PixelQA [2502.05352] ITBench : Evaluating AI Agents across Diverse Real-World IT How to Perform a Memory Leak Check: A Step-by-Step Guide ITBench : Evaluating AI Agents across Diverse Real-World IT Automatio… [2502.05352] ITBench: Evaluating AI Agents across Diverse ...", "date": "", "ddg_snippet": "A memory leak occurs when a process allocates memory from the paged or nonpaged pools, but doesn't free the memory. As a result, these limited pools of memory are depleted over time, causing Windows to slow down. If memory is completely depleted, failures may result. See full list on learn.microsoft.com Coming soon: Throughout 2024 we will be phasing out GitHub Issues as the feedback mechanism for content and replacing it with a new feedback system. For more information see: See full list on learn.microsoft.com Code repository for ITBench . Contribute to itbench -hub/ ITBench development by creating an account on GitHub. Aug 13, 2025 · What is the first step in identifying memory leaks in a codebase? The first step is to identify sections of your code where effective resource management is essential, particularly areas involving dynamic data allocation, and perform a memory leak check. How can monitoring tools assist in detecting memory leaks? Sep 4, 2024 · Memory leakage points a finger to a situation where a program allocates memory but fails to release it when it is no longer required which leads to a waste of resources and causes errors. Aug 13, 2025 · The article provides a comprehensive step -by- step guide on how to test for memory leaks in software development, emphasizing the importance of proper resource management to prevent performance degradation. What is a memory leak test? Detecting and Repairing Memory Leaks: The main purpose of testing for memory leakage is to identify states whereby an application allocates memory but cannot release it when no longer needed. How can a memory leak check help identify inefficiencies? Analyze Resource Usage: Carefully examine the profiling data to uncover discrepancies between allocated and deallocated resources. Focus on identifying patterns that may indicate resources that remain allocated without being released, which is a common source of inefficiencies that a memory leak check can help uncover. What are the effects of memory leaks in software applications? Improving Reliability : Memory leaks cause application crashes, Stucks, and other reliability problems. By actively testing for and repairing memory leaks, app developers can develop a more stable app with reliability, which can have a positive effect on its performance. What are the Consequences of Memory Leakage in Software Applications? What is itbench & how does it work? We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps). Why does exception management need a memory leak check? Overlooking Exception Management: Resource drains often occur during exception handling if allocations are not properly released. It is crucial to guarantee that resource management includes a memory leak check to ensure strength, even in error situations, to avert failures from happening during unexpected incidents. What makes itbench different from other telemetry benchmarks? Many benchmarks provide raw telemetry data, a key differentiator of ITBench is its alert-driven workflow , which mirrors how SREs are noti-fied of faults through golden-signal-alerts triggered from collected telemetry data. To further assess the importance of different telemetry sources, ITBench also supports au-tomated telemetry data masking. Feb 7, 2025 · Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and ...", "subpage_snippet": "", "source": "learn.microsoft.com", "link": "https://learn.microsoft.com/en-us/windows-hardware/drivers/debugger/finding-a-memory-leak", "content": "A memory leak occurs when a process allocates memory from the paged or nonpaged pools, but doesn't free the memory. As a result, these limited pools of memory are depleted over time, causing Windows to slow down. If memory is completely depleted, failures may result. See full list on learn.microsoft.com Coming soon: Throughout 2024 we will be phasing out GitHub Issues as the feedback mechanism for content and replacing it with a new feedback system. For more information see: See full list on learn.microsoft.com Code repository for ITBench . Contribute to itbench -hub/ ITBench development by creating an account on GitHub. Aug 13, 2025 · What is the first step in identifying memory leaks in a codebase? The first step is to identify sections of your code where effective resource management is essential, particularly areas involving dynamic data allocation, and perform a memory leak check. How can monitoring tools assist in detecting memory leaks? Sep 4, 2024 · Memory leakage points a finger to a situation where a program allocates memory but fails to release it when it is no longer required which leads to a waste of resources and causes errors. Aug 13, 2025 · The article provides a comprehensive step -by- step guide on how to test for memory leaks in software development, emphasizing the importance of proper resource management to prevent performance degradation. What is a memory leak test? Detecting and Repairing Memory Leaks: The main purpose of testing for memory leakage is to identify states whereby an application allocates memory but cannot release it when no longer needed. How can a memory leak check help identify inefficiencies? Analyze Resource Usage: Carefully examine the profiling data to uncover discrepancies between allocated and deallocated resources. Focus on identifying patterns that may indicate resources that remain allocated without being released, which is a common source of inefficiencies that a memory leak check can help uncover. What are the effects of memory leaks in software applications? Improving Reliability : Memory leaks cause application crashes, Stucks, and other reliability problems. By actively testing for and repairing memory leaks, app developers can develop a more stable app with reliability, which can have a positive effect on its performance. What are the Consequences of Memory Leakage in Software Applications? What is itbench & how does it work? We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps). Why does exception management need a memory leak check? Overlooking Exception Management: Resource drains often occur during exception handling if allocations are not properly released. It is crucial to guarantee that resource management includes a memory leak check to ensure strength, even in error situations, to avert failures from happening during unexpected incidents. What makes itbench different from other telemetry benchmarks? Many benchmarks provide raw telemetry data, a key differentiator of ITBench is its alert-driven workflow , which mirrors how SREs are noti-fied of faults through golden-signal-alerts triggered from collected telemetry data. To further assess the importance of different telemetry sources, ITBench also supports au-tomated telemetry data masking. Feb 7, 2025 · Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and ..."}
+{"idx": 2, "title": "GitHub - itbench-hub/ITBench: Code repository for ITBench", "date": "", "ddg_snippet": "Code repository for ITBench . Contribute to itbench -hub/ ITBench development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench", "content": "Code repository for ITBench . Contribute to itbench -hub/ ITBench development by creating an account on GitHub."}
+{"idx": 3, "title": "[2502.05352] ITBench: Evaluating AI Agents across Diverse ...", "date": "", "ddg_snippet": "Feb 7, 2025 · Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05352", "content": "Feb 7, 2025 · Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and ..."}
+{"idx": 4, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "... memory leak due to an exponentially growing cache ... Table 12 : Unique Scenarios available in ITBench . Scenario Pattern. Technologies Impacted. # Fault ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d2122a3a706c4c7cd0d382ee9b13da641bd1fe5a.pdf", "content": "... memory leak due to an exponentially growing cache ... Table 12 : Unique Scenarios available in ITBench . Scenario Pattern. Technologies Impacted. # Fault ..."}
+{"idx": 5, "title": "arXiv:2502.05352v1 [cs.AI] 7 Feb 2025", "date": "", "ddg_snippet": "by S Jha · 2025 · Cited by 3 — Table 12 : Unique Scenarios available in ITBench . Scenario Pattern. Technologies Impacted. # Fault ... propagation chain length, resolution steps ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352?", "content": "by S Jha · 2025 · Cited by 3 — Table 12 : Unique Scenarios available in ITBench . Scenario Pattern. Technologies Impacted. # Fault ... propagation chain length, resolution steps ..."}
+{"idx": 6, "title": "Computer Science Feb 2025", "date": "", "ddg_snippet": "7 Feb 2025 — Title: How to introduce an initial crack in phase field simulations to accurately predict the linear elastic fracture propagation threshold?", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-02?skip=1800&show=2000", "content": "7 Feb 2025 — Title: How to introduce an initial crack in phase field simulations to accurately predict the linear elastic fracture propagation threshold?"}
+{"idx": 7, "title": "Track: Poster Session 6 West", "date": "", "ddg_snippet": "17 Jul 2025 — A multi-antenna radar achieves very high resolution by computationally creating a large virtual sensing system using very few physical antennas.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50262", "content": "17 Jul 2025 — A multi-antenna radar achieves very high resolution by computationally creating a large virtual sensing system using very few physical antennas."}
+{"idx": 8, "title": "COMPUTER SCIENCE AND ENGINEERING", "date": "", "ddg_snippet": "... parameters to functions- Scope of variables- Storage classes. Arrays and ... memory -Declaring. Pointer Variables - Introduction to Pointers-Pointer ... 384 pages", "subpage_snippet": "", "source": "www.karunya.edu", "link": "https://www.karunya.edu/sites/default/files/img/pdf/Cst.pdf", "content": "... parameters to functions- Scope of variables- Storage classes. Arrays and ... memory -Declaring. Pointer Variables - Introduction to Pointers-Pointer ... 384 pages"}
+{"idx": 9, "title": "How to Perform a Memory Leak Check: A Step-by-Step Guide", "date": "", "ddg_snippet": "Aug 13, 2025 · What is the first step in identifying memory leaks in a codebase? The first step is to identify sections of your code where effective resource management is essential, particularly areas involving dynamic data allocation, and perform a memory leak check. How can monitoring tools assist in detecting memory leaks?", "subpage_snippet": "", "source": "blog.kodezi.com", "link": "https://blog.kodezi.com/how-to-perform-a-memory-leak-check-a-step-by-step-guide/", "content": "Aug 13, 2025 · What is the first step in identifying memory leaks in a codebase? The first step is to identify sections of your code where effective resource management is essential, particularly areas involving dynamic data allocation, and perform a memory leak check. How can monitoring tools assist in detecting memory leaks?"}
diff --git a/data/sampled_jsons/ITBench_complexity_formula_equation_6_page_28.jsonl b/data/sampled_jsons/ITBench_complexity_formula_equation_6_page_28.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..14da79b72fd5224129c328f2f16c3e80bd14e3a4
--- /dev/null
+++ b/data/sampled_jsons/ITBench_complexity_formula_equation_6_page_28.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Computational complexity of mathematical operations - Wikipedia", "date": "", "ddg_snippet": "The complexity of an elementary function is equivalent to that of its inverse, since all elementary functions are analytic and hence invertible by means of Newton's method.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Computational_complexity_of_mathematical_operations", "content": "The complexity of an elementary function is equivalent to that of its inverse, since all elementary functions are analytic and hence invertible by means of Newton's method."}
+{"idx": 1, "title": "Euler's formula - Wikipedia", "date": "", "ddg_snippet": "Euler's formula , named after Leonhard Euler, is a mathematical formula in complex analysis that establishes the fundamental relationship between the trigonometric functions and the complex exponential function.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Euler's_formula", "content": "Euler's formula , named after Leonhard Euler, is a mathematical formula in complex analysis that establishes the fundamental relationship between the trigonometric functions and the complex exponential function."}
+{"idx": 2, "title": "GitHub - itbench -hub/ ITBench : Code repository for ITBench", "date": "", "ddg_snippet": "Code repository for ITBench . Contribute to itbench -hub/ ITBench development by creating an account on GitHub. ITBench measures the performance of AI agents across a wide variety of complex and real-world inspired IT automation tasks targeting three key use cases", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itbench-hub/ITBench", "content": "Code repository for ITBench . Contribute to itbench -hub/ ITBench development by creating an account on GitHub. ITBench measures the performance of AI agents across a wide variety of complex and real-world inspired IT automation tasks targeting three key use cases"}
+{"idx": 3, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "We categorize scenarios as Easy, Medium, or Hard based on factors such as fault propagation chain length, number of resolution steps, and the diversity of technolo-gies involved, as described in Equation ( 6 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "We categorize scenarios as Easy, Medium, or Hard based on factors such as fault propagation chain length, number of resolution steps, and the diversity of technolo-gies involved, as described in Equation ( 6 )."}
+{"idx": 4, "title": "ITBench : Next-Gen Benchmarking for IT Automation Evaluation", "date": "", "ddg_snippet": "Learn more about ITBench , an open framework to benchmark AI agents for IT automation, focusing on reliability, efficiency, and real-world IT scenarios.In this blog, we introduce a novel ecosystem for benchmarking tools, ITBench , to address the more complex challenges of IT automation.", "subpage_snippet": "", "source": "dzone.com", "link": "https://dzone.com/articles/itbench-next-gen-benchmarking-it-automation", "content": "Learn more about ITBench , an open framework to benchmark AI agents for IT automation, focusing on reliability, efficiency, and real-world IT scenarios.In this blog, we introduce a novel ecosystem for benchmarking tools, ITBench , to address the more complex challenges of IT automation."}
+{"idx": 5, "title": "[2502.05352] ITBench: Evaluating AI Agents across Diverse ...", "date": "", "ddg_snippet": "Feb 7, 2025 · We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05352", "content": "Feb 7, 2025 · We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps)."}
+{"idx": 6, "title": "GitHub - IBM/ITBench-Utilities: Code repository for tools as ...", "date": "", "ddg_snippet": "IT-Bench Utilities This repository provides a toolkit for ITBench , including the containerized components used to run and evaluate agents.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IBM/ITBench-Utilities/", "content": "IT-Bench Utilities This repository provides a toolkit for ITBench , including the containerized components used to run and evaluate agents."}
+{"idx": 7, "title": "How to Calculate the Complexity of an Algorithm | by EL ...", "date": "", "ddg_snippet": "Nov 20, 2024 · Algorithm complexity is a cornerstone concept in computer science and software development. It provides a structured way to evaluate the efficiency of an algorithm based on its performance and...", "subpage_snippet": "", "source": "has1elb.medium.com", "link": "https://has1elb.medium.com/how-to-calculate-the-complexity-of-an-algorithm-0c49e17d271d", "content": "Nov 20, 2024 · Algorithm complexity is a cornerstone concept in computer science and software development. It provides a structured way to evaluate the efficiency of an algorithm based on its performance and..."}
+{"idx": 8, "title": "(PDF) ITBench : Evaluating AI Agents across Diverse Real-World IT...", "date": "", "ddg_snippet": "(d) FinOps scenario complexity . Figure 4: Characterization of ITBench scenarios. the formulation can be thought of as a POMDPgeometric mean. Equation ( 6 ) captures this relationship: 24. ITBench .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388882803_ITBench_Evaluating_AI_Agents_across_Diverse_Real-World_IT_Automation_Tasks", "content": "(d) FinOps scenario complexity . Figure 4: Characterization of ITBench scenarios. the formulation can be thought of as a POMDPgeometric mean. Equation ( 6 ) captures this relationship: 24. ITBench ."}
+{"idx": 9, "title": "[Literature Review] ITBench : Evaluating AI Agents across Diverse...", "date": "", "ddg_snippet": "This page provides the most accurate and concise summary worldwide for the paper titled ITBench : Evaluating AI Agents across Diverse Real-World IT Automation Tasks.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/itbench-evaluating-ai-agents-across-diverse-real-world-it-automation-tasks", "content": "This page provides the most accurate and concise summary worldwide for the paper titled ITBench : Evaluating AI Agents across Diverse Real-World IT Automation Tasks."}
diff --git a/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Matters_Table_1_Flowers_16-shot.jsonl b/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Matters_Table_1_Flowers_16-shot.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a94a33df5831091f6cf941962d6afb9ae0d0520e
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Matters_Table_1_Flowers_16-shot.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "Build-ing on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few- shot tasks. We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "Build-ing on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few- shot tasks. We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance."}
+{"idx": 1, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ... - GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ..."}
+{"idx": 2, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set", "date": "", "ddg_snippet": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance. Based on on this finding, we propose an improved self-supervised method tailored for few- shot scenarios, enhancing the transferability of representations from synthetic to real image domains.", "subpage_snippet": "", "source": "peihuali.org", "link": "http://peihuali.org/ImagineFSL/", "content": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance. Based on on this finding, we propose an improved self-supervised method tailored for few- shot scenarios, enhancing the transferability of representations from synthetic to real image domains."}
+{"idx": 3, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023/ImagineFSL · GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ..."}
+{"idx": 4, "title": "Releases: HaoyuanYang-2023/ImagineFSL_Preview - GitHub", "date": "", "ddg_snippet": "Official implementation of \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" [CVPR 2025 Highlight] - HaoyuanYang ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL_Preview/releases", "content": "Official implementation of \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" [CVPR 2025 Highlight] - HaoyuanYang ..."}
+{"idx": 5, "title": "PDF Supplementary Material for \"ImagineFSL: Self-Supervised Pretraining ...", "date": "", "ddg_snippet": "Specifically, ImagineFSL achieves gains of 6.2% and 5.8% in the 1-shot and 16-shot settings, respectively, while ImagineFSLLoRA shows gains of 6.3% and 2.9%. These results suggest that our methods exhibit superior scaling capabilities as the capacity of CLIP models increases.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "Specifically, ImagineFSL achieves gains of 6.2% and 5.8% in the 1-shot and 16-shot settings, respectively, while ImagineFSLLoRA shows gains of 6.3% and 2.9%. These results suggest that our methods exhibit superior scaling capabilities as the capacity of CLIP models increases."}
+{"idx": 6, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "We introduce a novel CLIP adaptation methodology called ImagineFSL , involving pretraining on the imagined base set followed by fine-tuning on downstream few- shot tasks. We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32717", "content": "We introduce a novel CLIP adaptation methodology called ImagineFSL , involving pretraining on the imagined base set followed by fine-tuning on downstream few- shot tasks. We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
+{"idx": 7, "title": "[2207.09176] Self-Supervision Can Be a Good Few-Shot Learner", "date": "", "ddg_snippet": "Rather than supervised pre-training focusing on the discriminable features of the seen classes, our self-supervised model has less bias toward the seen classes, resulting in better generalization for unseen classes. We explain that supervised pre-training and self-supervised pre-training are actually maximizing different MI objectives.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.09176", "content": "Rather than supervised pre-training focusing on the discriminable features of the seen classes, our self-supervised model has less bias toward the seen classes, resulting in better generalization for unseen classes. We explain that supervised pre-training and self-supervised pre-training are actually maximizing different MI objectives."}
+{"idx": 8, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few- shot tasks. We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based@CVPR2025@CVF", "content": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few- shot tasks. We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few- shot tasks. We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.html", "content": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few- shot tasks. We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter providing better performance."}
diff --git a/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_Few-shot_Learning__year_2024.jsonl b/data/sampled_jsons/ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_Few-shot_Learning__year_2024.jsonl
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index 0000000000000000000000000000000000000000..a5a558dd7b573a57be5dcf3ddf2105ec7c014295
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance. Based on on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of repre-sentations from synthetic to real image domains.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance. Based on on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of repre-sentations from synthetic to real image domains."}
+{"idx": 1, "title": "Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "by H Yang — The hyperparameters of fine-tuning for few - shot tasks are presented in Table S-2. For ImagineFSL , we use. AdamW optimizer with a cosine decay of learning rate.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "by H Yang — The hyperparameters of fine-tuning for few - shot tasks are presented in Table S-2. For ImagineFSL , we use. AdamW optimizer with a cosine decay of learning rate."}
+{"idx": 2, "title": "Low-Rank Few-Shot Adaptation of Vision-Language Models", "date": "", "ddg_snippet": "ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning · Haoyuan YangXiaoou LiJiaming LvXianjun ChengQilong Wang ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Low-Rank-Few-Shot-Adaptation-of-Vision-Language-Zanella-Ayed/42012c4e9dc8d787adb101548cefc42b8a4507d9", "content": "ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning · Haoyuan YangXiaoou LiJiaming LvXianjun ChengQilong Wang ..."}
+{"idx": 3, "title": "[PDF] DataDream: Few-shot Guided Dataset Generation", "date": "", "ddg_snippet": "ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning · Computer Science. Computer Vision and Pattern Recognition.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/54e9f08eb259f946346f375a106786238471a40d", "content": "ImagineFSL : Self - Supervised Pretraining Matters on Imagined Base Set for VLM - based Few - shot Learning · Computer Science. Computer Vision and Pattern Recognition."}
+{"idx": 4, "title": "A Closer Look at the Few-Shot Adaptation of Large Vision- ...", "date": "", "ddg_snippet": "20 Dec 2023 — Key takeaway: 'CLAP outperforms standard zero- shot predictions and is a more efficient alternative to standard few - shot adaptation of large ...", "subpage_snippet": "", "source": "k8s.consensus.app", "link": "https://k8s.consensus.app/papers/details/e285bdf4460c5e20aa25829948d799fd/", "content": "20 Dec 2023 — Key takeaway: 'CLAP outperforms standard zero- shot predictions and is a more efficient alternative to standard few - shot adaptation of large ..."}
+{"idx": 5, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 6, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "Adapting CLIP models for few-shot recognition has recently attracted significant attention. Despite considerable progress, these adaptations remain hindered by the pervasive challenge of data scarcity. Text-to-image models, capable of generating abundant photorealistic labeled images, offer a promising solution. However, existing approaches simply treat synthetic images as complements to real ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11094502", "content": "Adapting CLIP models for few-shot recognition has recently attracted significant attention. Despite considerable progress, these adaptations remain hindered by the pervasive challenge of data scarcity. Text-to-image models, capable of generating abundant photorealistic labeled images, offer a promising solution. However, existing approaches simply treat synthetic images as complements to real ..."}
+{"idx": 7, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set", "date": "", "ddg_snippet": "title = { ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning }, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},", "subpage_snippet": "", "source": "www.peihuali.org", "link": "https://www.peihuali.org/ImagineFSL/", "content": "title = { ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning }, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},"}
+{"idx": 8, "title": "Improving In-Context Few-Shot Learning via Self-Supervised Training", "date": "", "ddg_snippet": "Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot learning . In this paper, we propose to use self -supervision in an intermediate training stage between pretraining and downstream few-shot usage with the goal to teach the model to perform in-context few shot learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2205.01703", "content": "Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot learning . In this paper, we propose to use self -supervision in an intermediate training stage between pretraining and downstream few-shot usage with the goal to teach the model to perform in-context few shot learning ..."}
+{"idx": 9, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023/ImagineFSL · GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning \" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
diff --git a/data/sampled_jsons/Improved_Algorithms_for_Linear_Stochastic_Bandits_Abbasi-Yadkori_OFUL_Optimism.jsonl b/data/sampled_jsons/Improved_Algorithms_for_Linear_Stochastic_Bandits_Abbasi-Yadkori_OFUL_Optimism.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3095e9ddeeecbaaef6a20c3209ccda70ef5d5806
--- /dev/null
+++ b/data/sampled_jsons/Improved_Algorithms_for_Linear_Stochastic_Bandits_Abbasi-Yadkori_OFUL_Optimism.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improved Algorithms for Linear Stochastic Bandits (extended version)", "date": "", "ddg_snippet": "We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem. In particular, we ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/230627940_Improved_Algorithms_for_Linear_Stochastic_Bandits_extended_version", "content": "We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem. In particular, we ..."}
+{"idx": 1, "title": "PDF Improved Algorithms for Linear Stochastic Bandits - NeurIPS", "date": "", "ddg_snippet": "We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem. In particular, we show that a simple modification of Auer's UCB algorithm (Auer, 2002) achieves with high probability constant regret. More importantly, we modify and, consequently, improve the analysis of the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2011/file/e1d5be1c7f2f456670de3d53c7b54f4a-Paper.pdf", "content": "We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem. In particular, we show that a simple modification of Auer's UCB algorithm (Auer, 2002) achieves with high probability constant regret. More importantly, we modify and, consequently, improve the analysis of the ..."}
+{"idx": 2, "title": "PDF Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "References Yasin Abbasi-Yadkori , Andr ́as Antos, and Csaba Szepesv ́ari. Forced-exploration based algorithms for playing in stochastic linear bandits. In COLT Workshop on On-line Learning with Limited Feedback, 2009. Naoki Abe, Alan W. Biermann, and Philip M. Long. Reinforcement learning with immediate rewards and linear hypotheses.", "subpage_snippet": "", "source": "sites.ualberta.ca", "link": "https://sites.ualberta.ca/~szepesva/papers/linear-bandits-NeurIPS2011.pdf", "content": "References Yasin Abbasi-Yadkori , Andr ́as Antos, and Csaba Szepesv ́ari. Forced-exploration based algorithms for playing in stochastic linear bandits. In COLT Workshop on On-line Learning with Limited Feedback, 2009. Naoki Abe, Alan W. Biermann, and Philip M. Long. Reinforcement learning with immediate rewards and linear hypotheses."}
+{"idx": 3, "title": "Improved Algorithms for Linear Stochastic Bandits - Semantic Scholar", "date": "", "ddg_snippet": "A simple modification of Auer's UCB algorithm achieves with high probability constant regret and improves the regret bound by a logarithmic factor, though experiments show a vast improvement. We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem. In particular, we show that ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Improved-Algorithms-for-Linear-Stochastic-Bandits-Abbasi-Yadkori-Pál/6bea71fa6deb19c67e9586428f8f240e789fb3df", "content": "A simple modification of Auer's UCB algorithm achieves with high probability constant regret and improves the regret bound by a logarithmic factor, though experiments show a vast improvement. We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem. In particular, we show that ..."}
+{"idx": 4, "title": "PDF Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "Improved Algorithms for Linear Stochastic Bandits a NIPS article by Yasin Abbasi-Yadkori , David Pal, and Csaba Szepesvari", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/225a/40fff69bbdf9cfa0f06f99e52efeceedc1cc.pdf", "content": "Improved Algorithms for Linear Stochastic Bandits a NIPS article by Yasin Abbasi-Yadkori , David Pal, and Csaba Szepesvari"}
+{"idx": 5, "title": "PDF Improved Algorithms for Stochastic Linear Bandits Using Tail ... - NIPS", "date": "", "ddg_snippet": "1Y. Abbasi-Yadkori et al. (2011) Improved algorithms for linear stochastic bandits . NeurIPS New confidence sets Θt for stochastic linear bandits using a new tail bound for martingale mixtures New confidence sets Θt for stochastic linear bandits using a new tail bound", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2023/Slides/73845.pdf", "content": "1Y. Abbasi-Yadkori et al. (2011) Improved algorithms for linear stochastic bandits . NeurIPS New confidence sets Θt for stochastic linear bandits using a new tail bound for martingale mixtures New confidence sets Θt for stochastic linear bandits using a new tail bound"}
+{"idx": 6, "title": "Improved Algorithms for Linear Stochastic Bandits - ResearchGate", "date": "", "ddg_snippet": "The reason for this performance degeneration is because existing algorithms, such as OFUL ( Abbasi-Yadkori et al., 2011) and linear Thompson sampling (Agrawal and Goyal, 2013), utilize all the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/267562627_Improved_Algorithms_for_Linear_Stochastic_Bandits", "content": "The reason for this performance degeneration is because existing algorithms, such as OFUL ( Abbasi-Yadkori et al., 2011) and linear Thompson sampling (Agrawal and Goyal, 2013), utilize all the ..."}
+{"idx": 7, "title": "Improved Algorithms for Stochastic Linear Bandits", "date": "", "ddg_snippet": "UCB Algorithms for Linear Bandits . Confidence Sequences from Martingale Mixtures. Abbasi - Yadkori , Y., Antos, A., and Szepesvári, C. Forced-exploration based algorithms for playing in stochastic linear bandits .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TXoZiUZywf", "content": "UCB Algorithms for Linear Bandits . Confidence Sequences from Martingale Mixtures. Abbasi - Yadkori , Y., Antos, A., and Szepesvári, C. Forced-exploration based algorithms for playing in stochastic linear bandits ."}
+{"idx": 8, "title": "Improved algorithms for linear stochastic bandits | Proceedings of...", "date": "", "ddg_snippet": "Y. Abbasi - Yadkori , A. Antos, and Cs. Szepesvari. Forced-exploration based algorithms for playing in stochastic linear bandits .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2986459.2986717?cookieSet=1", "content": "Y. Abbasi - Yadkori , A. Antos, and Cs. Szepesvari. Forced-exploration based algorithms for playing in stochastic linear bandits ."}
+{"idx": 9, "title": "(PDF) Improved algorithms for linear stochastic bandits", "date": "", "ddg_snippet": "First page of “ Improved algorithms for linear stochastic bandits ” PDF Icon.Under this condition we construct an arm selection policy, called HOO (hierarchical optimistic optimization), with improved regret bounds compared to previous results for a large class of problems.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/31213011/Improved_algorithms_for_linear_stochastic_bandits", "content": "First page of “ Improved algorithms for linear stochastic bandits ” PDF Icon.Under this condition we construct an arm selection policy, called HOO (hierarchical optimistic optimization), with improved regret bounds compared to previous results for a large class of problems."}
diff --git a/data/sampled_jsons/Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice_Table_1_ImageNet-1k.jsonl b/data/sampled_jsons/Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice_Table_1_ImageNet-1k.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b6c1dcf538bba76828794d40182cadaec93a72e
--- /dev/null
+++ b/data/sampled_jsons/Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice_Table_1_ImageNet-1k.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate ...", "date": "", "ddg_snippet": "Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been identified as a key mechanism for improving scaling , enabling models to focus on the most informative synthetic samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.15588v1", "content": "Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been identified as a key mechanism for improving scaling , enabling models to focus on the most informative synthetic samples."}
+{"idx": 1, "title": "(PDF) Improving the Scaling Laws of Synthetic Data with ...", "date": "", "ddg_snippet": "Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been identified as a key mechanism for improving scaling , enabling models to focus on the most informative synthetic samples.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389274598_Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice", "content": "Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been identified as a key mechanism for improving scaling , enabling models to focus on the most informative synthetic samples."}
+{"idx": 2, "title": "Improving the Scaling Laws of Synthetic Data with ...", "date": "", "ddg_snippet": "by R Askari-Hemmat · Cited by 2 — ... ImageNet-1k , it generates 8x fewer samples with a 30% reduction in ... Improving the Scaling Laws of Synthetic Data with Deliberate Practice .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0LZRtvK871", "content": "by R Askari-Hemmat · Cited by 2 — ... ImageNet-1k , it generates 8x fewer samples with a 30% reduction in ... Improving the Scaling Laws of Synthetic Data with Deliberate Practice ."}
+{"idx": 3, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate ...", "date": "", "ddg_snippet": "... ImageNet-1k (right). DP significantly outperforms ... Table 1 : Comparison with previous work. DP ... Improving the Scaling Laws of Synthetic Data with Deliberate ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/5c501cdc79a2e4a109265b636c96445553efcb4d.pdf", "content": "... ImageNet-1k (right). DP significantly outperforms ... Table 1 : Comparison with previous work. DP ... Improving the Scaling Laws of Synthetic Data with Deliberate ..."}
+{"idx": 4, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate ...", "date": "", "ddg_snippet": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice ... ImageNet-1k , it generates 8 × fewer samples with ... ( Table 1 ). 2 Problem ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2502.15588/paper", "content": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice ... ImageNet-1k , it generates 8 × fewer samples with ... ( Table 1 ). 2 Problem ..."}
+{"idx": 5, "title": "Increasing the Utility of Synthetic Images through Chamfer ...", "date": "", "ddg_snippet": "14 Aug 2025 — Table 1 : Quantitative results on ImageNet-1k using LDM 1.5 and LDM 3.5M. Our Chamfer guidance consistently achieves state-of-the-art fidelity, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10631v1", "content": "14 Aug 2025 — Table 1 : Quantitative results on ImageNet-1k using LDM 1.5 and LDM 3.5M. Our Chamfer guidance consistently achieves state-of-the-art fidelity, ..."}
+{"idx": 6, "title": "Boosting Statistic Learning with Synthetic Data from Pretrained", "date": "", "ddg_snippet": "Despite the ability to produce large volumes of synthetic data , the proportion that effectively improves model performance is limited.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04992v1", "content": "Despite the ability to produce large volumes of synthetic data , the proportion that effectively improves model performance is limited."}
+{"idx": 7, "title": "SMARTER: A Data‑efficient Framework to Improve Toxicity", "date": "", "ddg_snippet": "... that SMARTER enables LLMs to achieve up to 13.5% macro-F1 improvement over standard few-shot baselines with a fraction of the full training data .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15174v1", "content": "... that SMARTER enables LLMs to achieve up to 13.5% macro-F1 improvement over standard few-shot baselines with a fraction of the full training data ."}
+{"idx": 8, "title": "Mind the Gap: Bridging Thought Leap for Improved", "date": "", "ddg_snippet": "... 1 (c), addressing the Thought Leap phenomenon leads to consistent improvements across different model architectures and datasets, with performance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14684v1", "content": "... 1 (c), addressing the Thought Leap phenomenon leads to consistent improvements across different model architectures and datasets, with performance ..."}
+{"idx": 9, "title": "The Thinking Therapist: Training Large Language Models to", "date": "", "ddg_snippet": "The second phase entailed using the synthetic ... We began the synthetic data generation process with the creation of 100 unique patient profiles.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09712v1", "content": "The second phase entailed using the synthetic ... We began the synthetic data generation process with the creation of 100 unique patient profiles."}
diff --git a/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_base_LLMs_fine-tu.jsonl b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_base_LLMs_fine-tu.jsonl
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index 0000000000000000000000000000000000000000..9c4802c27b06d9d11522f30637c97c3ed9de43f6
--- /dev/null
+++ b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_base_LLMs_fine-tu.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 67 — Our contribution is a study of the role of synthetic data in improving math reasoning capabilities of LLMs . We derive scaling laws for positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 67 — Our contribution is a study of the role of synthetic data in improving math reasoning capabilities of LLMs . We derive scaling laws for positive ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of", "date": "", "ddg_snippet": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96295/paper", "content": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks."}
+{"idx": 3, "title": "Offline RL on Sub-optimal Rollouts Scales Synthetic Data ...", "date": "", "ddg_snippet": "by A Setlur — Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=v2PV1yCFJk", "content": "by A Setlur — Training on model-generated synthetic data is a promising approach for finetuning LLMs , but it remains unclear when it helps or hurts. In this paper, we ..."}
+{"idx": 4, "title": "Mitigating Tail Narrowing in LLM Self-Improvement via ...", "date": "", "ddg_snippet": "by Y Ding · 2025 — RL · on incorrect synthetic data scales the efficiency · of LLM math reasoning by eight - fold . ... or even inverse scaling with more data during ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.533.pdf", "content": "by Y Ding · 2025 — RL · on incorrect synthetic data scales the efficiency · of LLM math reasoning by eight - fold . ... or even inverse scaling with more data during ..."}
+{"idx": 5, "title": "[PDF] Scaling LLM Test-Time Compute Optimally can be ...", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Mathematics, Computer Science. Neural Information Processing Systems.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/8292083dd8f6ae898ea0ee54a6b97997d1a51c9d", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Mathematics, Computer Science. Neural Information Processing Systems."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... fine-tuning of small LMs on synthetic data generated by larger LLMs .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=LLM-generated+synthetic+data", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold ... fine-tuning of small LMs on synthetic data generated by larger LLMs ."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Training on model-generated synthetic data is a promising approach ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=RL", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold · Training on model-generated synthetic data is a promising approach ..."}
+{"idx": 8, "title": "Achieving 8× Performance Gains with Reinforcement ...", "date": "", "ddg_snippet": "The researchers aim to understand synthetic data's impact on LLM capabilities via a study on math reasoning , a prevalent scenario where ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/syncedreview/achieving-8-performance-gains-with-reinforcement-learning-on-synthetic-data-in-large-language-cb6a9cfbffc9", "content": "The researchers aim to understand synthetic data's impact on LLM capabilities via a study on math reasoning , a prevalent scenario where ..."}
+{"idx": 9, "title": "A Critical Evaluation of AI Feedback for Aligning Large ...", "date": "", "ddg_snippet": "... fine - tuning LLMs on open-ended, particularly long-form ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Critical-Evaluation-of-AI-Feedback-for-Aligning-Sharma-Keh/087699924e3dc468a486e0763f1cc097824a60d2", "content": "... fine - tuning LLMs on open-ended, particularly long-form ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ."}
diff --git a/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_mathematical_reas_year_2024.jsonl b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_mathematical_reas_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..32f57fa8a7e8ba9419e0782091c9e952c785a226
--- /dev/null
+++ b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_mathematical_reas_year_2024.jsonl
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+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Since mathematical reasoning problems require step-by-step computation, simply arriving at an incorrect final answer does not mean that all steps in a negative.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "Since mathematical reasoning problems require step-by-step computation, simply arriving at an incorrect final answer does not mean that all steps in a negative."}
+{"idx": 1, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Since mathematical reasoning problems require step-by-step computation, simply arriving at an incorrect . final answer does not mean that all steps in a negative yˆ are incorrect .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "Since mathematical reasoning problems require step-by-step computation, simply arriving at an incorrect . final answer does not mean that all steps in a negative yˆ are incorrect ."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning (RL).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning (RL)."}
+{"idx": 4, "title": "AI-Powered Paper Summarization about the arXiv paper 2406.14532v1", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .AI-generated Key Points. Authors explore training language models on model-generated synthetic data for math reasoning tasks.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .AI-generated Key Points. Authors explore training language models on model-generated synthetic data for math reasoning tasks."}
+{"idx": 5, "title": "Bayesian beagle - RL on Incorrect Synthetic Data Scales the ...", "date": "", "ddg_snippet": "Finetuning LLMs with model-generated data can improve math reasoning , especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations.", "subpage_snippet": "", "source": "bayesian-beagle.netlify.app", "link": "https://bayesian-beagle.netlify.app/posts/rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold/2024-06-20-rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold", "content": "Finetuning LLMs with model-generated data can improve math reasoning , especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/es/overview/2406.14532v1", "content": "Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Specifically, they saw an 8 - fold increase in the efficiency of the LLM 's math reasoning abilities. The key insight is that the RL process is able to learn from the mistakes in the synthetic data , and use that knowledge to build more robust and flexible math reasoning capabilities in the LLM .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/rl-incorrect-synthetic-data-scales-efficiency-llm", "content": "Specifically, they saw an 8 - fold increase in the efficiency of the LLM 's math reasoning abilities. The key insight is that the RL process is able to learn from the mistakes in the synthetic data , and use that knowledge to build more robust and flexible math reasoning capabilities in the LLM ."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation."}
+{"idx": 9, "title": "8 × Math Gains, Cross-Modal Generation & Safety-First AI", "date": "", "ddg_snippet": "Explore the breakthroughs in AI safety, reasoning , and multimodality.At Turing, we’re scaling the frontier of multimodal LLM evaluation—where text, vision, and audio meet in real-world tasks.", "subpage_snippet": "", "source": "www.turing.com", "link": "https://www.turing.com/blog/agi-advance-newsletter-07", "content": "Explore the breakthroughs in AI safety, reasoning , and multimodality.At Turing, we’re scaling the frontier of multimodal LLM evaluation—where text, vision, and audio meet in real-world tasks."}
diff --git a/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_spurious_correlat_year_2024.jsonl b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_spurious_correlat_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..34cfbcc8911bba24e3ff75c2b828e67aa92aed71
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+++ b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_spurious_correlat_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 66 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "by A Setlur · 2024 · Cited by 66 — In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 67 — 4 When positive data from πsft contains spurious steps, scaling synthetic data leads to worse test errors. 7. Page 8 . RL on Incorrect Synthetic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 67 — 4 When positive data from πsft contains spurious steps, scaling synthetic data leads to worse test errors. 7. Page 8 . RL on Incorrect Synthetic ..."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of", "date": "", "ddg_snippet": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96295/paper", "content": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks."}
+{"idx": 4, "title": "RL on Synthetic Data Boosts LLM Math Reasoning", "date": "", "ddg_snippet": "The paper shows that reinforcement learning with negative synthetic data scales LLM math reasoning efficiency by eight-fold . It employs per-step ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The paper shows that reinforcement learning with negative synthetic data scales LLM math reasoning efficiency by eight-fold . It employs per-step ..."}
+{"idx": 5, "title": "MALT: Improving Reasoning with Multi-Agent LLM Training", "date": "", "ddg_snippet": "by SR Motwani · Cited by 22 — We demonstrate in Section 5 that this inference setting enhances performance compared to single-model approaches. The key insight, however, relies on leveraging.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=jXP9bgFack", "content": "by SR Motwani · Cited by 22 — We demonstrate in Section 5 that this inference setting enhances performance compared to single-model approaches. The key insight, however, relies on leveraging."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Simply training on synthetic data with positive pairs (correct problem-solution pairs) can lead to the model learning spurious correlations . Creatively ...", "subpage_snippet": "", "source": "www.threads.net", "link": "https://www.threads.net/@sung.kim.mw/post/C-EoASrpvSK/rl-on-incorrect-synthetic-data-scales-the-efficiency-of-llm-math-reasoning-by-ei", "content": "Simply training on synthetic data with positive pairs (correct problem-solution pairs) can lead to the model learning spurious correlations . Creatively ..."}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Simply training on synthetic data with positive pairs (correct problem-solution pairs) can lead to the model learning spurious correlations . Creatively ...", "subpage_snippet": "", "source": "www.threads.com", "link": "https://www.threads.com/@sung.kim.mw/post/C-EoASrpvSK/rl-on-incorrect-synthetic-data-scales-the-efficiency-of-llm-math-reasoning-by-ei", "content": "Simply training on synthetic data with positive pairs (correct problem-solution pairs) can lead to the model learning spurious correlations . Creatively ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold · Training on model-generated synthetic data is a promising approach ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=incorrect+set", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold · Training on model-generated synthetic data is a promising approach ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold · Training on model-generated synthetic data is a promising approach ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=LLM-generated+synthetic+data", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold · Training on model-generated synthetic data is a promising approach ..."}
diff --git a/data/sampled_jsons/Indyk_&_Motwani,_1998_Locality-Sensitive_Hashing.jsonl b/data/sampled_jsons/Indyk_&_Motwani,_1998_Locality-Sensitive_Hashing.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dbbabcc96c8d690ed5dd5d6cb932116a1592cba7
--- /dev/null
+++ b/data/sampled_jsons/Indyk_&_Motwani,_1998_Locality-Sensitive_Hashing.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fast Locality - Sensitive Hashing Frameworks", "date": "", "ddg_snippet": "Abstract The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general tech-nique for constructing a data structure to answer approximate near neighbor queries by using a distribution H over locality - sensitive hash functions that partition space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1708.07586", "content": "Abstract The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general tech-nique for constructing a data structure to answer approximate near neighbor queries by using a distribution H over locality - sensitive hash functions that partition space."}
+{"idx": 1, "title": "Local Sensitivity Hashing (L.S.H.): A Comprehensive... - Zilliz Learn", "date": "", "ddg_snippet": "Locality - Sensitive Hashing (LSH), introduced by Indyk - Motwani in 1998 , is a technique used in approximate nearest neighbor (ANN) searches that underpins and accelerates the efficiency of similarity searches.", "subpage_snippet": "", "source": "zilliz.com", "link": "https://zilliz.com/learn/Local-Sensitivity-Hashing-A-Comprehensive-Guide", "content": "Locality - Sensitive Hashing (LSH), introduced by Indyk - Motwani in 1998 , is a technique used in approximate nearest neighbor (ANN) searches that underpins and accelerates the efficiency of similarity searches."}
+{"idx": 2, "title": "Lecture 13: Nearest Neighbor Search and Locality Sensitive", "date": "", "ddg_snippet": "PLEB via Locality Sensitive Hashing . Other examples of locality sensitive hash functions.Theorem 4 ( Indyk , Motwani , 1998 ). Given a (r1, r2, p1, p2)- locality sensitive hash family, (r1, r2)-PLEB can be solved with constant probability using", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/~hy2/teaching/fall23-cos521/notes/NNS+&+LSH.pdf", "content": "PLEB via Locality Sensitive Hashing . Other examples of locality sensitive hash functions.Theorem 4 ( Indyk , Motwani , 1998 ). Given a (r1, r2, p1, p2)- locality sensitive hash family, (r1, r2)-PLEB can be solved with constant probability using"}
+{"idx": 3, "title": "Locality - Sensitive Hashing and Beyond", "date": "", "ddg_snippet": "Locality - Sensitive Hashing (LSH). Optimal LSH for a sphere.• Introduced in [ Indyk , Motwani 1998 ]. • Main idea: random partitions of Rd s.t. closer pairs of points collide more often.", "subpage_snippet": "", "source": "optml.mit.edu", "link": "https://optml.mit.edu/mit/optml++/ilya_slides.pdf", "content": "Locality - Sensitive Hashing (LSH). Optimal LSH for a sphere.• Introduced in [ Indyk , Motwani 1998 ]. • Main idea: random partitions of Rd s.t. closer pairs of points collide more often."}
+{"idx": 4, "title": "Fast Locality - Sensitive Hashing Frameworks for... | SpringerLink", "date": "", "ddg_snippet": "The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-32047-8_1", "content": "The Indyk - Motwani Locality - Sensitive Hashing (LSH) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution..."}
+{"idx": 5, "title": "Locality - Sensitive Hashing -Based Efficient Point Transformer with...", "date": "", "ddg_snippet": "Locality - Sensitive Hashing . LSH ( Indyk & Motwani , 1998 ) was proposed for efficient nearest-neighbor search. With high probability, it hashes close data points into. the same bucket and distant ones into different buckets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=vJx6fld6l0", "content": "Locality - Sensitive Hashing . LSH ( Indyk & Motwani , 1998 ) was proposed for efficient nearest-neighbor search. With high probability, it hashes close data points into. the same bucket and distant ones into different buckets."}
+{"idx": 6, "title": "Using locality - sensitive hashing to speed up tree estimation and...", "date": "", "ddg_snippet": "Theorem [ Indyk + Motwani 1998 ]: Can nd near neighbours to S in T with locality - sensitive hashing in o(n) time. Locality - sensitive hashing . I Idea: if S is close to a sequence in the tree, they probably match in a random set of k positions.", "subpage_snippet": "", "source": "www.cs.utexas.edu", "link": "https://www.cs.utexas.edu/~tandy/brown-symposium.pdf", "content": "Theorem [ Indyk + Motwani 1998 ]: Can nd near neighbours to S in T with locality - sensitive hashing in o(n) time. Locality - sensitive hashing . I Idea: if S is close to a sequence in the tree, they probably match in a random set of k positions."}
+{"idx": 7, "title": "CS-GY 9223 D: Lecture 4 Near neighbor search + locality sensitive ...", "date": "", "ddg_snippet": "locality sensitive hash functions. LSH for s(q, y) equal to Jaccard similarity: • Let c : {0, 1}d → [0, 1] be a single instantiation of MinHash. •Theorem ( Indyk , Motwani , 1998 ) Let q be the closest database vector to y. Return a vector q˜ with ∥q˜ − y∥0 ≤ C · ∥q − y∥0 in", "subpage_snippet": "", "source": "www.chrismusco.com", "link": "https://www.chrismusco.com/amlds2020/lectures/lec4.pdf", "content": "locality sensitive hash functions. LSH for s(q, y) equal to Jaccard similarity: • Let c : {0, 1}d → [0, 1] be a single instantiation of MinHash. •Theorem ( Indyk , Motwani , 1998 ) Let q be the closest database vector to y. Return a vector q˜ with ∥q˜ − y∥0 ≤ C · ∥q − y∥0 in"}
+{"idx": 8, "title": "(PDF) A locality - sensitive hash for real vectors", "date": "", "ddg_snippet": "locality - sensitive hashing lower bound proved by O'Donnell, Wu and Zhou (ICS 2011).P. Indyk and R. Motwani , Approximate nearest neigh- bor: toward removing the curse of dimensionality, Pro- ceedings of the 35th IEEE Symposium on Foundations of Computer Science, 1998 .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/1444489/A_locality_sensitive_hash_for_real_vectors", "content": "locality - sensitive hashing lower bound proved by O'Donnell, Wu and Zhou (ICS 2011).P. Indyk and R. Motwani , Approximate nearest neigh- bor: toward removing the curse of dimensionality, Pro- ceedings of the 35th IEEE Symposium on Foundations of Computer Science, 1998 ."}
+{"idx": 9, "title": "note6-LSH.pptx", "date": "", "ddg_snippet": "Min-‐ Hash -‐ ing. Locality -‐ Sensitive Hashing . The set of strings of length k that appear in the doc-‐ ument. Signatures: short integer vectors that represent the sets, and reect their similarity. Candidate pairs: those pairs of signatures that we need to test for similarity.", "subpage_snippet": "", "source": "www.cse.msu.edu", "link": "https://www.cse.msu.edu/~yannisun/cse836/notes/note6-LSH.pdf", "content": "Min-‐ Hash -‐ ing. Locality -‐ Sensitive Hashing . The set of strings of length k that appear in the doc-‐ ument. Signatures: short integer vectors that represent the sets, and reect their similarity. Candidate pairs: those pairs of signatures that we need to test for similarity."}
diff --git a/data/sampled_jsons/Indyk_Motwani_1998_LSH_fundamental_principle_locality_sensitive_hash_functions.jsonl b/data/sampled_jsons/Indyk_Motwani_1998_LSH_fundamental_principle_locality_sensitive_hash_functions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bba1fa57a1f58e00a6291194c8820c0bf39b30e9
--- /dev/null
+++ b/data/sampled_jsons/Indyk_Motwani_1998_LSH_fundamental_principle_locality_sensitive_hash_functions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Locality-sensitive hashing - Wikipedia", "date": "", "ddg_snippet": "In computer science, locality- sensitive hashing ( LSH ) is a fuzzy hashing technique that hashes similar input items into the same \"buckets\" with high probability. [1] The number of buckets is much smaller than the universe of possible input items. [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search. It differs from ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Locality-sensitive_hashing", "content": "In computer science, locality- sensitive hashing ( LSH ) is a fuzzy hashing technique that hashes similar input items into the same \"buckets\" with high probability. [1] The number of buckets is much smaller than the universe of possible input items. [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search. It differs from ..."}
+{"idx": 1, "title": "Local Sensitivity Hashing (L.S.H.): A Comprehensive Guide", "date": "", "ddg_snippet": "30 Mar 2024 — LSH is an approximate technique that can drastically enhance the efficiency of similarity search in high dimensions by intelligently mapping similar data ...", "subpage_snippet": "", "source": "zilliz.com", "link": "https://zilliz.com/learn/Local-Sensitivity-Hashing-A-Comprehensive-Guide", "content": "30 Mar 2024 — LSH is an approximate technique that can drastically enhance the efficiency of similarity search in high dimensions by intelligently mapping similar data ..."}
+{"idx": 2, "title": "Similarity Search in High Dimensions via Hashing", "date": "", "ddg_snippet": "by A Gionis · Cited by 5061 — In this section we present locality - sensitive hashing . ( LSH ). This technique was originally introduced by. Indyk and Motwani 24] for the purposes of devising. 12 pages", "subpage_snippet": "", "source": "www.cs.columbia.edu", "link": "https://www.cs.columbia.edu/~verma/classes/uml/ref/nn_lsh_gionis_indyk_motwani.pdf", "content": "by A Gionis · Cited by 5061 — In this section we present locality - sensitive hashing . ( LSH ). This technique was originally introduced by. Indyk and Motwani 24] for the purposes of devising. 12 pages"}
+{"idx": 3, "title": "Multi-metric locality sensitive hashing enhances alignment ...", "date": "", "ddg_snippet": "by H Nikaein · 2025 — Locality - Sensitive Hashing ( LSH ) is a widely used algorithm for estimating similarity between large datasets in bioinformatics, with applications in genome ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12360834/", "content": "by H Nikaein · 2025 — Locality - Sensitive Hashing ( LSH ) is a widely used algorithm for estimating similarity between large datasets in bioinformatics, with applications in genome ..."}
+{"idx": 4, "title": "4 Locality-sensitive hashing using stable distributions", "date": "", "ddg_snippet": "In this chapter, we introduce and analyze a novel locality - sensitive hashing family. The family is defined for the case where the distances are measured ... 13 pages", "subpage_snippet": "", "source": "graphics.stanford.edu", "link": "https://graphics.stanford.edu/courses/cs468-06-fall/Papers/13+lsh06.pdf", "content": "In this chapter, we introduce and analyze a novel locality - sensitive hashing family. The family is defined for the case where the distances are measured ... 13 pages"}
+{"idx": 5, "title": "Intelligent Probing for Locality Sensitive Hashing: Multi- ...", "date": "", "ddg_snippet": "by Q Lv · 2017 · Cited by 27 — Introduced by Indyk and Motwani in 1998, locality sensi- tive hashing (LSH) [8] uses a family of locality sensitive hash functions (i.e., certain random space ... 4 pages", "subpage_snippet": "", "source": "www.vldb.org", "link": "http://www.vldb.org/pvldb/vol10/p2021-lv.pdf", "content": "by Q Lv · 2017 · Cited by 27 — Introduced by Indyk and Motwani in 1998, locality sensi- tive hashing (LSH) [8] uses a family of locality sensitive hash functions (i.e., certain random space ... 4 pages"}
+{"idx": 6, "title": "LSH-Preserving Functions and Their Applications", "date": "", "ddg_snippet": "by F Chierichetti · 2015 · Cited by 58 — Locality sensitive hashing ( LSH ) is a key algorithmic tool that is widely used both in theory and practice. An important goal in the study of LSH is to ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/2816813", "content": "by F Chierichetti · 2015 · Cited by 58 — Locality sensitive hashing ( LSH ) is a key algorithmic tool that is widely used both in theory and practice. An important goal in the study of LSH is to ..."}
+{"idx": 7, "title": "[1708.07586] Fast Locality-Sensitive Hashing Frameworks for ... Fast Locality-Sensitive Hashing Frameworks for Approximate ... 1 Nearest Neighbor Search - Princeton University Locality-sensitive hashing - Wikipedia CS-GY 9223 D: Lecture 4 Near neighbor search + locality ... (PDF) Advances in Locality-Sensitive Hashing - ResearchGate [1708.07586] Fast Locality - Sensitive Hashing Frameworks for Approxi… Locality - sensitive hashing - Wikipedia Locality - sensitive hashing - Wikipedia 1 Nearest Neighbor Search - Princeton University Locality - sensitive hashing - Wikipedia Locality - sensitive hashing - Wikipedia Fast locality-sensitive hashing | Proceedings of the 17th ACM ...", "date": "", "ddg_snippet": "Aug 25, 2017 · The Indyk-Motwani Locality- Sensitive Hashing ( LSH ) framework (STOC 1998) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution H over locality- sensitive hash functions that partition space. The Indyk-Motwani Locality- Sensitive Hashing ( LSH ) framework (STOC 1998) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distri-bution over locality- sensitive hash functions that partition space. To solve this problem we will use a technique called \\locality sensitive hashing\" ( LSH ), which was introduced by Indyk and Motwani in 1998 [4] and has been very in uential, both in theory and in practical implementations of high-dimensional nearest neighbor search. De nition 1 (Locality Sensitive Hash Family). In computer science, locality- sensitive hashing ( LSH ) is a fuzzy hashing technique that hashes similar input items into the same \"buckets\" with high probability. [1] The number of buckets is much smaller than the universe of possible input items. [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search. It differs from ... CS-GY 9223 D: Lecture 4 Near neighbor search + locality sensitive hashing NYU Tandon School of Engineering, Prof. Christopher Musco Apr 18, 2016 · The approximate nearest neighbour (ANN) problem in high dimensions has a rich history, beginning with the seminal work on locality- sensitive hashing ( LSH ) by Indyk and Motwani (STOC 1998), and ... What is Indyk-Motwani locality-sensitive hashing (LSH)? The Indyk - Motwani Locality - Sensitive Hashing ( LSH ) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution H over locality - sensitive hash functions that partition space. What is locality-sensitive hashing (LSH)? In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same \"buckets\" with high probability. The number of buckets is much smaller than the universe of possible input items. What is TLSH (locality-sensitive hashing algorithm)? TLSH is locality-sensitive hashing algorithm designed for a range of security and digital forensic applications . The goal of TLSH is to generate hash digests for messages such that low distances between digests indicate that their corresponding messages are likely to be similar. An implementation of TLSH is available as open-source software. How to use a locality Sen-sitive hash family for near neighbor search? Proof of Theorem 4. The result applies to the following procedure for using a locality sen-sitive hash family for near neighbor search: Set k = log n= log(1=p2) and ` = 2n . Construct a new hash family G : U ! What is Locality preserving hashing? Locality-preserving hashing was initially devised as a way to facilitate data pipelining in implementations of massively parallel algorithms that use randomized routing and universal hashing to reduce memory contention and network congestion. A finite family of functions is defined to be an LSH family for What is semantic hashing? Semantic hashing is a technique that attempts to map input items to addresses such that closer inputs have higher semantic similarity. The hashcodes are found via training of an artificial neural network or graphical model. [citation needed] Aug 21, 2011 · Locality- sensitive hashing ( LSH ) is a basic primitive in several large-scale data processing applications, including nearest-neighbor search, de-duplication, clustering, etc. In this paper we propose a new and simple method to speed up the widely-used Euclidean realization of LSH . At the heart of our method is a fast way to estimate the Euclidean distance between two d -dimensional vectors ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1708.07586", "content": "Aug 25, 2017 · The Indyk-Motwani Locality- Sensitive Hashing ( LSH ) framework (STOC 1998) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution H over locality- sensitive hash functions that partition space. The Indyk-Motwani Locality- Sensitive Hashing ( LSH ) framework (STOC 1998) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distri-bution over locality- sensitive hash functions that partition space. To solve this problem we will use a technique called \\locality sensitive hashing\" ( LSH ), which was introduced by Indyk and Motwani in 1998 [4] and has been very in uential, both in theory and in practical implementations of high-dimensional nearest neighbor search. De nition 1 (Locality Sensitive Hash Family). In computer science, locality- sensitive hashing ( LSH ) is a fuzzy hashing technique that hashes similar input items into the same \"buckets\" with high probability. [1] The number of buckets is much smaller than the universe of possible input items. [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search. It differs from ... CS-GY 9223 D: Lecture 4 Near neighbor search + locality sensitive hashing NYU Tandon School of Engineering, Prof. Christopher Musco Apr 18, 2016 · The approximate nearest neighbour (ANN) problem in high dimensions has a rich history, beginning with the seminal work on locality- sensitive hashing ( LSH ) by Indyk and Motwani (STOC 1998), and ... What is Indyk-Motwani locality-sensitive hashing (LSH)? The Indyk - Motwani Locality - Sensitive Hashing ( LSH ) framework (STOC 1998 ) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution H over locality - sensitive hash functions that partition space. What is locality-sensitive hashing (LSH)? In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same \"buckets\" with high probability. The number of buckets is much smaller than the universe of possible input items. What is TLSH (locality-sensitive hashing algorithm)? TLSH is locality-sensitive hashing algorithm designed for a range of security and digital forensic applications . The goal of TLSH is to generate hash digests for messages such that low distances between digests indicate that their corresponding messages are likely to be similar. An implementation of TLSH is available as open-source software. How to use a locality Sen-sitive hash family for near neighbor search? Proof of Theorem 4. The result applies to the following procedure for using a locality sen-sitive hash family for near neighbor search: Set k = log n= log(1=p2) and ` = 2n . Construct a new hash family G : U ! What is Locality preserving hashing? Locality-preserving hashing was initially devised as a way to facilitate data pipelining in implementations of massively parallel algorithms that use randomized routing and universal hashing to reduce memory contention and network congestion. A finite family of functions is defined to be an LSH family for What is semantic hashing? Semantic hashing is a technique that attempts to map input items to addresses such that closer inputs have higher semantic similarity. The hashcodes are found via training of an artificial neural network or graphical model. [citation needed] Aug 21, 2011 · Locality- sensitive hashing ( LSH ) is a basic primitive in several large-scale data processing applications, including nearest-neighbor search, de-duplication, clustering, etc. In this paper we propose a new and simple method to speed up the widely-used Euclidean realization of LSH . At the heart of our method is a fast way to estimate the Euclidean distance between two d -dimensional vectors ..."}
+{"idx": 8, "title": "Fast Locality-Sensitive Hashing Frameworks for Approximate ...", "date": "", "ddg_snippet": "The Indyk-Motwani Locality- Sensitive Hashing ( LSH ) framework (STOC 1998) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distri-bution over locality- sensitive hash functions that partition space.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-32047-8_1.pdf", "content": "The Indyk-Motwani Locality- Sensitive Hashing ( LSH ) framework (STOC 1998) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distri-bution over locality- sensitive hash functions that partition space."}
+{"idx": 9, "title": "CS-GY 9223 D: Lecture 4 Near neighbor search + locality ...", "date": "", "ddg_snippet": "CS-GY 9223 D: Lecture 4 Near neighbor search + locality sensitive hashing NYU Tandon School of Engineering, Prof. Christopher Musco", "subpage_snippet": "", "source": "www.chrismusco.com", "link": "https://www.chrismusco.com/amlds2020/lectures/lec4.pdf", "content": "CS-GY 9223 D: Lecture 4 Near neighbor search + locality sensitive hashing NYU Tandon School of Engineering, Prof. Christopher Musco"}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_Dynamic_Scene_Reconstruction_Gaussian_Splat_year_2024.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_Dynamic_Scene_Reconstruction_Gaussian_Splat_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..43db0b0c678ce5df7e738626be52964dd71bbbfa
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_Dynamic_Scene_Reconstruction_Gaussian_Splat_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant - Wikipedia", "date": "", "ddg_snippet": "In physics and the philosophy of science, instant refers to an infinitesimal interval in time, whose passage is instantaneous.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Instant", "content": "In physics and the philosophy of science, instant refers to an infinitesimal interval in time, whose passage is instantaneous."}
+{"idx": 1, "title": "Instant", "date": "", "ddg_snippet": "Instant Financial offers fee-free earned wage access solutions for employees to access their pay before payday and manage financial wellness.", "subpage_snippet": "", "source": "app.instant.co", "link": "https://app.instant.co/", "content": "Instant Financial offers fee-free earned wage access solutions for employees to access their pay before payday and manage financial wellness."}
+{"idx": 2, "title": "INSTANT Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of INSTANT is an infinitesimal space of time; especially : a point in time separating two states. How to use instant in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/instant", "content": "The meaning of INSTANT is an infinitesimal space of time; especially : a point in time separating two states. How to use instant in a sentence."}
+{"idx": 3, "title": "INSTANT | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "INSTANT definition: 1. happening immediately, without any delay: 2. Instant food or drink is dried, usually in the…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/instant", "content": "INSTANT definition: 1. happening immediately, without any delay: 2. Instant food or drink is dried, usually in the…. Learn more."}
+{"idx": 4, "title": "instant noun - Definition, pictures, pronunciation and usage...", "date": "", "ddg_snippet": "Definition of instant noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/instant_2", "content": "Definition of instant noun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 5, "title": "instant - WordReference.com Dictionary of English", "date": "", "ddg_snippet": "noting a food or beverage requiring a minimal amount of time and effort to prepare, as by heating or the addition of milk or water, before being served or used: instant coffee; instant pudding.", "subpage_snippet": "", "source": "www.wordreference.com", "link": "https://www.wordreference.com/definition/instant", "content": "noting a food or beverage requiring a minimal amount of time and effort to prepare, as by heating or the addition of milk or water, before being served or used: instant coffee; instant pudding."}
+{"idx": 6, "title": "INSTANT Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Instant definition: an infinitesimal or very short space of time; a moment.. See examples of INSTANT used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/instant", "content": "Instant definition: an infinitesimal or very short space of time; a moment.. See examples of INSTANT used in a sentence."}
+{"idx": 7, "title": "INSTANT definition in American English | Collins English...", "date": "", "ddg_snippet": "If you say that something happens at a particular instant , you mean that it happens at exactly the time you have been referring to, and you are usually suggesting that it happens quickly or immediately.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/us/dictionary/english/instant", "content": "If you say that something happens at a particular instant , you mean that it happens at exactly the time you have been referring to, and you are usually suggesting that it happens quickly or immediately."}
+{"idx": 8, "title": "INSTANT Synonyms: 149 Similar and Opposite Words - ...", "date": "", "ddg_snippet": "Synonyms for INSTANT : instantaneous, immediate, split-second, rapid, summary, straightaway, quick, swift; Antonyms of INSTANT : slow, prolonged, sluggish, protracted, tardy, deferred, dilatory, delayed", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/thesaurus/instant", "content": "Synonyms for INSTANT : instantaneous, immediate, split-second, rapid, summary, straightaway, quick, swift; Antonyms of INSTANT : slow, prolonged, sluggish, protracted, tardy, deferred, dilatory, delayed"}
+{"idx": 9, "title": "Top Earned Wage Access Provider | Instant Financial Solutions", "date": "", "ddg_snippet": "As a leading earned wage access and digital tips provider, Instant ensures your employee benefits program offers instant access to pay for 100% of your employees.", "subpage_snippet": "", "source": "www.instant.co", "link": "https://www.instant.co/", "content": "As a leading earned wage access and digital tips provider, Instant ensures your employee benefits program offers instant access to pay for 100% of your employees."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_Implementation_details_training_dataset_AGM-Net.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_Implementation_details_training_dataset_AGM-Net.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..567aa5d0af81fdecc692d07dce57bf1acee1dbaa
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_Implementation_details_training_dataset_AGM-Net.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 62, 66, 69, 71] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 62, 66, 69, 71] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ..."}
+{"idx": 1, "title": "Scale-GS: Efficient Scalable Gaussian Splatting via Redundancy ...", "date": "", "ddg_snippet": "Abstract 3D Gaussian Splatting (3DGS) enables high-fidelity real-time rendering, a key requirement for immersive applications. However, the extension of 3DGS to dynamic scenes remains limitations on the substantial data volume of dense Gaussians and the prolonged training time required for each frame. This paper presents Scale-GS, a scalable Gaussian Splatting framework designed for efficient ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21444v1", "content": "Abstract 3D Gaussian Splatting (3DGS) enables high-fidelity real-time rendering, a key requirement for immersive applications. However, the extension of 3DGS to dynamic scenes remains limitations on the substantial data volume of dense Gaussians and the prolonged training time required for each frame. This paper presents Scale-GS, a scalable Gaussian Splatting framework designed for efficient ..."}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "Second, AGM-Net has been trained on four sequences from the N3DV indoor dataset . The limited size of the training data constrains its generalization capability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "Second, AGM-Net has been trained on four sequences from the N3DV indoor dataset . The limited size of the training data constrains its generalization capability."}
+{"idx": 3, "title": "Instant Gaussian Stream: Fast and Generalizable ... - ResearchGate", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 4, "title": "Scale-GS: Efficient Scalable Gaussian Splatting via Redundancy ...", "date": "", "ddg_snippet": "Abstract—3D Gaussian Splatting (3DGS) enables high-fidelity real-time rendering, a key requirement for immersive applica-tions. However, the extension of 3DGS to dynamic scenes remains limitations on the substantial data volume of dense Gaussians and the prolonged training time required for each frame. This paper presents Scale-GS, a scalable Gaussian Splatting framework designed for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.21444", "content": "Abstract—3D Gaussian Splatting (3DGS) enables high-fidelity real-time rendering, a key requirement for immersive applica-tions. However, the extension of 3DGS to dynamic scenes remains limitations on the substantial data volume of dense Gaussians and the prolonged training time required for each frame. This paper presents Scale-GS, a scalable Gaussian Splatting framework designed for ..."}
+{"idx": 5, "title": "InstantGaussianStream_2503.16979v1 | PDF", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene ... AGM - Net and performing online training ... Implementation details . Gaussians forward ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/889322844/InstantGaussianStream-2503-16979v1", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene ... AGM - Net and performing online training ... Implementation details . Gaussians forward ..."}
+{"idx": 6, "title": "[Literature Review] Instant Gaussian Stream: Fast and ...", "date": "", "ddg_snippet": "21 Mar 2025 — The paper \" Instant Gaussian Stream : Fast and Generalizable ... Implementation Details : Extensive training on large datasets is ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/instant-gaussian-stream-fast-and-generalizable-streaming-of-dynamic-scene-reconstruction-via-gaussian-splatting", "content": "21 Mar 2025 — The paper \" Instant Gaussian Stream : Fast and Generalizable ... Implementation Details : Extensive training on large datasets is ..."}
+{"idx": 7, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ..."}
+{"idx": 8, "title": "US11485517B1 - System and method for communicating a", "date": "", "ddg_snippet": "2021-11-15 Assigned to BETA AIR, LLC reassignment BETA AIR, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS ).", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11485517B1/en", "content": "2021-11-15 Assigned to BETA AIR, LLC reassignment BETA AIR, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS )."}
+{"idx": 9, "title": "US20190379976A1 - Systems and methods for using multiple", "date": "", "ddg_snippet": "... OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS ... hardware implementation of neural networks, neurons or parts of neurons using electronic means", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20190379976A1/en", "content": "... OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS ... hardware implementation of neural networks, neurons or parts of neurons using electronic means"}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_PSNR_Ours-l_Table_1.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_PSNR_Ours-l_Table_1.jsonl
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index 0000000000000000000000000000000000000000..12604b97be994cbe28f44a5cc4b472a5f211d213
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+++ b/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_PSNR_Ours-l_Table_1.jsonl
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+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Mar 21, 2025 · Table 1 : Comparison on the N3DV dataset, with results measured at a resolution of 1352 x 1014. † indicates that the evaluation was performed using the official code in the same experimental environment as ours , including the same initial point cloud.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "Mar 21, 2025 · Table 1 : Comparison on the N3DV dataset, with results measured at a resolution of 1352 x 1014. † indicates that the evaluation was performed using the official code in the same experimental environment as ours , including the same initial point cloud."}
+{"idx": 1, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "To promote the streaming framework to be more prac-tical, we introduce Instant Gaussian Stream (IGS), a streaming approach for dynamic scene reconstruction that achieves a per-frame reconstruction time of 2s+, mitigates error accumulation, and enhances view synthesis quality.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "To promote the streaming framework to be more prac-tical, we introduce Instant Gaussian Stream (IGS), a streaming approach for dynamic scene reconstruction that achieves a per-frame reconstruction time of 2s+, mitigates error accumulation, and enhances view synthesis quality."}
+{"idx": 2, "title": "How do you measure the PSNR of test set for N3DV in Table 1?", "date": "", "ddg_snippet": "Do you only report the PSNR for the first 10-second frames? Or, you split them into four chunks (each video is 10 seconds), train the model per chunk, and then measure the average PSNR between them?", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AlgoHunt/StreamRF/issues/1", "content": "Do you only report the PSNR for the first 10-second frames? Or, you split them into four chunks (each video is 10 seconds), train the model per chunk, and then measure the average PSNR between them?"}
+{"idx": 3, "title": "(PDF) Instant Gaussian Stream: Fast and Generalizable ...", "date": "", "ddg_snippet": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 4, "title": "CVPR Poster Instant Gaussian Stream", "date": "", "ddg_snippet": "Table 1 . Comparison on the N3DV dataset, with results measured at a resolution of 1352 x 1014. † indicates that the evaluation was performed using the ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33775", "content": "Table 1 . Comparison on the N3DV dataset, with results measured at a resolution of 1352 x 1014. † indicates that the evaluation was performed using the ..."}
+{"idx": 5, "title": "Dynamics-Aware Gaussian Splatting Streaming Towards ...", "date": "", "ddg_snippet": "Table 1 : Quantitative comparison on the N3DV dataset. The training time and reconstruction qualities are averaged over all 300 frames for each scene. † DyNeRF ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.14847v2", "content": "Table 1 : Quantitative comparison on the N3DV dataset. The training time and reconstruction qualities are averaged over all 300 frames for each scene. † DyNeRF ..."}
+{"idx": 6, "title": "InstantGaussianStream_2503.16979v1 | PDF", "date": "", "ddg_snippet": "The document presents the Instant Gaussian Stream (IGS), a novel framework for fast and generalizable streaming of dynamic scene reconstruction using ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/889322844/InstantGaussianStream-2503-16979v1", "content": "The document presents the Instant Gaussian Stream (IGS), a novel framework for fast and generalizable streaming of dynamic scene reconstruction using ..."}
+{"idx": 7, "title": "HiCoM: Hierarchical Coherent Motion for Streamable ...", "date": "", "ddg_snippet": "by Q Gao · 2024 · Cited by 11 — The right figure is tested on the N3DV [ 1 ] dataset, where the radius of the circle corresponds to the average storage per frame and the method in the top left ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/9370cc8d438b9ed8cb4344818a251d9a-Paper-Conference.pdf", "content": "by Q Gao · 2024 · Cited by 11 — The right figure is tested on the N3DV [ 1 ] dataset, where the radius of the circle corresponds to the average storage per frame and the method in the top left ..."}
+{"idx": 8, "title": "3DGStream: On-the-Fly Training of 3D Gaussians for ...", "date": "", "ddg_snippet": "... N3DV dataset. image. Table 1 . Quantitative comparison on the N3DV dataset. The training time, required storage and PSNR are averaged over the whole 300 ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/cvpr/31414/paper", "content": "... N3DV dataset. image. Table 1 . Quantitative comparison on the N3DV dataset. The training time, required storage and PSNR are averaged over the whole 300 ..."}
+{"idx": 9, "title": "Sun 3DGStream on-The-Fly Training of 3D Gaussians For ...", "date": "", "ddg_snippet": "The paper presents 3DGStream, a novel method for on-the-fly training of 3D Gaussians to efficiently stream photo-realistic Free-Viewpoint Videos (FVVs) of ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/822984980/Sun-3DGStream-on-The-Fly-Training-of-3D-Gaussians-for-Efficient-Streaming-of-CVPR-2024-Paper", "content": "The paper presents 3DGStream, a novel method for on-the-fly training of 3D Gaussians to efficiently stream photo-realistic Free-Viewpoint Videos (FVVs) of ..."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_dataset_test_sequences_coffee_room_plant_scene_Section_4.1.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_dataset_test_sequences_coffee_room_plant_scene_Section_4.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0c734287e3901e3ac7614205bb13fe77716eed62
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_dataset_test_sequences_coffee_room_plant_scene_Section_4.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Step 2: Train the Gaussian of Frame 0 In this step, we recommend first optimizing the Gaussian model for several thousand iterations using RaDe-GS, followed by compressing the Gaussian points with LightGaussian. This process ensures an efficient and high-quality reconstruction of the 3D scene .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Step 2: Train the Gaussian of Frame 0 In this step, we recommend first optimizing the Gaussian model for several thousand iterations using RaDe-GS, followed by compressing the Gaussian points with LightGaussian. This process ensures an efficient and high-quality reconstruction of the 3D scene ."}
+{"idx": 1, "title": "(PDF) Instant Gaussian Stream : Fast and Generalizable Streaming ...", "date": "", "ddg_snippet": "tical, we introduce Instant Gaussian Stream (IGS), a. streaming approach for dynamic scene reconstruction that. achieves a per-frame reconstruction time of 2s+, mitigates.Net has been trained on four sequences from the N 3 DV . indoor dataset .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "tical, we introduce Instant Gaussian Stream (IGS), a. streaming approach for dynamic scene reconstruction that. achieves a per-frame reconstruction time of 2s+, mitigates.Net has been trained on four sequences from the N 3 DV . indoor dataset ."}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream-ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream-ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality."}
+{"idx": 3, "title": "4DGC: Rate-Aware 4D Gaussian Compression for Efficient ...", "date": "", "ddg_snippet": "We present a qualitative comparison with ReRF, TeTriRF, and 3DGStream on the coffee martini sequence from the N3DV dataset and the trimming sequence from the MeetRoom dataset , as shown in the figure. Our approach achieves comparable reconstruction quality to 3DGStream at a substantially lower bitrate, achieving a compression rate exceeding 16×.", "subpage_snippet": "", "source": "waveviewer.github.io", "link": "https://waveviewer.github.io/4dgc/", "content": "We present a qualitative comparison with ReRF, TeTriRF, and 3DGStream on the coffee martini sequence from the N3DV dataset and the trimming sequence from the MeetRoom dataset , as shown in the figure. Our approach achieves comparable reconstruction quality to 3DGStream at a substantially lower bitrate, achieving a compression rate exceeding 16×."}
+{"idx": 4, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.16979", "content": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians."}
+{"idx": 5, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. Dataset Preparation: We split four sequences from the N 3 DV dataset into the training set , with the remaining two sequences", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. Dataset Preparation: We split four sequences from the N 3 DV dataset into the training set , with the remaining two sequences"}
+{"idx": 6, "title": "Instant Gaussian Stream : Fast and Generalizable... | alphaXiv", "date": "", "ddg_snippet": "Instant Gaussian Stream (IGS) is a novel framework that addresses these challenges by providing fast and generalizable streaming reconstruction of dynamic scenes .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.16979", "content": "Instant Gaussian Stream (IGS) is a novel framework that addresses these challenges by providing fast and generalizable streaming reconstruction of dynamic scenes ."}
+{"idx": 7, "title": "HiCoM: Hierarchical Coherent Motion for Dynamic Streamable Scenes ...", "date": "", "ddg_snippet": "The left panels display results on two different datasets ( N 3 DV and Meet Room ), highlighting the video resolution, training time, rendering speed, storage, and PSNR (Peak Signal-to-Noise Ratio) achieved by HiCoM.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/de4vwe4rbz/", "content": "The left panels display results on two different datasets ( N 3 DV and Meet Room ), highlighting the video resolution, training time, rendering speed, storage, and PSNR (Peak Signal-to-Noise Ratio) achieved by HiCoM."}
+{"idx": 8, "title": "Instant Gaussian Splats in Unreal Engine 5 with Luma AI", "date": "", "ddg_snippet": "The Luma AI plugin for Unreal Engine 5 optimizes the way artists work with gaussian splats and Nerf captures. Luma AI's tools simplify the process from capture to integration, addressing the previously challenging areas of gaussian and Nerf captures.", "subpage_snippet": "", "source": "www.onsetfacilities.com", "link": "https://www.onsetfacilities.com/post/instant-gaussian-splats-in-unreal-engine-5-with-luma-ai", "content": "The Luma AI plugin for Unreal Engine 5 optimizes the way artists work with gaussian splats and Nerf captures. Luma AI's tools simplify the process from capture to integration, addressing the previously challenging areas of gaussian and Nerf captures."}
+{"idx": 9, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_Table_1_train_time_3DGStream_16.93_2.67.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_Table_1_train_time_3DGStream_16.93_2.67.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..52d6f0780946b1b884b6259b2f9e4da876bdf5bb
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_Table_1_train_time_3DGStream_16.93_2.67.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for", "date": "", "ddg_snippet": "Finally, to achieve further efficiencies in terms of training time and storage, we utilize the differences between the 2D viewspace Gaussian ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.04469v1", "content": "Finally, to achieve further efficiencies in terms of training time and storage, we utilize the differences between the 2D viewspace Gaussian ..."}
+{"idx": 1, "title": "DynMF: Neural Motion Factorization for Real-time Dynamic View", "date": "", "ddg_snippet": "... framework consisting of a tiny set of learned basis queried only in time allows for rendering speed similar to 3D Gaussian Splatting, surpassing 120 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.00112v2", "content": "... framework consisting of a tiny set of learned basis queried only in time allows for rendering speed similar to 3D Gaussian Splatting, surpassing 120 ..."}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream-ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream-ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality."}
+{"idx": 3, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Step 2: Train the Gaussian of Frame 0 In this step, we recommend first optimizing the Gaussian model for several thousand iterations using RaDe-GS, followed by compressing the Gaussian points with LightGaussian. This process ensures an efficient and high-quality reconstruction of the 3D scene.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Step 2: Train the Gaussian of Frame 0 In this step, we recommend first optimizing the Gaussian model for several thousand iterations using RaDe-GS, followed by compressing the Gaussian points with LightGaussian. This process ensures an efficient and high-quality reconstruction of the 3D scene."}
+{"idx": 4, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Mar 21, 2025 · Our method outperforms 3DGStream in rendering quality, train time , and storage efficiency, achieving streaming with just 2.77s of per-frame reconstruction time , a significant improvement over 3DGStream .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "Mar 21, 2025 · Our method outperforms 3DGStream in rendering quality, train time , and storage efficiency, achieving streaming with just 2.77s of per-frame reconstruction time , a significant improvement over 3DGStream ."}
+{"idx": 5, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ... arXiv:2503.16979v1 [cs.CV] 21 Mar 2025 (PDF) Instant Gaussian Stream: Fast and Generalizable ...", "date": "", "ddg_snippet": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. oach (de-noted with † in the table ). Compared to 3DGStream and StreamRF, our method achieves a 6x reduction in train time , with an average delay of 2.67 seconds per frame, while maintaining compara Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.16979", "content": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. oach (de-noted with † in the table ). Compared to 3DGStream and StreamRF, our method achieves a 6x reduction in train time , with an average delay of 2.67 seconds per frame, while maintaining compara Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 6, "title": "(PDF) Instant Gaussian Stream: Fast and Generalizable ...", "date": "", "ddg_snippet": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 7, "title": "GitHub - SJoJoK/ 3 DGStream : [CVPR 2024 Highlight] Official...", "date": "", "ddg_snippet": "Per-frame Training Time : Average among all frames (including the first frame).[CVPR 2024 Highlight] Official repository for the paper \" 3 DGStream : On-the-fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SJoJoK/3DGStream", "content": "Per-frame Training Time : Average among all frames (including the first frame).[CVPR 2024 Highlight] Official repository for the paper \" 3 DGStream : On-the-fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos\"."}
+{"idx": 8, "title": "3 DGStream : On-the-Fly Training of 3D Gaussians for Efficient...", "date": "", "ddg_snippet": "the final published version of the proceedings is available on IEEE Xplore. 3 DGStream : On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_3DGStream_On-the-Fly_Training_of_3D_Gaussians_for_Efficient_Streaming_of_CVPR_2024_paper.pdf", "content": "the final published version of the proceedings is available on IEEE Xplore. 3 DGStream : On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos."}
+{"idx": 9, "title": "[2403.01444] 3 DGStream : On-the-Fly Training of 3D Gaussians for...", "date": "", "ddg_snippet": "To address these constraints, we introduce 3 DGStream , a method designed for efficient FVV streaming of real-world dynamic scenes. Our method achieves fast on-the-fly per-frame reconstruction within 12 seconds and real- time rendering at 200 FPS.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.01444", "content": "To address these constraints, we introduce 3 DGStream , a method designed for efficient FVV streaming of real-world dynamic scenes. Our method achieves fast on-the-fly per-frame reconstruction within 12 seconds and real- time rendering at 200 FPS."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_anchor-driven_motion_interpolation_formula.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_anchor-driven_motion_interpolation_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b62b5cf4d8c5bf2ea9136db16bf98923ee79ad55
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_anchor-driven_motion_interpolation_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "1. Introduction Reconstructing Free-Viewpoint Videos (FVV) from multi- generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion fea-tures into 3D space, using anchor points to drive the mo-tion of all Gaussians .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "1. Introduction Reconstructing Free-Viewpoint Videos (FVV) from multi- generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion fea-tures into 3D space, using anchor points to drive the mo-tion of all Gaussians ."}
+{"idx": 1, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians . This generalized Network generates the motion of Gaussians for each target frame in the time required for a single inference.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.16979", "content": "First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians . This generalized Network generates the motion of Gaussians for each target frame in the time required for a single inference."}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ... - GitHub", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting [CVPR25]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting [CVPR25]"}
+{"idx": 3, "title": "PDF Gaussian Interpolation Flows", "date": "", "ddg_snippet": "In this work, we explore an ODE ow-based approach for generative modeling, which we refer to as Gaussian Interpolation Flows (GIFs). This method is derived from the Gaussian stochastic interpolation detailed in Section 3.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume25/23-1515/23-1515.pdf", "content": "In this work, we explore an ODE ow-based approach for generative modeling, which we refer to as Gaussian Interpolation Flows (GIFs). This method is derived from the Gaussian stochastic interpolation detailed in Section 3."}
+{"idx": 4, "title": "[PDF] Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "This paper proposes Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, which introduces a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians . Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Instant-Gaussian-Stream:-Fast-and-Generalizable-of-Yan-Peng/b1554873f9eb74b1fe602a534cde1cce6cb298bd", "content": "This paper proposes Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, which introduces a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians . Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to ..."}
+{"idx": 5, "title": "DynSUP: Dynamic Gaussian Splatting from An Unposed Image Pair", "date": "", "ddg_snippet": "Second, we design an SE (3) field- driven Gaussian training method. It enables fine-grained motion modeling through learnable per- Gaussian transformations. Our method leads to high-fidelity novel view synthesis of dynamic scenes while accurately preserving temporal consistency and object motion .", "subpage_snippet": "", "source": "colin-de.github.io", "link": "https://colin-de.github.io/DynSUP/", "content": "Second, we design an SE (3) field- driven Gaussian training method. It enables fine-grained motion modeling through learnable per- Gaussian transformations. Our method leads to high-fidelity novel view synthesis of dynamic scenes while accurately preserving temporal consistency and object motion ."}
+{"idx": 6, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "n, limiting their broader application. In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and generalizable stream ng framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion fea-tures into 3D space, using anchor points", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "n, limiting their broader application. In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and generalizable stream ng framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion fea-tures into 3D space, using anchor points"}
+{"idx": 7, "title": "Cvpr'25开源 | 远超4dgs!北大新作igs：高达204 Fps的动态场景重建 - 知乎", "date": "", "ddg_snippet": "具体贡献如下： 针对单帧重建耗时问题，我们开发了广义锚点驱动高斯运动网络（ Anchor-driven Gaussian Motion Network, AGM-Net）。 该网络利用称为锚点的一组关键点承载运动特征，引导高斯基元的变换。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1920419056911095443", "content": "具体贡献如下： 针对单帧重建耗时问题，我们开发了广义锚点驱动高斯运动网络（ Anchor-driven Gaussian Motion Network, AGM-Net）。 该网络利用称为锚点的一组关键点承载运动特征，引导高斯基元的变换。"}
+{"idx": 8, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.html", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians ."}
+{"idx": 9, "title": "3D Reconstruction - Full Paper Collection - GitHub", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Tags: 3DGS ( Gaussian Splatting), 3D Reconstruction, Streaming, Anchor-driven Gaussian Motion Network, Key-frame-guided Streaming Strategy, Dynamic Scene Reconstruction", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcademicDissect/CVPR2025/blob/main/detailed_paper_collection/3D_Reconstruction.md", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Tags: 3DGS ( Gaussian Splatting), 3D Reconstruction, Streaming, Anchor-driven Gaussian Motion Network, Key-frame-guided Streaming Strategy, Dynamic Scene Reconstruction"}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_arXiv_2024_year_2024.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_arXiv_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c15f924650b9590978b1448a74bd15abcfed8da5
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_arXiv_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fast and Generalizable Streaming of Dynamic Scene ...", "date": "", "ddg_snippet": "14 Mar 2025 — In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "14 Mar 2025 — In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 1, "title": "Fast and Generalizable Streaming of Dynamic Scene ...", "date": "", "ddg_snippet": "by J Yan · 2025 · Cited by 6 — To promote the streaming framework to be more prac- tical, we introduce Instant Gaussian Stream (IGS), a streaming approach for dynamic scene reconstruction ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "by J Yan · 2025 · Cited by 6 — To promote the streaming framework to be more prac- tical, we introduce Instant Gaussian Stream (IGS), a streaming approach for dynamic scene reconstruction ..."}
+{"idx": 2, "title": "CVPR Poster Instant Gaussian Stream", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33775", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a ..."}
+{"idx": 3, "title": "Dynamics-Aware Gaussian Splatting Streaming Towards ...", "date": "", "ddg_snippet": "by Z Liu · 2024 — Our method achieves state-of-the-art performance in online 4D reconstruction, demonstrating the fastest on-the-fly training, superior representation quality, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.14847", "content": "by Z Liu · 2024 — Our method achieves state-of-the-art performance in online 4D reconstruction, demonstrating the fastest on-the-fly training, superior representation quality, ..."}
+{"idx": 4, "title": "Lee-JaeWon/2024-Arxiv-Paper-List-Gaussian-Splatting", "date": "", "ddg_snippet": "26 Dec 2024 — This is crawled to find out about the 2024 Gaussian Splatting papers in arxiv . There may be errors, so please leave a Pull Request or Issue ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Lee-JaeWon/2024-Arxiv-Paper-List-Gaussian-Splatting", "content": "26 Dec 2024 — This is crawled to find out about the 2024 Gaussian Splatting papers in arxiv . There may be errors, so please leave a Pull Request or Issue ..."}
+{"idx": 5, "title": "Dynamics-Aware Gaussian Splatting Streaming Towards ...", "date": "", "ddg_snippet": "S4D: Streaming 4D real-world reconstruction with Gaussians and 3d control points. arXiv preprint arXiv:2408.13036, 2024 . Kajiya and Von Herzen [1984] ↑", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.14847v2", "content": "S4D: Streaming 4D real-world reconstruction with Gaussians and 3d control points. arXiv preprint arXiv:2408.13036, 2024 . Kajiya and Von Herzen [1984] ↑"}
+{"idx": 6, "title": "On-the-Fly Training of 3D Gaussians for Efficient Streaming ...", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting · Jinbo YanRui Peng +5 authors. Rong Wang.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/3DGStream:-On-the-Fly-Training-of-3D-Gaussians-for-Sun-Jiao/f98ec8992d6a2c84e351794e6b0f9d351a2ce212", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting · Jinbo YanRui Peng +5 authors. Rong Wang."}
+{"idx": 7, "title": "Scale-GS: Efficient Scalable Gaussian Splatting via ...", "date": "", "ddg_snippet": "29 Aug 2025 — Instant Gaussian Stream (IGS ) [62] proposes enables single-pass motion computation guided by keyframes, reducing error accumulation and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21444v1", "content": "29 Aug 2025 — Instant Gaussian Stream (IGS ) [62] proposes enables single-pass motion computation guided by keyframes, reducing error accumulation and ..."}
+{"idx": 8, "title": "Jiahao Wu", "date": "", "ddg_snippet": "Instant gaussian stream : Fast and generalizable streaming of dynamic scene reconstruction via gaussian splatting ... arXiv preprint arXiv:2408.06543, 2024.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=qjqx4MYAAAAJ&hl=en", "content": "Instant gaussian stream : Fast and generalizable streaming of dynamic scene reconstruction via gaussian splatting ... arXiv preprint arXiv:2408.06543, 2024."}
+{"idx": 9, "title": "Jinbo Yan", "date": "", "ddg_snippet": "Instant gaussian stream : Fast and generalizable streaming of dynamic scene reconstruction via gaussian splatting. J Yan, R Peng, Z Wang, L Tang, J Yang, J Liang ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=VW1dtnsAAAAJ&hl=en", "content": "Instant gaussian stream : Fast and generalizable streaming of dynamic scene reconstruction via gaussian splatting. J Yan, R Peng, Z Wang, L Tang, J Yang, J Liang ..."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_equation_6_interpolation_motion_feature_z_i_z_k_year_2024.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_equation_6_interpolation_motion_feature_z_i_z_k_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f7b9fda8c8bfeb308d9eb676af3b0498dec6bc6f
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_equation_6_interpolation_motion_feature_z_i_z_k_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Robust LiDAR-Camera Calibration with 2D Gaussian ...", "date": "", "ddg_snippet": "by S Zhou · 2025 — We then use a bilinear interpolation function C(·) to approximate the color of wi based on the four nearest pixels. To ensure robustness, we ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.00525", "content": "by S Zhou · 2025 — We then use a bilinear interpolation function C(·) to approximate the color of wi based on the four nearest pixels. To ensure robustness, we ..."}
+{"idx": 1, "title": "Gaussian Mixture Reduction of Tracking Multiple Maneuvering ...", "date": "", "ddg_snippet": "by JL Williams · 2003 · Cited by 110 — linear propagation equations as a single Gaussian function to find the set of compo- nent Gaussian functions {f{X(k)| Zk −1,Ψu(k −1)}}. The ...", "subpage_snippet": "", "source": "scholar.afit.edu", "link": "https://scholar.afit.edu/cgi/viewcontent.cgi?article=5248&context=etd", "content": "by JL Williams · 2003 · Cited by 110 — linear propagation equations as a single Gaussian function to find the set of compo- nent Gaussian functions {f{X(k)| Zk −1,Ψu(k −1)}}. The ..."}
+{"idx": 2, "title": "a course on integration by parts - UMD Math Department", "date": "", "ddg_snippet": "It is mostly about the analysis on Gaussian space and how integration by parts can be useful in different contexts. The three examples we consider are (i) a ... 68 pages", "subpage_snippet": "", "source": "math.umd.edu", "link": "https://math.umd.edu/~ygu7/course/lectures.pdf", "content": "It is mostly about the analysis on Gaussian space and how integration by parts can be useful in different contexts. The three examples we consider are (i) a ... 68 pages"}
+{"idx": 3, "title": "Small Aid, Big Leap: Efficient Test-Time Adaptation for ...", "date": "", "ddg_snippet": "by X Chen · 2025 · Cited by 1 — An innovative collaboration mechanism between the frozen VLM and AdaptNet is proposed, by utilizing a confidence-based interpolation weight ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.02671", "content": "by X Chen · 2025 · Cited by 1 — An innovative collaboration mechanism between the frozen VLM and AdaptNet is proposed, by utilizing a confidence-based interpolation weight ..."}
+{"idx": 4, "title": "M11111111111111 111111Il", "date": "", "ddg_snippet": "by SR Allmaras · 1989 · Cited by 42 — The first contribution is a new algorithm for the solution of the 2-D unsteady Euler equations . The algorithm incorporates flux-splitting to capture shocks ...", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/104736/21503969.pdf?sequence=1&isAllowed=y", "content": "by SR Allmaras · 1989 · Cited by 42 — The first contribution is a new algorithm for the solution of the 2-D unsteady Euler equations . The algorithm incorporates flux-splitting to capture shocks ..."}
+{"idx": 5, "title": "6 Processes - Art Owen", "date": "", "ddg_snippet": "Gaussian Correlations. Gaussian Process Interpolations . Figure 6.5: This figure shows interpolation at three points using the Gaussian process model, with ... 84 pages", "subpage_snippet": "", "source": "artowen.su.domains", "link": "https://artowen.su.domains/mc/Ch-processes.pdf", "content": "Gaussian Correlations. Gaussian Process Interpolations . Figure 6.5: This figure shows interpolation at three points using the Gaussian process model, with ... 84 pages"}
+{"idx": 6, "title": "Inferring Latent Velocities from Weather Radar Data using ...", "date": "", "ddg_snippet": "by R Angell · Cited by 10 — This paper presents a Gaussian process (GP) model to reconstruct high-resolution full velocity fields across the entire US. The GP faithfully models all aspects ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "http://papers.neurips.cc/paper/8113-inferring-latent-velocities-from-weather-radar-data-using-gaussian-processes.pdf", "content": "by R Angell · Cited by 10 — This paper presents a Gaussian process (GP) model to reconstruct high-resolution full velocity fields across the entire US. The GP faithfully models all aspects ..."}
+{"idx": 7, "title": "Deep learning: a statistical viewpoint", "date": "", "ddg_snippet": "by PL Bartlett · 2021 · Cited by 455 — We focus specifically on the linear regime for neural networks, where the network can be approximated by a linear model. In this regime, we ... 89 pages", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~montanar/TEACHING/MATH276/REFS/review.pdf", "content": "by PL Bartlett · 2021 · Cited by 455 — We focus specifically on the linear regime for neural networks, where the network can be approximated by a linear model. In this regime, we ... 89 pages"}
+{"idx": 8, "title": "Probability in High Dimension - Princeton Math", "date": "", "ddg_snippet": "by R van Handel · Cited by 505 — These notes were written for the course APC 550: Probability in High Dimen- sion that I taught at Princeton in the Spring 2014 and Fall 2016 semesters. 326 pages", "subpage_snippet": "", "source": "web.math.princeton.edu", "link": "https://web.math.princeton.edu/~rvan/APC550.pdf", "content": "by R van Handel · Cited by 505 — These notes were written for the course APC 550: Probability in High Dimen- sion that I taught at Princeton in the Spring 2014 and Fall 2016 semesters. 326 pages"}
+{"idx": 9, "title": "A review on spectral data preprocessing techniques for ...", "date": "", "ddg_snippet": "by C Yan · Cited by 5 — Spectroscopic techniques are indispensable for material characterization, yet their weak signals remain highly prone to interference from environmental ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/iscience/pdf/S2589-0042(25)01020-X.pdf", "content": "by C Yan · Cited by 5 — Spectroscopic techniques are indispensable for material characterization, yet their weak signals remain highly prone to interference from environmental ..."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_paper_Section_4_Implementation_details_dataset_training_AGM-Net_test_sequenc_year_2024.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_paper_Section_4_Implementation_details_dataset_training_AGM-Net_test_sequenc_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cb7c206f17c51210c86bb8fd5c6596637fddc254
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_paper_Section_4_Implementation_details_dataset_training_AGM-Net_test_sequenc_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "In this paper , we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "In this paper , we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians ."}
+{"idx": 1, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ..."}
+{"idx": 2, "title": "[2411.14847] Dynamics-Aware Gaussian Splatting Streaming Towards Fast ...", "date": "", "ddg_snippet": "The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction. Existing approaches mainly rely on full-length multi-view videos, while there has been limited exploration of online reconstruction methods that enable on-the-fly training and per-timestep streaming. Current 3DGS-based streaming methods treat the Gaussian primitives uniformly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.14847", "content": "The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction. Existing approaches mainly rely on full-length multi-view videos, while there has been limited exploration of online reconstruction methods that enable on-the-fly training and per-timestep streaming. Current 3DGS-based streaming methods treat the Gaussian primitives uniformly ..."}
+{"idx": 3, "title": "3DGStream: On-the-fly Training of 3D Gaussians for Efficient Streaming ...", "date": "", "ddg_snippet": "Despite the remarkable advancements achieved by current neural rendering techniques, these methods generally require complete video sequences for offline training and are not capable of real-time rendering. To address these constraints, we introduce 3DGStream, a method designed for efficient FVV streaming of real-world dynamic scenes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01444v1", "content": "Despite the remarkable advancements achieved by current neural rendering techniques, these methods generally require complete video sequences for offline training and are not capable of real-time rendering. To address these constraints, we introduce 3DGStream, a method designed for efficient FVV streaming of real-world dynamic scenes."}
+{"idx": 4, "title": "Dynamics-Aware Gaussian Splatting Streaming Towards Fast On-the-Fly ...", "date": "", "ddg_snippet": "Abstract The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction from multi-view visual inputs. While existing approaches mainly rely on processing full-length multi-view videos for 4D reconstruction, there has been limited exploration of iterative online reconstruction methods that enable on-the-fly training and per-frame streaming ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.14847v1", "content": "Abstract The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction from multi-view visual inputs. While existing approaches mainly rely on processing full-length multi-view videos for 4D reconstruction, there has been limited exploration of iterative online reconstruction methods that enable on-the-fly training and per-frame streaming ..."}
+{"idx": 5, "title": "S4D: Streaming 4D Real-World Reconstruction with Gaussians and 3D ...", "date": "", "ddg_snippet": "The Neural 3D Video Synthesis Dataset includes six sequences , originally captured at a resolution of 2704 × 2028, which were downsampled to 1352 × 1014 for training purposes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.13036v2", "content": "The Neural 3D Video Synthesis Dataset includes six sequences , originally captured at a resolution of 2704 × 2028, which were downsampled to 1352 × 1014 for training purposes."}
+{"idx": 6, "title": "[2408.01126] IG-SLAM: Instant Gaussian SLAM - arXiv.org", "date": "", "ddg_snippet": "3D Gaussian Splatting has recently shown promising results as an alternative scene representation in SLAM systems to neural implicit representations. However, current methods either lack dense depth maps to supervise the mapping process or detailed training designs that consider the scale of the environment. To address these drawbacks, we present IG-SLAM, a dense RGB-only SLAM system that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.01126", "content": "3D Gaussian Splatting has recently shown promising results as an alternative scene representation in SLAM systems to neural implicit representations. However, current methods either lack dense depth maps to supervise the mapping process or detailed training designs that consider the scale of the environment. To address these drawbacks, we present IG-SLAM, a dense RGB-only SLAM system that ..."}
+{"idx": 7, "title": "Scale-GS: Efficient Scalable Gaussian Splatting via Redundancy ...", "date": "", "ddg_snippet": "This paper presents Scale-GS, a scalable Gaussian Splatting framework designed for efficient training in streaming tasks. Specifically, Gaussian spheres are hierarchically organized by scale within an anchor-based structure.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.21444", "content": "This paper presents Scale-GS, a scalable Gaussian Splatting framework designed for efficient training in streaming tasks. Specifically, Gaussian spheres are hierarchically organized by scale within an anchor-based structure."}
+{"idx": 8, "title": "Efficient 4D Gaussian Stream with Low Rank Adaptation*", "date": "", "ddg_snippet": "Abstract Recent methods have made significant progress in synthesizing novel views with long video sequences . This paper proposes a highly scalable method for dynamic novel view synthesis with continual learning. We leverage the 3D Gaussians to represent the scene and a low-rank adaptation-based deformation model to capture the dynamic scene changes. Our method continuously reconstructs the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.16575v1", "content": "Abstract Recent methods have made significant progress in synthesizing novel views with long video sequences . This paper proposes a highly scalable method for dynamic novel view synthesis with continual learning. We leverage the 3D Gaussians to represent the scene and a low-rank adaptation-based deformation model to capture the dynamic scene changes. Our method continuously reconstructs the ..."}
+{"idx": 9, "title": "InstantSplat: Sparse-view Gaussian Splatting in Seconds", "date": "", "ddg_snippet": "While neural 3D reconstruction has advanced substantially, its performance significantly degrades with sparse-view data, which limits its broader applicability, since SfM is often unreliable in sparse-view scenarios where feature matches are scarce. In this paper , we introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.20309", "content": "While neural 3D reconstruction has advanced substantially, its performance significantly degrades with sparse-view data, which limits its broader applicability, since SfM is often unreliable in sparse-view scenarios where feature matches are scarce. In this paper , we introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed ..."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_storage_efficiency_7.9_MB_33.6_33.2.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_storage_efficiency_7.9_MB_33.6_33.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..84a0fb409d411da058a1ce375a1aff3227a75956
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_storage_efficiency_7.9_MB_33.6_33.2.jsonl
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+{"idx": 0, "title": "GitHub - yjb6/IGS: [CVPR25 Highlight] Instant Gaussian Stream : Fast...", "date": "", "ddg_snippet": "title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting}It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting}It contains 1,200 optimized Gaussian points and requires 150GB of storage space. After extraction, the directory structure is as follows"}
+{"idx": 1, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "33 . 2 32.8 32.4.per, we propose Instant Gaussian Stream (IGS), a fast and view images is a valuable area of research, with appli", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "33 . 2 32.8 32.4.per, we propose Instant Gaussian Stream (IGS), a fast and view images is a valuable area of research, with appli"}
+{"idx": 2, "title": "(PDF) Instant Gaussian Stream : Fast and Generalizable Streaming ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.Method PSNR↑Train↓Render↑ Storage ↓. (dB) (s) (FPS) ( MB ). Offline training. Kplanes[ 17 ]32.17480.151.0. Realtime-4DGS[ 75 ]33.68-114", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.Method PSNR↑Train↓Render↑ Storage ↓. (dB) (s) (FPS) ( MB ). Offline training. Kplanes[ 17 ]32.17480.151.0. Realtime-4DGS[ 75 ]33.68-114"}
+{"idx": 3, "title": "Photiu.ai – Free Image Upscale Tool to Enhance Photo Quality", "date": "", "ddg_snippet": "Upgrade image quality with Photiu.ai's free AI Image Upscale tool. Instantly enhance your photos resolution online, no sign-up required!", "subpage_snippet": "", "source": "www.photiu.ai", "link": "https://www.photiu.ai/image-upscaler", "content": "Upgrade image quality with Photiu.ai's free AI Image Upscale tool. Instantly enhance your photos resolution online, no sign-up required!"}
+{"idx": 4, "title": "Google NotebookLM | AI Research Tool & Thinking Partner", "date": "", "ddg_snippet": "Instant insights. With all of your sources in place, NotebookLM gets to work and becomes a personalized AI expert in the information that matters most to you.", "subpage_snippet": "", "source": "notebooklm.google", "link": "https://notebooklm.google/", "content": "Instant insights. With all of your sources in place, NotebookLM gets to work and becomes a personalized AI expert in the information that matters most to you."}
+{"idx": 5, "title": "Bayes theorem, the geometry of changing beliefs - YouTube", "date": "", "ddg_snippet": "Perhaps the most important formula in probability.Help fund future projects: https://www.patreon.com/3blue1brownAn equally valuable form of support is to sim...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=HZGCoVF3YvM", "content": "Perhaps the most important formula in probability.Help fund future projects: https://www.patreon.com/3blue1brownAn equally valuable form of support is to sim..."}
+{"idx": 6, "title": "Два майора – Telegram", "date": "", "ddg_snippet": "89.6K views12:33. September 22. Два майора.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/dva_majors", "content": "89.6K views12:33. September 22. Два майора."}
+{"idx": 7, "title": "CrystalDiskInfo - скачать бесплатно CrystalDiskInfo 9.7.0", "date": "", "ddg_snippet": "Бесплатно. Windows. Категория: HDD утилиты. CrystalDiskInfo - отслеживает состояние жестких дисков, поддерживающих технологию S.M.A.R.T. Производит мониторинг и дает общую оценку \"здоровья\" вашего диска.", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-6420-crystaldiskinfo.html", "content": "Бесплатно. Windows. Категория: HDD утилиты. CrystalDiskInfo - отслеживает состояние жестких дисков, поддерживающих технологию S.M.A.R.T. Производит мониторинг и дает общую оценку \"здоровья\" вашего диска."}
+{"idx": 8, "title": "Погода в Самаре на 14 дней (Самарская область)... - Погода Mail", "date": "", "ddg_snippet": "06:28. Заход. 18:33. Долгота дня. 12:04.", "subpage_snippet": "", "source": "pogoda.mail.ru", "link": "https://pogoda.mail.ru/prognoz/samara/14dney/", "content": "06:28. Заход. 18:33. Долгота дня. 12:04."}
+{"idx": 9, "title": "GISMETEO: Погода в Перми на 10 дней, прогноз погоды Пермь на...", "date": "", "ddg_snippet": "Подробный прогноз погоды в Перми на десять дней.", "subpage_snippet": "", "source": "www.gismeteo.ru", "link": "https://www.gismeteo.ru/weather-perm-4476/10-days/", "content": "Подробный прогноз погоды в Перми на десять дней."}
diff --git a/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_paper_year_None.jsonl b/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_paper_year_None.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..da401c527c77c50bbe2edd665c4dd60002fca692
--- /dev/null
+++ b/data/sampled_jsons/Intervention_and_Conditioning_in_Causal_Bayesian_Networks_paper_year_None.jsonl
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+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.14728v1", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/intervention-and-conditioning-in-causal", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities."}
+{"idx": 2, "title": "(PDF) Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380847642_Intervention_and_Conditioning_in_Causal_Bayesian_Networks", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities."}
+{"idx": 3, "title": "2405.14728 - Intervention and Conditioning in Causal Bayesian ...", "date": "", "ddg_snippet": "This paper presents a method to accurately compute interventional probabilities in Causal Bayesian Networks using observational data and independence assumptions.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2405.14728", "content": "This paper presents a method to accurately compute interventional probabilities in Causal Bayesian Networks using observational data and independence assumptions."}
+{"idx": 4, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/intervention-conditioning-causal-bayesian-networks", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities."}
+{"idx": 5, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Powerdrill is an AI service centered around personal and enterprise datasets, designed to unlock the full potential of your data.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-Intervention-and-Conditioning-clxocrn6v0nat01657u2tbeu2", "content": "Powerdrill is an AI service centered around personal and enterprise datasets, designed to unlock the full potential of your data."}
+{"idx": 6, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Imagine being able to predict the ripple effects of one action across a network of variables. That’s exactly what this paper achieves with its cutting-edge approach to Causal Bayesian Networks (CBNs).", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/abby33459_intervention-and-conditioning-in-causal-bayesian-activity-7201047041542668288-8dOx", "content": "Imagine being able to predict the ripple effects of one action across a network of variables. That’s exactly what this paper achieves with its cutting-edge approach to Causal Bayesian Networks (CBNs)."}
+{"idx": 7, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "# This paper significantly advances causal inference by uniquely estimating probabilities in Causal Bayesian Networks (CBNs), enabling analysis using observational data, and simplifying calculations for crucial counterfactual probabilities.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/dc28fpk76s/", "content": "# This paper significantly advances causal inference by uniquely estimating probabilities in Causal Bayesian Networks (CBNs), enabling analysis using observational data, and simplifying calculations for crucial counterfactual probabilities."}
+{"idx": 8, "title": "(PDF) Causal Bayesian Networks for Causal AI Using pgmpy", "date": "", "ddg_snippet": "Intervention and conditioning in causal bayesian networks . In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), volume 37, pages 89019–89041, 2024.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/129346467/Causal_Bayesian_Networks_for_Causal_AI_Using_pgmpy", "content": "Intervention and conditioning in causal bayesian networks . In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), volume 37, pages 89019–89041, 2024."}
+{"idx": 9, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Intervention and Conditioning in Causal Bayesian Networks .", "subpage_snippet": "", "source": "sainyamgalhotra.com", "link": "https://sainyamgalhotra.com/publication/dblp-neurips24/", "content": "Intervention and Conditioning in Causal Bayesian Networks ."}
diff --git a/data/sampled_jsons/IoS_Intersection_over_Self_EntityErasure_formula_equation.jsonl b/data/sampled_jsons/IoS_Intersection_over_Self_EntityErasure_formula_equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4060c34b7cfd5fd5c140c4b20cdbd5d692116d95
--- /dev/null
+++ b/data/sampled_jsons/IoS_Intersection_over_Self_EntityErasure_formula_equation.jsonl
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+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "As shown Fig. 4, we use the pre-trained state-of-the-art open-world segmentation model EntitySeg [30] to predict the en-tity segmentation of the generated result, and then calculate the Intersection over Self ( IoS ) between each entity with in-painting mask by:", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "As shown Fig. 4, we use the pre-trained state-of-the-art open-world segmentation model EntitySeg [30] to predict the en-tity segmentation of the generated result, and then calculate the Intersection over Self ( IoS ) between each entity with in-painting mask by:"}
+{"idx": 1, "title": "(PDF) Cross-CAM: Focused Visual Explanations for Deep...", "date": "", "ddg_snippet": "The new weakly-supervised localization evaluation metric IoS ( Intersection over Self ) is proposed to effectively evaluate the focusing effect.Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/97030606/Cross_CAM_Focused_Visual_Explanations_for_Deep_Convolutional_Networks_via_Training_Set_Tracing", "content": "The new weakly-supervised localization evaluation metric IoS ( Intersection over Self ) is proposed to effectively evaluate the focusing effect.Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems."}
+{"idx": 2, "title": "Cross-CAM: Focused Visual Explanations for Deep... | SpringerLink", "date": "", "ddg_snippet": "On the ILSVRC-15 dataset, the proposed Cross-CAM is tested. The new weakly-supervised localization evaluation metric IoS ( Intersection over Self ) is proposed to effectively evaluate the focusing effect.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-10983-6_56", "content": "On the ILSVRC-15 dataset, the proposed Cross-CAM is tested. The new weakly-supervised localization evaluation metric IoS ( Intersection over Self ) is proposed to effectively evaluate the focusing effect."}
+{"idx": 3, "title": "Cross-CAM: Focused Visual Explanations for Deep Convolutional...", "date": "", "ddg_snippet": "On the ILSVRC-15 dataset, the proposed Cross-CAM is tested. The new weakly-supervised localization evaluation metric IoS ( Intersection over Self ) is proposed to effectively evaluate the focusing effect.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/362082671_Cross-CAM_Focused_Visual_Explanations_for_Deep_Convolutional_Networks_via_Training-Set_Tracing", "content": "On the ILSVRC-15 dataset, the proposed Cross-CAM is tested. The new weakly-supervised localization evaluation metric IoS ( Intersection over Self ) is proposed to effectively evaluate the focusing effect."}
+{"idx": 4, "title": "Knowledge Science, Engineering and... | springerprofessional.de", "date": "", "ddg_snippet": "Multi-hop Question Answering over Knowledge Graph (multi-hop KGQA) is a challenging task since it requires reasoning with multiple triplets over knowledge graph to find the correct answer entities .", "subpage_snippet": "", "source": "www.springerprofessional.de", "link": "https://www.springerprofessional.de/knowledge-science-engineering-and-management/23279764", "content": "Multi-hop Question Answering over Knowledge Graph (multi-hop KGQA) is a challenging task since it requires reasoning with multiple triplets over knowledge graph to find the correct answer entities ."}
+{"idx": 5, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/IoS_threshold_EntityErasure_paper_Section_4.1_newly_generated_sundry_year_2024.jsonl b/data/sampled_jsons/IoS_threshold_EntityErasure_paper_Section_4.1_newly_generated_sundry_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bcf733a40c653366403edc469adf2b4848a118f9
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+++ b/data/sampled_jsons/IoS_threshold_EntityErasure_paper_Section_4.1_newly_generated_sundry_year_2024.jsonl
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+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "In this paper , we present EntityErasure , a diffusion-based inpainting method that can generate high-quality object era-sure results free of sundries. Particularly, we propose to treat object erasure as a multi-entity amodal completion problem. To do so, we first perform amodal entity seg-mentation, and then utilize the segmentation output to guide", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "In this paper , we present EntityErasure , a diffusion-based inpainting method that can generate high-quality object era-sure results free of sundries. Particularly, we propose to treat object erasure as a multi-entity amodal completion problem. To do so, we first perform amodal entity seg-mentation, and then utilize the segmentation output to guide"}
+{"idx": 1, "title": "CVPR Poster EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "Abstract: This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34016", "content": "Abstract: This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate ..."}
+{"idx": 2, "title": "Regarding Guideline 1.2 - Safety - User Generated Content", "date": "", "ddg_snippet": "According AppStore Guideline 1.2 - Safety - User Generated Content for point - A method for filtering objectionable content What are the ways to implement \"A method for filtering objectionable co...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/55016105/regarding-guideline-1-2-safety-user-generated-content", "content": "According AppStore Guideline 1.2 - Safety - User Generated Content for point - A method for filtering objectionable content What are the ways to implement \"A method for filtering objectionable co..."}
+{"idx": 3, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html", "content": "This paper presents EntityErasure , a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as ..."}
+{"idx": 4, "title": "Inability to Install Custom Firmware Explained - Apple iPad Forum", "date": "", "ddg_snippet": "A very common question with iOS users (both jailbreak-ers and stock users) is whether or not they can downgrade or restore to a specific iOS that is not the latest/currently signed iOS , whether it be an iPhone, an iPad, or an iPod Touch. Before proceeding, I would like to denote some of the...", "subpage_snippet": "", "source": "www.ipadforums.net", "link": "https://www.ipadforums.net/threads/inability-to-install-custom-firmware-explained.116980/", "content": "A very common question with iOS users (both jailbreak-ers and stock users) is whether or not they can downgrade or restore to a specific iOS that is not the latest/currently signed iOS , whether it be an iPhone, an iPad, or an iPod Touch. Before proceeding, I would like to denote some of the..."}
+{"idx": 5, "title": "A guide fully covering the process of using Futurerestore to upgrade ...", "date": "", "ddg_snippet": "A guide fully covering the process of using Futurerestore to upgrade, downgrade, or re-restore to an unsigned iOS firmware.", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/TheRealKeto/7c5191c7495fb750e79f8ce0f0cdcdaa", "content": "A guide fully covering the process of using Futurerestore to upgrade, downgrade, or re-restore to an unsigned iOS firmware."}
+{"idx": 6, "title": "App Review Guidelines - Apple Developer", "date": "", "ddg_snippet": "The App Review Guidelines provide guidance and examples across a range of development topics, including user interface design, functionality, content, and the use of specific technologies. These guidelines are designed to help you prepare your apps for the approval process.", "subpage_snippet": "", "source": "developer.apple.com", "link": "https://developer.apple.com/app-store/review/guidelines/", "content": "The App Review Guidelines provide guidance and examples across a range of development topics, including user interface design, functionality, content, and the use of specific technologies. These guidelines are designed to help you prepare your apps for the approval process."}
+{"idx": 7, "title": "Firebase Apple SDK Release Notes", "date": "", "ddg_snippet": "Stay organized with collections Save and categorize content based on your preferences. To review release notes for the Firebase console and for other Firebase platforms and related SDKs, refer to the Firebase Release Notes.", "subpage_snippet": "", "source": "firebase.google.com", "link": "https://firebase.google.com/support/release-notes/ios", "content": "Stay organized with collections Save and categorize content based on your preferences. To review release notes for the Firebase console and for other Firebase platforms and related SDKs, refer to the Firebase Release Notes."}
+{"idx": 8, "title": "The ultimate guide to App Store rejections - RevenueCat", "date": "", "ddg_snippet": "Discover key tips for navigating App Store rejections. Avoid common pitfalls, meet guidelines & increase your chances of app approval.", "subpage_snippet": "", "source": "www.revenuecat.com", "link": "https://www.revenuecat.com/blog/growth/the-ultimate-guide-to-app-store-rejections/", "content": "Discover key tips for navigating App Store rejections. Avoid common pitfalls, meet guidelines & increase your chances of app approval."}
+{"idx": 9, "title": "Unsupervised Evaluation of Entity Resolution | Journal of Data and ...", "date": "", "ddg_snippet": "Entity resolution is the problem of identifying records that refer to the same entity from one or multiple databases. Applications of entity resolution range from health and social science research to national security and online commerce. Entity ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3721985", "content": "Entity resolution is the problem of identifying records that refer to the same entity from one or multiple databases. Applications of entity resolution range from health and social science research to national security and online commerce. Entity ..."}
diff --git a/data/sampled_jsons/Izacard_Grave_2021_passage_retrieval_k_performance_findings.jsonl b/data/sampled_jsons/Izacard_Grave_2021_passage_retrieval_k_performance_findings.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d08716c96a91b40ca1b0b541441332cebfa55ca7
--- /dev/null
+++ b/data/sampled_jsons/Izacard_Grave_2021_passage_retrieval_k_performance_findings.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Raven: In-Context Learning with Retrieval -Augmented...", "date": "", "ddg_snippet": "Gautier Izacard and Edouard Grave . Leveraging passage retrieval with generative models for open domain question answering. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 874–880, Online, April 2021 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2308.07922v2", "content": "Gautier Izacard and Edouard Grave . Leveraging passage retrieval with generative models for open domain question answering. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 874–880, Online, April 2021 ."}
+{"idx": 1, "title": "Leveraging Passage Retrieval with Generative Models for Open...", "date": "", "ddg_snippet": "Additionally, Izacard & Grave ( 2021 ) introduced fusion-in-decoder, which uses an encoder to process each passage in parallel and concatenates the hidden states for generation via a decoder.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/355430399_Leveraging_Passage_Retrieval_with_Generative_Models_for_Open_Domain_Question_Answering", "content": "Additionally, Izacard & Grave ( 2021 ) introduced fusion-in-decoder, which uses an encoder to process each passage in parallel and concatenates the hidden states for generation via a decoder."}
+{"idx": 2, "title": "Select and Augment: Enhanced Dense Retrieval Knowledge", "date": "", "ddg_snippet": "Izacard , G., & Grave , E. (2020). Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282.", "subpage_snippet": "", "source": "www.jair.org", "link": "https://www.jair.org/index.php/jair/article/download/14365/26972", "content": "Izacard , G., & Grave , E. (2020). Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282."}
+{"idx": 3, "title": "Pre-computed memory or on-the-y encoding? A hybrid approach to...", "date": "", "ddg_snippet": "Fusion-in-Decoder ( Izacard & Grave , 2021 ) consists of a T5 encoder-decoder model. For each input, a number of rel-evant text passages are retrieved, and the input is prepended to each passage . The resulting passages are encoded sepa-rately by the encoder, and the encoded...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=nlUAvrMbUZ", "content": "Fusion-in-Decoder ( Izacard & Grave , 2021 ) consists of a T5 encoder-decoder model. For each input, a number of rel-evant text passages are retrieved, and the input is prepended to each passage . The resulting passages are encoded sepa-rately by the encoder, and the encoded..."}
+{"idx": 4, "title": "Proceedings of the International Conference on Machine Learning 2022", "date": "", "ddg_snippet": "( Izacard & Grave , 2020a;b). For fair comparison, we dif-ferentiate models that use DPR for retrieval from those that leverage stronger ones.Izacard, G. and Grave, E. Distilling knowledge from reader to retriever for question answering.", "subpage_snippet": "", "source": "www.cs.ubc.ca", "link": "https://www.cs.ubc.ca/~kevinlb/papers/2022-ICML-KRLM-Frozen-LMs-as-Readers.pdf", "content": "( Izacard & Grave , 2020a;b). For fair comparison, we dif-ferentiate models that use DPR for retrieval from those that leverage stronger ones.Izacard, G. and Grave, E. Distilling knowledge from reader to retriever for question answering."}
+{"idx": 5, "title": "G enerate rather than r etrieve : L arge L angu", "date": "", "ddg_snippet": "Gautier Izacard and Edouard Grave . Leveraging passage retrieval with generative models for open domain question answering. In EACL 2021 , pp. 874–880, 2021 .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/generate-rather-than-retrieve-large-language-models-are-b24o7mgs.pdf", "content": "Gautier Izacard and Edouard Grave . Leveraging passage retrieval with generative models for open domain question answering. In EACL 2021 , pp. 874–880, 2021 ."}
+{"idx": 6, "title": "RankRAG: Unifying Context Ranking with", "date": "", "ddg_snippet": "Izacard , G. and Grave , E. Leveraging passage retrieval with generative models for open domain question answering. In EACL, 2021 .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/db93ccb6cf392f352570dd5af0a223d3-Paper-Conference.pdf", "content": "Izacard , G. and Grave , E. Leveraging passage retrieval with generative models for open domain question answering. In EACL, 2021 ."}
+{"idx": 7, "title": "Distilling Knowledge from Reader to Retriever for Question Answering", "date": "", "ddg_snippet": "Gautier Izacard and Edouard Grave . Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282, 2020.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03463398v1/document", "content": "Gautier Izacard and Edouard Grave . Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282, 2020."}
+{"idx": 8, "title": "Kg-fid: infusing knowledge graph in fusion- in-decoder...", "date": "", "ddg_snippet": "(2020); Izacard & Grave (2020a), we use the English Wikipedia as the text corpus, and apply the same preprocessing to divide them into disjoint passages with 100 words, which produces 21M passages in total.", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/kg-fid-infusing-knowledge-graph-in-fusion-in-decoder-for-2727459", "content": "(2020); Izacard & Grave (2020a), we use the English Wikipedia as the text corpus, and apply the same preprocessing to divide them into disjoint passages with 100 words, which produces 21M passages in total."}
+{"idx": 9, "title": "Recommendation Systems Are Five Years Ahead of RAG... | Medium", "date": "", "ddg_snippet": "FiD (Fusion-in-Decoder) ( Izacard & Grave 2020): RAG often has a limitation with a small number of retrieved passages ( k ). FiD addresses this by processing each retrieved passage concatenated with the input query independently through the encoder.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science-collective/recommendation-systems-are-five-years-ahead-of-rag-heres-how-rag-can-catch-up-f7ad569bc99a", "content": "FiD (Fusion-in-Decoder) ( Izacard & Grave 2020): RAG often has a limitation with a small number of retrieved passages ( k ). FiD addresses this by processing each retrieved passage concatenated with the input query independently through the encoder."}
diff --git a/data/sampled_jsons/Izacard_Grave_2021_retrieval_augmented_generation_k_performance_abstract.jsonl b/data/sampled_jsons/Izacard_Grave_2021_retrieval_augmented_generation_k_performance_abstract.jsonl
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index 0000000000000000000000000000000000000000..ad511c4c68defed9b3242780d29d56c77b024ad9
--- /dev/null
+++ b/data/sampled_jsons/Izacard_Grave_2021_retrieval_augmented_generation_k_performance_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Database-Augmented Query Representation for Information", "date": "", "ddg_snippet": "The experimental results show significant improvements of our DAQu in retrieval performance compared to other query augmentation baselines across ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.16013v3", "content": "The experimental results show significant improvements of our DAQu in retrieval performance compared to other query augmentation baselines across ..."}
+{"idx": 1, "title": "Benchmarking Large Language Models in Retrieval-Augmented", "date": "", "ddg_snippet": "Incorporating external knowledge via information retrieval , i.e., Retrieval - Augmented Generation (RAG), has been regarded as a promising way to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2309.01431v2", "content": "Incorporating external knowledge via information retrieval , i.e., Retrieval - Augmented Generation (RAG), has been regarded as a promising way to ..."}
+{"idx": 2, "title": "‘retrieval AI’ directory · Gwern.net", "date": "", "ddg_snippet": "... Generative AI and Machine Learning: ... Towards Generated Image Provenance Analysis Via Conceptual-Similar-Guided-SLIP Retrieval ”, Xia et al 2024", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/retrieval/index", "content": "... Generative AI and Machine Learning: ... Towards Generated Image Provenance Analysis Via Conceptual-Similar-Guided-SLIP Retrieval ”, Xia et al 2024"}
+{"idx": 3, "title": "(PDF) Achieving State-of-the-Art Open-Domain QA Performance", "date": "", "ddg_snippet": "... domain question answering (QA) has recently made significant progress, with generative models like Transformers demonstrating impressive performance ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/374031063_Achieving_State-of-the-Art_Open-Domain_QA_Performance_through_Fusion-in-Decoder_Method", "content": "... domain question answering (QA) has recently made significant progress, with generative models like Transformers demonstrating impressive performance ..."}
+{"idx": 4, "title": "Reasoning over Public and Private Data in Retrieval-Based", "date": "", "ddg_snippet": "... retrieval is now common in retrieval (Miller et al., 2016 ; Feldman and El-Yaniv, 2019 ; Asai et al., 2020 ; Xiong et al., 2021 ; Qi et al., 2021 ; ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00580/117168/Reasoning-over-Public-and-Private-Data-in", "content": "... retrieval is now common in retrieval (Miller et al., 2016 ; Feldman and El-Yaniv, 2019 ; Asai et al., 2020 ; Xiong et al., 2021 ; Qi et al., 2021 ; ..."}
+{"idx": 5, "title": "Reformulating Domain Adaptation of Large Language Models as", "date": "", "ddg_snippet": "Its performance (the green bar in Figure 1 ) even surpasses the retrieval -based GPT-4 generation on Chinese LegalQA.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.03328v3", "content": "Its performance (the green bar in Figure 1 ) even surpasses the retrieval -based GPT-4 generation on Chinese LegalQA."}
+{"idx": 6, "title": "Towards Adaptive Memory-Based Optimization for Enhanced", "date": "", "ddg_snippet": "Retrieval - Augmented Generation (RAG), by integrating non-parametric knowledge from external knowledge bases into models, has emerged as a promising ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.05312v4", "content": "Retrieval - Augmented Generation (RAG), by integrating non-parametric knowledge from external knowledge bases into models, has emerged as a promising ..."}
+{"idx": 7, "title": "Yingbo Zhou | DeepAI", "date": "", "ddg_snippet": "Fusion-in-decoder (Fid) ( Izacard and Grave , 2020) is a generative questi... ... Existing KBQA approaches, despite achieving strong performance on i.i ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/yingbo-zhou", "content": "Fusion-in-decoder (Fid) ( Izacard and Grave , 2020) is a generative questi... ... Existing KBQA approaches, despite achieving strong performance on i.i ..."}
+{"idx": 8, "title": "Kazuma Hashimoto - ACL Anthology", "date": "", "ddg_snippet": "Fusion-in-decoder (Fid) ( Izacard and Grave , 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/k/kazuma-hashimoto/", "content": "Fusion-in-decoder (Fid) ( Izacard and Grave , 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained ..."}
+{"idx": 9, "title": "eSapiens: A Platform for Secure and Auditable", "date": "", "ddg_snippet": "A key component is the THOR Agent , which handles structured SQL-style queries and generates actionable insights over enterprise databases.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09588v1", "content": "A key component is the THOR Agent , which handles structured SQL-style queries and generates actionable insights over enterprise databases."}
diff --git a/data/sampled_jsons/Izacard_Grave_Leveraging_Passage_Retrieval_abstract.jsonl b/data/sampled_jsons/Izacard_Grave_Leveraging_Passage_Retrieval_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4da58256717fe51aaca43d8827030fa61728961e
--- /dev/null
+++ b/data/sampled_jsons/Izacard_Grave_Leveraging_Passage_Retrieval_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Passage Retrieval with Generative Models for Open", "date": "", "ddg_snippet": "... izacard - grave -2021- leveraging , title = \" Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering \" , author = ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.eacl-main.74/", "content": "... izacard - grave -2021- leveraging , title = \" Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering \" , author = ..."}
+{"idx": 1, "title": "Édouard Grave - ACL Anthology", "date": "", "ddg_snippet": "pdf bib abs Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering Gautier Izacard | Edouard Grave Proceedings of the ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/e/edouard-grave/", "content": "pdf bib abs Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering Gautier Izacard | Edouard Grave Proceedings of the ..."}
+{"idx": 2, "title": "‘retrieval AI’ directory · Gwern.net", "date": "", "ddg_snippet": "RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval ”, Sarthi et al 2024 ... A Universal Emergent Decomposition of Retrieval ...", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/retrieval/index", "content": "RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval ”, Sarthi et al 2024 ... A Universal Emergent Decomposition of Retrieval ..."}
+{"idx": 3, "title": "Evaluating the Robustness of Retrieval-Augmented Generation to", "date": "", "ddg_snippet": "Retrieval augmented generation (RAG) systems provide a method for factually grounding the responses of a Large Language Model (LLM) by providing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03787v1", "content": "Retrieval augmented generation (RAG) systems provide a method for factually grounding the responses of a Large Language Model (LLM) by providing ..."}
+{"idx": 4, "title": "Representation Learning and Retrieval", "date": "", "ddg_snippet": "Gautier Izacard and Edouard Grave 's work \" Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering\" from July 2020 ...", "subpage_snippet": "", "source": "www.pragmatic.ml", "link": "https://www.pragmatic.ml/language-modeling-and-retrieval/", "content": "Gautier Izacard and Edouard Grave 's work \" Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering\" from July 2020 ..."}
+{"idx": 5, "title": "Kazuma Hashimoto - ACL Anthology", "date": "", "ddg_snippet": "Fusion-in-decoder (Fid) ( Izacard and Grave , 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/k/kazuma-hashimoto/", "content": "Fusion-in-decoder (Fid) ( Izacard and Grave , 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained ..."}
+{"idx": 6, "title": "An Artificial Intelligence Driven Semantic Similarity-Based", "date": "", "ddg_snippet": "Given the title and abstract of the target paper, 5 to 10 keywords were generated using a LLM, gemini-2.0-flash [ 9 ] , which recent studies have ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15292v1", "content": "Given the title and abstract of the target paper, 5 to 10 keywords were generated using a LLM, gemini-2.0-flash [ 9 ] , which recent studies have ..."}
+{"idx": 7, "title": "Building baby-NICER: A Memory-Driven Multi-Agent Journey -", "date": "", "ddg_snippet": "Retrieval ‑augmented generation mitigates this by replacing guessing with lookup : the agent queries its BigQuery vector store, fetches the most ...", "subpage_snippet": "", "source": "towardspeople.co.uk", "link": "https://towardspeople.co.uk/building-baby-nicer-a-memory-driven-multi-agent-journey/", "content": "Retrieval ‑augmented generation mitigates this by replacing guessing with lookup : the agent queries its BigQuery vector store, fetches the most ..."}
+{"idx": 8, "title": "Collective Intelligence - Human Purpose, Collective", "date": "", "ddg_snippet": "Retrieval ‑augmented generation mitigates this by replacing guessing with lookup : the agent queries its BigQuery vector store, fetches the most ...", "subpage_snippet": "", "source": "towardspeople.co.uk", "link": "https://towardspeople.co.uk/category/collective-intelligence/", "content": "Retrieval ‑augmented generation mitigates this by replacing guessing with lookup : the agent queries its BigQuery vector store, fetches the most ..."}
+{"idx": 9, "title": "Patterns for Building LLM-based Systems & Products", "date": "", "ddg_snippet": "A system or product can be made up of multiple components such as LLMs, prompt templates, retrieved context, and parameters like temperature.) A ...", "subpage_snippet": "", "source": "eugeneyan.com", "link": "https://eugeneyan.com/writing/llm-patterns/", "content": "A system or product can be made up of multiple components such as LLMs, prompt templates, retrieved context, and parameters like temperature.) A ..."}
diff --git a/data/sampled_jsons/JEflV4nRlH_What_Makes_and_Breaks_Safety_Fine-tuning_DPO_Direct_Preference_Optimization_explicit_rewa.jsonl b/data/sampled_jsons/JEflV4nRlH_What_Makes_and_Breaks_Safety_Fine-tuning_DPO_Direct_Preference_Optimization_explicit_rewa.jsonl
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index 0000000000000000000000000000000000000000..c37999e574d5fb7407e26e3dca3e08fb791b522c
--- /dev/null
+++ b/data/sampled_jsons/JEflV4nRlH_What_Makes_and_Breaks_Safety_Fine-tuning_DPO_Direct_Preference_Optimization_explicit_rewa.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Makes and Breaks Safety Fine - tuning ?", "date": "", "ddg_snippet": "3. Direct preference optimization ( DPO ) (Rafailov et al., 2023) also uses safe and unsafe outputs similar to RLHF, but differently does not require an additional reward model . Instead, the LLM is directly supervised to suppress unsafe outputs by the constructed objective function.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JEflV4nRlH", "content": "3. Direct preference optimization ( DPO ) (Rafailov et al., 2023) also uses safe and unsafe outputs similar to RLHF, but differently does not require an additional reward model . Instead, the LLM is directly supervised to suppress unsafe outputs by the constructed objective function."}
+{"idx": 1, "title": "What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study", "date": "", "ddg_snippet": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/JEflV4nRlH@OpenReview", "content": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment."}
+{"idx": 2, "title": "(PDF) What Makes and Breaks Safety Fine - tuning ? A Mechanistic...", "date": "", "ddg_snippet": "supervised safety fine - tuning , direct preference optimization , and unlearning—and. provide significant evidence demonstrating that these methods minimally transform. MLP weights to specifically align unsafe inputs into its weights’ null space.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382271359_What_Makes_and_Breaks_Safety_Fine-tuning_Mechanistic_Study", "content": "supervised safety fine - tuning , direct preference optimization , and unlearning—and. provide significant evidence demonstrating that these methods minimally transform. MLP weights to specifically align unsafe inputs into its weights’ null space."}
+{"idx": 3, "title": "fiveai/understanding_ safety _ finetuning : Official Code for What Makes ...", "date": "", "ddg_snippet": "What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study.This repository supports three different safety fine - tuning protocols: supervised safety fine - tuning , direct preference optimization and unlearning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study.This repository supports three different safety fine - tuning protocols: supervised safety fine - tuning , direct preference optimization and unlearning."}
+{"idx": 4, "title": "Fine Tuning SmolVLM for Human Alignment Using Direct Preference ...", "date": "", "ddg_snippet": "Direct Preference Optimization ( DPO ). From Rewards to Policies: The Change-of-Variables Insight. DPO Objective Function.Figure 4: Direct Preference Optimization (source: Po, 2024). Fine Tuning SmolVLM Using DPO .", "subpage_snippet": "", "source": "pyimagesearch.com", "link": "https://pyimagesearch.com/2025/08/04/fine-tuning-smolvlm-for-human-alignment-using-direct-preference-optimization/", "content": "Direct Preference Optimization ( DPO ). From Rewards to Policies: The Change-of-Variables Insight. DPO Objective Function.Figure 4: Direct Preference Optimization (source: Po, 2024). Fine Tuning SmolVLM Using DPO ."}
+{"idx": 5, "title": "What Makes and Breaks Safety Fine - tuning ? Mechanistic Study", "date": "", "ddg_snippet": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment.The researchers investigated three common safety fine - tuning techniques: supervised safety fine - tuning , direct preference optimization , and unlearning.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/what-makes-breaks-safety-fine-tuning-mechanistic", "content": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment.The researchers investigated three common safety fine - tuning techniques: supervised safety fine - tuning , direct preference optimization , and unlearning."}
+{"idx": 6, "title": "Reinforcement Learning - DPO , ORPO & KTO | Unsloth Documentation", "date": "", "ddg_snippet": "DPO ( Direct Preference Optimization ), ORPO (Odds Ratio Preference Optimization ), PPO, KTO Reward Modelling all work with Unsloth. We have Google Colab notebooks for reproducing GRPO, ORPO, DPO Zephyr, KTO and SimPO", "subpage_snippet": "", "source": "docs.unsloth.ai", "link": "https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/reinforcement-learning-dpo-orpo-and-kto", "content": "DPO ( Direct Preference Optimization ), ORPO (Odds Ratio Preference Optimization ), PPO, KTO Reward Modelling all work with Unsloth. We have Google Colab notebooks for reproducing GRPO, ORPO, DPO Zephyr, KTO and SimPO"}
+{"idx": 7, "title": "Optimizing LLMs with DPO in a RAGFramework:— Part... | Stackademic", "date": "", "ddg_snippet": "Direct Preference Optimization ( DPO ) offers a streamlined and effective alternative to traditional RLHF methods. By directly leveraging preference data without needing an additional reward model or extensive on-policy sampling, DPO simplifies the fine - tuning process while...", "subpage_snippet": "", "source": "blog.stackademic.com", "link": "https://blog.stackademic.com/optimizing-llms-with-dpo-in-a-ragframework-part-2-aligning-language-models-without-explicit-adec55867398", "content": "Direct Preference Optimization ( DPO ) offers a streamlined and effective alternative to traditional RLHF methods. By directly leveraging preference data without needing an additional reward model or extensive on-policy sampling, DPO simplifies the fine - tuning process while..."}
+{"idx": 8, "title": "DPO in LLM Training: Optimizing Models for Aligned Behavior", "date": "", "ddg_snippet": "Reward modeling and PPO-based fine - tuning are both resource-intensive. DPO sidesteps this by directly optimizing for the user’s preference signal using a classification-like objective.", "subpage_snippet": "", "source": "www.gocodeo.com", "link": "https://www.gocodeo.com/post/dpo-in-llm-training-optimizing-models-for-aligned-behavior", "content": "Reward modeling and PPO-based fine - tuning are both resource-intensive. DPO sidesteps this by directly optimizing for the user’s preference signal using a classification-like objective."}
+{"idx": 9, "title": "New AI Method Simplifies Fine - Tuning Language Models to Align with...", "date": "", "ddg_snippet": "The resulting Direct Preference Optimization ( DPO ) algorithm is more stable, performant, and computationally lightweight than existing RLHF methods. Technical Explanation. The paper proposes a new approach called Direct Preference Optimization ( DPO ) for fine - tuning large...", "subpage_snippet": "", "source": "practicaldev-herokuapp-com.global.ssl.fastly.net", "link": "https://practicaldev-herokuapp-com.global.ssl.fastly.net/aimodels-fyi/new-ai-method-simplifies-fine-tuning-language-models-to-align-with-human-preferences-26h9", "content": "The resulting Direct Preference Optimization ( DPO ) algorithm is more stable, performant, and computationally lightweight than existing RLHF methods. Technical Explanation. The paper proposes a new approach called Direct Preference Optimization ( DPO ) for fine - tuning large..."}
diff --git a/data/sampled_jsons/JNDcFOczOf_RA-PbRL-_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning.jsonl b/data/sampled_jsons/JNDcFOczOf_RA-PbRL-_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5861f00673f32eabcc07c1bf6d78b3874cc90ced
--- /dev/null
+++ b/data/sampled_jsons/JNDcFOczOf_RA-PbRL-_Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RA - PbRL : Provably Efficient Risk - Aware", "date": "", "ddg_snippet": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based Reinforcement Learning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JNDcFOczOf", "content": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based Reinforcement Learning ."}
+{"idx": 1, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "We also introduce Risk - Aware -PbRL ( RA - PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v3", "content": "We also introduce Risk - Aware -PbRL ( RA - PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes..."}
+{"idx": 2, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference - based Reinforcement Learning ..."}
+{"idx": 3, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/JNDcFOczOf@OpenReview", "content": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 4, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ra-pbrl-provably-efficient-risk-aware-preference", "content": "Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 5, "title": "GitHub - aguilarjose11/PbRLNeurips: Risk - Aware Preference -baser...", "date": "", "ddg_snippet": "Code for paper \" RA - PbRL : Provably Efficient Risk - Aware Preference - Based Reinforcement Learning \". Code Setup Documentation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/pbrlNeurips", "content": "Code for paper \" RA - PbRL : Provably Efficient Risk - Aware Preference - Based Reinforcement Learning \". Code Setup Documentation."}
+{"idx": 6, "title": "RA - PbRL : Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "# Risk - aware preference - based reinforcement learning ( PbRL ) addresses a critical gap in traditional PbRL , which predominantly focuses on maximizing average reward without considering risk.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "# Risk - aware preference - based reinforcement learning ( PbRL ) addresses a critical gap in traditional PbRL , which predominantly focuses on maximizing average reward without considering risk."}
+{"idx": 7, "title": "Human-in-the-loop: Provably Efficient Preference - based ...", "date": "", "ddg_snippet": "Human-in-the-loop: Provably Efficient Preference - based Reinforcement Learning with General Function Approximation.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag.html", "content": "Human-in-the-loop: Provably Efficient Preference - based Reinforcement Learning with General Function Approximation."}
+{"idx": 8, "title": "Preference - Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Preference - based reinforcement learning ( PbRL ) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Preference - based reinforcement learning ( PbRL ) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal."}
+{"idx": 9, "title": "A Survey of Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "Provable Oine Preference - Based Reinforcement Learning . In Proceedings of the International Conference on Learning Representa-tions (ICLR), 2024a.", "subpage_snippet": "", "source": "epub.ub.uni-muenchen.de", "link": "https://epub.ub.uni-muenchen.de/125328/1/2312.14925v2.pdf", "content": "Provable Oine Preference - Based Reinforcement Learning . In Proceedings of the International Conference on Learning Representa-tions (ICLR), 2024a."}
diff --git a/data/sampled_jsons/Jikang_Cheng_Zhiyuan_Yan_'Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector'.jsonl b/data/sampled_jsons/Jikang_Cheng_Zhiyuan_Yan_'Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector'.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5bc0867b52d6854f2c03e001988f47c6fd65b9eb
--- /dev/null
+++ b/data/sampled_jsons/Jikang_Cheng_Zhiyuan_Yan_'Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector'.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "by J Cheng · 2024 · Cited by 31 — In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from real to blendfake to deepfake to be a progressive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "by J Cheng · 2024 · Cited by 31 — In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from real to blendfake to deepfake to be a progressive ..."}
+{"idx": 1, "title": "Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "5 Nov 2024 — This paper proposes a novel training strategy for Deepfake detection using real, blendfake, and deepfake datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vh9yEPLeyD&referrer=[the+profile+of+Jikang+Cheng](/profile?id=~Jikang_Cheng1)", "content": "5 Nov 2024 — This paper proposes a novel training strategy for Deepfake detection using real, blendfake, and deepfake datasets."}
+{"idx": 2, "title": "Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "by J Cheng · 2024 · Cited by 31 — Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ? Intuitively, as ... 20 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/2718a032d15e0b80cd164b240220df89-Paper-Conference.pdf", "content": "by J Cheng · 2024 · Cited by 31 — Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ? Intuitively, as ... 20 pages"}
+{"idx": 3, "title": "Can we leave deepfake data behind in training deepfake detector?", "date": "", "ddg_snippet": "In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from \"real to blendfake to deepfake \" to be a progressive ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738607", "content": "In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from \"real to blendfake to deepfake \" to be a progressive ..."}
+{"idx": 4, "title": "Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "by J Cheng · 2024 · Cited by 31 — Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.17052", "content": "by J Cheng · 2024 · Cited by 31 — Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector?"}
+{"idx": 5, "title": "Can We Leave Deepfake Data Behind in Training ... - Liner", "date": "", "ddg_snippet": "This paper investigates if deepfake detectors can be trained effectively without deepfake data , proposing a method to improve real-to-fake transition ...", "subpage_snippet": "", "source": "liner.com", "link": "https://liner.com/review/can-we-leave-deepfake-data-behind-in-training-deepfake-detector", "content": "This paper investigates if deepfake detectors can be trained effectively without deepfake data , proposing a method to improve real-to-fake transition ..."}
+{"idx": 6, "title": "beautyremain/ProDet: The official code for paper \"Can We ...", "date": "", "ddg_snippet": "The official code for paper \" Can We Leave Deepfake Data Behind in Training Deepfake Detector \" (NIPS2024 poster) - beautyremain/ProDet.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "The official code for paper \" Can We Leave Deepfake Data Behind in Training Deepfake Detector \" (NIPS2024 poster) - beautyremain/ProDet."}
+{"idx": 7, "title": "Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "by J Cheng · Cited by 31 — Reversing a stereotype in research community, that is, deepfake is left behind during detector training . • Proposing to leverage the progressive transition from ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/93195.pdf", "content": "by J Cheng · Cited by 31 — Reversing a stereotype in research community, that is, deepfake is left behind during detector training . • Proposing to leverage the progressive transition from ..."}
+{"idx": 8, "title": "[Papierüberprüfung] Can We Leave Deepfake Data Behind in ...", "date": "", "ddg_snippet": "The paper titled \" Can We Leave Deepfake Data Behind in Training Deepfake Detector ?\" by Jikang Cheng et al. addresses a critical challenge in deepfake detection :", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/de/review/can-we-leave-deepfake-data-behind-in-training-deepfake-detector", "content": "The paper titled \" Can We Leave Deepfake Data Behind in Training Deepfake Detector ?\" by Jikang Cheng et al. addresses a critical challenge in deepfake detection :"}
+{"idx": 9, "title": "Zhiyuan Yan - Google 学术搜索", "date": "", "ddg_snippet": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ? J Cheng, Z Yan, Y Zhang, Y Luo, Z Wang, C Li. NeurIPS 2024, 2024. 31, 2024. Generalizing ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=U_u-mvoAAAAJ&hl=zh-CN", "content": "Can We Leave Deepfake Data Behind in Training Deepfake Detector ? J Cheng, Z Yan, Y Zhang, Y Luo, Z Wang, C Li. NeurIPS 2024, 2024. 31, 2024. Generalizing ..."}
diff --git a/data/sampled_jsons/John_Stewart_Fabila_Carrasco_homepage.jsonl b/data/sampled_jsons/John_Stewart_Fabila_Carrasco_homepage.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e86d791c7972d1629195e189264fcf847e8b9a3
--- /dev/null
+++ b/data/sampled_jsons/John_Stewart_Fabila_Carrasco_homepage.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "John Stewart Fabila-Carrasco - Google Scholar", "date": "", "ddg_snippet": "John Stewart Fabila - Carrasco University of Edinburgh Verified email at ed.ac.uk - Homepage Spectral Graph Theory Graph Signal Processing Analysis on graphs Network analysis ... Articles...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=5xrmd_YAAAAJ&hl=en", "content": "John Stewart Fabila - Carrasco University of Edinburgh Verified email at ed.ac.uk - Homepage Spectral Graph Theory Graph Signal Processing Analysis on graphs Network analysis ... Articles..."}
+{"idx": 1, "title": "John Stewart Fabila-Carrasco - Bio", "date": "", "ddg_snippet": "John Stewart Fabila - Carrasco - BioPage updated Google Sites Report abuse", "subpage_snippet": "", "source": "www.fabila.org", "link": "https://www.fabila.org/bio", "content": "John Stewart Fabila - Carrasco - BioPage updated Google Sites Report abuse"}
+{"idx": 2, "title": "John Stewart Fabila Carrasco - LinkedIn John Stewart Fabila Carrasco (0000-0003-3290-391X) - ORCID John Stewart Fabila Carrasco - OpenReview John Stewart Fabila-Carrasco - The Mathematics Genealogy Project", "date": "", "ddg_snippet": "My expertise in geospatial statistics and data visualization was instrumental in interpreting and presenting geographic data in a visually engaging and easily understandable format. Biography Dr John Stewart Fabila - Carrasco is a postdoctoral researcher at the University of Edinburgh. He is currently working on nonlinear analysis on graphs. John Stewart Fabila Carrasco Pronouns: he/him Postdoc, University of Edinburgh Joined September 2024 If you have additional information or corrections regarding this mathematician, please use the update form. To submit students of this mathematician, please use the new data form, noting this mathematician's MGP ID of 264009 for the advisor ID.", "subpage_snippet": "", "source": "uk.linkedin.com", "link": "https://uk.linkedin.com/in/john-stewart-fabila-carrasco-19077987", "content": "My expertise in geospatial statistics and data visualization was instrumental in interpreting and presenting geographic data in a visually engaging and easily understandable format. Biography Dr John Stewart Fabila - Carrasco is a postdoctoral researcher at the University of Edinburgh. He is currently working on nonlinear analysis on graphs. John Stewart Fabila Carrasco Pronouns: he/him Postdoc, University of Edinburgh Joined September 2024 If you have additional information or corrections regarding this mathematician, please use the update form. To submit students of this mathematician, please use the new data form, noting this mathematician's MGP ID of 264009 for the advisor ID."}
+{"idx": 3, "title": "John Stewart Fabila Carrasco (0000-0003-3290-391X) - ORCID", "date": "", "ddg_snippet": "Biography Dr John Stewart Fabila - Carrasco is a postdoctoral researcher at the University of Edinburgh. He is currently working on nonlinear analysis on graphs.", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0000-0003-3290-391X", "content": "Biography Dr John Stewart Fabila - Carrasco is a postdoctoral researcher at the University of Edinburgh. He is currently working on nonlinear analysis on graphs."}
+{"idx": 4, "title": "John Stewart Fabila Carrasco - OpenReview", "date": "", "ddg_snippet": "John Stewart Fabila Carrasco Pronouns: he/him Postdoc, University of Edinburgh Joined September 2024", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~John_Stewart_Fabila_Carrasco1", "content": "John Stewart Fabila Carrasco Pronouns: he/him Postdoc, University of Edinburgh Joined September 2024"}
+{"idx": 5, "title": "Fabiola Carrasco | YouTube Music", "date": "", "ddg_snippet": "Нравится Fabiola Carrasco ? Слушайте в приложении YouTube Music. Вас также ждет большая коллекция официальных треков, музыкальных видео, ремиксов, каверов и не только.", "subpage_snippet": "", "source": "music.youtube.com", "link": "https://music.youtube.com/channel/UCPxfhk8YOFX-kGgT8ySm6-Q", "content": "Нравится Fabiola Carrasco ? Слушайте в приложении YouTube Music. Вас также ждет большая коллекция официальных треков, музыкальных видео, ремиксов, каверов и не только."}
+{"idx": 6, "title": "Nolte: Jon Stewart and Stephen Colbert Make Jimmy...", "date": "", "ddg_snippet": "Jon Stewart returned to “The Daily Show” with a “government approved” program and referred to President Donald Trump as “Our Great Father” and “Dear Leader.” He joked about Trump’s recent visit to the United Kingdom and played a clip of a reporter’s question about Kimmel and...", "subpage_snippet": "", "source": "www.breitbart.com", "link": "https://www.breitbart.com/politics/2025/09/19/nolte-jon-stewart-and-stephen-colbert-make-jimmy-kimmels-suspension-all-about-stewart-and-colbert/", "content": "Jon Stewart returned to “The Daily Show” with a “government approved” program and referred to President Donald Trump as “Our Great Father” and “Dear Leader.” He joked about Trump’s recent visit to the United Kingdom and played a clip of a reporter’s question about Kimmel and..."}
+{"idx": 7, "title": "Fabiola Carrasco - Promotora de Salud - Puentes de Salud | LinkedIn", "date": "", "ddg_snippet": "View Fabiola Carrasco ’s profile on LinkedIn, a professional community of 1 billion members.11 others named Fabiola Carrasco in United States are on LinkedIn.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/fabiola-carrasco-ba1880a4", "content": "View Fabiola Carrasco ’s profile on LinkedIn, a professional community of 1 billion members.11 others named Fabiola Carrasco in United States are on LinkedIn."}
+{"idx": 8, "title": "Fabiola Carrasco | TikTok", "date": "", "ddg_snippet": "1507 me gusta,Vídeo de TikTok de Fabiola Carrasco (@fabiolacarrasco): «#fyp».Haciendo de todo menos buscar una relación sonido original - Yexa.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/fabiola-carrasco", "content": "1507 me gusta,Vídeo de TikTok de Fabiola Carrasco (@fabiolacarrasco): «#fyp».Haciendo de todo menos buscar una relación sonido original - Yexa."}
+{"idx": 9, "title": "Fabiola carga contra Rocío Carrasco : ‘si eso es así, ¿por qué no...", "date": "", "ddg_snippet": "Fabiola Martínez, también ha sido preguntada sobre este particular, y si bien ha aclarado que al menos hasta ahora no ha visto el documental de Rocío Carrasco , sí afirma que “sin conocer los detalles, como madre me resulta difícil entenderlo\".", "subpage_snippet": "", "source": "es.blastingnews.com", "link": "https://es.blastingnews.com/showbiz-y-tv/2021/04/fabiola-carga-contra-rocio-carrasco-si-eso-es-asi-por-que-no-lucho-por-sus-hijos-003310606.html", "content": "Fabiola Martínez, también ha sido preguntada sobre este particular, y si bien ha aclarado que al menos hasta ahora no ha visto el documental de Rocío Carrasco , sí afirma que “sin conocer los detalles, como madre me resulta difícil entenderlo\"."}
diff --git a/data/sampled_jsons/Kaplan_2020_scaling_laws_loss_data_size_exponent_-0.095_power_law_year_2020.jsonl b/data/sampled_jsons/Kaplan_2020_scaling_laws_loss_data_size_exponent_-0.095_power_law_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d7ff55a0d246ad63d28a3ef6abf3304a85fb56f
--- /dev/null
+++ b/data/sampled_jsons/Kaplan_2020_scaling_laws_loss_data_size_exponent_-0.095_power_law_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "\" Scaling laws \" are empirical statistical laws that predict LLM performance based on such factors.In mathematical terms, perplexity is the exponential of the average negative log likelihood per token.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "\" Scaling laws \" are empirical statistical laws that predict LLM performance based on such factors.In mathematical terms, perplexity is the exponential of the average negative log likelihood per token."}
+{"idx": 1, "title": "Exponent Calculator", "date": "", "ddg_snippet": "Basic exponent laws and rules.Thus, the only way for an to remain unchanged by multiplication, and this exponent law to remain true, is for a0 to be 1. When an exponent is a fraction where the numerator is 1, the nth root of the base is taken.", "subpage_snippet": "", "source": "www.calculator.net", "link": "https://www.calculator.net/exponent-calculator.html", "content": "Basic exponent laws and rules.Thus, the only way for an to remain unchanged by multiplication, and this exponent law to remain true, is for a0 to be 1. When an exponent is a fraction where the numerator is 1, the nth root of the base is taken."}
+{"idx": 2, "title": "Laws of Exponents", "date": "", "ddg_snippet": "Laws of Exponents . Exponents are also called Powers or Indices.The \" Laws of Exponents \" (also called \"Rules of Exponents \") come from three ideas: pencil paper. The exponent says how many times to use the number in a multiplication.", "subpage_snippet": "", "source": "www.mathsisfun.com", "link": "https://www.mathsisfun.com/algebra/exponent-laws.html", "content": "Laws of Exponents . Exponents are also called Powers or Indices.The \" Laws of Exponents \" (also called \"Rules of Exponents \") come from three ideas: pencil paper. The exponent says how many times to use the number in a multiplication."}
+{"idx": 3, "title": "python - Confidence interval for exponential curve fit - Stack Overflow", "date": "", "ddg_snippet": "I'm trying to obtain a confidence interval on an exponential fit to some x,y data (available here). Here's the MWE I have to find the best exponential fit to the data", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/24633664/confidence-interval-for-exponential-curve-fit/24636849", "content": "I'm trying to obtain a confidence interval on an exponential fit to some x,y data (available here). Here's the MWE I have to find the best exponential fit to the data"}
+{"idx": 4, "title": "Face Swap Online Free", "date": "", "ddg_snippet": "Step into your favorite moments with this AI- powered face swap GIF tool — sign in, upload a photo, and turn any GIF into a personalized animation in seconds.", "subpage_snippet": "", "source": "remaker.ai", "link": "https://remaker.ai/face-swap-free/", "content": "Step into your favorite moments with this AI- powered face swap GIF tool — sign in, upload a photo, and turn any GIF into a personalized animation in seconds."}
+{"idx": 5, "title": "Sandpiles with finite-range interactions", "date": "", "ddg_snippet": "Remarkably, all these observables exhibit power - law scaling for all values of. RR. , with exponents that vary systematically with.For large- scale data management, including the storage of avalanche toppling matrices, we relied on the input/output utilities provided by NumPy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13500v1", "content": "Remarkably, all these observables exhibit power - law scaling for all values of. RR. , with exponents that vary systematically with.For large- scale data management, including the storage of avalanche toppling matrices, we relied on the input/output utilities provided by NumPy."}
+{"idx": 6, "title": "The Mathematics of GPT Emergence: Why AI Gets Smart Suddenly", "date": "", "ddg_snippet": "Current Scaling Laws and Their Implications.Optimal scaling laws suggest we need 20 tokens of training data per model parameter. This means: GPT-4 (1.8T params): Needs 36T tokens (we used ~13T).", "subpage_snippet": "", "source": "ai.plainenglish.io", "link": "https://ai.plainenglish.io/the-mathematics-of-gpt-emergence-why-ai-gets-smart-suddenly-cd3c9f493cf1", "content": "Current Scaling Laws and Their Implications.Optimal scaling laws suggest we need 20 tokens of training data per model parameter. This means: GPT-4 (1.8T params): Needs 36T tokens (we used ~13T)."}
+{"idx": 7, "title": "Experimental investigation of tsunami waves generated by granular...", "date": "", "ddg_snippet": "A power law Am ∼ (V − V∗)α ts well the data with a power exponent α ≃ 0.58, signicantly lower than one.with a power exponent slightly smaller than one in our case. The dispersion of the data . when considering the best t by a power law is reduced to about 10%.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03889524v1/document", "content": "A power law Am ∼ (V − V∗)α ts well the data with a power exponent α ≃ 0.58, signicantly lower than one.with a power exponent slightly smaller than one in our case. The dispersion of the data . when considering the best t by a power law is reduced to about 10%."}
+{"idx": 8, "title": "Debt Rattle September 19 2025 - The Automatic Earth", "date": "", "ddg_snippet": "To do so, he suspended morality, the rule of law , and human decency in order serve what he and too many others see as a higher political purpose. Sadly, this moral madness is what is taught in our nation’s colleges.", "subpage_snippet": "", "source": "www.theautomaticearth.com", "link": "https://www.theautomaticearth.com/2025/09/debt-rattle-september-19-2025/", "content": "To do so, he suspended morality, the rule of law , and human decency in order serve what he and too many others see as a higher political purpose. Sadly, this moral madness is what is taught in our nation’s colleges."}
+{"idx": 9, "title": "Best Free Linode Alternative VPS | $100 Credit, 24/7 Support", "date": "", "ddg_snippet": "NVMe SSD. Experience 25x faster storage speeds for seamless data access and operations. 24/7 Support.Fully Redundant. Ensure zero downtime with N+1 hardware redundancy and fail-safe systems. An Enterprise-Grade VPS For Multi Purpose Needs.", "subpage_snippet": "", "source": "freevpshostings.com", "link": "https://freevpshostings.com/linode-akamai-alternative/", "content": "NVMe SSD. Experience 25x faster storage speeds for seamless data access and operations. 24/7 Support.Fully Redundant. Ensure zero downtime with N+1 hardware redundancy and fail-safe systems. An Enterprise-Grade VPS For Multi Purpose Needs."}
diff --git a/data/sampled_jsons/Kerner,_2024_CrowdStrike_outage.jsonl b/data/sampled_jsons/Kerner,_2024_CrowdStrike_outage.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ddc23c966e1e132d6f9e785d56d8bd1ceb15fff4
--- /dev/null
+++ b/data/sampled_jsons/Kerner,_2024_CrowdStrike_outage.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Reflection on the CrowdStrike IT Outage - Platypus", "date": "", "ddg_snippet": "18 Feb 2025 — Recovery time estimates ranged from days to months, but 99% of affected Windows systems were back online by the end of July ( Kerner 2024 ). The ...", "subpage_snippet": "", "source": "blog.castac.org", "link": "https://blog.castac.org/2025/02/major-internet-outages-are-getting-bigger-and-occurring-more-often-a-reflection-on-the-crowdstrike-it-outage/", "content": "18 Feb 2025 — Recovery time estimates ranged from days to months, but 99% of affected Windows systems were back online by the end of July ( Kerner 2024 ). The ..."}
+{"idx": 1, "title": "Crowdstrike: 79 Minutes", "date": "", "ddg_snippet": "The outage occurred on July 19, 2024, with millions of Windows systems failing and showing the infamous blue screen of death ( Kerner, 2024 ). The outage was not ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/crowdstrike-79-minutes-christian-calipusan-xpy4f", "content": "The outage occurred on July 19, 2024, with millions of Windows systems failing and showing the infamous blue screen of death ( Kerner, 2024 ). The outage was not ..."}
+{"idx": 2, "title": "Beyond infrastructure: Internet ecosystem resilience and ...", "date": "", "ddg_snippet": "by B Howell · 2025 · Cited by 1 — Kerner, 2024 . S.M. Kerner. CrowdStrike outage explained: What caused it and what's next. TechTarget (2024). Retrieved from. https://www.techtarget.com/whatis ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0308596125000953", "content": "by B Howell · 2025 · Cited by 1 — Kerner, 2024 . S.M. Kerner. CrowdStrike outage explained: What caused it and what's next. TechTarget (2024). Retrieved from. https://www.techtarget.com/whatis ..."}
+{"idx": 3, "title": "Into the next generation of digital protection: AI resiliency ...", "date": "", "ddg_snippet": "by E Noam · 2025 — This then affected Microsoft operating system Windows, and that, in turn, crashed Windows computers worldwide ( Kerner, 2024 ). ... Delta's CEO says the CrowdStrike ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0308596125000047", "content": "by E Noam · 2025 — This then affected Microsoft operating system Windows, and that, in turn, crashed Windows computers worldwide ( Kerner, 2024 ). ... Delta's CEO says the CrowdStrike ..."}
+{"idx": 4, "title": "Countering Autonomous Cyber Threats", "date": "", "ddg_snippet": "23 Oct 2024 — Kerner, (2024 ) ↑ Kerner, S. M. (2024). Crowdstrike outage explained: What caused it and what's next. https://www.techtarget.com/whatis ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.18312v1", "content": "23 Oct 2024 — Kerner, (2024 ) ↑ Kerner, S. M. (2024). Crowdstrike outage explained: What caused it and what's next. https://www.techtarget.com/whatis ..."}
+{"idx": 5, "title": "ITBench: Evaluating AI Agents across Diverse Real-World ...", "date": "", "ddg_snippet": "... ( Kerner, 2024 ). This incident underlined the critical need for intelligent IT ... Crowdstrike outage explained: What caused it and what's next. https ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44303", "content": "... ( Kerner, 2024 ). This incident underlined the critical need for intelligent IT ... Crowdstrike outage explained: What caused it and what's next. https ..."}
+{"idx": 6, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "The recent CrowdStrike outage highlighted these challenges ... ( Kerner, 2024 ). This incident underlined the ... Crowdstrike outage explained: What caused it and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d2122a3a706c4c7cd0d382ee9b13da641bd1fe5a.pdf", "content": "The recent CrowdStrike outage highlighted these challenges ... ( Kerner, 2024 ). This incident underlined the ... Crowdstrike outage explained: What caused it and ..."}
+{"idx": 7, "title": "Resilience in the IP Era: Navigating the Fragility of Modern ...", "date": "", "ddg_snippet": "(2024, July) What caused the crowdstrike outage : A detailed breakdown ... [156] S. M. Kerner. (2024 , Oct) Crowdstrike outage explained: What caused it ...", "subpage_snippet": "", "source": "d197for5662m48.cloudfront.net", "link": "https://d197for5662m48.cloudfront.net/documents/publicationstatus/270834/preprint_pdf/5183ca2445dde4ba762750deac539a43.pdf", "content": "(2024, July) What caused the crowdstrike outage : A detailed breakdown ... [156] S. M. Kerner. (2024 , Oct) Crowdstrike outage explained: What caused it ..."}
+{"idx": 8, "title": "Failing better in the infosphere: ontological uncertainties ...", "date": "", "ddg_snippet": "by NS Fouad · 2025 — to customers ( Kerner, 2024 ). Further, the dynamic nature of ... CrowdStrike outage explained: What caused it and what's next.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/pdf/10.1080/1369118X.2025.2492574", "content": "by NS Fouad · 2025 — to customers ( Kerner, 2024 ). Further, the dynamic nature of ... CrowdStrike outage explained: What caused it and what's next."}
+{"idx": 9, "title": "The Impact of AI on the Cyber Offense-Defense Balance ...", "date": "", "ddg_snippet": "by AJ Lohn · 2025 · Cited by 2 — An example presaging this combined future is Apple's agreement with OpenAI ( Kerner, 2024 ). Apple's. iPhones have a small efficient model that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.13371", "content": "by AJ Lohn · 2025 · Cited by 2 — An example presaging this combined future is Apple's agreement with OpenAI ( Kerner, 2024 ). Apple's. iPhones have a small efficient model that ..."}
diff --git a/data/sampled_jsons/Kerner_2024_CrowdStrike_financial_impact_economic_cost_year_2024.jsonl b/data/sampled_jsons/Kerner_2024_CrowdStrike_financial_impact_economic_cost_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..52fea9a4dd868e3a7a6d8de01a3055e2cfe610b7
--- /dev/null
+++ b/data/sampled_jsons/Kerner_2024_CrowdStrike_financial_impact_economic_cost_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "George Kurtz - Wikipedia", "date": "", "ddg_snippet": "In 2024 , his company CrowdStrike crashed millions of Windows computers around the world, causing billions of dollars in economic losses in what has ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/George_Kurtz", "content": "In 2024 , his company CrowdStrike crashed millions of Windows computers around the world, causing billions of dollars in economic losses in what has ..."}
+{"idx": 1, "title": "CrowdStrike update snafu affected 8.5 million Windows devices |", "date": "", "ddg_snippet": "In a blog post, Microsoft said while the percentage was small, the broad economic and societal impacts of the incident reflect the use of CrowdStrike ...", "subpage_snippet": "", "source": "www.computerweekly.com", "link": "https://www.computerweekly.com/news/366596373/CrowdStrike-update-snafu-affected-85-million-Windows-devices", "content": "In a blog post, Microsoft said while the percentage was small, the broad economic and societal impacts of the incident reflect the use of CrowdStrike ..."}
+{"idx": 2, "title": "Why I believe the global IT outage by Crowdstrike is not an", "date": "", "ddg_snippet": "... the world has crashed because of a deployment of an update to a software that was installed on them, that came from a company called Crowdstrike ...", "subpage_snippet": "", "source": "www.sott.net", "link": "https://www.sott.net/article/493303-Why-I-believe-the-global-IT-outage-by-Crowdstrike-is-not-an-accident", "content": "... the world has crashed because of a deployment of an update to a software that was installed on them, that came from a company called Crowdstrike ..."}
+{"idx": 3, "title": "Davos 2025: Digital supply chains at risk as world faces two", "date": "", "ddg_snippet": "Ahead of its 2025 annual meeting, the World Economic Forum (WEF) said experts, business leaders and politicians are predicting a stormy, turbulent or ...", "subpage_snippet": "", "source": "www.computerweekly.com", "link": "https://www.computerweekly.com/news/366618099/Davos-2025-Digital-supply-chains-at-risk-as-world-faces-two-years-of-turbulence", "content": "Ahead of its 2025 annual meeting, the World Economic Forum (WEF) said experts, business leaders and politicians are predicting a stormy, turbulent or ..."}
+{"idx": 4, "title": "Disaster recovery | News, analysis, and information from", "date": "", "ddg_snippet": "Understanding the two types of climate risk and how to plan for them can help organizations better prepare for disasters and improve their impact on ...", "subpage_snippet": "", "source": "www.computerweekly.com", "link": "https://www.computerweekly.com/resources/Disaster-recovery", "content": "Understanding the two types of climate risk and how to plan for them can help organizations better prepare for disasters and improve their impact on ..."}
+{"idx": 5, "title": "Cloud & Edge Computing Trends and Predictions 2025 From", "date": "", "ddg_snippet": "This trend is driven by the requirements to reduce cost , optimize operational efficiency and profit margins, increase performance, and reduce vendor ...", "subpage_snippet": "", "source": "www.itprotoday.com", "link": "https://www.itprotoday.com/cloud-computing/cloud-edge-computing-trends-and-predictions-2025-from-industry-insiders", "content": "This trend is driven by the requirements to reduce cost , optimize operational efficiency and profit margins, increase performance, and reduce vendor ..."}
+{"idx": 6, "title": "Cybersecurity Trends and Predictions 2025 From Industry", "date": "", "ddg_snippet": "Click here for Part 1 , which covers AI s impact on cybersecurity; ransomware; phishing and other attacks; identity theft, data security and privacy ...", "subpage_snippet": "", "source": "www.itprotoday.com", "link": "https://www.itprotoday.com/it-security/cybersecurity-trends-and-predictions-2025-from-industry-insiders-part-2", "content": "Click here for Part 1 , which covers AI s impact on cybersecurity; ransomware; phishing and other attacks; identity theft, data security and privacy ..."}
+{"idx": 7, "title": "Network World |", "date": "", "ddg_snippet": "Cisco financials catch AI demand, enterprise ... Nvidia allies with Armis, Check Point Software Technologies, CrowdStrike and World Wide Technology", "subpage_snippet": "", "source": "heartserased.com", "link": "http://heartserased.com/", "content": "Cisco financials catch AI demand, enterprise ... Nvidia allies with Armis, Check Point Software Technologies, CrowdStrike and World Wide Technology"}
+{"idx": 8, "title": "#RSAC: Solving the Ransomware Scourge Requires a Coordinated", "date": "", "ddg_snippet": "As ransomware attackers can be anywhere in the world, Reiner said that there are different tactics, including economic sanctions, that can and should ...", "subpage_snippet": "", "source": "www.infosecurity-magazine.com:443", "link": "https://www.infosecurity-magazine.com:443/news/rsac-solving-the-ransomware-scourge/", "content": "As ransomware attackers can be anywhere in the world, Reiner said that there are different tactics, including economic sanctions, that can and should ..."}
+{"idx": 9, "title": "TECHNOLOGY - CNBC Africa", "date": "", "ddg_snippet": "Designated non- financial businesses & professions boost ... Africa’s uniformity of purpose to tap immense economic power of AI, says TBI Africa MD", "subpage_snippet": "", "source": "www.cnbcafrica.com", "link": "https://www.cnbcafrica.com/section/technology/", "content": "Designated non- financial businesses & professions boost ... Africa’s uniformity of purpose to tap immense economic power of AI, says TBI Africa MD"}
diff --git a/data/sampled_jsons/Kerner_2024_CrowdStrike_outage_financial_cost_title.jsonl b/data/sampled_jsons/Kerner_2024_CrowdStrike_outage_financial_cost_title.jsonl
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index 0000000000000000000000000000000000000000..a3b0d74a2e940843fca352a93710daa5bba7003d
--- /dev/null
+++ b/data/sampled_jsons/Kerner_2024_CrowdStrike_outage_financial_cost_title.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "George Kurtz - Wikipedia", "date": "", "ddg_snippet": "On July 19, 2024 , CrowdStrike caused one of the largest information technology outages in history when it pushed out a software update that caused an ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/George_Kurtz", "content": "On July 19, 2024 , CrowdStrike caused one of the largest information technology outages in history when it pushed out a software update that caused an ..."}
+{"idx": 1, "title": "2024 CrowdStrike-related IT outages - Wikipedia", "date": "", "ddg_snippet": "On 19 July 2024 , the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. As a result, roughly 8.5 million systems crashed and were unable to properly restart [1] in what has been called the largest outage in the history of information technology [2 ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/2024_CrowdStrike-related_IT_outages", "content": "On 19 July 2024 , the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. As a result, roughly 8.5 million systems crashed and were unable to properly restart [1] in what has been called the largest outage in the history of information technology [2 ..."}
+{"idx": 2, "title": "CrowdStrike says most Falcon sensors now up and running |", "date": "", "ddg_snippet": "Meanwhile, CrowdStrike confirmed to Computer Weekly’s sister title TechTarget Security prior to the weekend that the logic error in its validator ...", "subpage_snippet": "", "source": "www.computerweekly.com", "link": "https://www.computerweekly.com/news/366599276/CrowdStrike-says-most-Falcon-sensors-now-up-and-running", "content": "Meanwhile, CrowdStrike confirmed to Computer Weekly’s sister title TechTarget Security prior to the weekend that the logic error in its validator ..."}
+{"idx": 3, "title": "CrowdStrike-Microsoft Outage To Cost $44M Per Fortune 500 ...", "date": "", "ddg_snippet": "Jul 25, 2024 · The massive CrowdStrike outage that affected millions of Microsoft devices is predicted to cost U.S. Fortune 500 companies $5.4 billion in total direct financial loss, with an average loss of $44 ...", "subpage_snippet": "", "source": "www.crn.com", "link": "https://www.crn.com/news/security/2024/crowdstrike-outage-costs-44-million-per-fortune-500-company", "content": "Jul 25, 2024 · The massive CrowdStrike outage that affected millions of Microsoft devices is predicted to cost U.S. Fortune 500 companies $5.4 billion in total direct financial loss, with an average loss of $44 ..."}
+{"idx": 4, "title": "CrowdStrike Outage Leads to Estimated Financial Loss of $5.4 ...", "date": "", "ddg_snippet": "Jul 29, 2024 · Guru Baran - July 29, 2024 A recent global IT outage linked to CrowdStrike , a leading cybersecurity company, has resulted in an estimated $5.4 billion in direct financial losses for Fortune 500 companies, according to a report released by cloud insurance firm Parametrix.", "subpage_snippet": "", "source": "cybersecuritynews.com", "link": "https://cybersecuritynews.com/crowdstrike-outage-loss-5-4-billion/", "content": "Jul 29, 2024 · Guru Baran - July 29, 2024 A recent global IT outage linked to CrowdStrike , a leading cybersecurity company, has resulted in an estimated $5.4 billion in direct financial losses for Fortune 500 companies, according to a report released by cloud insurance firm Parametrix."}
+{"idx": 5, "title": "CrowdStrike outage explained: What caused it and what’s next Counting the cost of CrowdStrike: the bug that bit billions The Financial Impact of the CrowdStrike Global IT Outage CrowdStrike outage will cost Fortune 500 companies $5.4 ... 2024 CrowdStrike -related IT outages - Wikipedia 2024 CrowdStrike -related IT outages - Wikipedia 2024 CrowdStrike -related IT outages - Wikipedia 2024 CrowdStrike -related IT outages - Wikipedia 2024 CrowdStrike -related IT outages - Wikipedia 2024 CrowdStrike -related IT outages - Wikipedia 2024 CrowdStrike-related IT outages - Wikipedia", "date": "", "ddg_snippet": "Oct 29, 2024 · What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ... Jul 26, 2024 · As eye-popping estimates emerge for the cost to enterprises of dealing with aftermath of last week’s CrowdStrike -induced outages , it’s crucial to break down the sources of these expenses and ... Jul 26, 2024 · Last week's CrowdStrike outage , caused by a faulty configuration update, led to widespread system crashes across various business sectors. This disruption highlighted the critical vulnerabilities in modern digital infrastructure, affecting but not limited to airlines, healthcare, financial markets, television broadcasting, and emergency services. Aug 3, 2024 · The days-long cyberincident — which grounded planes, shuttered businesses and stopped markets — cost Fortune 500 companies about $5.4 billion in damages, according to insurance company Parametrix. How many customers did CrowdStrike have in 2024? Archived from the original on 22 July 2024. Retrieved 19 July 2024. In its last earnings report, Crowdstrike declared a total of nearly 24,000 customers . ^ Singh, Manish (19 July 2024). \"Faulty CrowdStrike update causes major global IT outage, taking out banks, airlines and businesses globally\". TechCrunch. Did 'CrowdStrike' update skip checks? \"CrowdStrike update that caused global outage likely skipped checks , experts say\". Reuters. Archived from the original on 20 July 2024. Retrieved 21 July 2024. ^ \"Lesson from global Microsoft outage: Need for greater regulation and accountability\". The Indian Express. 20 July 2024. Archived from the original on 25 July 2024. Retrieved 25 July 2024. Did CrowdStrike update cause Linux outages in April? \" CrowdStrike updates caused Linux outages in April\". www.computing.co.uk. Archived from the original on 22 July 2024 . Retrieved 22 July 2024 . ^ a b \"Statement on Falcon Content Update for Windows Hosts\". Crowdstrike . Archived from the original on 20 July 2024 . Retrieved 19 July 2024 . Did CrowdStrike win the 2024 Pwnie Awards? CrowdStrike won the 2024 Pwnie Awards for the Most Epic Fail , which CrowdStrike president Michael Sentonas accepted in person at DEF CON's annual Pwnie Awards show. What happened to CrowdStrike's Falcon sensor security software? On 19 July 2024, the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. Why did CrowdStrike release a fault update? CrowdStrike's own post-incident investigation identified several errors that led to the release of a fault update to the \"Crowdstrike Sensor Detection Engine\": On 19 July at 04:09 UTC, CrowdStrike distributed a faulty configuration update for its Falcon sensor software running on Windows PCs and servers. On 19 July 2024 , the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. As a result, roughly 8.5 million systems crashed and were unable to properly restart [1] in what has been called the largest outage in the history of information technology [2 ...", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/WhatIs/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "Oct 29, 2024 · What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ... Jul 26, 2024 · As eye-popping estimates emerge for the cost to enterprises of dealing with aftermath of last week’s CrowdStrike -induced outages , it’s crucial to break down the sources of these expenses and ... Jul 26, 2024 · Last week's CrowdStrike outage , caused by a faulty configuration update, led to widespread system crashes across various business sectors. This disruption highlighted the critical vulnerabilities in modern digital infrastructure, affecting but not limited to airlines, healthcare, financial markets, television broadcasting, and emergency services. Aug 3, 2024 · The days-long cyberincident — which grounded planes, shuttered businesses and stopped markets — cost Fortune 500 companies about $5.4 billion in damages, according to insurance company Parametrix. How many customers did CrowdStrike have in 2024? Archived from the original on 22 July 2024. Retrieved 19 July 2024. In its last earnings report, Crowdstrike declared a total of nearly 24,000 customers . ^ Singh, Manish (19 July 2024). \"Faulty CrowdStrike update causes major global IT outage, taking out banks, airlines and businesses globally\". TechCrunch. Did 'CrowdStrike' update skip checks? \"CrowdStrike update that caused global outage likely skipped checks , experts say\". Reuters. Archived from the original on 20 July 2024. Retrieved 21 July 2024. ^ \"Lesson from global Microsoft outage: Need for greater regulation and accountability\". The Indian Express. 20 July 2024. Archived from the original on 25 July 2024. Retrieved 25 July 2024. Did CrowdStrike update cause Linux outages in April? \" CrowdStrike updates caused Linux outages in April\". www.computing.co.uk. Archived from the original on 22 July 2024 . Retrieved 22 July 2024 . ^ a b \"Statement on Falcon Content Update for Windows Hosts\". Crowdstrike . Archived from the original on 20 July 2024 . Retrieved 19 July 2024 . Did CrowdStrike win the 2024 Pwnie Awards? CrowdStrike won the 2024 Pwnie Awards for the Most Epic Fail , which CrowdStrike president Michael Sentonas accepted in person at DEF CON's annual Pwnie Awards show. What happened to CrowdStrike's Falcon sensor security software? On 19 July 2024, the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. Why did CrowdStrike release a fault update? CrowdStrike's own post-incident investigation identified several errors that led to the release of a fault update to the \"Crowdstrike Sensor Detection Engine\": On 19 July at 04:09 UTC, CrowdStrike distributed a faulty configuration update for its Falcon sensor software running on Windows PCs and servers. On 19 July 2024 , the American cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems with Microsoft Windows computers running the software. As a result, roughly 8.5 million systems crashed and were unable to properly restart [1] in what has been called the largest outage in the history of information technology [2 ..."}
+{"idx": 6, "title": "Counting the cost of CrowdStrike: the bug that bit billions", "date": "", "ddg_snippet": "Jul 26, 2024 · As eye-popping estimates emerge for the cost to enterprises of dealing with aftermath of last week’s CrowdStrike -induced outages , it’s crucial to break down the sources of these expenses and ...", "subpage_snippet": "", "source": "www.cio.com", "link": "https://www.cio.com/article/3478068/counting-the-cost-of-crowdstrike-the-bug-that-bit-billions.html", "content": "Jul 26, 2024 · As eye-popping estimates emerge for the cost to enterprises of dealing with aftermath of last week’s CrowdStrike -induced outages , it’s crucial to break down the sources of these expenses and ..."}
+{"idx": 7, "title": "The Financial Impact of the CrowdStrike Global IT Outage", "date": "", "ddg_snippet": "Jul 26, 2024 · Last week's CrowdStrike outage , caused by a faulty configuration update, led to widespread system crashes across various business sectors. This disruption highlighted the critical vulnerabilities in modern digital infrastructure, affecting but not limited to airlines, healthcare, financial markets, television broadcasting, and emergency services.", "subpage_snippet": "", "source": "www.guidewire.com", "link": "https://www.guidewire.com/resources/blog/technology/the-financial-impact-of-the-crowdstrike-global-it-outage", "content": "Jul 26, 2024 · Last week's CrowdStrike outage , caused by a faulty configuration update, led to widespread system crashes across various business sectors. This disruption highlighted the critical vulnerabilities in modern digital infrastructure, affecting but not limited to airlines, healthcare, financial markets, television broadcasting, and emergency services."}
+{"idx": 8, "title": "CrowdStrike outage will cost Fortune 500 companies $5.4 ...", "date": "", "ddg_snippet": "Aug 3, 2024 · The days-long cyberincident — which grounded planes, shuttered businesses and stopped markets — cost Fortune 500 companies about $5.4 billion in damages, according to insurance company Parametrix.", "subpage_snippet": "", "source": "fortune.com", "link": "https://fortune.com/2024/08/03/crowdstrike-outage-fortune-500-companies-5-4-billion-damages-uninsured-losses/", "content": "Aug 3, 2024 · The days-long cyberincident — which grounded planes, shuttered businesses and stopped markets — cost Fortune 500 companies about $5.4 billion in damages, according to insurance company Parametrix."}
+{"idx": 9, "title": "Database software | News, analysis, and information from", "date": "", "ddg_snippet": "MongoDB used its appearance at AWS re: Invent 2024 to tell us that a new cohort of organisations has joined the MongoDB AI Applications Program (MAAP ...", "subpage_snippet": "", "source": "www.computerweekly.com", "link": "https://www.computerweekly.com/resources/Database-software", "content": "MongoDB used its appearance at AWS re: Invent 2024 to tell us that a new cohort of organisations has joined the MongoDB AI Applications Program (MAAP ..."}
diff --git a/data/sampled_jsons/Kerner_2024_IT_outage_costs_financial_impact_year_2024.jsonl b/data/sampled_jsons/Kerner_2024_IT_outage_costs_financial_impact_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e9efc5427933dc369af7ba120f4699f22fb2858
--- /dev/null
+++ b/data/sampled_jsons/Kerner_2024_IT_outage_costs_financial_impact_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Explaining the largest IT outage in history and what’s next", "date": "", "ddg_snippet": "Jul 22, 2024 · IT expert Sean Michael Kerner shares an article on TechTarget explaining the outage , the businesses affected, and recovery times for businesses to get “back to normal”.", "subpage_snippet": "", "source": "www.nogalis.com", "link": "https://www.nogalis.com/2024/07/22/explaining-the-largest-it-outage-in-history-and-whats-next/", "content": "Jul 22, 2024 · IT expert Sean Michael Kerner shares an article on TechTarget explaining the outage , the businesses affected, and recovery times for businesses to get “back to normal”."}
+{"idx": 1, "title": "Major Internet Outages are Getting Bigger and Occurring More ...", "date": "", "ddg_snippet": "Jul 19, 2024 · The Global Payroll Association said that many workers would experience a delay in their monthly pay following the IT outage ( 2024 ). The outage also had a major financial and reputational impact on CrowdStrike.", "subpage_snippet": "", "source": "blog.castac.org", "link": "https://blog.castac.org/2025/02/major-internet-outages-are-getting-bigger-and-occurring-more-often-a-reflection-on-the-crowdstrike-it-outage/", "content": "Jul 19, 2024 · The Global Payroll Association said that many workers would experience a delay in their monthly pay following the IT outage ( 2024 ). The outage also had a major financial and reputational impact on CrowdStrike."}
+{"idx": 2, "title": "CrowdStrike outage explained: What caused it and what’s next Planning for the unexpected: Exploring the 2024 Global IT ... Exploring the Trifecta of Challenges in the Data Center ... Explaining the largest IT outage in history and what’s next Data Center Outages Decline for Fourth Straight Year, But ... One year on from the CrowdStrike outage: What have we learned?", "date": "", "ddg_snippet": "Oct 29, 2024 · What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike, affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. Jun 1, 2025 · The paper presents the results of an analysis of the 2024 Global IT Outage (GITO) disruptive event that occurred on 19 July 2024 , causing worldwide disruptions to air travel, health, systems, among others. Aug 3, 2024 · Data center outages have slightly decreased in frequency and severity, but their financial impact remains substantial. The survey found that about 20% of impactful outages cost over $1... Jul 22, 2024 · IT expert Sean Michael Kerner shares an article on TechTarget explaining the outage , the businesses affected, and recovery times for businesses to get “back to normal”. May 19, 2025 · Data center outages show a steady decline in a new Uptime Institute report, but power issues and rising costs remain key industry challenges. Jul 21, 2025 · The financial impact has been projected to be as much as $10bn, according to ratings agency Fitch, with airlines, banks, retailers, and government services significantly disrupted.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/WhatIs/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "Oct 29, 2024 · What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike, affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. Jun 1, 2025 · The paper presents the results of an analysis of the 2024 Global IT Outage (GITO) disruptive event that occurred on 19 July 2024 , causing worldwide disruptions to air travel, health, systems, among others. Aug 3, 2024 · Data center outages have slightly decreased in frequency and severity, but their financial impact remains substantial. The survey found that about 20% of impactful outages cost over $1... Jul 22, 2024 · IT expert Sean Michael Kerner shares an article on TechTarget explaining the outage , the businesses affected, and recovery times for businesses to get “back to normal”. May 19, 2025 · Data center outages show a steady decline in a new Uptime Institute report, but power issues and rising costs remain key industry challenges. Jul 21, 2025 · The financial impact has been projected to be as much as $10bn, according to ratings agency Fitch, with airlines, banks, retailers, and government services significantly disrupted."}
+{"idx": 3, "title": "Planning for the unexpected: Exploring the 2024 Global IT ...", "date": "", "ddg_snippet": "Jun 1, 2025 · The paper presents the results of an analysis of the 2024 Global IT Outage (GITO) disruptive event that occurred on 19 July 2024 , causing worldwide disruptions to air travel, health, systems, among others.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666188825000504", "content": "Jun 1, 2025 · The paper presents the results of an analysis of the 2024 Global IT Outage (GITO) disruptive event that occurred on 19 July 2024 , causing worldwide disruptions to air travel, health, systems, among others."}
+{"idx": 4, "title": "Exploring the Trifecta of Challenges in the Data Center ... Explaining the largest IT outage in history and what’s next Data Center Outages Decline for Fourth Straight Year, But ... One year on from the CrowdStrike outage: What have we learned?", "date": "", "ddg_snippet": "Aug 3, 2024 · Data center outages have slightly decreased in frequency and severity, but their financial impact remains substantial. The survey found that about 20% of impactful outages cost over $1... Jul 22, 2024 · IT expert Sean Michael Kerner shares an article on TechTarget explaining the outage , the businesses affected, and recovery times for businesses to get “back to normal”. May 19, 2025 · Data center outages show a steady decline in a new Uptime Institute report, but power issues and rising costs remain key industry challenges. Jul 21, 2025 · The financial impact has been projected to be as much as $10bn, according to ratings agency Fitch, with airlines, banks, retailers, and government services significantly disrupted.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/exploring-trifecta-challenges-data-center-industry-ej40e", "content": "Aug 3, 2024 · Data center outages have slightly decreased in frequency and severity, but their financial impact remains substantial. The survey found that about 20% of impactful outages cost over $1... Jul 22, 2024 · IT expert Sean Michael Kerner shares an article on TechTarget explaining the outage , the businesses affected, and recovery times for businesses to get “back to normal”. May 19, 2025 · Data center outages show a steady decline in a new Uptime Institute report, but power issues and rising costs remain key industry challenges. Jul 21, 2025 · The financial impact has been projected to be as much as $10bn, according to ratings agency Fitch, with airlines, banks, retailers, and government services significantly disrupted."}
+{"idx": 5, "title": "Data Center Outages Decline for Fourth Straight Year, But ...", "date": "", "ddg_snippet": "May 19, 2025 · Data center outages show a steady decline in a new Uptime Institute report, but power issues and rising costs remain key industry challenges.", "subpage_snippet": "", "source": "www.datacenterknowledge.com", "link": "https://www.datacenterknowledge.com/outages/data-center-outages-decline-for-fourth-straight-year-but-issues-persist", "content": "May 19, 2025 · Data center outages show a steady decline in a new Uptime Institute report, but power issues and rising costs remain key industry challenges."}
+{"idx": 6, "title": "One year on from the CrowdStrike outage: What have we learned?", "date": "", "ddg_snippet": "Jul 21, 2025 · The financial impact has been projected to be as much as $10bn, according to ratings agency Fitch, with airlines, banks, retailers, and government services significantly disrupted.", "subpage_snippet": "", "source": "www.computerweekly.com", "link": "https://www.computerweekly.com/opinion/One-year-on-from-the-CrowdStrike-outageWhat-have-we-learned", "content": "Jul 21, 2025 · The financial impact has been projected to be as much as $10bn, according to ratings agency Fitch, with airlines, banks, retailers, and government services significantly disrupted."}
+{"idx": 7, "title": "How to Combat Runaway Cloud Costs and 'Cloud-flation'", "date": "", "ddg_snippet": "Growing cloud costs is likely to be a major issue for many organizations in 2024 , according to a new report. ... IT leaders often get hit with a big ...", "subpage_snippet": "", "source": "www.datacenterknowledge.com", "link": "https://www.datacenterknowledge.com/cloud/how-to-combat-runaway-cloud-costs-and-cloud-flation-in-2024", "content": "Growing cloud costs is likely to be a major issue for many organizations in 2024 , according to a new report. ... IT leaders often get hit with a big ..."}
+{"idx": 8, "title": "Cloud Outage Could Cause $19 Billion in Losses, Lloyd's", "date": "", "ddg_snippet": "The new estimate on the impact of a catastrophic cloud outage isn ’ t the first time Lloyd ’ s has provided an insurance forecast for a ...", "subpage_snippet": "", "source": "www.eweek.com", "link": "https://www.eweek.com/cloud/lloyd-s-estimates-the-impact-of-a-u.s.-cloud-outage-at-19-billion/", "content": "The new estimate on the impact of a catastrophic cloud outage isn ’ t the first time Lloyd ’ s has provided an insurance forecast for a ..."}
+{"idx": 9, "title": "The CDK Global outage: Explaining how it happened", "date": "", "ddg_snippet": "The impact of the CDK Global ransomware attack is extensive as it caused widespread disruption across the automotive sector in North America.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/whatis/feature/The-CDK-Global-outage-Explaining-how-it-happened", "content": "The impact of the CDK Global ransomware attack is extensive as it caused widespread disruption across the automotive sector in North America."}
diff --git a/data/sampled_jsons/Kirilenko_Stepchenkova_Romsdahl_2015_Twitter_climate_change_causal_pathways_abstract.jsonl b/data/sampled_jsons/Kirilenko_Stepchenkova_Romsdahl_2015_Twitter_climate_change_causal_pathways_abstract.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Content Analysis Methods for Assessing Climate Change ...", "date": "", "ddg_snippet": "worldwide on climate change ( Kirilenko & Stepchenkova , 2014). To draw the samples from. Twitter , researchers usually use the Twitter API—an Application Programming Interface.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/309805948_Content_Analysis_Methods_for_Assessing_Climate_Change_Communication_and_Media_Portrayals", "content": "worldwide on climate change ( Kirilenko & Stepchenkova , 2014). To draw the samples from. Twitter , researchers usually use the Twitter API—an Application Programming Interface."}
+{"idx": 1, "title": "(PDF) How has the COVID-19 pandemic affected the climate change ...", "date": "", "ddg_snippet": "Abstract . Kirilenko , A.P., Molodtsova, T., Stepchenkova , S.O., 2015 . People as sensors: mass media and local temperature influence climate change discussion on Twitter . Glob.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/92551242/How_has_the_COVID_19_pandemic_affected_the_climate_change_debate_on_Twitter", "content": "Abstract . Kirilenko , A.P., Molodtsova, T., Stepchenkova , S.O., 2015 . People as sensors: mass media and local temperature influence climate change discussion on Twitter . Glob."}
+{"idx": 2, "title": "What is climate change ? A really simple guide", "date": "", "ddg_snippet": "Climate change is the long-term shift in the Earth's average temperatures and weather conditions. The world has been warming up quickly over the past 100 years or so. As a result, weather patterns are changing .", "subpage_snippet": "", "source": "www.bbc.com", "link": "https://www.bbc.com/news/articles/c9w15nggj58o", "content": "Climate change is the long-term shift in the Earth's average temperatures and weather conditions. The world has been warming up quickly over the past 100 years or so. As a result, weather patterns are changing ."}
+{"idx": 3, "title": "Exploring climate change on Twitter using seven aspects: Stance...", "date": "", "ddg_snippet": "Abstract . How do climate change deniers differ from believers? Is there any correlation between human sentiment and deviations from historic temperature?7. Kirilenko AP, Stepchenkova SO. Public microblogging on climate change : One year of Twitter worldwide.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9491544/", "content": "Abstract . How do climate change deniers differ from believers? Is there any correlation between human sentiment and deviations from historic temperature?7. Kirilenko AP, Stepchenkova SO. Public microblogging on climate change : One year of Twitter worldwide."}
+{"idx": 4, "title": "Gender differences in the climate change communication on Twitter", "date": "", "ddg_snippet": "Keywords Twitter , Climate change , Social media, Communication, Gender differences. Kirilenko and Stepchenkova (2014) investigated tweeting about climate change and mapped users, topics and news sources used by the tweeters.", "subpage_snippet": "", "source": "research.fit.edu", "link": "https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/climate-communications/youth-climate-amp-social-media/Holmberg--Hellsten.--2015.--Gender-Differences-In-The-CC-Communication-On-Twitter.pdf", "content": "Keywords Twitter , Climate change , Social media, Communication, Gender differences. Kirilenko and Stepchenkova (2014) investigated tweeting about climate change and mapped users, topics and news sources used by the tweeters."}
+{"idx": 5, "title": "What Is Climate Change ? | United Nations", "date": "", "ddg_snippet": "Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun’s activity or large volcanic eruptions.", "subpage_snippet": "", "source": "www.un.org", "link": "https://www.un.org/en/climatechange/what-is-climate-change", "content": "Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun’s activity or large volcanic eruptions."}
+{"idx": 6, "title": "What do people know about climate change „ and how confident are...", "date": "", "ddg_snippet": "4. Climate change is mainly caused by natural variations (such as changes in solar radiation intensity and volcanic eruptions). Kirilenko , A. P. and Stepchenkova , S. O. (2012). ‘ Climate change discourse in mass media: application of computer-assisted content analysis’.", "subpage_snippet": "", "source": "jcom.sissa.it", "link": "https://jcom.sissa.it/article/755/galley/1095/download/", "content": "4. Climate change is mainly caused by natural variations (such as changes in solar radiation intensity and volcanic eruptions). Kirilenko , A. P. and Stepchenkova , S. O. (2012). ‘ Climate change discourse in mass media: application of computer-assisted content analysis’."}
+{"idx": 7, "title": "Detecting Climate Change Deniers on Twitter ... | Semantic Scholar", "date": "", "ddg_snippet": "Climate change or global warming is a global threat to both human communities and natural systems.Taking Twitter data as an example, this study analyzed public discussions about climate change and global warming in year 2016.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Detecting-Climate-Change-Deniers-on-Twitter-Using-a-Chen-Zou/69ad5b2af09d62f2664cfd24340ba9d0cfebbcc6", "content": "Climate change or global warming is a global threat to both human communities and natural systems.Taking Twitter data as an example, this study analyzed public discussions about climate change and global warming in year 2016."}
+{"idx": 8, "title": "Men deny more than they believe about climate change on Twitter (X)", "date": "", "ddg_snippet": "Climate change and twitter have been in scholarly and academic attention for study of human behaviour expressed on the popular social media platform.", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0303007", "content": "Climate change and twitter have been in scholarly and academic attention for study of human behaviour expressed on the popular social media platform."}
+{"idx": 9, "title": "Data, concepts and methods for large-n comparative climate change ...", "date": "", "ddg_snippet": "Scopus search “ climate change adaptation” AND “Compar*”; check of abstracts for relevant search terms and synonyms (September 2017).Type and content: excluding abstracts without explicit reference to comparative climate change adaptation.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/199220827.pdf", "content": "Scopus search “ climate change adaptation” AND “Compar*”; check of abstracts for relevant search terms and synonyms (September 2017).Type and content: excluding abstracts without explicit reference to comparative climate change adaptation."}
diff --git a/data/sampled_jsons/Kirkpatrick_2017_overcoming_catastrophic_forgetting_abstract_PNAS_year_2017.jsonl b/data/sampled_jsons/Kirkpatrick_2017_overcoming_catastrophic_forgetting_abstract_PNAS_year_2017.jsonl
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index 0000000000000000000000000000000000000000..0462a0f7a98a9ae0ee94c014fe71db1565bee1f8
--- /dev/null
+++ b/data/sampled_jsons/Kirkpatrick_2017_overcoming_catastrophic_forgetting_abstract_PNAS_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Overcoming catastrophic forgetting in neural networks", "date": "", "ddg_snippet": "Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of ...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/28292907/", "content": "Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of ..."}
+{"idx": 1, "title": "[1612.00796] Overcoming catastrophic forgetting in neural", "date": "", "ddg_snippet": "View a PDF of the paper titled Overcoming catastrophic forgetting in neural networks, by James Kirkpatrick and 13 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1612.00796", "content": "View a PDF of the paper titled Overcoming catastrophic forgetting in neural networks, by James Kirkpatrick and 13 other authors"}
+{"idx": 2, "title": "Forgetting in neural networks just got less catastrophic", "date": "", "ddg_snippet": "The work is presented in \" Overcoming catastrophic forgetting in neural networks.\" It is in the Proceedings of the National Academy of Sciences ( PNAS ...", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2017-03-neural-networks-catastrophic.html", "content": "The work is presented in \" Overcoming catastrophic forgetting in neural networks.\" It is in the Proceedings of the National Academy of Sciences ( PNAS ..."}
+{"idx": 3, "title": "Can sleep protect memories from catastrophic forgetting? | eLife", "date": "", "ddg_snippet": "These ideas led to interesting attempts of solving the catastrophic forgetting problem in artificial neural networks ( Kemker and Kanan, 2017 ).", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/51005", "content": "These ideas led to interesting attempts of solving the catastrophic forgetting problem in artificial neural networks ( Kemker and Kanan, 2017 )."}
+{"idx": 4, "title": "Catastrophic Forgetting in Deep Learning: A Comprehensive", "date": "", "ddg_snippet": "... new tasks and update their knowledge without access to previous data, leading to a significant loss of accuracy known as Catastrophic Forgetting (CF).", "subpage_snippet": "", "source": "journals-sol.sbc.org.br", "link": "https://journals-sol.sbc.org.br/index.php/jbcs/article/view/3966", "content": "... new tasks and update their knowledge without access to previous data, leading to a significant loss of accuracy known as Catastrophic Forgetting (CF)."}
+{"idx": 5, "title": "Keep the General, Inject the Specific: Structured Dialogue", "date": "", "ddg_snippet": "... models struggle with a fundamental dilemma: direct adaptation approaches that inject domain-specific knowledge often trigger catastrophic forgetting ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00029v1", "content": "... models struggle with a fundamental dilemma: direct adaptation approaches that inject domain-specific knowledge often trigger catastrophic forgetting ..."}
+{"idx": 6, "title": "Investigating Critical Period Effects in Language Acquisition", "date": "", "ddg_snippet": "... Elastic Weight Consolidation ( EWC ; Kirkpatrick et al., 2017 ), a Bayesian regularizer used in machine learning to mitigate catastrophic forgetting ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00725/127669/Investigating-Critical-Period-Effects-in-Language", "content": "... Elastic Weight Consolidation ( EWC ; Kirkpatrick et al., 2017 ), a Bayesian regularizer used in machine learning to mitigate catastrophic forgetting ..."}
+{"idx": 7, "title": "Annals of Computer Science and Information Systems, Volume 31", "date": "", "ddg_snippet": "Atkinson et al., “Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting ,” Neurocomputing, vol.", "subpage_snippet": "", "source": "annals-csis.org", "link": "https://annals-csis.org/Volume_31/drp/267.html", "content": "Atkinson et al., “Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting ,” Neurocomputing, vol."}
+{"idx": 8, "title": "Learning Personalizable Clustered Embedding for Recommender", "date": "", "ddg_snippet": "Overcoming catastrophic forgetting in neural networks. In Proceedings of the National Academy of Sciences ( PNAS ) 114, 13 ( 2017 ), 3521--3526.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3665933", "content": "Overcoming catastrophic forgetting in neural networks. In Proceedings of the National Academy of Sciences ( PNAS ) 114, 13 ( 2017 ), 3521--3526."}
+{"idx": 9, "title": "Beyond gradients: Factorized, geometric control of interference", "date": "", "ddg_snippet": "When animals learn new skills they often generalize prior learning, and rarely forget or degrade it ( Dekker et al .2022 ; Franklin & Frank 2018 ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/103701", "content": "When animals learn new skills they often generalize prior learning, and rarely forget or degrade it ( Dekker et al .2022 ; Franklin & Frank 2018 ..."}
diff --git a/data/sampled_jsons/Koh_et_al._2020_concept_bottleneck_models_abstract_year_2020.jsonl b/data/sampled_jsons/Koh_et_al._2020_concept_bottleneck_models_abstract_year_2020.jsonl
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index 0000000000000000000000000000000000000000..6e9c750f4dea1211780c67965b34cea7ccfc0f9f
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2007.04612] Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.04612", "content": "Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 1, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "Earlier versions of concept bottleneck models were over-taken in predictive accuracy by end-to-end neural networks (e.g., Kumar et al . (2009) for face recognition and Lam-pert et al . (2009) for animal identification), leading to a perceived tradeoff between accuracy and interpretability in terms of concepts . In this paper, we propose a straightfor-ward method for turning any end-to-end neural ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a/koh20a.pdf", "content": "Earlier versions of concept bottleneck models were over-taken in predictive accuracy by end-to-end neural networks (e.g., Kumar et al . (2009) for face recognition and Lam-pert et al . (2009) for animal identification), leading to a perceived tradeoff between accuracy and interpretability in terms of concepts . In this paper, we propose a straightfor-ward method for turning any end-to-end neural ..."}
+{"idx": 2, "title": "(PDF) Interactive Concept Bottleneck Models - ResearchGate", "date": "", "ddg_snippet": "Dec 14, 2022 · Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366321114_Interactive_Concept_Bottleneck_Models", "content": "Dec 14, 2022 · Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict ..."}
+{"idx": 3, "title": "dblp: Concept Bottleneck Models. Interactive Concept Bottleneck Models - Google Research Concept Bottleneck Models - PMLR [2007.04612] Concept Bottleneck Models - arXiv.org Concept Bottleneck Models - proceedings .mlr.press Concept Bottleneck Models - proceedings .mlr.press Concept Bottleneck Models - proceedings .mlr.press [2007.04612] Concept Bottleneck Models - arXiv.org Concept Bottleneck Models - proceedings .mlr.press Concept bottleneck models | Proceedings of the 37th ...", "date": "", "ddg_snippet": "Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348 Abstract Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions. On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). What is a concept bottleneck X-ray grading model? On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Are concept bottleneck models over-taken by end-to-end neural networks? Earlier versions of concept bottleneck models were over-taken in predictive accuracy by end-to-end neural networks (e.g., Kumar et al. (2009) for face recognition and Lam-pert et al. (2009) for animal identification), leading to a perceived tradeoff between accuracy and interpretability in terms of concepts. What is a concept bottleneck model? Concept bottleneck models. Models that bottleneck on human-specified concepts —where the model first predicts the concepts, then uses only those predicted concepts to make a final prediction—have been previously used for specific applications (Kumar et al., 2009; Lampert et al., 2009). Do concept bottleneck models achieve com-petitive task accuracy? Table 1 shows that concept bottleneck models achieve com-petitive task accuracy with standard black-box models on both tasks, despite the bottleneck constraint (all numbers reported are on a held-out test set). How can we intervene on a concept bottleneck model? We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction . What are the disadvantages of concept bottleneck models? A drawback of concept bottleneck models is that they re-quire annotated concepts at training time . However, if the set of concepts are good enough, then fewer training exam-ples might be required to achieve a desired accuracy level (as in OAI). Jul 13, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/icml/KohNTMPKL20", "content": "Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348 Abstract Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions. On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). What is a concept bottleneck X-ray grading model? On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Are concept bottleneck models over-taken by end-to-end neural networks? Earlier versions of concept bottleneck models were over-taken in predictive accuracy by end-to-end neural networks (e.g., Kumar et al. (2009) for face recognition and Lam-pert et al. (2009) for animal identification), leading to a perceived tradeoff between accuracy and interpretability in terms of concepts. What is a concept bottleneck model? Concept bottleneck models. Models that bottleneck on human-specified concepts —where the model first predicts the concepts, then uses only those predicted concepts to make a final prediction—have been previously used for specific applications (Kumar et al., 2009; Lampert et al., 2009). Do concept bottleneck models achieve com-petitive task accuracy? Table 1 shows that concept bottleneck models achieve com-petitive task accuracy with standard black-box models on both tasks, despite the bottleneck constraint (all numbers reported are on a held-out test set). How can we intervene on a concept bottleneck model? We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction . What are the disadvantages of concept bottleneck models? A drawback of concept bottleneck models is that they re-quire annotated concepts at training time . However, if the set of concepts are good enough, then fewer training exam-ples might be required to achieve a desired accuracy level (as in OAI). Jul 13, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 4, "title": "Interactive Concept Bottleneck Models - Google Research", "date": "", "ddg_snippet": "Abstract Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/interactive-concept-bottleneck-models/", "content": "Abstract Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions."}
+{"idx": 5, "title": "Concept Bottleneck Models - PMLR", "date": "", "ddg_snippet": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a.html", "content": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 6, "title": "Concept bottleneck models | Proceedings of the 37th ...", "date": "", "ddg_snippet": "Jul 13, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3524938.3525433", "content": "Jul 13, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 7, "title": "Concept Bottleneck Language Models For protein design", "date": "", "ddg_snippet": "... concepts ) and predicting them ... Our approach builds on the foundation of concept bottleneck generative models (CBGMs) (Ismail et al ., 2023 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.06090v2", "content": "... concepts ) and predicting them ... Our approach builds on the foundation of concept bottleneck generative models (CBGMs) (Ismail et al ., 2023 ) ."}
+{"idx": 8, "title": "If Concept Bottlenecks are the Question, are Foundation Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) [ 41 ] combine two neural modules: a concept extractor f : 𝒳 → ℝ k : 𝑓 → 𝒳 superscript ℝ 𝑘 f ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.19774v2", "content": "Concept Bottleneck Models (CBMs) [ 41 ] combine two neural modules: a concept extractor f : 𝒳 → ℝ k : 𝑓 → 𝒳 superscript ℝ 𝑘 f ..."}
+{"idx": 9, "title": "CLIP-QDA: An Explainable Concept Bottleneck Model", "date": "", "ddg_snippet": "A class of networks that effectively exploits this notion is Concept Bottleneck Models (CBMs) ( Koh et al ., 2020 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.00110v3", "content": "A class of networks that effectively exploits this notion is Concept Bottleneck Models (CBMs) ( Koh et al ., 2020 ) ."}
diff --git a/data/sampled_jsons/Koh_et_al._2020_concept_bottleneck_models_year_2020.jsonl b/data/sampled_jsons/Koh_et_al._2020_concept_bottleneck_models_year_2020.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2007.04612] Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.04612", "content": "Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 1, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "Earlier versions of concept bottleneck models were over-taken in predictive accuracy by end-to-end neural networks (e.g., Kumar et al . (2009) for face recognition and Lam-pert et al . (2009) for animal identification), leading to a perceived tradeoff between accuracy and interpretability in terms of concepts. In this paper, we propose a straightfor-ward method for turning any end-to-end neural ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a/koh20a.pdf", "content": "Earlier versions of concept bottleneck models were over-taken in predictive accuracy by end-to-end neural networks (e.g., Kumar et al . (2009) for face recognition and Lam-pert et al . (2009) for animal identification), leading to a perceived tradeoff between accuracy and interpretability in terms of concepts. In this paper, we propose a straightfor-ward method for turning any end-to-end neural ..."}
+{"idx": 2, "title": "dblp: Concept Bottleneck Models. (PDF) Interactive Concept Bottleneck Models - ResearchGate Concept bottleneck models | Proceedings of the 37th ... GitHub - yewsiang/ConceptBottleneck: Concept Bottleneck ... Concept Bottleneck Models - proceedings .mlr.press Concept Bottleneck Models - proceedings .mlr.press Concept Bottleneck Models - proceedings .mlr.press Concept Bottleneck Models - proceedings .mlr.press Concept Bottleneck Models - proceedings .mlr.press Concept Bottleneck Models - proceedings .mlr.press Concept Bottleneck Models - Google Research", "date": "", "ddg_snippet": "Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348 Dec 14, 2022 · Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict ... Jul 13, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Concept Bottleneck Models , ICML 2020 . Contribute to yewsiang/ConceptBottleneck development by creating an account on GitHub . Are concept bottleneck models over-taken by end-to-end neural networks? Earlier versions of concept bottleneck models were over-taken in predictive accuracy by end-to-end neural networks (e.g., Kumar et al. (2009) for face recognition and Lam-pert et al. (2009) for animal identification), leading to a perceived tradeoff between accuracy and interpretability in terms of concepts. Do concept bottleneck models achieve com-petitive task accuracy? Table 1 shows that concept bottleneck models achieve com-petitive task accuracy with standard black-box models on both tasks, despite the bottleneck constraint (all numbers reported are on a held-out test set). What is a concept bottleneck model? Concept bottleneck models. Models that bottleneck on human-specified concepts —where the model first predicts the concepts, then uses only those predicted concepts to make a final prediction—have been previously used for specific applications (Kumar et al., 2009; Lampert et al., 2009). Do concept bottleneck models support concept interventions? Contributions. We systematically study variants of con-cept bottleneck models and contrast them with standard end-to-end models in different settings, with a focus on the previously-unexplored ability of concept bottleneck models to support concept interventions. What are the disadvantages of concept bottleneck models? A drawback of concept bottleneck models is that they re-quire annotated concepts at training time . However, if the set of concepts are good enough, then fewer training exam-ples might be required to achieve a desired accuracy level (as in OAI). Are concept bottleneck models effective on OAI? To study this, we subsampled the training and validation data and retrained each model (details in Appendix B.4). Concept bottleneck models are particularly effective on OAI : the sequential bottleneck model with 25% of the full dataset performs similarly to the standard model. On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models , while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”).", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/icml/KohNTMPKL20", "content": "Dec 15, 2020 · Dagstuhl > Home [–] Details and statistics DOI: — access: open type: Conference or Workshop Paper metadata version: 2020 -12-15 Pang Wei Koh , Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang: Concept Bottleneck Models . ICML 2020 : 5338-5348 Dec 14, 2022 · Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict ... Jul 13, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Concept Bottleneck Models , ICML 2020 . Contribute to yewsiang/ConceptBottleneck development by creating an account on GitHub . Are concept bottleneck models over-taken by end-to-end neural networks? Earlier versions of concept bottleneck models were over-taken in predictive accuracy by end-to-end neural networks (e.g., Kumar et al. (2009) for face recognition and Lam-pert et al. (2009) for animal identification), leading to a perceived tradeoff between accuracy and interpretability in terms of concepts. Do concept bottleneck models achieve com-petitive task accuracy? Table 1 shows that concept bottleneck models achieve com-petitive task accuracy with standard black-box models on both tasks, despite the bottleneck constraint (all numbers reported are on a held-out test set). What is a concept bottleneck model? Concept bottleneck models. Models that bottleneck on human-specified concepts —where the model first predicts the concepts, then uses only those predicted concepts to make a final prediction—have been previously used for specific applications (Kumar et al., 2009; Lampert et al., 2009). Do concept bottleneck models support concept interventions? Contributions. We systematically study variants of con-cept bottleneck models and contrast them with standard end-to-end models in different settings, with a focus on the previously-unexplored ability of concept bottleneck models to support concept interventions. What are the disadvantages of concept bottleneck models? A drawback of concept bottleneck models is that they re-quire annotated concepts at training time . However, if the set of concepts are good enough, then fewer training exam-ples might be required to achieve a desired accuracy level (as in OAI). Are concept bottleneck models effective on OAI? To study this, we subsampled the training and validation data and retrained each model (details in Appendix B.4). Concept bottleneck models are particularly effective on OAI : the sequential bottleneck model with 25% of the full dataset performs similarly to the standard model. On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models , while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”)."}
+{"idx": 3, "title": "(PDF) Interactive Concept Bottleneck Models - ResearchGate", "date": "", "ddg_snippet": "Dec 14, 2022 · Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366321114_Interactive_Concept_Bottleneck_Models", "content": "Dec 14, 2022 · Concept bottleneck models (CBMs) ( Koh et al . 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict ..."}
+{"idx": 4, "title": "Concept bottleneck models | Proceedings of the 37th ...", "date": "", "ddg_snippet": "Jul 13, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3524938.3525433", "content": "Jul 13, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 5, "title": "GitHub - yewsiang/ConceptBottleneck: Concept Bottleneck ...", "date": "", "ddg_snippet": "Concept Bottleneck Models , ICML 2020 . Contribute to yewsiang/ConceptBottleneck development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yewsiang/ConceptBottleneck", "content": "Concept Bottleneck Models , ICML 2020 . Contribute to yewsiang/ConceptBottleneck development by creating an account on GitHub ."}
+{"idx": 6, "title": "Concept Bottleneck Models - Google Research", "date": "", "ddg_snippet": "On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models , while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”).", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/concept-bottleneck-models/", "content": "On an x-ray dataset and bird species recognition dataset, concept bottleneck models achieve competitive predictive accuracy with standard end-to-end models , while allowing us to explain predictions in terms of high-level clinical concepts (“bone spurs”) and bird attributes (“wing color”)."}
+{"idx": 7, "title": "Concept Bottleneck Models - PMLR", "date": "", "ddg_snippet": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\").", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a.html", "content": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\")."}
+{"idx": 8, "title": "Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "Earlier versions of concept bottleneck models were over-taken in predictive accuracy by end-to-end neural networks (e.g., Kumar et al. (2009) for face recognition and Lampert et al. (2009) for animal identification), leading to a perceived tradeoff between accuracy and interpretability in terms of concepts . Recently, concept bottleneck models have started to re-emerge as targeted tools for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2007.04612", "content": "Earlier versions of concept bottleneck models were over-taken in predictive accuracy by end-to-end neural networks (e.g., Kumar et al. (2009) for face recognition and Lampert et al. (2009) for animal identification), leading to a perceived tradeoff between accuracy and interpretability in terms of concepts . Recently, concept bottleneck models have started to re-emerge as targeted tools for ..."}
+{"idx": 9, "title": "Interactive Concept Bottleneck Models - ojs.aaai.org", "date": "", "ddg_snippet": "Figure 1: Interactive Prediction: Panel (a) shows a concept bottleneck model ( Koh et al. 2020 ) that predicts a label y from an input x through an intermediate \" concept \" prediction layer (Figure adapted from Koh et al. ( 2020 )). Panel (b) shows our proposal: after predicting concepts , the system interactively queries the human for true values ci for concepts chosen so as to maximize ...", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/25736/25508", "content": "Figure 1: Interactive Prediction: Panel (a) shows a concept bottleneck model ( Koh et al. 2020 ) that predicts a label y from an input x through an intermediate \" concept \" prediction layer (Figure adapted from Koh et al. ( 2020 )). Panel (b) shows our proposal: after predicting concepts , the system interactively queries the human for true values ci for concepts chosen so as to maximize ..."}
diff --git a/data/sampled_jsons/Kwon_Alsabah_Devadas_circuit_fingerprinting_attacks_abstract_USENIX_2015.jsonl b/data/sampled_jsons/Kwon_Alsabah_Devadas_circuit_fingerprinting_attacks_abstract_USENIX_2015.jsonl
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index 0000000000000000000000000000000000000000..6013f1bb78d30b07a2e7d238b15ac53b3020687c
--- /dev/null
+++ b/data/sampled_jsons/Kwon_Alsabah_Devadas_circuit_fingerprinting_attacks_abstract_USENIX_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Circuit Fingerprinting Attacks: Passive Deanonymization of Tor ... - USENIX", "date": "", "ddg_snippet": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services Albert Kwon , Massachusetts Institute of Technology; Mashael AlSabah , Qatar Computing Research Institute, Qatar University, and Massachusetts Institute of Technology; David Lazar, Massachusetts Institute of Technology; Marc Dacier, Qatar Computing Research Institute; Srinivas Devadas , Massachusetts Institute of ...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/conference/usenixsecurity15/sec15-paper-kwon.pdf", "content": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services Albert Kwon , Massachusetts Institute of Technology; Mashael AlSabah , Qatar Computing Research Institute, Qatar University, and Massachusetts Institute of Technology; David Lazar, Massachusetts Institute of Technology; Marc Dacier, Qatar Computing Research Institute; Srinivas Devadas , Massachusetts Institute of ..."}
+{"idx": 1, "title": "Circuit fingerprinting attacks | Proceedings of the 24th USENIX ...", "date": "", "ddg_snippet": "Abstract This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively. In particular, we show that the circuits , paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit . We propose two attacks , under ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2831143.2831162", "content": "Abstract This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively. In particular, we show that the circuits , paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit . We propose two attacks , under ..."}
+{"idx": 2, "title": "Circuit fingerprinting attacks: Passive deanonymization of tor hidden ...", "date": "", "ddg_snippet": "This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively. In particular, we show that the circuits , paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit . We propose two attacks , under two ...", "subpage_snippet": "", "source": "academia.kaust.edu.sa", "link": "https://academia.kaust.edu.sa/en/publications/circuit-fingerprinting-attacks-passive-deanonymization-of-tor-hid", "content": "This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively. In particular, we show that the circuits , paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit . We propose two attacks , under two ..."}
+{"idx": 3, "title": "Discovering onion services through circuit fingerprinting attacks", "date": "", "ddg_snippet": "Circuit fingerprinting attack is a traffic analysis attack against Tor which break or reduce the anonymity that Tor aims to provide. Kwon et al. [1] discovered the fingerprint features of circuits and used the features to first propose a circuit fingerprinting attack . To measure the popularity of onion services, Jansen et al. [2] proposes a circuit fingerprinting attack that can be implemented ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2667295222000514", "content": "Circuit fingerprinting attack is a traffic analysis attack against Tor which break or reduce the anonymity that Tor aims to provide. Kwon et al. [1] discovered the fingerprint features of circuits and used the features to first propose a circuit fingerprinting attack . To measure the popularity of onion services, Jansen et al. [2] proposes a circuit fingerprinting attack that can be implemented ..."}
+{"idx": 4, "title": "PDF Circuit Fingerprinting Attacks: Passive Deanonymization of Tor Hidden ...", "date": "", "ddg_snippet": "In this paper, we present the first practical passive attack against hidden services and their users called circuit fingerprinting attack . Using our attack , an at-tacker can identify the presence of (client or server) hid-den service activity in the network with high accuracy.", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/devadas/pubs/circuit_finger.pdf", "content": "In this paper, we present the first practical passive attack against hidden services and their users called circuit fingerprinting attack . Using our attack , an at-tacker can identify the presence of (client or server) hid-den service activity in the network with high accuracy."}
+{"idx": 5, "title": "PDF Poster: Fingerprinting Hidden Service Circuits from a Tor Middle Relay", "date": "", "ddg_snippet": "Abstract—Kwon et al. recently showed that circuit fingerprint-ing attacks could be used to identify hidden service circuits , which is a key step towards linking Tor users and their activity online.", "subpage_snippet": "", "source": "www.ieee-security.org", "link": "https://www.ieee-security.org/TC/SP2017/poster-abstracts/IEEE-SP17_Posters_paper_36.pdf", "content": "Abstract—Kwon et al. recently showed that circuit fingerprint-ing attacks could be used to identify hidden service circuits , which is a key step towards linking Tor users and their activity online."}
+{"idx": 6, "title": "Circuit Fingerprinting Attacks: Passive Deanonymization of Tor Hidden ...", "date": "", "ddg_snippet": "A novel circuit fingerprinting attack is presented, which divides the circuit into the circuitgenerated by the client and the circuit generated by the onion service, and achieves highly accurate circuits fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Circuit-Fingerprinting-Attacks:-Passive-of-Tor-Kwon-Alsabah/fb4cb1f1c57ee56e9a014570debf7d3d6871ddf3/figure/2", "content": "A novel circuit fingerprinting attack is presented, which divides the circuit into the circuitgenerated by the client and the circuit generated by the onion service, and achieves highly accurate circuits fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor."}
+{"idx": 7, "title": "Circuit Fingerprinting Attacks_ Passive Deanonymization of Tor Hidden ...", "date": "", "ddg_snippet": "Parent Folder Circuit Fingerprinting Attacks _ Passive Deanonymization of Tor Hidden Services_ Albert Kwon _ Mashael AlSabah _ David Lazar_ Marc Dacier_ Srinivas Devadas _ 2015 .pdf PDF1 year ago522.52 kB www.superkuh.com> library> Computing>", "subpage_snippet": "", "source": "filepursuit.com", "link": "https://filepursuit.com/file/38307565-Circuit-Fingerprinting-Attacks-Passive-Deanonymization-of-Tor-Hidden-Services-Albert-Kwon-Mashael-AlSabah-David-Lazar-Marc-Dacier-Srinivas-Devadas-2015-pdf/", "content": "Parent Folder Circuit Fingerprinting Attacks _ Passive Deanonymization of Tor Hidden Services_ Albert Kwon _ Mashael AlSabah _ David Lazar_ Marc Dacier_ Srinivas Devadas _ 2015 .pdf PDF1 year ago522.52 kB www.superkuh.com> library> Computing>"}
+{"idx": 8, "title": "Circuit Fingerprinting Attacks: Passive Deanonymization of Tor ... - USENIX", "date": "", "ddg_snippet": "author = {Albert Kwon and Mashael AlSabah and David Lazar and Marc Dacier and Srinivas Devadas }, title = { Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services},", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/kwon", "content": "author = {Albert Kwon and Mashael AlSabah and David Lazar and Marc Dacier and Srinivas Devadas }, title = { Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services},"}
+{"idx": 9, "title": "A technical summary of the Usenix fingerprinting paper - Tor", "date": "", "ddg_snippet": "Albert Kwon , Mashael AlSabah , and others have a paper entitled Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services at the upcoming Usenix Security symposium in a few weeks.", "subpage_snippet": "", "source": "blog.torproject.org", "link": "https://blog.torproject.org/technical-summary-usenix-fingerprinting-paper/", "content": "Albert Kwon , Mashael AlSabah , and others have a paper entitled Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services at the upcoming Usenix Security symposium in a few weeks."}
diff --git a/data/sampled_jsons/LAUREL-40_Table_7_improvement_percentages.jsonl b/data/sampled_jsons/LAUREL-40_Table_7_improvement_percentages.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c45b779e3b98c6dd7f3498f0958bc409d54ea8f1
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+++ b/data/sampled_jsons/LAUREL-40_Table_7_improvement_percentages.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Free Online Calculator - Basic Arithmetic, Percentages , and More", "date": "", "ddg_snippet": "Discover a free online calculator with basic arithmetic functions, history tracking, and percentage calculations.", "subpage_snippet": "", "source": "linangdata.com", "link": "https://linangdata.com/calculator/", "content": "Discover a free online calculator with basic arithmetic functions, history tracking, and percentage calculations."}
+{"idx": 1, "title": "Percentage Calculator", "date": "", "ddg_snippet": "Percentage Calculator is a free online tool to calculate percentages .Why percentages matter? Percentages are used in a wide variety of contexts, from calculating discounts and taxes to measuring changes in stock prices and economic indicators.", "subpage_snippet": "", "source": "percentagecalculator.net", "link": "https://percentagecalculator.net/", "content": "Percentage Calculator is a free online tool to calculate percentages .Why percentages matter? Percentages are used in a wide variety of contexts, from calculating discounts and taxes to measuring changes in stock prices and economic indicators."}
+{"idx": 2, "title": "The complete Almaty, Kazakhstan earthquake report (up-to-date 2025).", "date": "", "ddg_snippet": "Magnitude. Earthquakes. Percentage .In the table below you will find the strongest earthquakes that occurred near Almaty in the past 10 years. You can use the tabs to find the heaviest historic earthquakes since the year 1900 or within a specific year or distance from Almaty.", "subpage_snippet": "", "source": "earthquakelist.org", "link": "https://earthquakelist.org/kazakhstan/almaty/almaty/", "content": "Magnitude. Earthquakes. Percentage .In the table below you will find the strongest earthquakes that occurred near Almaty in the past 10 years. You can use the tabs to find the heaviest historic earthquakes since the year 1900 or within a specific year or distance from Almaty."}
+{"idx": 3, "title": "40 Feet To Meters Converter | 40 ft To m Converter", "date": "", "ddg_snippet": "Convert 40 Foot to Meter with formula, common lengths conversion, conversion tables and more.", "subpage_snippet": "", "source": "feet-to-meters.appspot.com", "link": "https://feet-to-meters.appspot.com/40-feet-to-meters.html", "content": "Convert 40 Foot to Meter with formula, common lengths conversion, conversion tables and more."}
+{"idx": 4, "title": "Купить Ниссан Лаурель C35 в Новосибирске: продажа Nissan...", "date": "", "ddg_snippet": "21 объявление о продаже Ниссан Лаурель C35 б/у и новых в Новосибирске от 285 000 рублей – частные объявления. Узнать стоимость Nissan Laurel C35 и купить с пробегом на Drom.ru.", "subpage_snippet": "", "source": "novosibirsk.drom.ru", "link": "https://novosibirsk.drom.ru/nissan/laurel/generation8/", "content": "21 объявление о продаже Ниссан Лаурель C35 б/у и новых в Новосибирске от 285 000 рублей – частные объявления. Узнать стоимость Nissan Laurel C35 и купить с пробегом на Drom.ru."}
+{"idx": 5, "title": "Percentage - Formula | How To Calculate Percentage ?", "date": "", "ddg_snippet": "The percentage is a fraction with 100 as the denominator. Learn more about how to calculate percentages , and how to convert them into fractions, decimals, and vice versa along with solved examples.", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/commercial-math/percentages/", "content": "The percentage is a fraction with 100 as the denominator. Learn more about how to calculate percentages , and how to convert them into fractions, decimals, and vice versa along with solved examples."}
+{"idx": 6, "title": "40 Inches To Centimeters Converter | 40 in To cm Converter", "date": "", "ddg_snippet": "Convert 40 Inch to Centimeter with formula, common lengths conversion, conversion tables and more.Thus, for 40 inches in centimeter we get 101.6 cm. 40 Inch Conversion Table .", "subpage_snippet": "", "source": "inches-to-cm.appspot.com", "link": "https://inches-to-cm.appspot.com/40-inches-to-cm.html", "content": "Convert 40 Inch to Centimeter with formula, common lengths conversion, conversion tables and more.Thus, for 40 inches in centimeter we get 101.6 cm. 40 Inch Conversion Table ."}
+{"idx": 7, "title": "Гороскоп на сегодня | ГОРОСКОПЫ 365", "date": "", "ddg_snippet": "Дева. Весы. 00: 40 . Растущая Луна Первая фаза.", "subpage_snippet": "", "source": "goroskop365.ru", "link": "https://goroskop365.ru/", "content": "Дева. Весы. 00: 40 . Растущая Луна Первая фаза."}
+{"idx": 8, "title": "Как понять, что начались схватки: ощущения у первородящих...", "date": "", "ddg_snippet": "К 37 неделям беременности происходит физиологическая денервация матки: часть чувствительных нервных окончаний разрушается. Поэтому схватки в 35 недель могут быть более болезненными, чем при доношенной беременности в 40 недель...", "subpage_snippet": "", "source": "www.kp.ru", "link": "https://www.kp.ru/family/ya-mama/kak-ponyat-chto-nachalis-skhvatki/", "content": "К 37 неделям беременности происходит физиологическая денервация матки: часть чувствительных нервных окончаний разрушается. Поэтому схватки в 35 недель могут быть более болезненными, чем при доношенной беременности в 40 недель..."}
+{"idx": 9, "title": "Как я дообучал Tesseract и что из этого получилось / Хабр", "date": "", "ddg_snippet": "Заключение. Для обучения модели у меня было 40 размеченных страниц документов - картинок. Всего ставил 140 тыс. итераций тренировки. Ошибка при этом опустилась с 27.571 до 3.832.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/rosatom/articles/669020/", "content": "Заключение. Для обучения модели у меня было 40 размеченных страниц документов - картинок. Всего ставил 140 тыс. итераций тренировки. Ошибка при этом опустилась с 27.571 до 3.832."}
diff --git a/data/sampled_jsons/LEnergy_integral_expression_v_theta^2_nabla_x_s_theta^2_gQlxd3Mtru.jsonl b/data/sampled_jsons/LEnergy_integral_expression_v_theta^2_nabla_x_s_theta^2_gQlxd3Mtru.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/LEnergy_integral_expression_v_theta^2_nabla_x_s_theta^2_gQlxd3Mtru.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/LEnergy_loss_Equation_(10)_Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalance.jsonl b/data/sampled_jsons/LEnergy_loss_Equation_(10)_Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f15f88fcb35d72c2ab7ff0796eb51403e882fa0a
--- /dev/null
+++ b/data/sampled_jsons/LEnergy_loss_Equation_(10)_Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Introduction Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zhenyiizhang/DeepRUOT", "content": "Introduction Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 1, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Oct 1, 2024 · Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.00844", "content": "Oct 1, 2024 · Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 2, "title": "LEARNING STOCHASTIC DYNAMICS FROM SNAP SHOTS THROUGH ...", "date": "", "ddg_snippet": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dy- namics from observed snapshots .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/32b8a612105de5c22db337b774ce7b61-Paper-Conference.pdf", "content": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dy- namics from observed snapshots ."}
+{"idx": 3, "title": "LEARNING STOCHASTIC DYNAMICS FROM SNAP SHOTS THROUGH ...", "date": "", "ddg_snippet": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced opti- mal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots . Based on the RUOT form, our method models these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=4oXLrbMaV4&name=pdf", "content": "ABSTRACT Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced opti- mal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots . Based on the RUOT form, our method models these ..."}
+{"idx": 4, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v2", "content": "Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 5, "title": "Learning stochasticdynamics from snapshots through ...", "date": "", "ddg_snippet": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport ZhenyiZhang TiejunLi* Peking University PeijieZhou* *Joint corresponding authors", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/31800_i2NXVyQ.pdf", "content": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport ZhenyiZhang TiejunLi* Peking University PeijieZhou* *Joint corresponding authors"}
+{"idx": 6, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "We have introduced DeepRUOT for learning regularized unbalanced optimal transport (RUOT) and continuous unbalanced stochastic dynamics from time-series snapshot data. By leveraging Fisher regularization , our method transforms an SDE problem into an ODE constraint.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v5", "content": "We have introduced DeepRUOT for learning regularized unbalanced optimal transport (RUOT) and continuous unbalanced stochastic dynamics from time-series snapshot data. By leveraging Fisher regularization , our method transforms an SDE problem into an ODE constraint."}
+{"idx": 7, "title": "(PDF) Learning Stochastic Dynamics from Snapshots through ...", "date": "", "ddg_snippet": "Through regularized unbalanced optimal transport . Zhenyi zhang†, tiejun LI‡,and peijie zhou§. Abstract. Reconstructing dynamics using samples from sparsely time-resolved snapshots is an im", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384563451_Learning_Stochastic_Dynamics_from_Snapshots_through_Regularized_Unbalanced_Optimal_Transport", "content": "Through regularized unbalanced optimal transport . Zhenyi zhang†, tiejun LI‡,and peijie zhou§. Abstract. Reconstructing dynamics using samples from sparsely time-resolved snapshots is an im"}
+{"idx": 8, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT)...", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Learning-Stochastic-Dynamics-from-Snapshots-through-Regularized-Unbalanced-Optimal-Transport-39225f33-522f-44ce-b836-70679229a4ab", "content": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT)..."}
+{"idx": 9, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gQlxd3Mtru", "content": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
diff --git a/data/sampled_jsons/LEnergy_loss_Equation_(10)_repogithub.comzhenyiizhangDeepRUOT.jsonl b/data/sampled_jsons/LEnergy_loss_Equation_(10)_repogithub.comzhenyiizhangDeepRUOT.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ae0d0e9af529e7a077223fd8d40db15e180fd5e
--- /dev/null
+++ b/data/sampled_jsons/LEnergy_loss_Equation_(10)_repogithub.comzhenyiizhangDeepRUOT.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - zhenyiizhang / DeepRUOT : Learning stochastic dynamics...", "date": "", "ddg_snippet": "How to cite. If you find DeepRUOT useful in your research, please consider citing our work. Zhang, Z., Li, T., & Zhou, P. (2025). Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zhenyiizhang/DeepRUOT", "content": "How to cite. If you find DeepRUOT useful in your research, please consider citing our work. Zhang, Z., Li, T., & Zhou, P. (2025). Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport."}
+{"idx": 1, "title": "Installation and Setup | zhenyiizhang / DeepRUOT | DeepWiki", "date": "", "ddg_snippet": "This document provides detailed instructions for installing and configuring DeepRUOT , a deep learning framework for reconstructing dynamics from snapshot data using regularized unbalanced optimal tran.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/zhenyiizhang/DeepRUOT/1.1-installation-and-setup", "content": "This document provides detailed instructions for installing and configuring DeepRUOT , a deep learning framework for reconstructing dynamics from snapshot data using regularized unbalanced optimal tran."}
+{"idx": 2, "title": "Replace 'hub' with 'ingest' in any GitHub URL for a prompt-f...", "date": "", "ddg_snippet": "GitHub logo GitHub . GitHub star icon 12.5k. Prompt-friendly codebase. Turn any Git repository into a simple text digest of its codebase.Used once for cloning, then discarded from memory. No browser caching. Cloned repos are deleted after processing.", "subpage_snippet": "", "source": "gitingest.com", "link": "https://gitingest.com/", "content": "GitHub logo GitHub . GitHub star icon 12.5k. Prompt-friendly codebase. Turn any Git repository into a simple text digest of its codebase.Used once for cloning, then discarded from memory. No browser caching. Cloned repos are deleted after processing."}
+{"idx": 3, "title": "Learning stochastic dynamics from snapshots through regularized...", "date": "", "ddg_snippet": "Here we develop a new deep learning method ( DeepRUOT ) for learning general RUOT and inferring continuous unbalanced stochastic dynamics from samples based on the derived Fisher regularization form without requiring prior knowledge.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v2", "content": "Here we develop a new deep learning method ( DeepRUOT ) for learning general RUOT and inferring continuous unbalanced stochastic dynamics from samples based on the derived Fisher regularization form without requiring prior knowledge."}
+{"idx": 4, "title": "Ошибка \"irql not less or equal \" при работе в Windows 10 /11...", "date": "", "ddg_snippet": "OComp.info в Windows 10 .Как выглядит ошибка на практике, стоп-код - \"irql not less or equal \" (пример).", "subpage_snippet": "", "source": "ocomp.info", "link": "https://ocomp.info/irql-not-less-or-equal.html", "content": "OComp.info в Windows 10 .Как выглядит ошибка на практике, стоп-код - \"irql not less or equal \" (пример)."}
+{"idx": 5, "title": "Английский язык 5 класс Spotlight Английский в фокусе Ваулина.", "date": "", "ddg_snippet": "Подписаться в Телеграм. Раздел: Starter Unit (pp. 10 -24). Numbers (страница 20).Решение. reshalka. com .", "subpage_snippet": "", "source": "Reshalka.com", "link": "https://Reshalka.com/uchebniki/5-klass/english/vaulina/43", "content": "Подписаться в Телеграм. Раздел: Starter Unit (pp. 10 -24). Numbers (страница 20).Решение. reshalka. com ."}
+{"idx": 6, "title": "Download Instagram profile avatar • View profile photo", "date": "", "ddg_snippet": "anonyig. com .", "subpage_snippet": "", "source": "anonyig.com", "link": "https://anonyig.com/en/instagram-profile-viewer/", "content": "anonyig. com ."}
+{"idx": 7, "title": "простой ресайзер изображений: изменить размер фото... | Fotor", "date": "", "ddg_snippet": "GoArt. Конвертировать Изображения. DMCA. com Protection Status.", "subpage_snippet": "", "source": "www.fotor.com", "link": "https://www.fotor.com/ru/features/resize.html", "content": "GoArt. Конвертировать Изображения. DMCA. com Protection Status."}
+{"idx": 8, "title": "Калькулятор уравнений", "date": "", "ddg_snippet": "integral icon Интегралы. equation icon Уравнения.", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/equ/ru/", "content": "integral icon Интегралы. equation icon Уравнения."}
+{"idx": 9, "title": "Dynamic modeling, optimization, and deep learning for...", "date": "", "ddg_snippet": "In this study, we present GraphFP, a nonlinear Fokker-Planck equation on graph based model and dynamic inference framework, with the aim of reconstructing the cell state-transition complex potential energy landscape from time series single-cell transcriptomic data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389285565_Dynamic_modeling_optimization_and_deep_learning_for_high-dimensional_complex_biological_data", "content": "In this study, we present GraphFP, a nonlinear Fokker-Planck equation on graph based model and dynamic inference framework, with the aim of reconstructing the cell state-transition complex potential energy landscape from time series single-cell transcriptomic data."}
diff --git a/data/sampled_jsons/LHRS-Align_LHRS-Instruct_dataset_scale_annotations_number_Muhtar_year_2024.jsonl b/data/sampled_jsons/LHRS-Align_LHRS-Instruct_dataset_scale_annotations_number_Muhtar_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ff877036255ca6ba69ff141e4e98a199c8a07da3
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Align_LHRS-Instruct_dataset_scale_annotations_number_Muhtar_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2402.02544] LHRS-Bot: Empowering Remote Sensing with VGI ...", "date": "", "ddg_snippet": "Feb 4, 2024 · To bridge this gap, we construct a large- scale RS image-text dataset , LHRS-Align , and an informative RS-specific instruction dataset , LHRS-Instruct , leveraging the extensive volunteered geographic information (VGI) and globally available RS images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.02544", "content": "Feb 4, 2024 · To bridge this gap, we construct a large- scale RS image-text dataset , LHRS-Align , and an informative RS-specific instruction dataset , LHRS-Instruct , leveraging the extensive volunteered geographic information (VGI) and globally available RS images."}
+{"idx": 1, "title": "GitHub - NJU-LHRS/LHRS-Bot: VGI-Enhanced multimodal large ...", "date": "", "ddg_snippet": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !"}
+{"idx": 2, "title": "LHRS-Align|遥感技术数据集|图像文本对齐数据集", "date": "", "ddg_snippet": "Jul 16, 2024 · LHRS-Align 数据集是由Dilxat Muhtar 、Zhenshi Li、Feng Gu、Xueliang Zhang和Pengfeng Xiao等研究人员于2024年创建的，旨在推动遥感图像与志愿者地理信息（VGI）的融合研究。 该数据集的核心研究问题是如何利用大规模多模态语言模型（MLLM）增强遥感图像的理解与推理能力。", "subpage_snippet": "", "source": "www.selectdataset.com", "link": "https://www.selectdataset.com/dataset/065f30a28afb731ecc38b64d9d4d47f0", "content": "Jul 16, 2024 · LHRS-Align 数据集是由Dilxat Muhtar 、Zhenshi Li、Feng Gu、Xueliang Zhang和Pengfeng Xiao等研究人员于2024年创建的，旨在推动遥感图像与志愿者地理信息（VGI）的融合研究。 该数据集的核心研究问题是如何利用大规模多模态语言模型（MLLM）增强遥感图像的理解与推理能力。"}
+{"idx": 3, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "To unleash the potential of LLMs for RS image understanding, we curate a large- scale dataset , LHRS-Align , for RS-specific alignment, and LHRS-Instruct , a multimodal instruction-following dataset to enhance LHRS -Bot’s instruction-following capabilities.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-72904-1_26.pdf", "content": "To unleash the potential of LLMs for RS image understanding, we curate a large- scale dataset , LHRS-Align , for RS-specific alignment, and LHRS-Instruct , a multimodal instruction-following dataset to enhance LHRS -Bot’s instruction-following capabilities."}
+{"idx": 4, "title": "Require Training Dataset · Issue #35 · NJU-LHRS/LHRS-Bot", "date": "", "ddg_snippet": "Jan 31, 2025 · Can you please provide me your training datasets? These following: LHRS_Align _Recap, LHRS_Instruct LRV_ Instruct LHRS-Instruct -Plus Multi Task", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/issues/35", "content": "Jan 31, 2025 · Can you please provide me your training datasets? These following: LHRS_Align _Recap, LHRS_Instruct LRV_ Instruct LHRS-Instruct -Plus Multi Task"}
+{"idx": 5, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "Mar 18, 2024 · To bridge this gap, we construct a large- scale RS image-text dataset , LHRS 1 - Align , and an informative RS-specific instruction dataset , LHRS-Instruct , leveraging the extensive volunteered geographic information (VGI) and globally available RS images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.02544v3", "content": "Mar 18, 2024 · To bridge this gap, we construct a large- scale RS image-text dataset , LHRS 1 - Align , and an informative RS-specific instruction dataset , LHRS-Instruct , leveraging the extensive volunteered geographic information (VGI) and globally available RS images."}
+{"idx": 6, "title": "Landsat30-AU: A Vision-Language Dataset for Australian Landsat", "date": "", "ddg_snippet": "... the challenges of scale and label quality, we develop a semi-automatic bootstrapped pipeline that extends the methodologies of HRS- Align ( Muhtar et ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03127v1", "content": "... the challenges of scale and label quality, we develop a semi-automatic bootstrapped pipeline that extends the methodologies of HRS- Align ( Muhtar et ..."}
+{"idx": 7, "title": "ECVA | European Computer Vision Association", "date": "", "ddg_snippet": "To bridge this gap, we construct a large- scale RS image-text dataset , LHRS1 - Align , and an informative RS-specific instruction dataset , , leveraging the extensive volunteered geographic information (VGI) and globally available RS images.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/9511_ECCV_2024_paper.php", "content": "To bridge this gap, we construct a large- scale RS image-text dataset , LHRS1 - Align , and an informative RS-specific instruction dataset , , leveraging the extensive volunteered geographic information (VGI) and globally available RS images."}
+{"idx": 8, "title": "EarthGPT-X: Enabling MLLMs to Flexibly and Comprehensively", "date": "", "ddg_snippet": "Typical spatial understanding tasks in multi-modal learning include referring and grounding , which essentially require aligning regions in the image ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.12795v1", "content": "Typical spatial understanding tasks in multi-modal learning include referring and grounding , which essentially require aligning regions in the image ..."}
+{"idx": 9, "title": "VHM: Versatile and Honest Vision Language Model for Remote", "date": "", "ddg_snippet": "VHM is built on a large- scale remote sensing image-text dataset with rich-content captions (VersaD), and an honest instruction dataset comprising ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.20213v4", "content": "VHM is built on a large- scale remote sensing image-text dataset with rich-content captions (VersaD), and an honest instruction dataset comprising ..."}
diff --git a/data/sampled_jsons/LHRS-Bench_690_questions_annotations.jsonl b/data/sampled_jsons/LHRS-Bench_690_questions_annotations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..28bc5ff0f97eb4850be5c85370a7be9739a3c3db
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Bench_690_questions_annotations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ... - Springer", "date": "", "ddg_snippet": "LHRS - Bench contains 108 RS images with 690 question -answer pairs, covering five major evaluation dimensions and 11 sub-dimensions of questions . Each sample is a human-annotated single-choice question that may involve one or more evaluation dimensions simultaneously.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-72904-1_26", "content": "LHRS - Bench contains 108 RS images with 690 question -answer pairs, covering five major evaluation dimensions and 11 sub-dimensions of questions . Each sample is a human-annotated single-choice question that may involve one or more evaluation dimensions simultaneously."}
+{"idx": 1, "title": "LHRS-Bot: Empowering Remote Sensing with VGI ...", "date": "", "ddg_snippet": "LHRS-Bench includes 690 single-choice questions , spanning 5 top-level evaluation dimensions including 11 fine-grained categories to facilitate a comprehensive, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.02544v4", "content": "LHRS-Bench includes 690 single-choice questions , spanning 5 top-level evaluation dimensions including 11 fine-grained categories to facilitate a comprehensive, ..."}
+{"idx": 2, "title": "A Survey on Benchmarks of Multimodal Large Language ...", "date": "", "ddg_snippet": "16 Aug 2024 — This paper presents a comprehensive review of 180 benchmarks and evaluation for MLLMs, focusing on (1)perception and understanding, (2)cognition and reasoning, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.08632v1", "content": "16 Aug 2024 — This paper presents a comprehensive review of 180 benchmarks and evaluation for MLLMs, focusing on (1)perception and understanding, (2)cognition and reasoning, ..."}
+{"idx": 3, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "by F Wang · 2025 · Cited by 7 — LHRS-. Bench [38] provides 108 images and 690 questions , with. VQA questions spanning five dimensions. What's more,. RSSA [42] introduces a benchmark focused ... 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "by F Wang · 2025 · Cited by 7 — LHRS-. Bench [38] provides 108 images and 690 questions , with. VQA questions spanning five dimensions. What's more,. RSSA [42] introduces a benchmark focused ... 12 pages"}
+{"idx": 4, "title": "CVPR Poster XLRS-Bench: Could Your Multimodal LLMs ...", "date": "", "ddg_snippet": "14 Jun 2025 — LHRSBench [38] provides 108 images and 690 questions , with VQA questions spanning five dimensions. What's more, RSSA [42] introduces a ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/35068", "content": "14 Jun 2025 — LHRSBench [38] provides 108 images and 690 questions , with VQA questions spanning five dimensions. What's more, RSSA [42] introduces a ..."}
+{"idx": 5, "title": "[Literature Review] LHRS-Bot: Empowering Remote Sensing ...", "date": "", "ddg_snippet": "Manually constructed, high-quality benchmark to evaluate MLLMs in RS image understanding. Contains 690 single-choice questions spanning 5 top-level evaluation ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/lhrs-bot-empowering-remote-sensing-with-vgi-enhanced-large-multimodal-language-model", "content": "Manually constructed, high-quality benchmark to evaluate MLLMs in RS image understanding. Contains 690 single-choice questions spanning 5 top-level evaluation ..."}
+{"idx": 6, "title": "GitHub - NJU-LHRS/LHRS-Bot: VGI-Enhanced multimodal large language ...", "date": "", "ddg_snippet": "Introduction We are excited to introduce LHRS -Bot, a multimodal large language model (MLLM) that leverages globally available volunteer geographic information (VGI) and remote sensing images (RS). LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "Introduction We are excited to introduce LHRS -Bot, a multimodal large language model (MLLM) that leverages globally available volunteer geographic information (VGI) and remote sensing images (RS). LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain."}
+{"idx": 7, "title": "PDF LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ... - Springer", "date": "", "ddg_snippet": "LHRS - Bench includes 690 single-choice questions , span-ning 5 top-level evaluation dimensions including 11 fine-grained categories to facilitate a comprehensive, objective, and quantitative RS-specific evaluation. The main contributions of our work are summarized as follows:", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-72904-1_26.pdf", "content": "LHRS - Bench includes 690 single-choice questions , span-ning 5 top-level evaluation dimensions including 11 fine-grained categories to facilitate a comprehensive, objective, and quantitative RS-specific evaluation. The main contributions of our work are summarized as follows:"}
+{"idx": 8, "title": "[2402.02544] LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced ...", "date": "", "ddg_snippet": "Building on this foundation, we introduce LHRS -Bot, an MLLM tailored for RS image understanding through a novel multi-level vision-language alignment strategy and a curriculum learning method. Additionally, we introduce LHRS - Bench , a benchmark for thoroughly evaluating MLLMs' abilities in RS image understanding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.02544", "content": "Building on this foundation, we introduce LHRS -Bot, an MLLM tailored for RS image understanding through a novel multi-level vision-language alignment strategy and a curriculum learning method. Additionally, we introduce LHRS - Bench , a benchmark for thoroughly evaluating MLLMs' abilities in RS image understanding."}
+{"idx": 9, "title": "LHRS-Bot/main_bench_gen.py at main · NJU-LHRS/LHRS-Bot - GitHub", "date": "", "ddg_snippet": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS -Bot/main_bench_gen.py at main · NJU- LHRS / LHRS -Bot", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/main_bench_gen.py", "content": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS -Bot/main_bench_gen.py at main · NJU- LHRS / LHRS -Bot"}
diff --git a/data/sampled_jsons/LHRS-Bench_Muhtar_2024_dataset_scale_abstract_year_2024.jsonl b/data/sampled_jsons/LHRS-Bench_Muhtar_2024_dataset_scale_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..379663217a33d8b7fd9bb85409053d4f6cc397eb
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Bench_Muhtar_2024_dataset_scale_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LHRS-Bot-Nova: Improved Multimodal Large Language Model for ...", "date": "", "ddg_snippet": "Nov 14, 2024 · Beyond that, Muhtar et al. ( 2024 ) introduced LHRS -Bot, which leverages a newly proposed large- scale RS vision-language dataset and enhances visual features with a novel vision perceiver.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.09301v1", "content": "Nov 14, 2024 · Beyond that, Muhtar et al. ( 2024 ) introduced LHRS -Bot, which leverages a newly proposed large- scale RS vision-language dataset and enhances visual features with a novel vision perceiver."}
+{"idx": 1, "title": "[2402.02544] LHRS-Bot: Empowering Remote Sensing with VGI ...", "date": "", "ddg_snippet": "Feb 4, 2024 · To bridge this gap, we construct a large- scale RS image-text dataset , LHRS -Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.02544", "content": "Feb 4, 2024 · To bridge this gap, we construct a large- scale RS image-text dataset , LHRS -Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images."}
+{"idx": 2, "title": "GitHub - NJU-LHRS/LHRS-Bot: VGI-Enhanced multimodal large ...", "date": "", "ddg_snippet": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !"}
+{"idx": 3, "title": "LHRS-Bot-Nova: Improved multimodal large language model for ...", "date": "", "ddg_snippet": "Sep 1, 2025 · LHRS -Bot-Nova features an enhanced vision encoder and a novel bridge layer, enabling efficient visual compression and better language-vision alignment. To further enhance RS-oriented vision-language alignment, we propose a large- scale RS image-caption dataset , generated through feature-guided image recaptioning.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0924271625002230", "content": "Sep 1, 2025 · LHRS -Bot-Nova features an enhanced vision encoder and a novel bridge layer, enabling efficient visual compression and better language-vision alignment. To further enhance RS-oriented vision-language alignment, we propose a large- scale RS image-caption dataset , generated through feature-guided image recaptioning."}
+{"idx": 4, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ...", "date": "", "ddg_snippet": "To bridge this gap, we construct a large- scale RS image-text dataset , LHRS -Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240202544M/abstract", "content": "To bridge this gap, we construct a large- scale RS image-text dataset , LHRS -Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images."}
+{"idx": 5, "title": "dblp: LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced ...", "date": "", "ddg_snippet": "Dec 2, 2024 · Bibliographic details on LHRS -Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/eccv/MuhtarLGZX24", "content": "Dec 2, 2024 · Bibliographic details on LHRS -Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model."}
+{"idx": 6, "title": "LHRS -Bot: Empowering Remote Sensing with VGI-Enhanced Large...", "date": "", "ddg_snippet": "2023) and LHRS - Bench ( Muhtar et al. 2024 ) adapt existing remote sensing datasets to create visual reasoning benchmarks for LMMs, while Geochat (Kuckreja et al. 2024 ) primarily assesses regional perception capabilities.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386016876_LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model", "content": "2023) and LHRS - Bench ( Muhtar et al. 2024 ) adapt existing remote sensing datasets to create visual reasoning benchmarks for LMMs, while Geochat (Kuckreja et al. 2024 ) primarily assesses regional perception capabilities."}
+{"idx": 7, "title": "Explore datasets powering machine learning.", "date": "", "ddg_snippet": "Apply filters. Datasets . 506,574. Full-text search.23. JDhruv14/Bhagavad-Gita_ Dataset . Viewer • Updated 9 days ago •.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets", "content": "Apply filters. Datasets . 506,574. Full-text search.23. JDhruv14/Bhagavad-Gita_ Dataset . Viewer • Updated 9 days ago •."}
+{"idx": 8, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 9, "title": "SWE- Bench Pro (Commercial Dataset )", "date": "", "ddg_snippet": "Dataset Summary. SWE- Bench Pro is a large- scale benchmark containing 1865 total tasks across 41 professional repositories. The benchmark is composed of three distinct subsets: The Public Set : This set contains 731 instances and serves as the main public-facing benchmark.", "subpage_snippet": "", "source": "scale.com", "link": "https://scale.com/leaderboard/swe_bench_pro_commercial", "content": "Dataset Summary. SWE- Bench Pro is a large- scale benchmark containing 1865 total tasks across 41 professional repositories. The benchmark is composed of three distinct subsets: The Public Set : This set contains 731 instances and serves as the main public-facing benchmark."}
diff --git a/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model_filetypepdf.jsonl b/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a7012cf165f216621365f643412953543b0ba02c
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2402.02544] LHRS - Bot : Empowering Remote Sensing with ...", "date": "", "ddg_snippet": "View a PDF of the paper titled LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model , by Dilxat Muhtar and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.02544", "content": "View a PDF of the paper titled LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model , by Dilxat Muhtar and 4 other authors."}
+{"idx": 1, "title": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced ...", "date": "", "ddg_snippet": "LHRS - Bot [15] leverages large - Table 1: (Left) Comparison of EarthMind with existing EO LMMs. EarthMind supports both multigranular and multi-sensor understanding. ... Large Multimodal Models (LMMs) have demonstrated strong performance in various vision- language tasks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386016876_LHRS-Bot_Empowering_Remote_Sensing_with_VGI-Enhanced_Large_Multimodal_Language_Model", "content": "LHRS - Bot [15] leverages large - Table 1: (Left) Comparison of EarthMind with existing EO LMMs. EarthMind supports both multigranular and multi-sensor understanding. ... Large Multimodal Models (LMMs) have demonstrated strong performance in various vision- language tasks."}
+{"idx": 2, "title": "NJU-LHRS/ LHRS - Bot : VGI - Enhanced multimodal large language ...", "date": "", "ddg_snippet": "VGI - Enhanced multimodal large language model for remote sensing images. License.We are excited to introduce LHRS - Bot , a multimodal large language model (MLLM) that leverages globally available volunteer geographic information (VGI) and remote sensing images (RS).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "VGI - Enhanced multimodal large language model for remote sensing images. License.We are excited to introduce LHRS - Bot , a multimodal large language model (MLLM) that leverages globally available volunteer geographic information (VGI) and remote sensing images (RS)."}
+{"idx": 3, "title": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced ...", "date": "", "ddg_snippet": "The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/lhrs-bot-empowering-remote-sensing-with-vgi", "content": "The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains."}
+{"idx": 4, "title": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced ...", "date": "", "ddg_snippet": "Explores the use of large language models (LLMs) and multimodal large language models (MLLMs) in the field of remote sensing (RS) image understanding.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/lhrs-bot-empowering-remote-sensing-vgi-enhanced", "content": "Explores the use of large language models (LLMs) and multimodal large language models (MLLMs) in the field of remote sensing (RS) image understanding."}
+{"idx": 5, "title": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced ...", "date": "", "ddg_snippet": "The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/LHRS-Bot%3A-Empowering-Remote-Sensing-with-VGI-Enhanced-Large-Multimodal-Language-Model-6f64619b-c8b1-4e89-9b8e-01d7ac6e4107", "content": "The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains."}
+{"idx": 6, "title": "Multimodal Language Model for Remote Sensing", "date": "", "ddg_snippet": "Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model : LHRS - Bot .", "subpage_snippet": "", "source": "linnk.ai", "link": "https://linnk.ai/topic/multimodal-language-model-for-remote-sensing/", "content": "Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model : LHRS - Bot ."}
+{"idx": 7, "title": "https://github.com/NJU-LHRS/ LHRS - Bot summary | Ecosyste.ms...", "date": "", "ddg_snippet": "VGI - Enhanced multimodal large language model for remote sensing images. Language : Python. Size: 51.1 MB.", "subpage_snippet": "", "source": "summary.ecosyste.ms", "link": "https://summary.ecosyste.ms/projects/123293", "content": "VGI - Enhanced multimodal large language model for remote sensing images. Language : Python. Size: 51.1 MB."}
+{"idx": 8, "title": "Quality-Driven Curation of Remote Sensing Vision- Language Data via...", "date": "", "ddg_snippet": "LHRS - Bot : Empowering remote sensing with vgi - enhanced large multimodal language model . In European Conference on Computer Vision, pages 440–457.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.00743v1", "content": "LHRS - Bot : Empowering remote sensing with vgi - enhanced large multimodal language model . In European Conference on Computer Vision, pages 440–457."}
+{"idx": 9, "title": "GitHub - Jack-bo1220/Awesome- Remote - Sensing -Foundation- Models", "date": "", "ddg_snippet": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model . Large Language Models for Captioning and Retrieving Remote Sensing Images.", "subpage_snippet": "", "source": "www.hubp.de", "link": "https://www.hubp.de/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models", "content": "LHRS - Bot : Empowering Remote Sensing with VGI - Enhanced Large Multimodal Language Model . Large Language Models for Captioning and Retrieving Remote Sensing Images."}
diff --git a/data/sampled_jsons/LLaVA-1.5_vs_LLaVA-Next_architectural_differences_high_resolution_images.jsonl b/data/sampled_jsons/LLaVA-1.5_vs_LLaVA-Next_architectural_differences_high_resolution_images.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..76ffc0d598e34a16fc5e3623bb7c4265b704b144
--- /dev/null
+++ b/data/sampled_jsons/LLaVA-1.5_vs_LLaVA-Next_architectural_differences_high_resolution_images.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LLaVA : Large Language and Vision Assistant - GitHub", "date": "", "ddg_snippet": "With additional scaling to LLaVA -1.5, LLaVA -NeXT-34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/haotian-liu/LLaVA", "content": "With additional scaling to LLaVA -1.5, LLaVA -NeXT-34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before."}
+{"idx": 1, "title": "LLaVa - Hugging Face", "date": "", "ddg_snippet": "Dec 7, 2023 · Constructs a LLaVa processor which wraps a LLaVa image processor and a LLaMa tokenizer into a single processor. LlavaProcessor offers all the functionalities of LlavaImageProcessor and LlamaTokenizerFast.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/transformers/main/model_doc/llava", "content": "Dec 7, 2023 · Constructs a LLaVa processor which wraps a LLaVa image processor and a LLaMa tokenizer into a single processor. LlavaProcessor offers all the functionalities of LlavaImageProcessor and LlamaTokenizerFast."}
+{"idx": 2, "title": "LLaVA", "date": "", "ddg_snippet": "LLaVA Model. We introduce LLaVA (L arge L anguage- a nd- V ision A ssistant), an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.", "subpage_snippet": "", "source": "llava-vl.github.io", "link": "https://llava-vl.github.io/", "content": "LLaVA Model. We introduce LLaVA (L arge L anguage- a nd- V ision A ssistant), an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding."}
+{"idx": 3, "title": "LLaVA - LLaVA 多模态AI视觉理解平台", "date": "", "ddg_snippet": "6 days ago · LLaVA 是一款由微软与威斯康星大学合作研发的先进多模态AI模型，具备图像与语言双重理解能力。通过 LLaVA 在线平台，用户可以上传图像并与AI进行自然对话，轻松实现图像内容识别、场景分析、文档处理和智能问答。 LLaVA 在视觉理解上接近GPT-4水平，支持高分辨率图像处理和多场景应用，适用于教育 ...", "subpage_snippet": "", "source": "aitoolly.com", "link": "https://aitoolly.com/zh/product/llava", "content": "6 days ago · LLaVA 是一款由微软与威斯康星大学合作研发的先进多模态AI模型，具备图像与语言双重理解能力。通过 LLaVA 在线平台，用户可以上传图像并与AI进行自然对话，轻松实现图像内容识别、场景分析、文档处理和智能问答。 LLaVA 在视觉理解上接近GPT-4水平，支持高分辨率图像处理和多场景应用，适用于教育 ..."}
+{"idx": 4, "title": "LLaVA : Large Language and Vision Assistant - Microsoft Research", "date": "", "ddg_snippet": "LLaVA is an open-source project, collaborating with research community to advance the state-of-the-art in AI. LLaVA represents the first end-to-end trained large multimodal model (LMM) that achieves impressive chat capabilities mimicking spirits of the multimodal GPT-4.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/project/llava-large-language-and-vision-assistant/", "content": "LLaVA is an open-source project, collaborating with research community to advance the state-of-the-art in AI. LLaVA represents the first end-to-end trained large multimodal model (LMM) that achieves impressive chat capabilities mimicking spirits of the multimodal GPT-4."}
+{"idx": 5, "title": "Spec- LLaVA : Accelerating Vision-Language Models with Dynamic...", "date": "", "ddg_snippet": "Sep 15, 2025 · Vision-Language Models (VLMs) enable powerful multimodal reasoning but suffer from slow autoregressive inference, limiting their deployment in real-time applications. We introduce Spec- LLaVA , a system that applies speculative decoding to accelerate VLMs without sacrificing output quality. Spec- LLaVA pairs a lightweight draft VLM with a large target model: the draft speculates future tokens ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2509.11961", "content": "Sep 15, 2025 · Vision-Language Models (VLMs) enable powerful multimodal reasoning but suffer from slow autoregressive inference, limiting their deployment in real-time applications. We introduce Spec- LLaVA , a system that applies speculative decoding to accelerate VLMs without sacrificing output quality. Spec- LLaVA pairs a lightweight draft VLM with a large target model: the draft speculates future tokens ..."}
+{"idx": 6, "title": "LLaVA 系列—— LLaVA 、 LLaVA -1.5、 LLaVA -NeXT、 LLaVA -OneVision", "date": "", "ddg_snippet": "Mar 9, 2025 · L La VA 是一系列结构极简的多模态大模型。 不同于Flamingo的交叉注意力机制、BLIP系列的Q-Former， LL a VA 直接 使用简单的线性层将视觉特征映射为文本特征，在一系列的多模态任务上取得了很好的效果。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/28971454220", "content": "Mar 9, 2025 · L La VA 是一系列结构极简的多模态大模型。 不同于Flamingo的交叉注意力机制、BLIP系列的Q-Former， LL a VA 直接 使用简单的线性层将视觉特征映射为文本特征，在一系列的多模态任务上取得了很好的效果。"}
+{"idx": 7, "title": "【LLM多模态】 LLava 模型架构和训练过程 | CLIP模型-CSDN博客", "date": "", "ddg_snippet": "Aug 11, 2024 · L La VA 模型的架构，是将一个预训练的视觉编码器（CLIP ViT-L/14）与一个大规模 语言模型 （Vicuna）连接在一起。 这两个模型通过一个简单的映射矩阵连接，这个矩阵负责将视觉和语言特征对齐或转换，以便在一个统一的空间内对它们进行操作。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_35812205/article/details/136586853", "content": "Aug 11, 2024 · L La VA 模型的架构，是将一个预训练的视觉编码器（CLIP ViT-L/14）与一个大规模 语言模型 （Vicuna）连接在一起。 这两个模型通过一个简单的映射矩阵连接，这个矩阵负责将视觉和语言特征对齐或转换，以便在一个统一的空间内对它们进行操作。"}
+{"idx": 8, "title": "LLaVA （Large Language and Vision Assistant）大模型 - 知乎", "date": "", "ddg_snippet": "L La VA （Large Language and Vision Assistant）是一个由威斯康星大学麦迪逊分校、微软研究院和哥伦比亚大学研究者共同发布的多模态大模型。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/624928279", "content": "L La VA （Large Language and Vision Assistant）是一个由威斯康星大学麦迪逊分校、微软研究院和哥伦比亚大学研究者共同发布的多模态大模型。"}
+{"idx": 9, "title": "LLaVA 系列①—— LLaVA 的快速学习和简单调用（附详细代码+讲解）-CSDN博客", "date": "", "ddg_snippet": "Mar 21, 2025 · 【 LL a VA 模型介绍】 L La VA 主要由三部分构成，也就是下图中的：视觉编码器（Vision Encoder）、对齐层（Projection，我喜欢叫它对齐层，虽然直翻是“投影层”）、语言模型（Language Model）。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/Wang_Dou_Dou_/article/details/146390905", "content": "Mar 21, 2025 · 【 LL a VA 模型介绍】 L La VA 主要由三部分构成，也就是下图中的：视觉编码器（Vision Encoder）、对齐层（Projection，我喜欢叫它对齐层，虽然直翻是“投影层”）、语言模型（Language Model）。"}
diff --git a/data/sampled_jsons/LSH_locality_sensitive_hashing_(r1_r2_p1_p2)_sensitive_hash_family_definition.jsonl b/data/sampled_jsons/LSH_locality_sensitive_hashing_(r1_r2_p1_p2)_sensitive_hash_family_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af730523f22f36506c6664a053206207b5fed83a
--- /dev/null
+++ b/data/sampled_jsons/LSH_locality_sensitive_hashing_(r1_r2_p1_p2)_sensitive_hash_family_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Locality - sensitive hashing - Wikipedia", "date": "", "ddg_snippet": "In computer science, locality - sensitive hashing is a fuzzy hashing technique that hashes similar input items into the same \"buckets\" with high probability. The number of buckets is much smaller than the universe of possible input items.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Locality-sensitive_hashing", "content": "In computer science, locality - sensitive hashing is a fuzzy hashing technique that hashes similar input items into the same \"buckets\" with high probability. The number of buckets is much smaller than the universe of possible input items."}
+{"idx": 1, "title": "Locality Sensitive Hashing . An effective way of reducing the | Medium", "date": "", "ddg_snippet": "Locality sensitive hashing ( LSH ) is one such algorithm. Locality - sensitive hashing . Don’t read much into the figure for now. It’s just to give you the idea of the process flow.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/understanding-locality-sensitive-hashing-49f6d1f6134", "content": "Locality sensitive hashing ( LSH ) is one such algorithm. Locality - sensitive hashing . Don’t read much into the figure for now. It’s just to give you the idea of the process flow."}
+{"idx": 2, "title": "Locality Sensitive Hashing ( LSH ): The Illustrated Guide | Pinecone", "date": "", "ddg_snippet": "Locality sensitive hashing ( LSH ) allows us to do this. LSH consists of a variety of different methods. In this article, we’ll be covering the traditional approach — which consists of multiple steps — shingling, MinHashing, and the final banded LSH function.", "subpage_snippet": "", "source": "www.pinecone.io", "link": "https://www.pinecone.io/learn/series/faiss/locality-sensitive-hashing/", "content": "Locality sensitive hashing ( LSH ) allows us to do this. LSH consists of a variety of different methods. In this article, we’ll be covering the traditional approach — which consists of multiple steps — shingling, MinHashing, and the final banded LSH function."}
+{"idx": 3, "title": "4 Pictures that Explain LSH - Locality Sensitive Hashing Tutorial", "date": "", "ddg_snippet": "Locality sensitive hashing is a super useful trick. Most people use it for near-neighbor search but it’s also helpful for sketching algorithms and high-dimensional data analysis. This post will explain four common LSH functions. What is LSH ? Hash functions map objects to numbers, or bins.", "subpage_snippet": "", "source": "randorithms.com", "link": "https://randorithms.com/2019/09/19/Visual-LSH.html", "content": "Locality sensitive hashing is a super useful trick. Most people use it for near-neighbor search but it’s also helpful for sketching algorithms and high-dimensional data analysis. This post will explain four common LSH functions. What is LSH ? Hash functions map objects to numbers, or bins."}
+{"idx": 4, "title": "Approximate Nearest Neighbor with Locality Sensitive Hashing ( LSH )", "date": "", "ddg_snippet": "Learn to implement Locality Sensitive Hashing ( LSH ) for efficient approximate nearest neighbor searches in high-dimensional spaces. Unlock powerful search techniques!", "subpage_snippet": "", "source": "pyimagesearch.com", "link": "https://pyimagesearch.com/2025/01/27/approximate-nearest-neighbor-with-locality-sensitive-hashing-lsh/", "content": "Learn to implement Locality Sensitive Hashing ( LSH ) for efficient approximate nearest neighbor searches in high-dimensional spaces. Unlock powerful search techniques!"}
+{"idx": 5, "title": "Similarity Search in High Dimensions via Hashing | PDF", "date": "", "ddg_snippet": "This document discusses locality - sensitive hashing ( LSH ) for similarity search in high dimensions.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/similarity-search-in-high-dimensions-via-hashing/64430063?nway-content_model=D", "content": "This document discusses locality - sensitive hashing ( LSH ) for similarity search in high dimensions."}
+{"idx": 6, "title": "Sieving for shortest vectors in lattices using angular locality", "date": "", "ddg_snippet": "Let us now describe how locality - sensitive hashing can be used to speed up sieving algorithms, and in particular how we can speed up the NV-sieve of Nguyen and Vidick [43] using angular LSH . The same ideas can also be applied to the GaussSieve [40], as illustrated in Section 5.", "subpage_snippet": "", "source": "pure.tue.nl", "link": "https://pure.tue.nl/ws/portalfiles/portal/117660228/2014_744.pdf", "content": "Let us now describe how locality - sensitive hashing can be used to speed up sieving algorithms, and in particular how we can speed up the NV-sieve of Nguyen and Vidick [43] using angular LSH . The same ideas can also be applied to the GaussSieve [40], as illustrated in Section 5."}
+{"idx": 7, "title": "PPT - Locality sensitive hashing ( LSH ) PowerPoint Presentation...", "date": "", "ddg_snippet": "Locality sensitive hashing ( LSH ). Feb 14, 2012. Locality sensitive hashing ( LSH ). Nearest Neighbor Given a set P of n points in Rd. Nearest Neighbor Want to build a data structure to answer nearest neighbor queries.", "subpage_snippet": "", "source": "www.slideserve.com", "link": "https://www.slideserve.com/nevan/detecting-near-duplicates-for-web-crawling", "content": "Locality sensitive hashing ( LSH ). Feb 14, 2012. Locality sensitive hashing ( LSH ). Nearest Neighbor Given a set P of n points in Rd. Nearest Neighbor Want to build a data structure to answer nearest neighbor queries."}
+{"idx": 8, "title": "Text Similarity using K-Shingling, Minhashing and LSH ( Locality ...)", "date": "", "ddg_snippet": "The concept for locality - sensitive hashing ( LSH ) is that given the signature matrix of size n (row count), we will partition it into b bands, resulting in each band with r rows.", "subpage_snippet": "", "source": "towardsai.net", "link": "https://towardsai.net/p/l/text-similarity-using-k-shingling-minhashing-and-lshlocality-sensitive-hashing", "content": "The concept for locality - sensitive hashing ( LSH ) is that given the signature matrix of size n (row count), we will partition it into b bands, resulting in each band with r rows."}
+{"idx": 9, "title": "ScribNotes.ipynb - Colaboratory", "date": "", "ddg_snippet": "Locality Sensitive Hashing for Angle Distance.Notebook. Locality Sensitive Hashing . ↳ 38 cells hidden. Finding Nearest Points : Given a set of data points and a query data point, we have to find the data point closest to this query point.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/cs328-2022/CS328-Notes/blob/main/CS328-Notes/notebooks/2022_04_08_Locality_Sensitive_Hashing.ipynb", "content": "Locality Sensitive Hashing for Angle Distance.Notebook. Locality Sensitive Hashing . ↳ 38 cells hidden. Finding Nearest Points : Given a set of data points and a query data point, we have to find the data point closest to this query point."}
diff --git a/data/sampled_jsons/LSIF_loss_function_squared_error_density_ratio_equation_formula.jsonl b/data/sampled_jsons/LSIF_loss_function_squared_error_density_ratio_equation_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2be1e4809cc320b029dd1bddfbdd1e386b3b3e68
--- /dev/null
+++ b/data/sampled_jsons/LSIF_loss_function_squared_error_density_ratio_equation_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linking losses for density ratio and class-probability estimation", "date": "", "ddg_snippet": "The power family has a similar trend for < 1, but at = 1 the LSIF loss has uniform weighting over all possible values of the density ratio . Also of interest is that only the LSIF and square losses have wDR(0) < +1.", "subpage_snippet": "", "source": "akmenon.github.io", "link": "https://akmenon.github.io/papers/density-ratio/density-ratio-paper.pdf", "content": "The power family has a similar trend for < 1, but at = 1 the LSIF loss has uniform weighting over all possible values of the density ratio . Also of interest is that only the LSIF and square losses have wDR(0) < +1."}
+{"idx": 1, "title": "Squared loss - University of Wisconsin–Madison", "date": "", "ddg_snippet": "Squared loss Squared loss is a loss function that can be used in the learning setting in which we are predicting a real-valued variable y given an input variable x. That is, we are given the following scenario: let h be a hypothesis (i.e. a statisti-cal model).", "subpage_snippet": "", "source": "pages.cs.wisc.edu", "link": "https://pages.cs.wisc.edu/~matthewb/pages/notes/pdf/lossfunctions/SquaredLoss.pdf", "content": "Squared loss Squared loss is a loss function that can be used in the learning setting in which we are predicting a real-valued variable y given an input variable x. That is, we are given the following scenario: let h be a hypothesis (i.e. a statisti-cal model)."}
+{"idx": 2, "title": "Loss Functions in Deep Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 23, 2025 · Loss function helps in evaluation and optimization. Understanding different types of loss functions and their applications is important for designing effective deep learning models.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/deep-learning/loss-functions-in-deep-learning/", "content": "Jul 23, 2025 · Loss function helps in evaluation and optimization. Understanding different types of loss functions and their applications is important for designing effective deep learning models."}
+{"idx": 3, "title": "Squared Error Loss - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "As an example, the MSE function , discussed earlier, is the expected loss associated with the squared error loss function and the sum of squared errors cost is the respective empirical version.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/computer-science/squared-error-loss", "content": "As an example, the MSE function , discussed earlier, is the expected loss associated with the squared error loss function and the sum of squared errors cost is the respective empirical version."}
+{"idx": 4, "title": "Loss Functions — machine learning note documentation", "date": "", "ddg_snippet": "Generally, L2 loss converge faster than l1. But it prone to over-smooth for image processing, hence l1 and its variants used for img2img more than l2.", "subpage_snippet": "", "source": "machine-learning-note.readthedocs.io", "link": "https://machine-learning-note.readthedocs.io/en/latest/basic/loss_functions.html", "content": "Generally, L2 loss converge faster than l1. But it prone to over-smooth for image processing, hence l1 and its variants used for img2img more than l2."}
+{"idx": 5, "title": "Supplementary material for “Linking losses for density ratio ...", "date": "", "ddg_snippet": "Thus, from either equation , the weight function for the loss is w(c) = 1 c1−α · (1 - c)2+α . which is an instance of the (α, β) Beta family of weight ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v48/menon16-supp.pdf", "content": "Thus, from either equation , the weight function for the loss is w(c) = 1 c1−α · (1 - c)2+α . which is an instance of the (α, β) Beta family of weight ..."}
+{"idx": 6, "title": "α-Divergence Loss Function for Neural Density Ratio ...", "date": "", "ddg_snippet": "by Y Kitazawa · 2024 · Cited by 1 — Table 2 presents the gradient formulas for the divergence loss functions (as provided in Table 1) along with their asymptotic behavior of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.02041", "content": "by Y Kitazawa · 2024 · Cited by 1 — Table 2 presents the gradient formulas for the divergence loss functions (as provided in Table 1) along with their asymptotic behavior of the ..."}
+{"idx": 7, "title": "Direct Density Ratio Optimization: A Statistically Consistent ...", "date": "", "ddg_snippet": "by R Higuchi · 2025 · Cited by 1 — By directly optimizing the density ratio between preferred and unpreferred distribu- tions using Bregman divergence loss , DDRO theoretically.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.07558", "content": "by R Higuchi · 2025 · Cited by 1 — By directly optimizing the density ratio between preferred and unpreferred distribu- tions using Bregman divergence loss , DDRO theoretically."}
+{"idx": 8, "title": "A loss framework for calibrated anomaly detection", "date": "", "ddg_snippet": "by AK Menon · Cited by 23 — Given samples from a distribution, anomaly detection is the problem of determining if a given point lies in a low- density region.", "subpage_snippet": "", "source": "akmenon.github.io", "link": "https://akmenon.github.io/papers/proper-anomaly/proper-anomaly.pdf", "content": "by AK Menon · Cited by 23 — Given samples from a distribution, anomaly detection is the problem of determining if a given point lies in a low- density region."}
+{"idx": 9, "title": "Density Ratio Estimation with Doubly Strong Robustness", "date": "", "ddg_snippet": "by R Nagumo · Cited by 1 — We develop two density ratio estimation (DRE) methods with robustness to outliers. These are based on the divergence with a weight function .", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/nagumo24a/nagumo24a.pdf", "content": "by R Nagumo · Cited by 1 — We develop two density ratio estimation (DRE) methods with robustness to outliers. These are based on the divergence with a weight function ."}
diff --git a/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_Regret-Matching_Algorithms_g(z)_normalization_operato.jsonl b/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_Regret-Matching_Algorithms_g(z)_normalization_operato.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7bad1bc44d3e71c7231c380851f964612a0044c3
--- /dev/null
+++ b/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_Regret-Matching_Algorithms_g(z)_normalization_operato.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LAST-I CONVERGENCE PROPERTIES OF R M ALGORITHMS IN GAMES", "date": "", "ddg_snippet": "ABSTRACT We study last -iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games, virtually nothing is known about their last -iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LWeVVPuIx0", "content": "ABSTRACT We study last -iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games, virtually nothing is known about their last -iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by ..."}
+{"idx": 1, "title": "Last-Iterate Convergence Properties of Regret Matching ...", "date": "", "ddg_snippet": "We study last -iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+(RM+). Despite their widespread use for solving real games, virtually nothing is known about their last -iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "We study last -iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+(RM+). Despite their widespread use for solving real games, virtually nothing is known about their last -iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ..."}
+{"idx": 2, "title": "Regret Matching+: - Instability, average- and last-iterate ...", "date": "", "ddg_snippet": "instability. ) New algorithms for game solving: O(1=T) O(1=p average convergence T) best-iterate convergence , last -iterate convergence Why is this interesting?", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "instability. ) New algorithms for game solving: O(1=T) O(1=p average convergence T) best-iterate convergence , last -iterate convergence Why is this interesting?"}
+{"idx": 3, "title": "Last-Iterate Convergence Properties of Regret-Matching ...", "date": "", "ddg_snippet": "Abstract We study last -iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last -iterate convergence .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/7c20cba1f6aeeb219f9f9cbdde5c7382-Abstract-Conference.html", "content": "Abstract We study last -iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last -iterate convergence ."}
+{"idx": 4, "title": "Last-iterate Convergence in Extensive-Form Games", "date": "", "ddg_snippet": "Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games. However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence . Inspired by recent advances on last -iterate con- vergence of optimistic algorithms in zero-sum normal-form ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2021/file/77bb14f6132ea06dea456584b7d5581e-Paper.pdf", "content": "Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games. However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence . Inspired by recent advances on last -iterate con- vergence of optimistic algorithms in zero-sum normal-form ..."}
+{"idx": 5, "title": "Last-Iterate Convergence Properties of Regret-Matching ...", "date": "", "ddg_snippet": "Nov 1, 2023 · We study last -iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last -iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.00676", "content": "Nov 1, 2023 · We study last -iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last -iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ..."}
+{"idx": 6, "title": "Last-Iterate Convergence of Smooth Regret Matching ...", "date": "", "ddg_snippet": "Sep 26, 2024 · A primary obstacle in proving the last -iterate convergence for these algorithms is that their feedback is not the loss gradient of the vanilla games. This deviation results in the absence of crucial properties , \\eg, monotonicity or the weak Minty variation inequality (MVI), which are pivotal for establishing the last -iterate convergence .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=TU3wJQfKz8", "content": "Sep 26, 2024 · A primary obstacle in proving the last -iterate convergence for these algorithms is that their feedback is not the loss gradient of the vanilla games. This deviation results in the absence of crucial properties , \\eg, monotonicity or the weak Minty variation inequality (MVI), which are pivotal for establishing the last -iterate convergence ."}
+{"idx": 7, "title": "Last - Iterate Convergence Properties of Regret - Matching ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LWeVVPuIx0", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last - iterate convergence ."}
+{"idx": 8, "title": "Combining No- regret and Q-learning", "date": "", "ddg_snippet": "Furthermore, by leveraging last iterate converging no- regret algorithms (one of which we introduce), we show empirical last iterate convergence in all domains tested with LONR.", "subpage_snippet": "", "source": "ifaamas.org", "link": "https://ifaamas.org/Proceedings/aamas2020/pdfs/p593.pdf", "content": "Furthermore, by leveraging last iterate converging no- regret algorithms (one of which we introduce), we show empirical last iterate convergence in all domains tested with LONR."}
+{"idx": 9, "title": "On the Last Iterate Convergence of Momentum Methods", "date": "", "ddg_snippet": "For these algorithms , we show that the last iterate has optimal convergence O( √1 ) for.is equal to the Stochastic Heavy Ball method (SHB) with a specic choice of hyper-parameters. They prove a convergence rate for the last iterate of of O( √1 ) if T is given in advance, and is.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v167/li22a/li22a.pdf", "content": "For these algorithms , we show that the last iterate has optimal convergence O( √1 ) for.is equal to the Stochastic Heavy Ball method (SHB) with a specic choice of hyper-parameters. They prove a convergence rate for the last iterate of of O( √1 ) if T is given in advance, and is."}
diff --git a/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_normalization_.jsonl b/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_normalization_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fae7275bc9614f1ed06e64ff290d675b0f1b2c38
--- /dev/null
+++ b/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_normalization_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Last-i Convergence Properties of R M Algorithms in Games", "date": "", "ddg_snippet": "ABSTRACT We study last- iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last- iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LWeVVPuIx0", "content": "ABSTRACT We study last- iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last- iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by ..."}
+{"idx": 1, "title": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games", "date": "", "ddg_snippet": "We study last- iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching$^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last- iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.00676", "content": "We study last- iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching$^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last- iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ..."}
+{"idx": 2, "title": "PDF Regret Matching+: - Instability, average- and last-iterate convergence ...", "date": "", "ddg_snippet": "Instability, average- and last- iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Instability, average- and last- iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris"}
+{"idx": 3, "title": "PDF Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
+{"idx": 4, "title": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games", "date": "", "ddg_snippet": "We study last- iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching$^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last- iterate convergence .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LWeVVPuIx0", "content": "We study last- iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching$^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last- iterate convergence ."}
+{"idx": 5, "title": "PDF Last-iterate Convergence in Extensive-Form Games", "date": "", "ddg_snippet": "Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games . However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence . Inspired by recent advances on last- iterate con - vergence of optimistic algorithms in zero-sum normal-form ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2021/file/77bb14f6132ea06dea456584b7d5581e-Paper.pdf", "content": "Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games . However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence . Inspired by recent advances on last- iterate con - vergence of optimistic algorithms in zero-sum normal-form ..."}
+{"idx": 6, "title": "ICLR Poster Last-Iterate Convergence Properties of Regret-Matching ...", "date": "", "ddg_snippet": "Abstract: We study last- iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + (RM +). Despite their widespread use for solving real games , virtually nothing is known about their last- iterate convergence .", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/29992", "content": "Abstract: We study last- iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + (RM +). Despite their widespread use for solving real games , virtually nothing is known about their last- iterate convergence ."}
+{"idx": 7, "title": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games", "date": "", "ddg_snippet": "Last- Iterate Convergence Properties of Regret-Matching Algorithms in Games Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng May, 2025 Cite URL", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/cai-2025-last/", "content": "Last- Iterate Convergence Properties of Regret-Matching Algorithms in Games Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng May, 2025 Cite URL"}
+{"idx": 8, "title": "Gabriele Farina - Last-Iterate Convergence Properties of Regret ...", "date": "", "ddg_snippet": "We study last- iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last- iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2025/iclr25_rm_lastiterate/", "content": "We study last- iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last- iterate convergence . A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity. We start by showing ..."}
+{"idx": 9, "title": "arXiv:2311.00676v1 [cs.GT] 1 Nov 2023", "date": "", "ddg_snippet": "In this paper, we investigate the last- iterate convergence properties of regret-matching algorithms , a class of extremely popular methods for equilibrium computation in games .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.00676v1", "content": "In this paper, we investigate the last- iterate convergence properties of regret-matching algorithms , a class of extremely popular methods for equilibrium computation in games ."}
diff --git a/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_normalization_operator_Eq.jsonl b/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_normalization_operator_Eq.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..28baa97ba5e54cfe73ea6e34793984592d015376
--- /dev/null
+++ b/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_normalization_operator_Eq.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Last-Iterate Convergence Properties of Regret Matching", "date": "", "ddg_snippet": "Last - Iterate Convergence Properties of Regret Matching Algorithms in Games † † thanks: Authors are listed in alphabetic order.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "Last - Iterate Convergence Properties of Regret Matching Algorithms in Games † † thanks: Authors are listed in alphabetic order."}
+{"idx": 1, "title": "Rapid Learning in Constrained Minimax Games with Negative", "date": "", "ddg_snippet": "MMD provides a linear convergence rate to the regularized equilibrium by utilizing the influences of regularization on last - iterate convergence .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.00533v1", "content": "MMD provides a linear convergence rate to the regularized equilibrium by utilizing the influences of regularization on last - iterate convergence ."}
+{"idx": 2, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Implicit Learning Dynamics in Stackelberg Games : Equilibria Characterization, Convergence Analysis, and Empirical Study", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Implicit Learning Dynamics in Stackelberg Games : Equilibria Characterization, Convergence Analysis, and Empirical Study"}
+{"idx": 3, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Implicit Learning Dynamics in Stackelberg Games : Equilibria Characterization, Convergence Analysis, and Empirical Study", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Implicit Learning Dynamics in Stackelberg Games : Equilibria Characterization, Convergence Analysis, and Empirical Study"}
+{"idx": 4, "title": "Downloads", "date": "", "ddg_snippet": "Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning ... Algorithm with Order-Optimal ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning ... Algorithm with Order-Optimal ..."}
+{"idx": 5, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "... of Scaling Down Large Language Models: ... Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "... of Scaling Down Large Language Models: ... Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits"}
+{"idx": 6, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "... Bias in Multi-Task RNNs: Shared Attractors, Reuse of ... Polynomial-Time Linear-Swap Regret Minimization in Imperfect-Information Sequential Games", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html?filter=titles", "content": "... Bias in Multi-Task RNNs: Shared Attractors, Reuse of ... Polynomial-Time Linear-Swap Regret Minimization in Imperfect-Information Sequential Games"}
+{"idx": 7, "title": "Articles in Advance | Mathematics of Operations Research", "date": "", "ddg_snippet": "This work considers the effect of averaging, and more generally extrapolation, of the iterates of gradient descent in smooth convex optimization.", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/toc/moor/0/0", "content": "This work considers the effect of averaging, and more generally extrapolation, of the iterates of gradient descent in smooth convex optimization."}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "... Human Activities Using Wearable Sensors in ... AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2022", "content": "... Human Activities Using Wearable Sensors in ... AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators"}
+{"idx": 9, "title": "Graduate Alumni - Computing + Mathematical Sciences", "date": "", "ddg_snippet": "Test and Evaluation of Autonomous Systems: Reactive Test Synthesis and Task-Relevant Evaluation of Perception ... Control and State-Estimation of ...", "subpage_snippet": "", "source": "www.cms.caltech.edu", "link": "https://www.cms.caltech.edu/people/grads/alumni", "content": "Test and Evaluation of Autonomous Systems: Reactive Test Synthesis and Task-Relevant Evaluation of Perception ... Control and State-Estimation of ..."}
diff --git a/data/sampled_jsons/Lattimore_Szepesvari_optimism_critique_partial_monitoring.jsonl b/data/sampled_jsons/Lattimore_Szepesvari_optimism_critique_partial_monitoring.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b0b019d8eb99dbb712cfea7e7c41cc5c3bbf5740
--- /dev/null
+++ b/data/sampled_jsons/Lattimore_Szepesvari_optimism_critique_partial_monitoring.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tor Lattimore", "date": "", "ddg_snippet": "2020. An information-theoretic approach to minimax regret in partial monitoring . T Lattimore , C Szepesvári . Conference on Learning Theory, 2111-2139, 2019. 77 ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=fkDxJxcAAAAJ&hl=en", "content": "2020. An information-theoretic approach to minimax regret in partial monitoring . T Lattimore , C Szepesvári . Conference on Learning Theory, 2111-2139, 2019. 77 ..."}
+{"idx": 1, "title": "Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Exploration by Optimisation in Partial MonitoringTor Lattimore, Csaba SzepesváriWe provide a novel algorithm for adversarial k-action d-outcome partial ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v125/lattimore20a.html", "content": "Exploration by Optimisation in Partial MonitoringTor Lattimore, Csaba SzepesváriWe provide a novel algorithm for adversarial k-action d-outcome partial ..."}
+{"idx": 2, "title": "Partial monitoring - tor-lattimore.com", "date": "", "ddg_snippet": "Partial monitoring is a generalisation of the bandit framework that decouples the loss and the obser-vations. The framework is sufficiently rich to model bandits, linear bandits, full information games, dynamic pricing, bandits with graph feedback and many problems between and beyond these exam-ples.", "subpage_snippet": "", "source": "tor-lattimore.com", "link": "https://tor-lattimore.com/downloads/papers/2019-pm-simple.pdf", "content": "Partial monitoring is a generalisation of the bandit framework that decouples the loss and the obser-vations. The framework is sufficiently rich to model bandits, linear bandits, full information games, dynamic pricing, bandits with graph feedback and many problems between and beyond these exam-ples."}
+{"idx": 3, "title": "COLT 2020: Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Exploration by Optimisation in Partial Monitoring Tor Lattimore, Csaba Szepesvari [Proceedings link] [PDF] Subject areas: Bandit problems, Online learning Presented in: Session 2A, Session 2E [Zoom link for poster in Session 2A], [Zoom link for poster in Session 2E] Abstract", "subpage_snippet": "", "source": "www.learningtheory.org", "link": "https://www.learningtheory.org/colt2020/virtual/papers/paper_66.html", "content": "Exploration by Optimisation in Partial Monitoring Tor Lattimore, Csaba Szepesvari [Proceedings link] [PDF] Subject areas: Bandit problems, Online learning Presented in: Session 2A, Session 2E [Zoom link for poster in Session 2A], [Zoom link for poster in Session 2E] Abstract"}
+{"idx": 4, "title": "[1902.00470] An Information-Theoretic Approach to Minimax ... Tor Lattimore - Google Scholar Exploration by Optimisation in Partial Monitoring | Request PDF Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Feb 1, 2019 · View a PDF of the paper titled An Information-Theoretic Approach to Minimax Regret in Partial Monitoring , by Tor Lattimore and Csaba Szepesvari Google DeepMind - Cited by 8,821 - machine learning - learning theory - reinforcement learning Jul 12, 2019 · Recently, this condition has been shown by (Bartok, Pal, and Szepesvari , 2011) to imply the O (\\sqrt {T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured ... Johannes Kirschner, Tor Lattimore, A. Krause Computer Science ArXiv 2023 TLDR A simple and unified analysis of stochastic partial monitoring is presented, and a single algorithm, information-directed sampling (IDS), is (nearly) worst-case rate optimal in all finite-action games. Expand Highly Influenced PDF View 8 excerpts, cites methods and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1902.00470", "content": "Feb 1, 2019 · View a PDF of the paper titled An Information-Theoretic Approach to Minimax Regret in Partial Monitoring , by Tor Lattimore and Csaba Szepesvari Google DeepMind - Cited by 8,821 - machine learning - learning theory - reinforcement learning Jul 12, 2019 · Recently, this condition has been shown by (Bartok, Pal, and Szepesvari , 2011) to imply the O (\\sqrt {T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured ... Johannes Kirschner, Tor Lattimore, A. Krause Computer Science ArXiv 2023 TLDR A simple and unified analysis of stochastic partial monitoring is presented, and a single algorithm, information-directed sampling (IDS), is (nearly) worst-case rate optimal in all finite-action games. Expand Highly Influenced PDF View 8 excerpts, cites methods and ..."}
+{"idx": 5, "title": "Exploration by Optimisation in Partial Monitoring | Request PDF", "date": "", "ddg_snippet": "Jul 12, 2019 · Recently, this condition has been shown by (Bartok, Pal, and Szepesvari , 2011) to imply the O (\\sqrt {T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/334457375_Exploration_by_Optimisation_in_Partial_Monitoring", "content": "Jul 12, 2019 · Recently, this condition has been shown by (Bartok, Pal, and Szepesvari , 2011) to imply the O (\\sqrt {T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured ..."}
+{"idx": 6, "title": "Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Johannes Kirschner, Tor Lattimore, A. Krause Computer Science ArXiv 2023 TLDR A simple and unified analysis of stochastic partial monitoring is presented, and a single algorithm, information-directed sampling (IDS), is (nearly) worst-case rate optimal in all finite-action games. Expand Highly Influenced PDF View 8 excerpts, cites methods and ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Exploration-by-Optimisation-in-Partial-Monitoring-Lattimore-Szepesvari/c527571cea74c9a689d4d2b9a9e1608046f7ff95/figure/2", "content": "Johannes Kirschner, Tor Lattimore, A. Krause Computer Science ArXiv 2023 TLDR A simple and unified analysis of stochastic partial monitoring is presented, and a single algorithm, information-directed sampling (IDS), is (nearly) worst-case rate optimal in all finite-action games. Expand Highly Influenced PDF View 8 excerpts, cites methods and ..."}
+{"idx": 7, "title": "Linear Partial Monitoring for Sequential Decision Making ...", "date": "", "ddg_snippet": "by J Kirschner · 2023 · Cited by 7 — An algorithm with rate-optimal worst-case regret in all classes of games is by Lattimore and. Szepesvári (2019b). In the stochastic setting the hidden outcomes ... 45 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume24/22-1248/22-1248.pdf", "content": "by J Kirschner · 2023 · Cited by 7 — An algorithm with rate-optimal worst-case regret in all classes of games is by Lattimore and. Szepesvári (2019b). In the stochastic setting the hidden outcomes ... 45 pages"}
+{"idx": 8, "title": "The End of Optimism? An Asymptotic Analysis of Finite-Armed ...", "date": "", "ddg_snippet": "by T Lattimore · 2017 · Cited by 154 — Although related, the partial monitoring framework is more general than the bandit setting because the learner may not observe the reward even for the action ... 10 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v54/lattimore17a/lattimore17a.pdf", "content": "by T Lattimore · 2017 · Cited by 154 — Although related, the partial monitoring framework is more general than the bandit setting because the learner may not observe the reward even for the action ... 10 pages"}
+{"idx": 9, "title": "Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "by S Parisi · 2024 · Cited by 4 — Note that Lattimore and Szepesvari [36] already argued against optimism in partial monitoring [8], a generalization of the bandit framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.13909?", "content": "by S Parisi · 2024 · Cited by 4 — Note that Lattimore and Szepesvari [36] already argued against optimism in partial monitoring [8], a generalization of the bandit framework ..."}
diff --git a/data/sampled_jsons/Learned_Augmented_Residual_Layer_paper.jsonl b/data/sampled_jsons/Learned_Augmented_Residual_Layer_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb435cf6dd84872a595024ac5db057b892394f81
--- /dev/null
+++ b/data/sampled_jsons/Learned_Augmented_Residual_Layer_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Residual neural network - Wikipedia", "date": "", "ddg_snippet": "Residual learning re-parameterizes this subnetwork and lets the parameter layers represent a \" residual function\" F ( x ) = H ( x ) x {\\displaystyle F ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Residual_neural_network", "content": "Residual learning re-parameterizes this subnetwork and lets the parameter layers represent a \" residual function\" F ( x ) = H ( x ) x {\\displaystyle F ..."}
+{"idx": 1, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer (LAuReL) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.07501", "content": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer (LAuReL) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 2, "title": "ICML Poster LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "Abstract: One of the core pillars of efficient deep learning methods are architectural improvements, such as residual /skip connections, which have led to significantly better model convergence and quality.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43889", "content": "Abstract: One of the core pillars of efficient deep learning methods are architectural improvements, such as residual /skip connections, which have led to significantly better model convergence and quality."}
+{"idx": 3, "title": "Paper page - LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "Nov 11, 2024 · In this paper we introduce Learned Augmented Residual Layer (LAuReL) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.07501", "content": "Nov 11, 2024 · In this paper we introduce Learned Augmented Residual Layer (LAuReL) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 4, "title": "BAW2501/LAuReL-Learned-Augmented-Residual-Layer - GitHub", "date": "", "ddg_snippet": "Nov 20, 2024 · This repository contains my independent implementations of the three LAuReL variants described in the article titled \"LAuReL: Learned Augmented Residual Layer \".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/LAuReL-Learned-Augmented-Residual-Layer", "content": "Nov 20, 2024 · This repository contains my independent implementations of the three LAuReL variants described in the article titled \"LAuReL: Learned Augmented Residual Layer \"."}
+{"idx": 5, "title": "LAuReL: Learned Augmented Residual Layer - OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · In this paper , we introduce the Learned Augmented Residual Layer (LAuReL) --- a novel generalization of the canonical residual connection --- designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rUDRWP9WvZ", "content": "May 1, 2025 · In this paper , we introduce the Learned Augmented Residual Layer (LAuReL) --- a novel generalization of the canonical residual connection --- designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics."}
+{"idx": 6, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer (LAuReL)—a novel generalization of the canonical residual connection —with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v1", "content": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer (LAuReL)—a novel generalization of the canonical residual connection —with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 7, "title": "Google AI Introduces LAuReL (Learned Augmented Residual Layer ...", "date": "", "ddg_snippet": "Nov 17, 2024 · Researchers from Google Research, Mountain View, CA, and Google Research, New York, NY have proposed a novel method called Learned Augmented Residual Layer (LAUREL), which revolutionizes the traditional residual connection concept in neural networks.", "subpage_snippet": "", "source": "phdstudio.org", "link": "https://phdstudio.org/2024/11/17/google-ai-introduces-laurel-learned-augmented-residual-layer-revolutionizing-neural-networks-with-enhanced-residual-connections-for-efficient-model-performance-sajjad-ansari-artificial-intelligence/", "content": "Nov 17, 2024 · Researchers from Google Research, Mountain View, CA, and Google Research, New York, NY have proposed a novel method called Learned Augmented Residual Layer (LAUREL), which revolutionizes the traditional residual connection concept in neural networks."}
+{"idx": 8, "title": "A Variational Framework for Residual-Based Adaptivity in Neural", "date": "", "ddg_snippet": "The strategies for determining these weights are diverse, ranging from direct residual -based schemes to more complex adversarial or augmented ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14198v1", "content": "The strategies for determining these weights are diverse, ranging from direct residual -based schemes to more complex adversarial or augmented ..."}
+{"idx": 9, "title": "Transfer Learning-Based Deep Residual Learning for Speech", "date": "", "ddg_snippet": "This skip connection or shortcut allows the model to learn residual functions with reference to the layer inputs, rather than learning unreferenced ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.01632v1", "content": "This skip connection or shortcut allows the model to learn residual functions with reference to the layer inputs, rather than learning unreferenced ..."}
diff --git a/data/sampled_jsons/Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal_transport_equatio.jsonl b/data/sampled_jsons/Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal_transport_equatio.jsonl
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index 0000000000000000000000000000000000000000..b256eb3f5dfcda55e438810ee36cc40fa0f80568
--- /dev/null
+++ b/data/sampled_jsons/Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal_transport_equatio.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Abstract Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v1", "content": "Abstract Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 1, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Oct 1, 2024 · Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.00844", "content": "Oct 1, 2024 · Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 2, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport .Score-based generative modeling through stochastic differential equations . In International Conference on Learning Representations, 2021. Tong et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v5", "content": "4 Regularized Unbalanced Optimal Transport .Score-based generative modeling through stochastic differential equations . In International Conference on Learning Representations, 2021. Tong et al."}
+{"idx": 3, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport .Score-based generative modeling through stochastic differential equations . In International Conference on Learning Representations, 2021. Tong et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v4", "content": "4 Regularized Unbalanced Optimal Transport .Score-based generative modeling through stochastic differential equations . In International Conference on Learning Representations, 2021. Tong et al."}
+{"idx": 4, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "We have introduced DeepRUOT for learning regularized unbalanced optimal transport (RUOT) and continuous unbalanced stochastic dynamics from time-series snapshot data.Score-based generative modeling through stochastic differential equations .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v3", "content": "We have introduced DeepRUOT for learning regularized unbalanced optimal transport (RUOT) and continuous unbalanced stochastic dynamics from time-series snapshot data.Score-based generative modeling through stochastic differential equations ."}
+{"idx": 5, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "We have introduced DeepRUOT for learning regularized unbalanced optimal transport (RUOT) and continuous unbalanced stochastic dynamics from time-series snapshot data.Score-based generative modeling through stochastic differential equations .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v2", "content": "We have introduced DeepRUOT for learning regularized unbalanced optimal transport (RUOT) and continuous unbalanced stochastic dynamics from time-series snapshot data.Score-based generative modeling through stochastic differential equations ."}
+{"idx": 6, "title": "Variational Regularized Unbalanced Optimal Transport: Single ...", "date": "", "ddg_snippet": "May 17, 2025 · Recovering the dynamics from a few snapshots of a high-dimensional system is a challenging task in statistical physics and machine learning , with important applications in computational biology. Many algorithms have been developed to tackle this problem, based on frameworks such as optimal transport and the Schrödinger bridge. A notable recent framework is Regularized Unbalanced Optimal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.11823", "content": "May 17, 2025 · Recovering the dynamics from a few snapshots of a high-dimensional system is a challenging task in statistical physics and machine learning , with important applications in computational biology. Many algorithms have been developed to tackle this problem, based on frameworks such as optimal transport and the Schrödinger bridge. A notable recent framework is Regularized Unbalanced Optimal ..."}
+{"idx": 7, "title": "Variational Regularized Unbalanced Optimal Transport: Single ...", "date": "", "ddg_snippet": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning Representations, 2025a.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.11823", "content": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning Representations, 2025a."}
+{"idx": 8, "title": "[2011.05001] MMD-Regularized Unbalanced Optimal Transport Unbalanced Optimal Transport, from Theory to Numerics Learning stochastic dynamics from snapshots through regularized Learning stochastic dynamics from snapshots through regularized Learning stochastic dynamics from snapshots through regularized Learning stochastic dynamics from snapshots through regularized Learning stochastic dynamics from snapshots through regularized Learning stochastic dynamics from snapshots through regularized [2407.04583] Unbalanced optimal transport for stochastic ...", "date": "", "ddg_snippet": "Nov 10 , 2020 · We study the unbalanced optimal transport (UOT) problem, where the marginal constraints are enforced using Maximum Mean Discrepancy (MMD) regularization. Our work is motivated by the observation that the literature on UOT is focused on regularization based on $ϕ$-divergence (e.g., KL divergence). Despite the popularity of MMD, its role as a regularizer in the context of UOT seems less ... Nov 16, 2022 · Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learning pipelines is however plagued by several shortcomings. This includes its lack of robustness to outliers, its high computational costs, the need ... Can deep learning solve stochastic dynamics from sparsely time-resolved snapshots? Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots . Can regularized unbalanced optimal transport solve Schrödinger bridge problem? Recently, regularized unbalanced optimal transport (RUOT) offers a promising approach for modeling such stochastic unbalanced continuous dynamics (Baradat and Lavenant, 2021; Chen et al., 2022b; Buze and Duong, 2023; Janati et al., 2020). It can be viewed as an unbalanced relaxation of the dynamic formulation of the Schrödinger bridge problem. Does entropic optimal transport between unbalanced Gaussian measures have a closed form? Entropic optimal transport between unbalanced gaussian measures has a closed form . Advances in neural information processing systems 33, 10468–10479. Jiang, Q., Wan, L., 2024. A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data. How do we perform optimal transport matching? Specifically, we first address the mass matching loss to align the total number of cells. Subsequently, we normalize the distributions according to the matched masses, and then use these weights to perform optimal transport matching. This process is similar to using unbalanced optimal transport to evaluate the distance between two distributions. Does the unbalanced dynamic diffusion Schrödinger bridge solver work on synthetic dynamics? In this section, we evaluate the effectiveness of the unbalanced dynamic diffusion Schrödinger bridge solver on synthetic dynamics from two perspectives: (1) Compared to the balanced diffusion Schrödinger bridge, our approach accurately recovers the correct growth and transition processes, eliminating false transitions caused by neglecting growth. What is a stochastic interpolant? Stochastic interpolants: A unifying framework for flows and diffusions . arXiv preprint arXiv:2303.08797 . Albergo, M.S., Vanden-Eijnden, E., 2023. Building normalizing flows with stochastic interpolants, in: The Eleventh International Conference on Learning Representations. Baradat, A., Lavenant, H., 2021. Jul 5, 2024 · This paper aims to develop a globally consistent nearest-neighborhood algorithm that robustly extracts stochastic particle tracks from the reconstructed Gaussian particle distributions in all frames. Our tracking algorithm relies on the unbalanced optimal transport theory in the metric space of Gaussian measures.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.05001", "content": "Nov 10 , 2020 · We study the unbalanced optimal transport (UOT) problem, where the marginal constraints are enforced using Maximum Mean Discrepancy (MMD) regularization. Our work is motivated by the observation that the literature on UOT is focused on regularization based on $ϕ$-divergence (e.g., KL divergence). Despite the popularity of MMD, its role as a regularizer in the context of UOT seems less ... Nov 16, 2022 · Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learning pipelines is however plagued by several shortcomings. This includes its lack of robustness to outliers, its high computational costs, the need ... Can deep learning solve stochastic dynamics from sparsely time-resolved snapshots? Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots . Can regularized unbalanced optimal transport solve Schrödinger bridge problem? Recently, regularized unbalanced optimal transport (RUOT) offers a promising approach for modeling such stochastic unbalanced continuous dynamics (Baradat and Lavenant, 2021; Chen et al., 2022b; Buze and Duong, 2023; Janati et al., 2020). It can be viewed as an unbalanced relaxation of the dynamic formulation of the Schrödinger bridge problem. Does entropic optimal transport between unbalanced Gaussian measures have a closed form? Entropic optimal transport between unbalanced gaussian measures has a closed form . Advances in neural information processing systems 33, 10468–10479. Jiang, Q., Wan, L., 2024. A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data. How do we perform optimal transport matching? Specifically, we first address the mass matching loss to align the total number of cells. Subsequently, we normalize the distributions according to the matched masses, and then use these weights to perform optimal transport matching. This process is similar to using unbalanced optimal transport to evaluate the distance between two distributions. Does the unbalanced dynamic diffusion Schrödinger bridge solver work on synthetic dynamics? In this section, we evaluate the effectiveness of the unbalanced dynamic diffusion Schrödinger bridge solver on synthetic dynamics from two perspectives: (1) Compared to the balanced diffusion Schrödinger bridge, our approach accurately recovers the correct growth and transition processes, eliminating false transitions caused by neglecting growth. What is a stochastic interpolant? Stochastic interpolants: A unifying framework for flows and diffusions . arXiv preprint arXiv:2303.08797 . Albergo, M.S., Vanden-Eijnden, E., 2023. Building normalizing flows with stochastic interpolants, in: The Eleventh International Conference on Learning Representations. Baradat, A., Lavenant, H., 2021. Jul 5, 2024 · This paper aims to develop a globally consistent nearest-neighborhood algorithm that robustly extracts stochastic particle tracks from the reconstructed Gaussian particle distributions in all frames. Our tracking algorithm relies on the unbalanced optimal transport theory in the metric space of Gaussian measures."}
+{"idx": 9, "title": "Unbalanced Optimal Transport, from Theory to Numerics", "date": "", "ddg_snippet": "Nov 16, 2022 · Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learning pipelines is however plagued by several shortcomings. This includes its lack of robustness to outliers, its high computational costs, the need ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2211.08775", "content": "Nov 16, 2022 · Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learning pipelines is however plagued by several shortcomings. This includes its lack of robustness to outliers, its high computational costs, the need ..."}
diff --git a/data/sampled_jsons/Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal_transport_sitearx.jsonl b/data/sampled_jsons/Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal_transport_sitearx.jsonl
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index 0000000000000000000000000000000000000000..7e8e0859a3633a0065892f4d4cfdf80326960775
--- /dev/null
+++ b/data/sampled_jsons/Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal_transport_sitearx.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning from Samples: Inverse Problems over measures via", "date": "", "ddg_snippet": "... in this work on two specific settings where the forward optimization problem is derived from optimal transport (OT) with entropic regularization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.07124v1", "content": "... in this work on two specific settings where the forward optimization problem is derived from optimal transport (OT) with entropic regularization."}
+{"idx": 1, "title": "Consistent diffusion matrix estimation from population time", "date": "", "ddg_snippet": "... that has grown to prominence with advances in single-cell sequencing technologies is learning the behavior of individuals from population snapshots ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.14408v1", "content": "... that has grown to prominence with advances in single-cell sequencing technologies is learning the behavior of individuals from population snapshots ..."}
+{"idx": 2, "title": "Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the", "date": "", "ddg_snippet": "Scientists often want to make predictions beyond the observed time horizon of “ snapshot ” data following latent stochastic dynamics .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16082v1", "content": "Scientists often want to make predictions beyond the observed time horizon of “ snapshot ” data following latent stochastic dynamics ."}
+{"idx": 3, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v4", "content": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
+{"idx": 4, "title": "[2410.00844] Learning stochastic dynamics from snapshots ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport , by Zhenyi Zhang and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.00844", "content": "View a PDF of the paper titled Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport , by Zhenyi Zhang and 2 other authors."}
+{"idx": 5, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v5", "content": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
+{"idx": 6, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Recently, regularized unbalanced optimal transport (RUOT) offers a promising approach for modeling such stochastic unbalanced continuous dynamics (Baradat and Lavenant, 2021; Chen et al., 2022b; Buze and Duong, 2023; Janati et al., 2020) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v1", "content": "Recently, regularized unbalanced optimal transport (RUOT) offers a promising approach for modeling such stochastic unbalanced continuous dynamics (Baradat and Lavenant, 2021; Chen et al., 2022b; Buze and Duong, 2023; Janati et al., 2020) ."}
+{"idx": 7, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v2", "content": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
+{"idx": 8, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v3", "content": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
+{"idx": 9, "title": "Using Optimal Transport Aligned Latent Embeddings for Separated", "date": "", "ddg_snippet": "... learned by an autoencoder with the corresponding OT geodesics, we seek to learn low-dimensional representations of flow fields that are interpretable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07318v1", "content": "... learned by an autoencoder with the corresponding OT geodesics, we seek to learn low-dimensional representations of flow fields that are interpretable ..."}
diff --git a/data/sampled_jsons/Learning_without_Forgetting_Zhizhong_Li_Derek_Hoiem_abstract_ECCV_2016.jsonl b/data/sampled_jsons/Learning_without_Forgetting_Zhizhong_Li_Derek_Hoiem_abstract_ECCV_2016.jsonl
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index 0000000000000000000000000000000000000000..50a5a73ac83e6117f732727a3221743e42029e7d
--- /dev/null
+++ b/data/sampled_jsons/Learning_without_Forgetting_Zhizhong_Li_Derek_Hoiem_abstract_ECCV_2016.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1606.09282] Learning without Forgetting", "date": "", "ddg_snippet": "by Z Li · 2016 · Cited by 5971 — Abstract ... Authors: Zhizhong Li , Derek Hoiem . View a PDF of the paper titled Learning without Forgetting , by Zhizhong Li and 1 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1606.09282", "content": "by Z Li · 2016 · Cited by 5971 — Abstract ... Authors: Zhizhong Li , Derek Hoiem . View a PDF of the paper titled Learning without Forgetting , by Zhizhong Li and 1 other authors."}
+{"idx": 1, "title": "Learning without Forgetting", "date": "", "ddg_snippet": "by Z Li · Cited by 5973 — Learning without Forgetting . Zhizhong Li , Derek Hoiem , Member, IEEE. Abstract —When building a unified vision system or gradually adding new capabilities to a ... 13 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/ielaam/34/8520726/8107520-aam.pdf", "content": "by Z Li · Cited by 5973 — Learning without Forgetting . Zhizhong Li , Derek Hoiem , Member, IEEE. Abstract —When building a unified vision system or gradually adding new capabilities to a ... 13 pages"}
+{"idx": 2, "title": "Learning without Forgetting", "date": "", "ddg_snippet": "by Z Li · 2016 · Cited by 5959 — Learning without Forgetting . Zhizhong Li , Derek Hoiem , Member, IEEE. Abstract —When building a unified vision system or gradually adding new ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1606.09282", "content": "by Z Li · 2016 · Cited by 5959 — Learning without Forgetting . Zhizhong Li , Derek Hoiem , Member, IEEE. Abstract —When building a unified vision system or gradually adding new ..."}
+{"idx": 3, "title": "Learning without forgetting", "date": "", "ddg_snippet": "Learning Without Forgetting . Zhizhong Li (B) and Derek Hoiem . Department of Computer Science,. University of Illinois Urbana Champaign, Champaign, USA. {zli115 ... 16 pages", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/learning-without-forgetting-34a4300f5h.pdf", "content": "Learning Without Forgetting . Zhizhong Li (B) and Derek Hoiem . Department of Computer Science,. University of Illinois Urbana Champaign, Champaign, USA. {zli115 ... 16 pages"}
+{"idx": 4, "title": "Learning without forgetting", "date": "", "ddg_snippet": "by Z Li · 2016 · Cited by 5971 — Learning without forgetting . Zhizhong Li , Derek Hoiem · Electrical and Computer Engineering · Coordinated Science Lab · Siebel School of Computing and Data ...", "subpage_snippet": "", "source": "experts.illinois.edu", "link": "https://experts.illinois.edu/en/publications/learning-without-forgetting", "content": "by Z Li · 2016 · Cited by 5971 — Learning without forgetting . Zhizhong Li , Derek Hoiem · Electrical and Computer Engineering · Coordinated Science Lab · Siebel School of Computing and Data ..."}
+{"idx": 5, "title": "Learning without Forgetting | IEEE Transactions on Pattern ...", "date": "", "ddg_snippet": "Zhizhong Li . Department of Computer Science, University of Illinois, Urbana Champaign, IL. https://orcid.org/0000-0002-6068-7209 · View Profile. , Derek Hoiem .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/TPAMI.2017.2773081", "content": "Zhizhong Li . Department of Computer Science, University of Illinois, Urbana Champaign, IL. https://orcid.org/0000-0002-6068-7209 · View Profile. , Derek Hoiem ."}
+{"idx": 6, "title": "Repository for the Learning without Forgetting paper ...", "date": "", "ddg_snippet": "Repository for the Learning without Forgetting paper, ECCV 2016 ... Created by Zhizhong Li and Derek Hoiem at University of Illinois, Urbana Champaign.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lizhitwo/LearningWithoutForgetting", "content": "Repository for the Learning without Forgetting paper, ECCV 2016 ... Created by Zhizhong Li and Derek Hoiem at University of Illinois, Urbana Champaign."}
+{"idx": 7, "title": "(PDF) Learning without Forgetting (2018) | Zhizhong Li", "date": "", "ddg_snippet": "Learning Without Forgetting Zhizhong Li (B) and Derek Hoiem Department of Computer Science, University of Illinois Urbana Champaign, Champaign, USA {zli115 ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/learning-without-forgetting-1csfq4yxg5", "content": "Learning Without Forgetting Zhizhong Li (B) and Derek Hoiem Department of Computer Science, University of Illinois Urbana Champaign, Champaign, USA {zli115 ..."}
+{"idx": 8, "title": "Learning Without Forgetting | PDF | Artificial Neural Network", "date": "", "ddg_snippet": "Learning without Forgetting . Zhizhong Li , Derek Hoiem , Member, IEEE. Abstract —When building a unified vision system or gradually adding new capabilities to a ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/743721728/Learning-Without-Forgetting", "content": "Learning without Forgetting . Zhizhong Li , Derek Hoiem , Member, IEEE. Abstract —When building a unified vision system or gradually adding new capabilities to a ..."}
+{"idx": 9, "title": "LEARNING WITHOUT FORGETTING: TASK AWARE MULTI- ...", "date": "", "ddg_snippet": "Zhizhong Li and Derek Hoiem . Learning without forgetting . IEEE Trans. Pattern Anal. Mach. Intell.,. 40(12):2935–2947, 2018. doi: 10.1109/TPAMI.2017.2773081 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kJVVgJ-yCq", "content": "Zhizhong Li and Derek Hoiem . Learning without forgetting . IEEE Trans. Pattern Anal. Mach. Intell.,. 40(12):2935–2947, 2018. doi: 10.1109/TPAMI.2017.2773081 ..."}
diff --git a/data/sampled_jsons/Lemma_3.6_HDT_SRRW_average_neighborhood_size_cost_advantage_sitearxiv.org.jsonl b/data/sampled_jsons/Lemma_3.6_HDT_SRRW_average_neighborhood_size_cost_advantage_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c9bc9915e76a49f0a1f31258fea05a431e05c161
--- /dev/null
+++ b/data/sampled_jsons/Lemma_3.6_HDT_SRRW_average_neighborhood_size_cost_advantage_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Faster Neighborhood Attention: Reducing the Cost of Self ...", "date": "", "ddg_snippet": "window size and dilation factor, draws a spectrum of possible attention pat-terns between linear projection and self attention. Neighborhood attention, and more generally sliding window attention patterns, have long been bounded by infrastructure, particularly in higher-rank spaces (2-D and 3-D), calling for the development of custom kernels, which have been limited in either functionality, or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.04690v3", "content": "window size and dilation factor, draws a spectrum of possible attention pat-terns between linear projection and self attention. Neighborhood attention, and more generally sliding window attention patterns, have long been bounded by infrastructure, particularly in higher-rank spaces (2-D and 3-D), calling for the development of custom kernels, which have been limited in either functionality, or ..."}
+{"idx": 1, "title": "Faster Neighborhood Attention: Reducing the 𝒪 (𝑛²) Cost of ...", "date": "", "ddg_snippet": "Abstract Neighborhood attention reduces the cost of self attention by restricting each token’s attention span to its nearest neighbors. This restriction, parameterized by a window size and dilation factor, draws a spectrum of possible attention patterns between linear projection and self attention.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.04690", "content": "Abstract Neighborhood attention reduces the cost of self attention by restricting each token’s attention span to its nearest neighbors. This restriction, parameterized by a window size and dilation factor, draws a spectrum of possible attention patterns between linear projection and self attention."}
+{"idx": 2, "title": "[2310.10631] Llemma: An Open Language Model For Mathematics [1506.02438] High-Dimensional Continuous Control Using ... Consistent estimation of the basic neighborhood of Markov ... Generalized Hadamard three lines lemma - arXiv.org", "date": "", "ddg_snippet": "Oct 16, 2023 · We present Llemma, a large language model for mathematics. We continue pretraining Code Llama on the Proof-Pile-2, a mixture of scientific papers, web data containing mathematics, and mathematical code, yielding Llemma. On the MATH benchmark Llemma outperforms all known open base models, as well as the unreleased Minerva model suite on an equi-parameter basis. Moreover, Llemma is capable of ... Jun 8, 2015 · Abstract page for arXiv paper 1506.02438: High-Dimensional Continuous Control Using Generalized Advantage Estimation A modifi-cation of the Bayesian Information Criterion, replacing likelihood by pseudo-likelihood, is proved to provide strongly consistent estimation from observing a realization of the field on increasing finite regions: the estimated basic neighborhood equals the true one eventually al-most surely, not assuming any prior bound on the size of ... As previously mentioned, Lemma 4.2 allows us to use the Poisson representation formula in the strip ( Lemma B.1) to construct the phase functions corresponding to the solutions of the Euler-Lagrange equations (4.3) and (4.4).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.10631", "content": "Oct 16, 2023 · We present Llemma, a large language model for mathematics. We continue pretraining Code Llama on the Proof-Pile-2, a mixture of scientific papers, web data containing mathematics, and mathematical code, yielding Llemma. On the MATH benchmark Llemma outperforms all known open base models, as well as the unreleased Minerva model suite on an equi-parameter basis. Moreover, Llemma is capable of ... Jun 8, 2015 · Abstract page for arXiv paper 1506.02438: High-Dimensional Continuous Control Using Generalized Advantage Estimation A modifi-cation of the Bayesian Information Criterion, replacing likelihood by pseudo-likelihood, is proved to provide strongly consistent estimation from observing a realization of the field on increasing finite regions: the estimated basic neighborhood equals the true one eventually al-most surely, not assuming any prior bound on the size of ... As previously mentioned, Lemma 4.2 allows us to use the Poisson representation formula in the strip ( Lemma B.1) to construct the phase functions corresponding to the solutions of the Euler-Lagrange equations (4.3) and (4.4)."}
+{"idx": 3, "title": "[1506.02438] High-Dimensional Continuous Control Using ... Consistent estimation of the basic neighborhood of Markov ... Generalized Hadamard three lines lemma - arXiv.org", "date": "", "ddg_snippet": "Jun 8, 2015 · Abstract page for arXiv paper 1506.02438: High-Dimensional Continuous Control Using Generalized Advantage Estimation A modifi-cation of the Bayesian Information Criterion, replacing likelihood by pseudo-likelihood, is proved to provide strongly consistent estimation from observing a realization of the field on increasing finite regions: the estimated basic neighborhood equals the true one eventually al-most surely, not assuming any prior bound on the size of ... As previously mentioned, Lemma 4.2 allows us to use the Poisson representation formula in the strip ( Lemma B.1) to construct the phase functions corresponding to the solutions of the Euler-Lagrange equations (4.3) and (4.4).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1506.02438", "content": "Jun 8, 2015 · Abstract page for arXiv paper 1506.02438: High-Dimensional Continuous Control Using Generalized Advantage Estimation A modifi-cation of the Bayesian Information Criterion, replacing likelihood by pseudo-likelihood, is proved to provide strongly consistent estimation from observing a realization of the field on increasing finite regions: the estimated basic neighborhood equals the true one eventually al-most surely, not assuming any prior bound on the size of ... As previously mentioned, Lemma 4.2 allows us to use the Poisson representation formula in the strip ( Lemma B.1) to construct the phase functions corresponding to the solutions of the Euler-Lagrange equations (4.3) and (4.4)."}
+{"idx": 4, "title": "Consistent estimation of the basic neighborhood of Markov ...", "date": "", "ddg_snippet": "A modifi-cation of the Bayesian Information Criterion, replacing likelihood by pseudo-likelihood, is proved to provide strongly consistent estimation from observing a realization of the field on increasing finite regions: the estimated basic neighborhood equals the true one eventually al-most surely, not assuming any prior bound on the size of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/math/0605323.pdf", "content": "A modifi-cation of the Bayesian Information Criterion, replacing likelihood by pseudo-likelihood, is proved to provide strongly consistent estimation from observing a realization of the field on increasing finite regions: the estimated basic neighborhood equals the true one eventually al-most surely, not assuming any prior bound on the size of ..."}
+{"idx": 5, "title": "Generalized Hadamard three lines lemma - arXiv.org", "date": "", "ddg_snippet": "As previously mentioned, Lemma 4.2 allows us to use the Poisson representation formula in the strip ( Lemma B.1) to construct the phase functions corresponding to the solutions of the Euler-Lagrange equations (4.3) and (4.4).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.10117", "content": "As previously mentioned, Lemma 4.2 allows us to use the Poisson representation formula in the strip ( Lemma B.1) to construct the phase functions corresponding to the solutions of the Euler-Lagrange equations (4.3) and (4.4)."}
+{"idx": 6, "title": "Microsoft Word - submission arXiv", "date": "", "ddg_snippet": "This mean manifold is differentiable through Leibniz’s rule with a tangent plane from its differentiability. As the ball contracts the average manifold approaches the tangent plane, giving us Lemma 2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2002.10545", "content": "This mean manifold is differentiable through Leibniz’s rule with a tangent plane from its differentiability. As the ball contracts the average manifold approaches the tangent plane, giving us Lemma 2."}
+{"idx": 7, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "The discrepancy is even more pronounced in the Facebook graph in both plots, where the average degree (43.6 43.6 43.6) far exceeds that of p2p-Gnutella04 (7.4 7.4 7.4), supporting our discussion after Lemma 3.6 , where larger neighborhood size leads to more performance advantage of HDT -MCMC compared to SRRW .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v2", "content": "The discrepancy is even more pronounced in the Facebook graph in both plots, where the average degree (43.6 43.6 43.6) far exceeds that of p2p-Gnutella04 (7.4 7.4 7.4), supporting our discussion after Lemma 3.6 , where larger neighborhood size leads to more performance advantage of HDT -MCMC compared to SRRW ."}
+{"idx": 8, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient...", "date": "", "ddg_snippet": "Lemma 3 . 6 . The cost -based covariances between SRRW and HDT -MCMC in (11) and (12) are ordered as follows, suggesting a universal advantage . This factor becomes more pronounced in dense or nearly complete graphs, where the average neighborhood size is.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "Lemma 3 . 6 . The cost -based covariances between SRRW and HDT -MCMC in (11) and (12) are ordered as follows, suggesting a universal advantage . This factor becomes more pronounced in dense or nearly complete graphs, where the average neighborhood size is."}
+{"idx": 9, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient...", "date": "", "ddg_snippet": "The cost -based covariances between SRRW and HDT -MCMC in (11) and (12) are ordered as followsα\\alphaitalic_α. , suggesting a universal advantage . This factor becomes more pronounced in dense or nearly complete graphs, where the average neighborhood size is.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "The cost -based covariances between SRRW and HDT -MCMC in (11) and (12) are ordered as followsα\\alphaitalic_α. , suggesting a universal advantage . This factor becomes more pronounced in dense or nearly complete graphs, where the average neighborhood size is."}
diff --git a/data/sampled_jsons/Leutgeb_et_al._2004_hippocampus_place_cells_remapping.jsonl b/data/sampled_jsons/Leutgeb_et_al._2004_hippocampus_place_cells_remapping.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c0c35817b3fd162eedf0023193e69af3d4966e8b
--- /dev/null
+++ b/data/sampled_jsons/Leutgeb_et_al._2004_hippocampus_place_cells_remapping.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Hippocampal place cell remapping occurs with memory", "date": "", "ddg_snippet": "Remapping was assessed by comparing place cell population vector similarity before acquisition versus after extinction of avoidance.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372470805_Hippocampal_place_cell_remapping_occurs_with_memory_storage_of_aversive_experiences", "content": "Remapping was assessed by comparing place cell population vector similarity before acquisition versus after extinction of avoidance."}
+{"idx": 1, "title": "Hippocampal place cell remapping occurs with memory storage of", "date": "", "ddg_snippet": "Task learning induced partial remapping in CA1 place cells , allowing us to identify both remapping and stable cell populations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372636559_Hippocampal_place_cell_remapping_occurs_with_memory_storage_of_aversive_experiences", "content": "Task learning induced partial remapping in CA1 place cells , allowing us to identify both remapping and stable cell populations."}
+{"idx": 2, "title": "Hippocampal place cell remapping occurs with memory storage of", "date": "", "ddg_snippet": "Task learning induced partial remapping in CA1 place cells , allowing us to identify both remapping and stable cell populations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372634780_Hippocampal_place_cell_remapping_occurs_with_memory_storage_of_aversive_experiences", "content": "Task learning induced partial remapping in CA1 place cells , allowing us to identify both remapping and stable cell populations."}
+{"idx": 3, "title": "Hippocampal place cell remapping occurs with memory storage of", "date": "", "ddg_snippet": "Task learning induced partial remapping in CA1 place cells , allowing us to identify both remapping and stable cell populations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372634497_Hippocampal_place_cell_remapping_occurs_with_memory_storage_of_aversive_experiences", "content": "Task learning induced partial remapping in CA1 place cells , allowing us to identify both remapping and stable cell populations."}
+{"idx": 4, "title": "Hippocampal place cell remapping occurs with memory storage of", "date": "", "ddg_snippet": "... is encountered in an environment, place cells respond by remapping their firing fields in that environment ( Moita et al ., 2003 ; Moita et al ., 2004 ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/80661", "content": "... is encountered in an environment, place cells respond by remapping their firing fields in that environment ( Moita et al ., 2003 ; Moita et al ., 2004 ..."}
+{"idx": 5, "title": "Hippocampal Global Remapping Can Occur without Input from the", "date": "", "ddg_snippet": "... a reorganization of the spatial firing patterns of nearly all hippocampal cells ( Muller and Kubie, 1987; Lever et al ., 2002; Leutgeb et al ., 2004 ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell-reports/fulltext/S2211-1247(18)30292-4", "content": "... a reorganization of the spatial firing patterns of nearly all hippocampal cells ( Muller and Kubie, 1987; Lever et al ., 2002; Leutgeb et al ., 2004 ..."}
+{"idx": 6, "title": "Understanding memory through hippocampal remapping: Trends in", "date": "", "ddg_snippet": "Place cells have taught much, not only about spatial representation in the brain but also about the general working principles of the hippocampus and ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/trends/neurosciences/fulltext/S0166-2236(08)00167-7", "content": "Place cells have taught much, not only about spatial representation in the brain but also about the general working principles of the hippocampus and ..."}
+{"idx": 7, "title": ", 2005 and Muller, 1996) This “rate remapping” may", "date": "", "ddg_snippet": "This form of remapping has been reported when animals explore distinct recording enclosures in an otherwise constant environment ( Leutgeb et al ...", "subpage_snippet": "", "source": "atpase-signal.com", "link": "https://atpase-signal.com/2005-and-muller-1996-this-rate-remapping-may-reflect-the-s", "content": "This form of remapping has been reported when animals explore distinct recording enclosures in an otherwise constant environment ( Leutgeb et al ..."}
+{"idx": 8, "title": "Five Decades of Hippocampal Place Cells and EEG Rhythms in", "date": "", "ddg_snippet": "... with an environment ( e .g., color, shape, light vs darkness) could change the firing patterns of place cells ( Muller and Kubie, 1987 ; Quirk et al ...", "subpage_snippet": "", "source": "www.jneurosci.org", "link": "https://www.jneurosci.org/content/40/1/54", "content": "... with an environment ( e .g., color, shape, light vs darkness) could change the firing patterns of place cells ( Muller and Kubie, 1987 ; Quirk et al ..."}
+{"idx": 9, "title": "Reconceiving the hippocampal map as a topological template |", "date": "", "ddg_snippet": "Place cells appear to respond to a perplexing array of stimuli, from visual cues to head direction, goal planning, color changes, shape changes, and ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/03476", "content": "Place cells appear to respond to a perplexing array of stimuli, from visual cues to head direction, goal planning, color changes, shape changes, and ..."}
diff --git a/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Definition_4.2_per-instance_privacy_loss_year_2024.jsonl b/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Definition_4.2_per-instance_privacy_loss_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..96419f0c06ce932685a7805d0593182a5e71f8e4
--- /dev/null
+++ b/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Definition_4.2_per-instance_privacy_loss_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ..."}
+{"idx": 1, "title": "A survey on machine unlearning: Techniques and new emerged privacy ...", "date": "", "ddg_snippet": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions , implementation methods, and real-world applications.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2214212625000481", "content": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions , implementation methods, and real-world applications."}
+{"idx": 2, "title": "Per-instance Differential Privacy", "date": "", "ddg_snippet": "In this paper, we proposed to use per-instance di erential privacy (pDP) for quantifying the ne-grained privacy loss of a xed individual against randomized data analysis conducted on a xed data set.", "subpage_snippet": "", "source": "journalprivacyconfidentiality.org", "link": "https://journalprivacyconfidentiality.org/index.php/jpc/article/download/662/675/1038", "content": "In this paper, we proposed to use per-instance di erential privacy (pDP) for quantifying the ne-grained privacy loss of a xed individual against randomized data analysis conducted on a xed data set."}
+{"idx": 3, "title": "PDF Forget to Flourish: Leveraging Machine-Unlearning on Pretrained ...", "date": "", "ddg_snippet": "Hence, we propose bounded unlearning as a poisoning tool, where we maximize loss in a controlled manner on the pre-trained model for some noisy data points to increase privacy leakage of the fine-tuned LLM without compromising its utility.", "subpage_snippet": "", "source": "www.merl.com", "link": "https://www.merl.com/publications/docs/TR2024-168.pdf", "content": "Hence, we propose bounded unlearning as a poisoning tool, where we maximize loss in a controlled manner on the pre-trained model for some noisy data points to increase privacy leakage of the fine-tuned LLM without compromising its utility."}
+{"idx": 4, "title": "Inexact Unlearning Needs More Careful Evaluations to Avoid a False ...", "date": "", "ddg_snippet": "We formulate the membership inference game on machine unlearning and extensively evaluate the privacy protection of different unlearning algorithms using a strong per -sample U-MIA on both vision classifiers and language models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01218v1", "content": "We formulate the membership inference game on machine unlearning and extensively evaluate the privacy protection of different unlearning algorithms using a strong per -sample U-MIA on both vision classifiers and language models."}
+{"idx": 5, "title": "Finetune and Label Reversal: Privacy-preserving unlearning strategies ...", "date": "", "ddg_snippet": "With the increasing emphasis on data protection by governments, machine unlearning has become a highly researched and prominent topic of interest. Machine unlearning is the process of eliminating the influence of specific samples from a machine learning model.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0920548925000054", "content": "With the increasing emphasis on data protection by governments, machine unlearning has become a highly researched and prominent topic of interest. Machine unlearning is the process of eliminating the influence of specific samples from a machine learning model."}
+{"idx": 6, "title": "Certificates of Differential Privacy and Unlearning for Gradient-Based ...", "date": "", "ddg_snippet": "However, providing certificates of privacy or guarantees of the removal of a user's data from a trained machine learning model presents noteworthy technical challenges; these tasks are often termed differential privacy and machine unlearning , respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13433", "content": "However, providing certificates of privacy or guarantees of the removal of a user's data from a trained machine learning model presents noteworthy technical challenges; these tasks are often termed differential privacy and machine unlearning , respectively."}
+{"idx": 7, "title": "Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A ...", "date": "", "ddg_snippet": "Machine unlearning focuses on eficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing interest in inexact unlearning methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.13009", "content": "Machine unlearning focuses on eficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing interest in inexact unlearning methods."}
+{"idx": 8, "title": "Enhancing Privacy in Machine Unlearning: Posterior Perturbation Against ...", "date": "", "ddg_snippet": "Machine unlearning aims to safeguard data privacy by mitigating the data's impact on machine learning models. Nonetheless, machine unlearning practices can introduce new privacy vulnerabilities, leaving models susceptible to various forms of attack, such as confidence attack and label-only attacks. Existing defense methods encounter challenges in striking a balance between defending against ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-1551-3_16", "content": "Machine unlearning aims to safeguard data privacy by mitigating the data's impact on machine learning models. Nonetheless, machine unlearning practices can introduce new privacy vulnerabilities, leaving models susceptible to various forms of attack, such as confidence attack and label-only attacks. Existing defense methods encounter challenges in striking a balance between defending against ..."}
+{"idx": 9, "title": "Evaluating Machine Unlearning: Applications, Approaches, and Accuracy ...", "date": "", "ddg_snippet": "Unlearning at the Instance Level: Unlearning at the instance level focuses on eliminating single instances or data points from a learned model. This type of unlearning is beneficial when individual data points must be eliminated owing to privacy issues, data sensitivity, or biases.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/eng2.13081", "content": "Unlearning at the Instance Level: Unlearning at the instance level focuses on eliminating single instances or data points from a learned model. This type of unlearning is beneficial when individual data points must be eliminated owing to privacy issues, data sensitivity, or biases."}
diff --git a/data/sampled_jsons/Li_Hoiem_2016_Learning_without_Forgetting_abstract_year_2016.jsonl b/data/sampled_jsons/Li_Hoiem_2016_Learning_without_Forgetting_abstract_year_2016.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..59d10566c653bb85abb59919a433dc44e0db2a60
--- /dev/null
+++ b/data/sampled_jsons/Li_Hoiem_2016_Learning_without_Forgetting_abstract_year_2016.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1606.09282] Learning without Forgetting", "date": "", "ddg_snippet": "by Z Li · 2016 · Cited by 5981 — We propose our Learning without Forgetting method , which uses only new task data to train the network while preserving the original capabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1606.09282", "content": "by Z Li · 2016 · Cited by 5981 — We propose our Learning without Forgetting method , which uses only new task data to train the network while preserving the original capabilities."}
+{"idx": 1, "title": "Learning without Forgetting", "date": "", "ddg_snippet": "by Z Li · Cited by 5971 — Abstract —When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training. 13 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/ielaam/34/8520726/8107520-aam.pdf", "content": "by Z Li · Cited by 5971 — Abstract —When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training. 13 pages"}
+{"idx": 2, "title": "Learning without Forgetting", "date": "", "ddg_snippet": "by Z Li · 2016 · Cited by 5959 — Abstract —When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1606.09282", "content": "by Z Li · 2016 · Cited by 5959 — Abstract —When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training."}
+{"idx": 3, "title": "Learning without Forgetting | IEEE Transactions on Pattern ...", "date": "", "ddg_snippet": "We propose our Learning without Forgetting method , which uses only new task data to train the network while preserving the original capabilities.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/TPAMI.2017.2773081", "content": "We propose our Learning without Forgetting method , which uses only new task data to train the network while preserving the original capabilities."}
+{"idx": 4, "title": "Learning without forgetting", "date": "", "ddg_snippet": "by Z Li · 2016 · Cited by 5959 — Learning without forgetting . / Li, Zhizhong; Hoiem, Derek. Computer Vision - 14th European Conference, ECCV 2016, Proceedings. ed. / Bastian Leibe; Jiri Matas; ...", "subpage_snippet": "", "source": "experts.illinois.edu", "link": "https://experts.illinois.edu/en/publications/learning-without-forgetting", "content": "by Z Li · 2016 · Cited by 5959 — Learning without forgetting . / Li, Zhizhong; Hoiem, Derek. Computer Vision - 14th European Conference, ECCV 2016, Proceedings. ed. / Bastian Leibe; Jiri Matas; ..."}
+{"idx": 5, "title": "\"Learning without Forgetting\", Li & Hoiem 2016", "date": "", "ddg_snippet": "Transfer learning across CNNs. This takes the simultaneous-training approach of having two output layers, 1 for each separate task.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/4xlc1l/learning_without_forgetting_li_hoiem_2016/", "content": "Transfer learning across CNNs. This takes the simultaneous-training approach of having two output layers, 1 for each separate task."}
+{"idx": 6, "title": "Learning without forgetting", "date": "", "ddg_snippet": "We propose our Learning without Forgetting method , which uses only new task data to train the network while preserving the original capabili- ties. Our method ... 16 pages", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/learning-without-forgetting-34a4300f5h.pdf", "content": "We propose our Learning without Forgetting method , which uses only new task data to train the network while preserving the original capabili- ties. Our method ... 16 pages"}
+{"idx": 7, "title": "Repository for the Learning without Forgetting paper ...", "date": "", "ddg_snippet": "Learning without Forgetting aims at adding new capabilities (new tasks) to an existing Convolutional Neural Network, sharing representation with the original ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lizhitwo/LearningWithoutForgetting", "content": "Learning without Forgetting aims at adding new capabilities (new tasks) to an existing Convolutional Neural Network, sharing representation with the original ..."}
+{"idx": 8, "title": "Continuous learning in single-incremental-task scenarios", "date": "", "ddg_snippet": "by D Maltoni · 2019 · Cited by 445 — Learning Without Forgetting (LWF ) (Li & Hoiem, 2016) is a regularization strategy attempting to preserve the model accuracy on old tasks by imposing output ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0893608019300838", "content": "by D Maltoni · 2019 · Cited by 445 — Learning Without Forgetting (LWF ) (Li & Hoiem, 2016) is a regularization strategy attempting to preserve the model accuracy on old tasks by imposing output ..."}
+{"idx": 9, "title": "LEARNING WITHOUT FORGETTING FOR VISION- ...", "date": "", "ddg_snippet": "Zhizhong Li and Derek Hoiem. Learning without forgetting . In ECCV, pp. 614–629. Springer, 2016. Dongze Lian, Zhou Daquan, Jiashi Feng, and Xinchao ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/68268717af508361ae5c40ae4924e02d8cd4873e.pdf", "content": "Zhizhong Li and Derek Hoiem. Learning without forgetting . In ECCV, pp. 614–629. Springer, 2016. Dongze Lian, Zhou Daquan, Jiashi Feng, and Xinchao ..."}
diff --git a/data/sampled_jsons/Li_and_Chen_2024_feature_localization_generative_models_abstract_year_2024.jsonl b/data/sampled_jsons/Li_and_Chen_2024_feature_localization_generative_models_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8343ca1483664375a81b91d250de4d03f784322e
--- /dev/null
+++ b/data/sampled_jsons/Li_and_Chen_2024_feature_localization_generative_models_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Lyra: Generative 3D Scene Reconstruction via Video Diffusion", "date": "", "ddg_snippet": "Early works on multi-view image generation (Watson et al., 2023 ; Liu et al., 2023 ; Shi et al., 2024 ; Wang & Shi, 2023 ) mainly focus on object ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.19296v1", "content": "Early works on multi-view image generation (Watson et al., 2023 ; Liu et al., 2023 ; Shi et al., 2024 ; Wang & Shi, 2023 ) mainly focus on object ..."}
+{"idx": 1, "title": "Detecting LLM-Generated Korean Text through Linguistic Feature", "date": "", "ddg_snippet": "... captures a diverse range of real-life scenarios and linguistic features while addressing practical challenges in detecting LLM-generated Korean text.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00032v3", "content": "... captures a diverse range of real-life scenarios and linguistic features while addressing practical challenges in detecting LLM-generated Korean text."}
+{"idx": 2, "title": "Conditional Diffusion Models for CT Image Synthesis from CBCT:", "date": "", "ddg_snippet": "... generative models must respect both global and local anatomical context present in CBCT, although learning to correct for modality-specific ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.17790v1", "content": "... generative models must respect both global and local anatomical context present in CBCT, although learning to correct for modality-specific ..."}
+{"idx": 3, "title": "Xiaofei CHEN | Professor | PhD | Southern University of Science", "date": "", "ddg_snippet": "A read is counted each time someone views a publication summary (such as the title, abstract , and list of authors), clicks on a figure, or views or ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Xiaofei-Chen-2", "content": "A read is counted each time someone views a publication summary (such as the title, abstract , and list of authors), clicks on a figure, or views or ..."}
+{"idx": 4, "title": "ICSE 2024 - Artifact Evaluation - ICSE 2024", "date": "", "ddg_snippet": "... abstract describing your research artifact by the abstract deadline, and submit a 2 pages (max) PDF or Markdown file by the submission deadline at the ...", "subpage_snippet": "", "source": "conf.researchr.org", "link": "https://conf.researchr.org/track/icse-2024/icse-2024-artifact-evaluation", "content": "... abstract describing your research artifact by the abstract deadline, and submit a 2 pages (max) PDF or Markdown file by the submission deadline at the ..."}
+{"idx": 5, "title": "Generative Modeling | Climate Change AI", "date": "", "ddg_snippet": "Abstract : Accurate ocean modeling and coastal hazard prediction depend on high-resolution bathymetric data; yet, current worldwide datasets are too ...", "subpage_snippet": "", "source": "www.climatechange.ai", "link": "https://www.climatechange.ai/subject_areas/generative_modeling", "content": "Abstract : Accurate ocean modeling and coastal hazard prediction depend on high-resolution bathymetric data; yet, current worldwide datasets are too ..."}
+{"idx": 6, "title": "Refining medical large language models: key insights from", "date": "", "ddg_snippet": "... LLMs for healthcare, including advancements in pre-training models ( He et al., 2023 ), applications ( Yang et al., 2023 ; Liu et al., 2024a ), and ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-3216/", "content": "... LLMs for healthcare, including advancements in pre-training models ( He et al., 2023 ), applications ( Yang et al., 2023 ; Liu et al., 2024a ), and ..."}
+{"idx": 7, "title": "Mitigating Hallucinations in Multimodal LLMs via Object-aware", "date": "", "ddg_snippet": "... and He , Wang et al.( 2024 )Wang, Zhou, Huang, Xu, Zhang, Poon, and Chen , Jiang et al.( 2024 )Jiang, Zhang, Chen , Jin, and Liu , Sarkar ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20181v1", "content": "... and He , Wang et al.( 2024 )Wang, Zhou, Huang, Xu, Zhang, Poon, and Chen , Jiang et al.( 2024 )Jiang, Zhang, Chen , Jin, and Liu , Sarkar ..."}
+{"idx": 8, "title": "Publications", "date": "", "ddg_snippet": "... Feature Modeling Operations Pu Li , Jianwei Guo * , Huibin Li , Bedrich Benes, Dong-Ming Yan IEEE Conference on Computer Vision and Pattern Recognition ...", "subpage_snippet": "", "source": "jianweiguo.net", "link": "http://jianweiguo.net/publications/", "content": "... Feature Modeling Operations Pu Li , Jianwei Guo * , Huibin Li , Bedrich Benes, Dong-Ming Yan IEEE Conference on Computer Vision and Pattern Recognition ..."}
+{"idx": 9, "title": "ISSTA 2025 Contributors - ISSTA 2025", "date": "", "ddg_snippet": "... Note Generation for Android Apps using Large Language Models ... A Low-Cost Feature Interaction Fault Localization Approach for Software Product Lines", "subpage_snippet": "", "source": "conf.researchr.org", "link": "https://conf.researchr.org/people-index/issta-2025", "content": "... Note Generation for Android Apps using Large Language Models ... A Low-Cost Feature Interaction Fault Localization Approach for Software Product Lines"}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_numerical_results_experimental.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_numerical_results_experimental.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4199542f65db8cd8ed65fa94e6acea7c8ceaed03
--- /dev/null
+++ b/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_numerical_results_experimental.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Normal distribution - Wikipedia", "date": "", "ddg_snippet": "The truncated normal distribution results from rescaling a section of a single density function. Infinite divisibility and Cramér's theorem.. The standard approach to this problem is the maximum likelihood method, which requires maximization of the log- likelihood function", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Normal_distribution", "content": "The truncated normal distribution results from rescaling a section of a single density function. Infinite divisibility and Cramér's theorem.. The standard approach to this problem is the maximum likelihood method, which requires maximization of the log- likelihood function"}
+{"idx": 1, "title": "ICML Poster A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric."}
+{"idx": 2, "title": "(PDF) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Score- based methods: This recent approach reduces distribution estimation to score estimation through dynamical systems, with significant advancements in score- based diffusion models .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "Score- based methods: This recent approach reduces distribution estimation to score estimation through dynamical systems, with significant advancements in score- based diffusion models ."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models. It introduces a novel framework for learning conditional distributions of target variables given input variables.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models. It introduces a novel framework for learning conditional distributions of target variables given input variables."}
+{"idx": 5, "title": "Uncertainty Quantication in Logistic Regression", "date": "", "ddg_snippet": "Numerical experiments . Data and experimental settings. Results .The likelihood - based approach to statistical inference introduced in [17, 19] treats the relative likelihood function as a possibility distribution in the parameter space.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04489184/document", "content": "Numerical experiments . Data and experimental settings. Results .The likelihood - based approach to statistical inference introduced in [17, 19] treats the relative likelihood function as a possibility distribution in the parameter space."}
+{"idx": 6, "title": "Dynamic panel data modelling using maximum likelihood : an...", "date": "", "ddg_snippet": "The likelihood - based approach .This particular likelihood is useful in practice because it can be maximized using numerical opti-mization techniques available in standard software packages.", "subpage_snippet": "", "source": "statisticalhorizons.com", "link": "https://statisticalhorizons.com/wp-content/uploads/Dynamic-panel-data-modelling-using-maximum-likelihood-an-alternative-to-Arellano-Bond.pdf", "content": "The likelihood - based approach .This particular likelihood is useful in practice because it can be maximized using numerical opti-mization techniques available in standard software packages."}
+{"idx": 7, "title": "A primer on bayesian distributional", "date": "", "ddg_snippet": "Using the distributional regression approach in combination with MCMC simulation, it is rela-tively straightforward to provide correct inference for any desired quantity, e.g., the 100 year return levels. This results from the fact that all inferences are sampling based .", "subpage_snippet": "", "source": "www2.uibk.ac.at", "link": "https://www2.uibk.ac.at/downloads/c4041030/wpaper/2017-13.pdf", "content": "Using the distributional regression approach in combination with MCMC simulation, it is rela-tively straightforward to provide correct inference for any desired quantity, e.g., the 100 year return levels. This results from the fact that all inferences are sampling based ."}
+{"idx": 8, "title": "A distributional regression approach to income-related inequality of...", "date": "", "ddg_snippet": "Based on predicted health quantiles, we use both a parametric and a non-parametric approach to estimate the lower tail of the health distribution . Our data come from Wave 13 of the Household, Income and Labour Dynamics in Australia (HILDA) survey, collected in 2013-2014.", "subpage_snippet": "", "source": "equityhealthj.biomedcentral.com", "link": "https://equityhealthj.biomedcentral.com/articles/10.1186/s12939-020-01189-1", "content": "Based on predicted health quantiles, we use both a parametric and a non-parametric approach to estimate the lower tail of the health distribution . Our data come from Wave 13 of the Household, Income and Labour Dynamics in Australia (HILDA) survey, collected in 2013-2014."}
+{"idx": 9, "title": "Distributional regression forests for probabilistic precipitation...", "date": "", "ddg_snippet": "Distributional regression modeling is combined with tree- based modeling to obtain a novel and exible method for probabilistic forecasting. The resulting distributional trees and forests can capture abrupt and nonlinear eects and interactions in a data-driven way.", "subpage_snippet": "", "source": "www.econstor.eu", "link": "https://www.econstor.eu/bitstream/10419/184986/1/1023138042.pdf", "content": "Distributional regression modeling is combined with tree- based modeling to obtain a novel and exible method for probabilistic forecasting. The resulting distributional trees and forests can capture abrupt and nonlinear eects and interactions in a data-driven way."}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_2410.02025_MNIST_Table_2_sparse_Wasserstein.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_2410.02025_MNIST_Table_2_sparse_Wasserstein.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f2fbd1021f67a126a06045405d8df25582a152bc
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+++ b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_2410.02025_MNIST_Table_2_sparse_Wasserstein.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Supplementary Materials for “A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models”.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Supplementary Materials for “A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models”."}
+{"idx": 1, "title": "(PDF) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "arXiv: 2410 . 02025 v1 [math.ST] 2 Oct 2024. estimate it through learning the corresponding data sampling scheme.The second approach , based on adversarial learning, matches the empirical distribution of the data with a. distribution estimator using an adversarial loss.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "arXiv: 2410 . 02025 v1 [math.ST] 2 Oct 2024. estimate it through learning the corresponding data sampling scheme.The second approach , based on adversarial learning, matches the empirical distribution of the data with a. distribution estimator using an adversarial loss."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "First Exploration of Likelihood - Based Approach : This study is the first to explore a likelihood - based approach for distributional regression using conditional deep generative models, particularly in the context of full-dimensional noise and potentially singular underlying support .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "First Exploration of Likelihood - Based Approach : This study is the first to explore a likelihood - based approach for distributional regression using conditional deep generative models, particularly in the context of full-dimensional noise and potentially singular underlying support ."}
+{"idx": 4, "title": "Distribution Regression with Sliced Wasserstein ... - UCL Discovery", "date": "", "ddg_snippet": "Meunier, Dimitri; Pontil, Massimiliano; Ciliberto, Carlo; (2022) Distribution Regression with Sliced Wasserstein Kernels.In this work, we propose an OT- based estimator for distribution regression . We build on the Sliced Wasserstein distance to obtain an OT- based representation.", "subpage_snippet": "", "source": "discovery.ucl.ac.uk", "link": "https://discovery.ucl.ac.uk/id/eprint/10199640/", "content": "Meunier, Dimitri; Pontil, Massimiliano; Ciliberto, Carlo; (2022) Distribution Regression with Sliced Wasserstein Kernels.In this work, we propose an OT- based estimator for distribution regression . We build on the Sliced Wasserstein distance to obtain an OT- based representation."}
+{"idx": 5, "title": "On the computational complexity of finding a sparse Wasserstein ...", "date": "", "ddg_snippet": "The discrete Wasserstein barycenter problem is a minimum-cost mass transport problem for a set of probability measures with finite support. In this paper, we show that finding a barycenter of sparse support is hard, even in dimension 2 and for only 3 measures.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10878-021-00713-5", "content": "The discrete Wasserstein barycenter problem is a minimum-cost mass transport problem for a set of probability measures with finite support. In this paper, we show that finding a barycenter of sparse support is hard, even in dimension 2 and for only 3 measures."}
+{"idx": 6, "title": "[PDF] Discussion of: “Nonparametric regression ...” | Semantic Scholar", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Discussion-of:-“Nonparametric-regression-using-deep-Ghorbani-Mei/688b87f8e83080a024eb0fd7e55288de313aff8b", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models."}
+{"idx": 7, "title": "STATISTICS", "date": "", "ddg_snippet": "Distribution -on- distribution regression with Wasserstein metric: Mul-tivariate Gaussian case. Wasserstein regression with empirical measures and density estimation for sparse data.", "subpage_snippet": "", "source": "imstat.org", "link": "https://imstat.org/publications/aos/aos_53_4/aos_53_4.pdf", "content": "Distribution -on- distribution regression with Wasserstein metric: Mul-tivariate Gaussian case. Wasserstein regression with empirical measures and density estimation for sparse data."}
+{"idx": 8, "title": "Как обучить нейросеть на своих данных: пошаговый разбор", "date": "", "ddg_snippet": "Вот простой пример на Python с использованием Keras. Мы создадим нейросеть для распознавания рукописных цифр на базе датасета MNIST . Это базовая задача, но она даёт понимание структуры. from keras.models import Sequential.", "subpage_snippet": "", "source": "scrile.ru", "link": "https://scrile.ru/obuchenie-neiroseti/", "content": "Вот простой пример на Python с использованием Keras. Мы создадим нейросеть для распознавания рукописных цифр на базе датасета MNIST . Это базовая задача, но она даёт понимание структуры. from keras.models import Sequential."}
+{"idx": 9, "title": "All Departures | Antalya Airport", "date": "", "ddg_snippet": "Gender Based Violations And Harassment: GBVH. Active Announcement.", "subpage_snippet": "", "source": "www.antalya-airport.aero", "link": "https://www.antalya-airport.aero/passengers-visitors/flight-info/all-departures", "content": "Gender Based Violations And Harassment: GBVH. Active Announcement."}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Corollary_2_Table_2_MNIST.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Corollary_2_Table_2_MNIST.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2d7132b8bbdcb9dab190d477b39d0437e692f45e
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+++ b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Corollary_2_Table_2_MNIST.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using", "date": "", "ddg_snippet": "The second approach , based on adversarial learning, matches the empirical distribution of the data with a. distribution estimator using an adversarial loss. Representative examples include Goodfellow et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "The second approach , based on adversarial learning, matches the empirical distribution of the data with a. distribution estimator using an adversarial loss. Representative examples include Goodfellow et al."}
+{"idx": 1, "title": "(PDF) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "The second approach , based on adversarial learning, matches the empirical distribution of the data with a. distribution estimator using an adversarial loss. distributional regression using a conditional deep generative model, considering full-dimensional noise.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "The second approach , based on adversarial learning, matches the empirical distribution of the data with a. distribution estimator using an adversarial loss. distributional regression using a conditional deep generative model, considering full-dimensional noise."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Adversarial learning: This approach matches the empirical distribution of data with a distribution estimator using adversarial loss, exemplified by Goodfellow et al.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "Adversarial learning: This approach matches the empirical distribution of data with a distribution estimator using adversarial loss, exemplified by Goodfellow et al."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "The authors show that this likelihood - based approach outperforms other distribution regression methods on several benchmark tasks.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "The authors show that this likelihood - based approach outperforms other distribution regression methods on several benchmark tasks."}
+{"idx": 4, "title": "Modeling bounded data with a new unit distribution : regression ...", "date": "", "ddg_snippet": "Based on this distribution , a new regression model is developed to link bounded response variables to linear predictors, increasing its practical applicability. The maximum likelihood approach is used to estimate the parameters of the regression model and the suggested distribution .", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/id/68930f51ba35de58686da1c8", "content": "Based on this distribution , a new regression model is developed to link bounded response variables to linear predictors, increasing its practical applicability. The maximum likelihood approach is used to estimate the parameters of the regression model and the suggested distribution ."}
+{"idx": 5, "title": "An indirect nonparametric regression method for one-dimensional", "date": "", "ddg_snippet": "Keywords: distribution regression , warping transformation, nonparametric regression .This warping transformation- based approach for conditional distribution estimation in the original.", "subpage_snippet": "", "source": "vixra.org", "link": "https://vixra.org/pdf/1704.0277v1.pdf", "content": "Keywords: distribution regression , warping transformation, nonparametric regression .This warping transformation- based approach for conditional distribution estimation in the original."}
+{"idx": 6, "title": "MNIST _784", "date": "", "ddg_snippet": "emoji_events. Competitions. table _chart. Datasets.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/aadeshkoirala/mnist-784", "content": "emoji_events. Competitions. table _chart. Datasets."}
+{"idx": 7, "title": "Conditional Empirical Likelihood Approach", "date": "", "ddg_snippet": "Conditional Empirical Likelihood Approach to Statistical Analysis with Missing Data.From Table 2 .3, no substantial eect on the estimation of regression coecients resulting from the inclusion of two extra continuous covariates has been observed.", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "https://deepblue.lib.umich.edu/bitstream/handle/2027.42/99966/peisong_1.pdf?sequence=1", "content": "Conditional Empirical Likelihood Approach to Statistical Analysis with Missing Data.From Table 2 .3, no substantial eect on the estimation of regression coecients resulting from the inclusion of two extra continuous covariates has been observed."}
+{"idx": 8, "title": "GitHub - shap/shap: A game theoretic approach to explain the output...", "date": "", "ddg_snippet": "The plot below sorts features by the sum of SHAP value magnitudes over all samples, and uses SHAP values to show the distribution of the impacts each feature has on the model output. The color represents the feature value (red high, blue low).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shap/shap", "content": "The plot below sorts features by the sum of SHAP value magnitudes over all samples, and uses SHAP values to show the distribution of the impacts each feature has on the model output. The color represents the feature value (red high, blue low)."}
+{"idx": 9, "title": "Proceedings of the International Conference on Machine Learning 2022", "date": "", "ddg_snippet": "Then, we present results for distribution regression on the MNIST and Fashion MNIST datasets.Table 3 exten√ds the results of Table 2 to those two distances. The setting of the experiment is the same as in the main text.", "subpage_snippet": "", "source": "discovery.ucl.ac.uk", "link": "https://discovery.ucl.ac.uk/id/eprint/10199640/1/meunier22b.pdf", "content": "Then, we present results for distribution regression on the MNIST and Fashion MNIST datasets.Table 3 exten√ds the results of Table 2 to those two distances. The setting of the experiment is the same as in the main text."}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a0acf22d40404db95a2ef6350482e4541b2cedbd
--- /dev/null
+++ b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=V6hhhXoTSq", "content": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where ..."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ..."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-."}
+{"idx": 5, "title": "[Literature Review] A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "This page provides the most accurate and concise summary worldwide for the paper titled A Likelihood Based Approach to Distribution Regression Using Conditional ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/a-likelihood-based-approach-to-distribution-regression-using-conditional-deep-generative-models", "content": "This page provides the most accurate and concise summary worldwide for the paper titled A Likelihood Based Approach to Distribution Regression Using Conditional ..."}
+{"idx": 6, "title": "[PDF] A likelihood approach to nonparametric estimation of ...", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models · Shivam KumarYun YangLizhen Lin. Computer Science, Mathematics.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/9bfa1bf454384168d01d80aff788ea6ddd1fd6b1", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models · Shivam KumarYun YangLizhen Lin. Computer Science, Mathematics."}
+{"idx": 7, "title": "Shivam Kumar", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models . S Kumar, Y Yang, L Lin. arXiv preprint arXiv:2410.02025, 2024.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=cJI6e3cAAAAJ&hl=en", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models . S Kumar, Y Yang, L Lin. arXiv preprint arXiv:2410.02025, 2024."}
+{"idx": 8, "title": "Stat.ML Papers", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models https://t.co/8sc45EqtuL.", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/StatMLPapers/status/1842054334357401998", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models https://t.co/8sc45EqtuL."}
+{"idx": 9, "title": "Yun Yang", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Yun+Yang", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the ..."}
diff --git a/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_Magnani_et_al_2022_Rela.jsonl b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_Magnani_et_al_2022_Rela.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..460a4b035d873a7f3bd3f862b59b34e24efc553e
--- /dev/null
+++ b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_Magnani_et_al_2022_Rela.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani * 1 Marvin Pförtner * 1", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.05072", "content": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani * 1 Marvin Pförtner * 1"}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "We first review neural operators , with emphasis on Fourier neural operators , which serve as the primary case study for our analysis. Then, we provide an overview of multi-output Gaussian processes . 2.1. Neural Operators Neural operators (NOs) (Kovachki et al ., 2023) are neural network architectures that map between (infinite-dimensional) Banach spaces of functions . A neural operator is a ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46474/paper", "content": "We first review neural operators , with emphasis on Fourier neural operators , which serve as the primary case study for our analysis. Then, we provide an overview of multi-output Gaussian processes . 2.1. Neural Operators Neural operators (NOs) (Kovachki et al ., 2023) are neural network architectures that map between (infinite-dimensional) Banach spaces of functions . A neural operator is a ..."}
+{"idx": 2, "title": "Linearizing Neural Operators as Function-Valued GPs", "date": "", "ddg_snippet": "The paper presents NOLA, a framework that linearizes neural operators into function-valued Gaussian processes for uncertainty quantification in PDE models.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.05072", "content": "The paper presents NOLA, a framework that linearizes neural operators into function-valued Gaussian processes for uncertainty quantification in PDE models."}
+{"idx": 3, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be interpreted as a probabilistic version of the concept of currying from functional programming, yielding a function-valued ( Gaussian ) random process belief.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be interpreted as a probabilistic version of the concept of currying from functional programming, yielding a function-valued ( Gaussian ) random process belief."}
+{"idx": 4, "title": "LUNO: Linearized Predictive Uncertainty in Neural Operators", "date": "", "ddg_snippet": "This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes \" by Magnani et al . (2025).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno", "content": "This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes \" by Magnani et al . (2025)."}
+{"idx": 5, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "As for all statistical models, the predictions of these models are imperfect and exhibit errors. Such errors are particularly difficult to spot in the complex nonlinear behaviour of dynamical systems. We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes .", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240605072M/abstract", "content": "As for all statistical models, the predictions of these models are imperfect and exhibit errors. Such errors are particularly difficult to spot in the complex nonlinear behaviour of dynamical systems. We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes ."}
+{"idx": 6, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Spotlight Poster Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani · Marvin Pförtner · Tobias Weber · Philipp Hennig East Exhibition Hall A-B #E-1207", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Spotlight Poster Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani · Marvin Pförtner · Tobias Weber · Philipp Hennig East Exhibition Hall A-B #E-1207"}
+{"idx": 7, "title": "Emilia Magnani - dblp", "date": "", "ddg_snippet": "Emilia Magnani , Marvin Pförtner, Tobias Weber, Philipp Hennig: Linearization Turns Neural Operators into Function-Valued Gaussian Processes . CoRR abs/2406.05072 (2024)", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/206/6101", "content": "Emilia Magnani , Marvin Pförtner, Tobias Weber, Philipp Hennig: Linearization Turns Neural Operators into Function-Valued Gaussian Processes . CoRR abs/2406.05072 (2024)"}
+{"idx": 8, "title": "Excited to be at ICML presenting our work: \"Linearization turns neural ...", "date": "", "ddg_snippet": "Excited to be at ICML presenting our work : \" Linearization turns neural operators into function-valued Gaussian processes \"! If you're around, come by! Happy to chat about operator learning, UQ ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/emilia-magnani-58a513187_icml2025-neuraloperators-uncertaintyquantification-activity-7350598204704309248-DRKy", "content": "Excited to be at ICML presenting our work : \" Linearization turns neural operators into function-valued Gaussian processes \"! If you're around, come by! Happy to chat about operator learning, UQ ..."}
+{"idx": 9, "title": "PDF Abstract - ResearchGate", "date": "", "ddg_snippet": "ocesses. We then use Gaussian currying to construct function-valued Gaussian processes from neural operators with Gaussian weight po teriors. We discuss related work in Section 4 and showcase the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes/fulltext/6666938685a4ee7261b375f0/Linearization-Turns-Neural-Operators-into-Function-Valued-Gaussian-Processes.pdf", "content": "ocesses. We then use Gaussian currying to construct function-valued Gaussian processes from neural operators with Gaussian weight po teriors. We discuss related work in Section 4 and showcase the ..."}
diff --git a/data/sampled_jsons/Llama-2-7b-chat_initial_ASR_4.5_final_ASR_72_16x_improvement.jsonl b/data/sampled_jsons/Llama-2-7b-chat_initial_ASR_4.5_final_ASR_72_16x_improvement.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d80a8502557a82fb1b1c43d673b06f1afddf38c2
--- /dev/null
+++ b/data/sampled_jsons/Llama-2-7b-chat_initial_ASR_4.5_final_ASR_72_16x_improvement.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - run- llama / llama _index: LlamaIndex is the leading framework...", "date": "", "ddg_snippet": "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader.To build a simple vector store index using non-OpenAI LLMs, e.g. Llama 2 hosted on Replicate, where you can easily create a free trial API token", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/run-llama/llama_index", "content": "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader.To build a simple vector store index using non-OpenAI LLMs, e.g. Llama 2 hosted on Replicate, where you can easily create a free trial API token"}
+{"idx": 1, "title": "Chatbot Arena + | OpenLM.ai", "date": "", "ddg_snippet": "This leaderboard is based on the following benchmarks. Chatbot Arena - a crowdsourced, randomized battle platform for large language models (LLMs).", "subpage_snippet": "", "source": "openlm.ai", "link": "https://openlm.ai/chatbot-arena/", "content": "This leaderboard is based on the following benchmarks. Chatbot Arena - a crowdsourced, randomized battle platform for large language models (LLMs)."}
+{"idx": 2, "title": "Как дообучить LLaMA бесплатно и без программирования... / Хабр", "date": "", "ddg_snippet": "Что будем обучать и что потребуется. В данной статье я покажу как я дообучал LLaMA 7 B и LLaMA 2 7 B . Если готовы заплатить за аренду видеокарт, то можете обучить и модели покрупнее.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/755114/", "content": "Что будем обучать и что потребуется. В данной статье я покажу как я дообучал LLaMA 7 B и LLaMA 2 7 B . Если готовы заплатить за аренду видеокарт, то можете обучить и модели покрупнее."}
+{"idx": 3, "title": "Ollama Search", "date": "", "ddg_snippet": "Llama 2 based model fine tuned to improve Chinese dialogue ability. 7 b 13b. 166K Pulls.Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA to improve the helpfulness of LLM generated responses to user queries.", "subpage_snippet": "", "source": "ollama.com", "link": "https://ollama.com/search", "content": "Llama 2 based model fine tuned to improve Chinese dialogue ability. 7 b 13b. 166K Pulls.Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA to improve the helpfulness of LLM generated responses to user queries."}
+{"idx": 4, "title": "10 аналогов ChatGPT: топ нейросетей на... / Skillbox Media", "date": "", "ddg_snippet": "LLaMA 2 70 B Chat : версия цукерберговской LLaMA 2 с 70 миллиардами параметров, созданная специально для ведения диалогов.h2oGPT LLaMA 2 70 B Chat при включённом интернет-поиске не справилась с ответом на вопрос о лидерах чемпионата России по футболу.", "subpage_snippet": "", "source": "skillbox.ru", "link": "https://skillbox.ru/media/code/podborka-besplatnykh-analogov-chatgpt-v-2024-godu/", "content": "LLaMA 2 70 B Chat : версия цукерберговской LLaMA 2 с 70 миллиардами параметров, созданная специально для ведения диалогов.h2oGPT LLaMA 2 70 B Chat при включённом интернет-поиске не справилась с ответом на вопрос о лидерах чемпионата России по футболу."}
+{"idx": 5, "title": "Мое первое знакомство с ollama — miteigi nemoto на vc.ru", "date": "", "ddg_snippet": "Библиотека сильно упрощает скачивание и инференс моделей, предлагая разные квантизированные версии GGUF - 2 , 3, 6, 5, 8 бит или FP16, можно сразу работать с чат версией и одновременно по API выполнять запросы с режимом стриминга.", "subpage_snippet": "", "source": "vc.ru", "link": "https://vc.ru/id224943/1122503-moe-pervoe-znakomstvo-s-ollama", "content": "Библиотека сильно упрощает скачивание и инференс моделей, предлагая разные квантизированные версии GGUF - 2 , 3, 6, 5, 8 бит или FP16, можно сразу работать с чат версией и одновременно по API выполнять запросы с режимом стриминга."}
+{"idx": 6, "title": "Preparing for the era of 32K context: Early learnings and explorations", "date": "", "ddg_snippet": "Quality of 16 Core Scenarios in HELM v1.0 (evaluated on the same context length that fits LLaMA - 2 ). Building long-context applications via fine-tuning. The power of LLaMA - 2 - 7 B -32K is that it forms a powerful base model that one can fine-tune to build their own applications.", "subpage_snippet": "", "source": "www.together.ai", "link": "https://www.together.ai/blog/llama-2-7b-32k", "content": "Quality of 16 Core Scenarios in HELM v1.0 (evaluated on the same context length that fits LLaMA - 2 ). Building long-context applications via fine-tuning. The power of LLaMA - 2 - 7 B -32K is that it forms a powerful base model that one can fine-tune to build their own applications."}
+{"idx": 7, "title": "Mistral 7 B | The best 7 B model to date, Apache 2 .0", "date": "", "ddg_snippet": "Mistral 7 B is easy to fine-tune on any task. As a demonstration, we're providing a model fine-tuned for chat, which outperforms Llama 2 13 B chat . Performance in details. We compared Mistral 7 B to the Llama 2 family, and re-run all model evaluations ourselves for fair comparison.", "subpage_snippet": "", "source": "mistral.ai", "link": "https://mistral.ai/news/announcing-mistral-7b", "content": "Mistral 7 B is easy to fine-tune on any task. As a demonstration, we're providing a model fine-tuned for chat, which outperforms Llama 2 13 B chat . Performance in details. We compared Mistral 7 B to the Llama 2 family, and re-run all model evaluations ourselves for fair comparison."}
+{"idx": 8, "title": "HyPoradise: An Open Baseline for Generative Speech", "date": "", "ddg_snippet": "We finally report an initial prompting evaluation on CHiME-4 in zero-shot setting. Considering the task difficulty, T5 and LLaMA are employed for hypothesis correction.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=cAjZ3tMye6", "content": "We finally report an initial prompting evaluation on CHiME-4 in zero-shot setting. Considering the task difficulty, T5 and LLaMA are employed for hypothesis correction."}
+{"idx": 9, "title": "9 Best Uncensored Local LLM (7 To 20B) - Sci Fi Logic", "date": "", "ddg_snippet": "So FuseChat, It’s like the ultimate chat buddy, uncensored and all. It’s basically three super-powered chatbots mashed into one, each with their own thing going on.", "subpage_snippet": "", "source": "scifilogic.com", "link": "https://scifilogic.com/open-uncensored-llm-model/", "content": "So FuseChat, It’s like the ultimate chat buddy, uncensored and all. It’s basically three super-powered chatbots mashed into one, each with their own thing going on."}
diff --git a/data/sampled_jsons/Llama_sitearxiv.orghtml2406.14532v1.jsonl b/data/sampled_jsons/Llama_sitearxiv.orghtml2406.14532v1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1525daf799d4671f3d603b54ec2ed30f5f07135b
--- /dev/null
+++ b/data/sampled_jsons/Llama_sitearxiv.orghtml2406.14532v1.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. Villalobos et al. [2022] ↑ Pablo Villalobos ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. Villalobos et al. [2022] ↑ Pablo Villalobos ..."}
diff --git a/data/sampled_jsons/Locally_private_graph_neural_networks_Sajadmanesh_&_Gatica-Perez,_2021_abstract.jsonl b/data/sampled_jsons/Locally_private_graph_neural_networks_Sajadmanesh_&_Gatica-Perez,_2021_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ca6a25632fb66c19b3120b75d2b80bf9bf6fee24
--- /dev/null
+++ b/data/sampled_jsons/Locally_private_graph_neural_networks_Sajadmanesh_&_Gatica-Perez,_2021_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2006.05535] Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "by S Sajadmanesh · 2020 · Cited by 164 — In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "by S Sajadmanesh · 2020 · Cited by 164 — In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private ."}
+{"idx": 1, "title": "Locally Private Graph Neural Networks - ACM Digital Library", "date": "", "ddg_snippet": "13 Nov 2021 — In this paper, we study the problem of node data privacy, where graph nodes (e.g., social network users) have potentially sensitive data that is ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3460120.3484565", "content": "13 Nov 2021 — In this paper, we study the problem of node data privacy, where graph nodes (e.g., social network users) have potentially sensitive data that is ..."}
+{"idx": 2, "title": "Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "by S Sajadmanesh · 2020 · Cited by 164 — ABSTRACT. Graph Neural Networks (GNNs ) have demonstrated superior perfor- mance in learning node representations for various graph inference.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05535", "content": "by S Sajadmanesh · 2020 · Cited by 164 — ABSTRACT. Graph Neural Networks (GNNs ) have demonstrated superior perfor- mance in learning node representations for various graph inference."}
+{"idx": 3, "title": "Locally and Structurally Private Graph Neural Networks", "date": "", "ddg_snippet": "by RB Joshi · 2024 · Cited by 5 — We provide a method, Local Structural Perturbation Graph Neural Network , that adds noise to the neighborhood data of the node along with its features and label.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3624485", "content": "by RB Joshi · 2024 · Cited by 5 — We provide a method, Local Structural Perturbation Graph Neural Network , that adds noise to the neighborhood data of the node along with its features and label."}
+{"idx": 4, "title": "Locally Private Graph Neural Networks (ACM CCS 2021)", "date": "", "ddg_snippet": "In this paper, we study the problem of node data privacy, where graph nodes (e.g., social network users) have potentially sensitive data that is kept private , ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/LPGNN", "content": "In this paper, we study the problem of node data privacy, where graph nodes (e.g., social network users) have potentially sensitive data that is kept private , ..."}
+{"idx": 5, "title": "Going Deeper into Locally Differentially Private Graph ...", "date": "", "ddg_snippet": "by L He — In this paper, we present UPGNET, an LDP-based privacy-preserving graph learning framework that enhances utility while protecting user data privacy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2aKHuXdr7Q", "content": "by L He — In this paper, we present UPGNET, an LDP-based privacy-preserving graph learning framework that enhances utility while protecting user data privacy."}
+{"idx": 6, "title": "Going Deeper into Locally Differentially Private Graph Neural ...", "date": "", "ddg_snippet": "Abstract. Graph Neural Networks (GNNs ) have demon- strated superior performance in a variety of graph mining and learning tasks. However, when node.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/7efbd36f792e0a32a0c290ac11c17eaa63d6680d.pdf", "content": "Abstract. Graph Neural Networks (GNNs ) have demon- strated superior performance in a variety of graph mining and learning tasks. However, when node."}
+{"idx": 7, "title": "Going Deeper into Locally Differentially Private Graph ...", "date": "", "ddg_snippet": "16 Jul 2025 — Graph Neural Networks (GNNs ) have demonstrated superior performance in a variety of graph mining and learning tasks.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46579", "content": "16 Jul 2025 — Graph Neural Networks (GNNs ) have demonstrated superior performance in a variety of graph mining and learning tasks."}
+{"idx": 8, "title": "GAP: Differentially Private Graph Neural Networks with ...", "date": "", "ddg_snippet": "by S Sajadmanesh · Cited by 105 — In this paper, we study the problem of learning Graph Neural . Networks (GNNs) with Differential Privacy (DP). We pro- pose a novel differentially private GNN ... 18 pages", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/sec23fall-prepub-196-sajadmanesh.pdf", "content": "by S Sajadmanesh · Cited by 105 — In this paper, we study the problem of learning Graph Neural . Networks (GNNs) with Differential Privacy (DP). We pro- pose a novel differentially private GNN ... 18 pages"}
+{"idx": 9, "title": "Differentially private graph neural networks for ...", "date": "", "ddg_snippet": "by Y Li · 2025 · Cited by 4 — This study delves into the challenges encountered when implementing DP-SGD in GNNs (DPGNNs), focusing on factors associated with the GNN model.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0957417424026654", "content": "by Y Li · 2025 · Cited by 4 — This study delves into the challenges encountered when implementing DP-SGD in GNNs (DPGNNs), focusing on factors associated with the GNN model."}
diff --git a/data/sampled_jsons/Locally_private_graph_neural_networks_Sajadmanesh_Gatica-Perez_2021_abstract_year_2021.jsonl b/data/sampled_jsons/Locally_private_graph_neural_networks_Sajadmanesh_Gatica-Perez_2021_abstract_year_2021.jsonl
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index 0000000000000000000000000000000000000000..a1b4f5a928bfca6bb1155ba396536186a47f09fa
--- /dev/null
+++ b/data/sampled_jsons/Locally_private_graph_neural_networks_Sajadmanesh_Gatica-Perez_2021_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Common Neighborhood Estimation over Bipartite Graphs under", "date": "", "ddg_snippet": "Local differential privately anonymizing online social networks under hrg-based model. ... private learning on decentralized graphs with local ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3698803", "content": "Local differential privately anonymizing online social networks under hrg-based model. ... private learning on decentralized graphs with local ..."}
+{"idx": 1, "title": "Differentially Private Graph Diffusion with Applications in", "date": "", "ddg_snippet": "Graph diffusion, characterized by propagating signals across networks , is used in a variety of real-world applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.00077v5", "content": "Graph diffusion, characterized by propagating signals across networks , is used in a variety of real-world applications."}
+{"idx": 2, "title": "Differentially Private Relational Learning with Entity-level", "date": "", "ddg_snippet": "... graph structures are invaluable ... sajadmanesh2021locally, ) proposed a locally differentially private algorithm for graph neural networks (GNNs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08347v1", "content": "... graph structures are invaluable ... sajadmanesh2021locally, ) proposed a locally differentially private algorithm for graph neural networks (GNNs)."}
+{"idx": 3, "title": "CCS '21: 2021 ACM SIGSAC Conference on Computer and", "date": "", "ddg_snippet": "A Hard Label Black-box Adversarial Attack Against Graph Neural Networks Jiaming Mu , Binghui Wang , Qi Li 0002 , Kun Sun 0001 , Mingwei Xu , Zhuotao ...", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/ccs-2021", "content": "A Hard Label Black-box Adversarial Attack Against Graph Neural Networks Jiaming Mu , Binghui Wang , Qi Li 0002 , Kun Sun 0001 , Mingwei Xu , Zhuotao ..."}
+{"idx": 4, "title": "[2006.05535] Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica - Perez .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica - Perez ."}
+{"idx": 5, "title": "(PDF) Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Sajadmanesh and Gatica - Perez . 2021 . Stealing Links from Graph Neural Networks . In 30th USENIX Security.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348678319_Locally_Private_Graph_Neural_Networks", "content": "Sajadmanesh and Gatica - Perez . 2021 . Stealing Links from Graph Neural Networks . In 30th USENIX Security."}
+{"idx": 6, "title": "Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "ABSTRACT . Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks.ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez .", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/downloads/papers/2021/Sajadmanesh_CCS2021_2021.pdf", "content": "ABSTRACT . Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks.ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez ."}
+{"idx": 7, "title": "Locally Private Graph Neural Networks | Proceedings of the 2021 ...", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460120.3484565?cookieSet=1", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private ."}
+{"idx": 8, "title": "(Open Access) Locally Private Graph Neural Networks (2020)", "date": "", "ddg_snippet": "Sina Sajadmanesh , Daniel Gatica - Perez +1 moreIdiap Research Institute. - 09 Jun 2020. Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/locally-private-graph-neural-networks-1lusvir819", "content": "Sina Sajadmanesh , Daniel Gatica - Perez +1 moreIdiap Research Institute. - 09 Jun 2020. Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 9, "title": "Locally Private Graph Neural Networks - Paper Detail", "date": "", "ddg_snippet": "Sina Sajadmanesh , Daniel Gatica - Perez . Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/179057/locally-private-graph-neural-networks", "content": "Sina Sajadmanesh , Daniel Gatica - Perez . Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks."}
diff --git a/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_Initialization_section_'LM_initialization'_p_year_2023.jsonl b/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_Initialization_section_'LM_initialization'_p_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9780c190cc9d68b370f147e60af1fc3813b7c834
--- /dev/null
+++ b/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_Initialization_section_'LM_initialization'_p_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "With these considerations we propose SpeechSSM, the first speech language model to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates, based on recent advances in linear-time sequence modeling.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v1", "content": "With these considerations we propose SpeechSSM, the first speech language model to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates, based on recent advances in linear-time sequence modeling."}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language Models | AI Research ...", "date": "", "ddg_snippet": "The research also doesn't fully address multilingual capabilities. Conclusion This breakthrough represents a significant step toward natural, long-form speech generation . The combination of language modeling and audio processing techniques opens new possibilities for applications in audiobooks, virtual assistants, and educational content.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/long-form-speech-generation-spoken-language-models", "content": "The research also doesn't fully address multilingual capabilities. Conclusion This breakthrough represents a significant step toward natural, long-form speech generation . The combination of language modeling and audio processing techniques opens new possibilities for applications in audiobooks, virtual assistants, and educational content."}
+{"idx": 2, "title": "GitHub - google-deepmind/librispeech-long: LibriSpeech-Long is a ...", "date": "", "ddg_snippet": "About LibriSpeech- Long is a benchmark dataset for long-form speech generation and processing. Released as part of \" Long-Form Speech Generation with Spoken Language Models \" (arXiv 2024).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/google-deepmind/librispeech-long", "content": "About LibriSpeech- Long is a benchmark dataset for long-form speech generation and processing. Released as part of \" Long-Form Speech Generation with Spoken Language Models \" (arXiv 2024)."}
+{"idx": 3, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "SpeechSSM is derived, the first speech language model family to learn from and sample long-form spoken audio in a single decoding session without text intermediates and leverage recent advances in linear-time sequence modeling to greatly surpass current Transformer spoken LMs in coherence and efficiency on multi-minute generations while still matching them at the utterance level. We consider ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Long-Form-Speech-Generation-with-Spoken-Language-Park-Salazar/b70f44b066d7adcf89cdd0870a81e3266afdcb6d", "content": "SpeechSSM is derived, the first speech language model family to learn from and sample long-form spoken audio in a single decoding session without text intermediates and leverage recent advances in linear-time sequence modeling to greatly surpass current Transformer spoken LMs in coherence and efficiency on multi-minute generations while still matching them at the utterance level. We consider ..."}
+{"idx": 4, "title": "PDF Recent Advances in Speech Language Models: A Survey", "date": "", "ddg_snippet": "Text-based Large Language Models (LLMs) have recently gained signicant attention, pri- marily for their capabilities in text-based inter- actions. However, natural human interaction often relies on speech , highlighting the need for voice-based models . In this context, Speech Language Models (SpeechLMs) foundation models designed to understand and generate speech emerge as a promising solution ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.682.pdf", "content": "Text-based Large Language Models (LLMs) have recently gained signicant attention, pri- marily for their capabilities in text-based inter- actions. However, natural human interaction often relies on speech , highlighting the need for voice-based models . In this context, Speech Language Models (SpeechLMs) foundation models designed to understand and generate speech emerge as a promising solution ..."}
+{"idx": 5, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long -sequence training or extrapolation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.18603", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long -sequence training or extrapolation ..."}
+{"idx": 6, "title": "Long-Form Speech Generation with Spoken Language Models | Cool Papers ...", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long -sequence training or extrapolation ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/4AmFA0qNQ2@OpenReview", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long -sequence training or extrapolation ..."}
+{"idx": 7, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Our work makes initial progress on naturalistic, audio-native, long-form speech generation : We introduce SpeechSSM, the first spoken language model for long-form speech . Our 2B and 9B models : produces speech textlessly and in constant memory, for unbounded real-time generation ; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ...", "subpage_snippet": "", "source": "google.github.io", "link": "https://google.github.io/tacotron/publications/speechssm/", "content": "Our work makes initial progress on naturalistic, audio-native, long-form speech generation : We introduce SpeechSSM, the first spoken language model for long-form speech . Our 2B and 9B models : produces speech textlessly and in constant memory, for unbounded real-time generation ; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ..."}
+{"idx": 8, "title": "PDF Generative Spoken Language Model based on continuous word-sized audio ...", "date": "", "ddg_snippet": "Yet,inthespeech community, the standard input of spoken LMs are 20ms or 40ms- long discrete units (shorter thanaphoneme). Takinginspirationfromword- based LM , we introduce a Generative Spoken Language Model (GSLM) based on word-size continuous-valued audio embeddings that can generate diverse and expressive language out- put.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.emnlp-main.182.pdf", "content": "Yet,inthespeech community, the standard input of spoken LMs are 20ms or 40ms- long discrete units (shorter thanaphoneme). Takinginspirationfromword- based LM , we introduce a Generative Spoken Language Model (GSLM) based on word-size continuous-valued audio embeddings that can generate diverse and expressive language out- put."}
+{"idx": 9, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "The result is SpeechSSM, a new (textless) spoken language model family (2B, 9B) designed for long-form generation , being the first to model and generate unbounded long-form speech in bounded memory and the first state-space spoken LM .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.18603", "content": "The result is SpeechSSM, a new (textless) spoken language model family (2B, 9B) designed for long-form generation , being the first to model and generate unbounded long-form speech in bounded memory and the first state-space spoken LM ."}
diff --git a/data/sampled_jsons/LongLLMLingua_paper_abstract.jsonl b/data/sampled_jsons/LongLLMLingua_paper_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7c85c02003c8abbb2b15107770de417bde02fb6b
--- /dev/null
+++ b/data/sampled_jsons/LongLLMLingua_paper_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long ... LongLLMLingua: Accelerating and Enhancing LLMs in Long ... LongLLMLingua: ACCELERATING AND ENHANCING LLM L CONTEXT ... Paper page - LongLLMLingua: Accelerating and Enhancing LLMs ... LongLLMLingua: Accelerating and Enhancing LLMs in Long ... LongLLMLingua: Accelerating and Enhancing LLMs in Long ... LongLLMLingua: ACCELERATING AND ENHANCING LLM L CONTEXT SCENARIOS VIA LongLLMLingua : Accelerating and Enhancing LLMs in Long Context LongLLMLingua : Accelerating and Enhancing LLMs in Long Context LongLLMLingua : Accelerating and Enhancing LLMs in Long Context LongLLMLingua : Accelerating and Enhancing LLMs in Long Context LongLLMLingua : Accelerating and Enhancing LLMs in Long Context LongLLMLingua: Accelerating and Enhancing LLMs in Long ...", "date": "", "ddg_snippet": "Oct 10, 2023 · Abstract page for arXiv paper 2310.06839: LongLLMLingua : Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression Sep 15, 2025 · Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs’ perception of the ... ABSTRACT In long context scenarios, large language models (LLMs) face three main chal-lenges: higher computational/financial cost, longer latency, and inferior perfor-mance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ... Oct 10, 2023 · Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ... LongLLMLingua capitalizes on these distribution characteristics by employing prompt compression and reorganization. This strategy schedules and utilizes the limited but powerful context windows for LLMs more efficiently, effectively mitigating the \"Lost in the middle\" issue. View recent discussion. Abstract : In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving ... Do large language models perform well in long context scenarios? ARIOS VIA PROMPT COMPRESSIONAnonymous authorsPaper under double-blind reviewABSTRACTIn long context scenarios, large language models (LLMs) face three main chal lenges: higher computational/financial cost, longer latency, and inferior perfor-mance. Some studies reveal that the performance of LLMs depends on both the What is longllmlingua? LongLLMLingua capitalizes on these distribution characteristics by employing prompt compression and reorganization . This strategy schedules and utilizes the limited but powerful context windows for LLMs more efficiently, effectively mitigating the \"Lost in the middle\" issue. Does longllmlingua outperform original prompts? We evaluate LongLLMLingua across five benchmarks, i.e., NaturalQuestions, LongBench, ZeroSCROLLS , MuSicQue, and LooGLE, covering a variety of long context scenarios. Experimental results reveal that LongLLMLingua’s compressed prompts outperform original prompts in terms of performance, cost efficiency, and system latency. Is longllmlingua compressed prompt better than original prompts? Experiments on the multidocument QA, multi-hop QA, and long context benchmarks demonstrate that LongLLMLingua compressed prompt can derive higher performance than original prompts while both API costs for inference and the end-to-end system latency are largely reduced. If you find this repo helpful, please cite the following papers: Does longllmlingua improve performance? Our extensive evaluation across various long context scenarios demonstrates that LongLLMLingua not only enhances performance but also significantly reduces costs and latency. Can longllmlingua improve the NQ multi-document QA task? As illustrated in the figure above, LongLLMLingua can achieve up to a 21.4% improvement on the NQ Multi-document QA task while using only 1/4 of the tokens. Our main contributions are five-fold: We propose a question-aware coarse-to-fine compression method to improve the key information density in the prompt. Oct 10, 2023 · This work proposes LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges of higher computational cost, performance reduction, and position bias. In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.06839", "content": "Oct 10, 2023 · Abstract page for arXiv paper 2310.06839: LongLLMLingua : Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression Sep 15, 2025 · Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs’ perception of the ... ABSTRACT In long context scenarios, large language models (LLMs) face three main chal-lenges: higher computational/financial cost, longer latency, and inferior perfor-mance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ... Oct 10, 2023 · Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ... LongLLMLingua capitalizes on these distribution characteristics by employing prompt compression and reorganization. This strategy schedules and utilizes the limited but powerful context windows for LLMs more efficiently, effectively mitigating the \"Lost in the middle\" issue. View recent discussion. Abstract : In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving ... Do large language models perform well in long context scenarios? ARIOS VIA PROMPT COMPRESSIONAnonymous authorsPaper under double-blind reviewABSTRACTIn long context scenarios, large language models (LLMs) face three main chal lenges: higher computational/financial cost, longer latency, and inferior perfor-mance. Some studies reveal that the performance of LLMs depends on both the What is longllmlingua? LongLLMLingua capitalizes on these distribution characteristics by employing prompt compression and reorganization . This strategy schedules and utilizes the limited but powerful context windows for LLMs more efficiently, effectively mitigating the \"Lost in the middle\" issue. Does longllmlingua outperform original prompts? We evaluate LongLLMLingua across five benchmarks, i.e., NaturalQuestions, LongBench, ZeroSCROLLS , MuSicQue, and LooGLE, covering a variety of long context scenarios. Experimental results reveal that LongLLMLingua’s compressed prompts outperform original prompts in terms of performance, cost efficiency, and system latency. Is longllmlingua compressed prompt better than original prompts? Experiments on the multidocument QA, multi-hop QA, and long context benchmarks demonstrate that LongLLMLingua compressed prompt can derive higher performance than original prompts while both API costs for inference and the end-to-end system latency are largely reduced. If you find this repo helpful, please cite the following papers: Does longllmlingua improve performance? Our extensive evaluation across various long context scenarios demonstrates that LongLLMLingua not only enhances performance but also significantly reduces costs and latency. Can longllmlingua improve the NQ multi-document QA task? As illustrated in the figure above, LongLLMLingua can achieve up to a 21.4% improvement on the NQ Multi-document QA task while using only 1/4 of the tokens. Our main contributions are five-fold: We propose a question-aware coarse-to-fine compression method to improve the key information density in the prompt. Oct 10, 2023 · This work proposes LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges of higher computational cost, performance reduction, and position bias. In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research ..."}
+{"idx": 1, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long ...", "date": "", "ddg_snippet": "Sep 15, 2025 · Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs’ perception of the ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.91/", "content": "Sep 15, 2025 · Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs’ perception of the ..."}
+{"idx": 2, "title": "Paper page - LongLLMLingua: Accelerating and Enhancing LLMs ...", "date": "", "ddg_snippet": "Oct 10, 2023 · Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2310.06839", "content": "Oct 10, 2023 · Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ..."}
+{"idx": 3, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long ...", "date": "", "ddg_snippet": "LongLLMLingua capitalizes on these distribution characteristics by employing prompt compression and reorganization. This strategy schedules and utilizes the limited but powerful context windows for LLMs more efficiently, effectively mitigating the \"Lost in the middle\" issue.", "subpage_snippet": "", "source": "llmlingua.com", "link": "https://llmlingua.com/longllmlingua.html", "content": "LongLLMLingua capitalizes on these distribution characteristics by employing prompt compression and reorganization. This strategy schedules and utilizes the limited but powerful context windows for LLMs more efficiently, effectively mitigating the \"Lost in the middle\" issue."}
+{"idx": 4, "title": "LongLLMLingua: ACCELERATING AND ENHANCING LLM L CONTEXT ...", "date": "", "ddg_snippet": "ABSTRACT In long context scenarios, large language models (LLMs) face three main chal-lenges: higher computational/financial cost, longer latency, and inferior perfor-mance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=8dkp41et6U", "content": "ABSTRACT In long context scenarios, large language models (LLMs) face three main chal-lenges: higher computational/financial cost, longer latency, and inferior perfor-mance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ..."}
+{"idx": 5, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long ...", "date": "", "ddg_snippet": "View recent discussion. Abstract : In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2310.06839v2", "content": "View recent discussion. Abstract : In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving ..."}
+{"idx": 6, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long ...", "date": "", "ddg_snippet": "Oct 10, 2023 · This work proposes LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges of higher computational cost, performance reduction, and position bias. In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/LongLLMLingua:-Accelerating-and-Enhancing-LLMs-in-Jiang-Wu/4c0428917aeee6aa7bd434f337d039f35996b736", "content": "Oct 10, 2023 · This work proposes LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges of higher computational cost, performance reduction, and position bias. In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research ..."}
+{"idx": 7, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in ...", "date": "", "ddg_snippet": "by H Jiang · Cited by 299 — This paper proposes LongLLMLingua , a question-aware coarse-to-fine compression method to compress prompts and improve the key information density. The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8dkp41et6U", "content": "by H Jiang · Cited by 299 — This paper proposes LongLLMLingua , a question-aware coarse-to-fine compression method to compress prompts and improve the key information density. The ..."}
+{"idx": 8, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in ...", "date": "", "ddg_snippet": "by H Jiang · 2024 · Cited by 299 — Here, we investigate: (1) How effective is. LongLLM Lingua ? (2) How efficient is LongLLM-. Lingua? Implementation details In this paper, we use.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.91.pdf", "content": "by H Jiang · 2024 · Cited by 299 — Here, we investigate: (1) How effective is. LongLLM Lingua ? (2) How efficient is LongLLM-. Lingua? Implementation details In this paper, we use."}
+{"idx": 9, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long ...", "date": "", "ddg_snippet": "Microsoft researchers developed LongLLMLingua , a prompt compression framework that uses question-aware techniques to reduce input length for Large Language ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2310.06839v1", "content": "Microsoft researchers developed LongLLMLingua , a prompt compression framework that uses question-aware techniques to reduce input length for Large Language ..."}
diff --git a/data/sampled_jsons/Longpre_et_al._2021_Entity-Based_Knowledge_Conflicts_in_Question_Answering_abstract.jsonl b/data/sampled_jsons/Longpre_et_al._2021_Entity-Based_Knowledge_Conflicts_in_Question_Answering_abstract.jsonl
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index 0000000000000000000000000000000000000000..fe92a60ac5e84389171f1d816a342fb2d2429758
--- /dev/null
+++ b/data/sampled_jsons/Longpre_et_al._2021_Entity-Based_Knowledge_Conflicts_in_Question_Answering_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Sep 15, 2025 · Shayne Longpre , Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh. 2021 . Entity-Based Knowledge Conflicts in Question Answering . In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7052–7063, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Cite (Informal): Entity-Based Knowledge ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.emnlp-main.565/", "content": "Sep 15, 2025 · Shayne Longpre , Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh. 2021 . Entity-Based Knowledge Conflicts in Question Answering . In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7052–7063, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Cite (Informal): Entity-Based Knowledge ..."}
+{"idx": 1, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "EMNLP 2021 . Abstract Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric, learned at training time, and (2) contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular ...", "subpage_snippet": "", "source": "www.shaynelongpre.com", "link": "https://www.shaynelongpre.com/publication/kcqa-emnlp2021/", "content": "EMNLP 2021 . Abstract Knowledge -dependent tasks typically use two sources of knowledge , (1) parametric, learned at training time, and (2) contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular ..."}
+{"idx": 2, "title": "Entity-Based Knowledge Conflicts in Question Answering Entity-Based Knowledge Conflicts in Question Answeri Entity-Based Knowledge Conflicts in Question Answering Entity-Based Knowledge Conflicts in Question Answering Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Sep 10, 2021 · Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over ... ♠Apple ♥University of California, Irvine slongpre@mit.edu {kperisetla, nikhilr, cdubois} @apple.com {anthony.chen, sameer} @uci.edu See full list on par.nsf.gov Knowledge -dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the be-h... See full list on par.nsf.gov As our focus is entity - based knowledge conflicts , our first step identifies instances where the answer is a named entity . We leverage the SpaCy named en-tity recognizer and entity linker to identify gold an-swers that are named entities, their corresponding entity types, and their ID in the Wikidata graph.2 This allows us to gather auxiliary inform... See full list on par.nsf.gov In this work, we examine how conflicts between contextual and parametric knowledge affect ques-tion answering models. In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Fina... See full list on par.nsf.gov Nov 2, 2021 · Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. ( 2021 ) Longpre et al . EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. Empirical Methods in Natural Language Processing (EMNLP) 2021 · Apple Inc. and UC Irvine Our substitution framework for generating substituted instances which contains information that contradicts what may have been learned during pre-training or fine-tuning, introducing a knowledge conflict .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.05052", "content": "Sep 10, 2021 · Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over ... ♠Apple ♥University of California, Irvine slongpre@mit.edu {kperisetla, nikhilr, cdubois} @apple.com {anthony.chen, sameer} @uci.edu See full list on par.nsf.gov Knowledge -dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the be-h... See full list on par.nsf.gov As our focus is entity - based knowledge conflicts , our first step identifies instances where the answer is a named entity . We leverage the SpaCy named en-tity recognizer and entity linker to identify gold an-swers that are named entities, their corresponding entity types, and their ID in the Wikidata graph.2 This allows us to gather auxiliary inform... See full list on par.nsf.gov In this work, we examine how conflicts between contextual and parametric knowledge affect ques-tion answering models. In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Fina... See full list on par.nsf.gov Nov 2, 2021 · Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. ( 2021 ) Longpre et al . EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. Empirical Methods in Natural Language Processing (EMNLP) 2021 · Apple Inc. and UC Irvine Our substitution framework for generating substituted instances which contains information that contradicts what may have been learned during pre-training or fine-tuning, introducing a knowledge conflict ."}
+{"idx": 3, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Nov 2, 2021 · Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information.", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/entity-knowledge-conflicts", "content": "Nov 2, 2021 · Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information."}
+{"idx": 4, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Empirical Methods in Natural Language Processing (EMNLP) 2021 · Apple Inc. and UC Irvine Our substitution framework for generating substituted instances which contains information that contradicts what may have been learned during pre-training or fine-tuning, introducing a knowledge conflict .", "subpage_snippet": "", "source": "anthonywchen.github.io", "link": "https://anthonywchen.github.io/Papers/knowledge_conflicts/poster.pdf", "content": "Empirical Methods in Natural Language Processing (EMNLP) 2021 · Apple Inc. and UC Irvine Our substitution framework for generating substituted instances which contains information that contradicts what may have been learned during pre-training or fine-tuning, introducing a knowledge conflict ."}
+{"idx": 5, "title": "Entity-Based Knowledge Conflicts in Question Answeri Entity-Based Knowledge Conflicts in Question Answering Entity-Based Knowledge Conflicts in Question Answering Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "♠Apple ♥University of California, Irvine slongpre@mit.edu {kperisetla, nikhilr, cdubois} @apple.com {anthony.chen, sameer} @uci.edu See full list on par.nsf.gov Knowledge -dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the be-h... See full list on par.nsf.gov As our focus is entity - based knowledge conflicts , our first step identifies instances where the answer is a named entity . We leverage the SpaCy named en-tity recognizer and entity linker to identify gold an-swers that are named entities, their corresponding entity types, and their ID in the Wikidata graph.2 This allows us to gather auxiliary inform... See full list on par.nsf.gov In this work, we examine how conflicts between contextual and parametric knowledge affect ques-tion answering models. In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Fina... See full list on par.nsf.gov Nov 2, 2021 · Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. ( 2021 ) Longpre et al . EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. Empirical Methods in Natural Language Processing (EMNLP) 2021 · Apple Inc. and UC Irvine Our substitution framework for generating substituted instances which contains information that contradicts what may have been learned during pre-training or fine-tuning, introducing a knowledge conflict .", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10462823", "content": "♠Apple ♥University of California, Irvine slongpre@mit.edu {kperisetla, nikhilr, cdubois} @apple.com {anthony.chen, sameer} @uci.edu See full list on par.nsf.gov Knowledge -dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a pas-sage at inference time. To understand how models use these sources together, we for-malize the problem of knowledge conflicts , where the contextual information contradicts the learned information. Analyzing the be-h... See full list on par.nsf.gov As our focus is entity - based knowledge conflicts , our first step identifies instances where the answer is a named entity . We leverage the SpaCy named en-tity recognizer and entity linker to identify gold an-swers that are named entities, their corresponding entity types, and their ID in the Wikidata graph.2 This allows us to gather auxiliary inform... See full list on par.nsf.gov In this work, we examine how conflicts between contextual and parametric knowledge affect ques-tion answering models. In formalizing this prob-lem, we first contribute a substitution framework for creating knowledge conflicts and evaluating model behaviour. Using this framework, we con-duct a detailed examination of knowledge con-flicts in QA. Fina... See full list on par.nsf.gov Nov 2, 2021 · Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts , where the contextual information contradicts the learned information. ( 2021 ) Longpre et al . EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. Empirical Methods in Natural Language Processing (EMNLP) 2021 · Apple Inc. and UC Irvine Our substitution framework for generating substituted instances which contains information that contradicts what may have been learned during pre-training or fine-tuning, introducing a knowledge conflict ."}
+{"idx": 6, "title": "Entity-Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "( 2021 ) Longpre et al . EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time.", "subpage_snippet": "", "source": "www.mendeley.com", "link": "https://www.mendeley.com/catalogue/746fdfc7-46a9-3f17-a0d6-6fb3af14de1b/", "content": "( 2021 ) Longpre et al . EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time."}
+{"idx": 7, "title": "(PDF) Entity - Based Knowledge Conflicts in Question Answering", "date": "", "ddg_snippet": "Entity - Based Knowledge Conflicts in Question Answering . Shayne Longpre ∗Kartik Perisetla∗ Anthony Chen∗. ity to generalize to evolving knowledge and time-. dependent answers , not found in training (Guu. et al .,2020;Schuster et al ., 2021 ).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/354575176_Entity-Based_Knowledge_Conflicts_in_Question_Answering", "content": "Entity - Based Knowledge Conflicts in Question Answering . Shayne Longpre ∗Kartik Perisetla∗ Anthony Chen∗. ity to generalize to evolving knowledge and time-. dependent answers , not found in training (Guu. et al .,2020;Schuster et al ., 2021 )."}
+{"idx": 8, "title": "(Open Access) Entity - Based Knowledge Conflicts in Question ...", "date": "", "ddg_snippet": "Abstract : Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/entity-based-knowledge-conflicts-in-question-answering-4mty8e914i", "content": "Abstract : Knowledge -dependent tasks typically use two sources of knowledge : parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts ..."}
+{"idx": 9, "title": "GitHub - apple/ml- knowledge - conflicts : Entity - Based Knowledge ...", "date": "", "ddg_snippet": "@inproceedings{ longpre - etal - 2021 - entity , title = \" Entity - Based Knowledge Conflicts in Question Answering \", author = \" Longpre , Shayne and.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/apple/ml-knowledge-conflicts", "content": "@inproceedings{ longpre - etal - 2021 - entity , title = \" Entity - Based Knowledge Conflicts in Question Answering \", author = \" Longpre , Shayne and."}
diff --git a/data/sampled_jsons/Luo_Tseng_1992_coordinate_descent_convergence_optimization_year_1992.jsonl b/data/sampled_jsons/Luo_Tseng_1992_coordinate_descent_convergence_optimization_year_1992.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..da6e961a1834de65eabc7b05ad8773face54fc4c
--- /dev/null
+++ b/data/sampled_jsons/Luo_Tseng_1992_coordinate_descent_convergence_optimization_year_1992.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Coordinate descent - Wikipedia", "date": "", "ddg_snippet": "While this example shows that coordinate descent does not necessarily converge to the optimum, it is possible to show formal convergence under ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Coordinate_descent", "content": "While this example shows that coordinate descent does not necessarily converge to the optimum, it is possible to show formal convergence under ..."}
+{"idx": 1, "title": "PDF On the convergence of the coordinate descent method for convex ...", "date": "", "ddg_snippet": "Abstract. The coordinate descent method enjoys along history incon-vex differentiable min mization. Surprisingly, very little isknown about the convergence of the iterates generated by his method. Convergence typically requires restrictive assumptions such asthat thecost function has bounded l vel sets and is in some sense strictly convex. In a recent work, Luo and Tseng showed that the ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/bf00939948.pdf", "content": "Abstract. The coordinate descent method enjoys along history incon-vex differentiable min mization. Surprisingly, very little isknown about the convergence of the iterates generated by his method. Convergence typically requires restrictive assumptions such asthat thecost function has bounded l vel sets and is in some sense strictly convex. In a recent work, Luo and Tseng showed that the ..."}
+{"idx": 2, "title": "On the convergence of the coordinate descent method for convex ...", "date": "", "ddg_snippet": "On the convergence of the coordinate descent method for convex differentiable minimization Author (s) Luo , Zhi-Quan.; Tseng , Paul.; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/handle/1721.1/3164", "content": "On the convergence of the coordinate descent method for convex differentiable minimization Author (s) Luo , Zhi-Quan.; Tseng , Paul.; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems."}
+{"idx": 3, "title": "On the Linear Convergence of Descent Methods for Convex Essentially ...", "date": "", "ddg_snippet": "Z.-Q. Luo , P. Tseng , On the convergence of the coordinate descent method for convex differentiable minimization, J. Optim. Theory Appl., 72 ( 1992 ), 7-35, Laboratory for Information and Decision Systems Report No. P-1924, Massachusetts Institute of Technology, Cambridge, MA (1989; revised 1990)", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/0330025", "content": "Z.-Q. Luo , P. Tseng , On the convergence of the coordinate descent method for convex differentiable minimization, J. Optim. Theory Appl., 72 ( 1992 ), 7-35, Laboratory for Information and Decision Systems Report No. P-1924, Massachusetts Institute of Technology, Cambridge, MA (1989; revised 1990)"}
+{"idx": 4, "title": "Sci-Hub | On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "Luo , Z. Q., & Tseng , P. ( 1992 ). On the convergence of the coordinate descent method for convex differentiable minimization. Journal of Optimization Theory and ...", "subpage_snippet": "", "source": "sci-hub.se", "link": "https://sci-hub.se/10.1007/BF00939948", "content": "Luo , Z. Q., & Tseng , P. ( 1992 ). On the convergence of the coordinate descent method for convex differentiable minimization. Journal of Optimization Theory and ..."}
+{"idx": 5, "title": "[PDF] Convergence of a Block Coordinate Descent Method for ...", "date": "", "ddg_snippet": "We study the convergence properties of a (block) coordinate descent method applied to minimize a nondifferentiable (nonconvex) function f(x1, . . . , xN) with certain separability and regularity properties. Assuming that f is continuous on a compact level set, the subsequence convergence of the iterates to a stationary point is shown when either f is pseudoconvex in every pair of coordinate ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Convergence-of-a-Block-Coordinate-Descent-Method-Tseng/3d95ae9504cfb904216be4f2bfda9315d0476986", "content": "We study the convergence properties of a (block) coordinate descent method applied to minimize a nondifferentiable (nonconvex) function f(x1, . . . , xN) with certain separability and regularity properties. Assuming that f is continuous on a compact level set, the subsequence convergence of the iterates to a stationary point is shown when either f is pseudoconvex in every pair of coordinate ..."}
+{"idx": 6, "title": "On the convergence of the coordinate descent method for convex ...", "date": "", "ddg_snippet": "article On the convergence of the coordinate descent method for convex differentiable minimization Authors: Z. Q. Luo , P. Tseng Authors Info & Claims", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/BF00939948", "content": "article On the convergence of the coordinate descent method for convex differentiable minimization Authors: Z. Q. Luo , P. Tseng Authors Info & Claims"}
+{"idx": 7, "title": "On the convergence of the coordinate descent method for convex ...", "date": "", "ddg_snippet": "Scholar articles On the convergence of the coordinate descent method for convex differentiable minimization ZQ Luo , P Tseng - Journal of Optimization Theory and Applications, 1992 Cited by 710 Related articles All 9 versions", "subpage_snippet": "", "source": "xs.typicalgame.com", "link": "https://xs.typicalgame.com/citations?view_op=view_citation&hl=en&user=dW3gcXoAAAAJ&citation_for_view=dW3gcXoAAAAJ:-95Q15plzcUC", "content": "Scholar articles On the convergence of the coordinate descent method for convex differentiable minimization ZQ Luo , P Tseng - Journal of Optimization Theory and Applications, 1992 Cited by 710 Related articles All 9 versions"}
+{"idx": 8, "title": "On the convergence of the coordinate descent method for convex ...", "date": "", "ddg_snippet": "The coordinate descent method enjoys a long history in convex differentiable minimization. Surprisingly, very little is known about the convergence of the iterates generated by this method. Convergence typically requires restrictive assumptions such as that the cost function has bounded level sets and is in some sense strictly convex. In a recent work, Luo and Tseng showed that the iterates ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/BF00939948", "content": "The coordinate descent method enjoys a long history in convex differentiable minimization. Surprisingly, very little is known about the convergence of the iterates generated by this method. Convergence typically requires restrictive assumptions such as that the cost function has bounded level sets and is in some sense strictly convex. In a recent work, Luo and Tseng showed that the iterates ..."}
+{"idx": 9, "title": "On the convergence of the coordinate descent method for convex ...", "date": "", "ddg_snippet": "Published in Journal of Optimization Theory and Applications by Springer Nature. 1992 Volume 72, p7-35", "subpage_snippet": "", "source": "scholar.archive.org", "link": "https://scholar.archive.org/fatcat/release/3nayugjzv5bvnkssglxvkjn4e4", "content": "Published in Journal of Optimization Theory and Applications by Springer Nature. 1992 Volume 72, p7-35"}
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+{"idx": 1, "title": "Alareeni Et Al (2022) - FinTech, Entrepreneurship and ...", "date": "", "ddg_snippet": "Dai, A.M., Le, Q.V.: Semi - supervised sequence learning . In: Advances in Neural Information Processing Systems (2015) Gill, S., et al.: Twitter and the ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/726658725/Alareeni-Et-Al-2022-FinTech-Entrepreneurship-and-Business-Development-ICBT-2021", "content": "Dai, A.M., Le, Q.V.: Semi - supervised sequence learning . In: Advances in Neural Information Processing Systems (2015) Gill, S., et al.: Twitter and the ..."}
+{"idx": 2, "title": "Paper Digest: COVID-19 Related Papers (Computer Science)", "date": "", "ddg_snippet": "7 Apr 2020 — Trust Based Access Control with Hybrid ... Identifying Misinformation Spreaders: A Graph- Based Semi - Supervised Learning Approach", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2020/04/covid-19-related-papers-computer-science/", "content": "7 Apr 2020 — Trust Based Access Control with Hybrid ... Identifying Misinformation Spreaders: A Graph- Based Semi - Supervised Learning Approach"}
+{"idx": 3, "title": "SelectedSet - Journal of Mood & Anxiety Disorders", "date": "", "ddg_snippet": "... M , Gradisar M , Bartel K , Whittall H, Kahn M . Sleep Med. 2022 Dec;100:174-182. doi: 10.1016/j.sleep.2022.08.004. Epub 2022 Aug 20. Pillion M , Sleep Med, 2022 ...", "subpage_snippet": "", "source": "www.jmoodanxdisorders.org", "link": "https://www.jmoodanxdisorders.org/cms/10.1016/j.xjmad.2023.100018/attachment/267399f7-e627-4fb1-b33e-f5f284f2e28e/mmc1.xlsx", "content": "... M , Gradisar M , Bartel K , Whittall H, Kahn M . Sleep Med. 2022 Dec;100:174-182. doi: 10.1016/j.sleep.2022.08.004. Epub 2022 Aug 20. Pillion M , Sleep Med, 2022 ..."}
+{"idx": 4, "title": "Asian Military Evolutions: Civil–Military Relations in Asia ...", "date": "", "ddg_snippet": "This book explores civil-military relations in Asia. With chapters on individual countries in the region , it provides a", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/asian-military-evolutions-civilmilitary-relations-in-asia-9781529229349.html", "content": "This book explores civil-military relations in Asia. With chapters on individual countries in the region , it provides a"}
+{"idx": 5, "title": "Emerging Trends in Smart Societies: Interdisciplinary ...", "date": "", "ddg_snippet": "Problem- based learning in K -8 mathematics and science education : a literature review. Interdisciplinary Journal of Problem- based Learning , 11(2). Munawaroh ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/123197891/Emerging_Trends_in_Smart_Societies_Interdisciplinary_Perspectives", "content": "Problem- based learning in K -8 mathematics and science education : a literature review. Interdisciplinary Journal of Problem- based Learning , 11(2). Munawaroh ..."}
+{"idx": 6, "title": "May - June 2020: ISSN: 0193-4120 | PDF", "date": "", "ddg_snippet": "30 Jun 2020 — Semi Supervised Learning Using Segmentation and Clustering for ... Deep Learning- Based Segmentation of the Coronary Artery Region in the", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/643939057/d288140600450e4d5207a04027ff0aea-pdf", "content": "30 Jun 2020 — Semi Supervised Learning Using Segmentation and Clustering for ... Deep Learning- Based Segmentation of the Coronary Artery Region in the"}
+{"idx": 7, "title": "A review and critical analysis of multimodal datasets for ...", "date": "", "ddg_snippet": "by S Al-Azani · 2025 — In addition, datasets primarily focused on psychological research related to emotional stimuli and cognitive reactions, such as (Diconne et al.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11271-1", "content": "by S Al-Azani · 2025 — In addition, datasets primarily focused on psychological research related to emotional stimuli and cognitive reactions, such as (Diconne et al."}
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+{"idx": 9, "title": "Hybrid Intelligent Systems: 19th International Conference ...", "date": "", "ddg_snippet": "The investigation described in this paper focuses on four IBL algorithms that implement data reduction, which have been empirically evaluated in data sets from ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/hybrid-intelligent-systems-19th-international-conference-on-hybrid-intelligent-systems-his-2019-held-in-bhopal-india-december-10-12-2019-1st-ed-9783030493356-9783030493363.html", "content": "The investigation described in this paper focuses on four IBL algorithms that implement data reduction, which have been empirically evaluated in data sets from ..."}
diff --git a/data/sampled_jsons/MA-OSMA_multi-agent_submodular_coordination_paper.jsonl b/data/sampled_jsons/MA-OSMA_multi-agent_submodular_coordination_paper.jsonl
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index 0000000000000000000000000000000000000000..2a7e86443ce51ed7df7eba2390f2ebdfeed9c721
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+{"idx": 0, "title": "Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "Feb 7, 2025 · To address these challenges, we firstly present a MA-OSMA algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05028", "content": "Feb 7, 2025 · To address these challenges, we firstly present a MA-OSMA algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques."}
+{"idx": 1, "title": "N -O ONLINE LEARNING FOR MULTI A S COORDINATION: TIGHT AP ...", "date": "", "ddg_snippet": "poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address these challenges, we firstly present a MA-OSMA algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, th", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i8dYPGdB1C", "content": "poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address these challenges, we firstly present a MA-OSMA algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, th"}
+{"idx": 2, "title": "Multi-Agent Coordination across Diverse Applications: A Survey Multi-Agent Maximization of a Monotone Submodular Function ... NeurIPS Poster Effective Policy Learning for Multi-Agent ... Multi-Agent Submodular Optimization - drops.dagstuhl.de Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "Feb 20, 2025 · Multi-agent coordination studies the underlying mechanism enabling the trending spread of diverse multi-agent systems (MAS) and has received increasing attention, driven by the expansion of emerging applications and rapid AI advances. This survey outlines the current state of coordination research across applications through a unified understanding that answers four fundamental coordination ... This paper studies distributed submodular optimization subject to partition matroid. We work in the value oracle model where the only access of the agents to the utility function is through a black box that returns the utility function value. The agents are communicating over a connected undirected graph and have access only to their own strategy set. As known in the literature, submodular ... Abstract: In this paper , we present two effective policy learning algorithms for multi-agent online coordination ( MA -OC) problem. The first one, ** MA -SPL**, not only can achieve the optimal (1 − c e) -approximation guarantee for the MA -OC problem with submodular objectives but also can handle the unexplored α -weakly DR- submodular and (γ, β) -weakly submodular scenarios, where c is the ... Aug 13, 2018 · This was introduced in the minimization setting by Goel et al. In this paper we explore the extent to which the approximability of the multi-agent problems are linked to their single- agent versions, referred to informally as the multi-agent gap. We present different reductions that transform a multi-agent problem into a single- agent one. To address these challenges, we firstly present a MA-OSMA MA-OSMA algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.14743", "content": "Feb 20, 2025 · Multi-agent coordination studies the underlying mechanism enabling the trending spread of diverse multi-agent systems (MAS) and has received increasing attention, driven by the expansion of emerging applications and rapid AI advances. This survey outlines the current state of coordination research across applications through a unified understanding that answers four fundamental coordination ... This paper studies distributed submodular optimization subject to partition matroid. We work in the value oracle model where the only access of the agents to the utility function is through a black box that returns the utility function value. The agents are communicating over a connected undirected graph and have access only to their own strategy set. As known in the literature, submodular ... Abstract: In this paper , we present two effective policy learning algorithms for multi-agent online coordination ( MA -OC) problem. The first one, ** MA -SPL**, not only can achieve the optimal (1 − c e) -approximation guarantee for the MA -OC problem with submodular objectives but also can handle the unexplored α -weakly DR- submodular and (γ, β) -weakly submodular scenarios, where c is the ... Aug 13, 2018 · This was introduced in the minimization setting by Goel et al. In this paper we explore the extent to which the approximability of the multi-agent problems are linked to their single- agent versions, referred to informally as the multi-agent gap. We present different reductions that transform a multi-agent problem into a single- agent one. To address these challenges, we firstly present a MA-OSMA MA-OSMA algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques."}
+{"idx": 3, "title": "Multi-Agent Maximization of a Monotone Submodular Function ...", "date": "", "ddg_snippet": "This paper studies distributed submodular optimization subject to partition matroid. We work in the value oracle model where the only access of the agents to the utility function is through a black box that returns the utility function value. The agents are communicating over a connected undirected graph and have access only to their own strategy set. As known in the literature, submodular ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9682818", "content": "This paper studies distributed submodular optimization subject to partition matroid. We work in the value oracle model where the only access of the agents to the utility function is through a black box that returns the utility function value. The agents are communicating over a connected undirected graph and have access only to their own strategy set. As known in the literature, submodular ..."}
+{"idx": 4, "title": "NeurIPS Poster Effective Policy Learning for Multi-Agent ...", "date": "", "ddg_snippet": "Abstract: In this paper , we present two effective policy learning algorithms for multi-agent online coordination ( MA -OC) problem. The first one, ** MA -SPL**, not only can achieve the optimal (1 − c e) -approximation guarantee for the MA -OC problem with submodular objectives but also can handle the unexplored α -weakly DR- submodular and (γ, β) -weakly submodular scenarios, where c is the ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2025/poster/116516", "content": "Abstract: In this paper , we present two effective policy learning algorithms for multi-agent online coordination ( MA -OC) problem. The first one, ** MA -SPL**, not only can achieve the optimal (1 − c e) -approximation guarantee for the MA -OC problem with submodular objectives but also can handle the unexplored α -weakly DR- submodular and (γ, β) -weakly submodular scenarios, where c is the ..."}
+{"idx": 5, "title": "Multi-Agent Submodular Optimization - drops.dagstuhl.de", "date": "", "ddg_snippet": "Aug 13, 2018 · This was introduced in the minimization setting by Goel et al. In this paper we explore the extent to which the approximability of the multi-agent problems are linked to their single- agent versions, referred to informally as the multi-agent gap. We present different reductions that transform a multi-agent problem into a single- agent one.", "subpage_snippet": "", "source": "drops.dagstuhl.de", "link": "https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2018.23", "content": "Aug 13, 2018 · This was introduced in the minimization setting by Goel et al. In this paper we explore the extent to which the approximability of the multi-agent problems are linked to their single- agent versions, referred to informally as the multi-agent gap. We present different reductions that transform a multi-agent problem into a single- agent one."}
+{"idx": 6, "title": "Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "To address these challenges, we firstly present a MA-OSMA MA-OSMA algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/3340ee1e4a8bad8d32c35721712b4d0a-Abstract-Conference.html", "content": "To address these challenges, we firstly present a MA-OSMA MA-OSMA algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques."}
+{"idx": 7, "title": "ICLR Poster Near-Optimal Online Learning for Multi - Agent ...", "date": "", "ddg_snippet": "Abstract: Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control.the joint curvature of submodular objectives.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28714", "content": "Abstract: Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control.the joint curvature of submodular objectives."}
+{"idx": 8, "title": "(PDF) Near-Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Multi - agent submodular maximization( MA -SM) problem. involves coordinating multiple agents to collaboratively maximize a submodular utility function. A commonly used solution for MA -SM problem heavily depends on the distributed implemen", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388847678_Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_Tight_Approximation_and_Communication_Efficiency", "content": "Multi - agent submodular maximization( MA -SM) problem. involves coordinating multiple agents to collaboratively maximize a submodular utility function. A commonly used solution for MA -SM problem heavily depends on the distributed implemen"}
+{"idx": 9, "title": "Near-Optimal Online Learning for Multi - Agent Submodular ... | alphaXiv", "date": "", "ddg_snippet": "Abstract: Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2502.05028", "content": "Abstract: Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control."}
diff --git a/data/sampled_jsons/MADE_Masked_Autoencoder_MLP_architecture_neural_network_layers.jsonl b/data/sampled_jsons/MADE_Masked_Autoencoder_MLP_architecture_neural_network_layers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f32bcd1901bdd67d6a912434d995323d06e4cf5
--- /dev/null
+++ b/data/sampled_jsons/MADE_Masked_Autoencoder_MLP_architecture_neural_network_layers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "C.S.A. domestically-made revolvers | Small Arms & Ammunition", "date": "", "ddg_snippet": "Mar 18, 2016 · There were other C.S.A. domestic manufacturers of pistols of course, smaller than the four outfits named above. For example there was Dance Brothers (of Galveston, Texas) which made a .44-cal. pistol that looked like this Elsewhere in Texas the Tucker & Sherrod company was making .44-cal. Dragoon revolvers like these And in still another part of Texas (Sisterdale) the Sisterdale Dragoon .44 ...", "subpage_snippet": "", "source": "civilwartalk.com", "link": "https://civilwartalk.com/threads/c-s-a-domestically-made-revolvers.130803/", "content": "Mar 18, 2016 · There were other C.S.A. domestic manufacturers of pistols of course, smaller than the four outfits named above. For example there was Dance Brothers (of Galveston, Texas) which made a .44-cal. pistol that looked like this Elsewhere in Texas the Tucker & Sherrod company was making .44-cal. Dragoon revolvers like these And in still another part of Texas (Sisterdale) the Sisterdale Dragoon .44 ..."}
+{"idx": 1, "title": "The \"Abiding Mystery\" of those India Made Muskets That Blow Up", "date": "", "ddg_snippet": "Dec 27, 2011 · I also made up a collapsible N-SSA target frame and carried it along with targets to hang from it like soft drink cans, charcoal briquets, and clay pigeons. When I showed up at the Picnic bright and early that Saturday Morning I was pleased to find something like 20 SCV members decked out in their uniforms and carrying their replica rifle-muskets.", "subpage_snippet": "", "source": "civilwartalk.com", "link": "https://civilwartalk.com/threads/the-abiding-mystery-of-those-india-made-muskets-that-blow-up.92642/", "content": "Dec 27, 2011 · I also made up a collapsible N-SSA target frame and carried it along with targets to hang from it like soft drink cans, charcoal briquets, and clay pigeons. When I showed up at the Picnic bright and early that Saturday Morning I was pleased to find something like 20 SCV members decked out in their uniforms and carrying their replica rifle-muskets."}
+{"idx": 2, "title": "The long shot made with a Henry 1860 by Gus in the \"Lonesome Dove...", "date": "", "ddg_snippet": "Aug 12, 2011 · The long shot made with a Henry 1860 by Gus in the \"Lonesome Dove\" movie. Could a 1860 Henry make such a shot?", "subpage_snippet": "", "source": "civilwartalk.com", "link": "https://civilwartalk.com/threads/the-long-shot-made-with-a-henry-1860-by-gus-in-the-lonesome-dove-movie-could-a-1860-henry-make-such-a-shot.205898/page-2", "content": "Aug 12, 2011 · The long shot made with a Henry 1860 by Gus in the \"Lonesome Dove\" movie. Could a 1860 Henry make such a shot?"}
+{"idx": 3, "title": "Why is there so much false information concerning Brass framed...", "date": "", "ddg_snippet": "Aug 24, 2012 · I have read so much hog wash about why the south used brass to make the frames for the revolvers. it has been told that there was a scarcity of iron in the south due to northern blockades that they made the frames out of brass when in fact it was the brass that was much more scarce. if the...", "subpage_snippet": "", "source": "civilwartalk.com", "link": "https://civilwartalk.com/threads/why-is-there-so-much-false-information-concerning-brass-framed-revolvers.75730/", "content": "Aug 24, 2012 · I have read so much hog wash about why the south used brass to make the frames for the revolvers. it has been told that there was a scarcity of iron in the south due to northern blockades that they made the frames out of brass when in fact it was the brass that was much more scarce. if the..."}
+{"idx": 4, "title": "Case Shot or Canister? | Cannons / Artillery & Crew Served...", "date": "", "ddg_snippet": "Mar 15, 2013 · Most case-shot was made of lead, both north and south. Later in the War when lead supplies were short, the Confederacy switched to iron case-shot balls. Most all canister consisted of iron balls. Thanks! Great explanation that even I can understand. Now on to \"shells\" - I got the ones that were hollow cannonballs with powder inside.", "subpage_snippet": "", "source": "civilwartalk.com", "link": "https://civilwartalk.com/threads/case-shot-or-canister.142261/", "content": "Mar 15, 2013 · Most case-shot was made of lead, both north and south. Later in the War when lead supplies were short, the Confederacy switched to iron case-shot balls. Most all canister consisted of iron balls. Thanks! Great explanation that even I can understand. Now on to \"shells\" - I got the ones that were hollow cannonballs with powder inside."}
+{"idx": 5, "title": "Lee's biggest mistakes at Gettysburg | Gettysburg", "date": "", "ddg_snippet": "Aug 20, 2021 · They all made mistakes, and honestly, it was extremely hard coordinating that many men at any one time. Having said all of that I've recently purchased Sears Gettysburg book and have been re-reading some of my other purchases, and the once the battle commenced on July 1st, I feel like the biggest mistake Lee made that he had the most control ...", "subpage_snippet": "", "source": "civilwartalk.com", "link": "https://civilwartalk.com/threads/lees-biggest-mistakes-at-gettysburg.188894/", "content": "Aug 20, 2021 · They all made mistakes, and honestly, it was extremely hard coordinating that many men at any one time. Having said all of that I've recently purchased Sears Gettysburg book and have been re-reading some of my other purchases, and the once the battle commenced on July 1st, I feel like the biggest mistake Lee made that he had the most control ..."}
+{"idx": 6, "title": "Real Confederate Bowie Knives | Edged Weapons - American Civil...", "date": "", "ddg_snippet": "Nov 2, 2019 · When researching book and scouring as many images of soldiers armed with knives, majority of Union soldiers had Sheffield bowies or side knives, but most knife-toting Confederates had \"local- made \" especially early war where it was an honor to use Southern made arms. I agree, some of the home grown edged weapons were very good.", "subpage_snippet": "", "source": "civilwartalk.com", "link": "https://civilwartalk.com/threads/real-confederate-bowie-knives.184649/", "content": "Nov 2, 2019 · When researching book and scouring as many images of soldiers armed with knives, majority of Union soldiers had Sheffield bowies or side knives, but most knife-toting Confederates had \"local- made \" especially early war where it was an honor to use Southern made arms. I agree, some of the home grown edged weapons were very good."}
+{"idx": 7, "title": "Fact Check! 1861 Springfield Rifle-Musket | Small Arms &...", "date": "", "ddg_snippet": "Apr 1, 1999 · The well made interchangeable parts made it the realization of a dream for ordnance men. It was a simple and inexpensive arm that influenced small arms development well into the 20th century.", "subpage_snippet": "", "source": "civilwartalk.com", "link": "https://civilwartalk.com/threads/fact-check-1861-springfield-rifle-musket.19184/", "content": "Apr 1, 1999 · The well made interchangeable parts made it the realization of a dream for ordnance men. It was a simple and inexpensive arm that influenced small arms development well into the 20th century."}
+{"idx": 8, "title": "Union Cases - 1 of 13 | Period Photos & Examinations", "date": "", "ddg_snippet": "Jan 24, 2013 · When these components were mixed together, heated and pressed into a mold, the parts of a Union Case are formed and because of the way they were made , the parts of the cases could take on fine, and in some cases, elaborate details which most Union cases display.", "subpage_snippet": "", "source": "civilwartalk.com", "link": "https://civilwartalk.com/threads/union-cases-1-of-13.105126/", "content": "Jan 24, 2013 · When these components were mixed together, heated and pressed into a mold, the parts of a Union Case are formed and because of the way they were made , the parts of the cases could take on fine, and in some cases, elaborate details which most Union cases display."}
+{"idx": 9, "title": "J D Chevalier Bowie Knife | Edged Weapons - civilwartalk.com", "date": "", "ddg_snippet": "Oct 9, 2024 · Hello everyone, I have a J D Chevalier, New York Bowie knife that has been etched with a tribute to the 39th Infantry of the Garibaldi Guard of New York. They served the Union army during the civil under the command of Frederick George D'Utassy from 1861 to 1863. J D Chevalier actually was a...", "subpage_snippet": "", "source": "civilwartalk.com", "link": "https://civilwartalk.com/threads/j-d-chevalier-bowie-knife.212398/", "content": "Oct 9, 2024 · Hello everyone, I have a J D Chevalier, New York Bowie knife that has been etched with a tribute to the 39th Infantry of the Garibaldi Guard of New York. They served the Union army during the civil under the command of Frederick George D'Utassy from 1861 to 1863. J D Chevalier actually was a..."}
diff --git a/data/sampled_jsons/MADE_Masked_Autoencoder_for_Distribution_Estimation_architecture.jsonl b/data/sampled_jsons/MADE_Masked_Autoencoder_for_Distribution_Estimation_architecture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..476a04ec291425e948b9b60a8ceb7446895eca55
--- /dev/null
+++ b/data/sampled_jsons/MADE_Masked_Autoencoder_for_Distribution_Estimation_architecture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MADE — Masked Autoencoder for Distribution Estimation | Medium", "date": "", "ddg_snippet": "An in-depth explanation of Masked Autoencoder for distribution estimation ( MADE ).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/towards-data-science/made-masked-autoencoder-for-distribution-estimation-fc95aaca8467", "content": "An in-depth explanation of Masked Autoencoder for distribution estimation ( MADE )."}
+{"idx": 1, "title": "MADE : Masked Autoencoder for Distribution Estimation", "date": "", "ddg_snippet": "The resulting Masked Autoencoder Distribution Estimator ( MADE ) preserves the efciency of a single pass through a regular autoencoder . Implementation on a GPU is straightforward, making the method scalable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1502.03509", "content": "The resulting Masked Autoencoder Distribution Estimator ( MADE ) preserves the efciency of a single pass through a regular autoencoder . Implementation on a GPU is straightforward, making the method scalable."}
+{"idx": 2, "title": "(PDF) MADE : Masked Autoencoder for Distribution Estimation", "date": "", "ddg_snippet": "by the network. MADE : Masked Autoencoder for Distribution Estimation . Table 4.We present Neural Autoregressive Distribution Estimation (NADE) models, which are neu- ral network architectures applied to the problem of unsupervised distribution and density esitmation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/272194203_MADE_Masked_Autoencoder_for_Distribution_Estimation", "content": "by the network. MADE : Masked Autoencoder for Distribution Estimation . Table 4.We present Neural Autoregressive Distribution Estimation (NADE) models, which are neu- ral network architectures applied to the problem of unsupervised distribution and density esitmation."}
+{"idx": 3, "title": "Distribution estimation with Masked Autoencoders | Ritchie Vink", "date": "", "ddg_snippet": "If we bully the autoencoders just a bit more, by also blinding them partially, we can actually make them learn $P(x)$, i.e. the distribution of $x$. Germain, Gregor & Larochelle $^{[2]}$, posted their findings in the paper MADE : Masked Autoencoder for Density Estimation .", "subpage_snippet": "", "source": "www.ritchievink.com", "link": "https://www.ritchievink.com/blog/2019/10/25/distribution-estimation-with-masked-autoencoders/", "content": "If we bully the autoencoders just a bit more, by also blinding them partially, we can actually make them learn $P(x)$, i.e. the distribution of $x$. Germain, Gregor & Larochelle $^{[2]}$, posted their findings in the paper MADE : Masked Autoencoder for Density Estimation ."}
+{"idx": 4, "title": "MADE : Masked Autoencoder for Distribution Estimation | DeepAI", "date": "", "ddg_snippet": "There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/made-masked-autoencoder-for-distribution-estimation", "content": "There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models."}
+{"idx": 5, "title": "Masked Autoencoder for Distribution Estimation ( MADE )...", "date": "", "ddg_snippet": "This property is formally referred to as “autoregression” (dependence on itself), and is implemented in MADE by introducing masks for the weights of the neural network that is used to estimate the distribution of the variable’s element. More concretely, this is achieved by masking all...", "subpage_snippet": "", "source": "dosssman.github.io", "link": "https://dosssman.github.io/posts/2020-07-28-made/", "content": "This property is formally referred to as “autoregression” (dependence on itself), and is implemented in MADE by introducing masks for the weights of the neural network that is used to estimate the distribution of the variable’s element. More concretely, this is achieved by masking all..."}
+{"idx": 6, "title": "MADE : Masked Autoencoder for Distribution Estimation", "date": "", "ddg_snippet": "- Density Estimation . UCI GAS. MADE MoG.There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/made-masked-autoencoder-for-distribution", "content": "- Density Estimation . UCI GAS. MADE MoG.There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples."}
+{"idx": 7, "title": "made .pdf", "date": "", "ddg_snippet": "MADE : Masked Autoencoder for Distribution Estimation .The resulting Masked Autoencoder Distribution . Estimator ( MADE ) preserves the efficiency of a single pass.", "subpage_snippet": "", "source": "docs.google.com", "link": "https://docs.google.com/viewer?url=homepages.inf.ed.ac.uk/imurray2/pub/15made/made.pdf", "content": "MADE : Masked Autoencoder for Distribution Estimation .The resulting Masked Autoencoder Distribution . Estimator ( MADE ) preserves the efficiency of a single pass."}
+{"idx": 8, "title": "Deep Dive into MADE ( Masked Autoencoder for Distribution ...)", "date": "", "ddg_snippet": "In this post I will talk about the Masked Autoencoder for Distribution Estimation MADE which was covered in a paper in 2015 as linked above. I will follow the implementation from University of Berkeley’s Deep Unsupervised Learning course which can be found here.", "subpage_snippet": "", "source": "nsanghi.com", "link": "https://nsanghi.com/blog/made/", "content": "In this post I will talk about the Masked Autoencoder for Distribution Estimation MADE which was covered in a paper in 2015 as linked above. I will follow the implementation from University of Berkeley’s Deep Unsupervised Learning course which can be found here."}
+{"idx": 9, "title": "[PDF] MADE : Masked Autoencoder for Distribution Estimation", "date": "", "ddg_snippet": "We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder 's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/MADE:-Masked-Autoencoder-for-Distribution-Germain-Gregor/90f72fbbe5f0a29e627db28999e01a30a9655bc6", "content": "We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder 's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering."}
diff --git a/data/sampled_jsons/MATH_dataset_critical_window_frequency_0.33_0.41_CW_correct_CW_incorrect_percentage_increase.jsonl b/data/sampled_jsons/MATH_dataset_critical_window_frequency_0.33_0.41_CW_correct_CW_incorrect_percentage_increase.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..40234a64041c8cacb2d6e37868078a8530d6d953
--- /dev/null
+++ b/data/sampled_jsons/MATH_dataset_critical_window_frequency_0.33_0.41_CW_correct_CW_incorrect_percentage_increase.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Percentage Increase Calculator", "date": "", "ddg_snippet": "Free. Windows , Mac OS, Linux, iOS, Android. Percentage increase calculator finds the increase from one value to another as a percentage of the first value. Shows you how to find percentage increase with percent increase formula.", "subpage_snippet": "", "source": "www.calculatorsoup.com", "link": "https://www.calculatorsoup.com/calculators/algebra/percentage-increase-calculator.php", "content": "Free. Windows , Mac OS, Linux, iOS, Android. Percentage increase calculator finds the increase from one value to another as a percentage of the first value. Shows you how to find percentage increase with percent increase formula."}
+{"idx": 1, "title": "Ошибка CRITICAL PROCESS DIED Windows 10 | remontka.pro", "date": "", "ddg_snippet": "Как исправить ошибку CRITICAL PROCESS DIED на синем экране в Windows 10 при работе и загрузке системы.", "subpage_snippet": "", "source": "remontka.pro", "link": "https://remontka.pro/critical-process-died-windows-10/", "content": "Как исправить ошибку CRITICAL PROCESS DIED на синем экране в Windows 10 при работе и загрузке системы."}
+{"idx": 2, "title": "Percent Increase and Decrease Word Problems - YouTube", "date": "", "ddg_snippet": "This math video tutorial explains how to calculate the percent of change using the percent increase and decrease formula.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=T6-0MwmCpE8", "content": "This math video tutorial explains how to calculate the percent of change using the percent increase and decrease formula."}
+{"idx": 3, "title": "Google Sheets Percentage Formula (5+ Easy Examples)", "date": "", "ddg_snippet": "Here's how to calculate percentage in Google Sheets. I cover the formula and various examples with screenshots and video.", "subpage_snippet": "", "source": "spreadsheetpoint.com", "link": "https://spreadsheetpoint.com/calculate-percentage-in-google-sheets/", "content": "Here's how to calculate percentage in Google Sheets. I cover the formula and various examples with screenshots and video."}
+{"idx": 4, "title": "How to calculate a Percentage in Python | bobbyhadz", "date": "", "ddg_snippet": "# Table of ContentsGetting the percentage increase /decrease between two numbersCalculate a Percentage Increase /Decrease from User Input in Python", "subpage_snippet": "", "source": "bobbyhadz.com", "link": "https://bobbyhadz.com/blog/python-calculate-percentage", "content": "# Table of ContentsGetting the percentage increase /decrease between two numbersCalculate a Percentage Increase /Decrease from User Input in Python"}
+{"idx": 5, "title": "Percentage - Formula | How To Calculate Percentage ?", "date": "", "ddg_snippet": "Percentage increase refers to the percentage change in the value when it is increased over a period of time. For example, population increase , increase in the number of bacteria on a surface, etc. Percentage Increase = ( Increased Value-Original value)/Original value × 100.", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/commercial-math/percentages/", "content": "Percentage increase refers to the percentage change in the value when it is increased over a period of time. For example, population increase , increase in the number of bacteria on a surface, etc. Percentage Increase = ( Increased Value-Original value)/Original value × 100."}
+{"idx": 6, "title": "PowerShell Round to 2 Decimal Places [5 Methods]", "date": "", "ddg_snippet": "Master PowerShell decimal rounding with 5 proven methods. Learn [ Math ]::Round, ToString, -f operator & more. Perfect for beginners & pros. Examples included.", "subpage_snippet": "", "source": "powershellfaqs.com", "link": "https://powershellfaqs.com/powershell-round-to-2-decimal-places/", "content": "Master PowerShell decimal rounding with 5 proven methods. Learn [ Math ]::Round, ToString, -f operator & more. Perfect for beginners & pros. Examples included."}
+{"idx": 7, "title": "Computerized Analysis of Verbal Fluency: Normative Data and the...", "date": "", "ddg_snippet": "CW = correct words; RW% = percentage of repeated words; TYP = typicality, the median number of participants who produced each word (higher numbers indicate more typical words); SOI = semantic organization index; LWF = log word frequency ; SYLL = mean syllable count; ESW...", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0166439&type=printable", "content": "CW = correct words; RW% = percentage of repeated words; TYP = typicality, the median number of participants who produced each word (higher numbers indicate more typical words); SOI = semantic organization index; LWF = log word frequency ; SYLL = mean syllable count; ESW..."}
+{"idx": 8, "title": "Кета холодного копчения. как правильно засолить...", "date": "", "ddg_snippet": "Сегодня коптим красную рыбку) Рецепт как всегда простой: На 1 литр рассола: Соль - 90 гр. Сахар - 20 гр. Реклама. ИП Данилов А. Г. ИНН 773386751060 erid LjN8K 41 cw .", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/36c4ad2d6583405c941ceb05c59c7d21/", "content": "Сегодня коптим красную рыбку) Рецепт как всегда простой: На 1 литр рассола: Соль - 90 гр. Сахар - 20 гр. Реклама. ИП Данилов А. Г. ИНН 773386751060 erid LjN8K 41 cw ."}
+{"idx": 9, "title": "Аргументы и Факты — последние новости России и мира сегодня", "date": "", "ddg_snippet": "01:41, Политика. Волошин прокомментировал реакцию в ООН на обвинения Эстонии в адрес России.", "subpage_snippet": "", "source": "aif.ru", "link": "https://aif.ru/", "content": "01:41, Политика. Волошин прокомментировал реакцию в ООН на обвинения Эстонии в адрес России."}
diff --git a/data/sampled_jsons/METransformer_BLEU-4_0.124_Wang_Liu_Zhou_year_2023.jsonl b/data/sampled_jsons/METransformer_BLEU-4_0.124_Wang_Liu_Zhou_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dad526646aa03758be60cfcd94e619749d398be6
--- /dev/null
+++ b/data/sampled_jsons/METransformer_BLEU-4_0.124_Wang_Liu_Zhou_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "by Z Wang · 2023 · Cited by 166 — Ours( METransformer ). 0.386. 0.250. 0.169. 0.124 . 0.291. 0.152. 0.362. Table 1 ... Bleu 4 and CIDEr scores by using different numbers of expert tokens on IU ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "by Z Wang · 2023 · Cited by 166 — Ours( METransformer ). 0.386. 0.250. 0.169. 0.124 . 0.291. 0.152. 0.362. Table 1 ... Bleu 4 and CIDEr scores by using different numbers of expert tokens on IU ... 10 pages"}
+{"idx": 1, "title": "Supplementary Material METransformer: Radiology Report ...", "date": "", "ddg_snippet": "by Z Wang — Hyper-parameter study of λ on IU-Xray dataset. λ BLEU 4 ROUGE METEOR CIDEr Overall ... 0.124 . 0.289. 0.149. 0.361 0.992. 1. 0.122. 0.286. 0.147. 0.360 0.982. 2.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_METransformer_Radiology_Report_CVPR_2023_supplemental.pdf", "content": "by Z Wang — Hyper-parameter study of λ on IU-Xray dataset. λ BLEU 4 ROUGE METEOR CIDEr Overall ... 0.124 . 0.289. 0.149. 0.361 0.992. 1. 0.122. 0.286. 0.147. 0.360 0.982. 2."}
+{"idx": 2, "title": "R2GenGPT: Radiology Report Generation with frozen LLMs", "date": "", "ddg_snippet": "Nov 1, 2023 · For instance, our BLEU_4 score is improved from 0.124 to 0.134, marking an 8.1 % increase. However, we achieved a CIDEr score of 0.269, which is lower than METransformer 's 0.362. This discrepancy is because METransformer employs an expert voting strategy similar to an ensemble approach to enhance the CIDEr metric.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2950162823000334", "content": "Nov 1, 2023 · For instance, our BLEU_4 score is improved from 0.124 to 0.134, marking an 8.1 % increase. However, we achieved a CIDEr score of 0.269, which is lower than METransformer 's 0.362. This discrepancy is because METransformer employs an expert voting strategy similar to an ensemble approach to enhance the CIDEr metric."}
+{"idx": 3, "title": "MRScore: Evaluating Medical Report with LLM-based Reward System", "date": "", "ddg_snippet": "Wang , Z., Liu , L., Wang , L., Zhou , L.: Metransformer : Radiology report genera-tion by transformer with multiple learnable expert tokens. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11558– 11567 (2023)", "subpage_snippet": "", "source": "papers.miccai.org", "link": "https://papers.miccai.org/miccai-2024/paper/1151_paper.pdf", "content": "Wang , Z., Liu , L., Wang , L., Zhou , L.: Metransformer : Radiology report genera-tion by transformer with multiple learnable expert tokens. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11558– 11567 (2023)"}
+{"idx": 4, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "Springer, 2022. Wang et al. [2023] Zhanyu Wang , Lingqiao Liu , Lei Wang , and Luping Zhou . METransformer : Radiology report generation by transformer with multiple learnable expert tokens. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11558–11567, 2023.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "Springer, 2022. Wang et al. [2023] Zhanyu Wang , Lingqiao Liu , Lei Wang , and Luping Zhou . METransformer : Radiology report generation by transformer with multiple learnable expert tokens. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11558–11567, 2023."}
+{"idx": 5, "title": "S4M: Generating Radiology Reports by A Single Model for ...", "date": "", "ddg_snippet": "May 26, 2023 · [8] Wang , Z.; Liu , L.; W ang , L.; Zhou , L. METransformer : Radiology Report Generation by Transformer with", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371124054_S4M_Generating_Radiology_Reports_by_A_Single_Model_for_Multiple_Body_Parts", "content": "May 26, 2023 · [8] Wang , Z.; Liu , L.; W ang , L.; Zhou , L. METransformer : Radiology Report Generation by Transformer with"}
+{"idx": 6, "title": "Graph-guided topic modeling and multi-level context-aware ...", "date": "", "ddg_snippet": "Z. Wang , L. Liu , L. Wang , L. Zhou , Metransformer : Radiology report generation by transformer with multiple learnable expert tokens, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 11558–11567.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S174680942501064X", "content": "Z. Wang , L. Liu , L. Wang , L. Zhou , Metransformer : Radiology report generation by transformer with multiple learnable expert tokens, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 11558–11567."}
+{"idx": 7, "title": "A Systematic Evaluation of GPT-4V’s Multimodal Capability for ...", "date": "", "ddg_snippet": "Jan 30, 2024 · Yunyi Liu Yingshu Li Zhanyu Wang Xinyu Liang Lei Wang Lingqiao Liu Leyang Cui Zhaopeng Tu Longyue Wang vinnylywang@tencent.com Luping Zhou luping. zhou @sydney.edu.au Electrical and Computer Engineering, The University of Sydney, NSW, 2006, Australia School of Computing and Information Technology, The University of Wollongong, NSW, 2522, Australia School of Computer and Mathematical Sciences ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.20381v5", "content": "Jan 30, 2024 · Yunyi Liu Yingshu Li Zhanyu Wang Xinyu Liang Lei Wang Lingqiao Liu Leyang Cui Zhaopeng Tu Longyue Wang vinnylywang@tencent.com Luping Zhou luping. zhou @sydney.edu.au Electrical and Computer Engineering, The University of Sydney, NSW, 2006, Australia School of Computing and Information Technology, The University of Wollongong, NSW, 2522, Australia School of Computer and Mathematical Sciences ..."}
+{"idx": 8, "title": "MedKit: Multi-level feature distillation with knowledge ...", "date": "", "ddg_snippet": "Wang , Liu , Wang , & Zhou [2023c] proposed the R2GenGPT framework, which bridges the modality gap by aligning visual features with the LLM embedding space through a lightweight visual alignment module, enabling highly efficient fine-tuning.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S095741742502620X", "content": "Wang , Liu , Wang , & Zhou [2023c] proposed the R2GenGPT framework, which bridges the modality gap by aligning visual features with the LLM embedding space through a lightweight visual alignment module, enabling highly efficient fine-tuning."}
+{"idx": 9, "title": "arXiv:2304.02211v1 [cs.CV] 5 Apr 2023", "date": "", "ddg_snippet": "by Z Wang · 2023 · Cited by 161 — Ours( METransformer ). 0.386. 0.250. 0.169. 0.124 . 0.291. 0.152. 0.362. Table ... Bleu 4 and CIDEr scores by using different numbers of expert ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.02211", "content": "by Z Wang · 2023 · Cited by 161 — Ours( METransformer ). 0.386. 0.250. 0.169. 0.124 . 0.291. 0.152. 0.362. Table ... Bleu 4 and CIDEr scores by using different numbers of expert ..."}
diff --git a/data/sampled_jsons/METransformer_BLEU-4_MIMIC-CXR_2023.jsonl b/data/sampled_jsons/METransformer_BLEU-4_MIMIC-CXR_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..33c194227b4eebd564b39dd9c5cf1265dd0a5ab5
--- /dev/null
+++ b/data/sampled_jsons/METransformer_BLEU-4_MIMIC-CXR_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "To show the impacts of the expert tokens, we train METransformer with different numbers of expert tokens, i.e., num expert ∈ {1, 3, 5, 7, 9} and the re-sults on IU-Xray and MIMIC - CXR are shown in Figure.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "To show the impacts of the expert tokens, we train METransformer with different numbers of expert tokens, i.e., num expert ∈ {1, 3, 5, 7, 9} and the re-sults on IU-Xray and MIMIC - CXR are shown in Figure."}
+{"idx": 1, "title": "R2GenGPT: Radiology Report Generation with frozen LLMs", "date": "", "ddg_snippet": "Nov 1, 2023 · In the MIMIC - CXR dataset, apart from CIDEr, we significantly outperform the latest METransformer40 method across all metrics. For instance, our BLEU _ 4 score is improved from 0.124 to 0.134, marking an 8.1 % increase.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2950162823000334", "content": "Nov 1, 2023 · In the MIMIC - CXR dataset, apart from CIDEr, we significantly outperform the latest METransformer40 method across all metrics. For instance, our BLEU _ 4 score is improved from 0.124 to 0.134, marking an 8.1 % increase."}
+{"idx": 2, "title": "Reason Like a Radiologist: Chain-of-Thought and Reinforcement ...", "date": "", "ddg_snippet": "In the Downstream Adapter Phase, we train the Adapter using two radiology report generation benchmarks: MIMIC - CXR and IU X-Ray (Demner-Fushman et al., 2016). MIMIC - CXR comprises 377,110 chest X-ray images paired with 227,835 radiology reports, while IU X-Ray includes 7,470 images and 3,955 reports.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.18453", "content": "In the Downstream Adapter Phase, we train the Adapter using two radiology report generation benchmarks: MIMIC - CXR and IU X-Ray (Demner-Fushman et al., 2016). MIMIC - CXR comprises 377,110 chest X-ray images paired with 227,835 radiology reports, while IU X-Ray includes 7,470 images and 3,955 reports."}
+{"idx": 3, "title": "KARGEN: Knowledge-Enhanced Automated Radiology Report ... Label knowledge guided transformer for automatic radiology ... KARGEN: Knowledge-enhanced Automated Radiology Report ... arXiv:2408.09743v1 [cs.CV] 19 Aug 2024", "date": "", "ddg_snippet": "Oct 4 , 2024 · We explore two fusion methods to automatically prioritize and select the most relevant features. The fused features are employed by LLM to generate reports that are more sensitive to diseases and of improved quality. Our approach demonstrates promising results on the MIMIC - CXR and IU-Xray datasets. Our code will be available on GitHub. Sep 1, 2025 · To address this, we propose the label knowledge guided transformer model for generating radiology reports. Specifically, our model incorporates a Multi Feature Extraction module and a Dual-branch Collaborative Attention module. Ablation Study: Table 3 summarizes our ablation study on the MIMIC - CXR dataset, singling out the contribution of each component, including knowledge-enhanced disease-related features, Graph Convolutional Network (GCN), and fusion methods. their method using only with Findings. Our method demonstrates competitive performance, achieving a BLEU - 4 score of 0.206, which surpasses existing methods, highlighting the effectiveness of our context-guided eficient X-ra", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-72086-4_36", "content": "Oct 4 , 2024 · We explore two fusion methods to automatically prioritize and select the most relevant features. The fused features are employed by LLM to generate reports that are more sensitive to diseases and of improved quality. Our approach demonstrates promising results on the MIMIC - CXR and IU-Xray datasets. Our code will be available on GitHub. Sep 1, 2025 · To address this, we propose the label knowledge guided transformer model for generating radiology reports. Specifically, our model incorporates a Multi Feature Extraction module and a Dual-branch Collaborative Attention module. Ablation Study: Table 3 summarizes our ablation study on the MIMIC - CXR dataset, singling out the contribution of each component, including knowledge-enhanced disease-related features, Graph Convolutional Network (GCN), and fusion methods. their method using only with Findings. Our method demonstrates competitive performance, achieving a BLEU - 4 score of 0.206, which surpasses existing methods, highlighting the effectiveness of our context-guided eficient X-ra"}
+{"idx": 4, "title": "KARGEN: Knowledge-enhanced Automated Radiology Report ...", "date": "", "ddg_snippet": "Ablation Study: Table 3 summarizes our ablation study on the MIMIC - CXR dataset, singling out the contribution of each component, including knowledge-enhanced disease-related features, Graph Convolutional Network (GCN), and fusion methods.", "subpage_snippet": "", "source": "papers.miccai.org", "link": "https://papers.miccai.org/miccai-2024/paper/0877_paper.pdf", "content": "Ablation Study: Table 3 summarizes our ablation study on the MIMIC - CXR dataset, singling out the contribution of each component, including knowledge-enhanced disease-related features, Graph Convolutional Network (GCN), and fusion methods."}
+{"idx": 5, "title": "arXiv:2408.09743v1 [cs.CV] 19 Aug 2024", "date": "", "ddg_snippet": "their method using only with Findings. Our method demonstrates competitive performance, achieving a BLEU - 4 score of 0.206, which surpasses existing methods, highlighting the effectiveness of our context-guided eficient X-ra", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.09743", "content": "their method using only with Findings. Our method demonstrates competitive performance, achieving a BLEU - 4 score of 0.206, which surpasses existing methods, highlighting the effectiveness of our context-guided eficient X-ra"}
+{"idx": 6, "title": "PriorRG: Prior-Guided Contrastive Pre-training and", "date": "", "ddg_snippet": "Extensive experiments on MIMIC - CXR and MIMIC - ABN datasets demonstrate that PriorRG outperforms state-of-the-art methods, achieving a 3.6% BLEU - 4 and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05353v1", "content": "Extensive experiments on MIMIC - CXR and MIMIC - ABN datasets demonstrate that PriorRG outperforms state-of-the-art methods, achieving a 3.6% BLEU - 4 and ..."}
+{"idx": 7, "title": "CheXPO: Preference Optimization for Chest X-ray VLMs with", "date": "", "ddg_snippet": "Our model, CheX-Phi3.5V , outperforms existing methods on the MIMIC - CXR -VQA and Medical-Diff-VQA benchmarks, demonstrating improved interpretability ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.06959v1", "content": "Our model, CheX-Phi3.5V , outperforms existing methods on the MIMIC - CXR -VQA and Medical-Diff-VQA benchmarks, demonstrating improved interpretability ..."}
+{"idx": 8, "title": "Activating Associative Disease-Aware Vision Token Memory for", "date": "", "ddg_snippet": "... reports based on a large language model and achieves state-of-the-art performance on multiple benchmark datasets, including the IU X-ray, MIMIC - CXR ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.03458v1", "content": "... reports based on a large language model and achieves state-of-the-art performance on multiple benchmark datasets, including the IU X-ray, MIMIC - CXR ..."}
+{"idx": 9, "title": "Advancing Multimodal Medical Capabilities of Gemini", "date": "", "ddg_snippet": "... LLMs) and large multimodal models (LMMs) such as Flamingo (Alayrac et al., 2022 ) , PaLI (Chen et al., 2022 ) , GPT- 4 (Achiam et al., 2023 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.03162v1", "content": "... LLMs) and large multimodal models (LMMs) such as Flamingo (Alayrac et al., 2022 ) , PaLI (Chen et al., 2022 ) , GPT- 4 (Achiam et al., 2023 ..."}
diff --git a/data/sampled_jsons/METransformer_MIMIC-CXR_BLEU-4_score_0.124_Wang_Liu_2023.jsonl b/data/sampled_jsons/METransformer_MIMIC-CXR_BLEU-4_score_0.124_Wang_Liu_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..524a2f6bba268345e9d130d1122273de2c851488
--- /dev/null
+++ b/data/sampled_jsons/METransformer_MIMIC-CXR_BLEU-4_score_0.124_Wang_Liu_2023.jsonl
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+{"idx": 0, "title": "METransformer : Radiology Report Generation by Transformer With...", "date": "", "ddg_snippet": "MIMIC - CXR . Methods. BLEU-1 BLEU-2 BLEU-3 BLEU - 4 ROUGE METEOR.[48] Yuanen Zhou, Meng Wang , Daqing Liu , Zhenzhen Hu, and Hanwang Zhang. More grounded image captioning by dis-tilling image-text matching model. In CVPR, 2020.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "MIMIC - CXR . Methods. BLEU-1 BLEU-2 BLEU-3 BLEU - 4 ROUGE METEOR.[48] Yuanen Zhou, Meng Wang , Daqing Liu , Zhenzhen Hu, and Hanwang Zhang. More grounded image captioning by dis-tilling image-text matching model. In CVPR, 2020."}
+{"idx": 1, "title": "(PDF) METransformer : Radiology Report Generation by Transformer...", "date": "", "ddg_snippet": "MIMIC - CXR , METransformer is the best performer across. all metrics. Especially, our CIDEr score is up to 0.362", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/369823367_METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens", "content": "MIMIC - CXR , METransformer is the best performer across. all metrics. Especially, our CIDEr score is up to 0.362"}
+{"idx": 2, "title": "[2304.02211] METransformer : Radiology Report Generation by...", "date": "", "ddg_snippet": "View a PDF of the paper titled METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens, by Zhanyu Wang and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.02211", "content": "View a PDF of the paper titled METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens, by Zhanyu Wang and 3 other authors."}
+{"idx": 3, "title": "Tigoals - Sport Live Streaming, Live football and basketball, Live Score ...", "date": "", "ddg_snippet": "Tigoals - Sport Live Streaming, Live football and basketball, Live Score , Fixture, Results, Football Livestream. tigoals213.", "subpage_snippet": "", "source": "www.tigoals213.com", "link": "https://www.tigoals213.com/", "content": "Tigoals - Sport Live Streaming, Live football and basketball, Live Score , Fixture, Results, Football Livestream. tigoals213."}
+{"idx": 4, "title": "Та, что сбежала: История Кары Робинсон - русский трейлер 2023 ...", "date": "", "ddg_snippet": "3 мин 1 с. Видео от 4 сентября 2023 в хорошем качестве, без регистрации в бесплатном видеокаталоге ВКонтакте!", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/video-218925083_456239362", "content": "3 мин 1 с. Видео от 4 сентября 2023 в хорошем качестве, без регистрации в бесплатном видеокаталоге ВКонтакте!"}
+{"idx": 5, "title": "Chelsea 6-0 Everton | PALMER scores FOUR ! | Highlights... - YouTube", "date": "", "ddg_snippet": "Matchday 31 - Extended highlights of Chelsea's 6-0 Premier League win against Everton at Stamford Bridge...GOALS & HIGHLIGHTS:00:00 - Intro00:23 - Teams01:49...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=DyvKz9x5fcI", "content": "Matchday 31 - Extended highlights of Chelsea's 6-0 Premier League win against Everton at Stamford Bridge...GOALS & HIGHLIGHTS:00:00 - Intro00:23 - Teams01:49..."}
+{"idx": 6, "title": "PriorRG: Prior-Guided Contrastive Pre-training and", "date": "", "ddg_snippet": "... on MIMIC - CXR and MIMIC - ABN datasets demonstrate that PriorRG outperforms state-of-the-art methods, achieving a 3.6% BLEU - 4 and 3.8% F1 score ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05353v1", "content": "... on MIMIC - CXR and MIMIC - ABN datasets demonstrate that PriorRG outperforms state-of-the-art methods, achieving a 3.6% BLEU - 4 and 3.8% F1 score ..."}
+{"idx": 7, "title": "Activating Associative Disease-Aware Vision Token Memory for", "date": "", "ddg_snippet": "Xiao Wang , Member, IEEE , Fuling Wang , Haowen Wang *, Bo Jiang*, Chuanfu Li, Yaowei Wang , Member, IEEE , Yonghong Tian, Fellow, IEEE , Jin Tang ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.03458v1", "content": "Xiao Wang , Member, IEEE , Fuling Wang , Haowen Wang *, Bo Jiang*, Chuanfu Li, Yaowei Wang , Member, IEEE , Yonghong Tian, Fellow, IEEE , Jin Tang ..."}
+{"idx": 8, "title": "CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical", "date": "", "ddg_snippet": "After revisiting the mainstream algorithms of X-ray image medical report generation, we find that datasets like IU X-ray and MIMIC - CXR are widely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00379v1", "content": "After revisiting the mainstream algorithms of X-ray image medical report generation, we find that datasets like IU X-ray and MIMIC - CXR are widely ..."}
+{"idx": 9, "title": "Contrastive Learning with Counterfactual", "date": "", "ddg_snippet": "MIMIC - CXR [18], the most extensive radiology dataset publicly available, includes 368,960 images and 222,758 reports. Wang , Z., Liu , L., Wang , L., Zhou, L.: Metransformer : Radiology report genera-tion by transformer with multiple learnable expert tokens.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/05958.pdf", "content": "MIMIC - CXR [18], the most extensive radiology dataset publicly available, includes 368,960 images and 222,758 reports. Wang , Z., Liu , L., Wang , L., Zhou, L.: Metransformer : Radiology report genera-tion by transformer with multiple learnable expert tokens."}
diff --git a/data/sampled_jsons/ML-1M_6040_Amazon_Beauty_22363_Amazon_Toys_19412_Amazon_Games_28718_train_interactions_density.jsonl b/data/sampled_jsons/ML-1M_6040_Amazon_Beauty_22363_Amazon_Toys_19412_Amazon_Games_28718_train_interactions_density.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ea84c2998ea7a90ed3af9d97757d6edc4a848460
--- /dev/null
+++ b/data/sampled_jsons/ML-1M_6040_Amazon_Beauty_22363_Amazon_Toys_19412_Amazon_Games_28718_train_interactions_density.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Amazon .com: Toys & Games", "date": "", "ddg_snippet": "Amazon 's Toys & Games store features thousands of products, including dolls, action figures, games , advent calendars, building toys , stuffed animals, and much more.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/toys/b?ie=UTF8&node=165793011", "content": "Amazon 's Toys & Games store features thousands of products, including dolls, action figures, games , advent calendars, building toys , stuffed animals, and much more."}
+{"idx": 1, "title": "The Official Amazon Games Website | Amazon Games", "date": "", "ddg_snippet": "Amazon Game logo in gold.Aug 27, 2025 March of Giants, a New Free-to-Play War MOBA From Amazon Games Montreal, Begins Closed Alpha Testing.", "subpage_snippet": "", "source": "www.amazongames.com", "link": "https://www.amazongames.com/en-us/", "content": "Amazon Game logo in gold.Aug 27, 2025 March of Giants, a New Free-to-Play War MOBA From Amazon Games Montreal, Begins Closed Alpha Testing."}
+{"idx": 2, "title": "Wonder Woman 1984 | Young Diana Takes on The Amazon Games", "date": "", "ddg_snippet": "The first amazon games for Young Diana of Themyscira. She receives an important lesson from Antiope (Robin Wright).#wonderwoman1984 #WW84SUBSCRIBE to Warner ...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=u1NlmFa0-68", "content": "The first amazon games for Young Diana of Themyscira. She receives an important lesson from Antiope (Robin Wright).#wonderwoman1984 #WW84SUBSCRIBE to Warner ..."}
+{"idx": 3, "title": "Top wheely bug amazon Sale", "date": "", "ddg_snippet": "wheely bug amazon , Lion Balance Bug Ride On Wheely Toy with Wooden Base for Children by Beehive Toys Amazon Toys Games Sale.", "subpage_snippet": "", "source": "budzhak-info.in.ua", "link": "http://budzhak-info.in.ua/?b=297645728", "content": "wheely bug amazon , Lion Balance Bug Ride On Wheely Toy with Wooden Base for Children by Beehive Toys Amazon Toys Games Sale."}
+{"idx": 4, "title": "Amazon ’s Best Toys In 2025 List Is Here - Forbes Vetted", "date": "", "ddg_snippet": "Amazon 's annual ' Toys We Love' holiday preview is filled with the retailer's top toys of 2024 for kids of all ages, including 100 new items.", "subpage_snippet": "", "source": "www.forbes.com", "link": "https://www.forbes.com/sites/forbes-personal-shopper/2024/09/11/best-amazon-toys-we-love/", "content": "Amazon 's annual ' Toys We Love' holiday preview is filled with the retailer's top toys of 2024 for kids of all ages, including 100 new items."}
+{"idx": 5, "title": "Buy Tiny ML Book Online at Low Prices in India | Tiny ML ... - Amazon .in", "date": "", "ddg_snippet": "Read Tiny ML book reviews & author details and more at Amazon .in.Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data", "subpage_snippet": "", "source": "www.amazon.in", "link": "https://www.amazon.in/Tiny-ML-Pete-Warden/dp/1492052043", "content": "Read Tiny ML book reviews & author details and more at Amazon .in.Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data"}
+{"idx": 6, "title": "countryliving.com/shopping/gifts/g40627460/best-selling- toys - amazon", "date": "", "ddg_snippet": "17 Best Selling Toys on Amazon for Boys and Girls.", "subpage_snippet": "", "source": "www.countryliving.com", "link": "https://www.countryliving.com/shopping/gifts/g40627460/best-selling-toys-amazon/", "content": "17 Best Selling Toys on Amazon for Boys and Girls."}
+{"idx": 7, "title": "Johnson's Baby Soft Lotion, 200 ml : Buy... - Souq is now Amazon .eg", "date": "", "ddg_snippet": "All Categories Amazon Devices Amazon Fashion Amazon Resale Arts, Crafts & Sewing Automotive Parts & Accessories Baby Beauty & Personal Care Books Electronics Gift Cards Grocery & Gourmet Food Health, Household & Baby Care HomeOptions Available. Size. 200 ml (Pack of 1).", "subpage_snippet": "", "source": "www.amazon.eg", "link": "https://www.amazon.eg/-/en/Johnsons-Baby-Soft-Lotion-200ml/dp/B07T2CBVYY", "content": "All Categories Amazon Devices Amazon Fashion Amazon Resale Arts, Crafts & Sewing Automotive Parts & Accessories Baby Beauty & Personal Care Books Electronics Gift Cards Grocery & Gourmet Food Health, Household & Baby Care HomeOptions Available. Size. 200 ml (Pack of 1)."}
+{"idx": 8, "title": "Enterogermina Adult Probiotic Liquid - 4 Billion | Beauty Care Bag", "date": "", "ddg_snippet": "Improve your gut health with Enterogermina Adult Probiotic Liquid, packed with 4 billion CFU per 5 mL . Try it today!", "subpage_snippet": "", "source": "beautycarebag.com", "link": "https://beautycarebag.com/products/enterogermina-adult-probiotic-liquid-4-billion-cfu-5ml-20vials", "content": "Improve your gut health with Enterogermina Adult Probiotic Liquid, packed with 4 billion CFU per 5 mL . Try it today!"}
+{"idx": 9, "title": "(PDF) Tensor-Based Sequential Learning via Hankel Matrix...", "date": "", "ddg_snippet": "Dataset #users #items average median density . Amazon Beauty 22363 12101 8.9 6 0.07%. Amazon datasets. We were unable to identify the cause of it and provide.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366910946_Tensor-based_Sequential_Learning_via_Hankel_Matrix_Representation_for_Next_Item_Recommendations", "content": "Dataset #users #items average median density . Amazon Beauty 22363 12101 8.9 6 0.07%. Amazon datasets. We were unable to identify the cause of it and provide."}
diff --git a/data/sampled_jsons/ML-1M_900188_6040_Amazon_Beauty_198439_22363_Amazon_Toys_167597_19412_Amazon_Games_185142_28718_trai.jsonl b/data/sampled_jsons/ML-1M_900188_6040_Amazon_Beauty_198439_22363_Amazon_Toys_167597_19412_Amazon_Games_185142_28718_trai.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7e196715a6171e1fc67de84d2ca774201df4edcb
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Amazon .com: Toys & Games", "date": "", "ddg_snippet": "Amazon 's Toys & Games store features thousands of products, including dolls, action figures, games , advent calendars, building toys , stuffed animals, and much more.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/toys/b?ie=UTF8&node=165793011", "content": "Amazon 's Toys & Games store features thousands of products, including dolls, action figures, games , advent calendars, building toys , stuffed animals, and much more."}
+{"idx": 1, "title": "The Official Amazon Games Website | Amazon Games", "date": "", "ddg_snippet": "Amazon Game logo in gold.Aug 27, 2025 March of Giants, a New Free-to-Play War MOBA From Amazon Games Montreal, Begins Closed Alpha Testing.", "subpage_snippet": "", "source": "www.amazongames.com", "link": "https://www.amazongames.com/en-us/", "content": "Amazon Game logo in gold.Aug 27, 2025 March of Giants, a New Free-to-Play War MOBA From Amazon Games Montreal, Begins Closed Alpha Testing."}
+{"idx": 2, "title": "Wonder Woman 1984 | Young Diana Takes on The Amazon Games", "date": "", "ddg_snippet": "The first amazon games for Young Diana of Themyscira. She receives an important lesson from Antiope (Robin Wright).#wonderwoman1984 #WW84SUBSCRIBE to Warner ...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=u1NlmFa0-68", "content": "The first amazon games for Young Diana of Themyscira. She receives an important lesson from Antiope (Robin Wright).#wonderwoman1984 #WW84SUBSCRIBE to Warner ..."}
+{"idx": 3, "title": "Amazon ’s Best Toys In 2025 List Is Here - Forbes Vetted", "date": "", "ddg_snippet": "Amazon 's annual ' Toys We Love' holiday preview is filled with the retailer's top toys of 2024 for kids of all ages, including 100 new items.", "subpage_snippet": "", "source": "www.forbes.com", "link": "https://www.forbes.com/sites/forbes-personal-shopper/2024/09/11/best-amazon-toys-we-love/", "content": "Amazon 's annual ' Toys We Love' holiday preview is filled with the retailer's top toys of 2024 for kids of all ages, including 100 new items."}
+{"idx": 4, "title": "Amazon .it: Train dreams - Johnson, Denis, Pareschi, S. - Libri", "date": "", "ddg_snippet": "Train dreams Copertina flessibile – 23 aprile 2013. di Denis Johnson (Autore), S. Pareschi (Traduttore).Posizione nella classifica Bestseller di Amazon : n. 51.277 in Libri (Visualizza i Top 100 nella categoria Libri).", "subpage_snippet": "", "source": "www.amazon.it", "link": "https://www.amazon.it/Train-dreams-Denis-Johnson/dp/8804624515", "content": "Train dreams Copertina flessibile – 23 aprile 2013. di Denis Johnson (Autore), S. Pareschi (Traduttore).Posizione nella classifica Bestseller di Amazon : n. 51.277 in Libri (Visualizza i Top 100 nella categoria Libri)."}
+{"idx": 5, "title": "Universe of HD Wallpapers - WallpaperCat", "date": "", "ddg_snippet": "Discover the World of 100,000+ HD wallpapers on WallpaperCat. We are on a mission to make every screen beautiful one wallpaper at a time.4K. 3840x2160. Train , Cartoon Railway, HD Sea Landscape, AI Wallpaper.", "subpage_snippet": "", "source": "wallpapercat.com", "link": "https://wallpapercat.com/", "content": "Discover the World of 100,000+ HD wallpapers on WallpaperCat. We are on a mission to make every screen beautiful one wallpaper at a time.4K. 3840x2160. Train , Cartoon Railway, HD Sea Landscape, AI Wallpaper."}
+{"idx": 6, "title": "أمازون السعودية: تسوق أونلاين | أسعار مخفضة على...", "date": "", "ddg_snippet": "جميع الأقسام آلات موسيقية أجهزة Amazon أدوات وتحسينات المنزل أزياء Amazon أسواق العثيم ألعاب الفيديو أمازون بازار اشترك ووفّر الأجهزة المنزلية الألعاب والدمى الإلكترونيات البقالة والطعام الفاخر الجمال والعناية الشخصية الرياضة واللياقة البدنية...", "subpage_snippet": "", "source": "www.amazon.sa", "link": "https://www.amazon.sa/", "content": "جميع الأقسام آلات موسيقية أجهزة Amazon أدوات وتحسينات المنزل أزياء Amazon أسواق العثيم ألعاب الفيديو أمازون بازار اشترك ووفّر الأجهزة المنزلية الألعاب والدمى الإلكترونيات البقالة والطعام الفاخر الجمال والعناية الشخصية الرياضة واللياقة البدنية..."}
+{"idx": 7, "title": "“ I Want To Enjoy Every Minute That I Can”: For... | British Vogue", "date": "", "ddg_snippet": "Amazon Advertising. Aniview. 1-10 / 101.He has a training team who test and track everything from his body composition and metabolic rate to his hand grip strength and heat tolerance. His meal plans are tailored to nutritional deficiencies detected in regular blood work.", "subpage_snippet": "", "source": "www.vogue.co.uk", "link": "https://www.vogue.co.uk/article/lando-norris-interview", "content": "Amazon Advertising. Aniview. 1-10 / 101.He has a training team who test and track everything from his body composition and metabolic rate to his hand grip strength and heat tolerance. His meal plans are tailored to nutritional deficiencies detected in regular blood work."}
+{"idx": 8, "title": "AliExpress - Affordable Chinese Stores & Free Shipping - Online...", "date": "", "ddg_snippet": "Passion shouldn't cost a fortune. On AliExpress, shop online for over 111 million affordable products from China on Fashion, Men's and Women's Clothing, Electronics, Toys , Tools, Home & Garden on our marketplace with free shipping! Login app for coupons.", "subpage_snippet": "", "source": "www.aliexpress.com", "link": "https://www.aliexpress.com/", "content": "Passion shouldn't cost a fortune. On AliExpress, shop online for over 111 million affordable products from China on Fashion, Men's and Women's Clothing, Electronics, Toys , Tools, Home & Garden on our marketplace with free shipping! Login app for coupons."}
+{"idx": 9, "title": "Инфоцентр AfterShock • Каким будет завтра?", "date": "", "ddg_snippet": "Ресурс №1 в рунете по кризису во всех аспектах - пирамида долгов, энергетический шок, геополитический передел. Новости, аналитика, прогнозы, экспертиза...", "subpage_snippet": "", "source": "AfterShock.news", "link": "https://AfterShock.news/", "content": "Ресурс №1 в рунете по кризису во всех аспектах - пирамида долгов, энергетический шок, геополитический передел. Новости, аналитика, прогнозы, экспертиза..."}
diff --git a/data/sampled_jsons/MLD_FID_0.077_HumanAct12_action-to-motion_year_2023.jsonl b/data/sampled_jsons/MLD_FID_0.077_HumanAct12_action-to-motion_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..473b2a395f5a16a249870d6494cc80f28f130185
--- /dev/null
+++ b/data/sampled_jsons/MLD_FID_0.077_HumanAct12_action-to-motion_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HumanAct 12 Benchmark ( Motion Synthesis) | Papers With Code", "date": "", "ddg_snippet": "Motion Synthesis on HumanAct 12 . Leaderboard. Dataset. View. Accuracy FID Multimodality.2022. 2. MLD . 0.964. 0 . 077 .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/sota/motion-synthesis-on-humanact12?p=human-motion-diffusion-model", "content": "Motion Synthesis on HumanAct 12 . Leaderboard. Dataset. View. Accuracy FID Multimodality.2022. 2. MLD . 0.964. 0 . 077 ."}
+{"idx": 1, "title": "GitHub - ChenFengYe/ motion -latent-diffusion: [CVPR 2023] Executing...", "date": "", "ddg_snippet": "Motion Latent Diffusion ( MLD ) is a text- to - motion and action - to - motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data. News.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion", "content": "Motion Latent Diffusion ( MLD ) is a text- to - motion and action - to - motion diffusion model. Our work achieves state-of-the-art motion quality and two orders of magnitude faster than previous diffusion models on raw motion data. News."}
+{"idx": 2, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Lastly, action - to - motion task requires action-conditioned motions similar to action recognition datasets.Table 3. Comparison of action-conditional motion synthesis on UESTC [26] and HumanAct 12 [19] dataset: FIDtrain, FIDtrain indi-cate the evaluated splits.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.04048", "content": "Lastly, action - to - motion task requires action-conditioned motions similar to action recognition datasets.Table 3. Comparison of action-conditional motion synthesis on UESTC [26] and HumanAct 12 [19] dataset: FIDtrain, FIDtrain indi-cate the evaluated splits."}
+{"idx": 3, "title": "(PDF) LS-GAN: Human Motion Synthesis with Latent-space GANs", "date": "", "ddg_snippet": "Action - to - motion : HumanAct 12 [16] is a action - to -. motion language dataset that provides 1,191 raw motion se-. quences and 12 action categories. Methods R Precision ↑ FID ↓MM Dist↓Diversity→MModality↑.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387744893_LS-GAN_Human_Motion_Synthesis_with_Latent-space_GANs", "content": "Action - to - motion : HumanAct 12 [16] is a action - to -. motion language dataset that provides 1,191 raw motion se-. quences and 12 action categories. Methods R Precision ↑ FID ↓MM Dist↓Diversity→MModality↑."}
+{"idx": 4, "title": "EMDM: Efficient Motion Diffusion Model for", "date": "", "ddg_snippet": "HumanAct 12 [21] provides 1191 motion sequences and 12 action categories. Motion quality. We use Frechet Inception Distance ( FID ) as a principal metric to evaluate the feature distributions between the generated and real motions .", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00168.pdf", "content": "HumanAct 12 [21] provides 1191 motion sequences and 12 action categories. Motion quality. We use Frechet Inception Distance ( FID ) as a principal metric to evaluate the feature distributions between the generated and real motions ."}
+{"idx": 5, "title": "MotionMix: Weakly-Supervised Diffusion for Controllable Motion ...", "date": "", "ddg_snippet": "4.2 Action - to - motion . • Implementation Details. Following the experimental setup by Tevet et al., we train the MDM (MotionMix) from scratch on the HumanAct 12 and UESTC datasets for 750K and 2M steps, respectively.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/27988/27993", "content": "4.2 Action - to - motion . • Implementation Details. Following the experimental setup by Tevet et al., we train the MDM (MotionMix) from scratch on the HumanAct 12 and UESTC datasets for 750K and 2M steps, respectively."}
+{"idx": 6, "title": "Comparative Evaluation on Human Motion Generation and Control...", "date": "", "ddg_snippet": "The MLD [12] method focuses exclusively in conditional human motion generation, crafting believable sequences of human motion based on diverse conditional inputs, such as action categories or textual descriptions.", "subpage_snippet": "", "source": "dspace.lib.ntua.gr", "link": "https://dspace.lib.ntua.gr/xmlui/bitstream/handle/123456789/59704/George_Papoulias_Thesis___DSML.pdf?sequence=1&isAllowed=y", "content": "The MLD [12] method focuses exclusively in conditional human motion generation, crafting believable sequences of human motion based on diverse conditional inputs, such as action categories or textual descriptions."}
+{"idx": 7, "title": "Motion Binary Latent Diffusion", "date": "", "ddg_snippet": "The motion data comes from HumanAct 12 and AMASS (Mahmood et al., 2019), which are collections of several smaller motion captured datasets. Authors of HumanML3D have standarized the motion sequences to 20FPS and to a default human skeletal template.", "subpage_snippet": "", "source": "sergioescalera.com", "link": "https://sergioescalera.com/wp-content/uploads/2024/01/TFM-Alex-Pujol.pdf", "content": "The motion data comes from HumanAct 12 and AMASS (Mahmood et al., 2019), which are collections of several smaller motion captured datasets. Authors of HumanML3D have standarized the motion sequences to 20FPS and to a default human skeletal template."}
+{"idx": 8, "title": "The motion -latent-diffusion from chenfengye - Code Monkey", "date": "", "ddg_snippet": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model from Code Monkey.Now I want to use this ckpt for test just like the 1222_ mld _humanml3d_ FID 041.ckpt in your template. But the state_dict is wrong.", "subpage_snippet": "", "source": "codemonkey.link", "link": "https://codemonkey.link/chenfengye/motion-latent-diffusion", "content": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model from Code Monkey.Now I want to use this ckpt for test just like the 1222_ mld _humanml3d_ FID 041.ckpt in your template. But the state_dict is wrong."}
+{"idx": 9, "title": "Rainbow sybmol hashes starting FID 4M - Numbers-To-Words.com", "date": "", "ddg_snippet": "Below you can see the hash for FID 4M. If the attempt to recover the password was unsuccessful, look at the other options below. FID 4M1Y FID 4M1Z FID 4M10 FID 4M11 FID 4M12 FID 4M13 FID 4M14 FID 4M15 FID 4M16 FID 4M17 FID 4M18 FID 4M19.", "subpage_snippet": "", "source": "numbers-to-words.com", "link": "https://numbers-to-words.com/hash/FID4M", "content": "Below you can see the hash for FID 4M. If the attempt to recover the password was unsuccessful, look at the other options below. FID 4M1Y FID 4M1Z FID 4M10 FID 4M11 FID 4M12 FID 4M13 FID 4M14 FID 4M15 FID 4M16 FID 4M17 FID 4M18 FID 4M19."}
diff --git a/data/sampled_jsons/Ma_et_al._online_matching.jsonl b/data/sampled_jsons/Ma_et_al._online_matching.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f96675d76b4300a09e0062167122492ea72cc72a
--- /dev/null
+++ b/data/sampled_jsons/Ma_et_al._online_matching.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Online Matching with Delays and Stochastic Arrival Times | Theory of...", "date": "", "ddg_snippet": "This can be modeled as an online problem called Min-cost Perfect Matching with Delays (MPMD). In the case when agents arrive in an adversarial order, no online algorithm can achieve a constant-competitive ratio.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00224-024-10207-6", "content": "This can be modeled as an online problem called Min-cost Perfect Matching with Delays (MPMD). In the case when agents arrive in an adversarial order, no online algorithm can achieve a constant-competitive ratio."}
+{"idx": 1, "title": "Online matching and preferences in future electricity markets", "date": "", "ddg_snippet": "An upper bound on the sub-optimality of online matching algorithms, compared to an offline double auction, is also provided.Esch, Helge Stefan ; Moret, Fabio ; Pinson, Pierre et al . /", "subpage_snippet": "", "source": "orbit.dtu.dk", "link": "https://orbit.dtu.dk/en/publications/online-matching-and-preferences-in-future-electricity-markets", "content": "An upper bound on the sub-optimality of online matching algorithms, compared to an offline double auction, is also provided.Esch, Helge Stefan ; Moret, Fabio ; Pinson, Pierre et al . /"}
+{"idx": 2, "title": "Online spatio-temporal matching in stochastic and dynamic domains", "date": "", "ddg_snippet": "Therefore, unlike in online bipartite matching that assigns one service to only one customer, in this paper, we match one service to multiple cus-tomers (with one customer at any specic point) over time. A recent work by Dick-erson et . al .", "subpage_snippet": "", "source": "ink.library.smu.edu.sg", "link": "https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5332&context=sis_research", "content": "Therefore, unlike in online bipartite matching that assigns one service to only one customer, in this paper, we match one service to multiple cus-tomers (with one customer at any specic point) over time. A recent work by Dick-erson et . al ."}
+{"idx": 3, "title": "Online Learning and Matching for Resource Allocation Problems", "date": "", "ddg_snippet": "[12] A. Mehta et al ., Online matching and ad allocation, Foundations and Trends R in Theoretical. Computer Science, 8 (2013), pp. 265–368.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1911.07409", "content": "[12] A. Mehta et al ., Online matching and ad allocation, Foundations and Trends R in Theoretical. Computer Science, 8 (2013), pp. 265–368."}
+{"idx": 4, "title": "Fairness in Matching under Uncertainty", "date": "", "ddg_snippet": "A. Mehta et al . Online matching and ad allocation.R. Salakhutdinov and A. Mnih. Bayesian probabilistic ma -trix factorization using markov chain monte carlo. In Proc. 25th Intl.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oDR4MurRrT", "content": "A. Mehta et al . Online matching and ad allocation.R. Salakhutdinov and A. Mnih. Bayesian probabilistic ma -trix factorization using markov chain monte carlo. In Proc. 25th Intl."}
+{"idx": 5, "title": "Online Parallel Accumulation–Serial Fragmentation (PASEF) with...", "date": "", "ddg_snippet": "Online PASEF achieves a remarkable sensitivity with more than 2,500 proteins identified in 30 min runs of only 10 ng HeLa digest.", "subpage_snippet": "", "source": "www.mcponline.org", "link": "https://www.mcponline.org/article/S1535-9476(20)32012-0/fulltext", "content": "Online PASEF achieves a remarkable sensitivity with more than 2,500 proteins identified in 30 min runs of only 10 ng HeLa digest."}
+{"idx": 6, "title": "Картинки Google", "date": "", "ddg_snippet": "20 Best Sites To Watch Movies Online without Registration ...", "subpage_snippet": "", "source": "images.google.com", "link": "https://images.google.com/", "content": "20 Best Sites To Watch Movies Online without Registration ..."}
+{"idx": 7, "title": "Another Look at DPR: Reproduction of Training and Replication of...", "date": "", "ddg_snippet": "620 X. Ma et al . Table 1. Retrieval eectiveness comparing results from the original DPR paper (“orig”) and our reproduction attempt (“repro”). Online (2021).", "subpage_snippet": "", "source": "cs.uwaterloo.ca", "link": "https://cs.uwaterloo.ca/~jimmylin/publications/Ma_etal_ECIR2022.pdf", "content": "620 X. Ma et al . Table 1. Retrieval eectiveness comparing results from the original DPR paper (“orig”) and our reproduction attempt (“repro”). Online (2021)."}
+{"idx": 8, "title": "Macmillan Education Everywhere", "date": "", "ddg_snippet": "Register today. Easy access to innovative content, tools and resources. Motivate and engage learners of all ages & abilities. Make teaching rewarding and more effective.", "subpage_snippet": "", "source": "www.macmillaneducationeverywhere.com", "link": "https://www.macmillaneducationeverywhere.com/", "content": "Register today. Easy access to innovative content, tools and resources. Motivate and engage learners of all ages & abilities. Make teaching rewarding and more effective."}
+{"idx": 9, "title": "Since MLD offers attractive symbol error performance, there", "date": "", "ddg_snippet": "MA et al .: Block alternating likelihood maximization approach to multiuser detection. Table II simulation settings in example 1.", "subpage_snippet": "", "source": "www.ece.mcmaster.ca", "link": "https://www.ece.mcmaster.ca/~davidson/pubs/Ma_etal_BALM_MUD.pdf", "content": "MA et al .: Block alternating likelihood maximization approach to multiuser detection. Table II simulation settings in example 1."}
diff --git a/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_Section_5_spurious_correlation_Schubert_polynomials_p.jsonl b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_Section_5_spurious_correlation_Schubert_polynomials_p.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a69f7159a03dd03a238d3ca8ecd3aa760155eff2
--- /dev/null
+++ b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_Section_5_spurious_correlation_Schubert_polynomials_p.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine - Wikipedia", "date": "", "ddg_snippet": "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Machine", "content": "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines ."}
+{"idx": 1, "title": "MACHINE Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Machine definition: an apparatus consisting of interrelated parts with separate functions, used in the performance of some kind of work.. See examples of MACHINE used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/machine", "content": "Machine definition: an apparatus consisting of interrelated parts with separate functions, used in the performance of some kind of work.. See examples of MACHINE used in a sentence."}
+{"idx": 2, "title": "MACHINE Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of MACHINE is a mechanically, electrically, or electronically operated device for performing a task. How to use machine in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/machine", "content": "The meaning of MACHINE is a mechanically, electrically, or electronically operated device for performing a task. How to use machine in a sentence."}
+{"idx": 3, "title": "Machine | Definition, Mechanisms & Efficiency | Britannica", "date": "", "ddg_snippet": "machine , device, having a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/technology/machine", "content": "machine , device, having a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks."}
+{"idx": 4, "title": "MACHINE | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "MACHINE definition: 1. a piece of equipment with several moving parts that uses power to do a particular type of work…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/machine", "content": "MACHINE definition: 1. a piece of equipment with several moving parts that uses power to do a particular type of work…. Learn more."}
+{"idx": 5, "title": "machine , n. meanings, etymology and more | Oxford English...", "date": "", "ddg_snippet": "machine , n. meanings, etymology, pronunciation and more in the Oxford English Dictionary", "subpage_snippet": "", "source": "www.oed.com", "link": "https://www.oed.com/dictionary/machine_n", "content": "machine , n. meanings, etymology, pronunciation and more in the Oxford English Dictionary"}
+{"idx": 6, "title": "What Is A Machine ? Its Types and How it Works - Mech Lesson", "date": "", "ddg_snippet": "A machine is a mechanical device that uses power to apply force and control motion to perform work efficiently . Machines range from simple tools like pulleys and levers to complex systems like engines, robots, and manufacturing equipment.", "subpage_snippet": "", "source": "mechlesson.com", "link": "https://mechlesson.com/machine/", "content": "A machine is a mechanical device that uses power to apply force and control motion to perform work efficiently . Machines range from simple tools like pulleys and levers to complex systems like engines, robots, and manufacturing equipment."}
+{"idx": 7, "title": "machine - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "2 days ago · (figuratively) A person or organisation that seemingly acts like a machine , being particularly efficient, single-minded, or unemotional. Bruce Campbell was a \"demon-killing machine \" because he made quick work of killing demons. The government has become a money-making machine .", "subpage_snippet": "", "source": "en.wiktionary.org", "link": "https://en.wiktionary.org/wiki/machine", "content": "2 days ago · (figuratively) A person or organisation that seemingly acts like a machine , being particularly efficient, single-minded, or unemotional. Bruce Campbell was a \"demon-killing machine \" because he made quick work of killing demons. The government has become a money-making machine ."}
+{"idx": 8, "title": "Machine - definition of machine by The Free Dictionary", "date": "", "ddg_snippet": "Of, relating to, or felt to resemble a machine : machine repairs; machine politics.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/machine", "content": "Of, relating to, or felt to resemble a machine : machine repairs; machine politics."}
+{"idx": 9, "title": "The Smashing Machine | Official Trailer HD | A24 - YouTube", "date": "", "ddg_snippet": "Special Blu-ray & 4K editions of Danny & Michael Philippou's sinister tale of domestic and occult horror. Special features include a commentary with the directors, one deleted scene, and an...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=aRpnP3LZ99g", "content": "Special Blu-ray & 4K editions of Danny & Michael Philippou's sinister tale of domestic and occult horror. Special features include a commentary with the directors, one deleted scene, and an..."}
diff --git a/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_dataset_Schubert_polynomials_n=6_Table_1_accuracy_year_2023.jsonl b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_dataset_Schubert_polynomials_n=6_Table_1_accuracy_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d159ff297e4f830826f817dd7cc1b6edfbe83ef5
--- /dev/null
+++ b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_dataset_Schubert_polynomials_n=6_Table_1_accuracy_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics : A Suite of...", "date": "", "ddg_snippet": "Machine Learning meets Algebraic Combinatoric. How hard is it?: We provide both accuracy ( Table 4) and macro F 1 -scores ( Table 5) for these imbalanced datasets .Statistics for the Schubert polynomial structure constants dataset for n = 6 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "Machine Learning meets Algebraic Combinatoric. How hard is it?: We provide both accuracy ( Table 4) and macro F 1 -scores ( Table 5) for these imbalanced datasets .Statistics for the Schubert polynomial structure constants dataset for n = 6 ."}
+{"idx": 1, "title": "Schubert Polynomials - Combinatorics", "date": "", "ddg_snippet": "Algebraic combinatorics . Combinatorial Hopf algebras . Poirier-Reutenauer Hopf algebra of standard tableaux. Word Quasi-symmetric functions.", "subpage_snippet": "", "source": "doc.sagemath.org", "link": "https://doc.sagemath.org/html/en/reference/combinat/sage/combinat/schubert_polynomial.html", "content": "Algebraic combinatorics . Combinatorial Hopf algebras . Poirier-Reutenauer Hopf algebra of standard tableaux. Word Quasi-symmetric functions."}
+{"idx": 2, "title": "pnnl/ML4AlgComb: ML Benchmarks in Algebraic Combinatorics", "date": "", "ddg_snippet": "Algebraic Combinatorics Dataset Repository. Schubert polynomial structure constants: Schubert polynomials are a family of polynomials indexed by permutations of $S_ n $.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "Algebraic Combinatorics Dataset Repository. Schubert polynomial structure constants: Schubert polynomials are a family of polynomials indexed by permutations of $S_ n $."}
+{"idx": 3, "title": "NeurIPS Machine Learning meets Algebraic Combinatorics : A Suite...", "date": "", "ddg_snippet": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either classic or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/98521", "content": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either classic or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra ."}
+{"idx": 4, "title": "Paper page - Machine Learning meets Algebraic Combinatorics ...", "date": "", "ddg_snippet": "A collection of datasets , Algebraic Combinatorics Dataset Repository, supports conjecture generation in algebraic combinatorics using machine learning models.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.06366", "content": "A collection of datasets , Algebraic Combinatorics Dataset Repository, supports conjecture generation in algebraic combinatorics using machine learning models."}
+{"idx": 5, "title": "Diagonal degenerations of matrix Schubert varieties", "date": "", "ddg_snippet": "Knutson and Miller (2005) established a connection between the anti-diagonal Gröbner degenerations of matrix Schubert varieties and the pre-existing combinatorics of pipe dreams.", "subpage_snippet": "", "source": "alco.centre-mersenne.org", "link": "https://alco.centre-mersenne.org/articles/10.5802/alco.296/", "content": "Knutson and Miller (2005) established a connection between the anti-diagonal Gröbner degenerations of matrix Schubert varieties and the pre-existing combinatorics of pipe dreams."}
+{"idx": 6, "title": "(Open Access) Newton polytopes in algebraic combinatorics (2019)", "date": "", "ddg_snippet": "This generalized permutahedron conjecturally has positive Ehrhart polynomial . We conjecture it describes the Newton polytope of Schubert and key polynomials .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/newton-polytopes-in-algebraic-combinatorics-2sieuynbfg", "content": "This generalized permutahedron conjecturally has positive Ehrhart polynomial . We conjecture it describes the Newton polytope of Schubert and key polynomials ."}
+{"idx": 7, "title": "The Algebraic Combinatorics Seminar", "date": "", "ddg_snippet": "The Algebraic Combinatorics Seminar. Scheduled for Fridays at 3:15 PM.A longstanding problem in algebraic combinatorics is that of providing a manifestly positive formula for multiplying two Schubert classes in the cohomology ring of the full flag manifold.", "subpage_snippet": "", "source": "garsia.math.yorku.ca", "link": "https://garsia.math.yorku.ca/fieldseminar/y13-14.html", "content": "The Algebraic Combinatorics Seminar. Scheduled for Fridays at 3:15 PM.A longstanding problem in algebraic combinatorics is that of providing a manifestly positive formula for multiplying two Schubert classes in the cohomology ring of the full flag manifold."}
+{"idx": 8, "title": "PNNL Research Featured at World's Second-Largest Machine ... | PNNL", "date": "", "ddg_snippet": "Greater accuracy and sustainability. PNNL intern Chuan Liu presented the final PNNL featured poster, “An Expressive and Self-Adaptive Dynamical System for Efficient Function Learning .”", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/news-media/pnnl-research-featured-worlds-second-largest-machine-learning-conference", "content": "Greater accuracy and sustainability. PNNL intern Chuan Liu presented the final PNNL featured poster, “An Expressive and Self-Adaptive Dynamical System for Efficient Function Learning .”"}
+{"idx": 9, "title": "The Prism tableau model for Schubert polynomials", "date": "", "ddg_snippet": "Anna Weigandt, Alexander Yong. The Prism tableau model for Schubert polynomials . 28-th Inter-national Conference on Formal Power Series and Algebraic Combinatorics , Simon Fraser University, Jul 2016, Vancouver, Canada.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-02168181/document", "content": "Anna Weigandt, Alexander Yong. The Prism tableau model for Schubert polynomials . 28-th Inter-national Conference on Formal Power Series and Algebraic Combinatorics , Simon Fraser University, Jul 2016, Vancouver, Canada."}
diff --git a/data/sampled_jsons/Magnani_et_al_2022_uncertainty_estimation_neural_operators.jsonl b/data/sampled_jsons/Magnani_et_al_2022_uncertainty_estimation_neural_operators.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4ec980bffca248276c75ae3594d47f068b1b2986
--- /dev/null
+++ b/data/sampled_jsons/Magnani_et_al_2022_uncertainty_estimation_neural_operators.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2208.01565] Approximate Bayesian Neural Operators: Uncertainty ...", "date": "", "ddg_snippet": "Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2208.01565", "content": "Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically ..."}
+{"idx": 1, "title": "Uncertainty Quantification for Fourier Neural Operators", "date": "", "ddg_snippet": "The introduction of Laplace approximation for uncertainty quantification in FNOs is somewhat novel, as recognized by the authors and properly cited previous related works, e.g. Magnani et al .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=knSgoNJcnV", "content": "The introduction of Laplace approximation for uncertainty quantification in FNOs is somewhat novel, as recognized by the authors and properly cited previous related works, e.g. Magnani et al ."}
+{"idx": 2, "title": "Approximate Bayesian Neural Operators: Uncertainty Quantification for ...", "date": "", "ddg_snippet": "We extend previous results on neural operators by providing them with uncertainty quantification. As a result, our approach is able to identify cases, and provide structured uncertainty estimates, where the neural operator fails to predict well. READ FULL TEXT Emilia Magnani 3 publications Nicholas Krämer 8 publications Runa Eschenhagen 8 ...", "subpage_snippet": "", "source": "cdnjs.deepai.org", "link": "https://cdnjs.deepai.org/publication/approximate-bayesian-neural-operators-uncertainty-quantification-for-parametric-pdes", "content": "We extend previous results on neural operators by providing them with uncertainty quantification. As a result, our approach is able to identify cases, and provide structured uncertainty estimates, where the neural operator fails to predict well. READ FULL TEXT Emilia Magnani 3 publications Nicholas Krämer 8 publications Runa Eschenhagen 8 ..."}
+{"idx": 3, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian ...", "date": "", "ddg_snippet": "Although neural operators have demonstrated strong pre-dictive capabilities, they are unable to quantify the inherent uncertainty in their predictions. Predictive uncertainty quan-tification is indispensable for many downstream tasks, such as decision-making in safety-critical scenarios. For exam-ple, a neural operator trained on past climate data should increase predictive uncertainty under ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "Although neural operators have demonstrated strong pre-dictive capabilities, they are unable to quantify the inherent uncertainty in their predictions. Predictive uncertainty quan-tification is indispensable for many downstream tasks, such as decision-making in safety-critical scenarios. For exam-ple, a neural operator trained on past climate data should increase predictive uncertainty under ..."}
+{"idx": 4, "title": "Scalable uncertainty quantification for deep operator networks using ...", "date": "", "ddg_snippet": "Leveraging the DeepONet architecture for operator learning and the flexibility of randomized prior networks for uncertainty quantification, we propose a novel model class called UQDeepONet that facilitates scalable uncertainty estimation in operator learning.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782522004595", "content": "Leveraging the DeepONet architecture for operator learning and the flexibility of randomized prior networks for uncertainty quantification, we propose a novel model class called UQDeepONet that facilitates scalable uncertainty estimation in operator learning."}
+{"idx": 5, "title": "Calibrated Uncertainty Quantification for Operator Learning via ...", "date": "", "ddg_snippet": "Recent works on uncertainty quantification for operator learning are either heuristic (Guo et al ., 2023; Akhare et al ., 2023; Nehme et al ., 2023), or rely on Gaussian assumptions and approximations that may not hold in real-world settings ( Magnani et al ., 2022 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.01960v1", "content": "Recent works on uncertainty quantification for operator learning are either heuristic (Guo et al ., 2023; Akhare et al ., 2023; Nehme et al ., 2023), or rely on Gaussian assumptions and approximations that may not hold in real-world settings ( Magnani et al ., 2022 )."}
+{"idx": 6, "title": "Uncertainty quantification in Neural Networks by Approximate Bayesian ...", "date": "", "ddg_snippet": "Particularly, Deep Neural Networks (DNN) have revolutionized the world of machine learning, with more complex models that have made computer vision (Voulodimos et al ., 2018) and speech recognition (Arora and Singh, 2012) a reality, in some cases even reaching human accuracy (Sturman et al ., 2020).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197621003596", "content": "Particularly, Deep Neural Networks (DNN) have revolutionized the world of machine learning, with more complex models that have made computer vision (Voulodimos et al ., 2018) and speech recognition (Arora and Singh, 2012) a reality, in some cases even reaching human accuracy (Sturman et al ., 2020)."}
+{"idx": 7, "title": "Uncertainty Quantification for Fourier Neu-ral Operators", "date": "", "ddg_snippet": "h in terms of performance and computational cost (Daxberger et al ., 2021). While Laplace approximations have been previously explored for Graph Neural Operators ( Magnani et al ., 2022 ), its applicatio remains unexplored within the context of Fourier Neural Operators (FNOs). In this paper, we hence focus on FNOs and e", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=knSgoNJcnV", "content": "h in terms of performance and computational cost (Daxberger et al ., 2021). While Laplace approximations have been previously explored for Graph Neural Operators ( Magnani et al ., 2022 ), its applicatio remains unexplored within the context of Fourier Neural Operators (FNOs). In this paper, we hence focus on FNOs and e"}
+{"idx": 8, "title": "Uncertainty Estimation With Neural Processes for Meta-Continual ...", "date": "", "ddg_snippet": "Regarding this, we introduce a member meta-continual learning with neural process (MCLNP) for uncertainty estimation . We enable two levels of uncertainty estimations : the local uncertainty on certain points and the global uncertainty p(z) that represents the function evolution in dynamic environments.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9933466", "content": "Regarding this, we introduce a member meta-continual learning with neural process (MCLNP) for uncertainty estimation . We enable two levels of uncertainty estimations : the local uncertainty on certain points and the global uncertainty p(z) that represents the function evolution in dynamic environments."}
+{"idx": 9, "title": "LUNO: Linearized Predictive Uncertainty in Neural Operators", "date": "", "ddg_snippet": "luno - Linearized Uncertainty for Neural Operators This repository contains the main algorithm of the paper \"Linearization Turns Neural Operators into Function-Valued Gaussian Processes\" by Magnani et al . (2025).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno", "content": "luno - Linearized Uncertainty for Neural Operators This repository contains the main algorithm of the paper \"Linearization Turns Neural Operators into Function-Valued Gaussian Processes\" by Magnani et al . (2025)."}
diff --git "a/data/sampled_jsons/Malus'_law_cos\302\262(\316\270\342\202\201_-_\316\270\342\202\202)_polarizers_transmission_factor.jsonl" "b/data/sampled_jsons/Malus'_law_cos\302\262(\316\270\342\202\201_-_\316\270\342\202\202)_polarizers_transmission_factor.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..bb60920670746b9b841f78c7c2d9a45800022f23
--- /dev/null
+++ "b/data/sampled_jsons/Malus'_law_cos\302\262(\316\270\342\202\201_-_\316\270\342\202\202)_polarizers_transmission_factor.jsonl"
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+{"idx": 1, "title": "下载Malus加速器 | Malus加速器，海外华人留学生追剧听歌游戏必备的回...", "date": "", "ddg_snippet": "立即开启Malus回国加速，帮你访问和加速爱奇艺、哔哩哔哩、腾讯视频、QQ音乐和网易云音乐等国内服务，稳定连接，超低延迟，免费体验。", "subpage_snippet": "", "source": "getmalus.com", "link": "https://getmalus.com/download", "content": "立即开启Malus回国加速，帮你访问和加速爱奇艺、哔哩哔哩、腾讯视频、QQ音乐和网易云音乐等国内服务，稳定连接，超低延迟，免费体验。"}
+{"idx": 2, "title": "Download Malus VPN | Malus game accelerator, free VPN for...", "date": "", "ddg_snippet": "Malus can meet various needs for accelerating your return to China from overseas, supporting acceleration for audio and video streaming, gaming, live streaming, and office applications.", "subpage_snippet": "", "source": "getmalus.com", "link": "https://getmalus.com/en/download", "content": "Malus can meet various needs for accelerating your return to China from overseas, supporting acceleration for audio and video streaming, gaming, live streaming, and office applications."}
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diff --git a/data/sampled_jsons/Marvin_Li_Aayush_Karan_Sitan_Chen_github_repository_year_2024.jsonl b/data/sampled_jsons/Marvin_Li_Aayush_Karan_Sitan_Chen_github_repository_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d798069ad7e223160712d19cd23068970cb249ed
--- /dev/null
+++ b/data/sampled_jsons/Marvin_Li_Aayush_Karan_Sitan_Chen_github_repository_year_2024.jsonl
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+{"idx": 0, "title": "Hi, my name is Marvin. - Marvin Li", "date": "", "ddg_snippet": "Blink of an Eye: A Simple Theory for Feature Localization in Generative Models Marvin Li , Aayush Karan , Sitan Chen . ICML, 2025 (Oral, top 1% of submissions) arXiv / code A unifying theory showing why and when features suddenly “lock in” during generation in both diffusion and autoregressive models.", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/", "content": "Blink of an Eye: A Simple Theory for Feature Localization in Generative Models Marvin Li , Aayush Karan , Sitan Chen . ICML, 2025 (Oral, top 1% of submissions) arXiv / code A unifying theory showing why and when features suddenly “lock in” during generation in both diffusion and autoregressive models."}
+{"idx": 1, "title": "GitHub - marvinli-harvard/critical-windows", "date": "", "ddg_snippet": "Overview This repo provides some experimental tools to investigate the phenomena of feature emergence in diffusion models, where features of the final outputted images like color, background, or clothing type are fossilized in narrow intervals of the reverse denoising process. This code accompanies the ICML 2024 paper ( Li and Chen , 2024).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/marvinli-harvard/critical-windows", "content": "Overview This repo provides some experimental tools to investigate the phenomena of feature emergence in diffusion models, where features of the final outputted images like color, background, or clothing type are fossilized in narrow intervals of the reverse denoising process. This code accompanies the ICML 2024 paper ( Li and Chen , 2024)."}
+{"idx": 2, "title": "Homepage: Sitan Chen", "date": "", "ddg_snippet": "Marvin Li , Aayush Karan , Sitan Chen ICML 2025 Oral presentation Gradient Dynamics for Low-Rank Fine-Tuning Beyond Kernels [pdf] [slides] Arif Kerem Dayi, Sitan Chen COLT 2025 Predicting Quantum Channels Over General Product Distributions [pdf] Sitan Chen , Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li COLT 2025", "subpage_snippet": "", "source": "www.sitanchen.com", "link": "https://www.sitanchen.com/", "content": "Marvin Li , Aayush Karan , Sitan Chen ICML 2025 Oral presentation Gradient Dynamics for Low-Rank Fine-Tuning Beyond Kernels [pdf] [slides] Arif Kerem Dayi, Sitan Chen COLT 2025 Predicting Quantum Channels Over General Product Distributions [pdf] Sitan Chen , Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li COLT 2025"}
+{"idx": 3, "title": "Marvin Li - OpenReview", "date": "", "ddg_snippet": "Marvin Li , Aayush Karan , Sitan Chen FPI-ICLR2025 Poster Blink of an eye: a simple theory for feature localization in generative models Marvin Li , Aayush Karan , Sitan Chen ICML 2025 oral Bias Begets Bias: the Impact of Biased Embeddings on Diffusion Models Sahil Kuchlous, Marvin Li , Jeffrey George Wang", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Marvin_Li1", "content": "Marvin Li , Aayush Karan , Sitan Chen FPI-ICLR2025 Poster Blink of an eye: a simple theory for feature localization in generative models Marvin Li , Aayush Karan , Sitan Chen ICML 2025 oral Bias Begets Bias: the Impact of Biased Embeddings on Diffusion Models Sahil Kuchlous, Marvin Li , Jeffrey George Wang"}
+{"idx": 4, "title": "Marvin Li - Google Scholar", "date": "", "ddg_snippet": "Patricia Glibert Horn Point Laboratory, University of Maryland Center for Environmental Science Vyacheslav Lyubchich University of Maryland Center for Environmental Science Sahil Kuchlous Student, Harvard University Aayush Karan Harvard University", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=NhMTzpsAAAAJ&hl=en", "content": "Patricia Glibert Horn Point Laboratory, University of Maryland Center for Environmental Science Vyacheslav Lyubchich University of Maryland Center for Environmental Science Sahil Kuchlous Student, Harvard University Aayush Karan Harvard University"}
+{"idx": 5, "title": "CV - Marvin Li", "date": "", "ddg_snippet": "Blink of an Eye: A Simple Theory for Feature Localization in Generative Models Marvin Li , Aayush Karan , and Sitan Chen . (2025). \"Blink of an Eye: A Simple Theory for Feature Localization in Generative Models.\" Proceedings of the 42nd International Conference on Machine Learning (ICML).", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/cv/", "content": "Blink of an Eye: A Simple Theory for Feature Localization in Generative Models Marvin Li , Aayush Karan , and Sitan Chen . (2025). \"Blink of an Eye: A Simple Theory for Feature Localization in Generative Models.\" Proceedings of the 42nd International Conference on Machine Learning (ICML)."}
+{"idx": 6, "title": "Homepage: Sitan Chen", "date": "", "ddg_snippet": "Blink of an Eye: A Simple Theory for Feature Localization in Generative Models [pdf] Marvin Li , Aayush Karan , Sitan Chen ICML 2025 Oral presentation.", "subpage_snippet": "", "source": "sitanchen.com", "link": "https://sitanchen.com/", "content": "Blink of an Eye: A Simple Theory for Feature Localization in Generative Models [pdf] Marvin Li , Aayush Karan , Sitan Chen ICML 2025 Oral presentation."}
+{"idx": 7, "title": "Marvin Li - Google Akademik", "date": "", "ddg_snippet": "Aayush Karan Aayush KaranHarvard Universityg.harvard.edu üzerinde doğrulanmış e-posta adresine sahip. Takip et. Marvin Li .MF Li , PM Glibert, V Lyubchich. Journal of Marine Science and Engineering 9 (9), 999, 2021.", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=NhMTzpsAAAAJ&hl=tr", "content": "Aayush Karan Aayush KaranHarvard Universityg.harvard.edu üzerinde doğrulanmış e-posta adresine sahip. Takip et. Marvin Li .MF Li , PM Glibert, V Lyubchich. Journal of Marine Science and Engineering 9 (9), 999, 2021."}
+{"idx": 8, "title": "ICML Poster Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "Marvin Li · Aayush Karan · Sitan Chen . [ Abstract ].", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45312", "content": "Marvin Li · Aayush Karan · Sitan Chen . [ Abstract ]."}
+{"idx": 9, "title": "Blink of an eye: a simple theory for feature localization in generative...", "date": "", "ddg_snippet": "Published 2/4/2025 by Marvin Li , Aayush Karan , Sitan Chen .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/blink-eye-simple-theory-feature-localization-generative", "content": "Published 2/4/2025 by Marvin Li , Aayush Karan , Sitan Chen ."}
diff --git a/data/sampled_jsons/Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_Table_1_values.jsonl b/data/sampled_jsons/Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_Table_1_values.jsonl
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index 0000000000000000000000000000000000000000..70f1210c6cfa90e47329da251e1645036d16f26e
--- /dev/null
+++ b/data/sampled_jsons/Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_Table_1_values.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Medical Large Language Model Benchmarks Should Prioritize ...", "date": "", "ddg_snippet": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks —a tradition inherited from mainstream machine learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10694v1", "content": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks —a tradition inherited from mainstream machine learning."}
+{"idx": 1, "title": "Position: Medical Large Language Model Benchmarks Should ...", "date": "", "ddg_snippet": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks —a tradition inherited from mainstream machine learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YuMEUNNpeb", "content": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks —a tradition inherited from mainstream machine learning."}
+{"idx": 2, "title": "Latest AI Research LLM Agents, Medical LLMs, And More", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity argues that medical LLM benchmarks should prioritize construct validity . This means that the benchmarks should accurately measure the underlying abilities they are intended to assess.", "subpage_snippet": "", "source": "codemeld.org", "link": "https://codemeld.org/blog/latest-ai-research-llm-agents", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity argues that medical LLM benchmarks should prioritize construct validity . This means that the benchmarks should accurately measure the underlying abilities they are intended to assess."}
+{"idx": 3, "title": "Inioluwa Deborah Raji's research works | University of California...", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Preprint. File available.In the psychological testing literature, \" construct validity \" refers to the ability of a test to measure an underlying \" construct \", that is the actual conceptual target of evaluation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Inioluwa-Deborah-Raji-2148191730", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Preprint. File available.In the psychological testing literature, \" construct validity \" refers to the ability of a test to measure an underlying \" construct \", that is the actual conceptual target of evaluation."}
+{"idx": 4, "title": "ICML 2025 Sneak Peek: The New Laws of AI | Medium", "date": "", "ddg_snippet": "Another paper, “Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity ,” argues that current benchmarks for high-stakes domains like medicine fail to meet the rigorous standards of scientific validity required for trustworthy evaluation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/foundation-models-deep-dive/icml-2025-sneak-peek-the-new-laws-of-ai-1210789c66a2", "content": "Another paper, “Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity ,” argues that current benchmarks for high-stakes domains like medicine fail to meet the rigorous standards of scientific validity required for trustworthy evaluation."}
+{"idx": 5, "title": "Articles by Frances Dean | Synthical", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 12 March 2025 by Ahmed Alaa and others. Computation and Language .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/d8b88aa7-cbcf-4d81-a315-a1ff4be5f516/articles", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 12 March 2025 by Ahmed Alaa and others. Computation and Language ."}
+{"idx": 6, "title": "Franny Dean - Google Scholar", "date": "", "ddg_snippet": "2021. Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Y04UhM4AAAAJ&hl=en", "content": "2021. Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity ."}
+{"idx": 7, "title": "Latest 15 Papers - June 08, 2025 - Githubissues", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity .", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/somewordstoolate/DailyArXiv/17", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity ."}
+{"idx": 8, "title": "GitHub - Xuchen-Li/llm-arxiv-daily: Automatically update arXiv papers...", "date": "", "ddg_snippet": "MKG-Rank: Enhancing Large Language Models with Knowledge Graph for Multilingual Medical Question Answering. Feiyang Li et.al. Medical Large Language Model Benchmarks Should Prioritize Construct Validity .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xuchen-Li/llm-arxiv-daily", "content": "MKG-Rank: Enhancing Large Language Models with Knowledge Graph for Multilingual Medical Question Answering. Feiyang Li et.al. Medical Large Language Model Benchmarks Should Prioritize Construct Validity ."}
+{"idx": 9, "title": "Paper page - Clinical knowledge in LLMs does not translate to human...", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity (2025).", "subpage_snippet": "", "source": "hf.qhduan.com", "link": "https://hf.qhduan.com/papers/2504.18919", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity (2025)."}
diff --git a/data/sampled_jsons/Merrill_et_al._2024_'The_Illusion_of_State_in_State-Space_Models'_'we_show_that_these_SSMs_cannot_si.jsonl b/data/sampled_jsons/Merrill_et_al._2024_'The_Illusion_of_State_in_State-Space_Models'_'we_show_that_these_SSMs_cannot_si.jsonl
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index 0000000000000000000000000000000000000000..0e585729f91616f0c7b0241e122cd6d9dd9363ab
--- /dev/null
+++ b/data/sampled_jsons/Merrill_et_al._2024_'The_Illusion_of_State_in_State-Space_Models'_'we_show_that_these_SSMs_cannot_si.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2404.08819] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Apr 12, 2024 · State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "Apr 12, 2024 · State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 1, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "To supplement our formal analysis, we report experiments showing that Mamba-style SSMs indeed struggle with state tracking. Thus, despite its recurrent formulation, the \" state ” in an SSM is an illusion : SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/merrill24a.html", "content": "To supplement our formal analysis, we report experiments showing that Mamba-style SSMs indeed struggle with state tracking. Thus, despite its recurrent formulation, the \" state ” in an SSM is an illusion : SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve ..."}
+{"idx": 2, "title": "The illusion of state in state-space models | Proceedings of ...", "date": "", "ddg_snippet": "Jul 21, 2024 · State-space models ( SSMs ) have emerged as a potential alternative to transformers. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3693514", "content": "Jul 21, 2024 · State-space models ( SSMs ) have emerged as a potential alternative to transformers. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent ..."}
+{"idx": 3, "title": "The Illusion of State in State-Space Models - Jackson Petty", "date": "", "ddg_snippet": "Apr 16, 2024 · State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill and Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "jacksonpetty.org", "link": "https://jacksonpetty.org/ssm-illusion/", "content": "Apr 16, 2024 · State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill and Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 4, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Abstract:Structured state-space models ( SSMs ) such as S4, stemming from the seminal work of Gu et al ., are gaining popularity as effective approaches for modeling sequential data. Deep SSMs demonstrate outstanding performance across a diverse set of domains, at a reduced training and inference cost compared to attention-based transformers. Recent developments show that if the linear recurrence ...", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2404.08819", "content": "Abstract:Structured state-space models ( SSMs ) such as S4, stemming from the seminal work of Gu et al ., are gaining popularity as effective approaches for modeling sequential data. Deep SSMs demonstrate outstanding performance across a diverse set of domains, at a reduced training and inference cost compared to attention-based transformers. Recent developments show that if the linear recurrence ..."}
+{"idx": 5, "title": "The Illusion of State in State-Space Models - Semantic Scholar", "date": "", "ddg_snippet": "Apr 12, 2024 · Analysis of state-space models reveals that SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems. State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Illusion-of-State-in-State-Space-Models-Merrill-Petty/917479a7a72ee7c1fb320c14d770e30ef322ef28", "content": "Apr 12, 2024 · Analysis of state-space models reveals that SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems. State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer ..."}
+{"idx": 6, "title": "ICML Poster The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Abstract: State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/poster/34075", "content": "Abstract: State-space models ( SSMs ) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to ..."}
+{"idx": 7, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "In a different line of work, state space model (SSM) architectures (Gu et al ., 2021 , 2022a ; Fu et al ., 2023 ; Gu & Dao, 2023 ; Wang et al ., ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v3", "content": "In a different line of work, state space model (SSM) architectures (Gu et al ., 2021 , 2022a ; Fu et al ., 2023 ; Gu & Dao, 2023 ; Wang et al ., ..."}
+{"idx": 8, "title": "Understanding and Mitigating Bottlenecks of State Space Models", "date": "", "ddg_snippet": "These models are grounded in HiPPO theory (Gu et al ., 2020 ) , which demonstrates that a first-order Ordinary Differential Equation (ODE) can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.00658v2", "content": "These models are grounded in HiPPO theory (Gu et al ., 2020 ) , which demonstrates that a first-order Ordinary Differential Equation (ODE) can ..."}
+{"idx": 9, "title": "Overcoming Long-Context Limitations of State-Space Models via", "date": "", "ddg_snippet": "Through theoretical analysis on joint recall , we demonstrate that integrating state - space models (SSMs) with context-dependent sparse attention ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.00449v1", "content": "Through theoretical analysis on joint recall , we demonstrate that integrating state - space models (SSMs) with context-dependent sparse attention ..."}
diff --git a/data/sampled_jsons/Merrill_et_al._2024_'The_Illusion_of_State_in_State-Space_Models'_full_abstract_text.jsonl b/data/sampled_jsons/Merrill_et_al._2024_'The_Illusion_of_State_in_State-Space_Models'_full_abstract_text.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4ee123c4a31ef52eac10f5b9712567cfc43fbccb
--- /dev/null
+++ b/data/sampled_jsons/Merrill_et_al._2024_'The_Illusion_of_State_in_State-Space_Models'_full_abstract_text.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "In a different line of work, state space model (SSM) architectures (Gu et al ., 2021 , 2022a ; Fu et al ., 2023 ; Gu & Dao, 2023 ; Wang et al ., ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v3", "content": "In a different line of work, state space model (SSM) architectures (Gu et al ., 2021 , 2022a ; Fu et al ., 2023 ; Gu & Dao, 2023 ; Wang et al ., ..."}
+{"idx": 1, "title": "The Expressive Capacity of State Space Models: A Formal", "date": "", "ddg_snippet": "These SSMs are recurrent models that—while formulated in terms of iterative state updates—allow efficient parallelization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17394v2", "content": "These SSMs are recurrent models that—while formulated in terms of iterative state updates—allow efficient parallelization."}
+{"idx": 2, "title": "Overcoming Long-Context Limitations of State-Space Models via", "date": "", "ddg_snippet": "While state - space models (SSMs) offer alternative sub-quadratic solutions, they struggle to capture long-range dependencies effectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.00449v1", "content": "While state - space models (SSMs) offer alternative sub-quadratic solutions, they struggle to capture long-range dependencies effectively."}
+{"idx": 3, "title": "(How) Do Language Models Track State?", "date": "", "ddg_snippet": "Inferring common ground in discourse (Li et al ., 2021 ) , navigating the environment (Vafa et al ., 2024 ) , reasoning about code ( Merrill ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.02854v1", "content": "Inferring common ground in discourse (Li et al ., 2021 ) , navigating the environment (Vafa et al ., 2024 ) , reasoning about code ( Merrill ..."}
+{"idx": 4, "title": "Optical Illusion - The Latest in AI", "date": "", "ddg_snippet": "... models , particularly diffusion models , to generate and analyze illusions , investigating their robustness and susceptibility to adversarial attacks ...", "subpage_snippet": "", "source": "thelatestinai.com", "link": "https://thelatestinai.com/topic/optical-illusion", "content": "... models , particularly diffusion models , to generate and analyze illusions , investigating their robustness and susceptibility to adversarial attacks ..."}
+{"idx": 5, "title": "(PDF) Evolved navigation theory and the descent illusion", "date": "", "ddg_snippet": "Only ENT predicted greater height perceived from the top than from the bottom of a vertical surface (because descent results in falls more often than ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/6167752_Evolved_navigation_theory_and_the_descent_illusion", "content": "Only ENT predicted greater height perceived from the top than from the bottom of a vertical surface (because descent results in falls more often than ..."}
+{"idx": 6, "title": "EpochCore: Digital Hardware Accelerator For Structured", "date": "", "ddg_snippet": "To address this limitation, recent research has explored alternative architectures based on State Space Models (SSMs) [ 25 ] , which offer a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21394v1", "content": "To address this limitation, recent research has explored alternative architectures based on State Space Models (SSMs) [ 25 ] , which offer a ..."}
+{"idx": 7, "title": "‘RNN’ directory · Gwern.net", "date": "", "ddg_snippet": "The Illusion of State in State - Space Models ”, Merrill et al 2024 ... State Space Models With Mixture of Experts ”, Pióro et al 2024", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/rnn/index", "content": "The Illusion of State in State - Space Models ”, Merrill et al 2024 ... State Space Models With Mixture of Experts ”, Pióro et al 2024"}
+{"idx": 8, "title": "Sources of richness and ineffability for phenomenally conscious", "date": "", "ddg_snippet": "Conscious states — state that there is something it is like to be in —seem both rich or full of detail and ineffable or hard to fully describe or ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/nc/article/2024/1/niae001/7623923", "content": "Conscious states — state that there is something it is like to be in —seem both rich or full of detail and ineffable or hard to fully describe or ..."}
+{"idx": 9, "title": "How LLMs Really Work - Ves Ivanov", "date": "", "ddg_snippet": "Because language encodes logic, structure, culture, and context, learning to predict it also requires the model to internalize a lot of the same ...", "subpage_snippet": "", "source": "vesivanov.com", "link": "https://vesivanov.com/how-llms-work/", "content": "Because language encodes logic, structure, culture, and context, learning to predict it also requires the model to internalize a lot of the same ..."}
diff --git a/data/sampled_jsons/Merrill_et_al._2024_The_Illusion_of_State_in_State-Space_Models.jsonl b/data/sampled_jsons/Merrill_et_al._2024_The_Illusion_of_State_in_State-Space_Models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c742e91bd2e23927781f3f3dd475039f52105be1
--- /dev/null
+++ b/data/sampled_jsons/Merrill_et_al._2024_The_Illusion_of_State_in_State-Space_Models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2404.08819] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Apr 12, 2024 · Thus, despite its recurrent formulation, the \" state \" in an SSM is an illusion : SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state-tracking problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "Apr 12, 2024 · Thus, despite its recurrent formulation, the \" state \" in an SSM is an illusion : SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state-tracking problems."}
+{"idx": 1, "title": "ICML Poster The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Abstract: State - space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/poster/34075", "content": "Abstract: State - space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture."}
+{"idx": 2, "title": "The Illusion of State in State-Space Models - Semantic Scholar", "date": "", "ddg_snippet": "Apr 12, 2024 · The LTC-based structural state - space model , dubbed Liquid-S4, achieves the new state-of-the-art generalization across sequence modeling tasks with long-term dependencies such as image, text, audio, and medical time-series, with an average performance of 87.32% on the Long-Range Arena benchmark.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Illusion-of-State-in-State-Space-Models-Merrill-Petty/917479a7a72ee7c1fb320c14d770e30ef322ef28", "content": "Apr 12, 2024 · The LTC-based structural state - space model , dubbed Liquid-S4, achieves the new state-of-the-art generalization across sequence modeling tasks with long-term dependencies such as image, text, audio, and medical time-series, with an average performance of 87.32% on the Long-Range Arena benchmark."}
+{"idx": 3, "title": "The Illusion of State in State-Space Models (Conference Paper ...", "date": "", "ddg_snippet": "In this study, we evaluate the ICL performance of SSMs , focusing on Mamba, against Transformer models across various tasks. Our results show that SSMs perform comparably to Transformers in standard regression ICL tasks, while outperforming them in tasks like sparse parity learning.", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10535878-illusion-state-state-space-models", "content": "In this study, we evaluate the ICL performance of SSMs , focusing on Mamba, against Transformer models across various tasks. Our results show that SSMs perform comparably to Transformers in standard regression ICL tasks, while outperforming them in tasks like sparse parity learning."}
+{"idx": 4, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Thus, despite its recurrent formulation, the \" state ” in an SSM is an illusion : SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state-tracking problems.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/merrill24a.html", "content": "Thus, despite its recurrent formulation, the \" state ” in an SSM is an illusion : SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state-tracking problems."}
+{"idx": 5, "title": "The Illusion of State in State-Space Models: Paper and Code", "date": "", "ddg_snippet": "It follows that SSMs are provably unable to accurately track chess moves with certain notation, evaluate code, or track entities in a long narrative. To supplement our formal analysis, we report experiments showing that Mamba-style SSMs indeed struggle with state tracking.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/the-illusion-of-state-in-state-space-models", "content": "It follows that SSMs are provably unable to accurately track chess moves with certain notation, evaluate code, or track entities in a long narrative. To supplement our formal analysis, we report experiments showing that Mamba-style SSMs indeed struggle with state tracking."}
+{"idx": 6, "title": "The illusion of state in state-space models | Proceedings of ...", "date": "", "ddg_snippet": "Jul 21, 2024 · Thus, despite their recurrent formulation, the \" state \" in common SSMs is an illusion : S4, Mamba, and related models have similar expressiveness limitations to nonrecurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3693514", "content": "Jul 21, 2024 · Thus, despite their recurrent formulation, the \" state \" in common SSMs is an illusion : S4, Mamba, and related models have similar expressiveness limitations to nonrecurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems."}
+{"idx": 7, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "by W Merrill · 2024 · Cited by 89 — Abstract. State-space models (SSMs) have emerged as a potential alternative to transformers . One theoret- ical weakness of transformers is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.08819", "content": "by W Merrill · 2024 · Cited by 89 — Abstract. State-space models (SSMs) have emerged as a potential alternative to transformers . One theoret- ical weakness of transformers is ..."}
+{"idx": 8, "title": "[R] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "The ssm conv operation relies on your discretisation and a combination of stacked As and the B matrices which imo fundamentally tell different stories.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/1c5z6ua/r_the_illusion_of_state_in_statespace_models/", "content": "The ssm conv operation relies on your discretisation and a combination of stacked As and the B matrices which imo fundamentally tell different stories."}
+{"idx": 9, "title": "The Illusion of State in State-Space Models (Conference Paper)", "date": "", "ddg_snippet": "State - space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10535878", "content": "State - space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ..."}
diff --git a/data/sampled_jsons/Mind2Web_Deng_2024_task_construction_method_automatic_manual_annotation.jsonl b/data/sampled_jsons/Mind2Web_Deng_2024_task_construction_method_automatic_manual_annotation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..82b35897dc4320eb13ca28a1386c57e54b37823e
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_Deng_2024_task_construction_method_automatic_manual_annotation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "WebVoyager (Self-reported) Online-Mind2Web (Manual Evaluation ...", "date": "", "ddg_snippet": "ments) per task , but are limited by annotation quality, sensitivity to webpage updates, small scale, and poor scalability. AssistantBench (Yoran et al., 2024 ) focuses on information-seeking tasks with static answers and evaluate", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.01382", "content": "ments) per task , but are limited by annotation quality, sensitivity to webpage updates, small scale, and poor scalability. AssistantBench (Yoran et al., 2024 ) focuses on information-seeking tasks with static answers and evaluate"}
+{"idx": 1, "title": "Mind2Web: Towards a Generalist Agent for the Web - GitHub Mind2Web - GitHub Pages MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS Mind2Web: Towards a Generalist Agent for the Web OSU-NLP-Group/Mind2Web-2 - GitHub Mind2Web : Towards a Generalist Agent for the Web - GitHub MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS Mind2Web : Towards a Generalist Agent for the Web - GitHub On the Multi-turn Instruction Following for Conversational Web Agents MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS Mind2Web : Towards a Generalist Agent for the Web - GitHub On the Multi-turn Instruction Following for Conversational ...", "date": "", "ddg_snippet": "Dataset, code, and models for the paper \" Mind2Web : Towards a Generalist Agent for the Web\". Check project website for demos and data exploration. Release process: •Dataset •Data used in the paper with textual context •Data with full traces and snapshots See full list on github.com Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Please check our website to explore the dataset. See full list on github.com The training set is hosted on Huggingface. We only provide a zip file for the test splits to prevent potential data contamination from large lagnuage models crawling the test set for training. Please download the test set here and unzip it with password mind2web . The structure is the same as the training set and you can still load it with Huggingface datasets. Please DO NOT redistribute the unzipped data files online. Clone the training data from Huggingface: And then download and unzip the test data into the same directory. After that, the directory structure should look like this: See full list on github.com Data Splits •train: 1,009 instances•test:•Cross Task : 252 instances, tasks from the same website are seen during training•Cross Website: 177 instances, websites are not seen during training•Cross Domain: 912 instances, entire domains are not seen during training Data Fields •\"annotation_id\" (str): unique id for each task •\"website\" (str): website name•\"domain\" (str): website domain•\"subdomain\" (str): website subdomain•\"confirmed_task\" (str): task description•\"action_reprs\" (list[str]): human readable string representation of the action sequence•\"actions\" (list[dict]): list of actions (steps) to complete the task •\"action_uid\" (str): unique id for each action (step)•\"raw_html\" (str): raw html of the page before the action is performed•\"cleaned_html\" (str): cleaned html of the page before the action is performed•\"operation\" (dict): operation to perform•\"op\" (str): operation type, one of CLICK, TYPE, SELECT•\"original_op\" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used•\"value\" (str): optional value for the operation, e.g., text to type, option to select•\"pos_candidates\" (list[dict]): ground truth elements. Here we only include positive elements that exist in \"cleaned_html\" after our preprocessing, so \"pos_candidates\" might be empty. The original labeled element can always be found in the \"raw_html\".•\"tag\" (str): tag of the element•\"is_original_target\" (bool): whether the element is the original target labeled by the annotator•\"is_top_level_target\" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details.•\"backend_node_id\" (str): unique id for the element•\"attributes\" (str): serialized attributes of the element, use json.loads to convert back to dict•\"neg_candidates\" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as \"pos_candidates\" See full list on github.com The raw dump contains the original trace file, network traffic stored in har file, recordings and various snapshots extracted from the trace file. Due to the size of the raw dump, the data is shared via Globus with OSC. Please check the instruction here for an overview of Globus. You can either login with your Google account, or check with your institution as they might have an institutional Globus account. If you have any trouble accessing the data, please contact us. The raw dump is organized in the following structure: We have the following files for each task : •session.har.zip: network traffic stored in har file, can be used for replaying. Please see here for more details. Note that matching network requests is non-trivial as even the same action may trigger different requests due to the dynamic nature of the web (datetime, random generator). We do not use this in our work but would be worth exploring. See full list on github.com Evaluation You can find the trained DeBERTa-v3-base model on Huggingface Model Hub. You can run evaluation with the following command:•model_path: path to the model or model name on Huggingface Model Hub.•data_path: path to the dataset directory, e.g., ${BASE_DIR}/ Mind2Web .•split_file: path to the split file, e.g., data/test_website/*.json.•output_dir: path to the output directory. You will see two files: results_*.json with the evaluation metrics, and scores_*.pkl with the prediction scores which can be used for the action prediction module. Fine-tuning To fine-tune the model, you can simply run:•model: Model config to load.It uses config file in candidate_generation/conf/config.yaml. The checkpoints will be saved under the workdir configured by hydra. The candidate generation model we use is a encoder-only DeBERTa model that outputs a score for a pair of query and candidate, and the implementation is based on SentenceTransformer's Cross-Encoders, please check the documentation for more details. See full list on github.com Evaluation You can find the trained flan-t5-base, flan-t5-large and flan-t5-xl models on Huggingface Model Hub.To run evaluation on the test splits, set the parameters in action_prediction/conf/config.yaml accordingly. We use hydra for managing the configuration.you can then run the evaluation using the following command:•model_path: Path to the model you want to evaluate, or the model name on Huggingface Model Hub.•model: Model config to load, it should match the model to load.•output_path: We will write model outputs to the directory, you will see three files: *_outputs*.json, *_predictions*.json and *_results*.json.•top_k: Number of candidates to consider for each action, we use 50 in the paper for most experiments. Fine-tuning To fine-tune the model, you can use the following command:It uses the same config file as above, action_prediction/conf/config.yaml. The checkpoints will be saved under the workdir configured by hydra. The action prediction model we use is based on the seq2seq T5 model, and the implementation is largely based on Huggingface Transformers's Seq2SeqTrainer, please check the documentation for more details. See full list on github.com You will need the dataset, candidate generation results, and your own openai_api key to run evaluation with LLMs. You can find the 3-shot prompt we use under: src/action_prediction/llm_prompt.json. As described in the paper, we use a multi-choice QA formulation for selecting the target element; an example is shown below: To run evaluation on the test splits, set the parameters in action_prediction/conf/config.yaml similar as above. you can then run the evaluation using the following command: •output_path: We will write model outputs to the directory, you will see three files: *_outputs*.json, *_predictions*.json and *_results*.json. •llm: Openai model you want to use. See full list on github.com Xiang Deng , Huan Sun, Yu Su, The Ohio State University See full list on github.com The Mind2Web dataset is licensed under a Creative Commons Attribution 4.0 International License. Code under this repo is licensed under a MIT License. See full list on github.com Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ... 31 domains and crowdsourced action sequences for the tasks , MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks , 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on MIND2WEB , we conduct an initial exploration of using large language models ... Jun 9, 2023 · We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks , thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ... Jul 14, 2025 · Mind2Web 2 Mind2Web 2 is a benchmark for agentic search systems, featuring Agent-as-a-Judge methodology for comprehensive, rigorous, and reliable assessment on long-horizon and complex tasks that involve complex and real-time information synthesis. What is mind2web? Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. How many domains does mind2web have? 31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. What's new in mind2web 2024? 2024 /3/18: Multimodal- Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024 /1/3: Update! Check out our latest follow-up, SeeAct, enabling everyone to use GPT-4V-based web agents with one click! Try it out and have fun! What is the construction process in mind2web? In specic, the construction process contains three main steps: 1) Organize Conversation Sessions Given the same context, i.e., the same domain and website in Mind2Web, set up a conversation session with consecutive topics from multiple individual task instructions. Is mind2web better than a full-fledged website? Recent studies [5, 21, 35] have utilized similar techniques for mobile applications, however, these are often simpler and offer fewer functions compared with full-fledged websites. In contrast, MIND2WEB aims to adapt to a realistic web environment, characterized by its high diversity. Also related is the research on web automation systems [1, 19]. When will mind2web release? 2025/3/25 : Online-Mind2Web released! 2024/3/18: Multimodal-Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024/1/3: Update! Check out our latest follow-up, SeeAct, enabling everyone to use GPT-4V-based web agents with one click! However, prompt-based methods typi- callyfailtocompetewithne-tunedmethods( Gur et al., 2024 ; Deng et al. , 2023 ) in advanced set- tings, such as Mind2Web . In this work, we pro- pose a new task , namely conversational web navi- gation, which requires multi-turn interaction capa- bilities with both users and the environment.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/Mind2Web", "content": "Dataset, code, and models for the paper \" Mind2Web : Towards a Generalist Agent for the Web\". Check project website for demos and data exploration. Release process: •Dataset •Data used in the paper with textual context •Data with full traces and snapshots See full list on github.com Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Please check our website to explore the dataset. See full list on github.com The training set is hosted on Huggingface. We only provide a zip file for the test splits to prevent potential data contamination from large lagnuage models crawling the test set for training. Please download the test set here and unzip it with password mind2web . The structure is the same as the training set and you can still load it with Huggingface datasets. Please DO NOT redistribute the unzipped data files online. Clone the training data from Huggingface: And then download and unzip the test data into the same directory. After that, the directory structure should look like this: See full list on github.com Data Splits •train: 1,009 instances•test:•Cross Task : 252 instances, tasks from the same website are seen during training•Cross Website: 177 instances, websites are not seen during training•Cross Domain: 912 instances, entire domains are not seen during training Data Fields •\"annotation_id\" (str): unique id for each task •\"website\" (str): website name•\"domain\" (str): website domain•\"subdomain\" (str): website subdomain•\"confirmed_task\" (str): task description•\"action_reprs\" (list[str]): human readable string representation of the action sequence•\"actions\" (list[dict]): list of actions (steps) to complete the task •\"action_uid\" (str): unique id for each action (step)•\"raw_html\" (str): raw html of the page before the action is performed•\"cleaned_html\" (str): cleaned html of the page before the action is performed•\"operation\" (dict): operation to perform•\"op\" (str): operation type, one of CLICK, TYPE, SELECT•\"original_op\" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used•\"value\" (str): optional value for the operation, e.g., text to type, option to select•\"pos_candidates\" (list[dict]): ground truth elements. Here we only include positive elements that exist in \"cleaned_html\" after our preprocessing, so \"pos_candidates\" might be empty. The original labeled element can always be found in the \"raw_html\".•\"tag\" (str): tag of the element•\"is_original_target\" (bool): whether the element is the original target labeled by the annotator•\"is_top_level_target\" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details.•\"backend_node_id\" (str): unique id for the element•\"attributes\" (str): serialized attributes of the element, use json.loads to convert back to dict•\"neg_candidates\" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as \"pos_candidates\" See full list on github.com The raw dump contains the original trace file, network traffic stored in har file, recordings and various snapshots extracted from the trace file. Due to the size of the raw dump, the data is shared via Globus with OSC. Please check the instruction here for an overview of Globus. You can either login with your Google account, or check with your institution as they might have an institutional Globus account. If you have any trouble accessing the data, please contact us. The raw dump is organized in the following structure: We have the following files for each task : •session.har.zip: network traffic stored in har file, can be used for replaying. Please see here for more details. Note that matching network requests is non-trivial as even the same action may trigger different requests due to the dynamic nature of the web (datetime, random generator). We do not use this in our work but would be worth exploring. See full list on github.com Evaluation You can find the trained DeBERTa-v3-base model on Huggingface Model Hub. You can run evaluation with the following command:•model_path: path to the model or model name on Huggingface Model Hub.•data_path: path to the dataset directory, e.g., ${BASE_DIR}/ Mind2Web .•split_file: path to the split file, e.g., data/test_website/*.json.•output_dir: path to the output directory. You will see two files: results_*.json with the evaluation metrics, and scores_*.pkl with the prediction scores which can be used for the action prediction module. Fine-tuning To fine-tune the model, you can simply run:•model: Model config to load.It uses config file in candidate_generation/conf/config.yaml. The checkpoints will be saved under the workdir configured by hydra. The candidate generation model we use is a encoder-only DeBERTa model that outputs a score for a pair of query and candidate, and the implementation is based on SentenceTransformer's Cross-Encoders, please check the documentation for more details. See full list on github.com Evaluation You can find the trained flan-t5-base, flan-t5-large and flan-t5-xl models on Huggingface Model Hub.To run evaluation on the test splits, set the parameters in action_prediction/conf/config.yaml accordingly. We use hydra for managing the configuration.you can then run the evaluation using the following command:•model_path: Path to the model you want to evaluate, or the model name on Huggingface Model Hub.•model: Model config to load, it should match the model to load.•output_path: We will write model outputs to the directory, you will see three files: *_outputs*.json, *_predictions*.json and *_results*.json.•top_k: Number of candidates to consider for each action, we use 50 in the paper for most experiments. Fine-tuning To fine-tune the model, you can use the following command:It uses the same config file as above, action_prediction/conf/config.yaml. The checkpoints will be saved under the workdir configured by hydra. The action prediction model we use is based on the seq2seq T5 model, and the implementation is largely based on Huggingface Transformers's Seq2SeqTrainer, please check the documentation for more details. See full list on github.com You will need the dataset, candidate generation results, and your own openai_api key to run evaluation with LLMs. You can find the 3-shot prompt we use under: src/action_prediction/llm_prompt.json. As described in the paper, we use a multi-choice QA formulation for selecting the target element; an example is shown below: To run evaluation on the test splits, set the parameters in action_prediction/conf/config.yaml similar as above. you can then run the evaluation using the following command: •output_path: We will write model outputs to the directory, you will see three files: *_outputs*.json, *_predictions*.json and *_results*.json. •llm: Openai model you want to use. See full list on github.com Xiang Deng , Huan Sun, Yu Su, The Ohio State University See full list on github.com The Mind2Web dataset is licensed under a Creative Commons Attribution 4.0 International License. Code under this repo is licensed under a MIT License. See full list on github.com Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ... 31 domains and crowdsourced action sequences for the tasks , MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks , 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on MIND2WEB , we conduct an initial exploration of using large language models ... Jun 9, 2023 · We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks , thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ... Jul 14, 2025 · Mind2Web 2 Mind2Web 2 is a benchmark for agentic search systems, featuring Agent-as-a-Judge methodology for comprehensive, rigorous, and reliable assessment on long-horizon and complex tasks that involve complex and real-time information synthesis. What is mind2web? Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. How many domains does mind2web have? 31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. What's new in mind2web 2024? 2024 /3/18: Multimodal- Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024 /1/3: Update! Check out our latest follow-up, SeeAct, enabling everyone to use GPT-4V-based web agents with one click! Try it out and have fun! What is the construction process in mind2web? In specic, the construction process contains three main steps: 1) Organize Conversation Sessions Given the same context, i.e., the same domain and website in Mind2Web, set up a conversation session with consecutive topics from multiple individual task instructions. Is mind2web better than a full-fledged website? Recent studies [5, 21, 35] have utilized similar techniques for mobile applications, however, these are often simpler and offer fewer functions compared with full-fledged websites. In contrast, MIND2WEB aims to adapt to a realistic web environment, characterized by its high diversity. Also related is the research on web automation systems [1, 19]. When will mind2web release? 2025/3/25 : Online-Mind2Web released! 2024/3/18: Multimodal-Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024/1/3: Update! Check out our latest follow-up, SeeAct, enabling everyone to use GPT-4V-based web agents with one click! However, prompt-based methods typi- callyfailtocompetewithne-tunedmethods( Gur et al., 2024 ; Deng et al. , 2023 ) in advanced set- tings, such as Mind2Web . In this work, we pro- pose a new task , namely conversational web navi- gation, which requires multi-turn interaction capa- bilities with both users and the environment."}
+{"idx": 2, "title": "Mind2Web - GitHub Pages", "date": "", "ddg_snippet": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ...", "subpage_snippet": "", "source": "osu-nlp-group.github.io", "link": "https://osu-nlp-group.github.io/Mind2Web/", "content": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ..."}
+{"idx": 3, "title": "MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS", "date": "", "ddg_snippet": "31 domains and crowdsourced action sequences for the tasks , MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks , 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on MIND2WEB , we conduct an initial exploration of using large language models ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/5950bf290a1570ea401bf98882128160-Paper-Datasets_and_Benchmarks.pdf", "content": "31 domains and crowdsourced action sequences for the tasks , MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks , 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on MIND2WEB , we conduct an initial exploration of using large language models ..."}
+{"idx": 4, "title": "Mind2Web: Towards a Generalist Agent for the Web OSU-NLP-Group/Mind2Web-2 - GitHub Mind2Web : Towards a Generalist Agent for the Web - GitHub MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS Mind2Web : Towards a Generalist Agent for the Web - GitHub On the Multi-turn Instruction Following for Conversational Web Agents MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS Mind2Web : Towards a Generalist Agent for the Web - GitHub On the Multi-turn Instruction Following for Conversational ...", "date": "", "ddg_snippet": "Jun 9, 2023 · We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks , thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ... Jul 14, 2025 · Mind2Web 2 Mind2Web 2 is a benchmark for agentic search systems, featuring Agent-as-a-Judge methodology for comprehensive, rigorous, and reliable assessment on long-horizon and complex tasks that involve complex and real-time information synthesis. What is mind2web? Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. How many domains does mind2web have? 31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. What's new in mind2web 2024? 2024 /3/18: Multimodal- Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024 /1/3: Update! Check out our latest follow-up, SeeAct, enabling everyone to use GPT-4V-based web agents with one click! Try it out and have fun! What is the construction process in mind2web? In specic, the construction process contains three main steps: 1) Organize Conversation Sessions Given the same context, i.e., the same domain and website in Mind2Web, set up a conversation session with consecutive topics from multiple individual task instructions. Is mind2web better than a full-fledged website? Recent studies [5, 21, 35] have utilized similar techniques for mobile applications, however, these are often simpler and offer fewer functions compared with full-fledged websites. In contrast, MIND2WEB aims to adapt to a realistic web environment, characterized by its high diversity. Also related is the research on web automation systems [1, 19]. When will mind2web release? 2025/3/25 : Online-Mind2Web released! 2024/3/18: Multimodal-Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024/1/3: Update! Check out our latest follow-up, SeeAct, enabling everyone to use GPT-4V-based web agents with one click! However, prompt-based methods typi- callyfailtocompetewithne-tunedmethods( Gur et al., 2024 ; Deng et al. , 2023 ) in advanced set- tings, such as Mind2Web . In this work, we pro- pose a new task , namely conversational web navi- gation, which requires multi-turn interaction capa- bilities with both users and the environment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.06070", "content": "Jun 9, 2023 · We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks , thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ... Jul 14, 2025 · Mind2Web 2 Mind2Web 2 is a benchmark for agentic search systems, featuring Agent-as-a-Judge methodology for comprehensive, rigorous, and reliable assessment on long-horizon and complex tasks that involve complex and real-time information synthesis. What is mind2web? Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. How many domains does mind2web have? 31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. What's new in mind2web 2024? 2024 /3/18: Multimodal- Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024 /1/3: Update! Check out our latest follow-up, SeeAct, enabling everyone to use GPT-4V-based web agents with one click! Try it out and have fun! What is the construction process in mind2web? In specic, the construction process contains three main steps: 1) Organize Conversation Sessions Given the same context, i.e., the same domain and website in Mind2Web, set up a conversation session with consecutive topics from multiple individual task instructions. Is mind2web better than a full-fledged website? Recent studies [5, 21, 35] have utilized similar techniques for mobile applications, however, these are often simpler and offer fewer functions compared with full-fledged websites. In contrast, MIND2WEB aims to adapt to a realistic web environment, characterized by its high diversity. Also related is the research on web automation systems [1, 19]. When will mind2web release? 2025/3/25 : Online-Mind2Web released! 2024/3/18: Multimodal-Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024/1/3: Update! Check out our latest follow-up, SeeAct, enabling everyone to use GPT-4V-based web agents with one click! However, prompt-based methods typi- callyfailtocompetewithne-tunedmethods( Gur et al., 2024 ; Deng et al. , 2023 ) in advanced set- tings, such as Mind2Web . In this work, we pro- pose a new task , namely conversational web navi- gation, which requires multi-turn interaction capa- bilities with both users and the environment."}
+{"idx": 5, "title": "OSU-NLP-Group/Mind2Web-2 - GitHub", "date": "", "ddg_snippet": "Jul 14, 2025 · Mind2Web 2 Mind2Web 2 is a benchmark for agentic search systems, featuring Agent-as-a-Judge methodology for comprehensive, rigorous, and reliable assessment on long-horizon and complex tasks that involve complex and real-time information synthesis.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/Mind2Web-2", "content": "Jul 14, 2025 · Mind2Web 2 Mind2Web 2 is a benchmark for agentic search systems, featuring Agent-as-a-Judge methodology for comprehensive, rigorous, and reliable assessment on long-horizon and complex tasks that involve complex and real-time information synthesis."}
+{"idx": 6, "title": "Меган: К вашим услугам | Subservience ( 2024 ) — Видео от КИНОБРО", "date": "", "ddg_snippet": "Всегда доступные фильмы! Подписывайся! Меган: К вашим услугам | Subservience ( 2024 ) Жанр: триллер, фантастика Пока жена Ника находится в больнице в тяжелом состоянии, хранительницей очага становится высокотехнологичный робот Меган.", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/video-220018529_456242926", "content": "Всегда доступные фильмы! Подписывайся! Меган: К вашим услугам | Subservience ( 2024 ) Жанр: триллер, фантастика Пока жена Ника находится в больнице в тяжелом состоянии, хранительницей очага становится высокотехнологичный робот Меган."}
+{"idx": 7, "title": "On the Multi-turn Instruction Following for Conversational ...", "date": "", "ddg_snippet": "However, prompt-based methods typi- callyfailtocompetewithne-tunedmethods( Gur et al., 2024 ; Deng et al. , 2023 ) in advanced set- tings, such as Mind2Web . In this work, we pro- pose a new task , namely conversational web navi- gation, which requires multi-turn interaction capa- bilities with both users and the environment.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.477.pdf", "content": "However, prompt-based methods typi- callyfailtocompetewithne-tunedmethods( Gur et al., 2024 ; Deng et al. , 2023 ) in advanced set- tings, such as Mind2Web . In this work, we pro- pose a new task , namely conversational web navi- gation, which requires multi-turn interaction capa- bilities with both users and the environment."}
+{"idx": 8, "title": "Mind 2 Web 2: Evaluating Agentic Search with Agent-as-a-Judge", "date": "", "ddg_snippet": "The tasks in Mind 2 Web 2 shall have the following character-istics: (1) Realistic and diverse. Tasks must reflect practical user needs in diverse domains, providing substantial real-world value when solved.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lVGl3E6tq8", "content": "The tasks in Mind 2 Web 2 shall have the following character-istics: (1) Realistic and diverse. Tasks must reflect practical user needs in diverse domains, providing substantial real-world value when solved."}
+{"idx": 9, "title": "Small Models, Big Results: Achieving Superior Intent Extraction...", "date": "", "ddg_snippet": "Mind 2 Web (CC BY 4.0 license) ( Deng et al., 2024 ): Has 2,350 human demonstrations on web-sites. Each user trajectory is on average 7.3 steps long and contains screenshots and actions for each step, as well as a high level description of the task the human was asked to perform.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.12423", "content": "Mind 2 Web (CC BY 4.0 license) ( Deng et al., 2024 ): Has 2,350 human demonstrations on web-sites. Each user trajectory is on average 7.3 steps long and contains screenshots and actions for each step, as well as a high level description of the task the human was asked to perform."}
diff --git a/data/sampled_jsons/Mind2Web_Deng_et_al_2024_original_paper.jsonl b/data/sampled_jsons/Mind2Web_Deng_et_al_2024_original_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0b99653027b5e647476ab491860ae62f83a6c892
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_Deng_et_al_2024_original_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.06070", "content": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ..."}
+{"idx": 1, "title": "PDF MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS", "date": "", "ddg_snippet": "31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on MIND2WEB , we conduct an initial exploration of using large language models ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/5950bf290a1570ea401bf98882128160-Paper-Datasets_and_Benchmarks.pdf", "content": "31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on MIND2WEB , we conduct an initial exploration of using large language models ..."}
+{"idx": 2, "title": "MIND2WEB | Proceedings of the 37th International Conference on Neural ...", "date": "", "ddg_snippet": "We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3667342", "content": "We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ..."}
+{"idx": 3, "title": "Paper page - Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2306.06070", "content": "With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad ..."}
+{"idx": 4, "title": "Mind2Web - GitHub Pages", "date": "", "ddg_snippet": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ...", "subpage_snippet": "", "source": "osu-nlp-group.github.io", "link": "https://osu-nlp-group.github.io/Mind2Web/", "content": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ..."}
+{"idx": 5, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Mind2Web:-Towards-a-Generalist-Agent-for-the-Web-Deng-Gu/58f8925a8b87054ad0635a6398a7fe24935b1604", "content": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents."}
+{"idx": 6, "title": "Mind2Web: Towards a Generalist Agent for the Web - GitHub", "date": "", "ddg_snippet": "Dataset, code, and models for the paper \" Mind2Web : Towards a Generalist Agent for the Web\". Check project website for demos and data exploration. Updates: 2025/3/25: Online- Mind2Web released! 2024/3/18: Multimodal- Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024/1/3: Update ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/Mind2Web", "content": "Dataset, code, and models for the paper \" Mind2Web : Towards a Generalist Agent for the Web\". Check project website for demos and data exploration. Updates: 2025/3/25: Online- Mind2Web released! 2024/3/18: Multimodal- Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024/1/3: Update ..."}
+{"idx": 7, "title": "arXiv:2402.15057v1 [cs.CL] 23 Feb 2024", "date": "", "ddg_snippet": "ented prompting (Zheng et al ., 2024b). How-ever, prompt-based methods typically fail to com-pete with fine-tuned methods (Gur et al ., 2024 ; Deng et al ., 2023) n advanced settings, such as Mind2Web . In this work, we propose a new task, namely conversational web navigation, which re-quires multi-turn interaction capabiliti", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.15057", "content": "ented prompting (Zheng et al ., 2024b). How-ever, prompt-based methods typically fail to com-pete with fine-tuned methods (Gur et al ., 2024 ; Deng et al ., 2023) n advanced settings, such as Mind2Web . In this work, we propose a new task, namely conversational web navigation, which re-quires multi-turn interaction capabiliti"}
+{"idx": 8, "title": "NeurIPS 2023 Mind2web Towards A Generalist Agent For The Web Paper ...", "date": "", "ddg_snippet": "M IND 2W EB: Towards a Generalist Agent for the Web Xiang Deng ∗ Yu Gu Boyuan Zheng Shijie Chen Samuel Stevens Boshi Wang Huan Sun∗ Yu Su∗ The Ohio State University https://osu-nlp-group.github.io/ Mind2Web Abstract We introduce M IND 2W EB, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/809106936/NeurIPS-2023-Mind2web-Towards-a-Generalist-Agent-for-the-Web-Paper-Datasets-and-Benchmarks", "content": "M IND 2W EB: Towards a Generalist Agent for the Web Xiang Deng ∗ Yu Gu Boyuan Zheng Shijie Chen Samuel Stevens Boshi Wang Huan Sun∗ Yu Su∗ The Ohio State University https://osu-nlp-group.github.io/ Mind2Web Abstract We introduce M IND 2W EB, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any ..."}
+{"idx": 9, "title": "Mind2Web: Towards a Generalist Agent for the Web - NeurIPS", "date": "", "ddg_snippet": "Authors Xiang Deng , Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, Yu Su Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/5950bf290a1570ea401bf98882128160-Abstract-Datasets_and_Benchmarks.html", "content": "Authors Xiang Deng , Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, Yu Su Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and ..."}
diff --git a/data/sampled_jsons/Mind2Web_construction_process_crowdsourced_human_annotation_manual.jsonl b/data/sampled_jsons/Mind2Web_construction_process_crowdsourced_human_annotation_manual.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..98c1b9117794c9254e5f0508700dbc310986d53f
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_construction_process_crowdsourced_human_annotation_manual.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "In light of this, we present Mind2Web , a new dataset with natural language tasks and manually annotated action sequences for developing and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.06070v3", "content": "In light of this, we present Mind2Web , a new dataset with natural language tasks and manually annotated action sequences for developing and ..."}
+{"idx": 1, "title": "(PDF) Integrating Crowdsourcing and Human ... - Academia.edu", "date": "", "ddg_snippet": "Integrating Crowdsourcing and Human Computation for Complex Video Annotation Tasks.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/108079776/Integrating_Crowdsourcing_and_Human_Computation_for_Complex_Video_Annotation_Tasks", "content": "Integrating Crowdsourcing and Human Computation for Complex Video Annotation Tasks."}
+{"idx": 2, "title": "Truth is a Lie: 7 Myths about Human Annotation @CogComputing...", "date": "", "ddg_snippet": "The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods. Crowdsourcing -based approaches are gaining popularity in the attempt to solve the issues related to the volume of data and lack of annotators.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/truth-is-a-lie-7-myths-about-human-annotation-cogcomputing-forum-2014/38231843", "content": "The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods. Crowdsourcing -based approaches are gaining popularity in the attempt to solve the issues related to the volume of data and lack of annotators."}
+{"idx": 3, "title": "[PDF] How reliable are annotations via crowdsourcing : a study about...", "date": "", "ddg_snippet": "This study is applied to annotations of images with multiple concepts. A subset of the images employed in the latest ImageCLEF Photo Annotation competition was manually annotated by expert annotators and non-experts with Mechanical Turk.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/How-reliable-are-annotations-via-crowdsourcing:-a-Nowak-Rüger/4e227d4919caa1780ec2cd67e834430037e294bb", "content": "This study is applied to annotations of images with multiple concepts. A subset of the images employed in the latest ImageCLEF Photo Annotation competition was manually annotated by expert annotators and non-experts with Mechanical Turk."}
+{"idx": 4, "title": "MMInA: Benchmarking Multihop Multimodal Internet Agents", "date": "", "ddg_snippet": "Our 1,050 human -written tasks challenge agents to process multimodal inputs across multiple website hops and execute complex, multi-step reasoning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.09992v2", "content": "Our 1,050 human -written tasks challenge agents to process multimodal inputs across multiple website hops and execute complex, multi-step reasoning ..."}
+{"idx": 5, "title": "On the Effects of Data Scale on UI Control Agents", "date": "", "ddg_snippet": "... 39 , 17 , 7 ] that accomplish human ... First, for every task, it contains both low-level and high-level instructions generated by human annotators.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.03679v6", "content": "... 39 , 17 , 7 ] that accomplish human ... First, for every task, it contains both low-level and high-level instructions generated by human annotators."}
+{"idx": 6, "title": "Large Language Model Agent: A Survey on Methodology,", "date": "", "ddg_snippet": "... task processors , unifying perception, decision-making, and action within semantic space through generative architectures, thereby forming human -like ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.21460v1", "content": "... task processors , unifying perception, decision-making, and action within semantic space through generative architectures, thereby forming human -like ..."}
+{"idx": 7, "title": "Survey on Evaluation of LLM-based Agents", "date": "", "ddg_snippet": "2025 ) , model_section, text width=12cm] ] [Software Engineering Agents (§ 3.2 ), model_section [ HumanEval Chen et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16416v1", "content": "2025 ) , model_section, text width=12cm] ] [Software Engineering Agents (§ 3.2 ), model_section [ HumanEval Chen et al."}
+{"idx": 8, "title": "Annotator Rationales for Labeling Tasks in Crowdsourcing", "date": "", "ddg_snippet": "We propose a novel three-stage FIND-RESOLVE-LABEL workflow for crowdsourced annotation to reduce ambiguity in task instructions and thus improve annotation quality.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/345438557_Annotator_Rationales_for_Labeling_Tasks_in_Crowdsourcing", "content": "We propose a novel three-stage FIND-RESOLVE-LABEL workflow for crowdsourced annotation to reduce ambiguity in task instructions and thus improve annotation quality."}
+{"idx": 9, "title": "Advancing Fine-Grained Emotion Recognition in Short Text", "date": "", "ddg_snippet": "Crowdsourcing Human Annotations Online data annotation via crowdsourcing became available with the development of the web and its economy. It provides a way to outsource expensive and time-consuming manual data annotation to “an undened...", "subpage_snippet": "", "source": "infoscience.epfl.ch", "link": "https://infoscience.epfl.ch/server/api/core/bitstreams/57916240-86cf-48f2-a520-9f572c0a5f92/content", "content": "Crowdsourcing Human Annotations Online data annotation via crowdsourcing became available with the development of the web and its economy. It provides a way to outsource expensive and time-consuming manual data annotation to “an undened..."}
diff --git a/data/sampled_jsons/Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them_Shen_Lee_2019_ICML.jsonl b/data/sampled_jsons/Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them_Shen_Lee_2019_ICML.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dd1f61051fa44f574706764bf5ffb49df96e3ba8
--- /dev/null
+++ b/data/sampled_jsons/Minimizing_the_sum_of_many_functions_is_as_easy_as_minimizing_one_of_them_Shen_Lee_2019_ICML.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "One-Shot Federated Learning based on Bayesian Ensemble", "date": "", "ddg_snippet": "In this paper, we analyze the One -Shot FL problem through the lens of Bayesian inference and propose FedBEns, an algorithm that leverages the inherent ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44060", "content": "In this paper, we analyze the One -Shot FL problem through the lens of Bayesian inference and propose FedBEns, an algorithm that leverages the inherent ..."}
+{"idx": 1, "title": "Federated learning for minimizing nonsmooth convex loss ...", "date": "", "ddg_snippet": "by LY Wei · 2023 · Cited by 16 — This paper aims at providing an understanding of federated learning in the situation that the loss functions are nonsmooth and gradient information is ...", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/doi/10.3934/mfc.2023026", "content": "by LY Wei · 2023 · Cited by 16 — This paper aims at providing an understanding of federated learning in the situation that the loss functions are nonsmooth and gradient information is ..."}
+{"idx": 2, "title": "Efficient Loss Function by Minimizing the Detrimental Effect of ...", "date": "", "ddg_snippet": "by Y Yu · 2023 · Cited by 11 — This paper reveals that the relative error in the gradients caused by floating- point errors is one of the fundamental reasons for the failure of gradient-based ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Efficient_Loss_Function_by_Minimizing_the_Detrimental_Effect_of_Floating-Point_CVPR_2023_paper.pdf", "content": "by Y Yu · 2023 · Cited by 11 — This paper reveals that the relative error in the gradients caused by floating- point errors is one of the fundamental reasons for the failure of gradient-based ... 11 pages"}
+{"idx": 3, "title": "Loss Functions in Deep Learning: A Comprehensive Review", "date": "", "ddg_snippet": "This paper presents a comprehensive review of loss functions , covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.04242v1", "content": "This paper presents a comprehensive review of loss functions , covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions ."}
+{"idx": 4, "title": "A comprehensive survey of loss functions and metrics in ...", "date": "", "ddg_snippet": "by J Terven · 2025 · Cited by 48 — This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11198-7", "content": "by J Terven · 2025 · Cited by 48 — This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights"}
+{"idx": 5, "title": "Efficient Core-set Selection for Deep Learning Through ...", "date": "", "ddg_snippet": "16 Jul 2025 — 1 . We propose a novel CS objective based on minimizing the sum of squared loss, which balances convergence between the core-set and non-core-set ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45105", "content": "16 Jul 2025 — 1 . We propose a novel CS objective based on minimizing the sum of squared loss, which balances convergence between the core-set and non-core-set ..."}
+{"idx": 6, "title": "Fast, Accurate Manifold Denoising by Tunneling ...", "date": "", "ddg_snippet": "We have two technical innovations: (i) online learning methods which learn to optimize over the manifold of clean signals using only noisy data, effectively “ ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44296", "content": "We have two technical innovations: (i) online learning methods which learn to optimize over the manifold of clean signals using only noisy data, effectively “ ..."}
+{"idx": 7, "title": "Sharpness-Aware Minimization: General Analysis and ...", "date": "", "ddg_snippet": "by D Oikonomou · Cited by 1 — Sharpness-Aware Minimization (SAM ) has emerged as a powerful method for improving generalization in machine learning models by minimizing the sharpness of the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8rvqpiTTFv", "content": "by D Oikonomou · Cited by 1 — Sharpness-Aware Minimization (SAM ) has emerged as a powerful method for improving generalization in machine learning models by minimizing the sharpness of the ..."}
+{"idx": 8, "title": "Reducing Variance of Stochastic Optimization for ...", "date": "", "ddg_snippet": "15 Jul 2025 — To improve the convergence rate by mitigating the high variance associated with the existing unbiased loss function , we propose a novel ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45762", "content": "15 Jul 2025 — To improve the convergence rate by mitigating the high variance associated with the existing unbiased loss function , we propose a novel ..."}
+{"idx": 9, "title": "A Single-Step, Sharpness-Aware Minimization is All You ...", "date": "", "ddg_snippet": "A single -step, sharpness-aware minimization is all you need to achieve efficient and accurate sparse training.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/95533", "content": "A single -step, sharpness-aware minimization is all you need to achieve efficient and accurate sparse training."}
diff --git a/data/sampled_jsons/ModelGo_Licenses.jsonl b/data/sampled_jsons/ModelGo_Licenses.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f525f33e77627243763d23f8e0348a68cfc09880
--- /dev/null
+++ b/data/sampled_jsons/ModelGo_Licenses.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Standard Way for Model Publishing | ModelGo Licenses", "date": "", "ddg_snippet": "ModelGo licenses provide CreativeCommons-style licensing solutions to meet your specific needs in publishing AI models. The goal of ModelGo is to facilitate managed sharing of models while protecting Intellectual Property, striking a balance between openness and control.", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/", "content": "ModelGo licenses provide CreativeCommons-style licensing solutions to meet your specific needs in publishing AI models. The goal of ModelGo is to facilitate managed sharing of models while protecting Intellectual Property, striking a balance between openness and control."}
+{"idx": 1, "title": "GitHub - Xtra-Computing/ModelGo", "date": "", "ddg_snippet": "Why we need ModelGo Licenses Set? To facilitate managed sharing of models while protecting your Intellectual Property. ModelGo licenses offer flexible options to fulfill your specific licensing needs about using and distributing your deep learning models while protecting your Intellectual Property (IP).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xtra-Computing/ModelGo", "content": "Why we need ModelGo Licenses Set? To facilitate managed sharing of models while protecting your Intellectual Property. ModelGo licenses offer flexible options to fulfill your specific licensing needs about using and distributing your deep learning models while protecting your Intellectual Property (IP)."}
+{"idx": 2, "title": "ModelGo: A Pratical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "Furthermore, our case studies can cover 569 all events listed in Table 1, and the their details and findings are 570 provided in the following section. 571 It's worth noting that, as a license compliance analysis tool, 572 ModelGo's goal is to report potential legal risks in ML projects 573 related to licenses .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Et9rHdWGAZ", "content": "Furthermore, our case studies can cover 569 all events listed in Table 1, and the their details and findings are 570 provided in the following section. 571 It's worth noting that, as a license compliance analysis tool, 572 ModelGo's goal is to report potential legal risks in ML projects 573 related to licenses ."}
+{"idx": 3, "title": "Understanding ModelGo | ModelGo Licenses", "date": "", "ddg_snippet": "ModelGo licenses consist of six sections. Section 2, \" License Rights,\" is the primary provision that grants rights licenses and states the restrictions of use and distribution. ModelGo licenses include a Disclaimer and Limitation of Liability (Section3, 4). Additionally, our licenses include terms, as stated in Section 6, that allow you to modify the license text, provided you furnish a ...", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/learn-more/understanding-modelgo", "content": "ModelGo licenses consist of six sections. Section 2, \" License Rights,\" is the primary provision that grants rights licenses and states the restrictions of use and distribution. ModelGo licenses include a Disclaimer and Limitation of Liability (Section3, 4). Additionally, our licenses include terms, as stated in Section 6, that allow you to modify the license text, provided you furnish a ..."}
+{"idx": 4, "title": "Using ModelGo Licenses | ModelGo Licenses", "date": "", "ddg_snippet": "ModelGo Licenses include eight variants, grouped into Permissive Licenses , Conditional Permissive Licenses , and Stringent Licenses . Please select the one that best suits your needs.", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/get-started/using-modelgo-licenses", "content": "ModelGo Licenses include eight variants, grouped into Permissive Licenses , Conditional Permissive Licenses , and Stringent Licenses . Please select the one that best suits your needs."}
+{"idx": 5, "title": "ModelGo/README.md at main · Xtra-Computing/ModelGo · GitHub", "date": "", "ddg_snippet": "ModelGo licenses offer flexible options to fulfill your specific licensing needs about using and distributing your deep learning models while protecting your Intellectual Property (IP).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xtra-Computing/ModelGo/blob/main/README.md", "content": "ModelGo licenses offer flexible options to fulfill your specific licensing needs about using and distributing your deep learning models while protecting your Intellectual Property (IP)."}
+{"idx": 6, "title": "NUS - Institute of Data Science", "date": "", "ddg_snippet": "ModelGo License Options ModelGo licenses offer you five publishing options: BY - Downstream model users must give credit to you, retain your attribution information, keep the original license and notice in their shared copies and modifications. NC - Downstream model users must run and distribute your models, derivatives of your models, and generated content of your models for Non-Commercial ...", "subpage_snippet": "", "source": "ids.nus.edu.sg", "link": "https://ids.nus.edu.sg/modelgo.html", "content": "ModelGo License Options ModelGo licenses offer you five publishing options: BY - Downstream model users must give credit to you, retain your attribution information, keep the original license and notice in their shared copies and modifications. NC - Downstream model users must run and distribute your models, derivatives of your models, and generated content of your models for Non-Commercial ..."}
+{"idx": 7, "title": "ICML Poster Position: Current Model Licensing Practices are Dragging Us ...", "date": "", "ddg_snippet": "Our findings suggest that current model licensing practices have already led to a quagmire of legal problems for developers and users alike, creating an urgent need for input and attention from the ML community.We also introduce ModelGo Licenses (MGLs), a new draft license framework designed to promote clearer, more consistent, and legally ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40180", "content": "Our findings suggest that current model licensing practices have already led to a quagmire of legal problems for developers and users alike, creating an urgent need for input and attention from the ML community.We also introduce ModelGo Licenses (MGLs), a new draft license framework designed to promote clearer, more consistent, and legally ..."}
+{"idx": 8, "title": "PDF ML-Asset Management: Curation, Discovery, and Utilization", "date": "", "ddg_snippet": "It evaluates licensing-related issues such as rights granting, term con-flicts, and incompatibility between licenses . ModelGo Licenses [11]: a new Creative Commons-style model-specific license set designed for general model publication. It supports flexible licensing options to meet diverse model sharing needs.", "subpage_snippet": "", "source": "www.vldb.org", "link": "https://www.vldb.org/pvldb/vol18/p5493-wang.pdf", "content": "It evaluates licensing-related issues such as rights granting, term con-flicts, and incompatibility between licenses . ModelGo Licenses [11]: a new Creative Commons-style model-specific license set designed for general model publication. It supports flexible licensing options to meet diverse model sharing needs."}
+{"idx": 9, "title": "ids.nus.edu.sg", "date": "", "ddg_snippet": "ModelGo Attribution License Version 2.0, May 2025 By exercising the rights granted in Section 2.1, You acknowledge and agree that You have read, understood, and agree to be bound by the terms and conditions of this License . If You do not agree to any terms and/or conditions of this License , then the Licensor grants You no rights under this License . 1. DEFINITIONS \"Complementary Materials ...", "subpage_snippet": "", "source": "ids.nus.edu.sg", "link": "https://ids.nus.edu.sg/docs/modelgo/v2/MG-BY/LICENSE", "content": "ModelGo Attribution License Version 2.0, May 2025 By exercising the rights granted in Section 2.1, You acknowledge and agree that You have read, understood, and agree to be bound by the terms and conditions of this License . If You do not agree to any terms and/or conditions of this License , then the Licensor grants You no rights under this License . 1. DEFINITIONS \"Complementary Materials ..."}
diff --git a/data/sampled_jsons/ModelGo_Licenses_MGL_model_licensing_AI_year_2024.jsonl b/data/sampled_jsons/ModelGo_Licenses_MGL_model_licensing_AI_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ded5cd129dd4cf0fff62dae24ea9a80657dba185
--- /dev/null
+++ b/data/sampled_jsons/ModelGo_Licenses_MGL_model_licensing_AI_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Standard Way for Model Publishing | ModelGo Licenses", "date": "", "ddg_snippet": "Jun 26, 2025 · ModelGo licenses provide CreativeCommons-style licensing solutions to meet your specific needs in publishing AI models. The goal of ModelGo is to facilitate managed sharing of models while protecting Intellectual Property, striking a balance between openness and control.", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/", "content": "Jun 26, 2025 · ModelGo licenses provide CreativeCommons-style licensing solutions to meet your specific needs in publishing AI models. The goal of ModelGo is to facilitate managed sharing of models while protecting Intellectual Property, striking a balance between openness and control."}
+{"idx": 1, "title": "ModelGo: A Pratical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "22 for auditing potential legal risks in machine learning projects to en- 23 hance compliance and fairness. With ModelGo , we present license 24 assessment reports based on 5 use cases with diverse model -reusing 25 scenarios, rendered by real-world machine learning components. 26 Finally, we summarize the reasons behind license conflicts and", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Et9rHdWGAZ", "content": "22 for auditing potential legal risks in machine learning projects to en- 23 hance compliance and fairness. With ModelGo , we present license 24 assessment reports based on 5 use cases with diverse model -reusing 25 scenarios, rendered by real-world machine learning components. 26 Finally, we summarize the reasons behind license conflicts and"}
+{"idx": 2, "title": "GitHub - Xtra-Computing/ModelGo", "date": "", "ddg_snippet": "Why we need ModelGo Licenses Set? To facilitate managed sharing of models while protecting your Intellectual Property. ModelGo licenses offer flexible options to fulfill your specific licensing needs about using and distributing your deep learning models while protecting your Intellectual Property (IP).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xtra-Computing/ModelGo", "content": "Why we need ModelGo Licenses Set? To facilitate managed sharing of models while protecting your Intellectual Property. ModelGo licenses offer flexible options to fulfill your specific licensing needs about using and distributing your deep learning models while protecting your Intellectual Property (IP)."}
+{"idx": 3, "title": "ModelGo: A Practical Tool for Machine Learning License ...", "date": "", "ddg_snippet": "May 13, 2024 · In this paper, we introduce ModelGo , a practical tool for auditing potential legal risks in machine learning projects to enhance compliance and fairness. With ModelGo , we present license assessment reports based on five use cases with diverse model -reusing scenarios, rendered by real-world machine learning components.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3589334.3645520", "content": "May 13, 2024 · In this paper, we introduce ModelGo , a practical tool for auditing potential legal risks in machine learning projects to enhance compliance and fairness. With ModelGo , we present license assessment reports based on five use cases with diverse model -reusing scenarios, rendered by real-world machine learning components."}
+{"idx": 4, "title": "We have released ModelGO, a licensing framework for sharing ...", "date": "", "ddg_snippet": "We have released ModelGO , a licensing framework for sharing and publishing AI models. We hope to make it like MIT or Apache licenses for AI models.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/bingsheng-he-7734b131_we-have-released-modelgo-a-licensing-framework-activity-7299218622940397571-OeRk", "content": "We have released ModelGO , a licensing framework for sharing and publishing AI models. We hope to make it like MIT or Apache licenses for AI models."}
+{"idx": 5, "title": "ICML Poster Position: Current Model Licensing Practices are ...", "date": "", "ddg_snippet": "Our findings suggest that current model licensing practices have already led to a quagmire of legal problems for developers and users alike, creating an urgent need for input and attention from the ML community.We also introduce ModelGo Licenses (MGLs), a new draft license framework designed to promote clearer, more consistent, and legally ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40180", "content": "Our findings suggest that current model licensing practices have already led to a quagmire of legal problems for developers and users alike, creating an urgent need for input and attention from the ML community.We also introduce ModelGo Licenses (MGLs), a new draft license framework designed to promote clearer, more consistent, and legally ..."}
+{"idx": 6, "title": "ICML Oral Position: Current Model Licensing Practices are Dragging...", "date": "", "ddg_snippet": "Developers are often required to choose a license to publish and govern the use of their models . Popular options include Apache-2.0, OpenRAIL (Responsible AI Licenses ), Creative Commons Licenses (CCs), Llama2, and GPL-3.0.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40181", "content": "Developers are often required to choose a license to publish and govern the use of their models . Popular options include Apache-2.0, OpenRAIL (Responsible AI Licenses ), Creative Commons Licenses (CCs), Llama2, and GPL-3.0."}
+{"idx": 7, "title": "ModelGo : A Practical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "Behavioral Use Licensing for Responsible AI .Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence ( AI ).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380539201_ModelGo_A_Practical_Tool_for_Machine_Learning_License_Analysis", "content": "Behavioral Use Licensing for Responsible AI .Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence ( AI )."}
+{"idx": 8, "title": "[ License -review] Notice requirement for model output: OSD-compliant...", "date": "", "ddg_snippet": "Previous message (by thread): [ License -review] Notice requirement for model output: OSD-compliant or not? ( ModelGo ).I tend to agree. The output of a program (and of an AI system/ model alike) should not be subject matter controlled by the maker of them.", "subpage_snippet": "", "source": "lists.opensource.org", "link": "https://lists.opensource.org/pipermail/license-review_lists.opensource.org/2025-March/005706.html", "content": "Previous message (by thread): [ License -review] Notice requirement for model output: OSD-compliant or not? ( ModelGo ).I tend to agree. The output of a program (and of an AI system/ model alike) should not be subject matter controlled by the maker of them."}
+{"idx": 9, "title": "AI Model Licensing Guidelines | Restackio", "date": "", "ddg_snippet": "AI Model Licensing Guidelines. Last updated on 05/30/25. Explore essential guidelines for licensing AI models , focusing on compliance and best practices for model versioning.", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/model-versioning-answer-ai-model-licensing-guidelines-cat-ai", "content": "AI Model Licensing Guidelines. Last updated on 05/30/25. Explore essential guidelines for licensing AI models , focusing on compliance and best practices for model versioning."}
diff --git a/data/sampled_jsons/MovieLens_100k_dataset_origin_source.jsonl b/data/sampled_jsons/MovieLens_100k_dataset_origin_source.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e2b626084cb88e5db5552e57bc1c5f73864e711f
--- /dev/null
+++ b/data/sampled_jsons/MovieLens_100k_dataset_origin_source.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - vectorsss/ movielens _ 100 k _1m_extension: This repository is...", "date": "", "ddg_snippet": "This repository is an extension for movielens 100 k and 1m. Including the imdbId, tmdbId and the url for poster.Update: For dataset ml-1m and ml- 100 k , I fetch those movies metadata from OMDB. You could see some new files in the corresponding folder.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vectorsss/movielens_100k_1m_extension", "content": "This repository is an extension for movielens 100 k and 1m. Including the imdbId, tmdbId and the url for poster.Update: For dataset ml-1m and ml- 100 k , I fetch those movies metadata from OMDB. You could see some new files in the corresponding folder."}
+{"idx": 1, "title": "movielens .ipynb - Colab", "date": "", "ddg_snippet": "Getting the Data . The MovieLens dataset is hosted by the GroupLens website. Several versions are available. We will use the MovieLens 100 K dataset :cite: Herlocker.Konstan.Borchers.ea.1999 .", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/d2l-ai/d2l-en-colab/blob/master/chapter_recommender-systems/movielens.ipynb", "content": "Getting the Data . The MovieLens dataset is hosted by the GroupLens website. Several versions are available. We will use the MovieLens 100 K dataset :cite: Herlocker.Konstan.Borchers.ea.1999 ."}
+{"idx": 2, "title": "MovieLens | GroupLens", "date": "", "ddg_snippet": "MovieLens 100 K Dataset .Stable benchmark dataset . 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users. Includes tag genome data with 12 million relevance scores across 1,100 tags.", "subpage_snippet": "", "source": "grouplens.org", "link": "https://grouplens.org/datasets/movielens/", "content": "MovieLens 100 K Dataset .Stable benchmark dataset . 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users. Includes tag genome data with 12 million relevance scores across 1,100 tags."}
+{"idx": 3, "title": "movielens | TensorFlow Datasets", "date": "", "ddg_snippet": "Source code: tfds.structured. Movielens .Config description: This dataset contains 100,000 ratings from 943 users on 1,682 movies. This dataset is the oldest version of the MovieLens dataset .", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/datasets/catalog/movielens", "content": "Source code: tfds.structured. Movielens .Config description: This dataset contains 100,000 ratings from 943 users on 1,682 movies. This dataset is the oldest version of the MovieLens dataset ."}
+{"idx": 4, "title": "21.2. The MovieLens Dataset — Dive into Deep Learning...", "date": "", "ddg_snippet": "We will use the MovieLens 100 K dataset (Herlocker et al., 1999) .We split the dataset into training and test sets . The following function provides two split modes including random and seq-aware .", "subpage_snippet": "", "source": "d2l.ai", "link": "https://d2l.ai/chapter_recommender-systems/movielens.html", "content": "We will use the MovieLens 100 K dataset (Herlocker et al., 1999) .We split the dataset into training and test sets . The following function provides two split modes including random and seq-aware ."}
+{"idx": 5, "title": "torch_geometric. datasets . MovieLens 100 K — pytorch_geometric...", "date": "", "ddg_snippet": "torch_geometric. datasets . MovieLens 100 K . Edit on GitHub.transform (callable, optional) – A function/transform that takes in an torch_geometric. data .HeteroData object and returns a transformed version. The data object will be transformed before every access. (default: None).", "subpage_snippet": "", "source": "pytorch-geometric.readthedocs.io", "link": "https://pytorch-geometric.readthedocs.io/en/2.6.0/generated/torch_geometric.datasets.MovieLens100K.html", "content": "torch_geometric. datasets . MovieLens 100 K . Edit on GitHub.transform (callable, optional) – A function/transform that takes in an torch_geometric. data .HeteroData object and returns a transformed version. The data object will be transformed before every access. (default: None)."}
+{"idx": 6, "title": "ml 100 k : Movielens 100 K Dataset in rrecsys: Environment for...", "date": "", "ddg_snippet": "Description Source . Description. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota. This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies.", "subpage_snippet": "", "source": "rdrr.io", "link": "https://rdrr.io/cran/rrecsys/man/ml100k.html", "content": "Description Source . Description. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota. This data set consists of: 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies."}
+{"idx": 7, "title": "MovieLens 100 K Dataset", "date": "", "ddg_snippet": "MovieLens 100 K Dataset . Stable benchmark dataset . 100,000 ratings from 1000 users on 1700 movies. MovieLens 100 K Dataset . Data Card Code (213) Discussion (1) Suggestions (0).", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/prajitdatta/movielens-100k-dataset/suggestions?status=pending", "content": "MovieLens 100 K Dataset . Stable benchmark dataset . 100,000 ratings from 1000 users on 1700 movies. MovieLens 100 K Dataset . Data Card Code (213) Discussion (1) Suggestions (0)."}
+{"idx": 8, "title": "The MovieLens 100 k dataset - Machine Learning with Spark - Second...", "date": "", "ddg_snippet": "The MovieLens 100 k dataset is a set of 100,000 data points related to ratings given by a set of users to a set of movies. It also contains movie metadata and user profiles. While it is a small dataset , you can quickly download it and run Spark code on it.", "subpage_snippet": "", "source": "www.oreilly.com", "link": "https://www.oreilly.com/library/view/machine-learning-with/9781785889936/358ef204-9150-4116-a5ae-e36c68804814.xhtml", "content": "The MovieLens 100 k dataset is a set of 100,000 data points related to ratings given by a set of users to a set of movies. It also contains movie metadata and user profiles. While it is a small dataset , you can quickly download it and run Spark code on it."}
+{"idx": 9, "title": "Deep Diving into The Movie Lens Dataset - Freedium", "date": "", "ddg_snippet": "Comprehensive Data Explorations with Matplotlib. Deep Diving into The Movie Lens Dataset .Specifically, we will be using the MovieLens 100 K movie ratings dataset which consists of 1000 users on 1700 movies.", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/a388be12a355", "content": "Comprehensive Data Explorations with Matplotlib. Deep Diving into The Movie Lens Dataset .Specifically, we will be using the MovieLens 100 K movie ratings dataset which consists of 1000 users on 1700 movies."}
diff --git a/data/sampled_jsons/MultiPDENet_MaNN_Block_Section_3.2.4_macro_neural_network.jsonl b/data/sampled_jsons/MultiPDENet_MaNN_Block_Section_3.2.4_macro_neural_network.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..644f69800c4e6b60580a57afc2690008b622f135
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_MaNN_Block_Section_3.2.4_macro_neural_network.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "15 Jul 2025 — The MaNN Block (see Section 3.2.4 ) refines these incremental updates ... MaNN Blocks , operating at micro- and macro -scales, respectively.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46029", "content": "15 Jul 2025 — The MaNN Block (see Section 3.2.4 ) refines these incremental updates ... MaNN Blocks , operating at micro- and macro -scales, respectively."}
+{"idx": 1, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "The MaNN Block (see Section 3.2.4) refines these incremental updates ... The Correction Block leverages a neural network to refine the coarse solution ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/046ccccd77df13df48f47ff1081b58969771121a.pdf", "content": "The MaNN Block (see Section 3.2.4) refines these incremental updates ... The Correction Block leverages a neural network to refine the coarse solution ..."}
+{"idx": 2, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows."}
+{"idx": 3, "title": "PyTorch implementation of Memory Augmented Neural Network 6 Neural Networks – 6.390 - Intro to Machine Learning An In-Memory Computing SRAM Macro for Memory-Augmented Neural ... Memory Augmented Neural Network for Meta Learning - Medium Memory Augmented Neural Network for Meta Learning — Case Study | … 6 Neural Networks – 6.390 - Intro to Machine Learning A Mathematical Framework for the Analysis of Neural Networks A Mathematical Framework for the Analysis of Neural Networks A Mathematical Framework for the Analysis of Neural Networks A Mathematical Framework for the Analysis of Neural Networks A Mathematical Framework for the Analysis of Neural Networks", "date": "", "ddg_snippet": "Download the omniglot Dataset from here and put all the images(evaluation + background) in one folder. Then run resize_images.py there. Basic implementaion is in mann_pytorch.ipynb See full list on github.com •Basic Implementation of MANN using LSTM •Fix Training loss error •PreFetching Dataloader added •Training for:- •5 shot 1 way • 4 shot 2 way See full list on github.com You’ve probably been hearing a lot about “ neural networks .” Now that we have several useful machine-learning concepts (hypothesis classes, classification, regression, gradient descent, regularization, etc.), we are well equipped to understand neural networks in detail. This is, in some sense, the “third wave” of neural nets. Dec 2 , 2021 · In this brief, we present an SRAM macro designed for accelerating Memory-Augmented Neural Network ( MANN ). We first propose algorithmic optimizations for a few-shot learning algorithm employing MANN for efficient hardware implementation. May 11, 2020 · Memory Augmented Neural Network [1] ( MANN ) is one of them which inspired the use of external memory from Neural Turing Machine [2]. Press enter or click to view image in full size What is memory augmented neural network (Mann)? Many researches have been carried out and novel architectures have been proposed to accomplish Meta Learning task. Memory Augmented Neural Network (MANN) is one of them which inspired the use of external memory from Neural Turing Machine . A gentle introduction to MANN. Using MANN for a few shot classification in Omniglot data set. What is the “third wave” of neural networks? You’ve probably been hearing a lot about “neural networks.” Now that we have several useful machine-learning concepts (hypothesis classes, classification, regression, gradient descent, regularization, etc.), we are well equipped to understand neural networks in detail . This is, in some sense, the “third wave” of neural nets. What is a deep neural network (DNN)? Over the past decade, Deep Neural Networks (DNNs) have become very popular models for processing large amounts of data because of their successful application in a wide variety of elds. These models are layered, often containing parametrized linear and non-linear transformations at each layer in the network. What are the three types of neural networks? We will dedicate this chapter to expressing three common neural network structures within this generic framework: the Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Deep Auto-Encoder (DAE) . What are recurring neural networks (RNNs)? We will now extend the generic framework even further to handle Recurrent Neural Networks (RNNs), the sequence-parsing network structure containing a recurring latent, or hidden, state that evolves at each layer of the net-work. This will involve the development of new notation, but we will remain as consistent as possible with previous chapters. What is bidirectional recurrent neural network (BRNN)? Another extension is the Bidirectional Recurrent Neural Network (BRNN), which we examine in subsection 5.3.2. BRNNs parse the sequence both forwards and backwards , if the entire sequence is known at the start, allowing the network to capture more information about the sequence. In this thesis, we explore one way to approach this problem: we develop a generic mathematical framework for representing neural networks , and demonstrate how this framework can be used to represent speci c neural network architectures. In chapter 1, we start by exploring mathematical contributions to neural networks .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/m2kulkarni/MANN", "content": "Download the omniglot Dataset from here and put all the images(evaluation + background) in one folder. Then run resize_images.py there. Basic implementaion is in mann_pytorch.ipynb See full list on github.com •Basic Implementation of MANN using LSTM •Fix Training loss error •PreFetching Dataloader added •Training for:- •5 shot 1 way • 4 shot 2 way See full list on github.com You’ve probably been hearing a lot about “ neural networks .” Now that we have several useful machine-learning concepts (hypothesis classes, classification, regression, gradient descent, regularization, etc.), we are well equipped to understand neural networks in detail. This is, in some sense, the “third wave” of neural nets. Dec 2 , 2021 · In this brief, we present an SRAM macro designed for accelerating Memory-Augmented Neural Network ( MANN ). We first propose algorithmic optimizations for a few-shot learning algorithm employing MANN for efficient hardware implementation. May 11, 2020 · Memory Augmented Neural Network [1] ( MANN ) is one of them which inspired the use of external memory from Neural Turing Machine [2]. Press enter or click to view image in full size What is memory augmented neural network (Mann)? Many researches have been carried out and novel architectures have been proposed to accomplish Meta Learning task. Memory Augmented Neural Network (MANN) is one of them which inspired the use of external memory from Neural Turing Machine . A gentle introduction to MANN. Using MANN for a few shot classification in Omniglot data set. What is the “third wave” of neural networks? You’ve probably been hearing a lot about “neural networks.” Now that we have several useful machine-learning concepts (hypothesis classes, classification, regression, gradient descent, regularization, etc.), we are well equipped to understand neural networks in detail . This is, in some sense, the “third wave” of neural nets. What is a deep neural network (DNN)? Over the past decade, Deep Neural Networks (DNNs) have become very popular models for processing large amounts of data because of their successful application in a wide variety of elds. These models are layered, often containing parametrized linear and non-linear transformations at each layer in the network. What are the three types of neural networks? We will dedicate this chapter to expressing three common neural network structures within this generic framework: the Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Deep Auto-Encoder (DAE) . What are recurring neural networks (RNNs)? We will now extend the generic framework even further to handle Recurrent Neural Networks (RNNs), the sequence-parsing network structure containing a recurring latent, or hidden, state that evolves at each layer of the net-work. This will involve the development of new notation, but we will remain as consistent as possible with previous chapters. What is bidirectional recurrent neural network (BRNN)? Another extension is the Bidirectional Recurrent Neural Network (BRNN), which we examine in subsection 5.3.2. BRNNs parse the sequence both forwards and backwards , if the entire sequence is known at the start, allowing the network to capture more information about the sequence. In this thesis, we explore one way to approach this problem: we develop a generic mathematical framework for representing neural networks , and demonstrate how this framework can be used to represent speci c neural network architectures. In chapter 1, we start by exploring mathematical contributions to neural networks ."}
+{"idx": 4, "title": "6 Neural Networks – 6.390 - Intro to Machine Learning", "date": "", "ddg_snippet": "You’ve probably been hearing a lot about “ neural networks .” Now that we have several useful machine-learning concepts (hypothesis classes, classification, regression, gradient descent, regularization, etc.), we are well equipped to understand neural networks in detail. This is, in some sense, the “third wave” of neural nets.", "subpage_snippet": "", "source": "introml.mit.edu", "link": "https://introml.mit.edu/notes/neural_networks.html", "content": "You’ve probably been hearing a lot about “ neural networks .” Now that we have several useful machine-learning concepts (hypothesis classes, classification, regression, gradient descent, regularization, etc.), we are well equipped to understand neural networks in detail. This is, in some sense, the “third wave” of neural nets."}
+{"idx": 5, "title": "An In-Memory Computing SRAM Macro for Memory-Augmented Neural ...", "date": "", "ddg_snippet": "Dec 2 , 2021 · In this brief, we present an SRAM macro designed for accelerating Memory-Augmented Neural Network ( MANN ). We first propose algorithmic optimizations for a few-shot learning algorithm employing MANN for efficient hardware implementation.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9632823", "content": "Dec 2 , 2021 · In this brief, we present an SRAM macro designed for accelerating Memory-Augmented Neural Network ( MANN ). We first propose algorithmic optimizations for a few-shot learning algorithm employing MANN for efficient hardware implementation."}
+{"idx": 6, "title": "Memory Augmented Neural Network for Meta Learning - Medium", "date": "", "ddg_snippet": "May 11, 2020 · Memory Augmented Neural Network [1] ( MANN ) is one of them which inspired the use of external memory from Neural Turing Machine [2]. Press enter or click to view image in full size", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/the-ai-team/memory-augmented-neural-network-for-meta-learning-case-study-56af9cc81ae2", "content": "May 11, 2020 · Memory Augmented Neural Network [1] ( MANN ) is one of them which inspired the use of external memory from Neural Turing Machine [2]. Press enter or click to view image in full size"}
+{"idx": 7, "title": "A Mathematical Framework for the Analysis of Neural Networks", "date": "", "ddg_snippet": "In this thesis, we explore one way to approach this problem: we develop a generic mathematical framework for representing neural networks , and demonstrate how this framework can be used to represent speci c neural network architectures. In chapter 1, we start by exploring mathematical contributions to neural networks .", "subpage_snippet": "", "source": "uwspace.uwaterloo.ca", "link": "https://uwspace.uwaterloo.ca/bitstreams/240b59bb-39f5-4e9b-860e-6754e2d2319f/download", "content": "In this thesis, we explore one way to approach this problem: we develop a generic mathematical framework for representing neural networks , and demonstrate how this framework can be used to represent speci c neural network architectures. In chapter 1, we start by exploring mathematical contributions to neural networks ."}
+{"idx": 8, "title": "MultiPDENet : PDE-embedded Learning with Multi-time-stepping for...", "date": "", "ddg_snippet": "The Correction Block within MultiPDENet utilizes a neural network , specifically the Fourier Neural Operator (FNO), to refine coarse solutions.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-multipdenet-pde-embedded-learning-with-cm6gypmal2nr407s36ekweuc5", "content": "The Correction Block within MultiPDENet utilizes a neural network , specifically the Fourier Neural Operator (FNO), to refine coarse solutions."}
+{"idx": 9, "title": "[2501.15987] MultiPDENet : PDE-embedded Learning with...", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.15987", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows."}
diff --git a/data/sampled_jsons/Murai_et_al._BIT-VO_in-pixel_processing_feature_tracking.jsonl b/data/sampled_jsons/Murai_et_al._BIT-VO_in-pixel_processing_feature_tracking.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..80c6284936436048eb46a0f1345a44d3ad1a92f2
--- /dev/null
+++ b/data/sampled_jsons/Murai_et_al._BIT-VO_in-pixel_processing_feature_tracking.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Murai MY | Berita Selebriti Malaysia", "date": "", "ddg_snippet": "Murai MY adalah portal berita yang menyajikan informasi hiburan dan dilengkapi dengan gambar eksklusif serta video terkini. Mulai dari gosip selebriti Malaysia, Indonesia dan seluruh negara, filem-filem Hollywood, muzik generasi sehinggalah Hotlist selebriti popular.", "subpage_snippet": "", "source": "murai.my", "link": "https://murai.my/", "content": "Murai MY adalah portal berita yang menyajikan informasi hiburan dan dilengkapi dengan gambar eksklusif serta video terkini. Mulai dari gosip selebriti Malaysia, Indonesia dan seluruh negara, filem-filem Hollywood, muzik generasi sehinggalah Hotlist selebriti popular."}
+{"idx": 1, "title": "Selebriti Archives - Murai MY", "date": "", "ddg_snippet": "MURAI yakin ramai yang tahu tentang kisah cinta antara Salman Khan dan Aishwarya Rai. Seluruh dunia telah menyaksikan bagaimana mereka...", "subpage_snippet": "", "source": "murai.my", "link": "https://murai.my/selebriti/", "content": "MURAI yakin ramai yang tahu tentang kisah cinta antara Salman Khan dan Aishwarya Rai. Seluruh dunia telah menyaksikan bagaimana mereka..."}
+{"idx": 2, "title": "“Sumpah Turn Off Gila, Sorry Anju, Unfollow Done ... - murai.my", "date": "", "ddg_snippet": "Aug 18, 2025 · Murai dengar-dengar, tujuan asal Anju join video itu sebenarnya untuk promosi produk. Tapi biasalah, bila bab melibatkan nama besar yang pernah ada kes panas, orang ramai memang tak boleh nak ‘move on’ begitu saja.", "subpage_snippet": "", "source": "murai.my", "link": "https://murai.my/zarina-anjoulie-daddy-ash-netizen-kecewa/", "content": "Aug 18, 2025 · Murai dengar-dengar, tujuan asal Anju join video itu sebenarnya untuk promosi produk. Tapi biasalah, bila bab melibatkan nama besar yang pernah ada kes panas, orang ramai memang tak boleh nak ‘move on’ begitu saja."}
+{"idx": 3, "title": "“Ramai Kurang Senang, Kami Tidak Menyiarkan Episod ... - murai.my", "date": "", "ddg_snippet": "Jul 31, 2025 · Episod terbaru bagi rancangan podcast ‘Apa Cerita’ yang dikendalikan oleh Asyraf Khalid telah mendapat kecama hebat daripada orang ramai termasuklah Caprice. Episod tersebut dikecam sel…", "subpage_snippet": "", "source": "murai.my", "link": "https://murai.my/ramai-kurang-senang-kami-tidak-menyiarkan-episod-tersebut-asyraf-khalid/", "content": "Jul 31, 2025 · Episod terbaru bagi rancangan podcast ‘Apa Cerita’ yang dikendalikan oleh Asyraf Khalid telah mendapat kecama hebat daripada orang ramai termasuklah Caprice. Episod tersebut dikecam sel…"}
+{"idx": 4, "title": "COO myBurgerLab Kena Pecat… Salah Siapa Sebenarnya? - murai.my", "date": "", "ddg_snippet": "Aug 22, 2025 · Rangkaian burger popular tempatan, myBurgerLab, baru-baru ini umumkan mereka terpaksa ambil langkah tegas dengan pecat Ketua Pegawai Operasi (COO) mereka, Andrew Chong. Semua ni gara-gara satu hant…", "subpage_snippet": "", "source": "murai.my", "link": "https://murai.my/coo-myburgerlab-kena-pecat-salah-siapa-sebenarnya/", "content": "Aug 22, 2025 · Rangkaian burger popular tempatan, myBurgerLab, baru-baru ini umumkan mereka terpaksa ambil langkah tegas dengan pecat Ketua Pegawai Operasi (COO) mereka, Andrew Chong. Semua ni gara-gara satu hant…"}
+{"idx": 5, "title": "Nadhif Basamalah Live September Ini! - murai.my", "date": "", "ddg_snippet": "Aug 17, 2025 · Peminat muzik balada, bersedialah! Penjaga Hati anda, Nadhif Basamalah, akan hadir secara live di Kuala Lumpur pada 11 September 2025 sempena konsert jelajah Nadhif Basamalah – Menjaga Hati Asia To…", "subpage_snippet": "", "source": "murai.my", "link": "https://murai.my/penjaga-hati-bakal-gegar-kl-nadhif-basamalah-live-september-ini/", "content": "Aug 17, 2025 · Peminat muzik balada, bersedialah! Penjaga Hati anda, Nadhif Basamalah, akan hadir secara live di Kuala Lumpur pada 11 September 2025 sempena konsert jelajah Nadhif Basamalah – Menjaga Hati Asia To…"}
+{"idx": 6, "title": "Wajah Ibunda Tengku Amir Shah Dalam Majalah Lama Buat ... -...", "date": "", "ddg_snippet": "Aug 1, 2025 · Serius MURAI cakap, penampilan Cik Puan Lisa waktu tu memang ‘timeless beauty’. Dengan senyuman manis, raut wajah kacukan dan aura lembut yang cukup mempersona, ramai netizen menyamakan kecantikannya macam heroin filem Melayu zaman 80-an!", "subpage_snippet": "", "source": "murai.my", "link": "https://murai.my/ibunda-tengku-amir-shah-cik-puan-nur-lisa-idris/", "content": "Aug 1, 2025 · Serius MURAI cakap, penampilan Cik Puan Lisa waktu tu memang ‘timeless beauty’. Dengan senyuman manis, raut wajah kacukan dan aura lembut yang cukup mempersona, ramai netizen menyamakan kecantikannya macam heroin filem Melayu zaman 80-an!"}
+{"idx": 7, "title": "Selepas Bergelar Miss Universe Malaysia 2025, Chloe Lim ... -...", "date": "", "ddg_snippet": "6 days ago · MURAI rasa, kalau beginilah aura Miss Universe Malaysia 2025, memang Malaysia boleh berbangga. Chloe bukan saja wakil kita di pentas dunia nanti, tapi bukti hidup bahawa kecantikan dan kebijaksanaan boleh jalan seiring.", "subpage_snippet": "", "source": "murai.my", "link": "https://murai.my/selepas-bergelar-miss-universe-malaysia-2025-chloe-lim-dilihat-bergandingan-dengan-menteri/", "content": "6 days ago · MURAI rasa, kalau beginilah aura Miss Universe Malaysia 2025, memang Malaysia boleh berbangga. Chloe bukan saja wakil kita di pentas dunia nanti, tapi bukti hidup bahawa kecantikan dan kebijaksanaan boleh jalan seiring."}
+{"idx": 8, "title": "Nak Lari Jauh Tanpa Drama? Ini Cara Pilih Outfit Running ... -...", "date": "", "ddg_snippet": "Sep 12, 2025 · Extra tips dari MURAI : Kalau nak feel lebih seronok, join community run lululemon setiap Ahad. Selain boleh test gear baru, kalian boleh rasa sendiri vibe “Running Gives”, lagi banyak lari, lagi banyak “feel good” yang kalian dapat.", "subpage_snippet": "", "source": "murai.my", "link": "https://murai.my/nak-lari-jauh-tanpa-drama-ini-cara-pilih-outfit-running-yang-betul/", "content": "Sep 12, 2025 · Extra tips dari MURAI : Kalau nak feel lebih seronok, join community run lululemon setiap Ahad. Selain boleh test gear baru, kalian boleh rasa sendiri vibe “Running Gives”, lagi banyak lari, lagi banyak “feel good” yang kalian dapat."}
+{"idx": 9, "title": "\"Adakah Saya Layak Untuk Program Rebat Bil Elektrik RM40 ... -...", "date": "", "ddg_snippet": "Aug 20, 2025 · Rasanya di kalangan pembaca MURAI , kamu mesti pernah dapat diskaun misteri dalam bil elektrik bulanan kan? Kalau kamu nak tahu, diskaun misteri tersebut adalah daripada Tenaga Nasional Berhad (TNB) menerusi Program Rebat Bil Elektrik RM40.", "subpage_snippet": "", "source": "murai.my", "link": "https://murai.my/adakah-saya-layak-untuk-program-rebat-bil-elektrik-rm40-jom-semak-kelayakan/", "content": "Aug 20, 2025 · Rasanya di kalangan pembaca MURAI , kamu mesti pernah dapat diskaun misteri dalam bil elektrik bulanan kan? Kalau kamu nak tahu, diskaun misteri tersebut adalah daripada Tenaga Nasional Berhad (TNB) menerusi Program Rebat Bil Elektrik RM40."}
diff --git a/data/sampled_jsons/N-step_return_formula_gamma_rewards_V_target.jsonl b/data/sampled_jsons/N-step_return_formula_gamma_rewards_V_target.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..73464e348072b71ed19f8bbde6e2bd430ed2cb6a
--- /dev/null
+++ b/data/sampled_jsons/N-step_return_formula_gamma_rewards_V_target.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "n-step Bootstrapping | Home - Peter Wang", "date": "", "ddg_snippet": "The n n - step return uses the value function V t + n 1 V t+n−1 to correct for the missing rewards beyond R t + n Rt+n. An important property of n n - step returns is that their expectation is guaranteed to be a better estimate of v π vπ than V t + n 1 V t+n−1 is, in a worst-state sense.", "subpage_snippet": "", "source": "pwang649.github.io", "link": "https://pwang649.github.io/myWiki/Reinforcement+Learning/Reinforcement+Learning+-+n-step", "content": "The n n - step return uses the value function V t + n 1 V t+n−1 to correct for the missing rewards beyond R t + n Rt+n. An important property of n n - step returns is that their expectation is guaranteed to be a better estimate of v π vπ than V t + n 1 V t+n−1 is, in a worst-state sense."}
+{"idx": 1, "title": "n-step Bootstrapping in Reinforcement Learning - Medium", "date": "", "ddg_snippet": "The value function is updated after observing a sequence of future rewards , which are discounted using a factor γ ( gamma ) to give less importance to distant rewards . Here's the formula :", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@amit25173/n-step-bootstrapping-in-reinforcement-learning-e4f70f264933", "content": "The value function is updated after observing a sequence of future rewards , which are discounted using a factor γ ( gamma ) to give less importance to distant rewards . Here's the formula :"}
+{"idx": 2, "title": "WTF-DeepRL/09_NStepDQN/readme.md at master - GitHub", "date": "", "ddg_snippet": "# compute \"target q-values\" for loss - it's what's inside square parentheses in the above formula . target_qvalues_for_actions = rewards + (self. gamma**self. n_multi_step) *next_state_values", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AmazingAng/WTF-DeepRL/blob/master/09_NStepDQN/readme.md", "content": "# compute \"target q-values\" for loss - it's what's inside square parentheses in the above formula . target_qvalues_for_actions = rewards + (self. gamma**self. n_multi_step) *next_state_values"}
+{"idx": 3, "title": "RL-UNIT5 - N-Step Returns & TD (λ) Algorithm in ... - Studocu", "date": "", "ddg_snippet": "Rt+k is the reward received k steps after time t. γ\\ gamma is the discount factor (how much future rewards are discounted). V (St+n) is the estimated value of the state after n steps .", "subpage_snippet": "", "source": "www.studocu.com", "link": "https://www.studocu.com/in/document/jawaharlal-nehru-technological-university-hyderabad/reinforcement-learning/rl-unit5-rl-notes/116715614", "content": "Rt+k is the reward received k steps after time t. γ\\ gamma is the discount factor (how much future rewards are discounted). V (St+n) is the estimated value of the state after n steps ."}
+{"idx": 4, "title": "Nstep Experience Replay - cpprb", "date": "", "ddg_snippet": "1 Overview To reduce fluctuation of random sampling effect especially at bootstrap phase, N-step reward (discounted summation) are useful. By expanding Bellman equation, a N-step target of Q function becomes \\ (\\sum _ {k=0}^ {N-1} \\ gamma ^k r_ {t+k} + \\ gamma ^ N \\max _ {a} Q (s_ {t+N},a)\\). According to W. Fedus et al., N-step reward can utilize larger buffer more effectively. Even though ...", "subpage_snippet": "", "source": "ymd_h.gitlab.io", "link": "https://ymd_h.gitlab.io/cpprb/features/nstep/", "content": "1 Overview To reduce fluctuation of random sampling effect especially at bootstrap phase, N-step reward (discounted summation) are useful. By expanding Bellman equation, a N-step target of Q function becomes \\ (\\sum _ {k=0}^ {N-1} \\ gamma ^k r_ {t+k} + \\ gamma ^ N \\max _ {a} Q (s_ {t+N},a)\\). According to W. Fedus et al., N-step reward can utilize larger buffer more effectively. Even though ..."}
+{"idx": 5, "title": "Calculating Return in Reinforcement Learning - apxml.com", "date": "", "ddg_snippet": "We introduce a discount factor, denoted by the Greek letter gamma (γ γ), where 0 ≤ γ ≤ 1 0 ≤ γ ≤ 1. The idea is to give less weight to rewards received further in the future compared to immediate rewards . The discounted return G t Gt is defined as the sum of future rewards , where each reward R t + k + 1 Rt+k+1 is multiplied by γ k ...", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/intro-to-reinforcement-learning/chapter-2-markov-decision-processes-mdps/return-cumulative-future-rewards", "content": "We introduce a discount factor, denoted by the Greek letter gamma (γ γ), where 0 ≤ γ ≤ 1 0 ≤ γ ≤ 1. The idea is to give less weight to rewards received further in the future compared to immediate rewards . The discounted return G t Gt is defined as the sum of future rewards , where each reward R t + k + 1 Rt+k+1 is multiplied by γ k ..."}
+{"idx": 6, "title": "N-step Returns - SERP AI", "date": "", "ddg_snippet": "N-step Returns : A Fundamental Technique in Reinforcement Learning | SERP AIhome / posts / n step returns", "subpage_snippet": "", "source": "serp.ai", "link": "https://serp.ai/posts/n-step-returns/", "content": "N-step Returns : A Fundamental Technique in Reinforcement Learning | SERP AIhome / posts / n step returns"}
+{"idx": 7, "title": "n-step reinforcement learning - GitHub Pages", "date": "", "ddg_snippet": "That is, the reward of the entire future from step t is estimated as the reward at t plus the estimated (discounted) future reward from t + 1. V (s t + 1) is estimated using the maximum expected return (Q-learning) or the estimated value of the next action (SARSA). This is a one- step return . Truncated Discounted Rewards # However, we can estimate a two- step return :", "subpage_snippet": "", "source": "gibberblot.github.io", "link": "https://gibberblot.github.io/rl-notes/single-agent/n-step.html", "content": "That is, the reward of the entire future from step t is estimated as the reward at t plus the estimated (discounted) future reward from t + 1. V (s t + 1) is estimated using the maximum expected return (Q-learning) or the estimated value of the next action (SARSA). This is a one- step return . Truncated Discounted Rewards # However, we can estimate a two- step return :"}
+{"idx": 8, "title": "N-step TD Method. The unification of SARSA and Monte… | by ... - Medium", "date": "", "ddg_snippet": "The update of one- step TD methods, on the other hand, is based on just the one next reward , bootstrapping from the value of the state one step later as a proxy for the remaining rewards .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/zero-equals-false/n-step-td-method-157d3875b9cb", "content": "The update of one- step TD methods, on the other hand, is based on just the one next reward , bootstrapping from the value of the state one step later as a proxy for the remaining rewards ."}
+{"idx": 9, "title": "REINFORCE Algorithm - GeeksforGeeks", "date": "", "ddg_snippet": "This generates a trajectory consisting of states, actions and rewards . Calculate Returns : For each time step t, calculate the return G_t which is the total reward obtained from time t onwards. Typically, this is the discounted sum of rewards : G_t = \\sum_ {k=t}^T \\ gamma ^ {k-t}", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/reinforce-algorithm/", "content": "This generates a trajectory consisting of states, actions and rewards . Calculate Returns : For each time step t, calculate the return G_t which is the total reward obtained from time t onwards. Typically, this is the discounted sum of rewards : G_t = \\sum_ {k=t}^T \\ gamma ^ {k-t}"}
diff --git a/data/sampled_jsons/N3DV_dataset_sequences_training_test.jsonl b/data/sampled_jsons/N3DV_dataset_sequences_training_test.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7cc3a1509b0d680c96eabefbd843a21636e50734
--- /dev/null
+++ b/data/sampled_jsons/N3DV_dataset_sequences_training_test.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "3DGStream: On-the-Fly Training of 3D Gaussians for ...", "date": "", "ddg_snippet": "For the training of initial 3DGs, we fine-tune the learning rates on the N3DV dataset based on the default settings of 3DG-S, and apply them to the Meet Room ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01444v4", "content": "For the training of initial 3DGs, we fine-tune the learning rates on the N3DV dataset based on the default settings of 3DG-S, and apply them to the Meet Room ..."}
+{"idx": 1, "title": "3DGStream: On-the-Fly Training of 3D Gaussians for Efficient ...", "date": "", "ddg_snippet": "by J Sun · 2024 · Cited by 110 — To demonstrate 3DGStream's competitive image quality, we drew comparisons with the quantitative results reported for the N3DV dataset in the respective papers ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_3DGStream_On-the-Fly_Training_of_3D_Gaussians_for_Efficient_Streaming_of_CVPR_2024_paper.pdf", "content": "by J Sun · 2024 · Cited by 110 — To demonstrate 3DGStream's competitive image quality, we drew comparisons with the quantitative results reported for the N3DV dataset in the respective papers ..."}
+{"idx": 2, "title": "Fast and Generalizable Streaming of Dynamic Scene ...", "date": "", "ddg_snippet": "14 Mar 2025 — Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , { c u t r ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "14 Mar 2025 — Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , { c u t r ..."}
+{"idx": 3, "title": "Fast and Generalizable Streaming of Dynamic Scene ...", "date": "", "ddg_snippet": "by J Yan · 2025 · Cited by 6 — Dataset Preparation: We split four sequences from the. N3DV dataset into the training set, with the remaining two sequences , {cut roasted beef,sear steak}, used ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "by J Yan · 2025 · Cited by 6 — Dataset Preparation: We split four sequences from the. N3DV dataset into the training set, with the remaining two sequences , {cut roasted beef,sear steak}, used ..."}
+{"idx": 4, "title": "yjb6/IGS: [CVPR25 Highlight] Instant Gaussian Stream: ...", "date": "", "ddg_snippet": "Our Training Dataset . Download our processed data from 4 sequences of N3DV , which can be directly used for training . It contains 1,200 optimized Gaussian ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Our Training Dataset . Download our processed data from 4 sequences of N3DV , which can be directly used for training . It contains 1,200 optimized Gaussian ..."}
+{"idx": 5, "title": "Streaming Radiance Fields for 3D Video Synthesis ...", "date": "", "ddg_snippet": "We capture the Meet Room dataset by using 13 Azure Kinect DK [1] cameras. We use the view in centre for test and the rest views of 12 cameras for training .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/57c2cc952f388f6185db98f441351c96-Supplemental-Conference.pdf", "content": "We capture the Meet Room dataset by using 13 Azure Kinect DK [1] cameras. We use the view in centre for test and the rest views of 12 cameras for training ."}
+{"idx": 6, "title": "Streaming Radiance Fields for 3D Video Synthesis", "date": "", "ddg_snippet": "by L LI · 2022 · Cited by 98 — For the experiments on the N3DV dataset , we train the pilot model with 750 iterations and then tune the full model with 500 iterations. We use RMSprop to ... 14 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/57c2cc952f388f6185db98f441351c96-Paper-Conference.pdf", "content": "by L LI · 2022 · Cited by 98 — For the experiments on the N3DV dataset , we train the pilot model with 750 iterations and then tune the full model with 500 iterations. We use RMSprop to ... 14 pages"}
+{"idx": 7, "title": "Instant Gaussian Stream: Fast and Generalizable ...", "date": "", "ddg_snippet": "14 Jun 2025 — Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , {cut roasted ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/cvpr/33775/paper", "content": "14 Jun 2025 — Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , {cut roasted ..."}
+{"idx": 8, "title": "Streaming Radiance Fields for 3D Video Synthesis", "date": "", "ddg_snippet": "by L Li · 2022 · Cited by 98 — We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oMhmv3hLOF2", "content": "by L Li · 2022 · Cited by 98 — We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world ..."}
+{"idx": 9, "title": "Swift4D: Adaptive divide-and-conquer Gaussian Splatting ...", "date": "", "ddg_snippet": "by J Wu · Cited by 7 — In this paper we propose Swift4D, a divide-and-conquer 3D Gaussian Splatting method that can handle static and dynamic primitives separately.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=c1RhJVTPwT", "content": "by J Wu · Cited by 7 — In this paper we propose Swift4D, a divide-and-conquer 3D Gaussian Splatting method that can handle static and dynamic primitives separately."}
diff --git a/data/sampled_jsons/NVIDIA_open-source_toolkit_optimizing_deploying_quantized_models_Jetson_devices.jsonl b/data/sampled_jsons/NVIDIA_open-source_toolkit_optimizing_deploying_quantized_models_Jetson_devices.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..abf42000ecd75cacb6352c944dad694f472f15fd
--- /dev/null
+++ b/data/sampled_jsons/NVIDIA_open-source_toolkit_optimizing_deploying_quantized_models_Jetson_devices.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Accelerating Quantized Networks with the NVIDIA QAT Toolkit ...", "date": "", "ddg_snippet": "Jun 16, 2022 · The NVIDIA Quantization-Aware Training (QAT) Toolkit for TensorFlow 2 enables easy quantization of networks for optimal TensorRT deployment on NVIDIA GPUs. Quantization-aware training simulates lower precision behavior during training by adding quantize and de-quantize nodes, minimizing accuracy loss and allowing for fine-tuning of model quantization and hyperparameters. The toolkit provides ...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/accelerating-quantized-networks-with-qat-toolkit-and-tensorrt/", "content": "Jun 16, 2022 · The NVIDIA Quantization-Aware Training (QAT) Toolkit for TensorFlow 2 enables easy quantization of networks for optimal TensorRT deployment on NVIDIA GPUs. Quantization-aware training simulates lower precision behavior during training by adding quantize and de-quantize nodes, minimizing accuracy loss and allowing for fine-tuning of model quantization and hyperparameters. The toolkit provides ..."}
+{"idx": 1, "title": "Post-Training Quantization of LLMs with NVIDIA NeMo and ...", "date": "", "ddg_snippet": "Sep 10, 2024 · The recent NeMo container release is a self-contained toolkit coming with all the required dependencies for applying PTQ and deploying quantized LLMs. NeMo and TensorRT Model Optimizer offer a broad range of models suitable for quantization, including the following families: GPT Llama Gemma StarCoder", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/post-training-quantization-of-llms-with-nvidia-nemo-and-nvidia-tensorrt-model-optimizer/", "content": "Sep 10, 2024 · The recent NeMo container release is a self-contained toolkit coming with all the required dependencies for applying PTQ and deploying quantized LLMs. NeMo and TensorRT Model Optimizer offer a broad range of models suitable for quantization, including the following families: GPT Llama Gemma StarCoder"}
+{"idx": 2, "title": "RTX-AI-Toolkit/llm-deployment/TensorRT-LLM_deployment.md at ...", "date": "", "ddg_snippet": "TensorRT-LLM is NVIDIA 's library for high-performance LLM inference across on- device and data center platforms. To deploy a quantized model using TensorRT-LLM, you first need to quantize the model using NVIDIA 's TensorRT Model Optimizer library. This guide provides a step-by-step process for quantizing and deploying LLMs trained with the RTX AI Toolkit .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/RTX-AI-Toolkit/blob/main/llm-deployment/TensorRT-LLM_deployment.md", "content": "TensorRT-LLM is NVIDIA 's library for high-performance LLM inference across on- device and data center platforms. To deploy a quantized model using TensorRT-LLM, you first need to quantize the model using NVIDIA 's TensorRT Model Optimizer library. This guide provides a step-by-step process for quantizing and deploying LLMs trained with the RTX AI Toolkit ."}
+{"idx": 3, "title": "NVIDIA TensorRT-LLM for Quantized Models - apxml.com", "date": "", "ddg_snippet": "While general-purpose deployment frameworks offer flexibility, achieving maximum performance for quantized Large Language Models (LLMs) on NVIDIA GPUs often requires specialized optimization . NVIDIA 's TensorRT-LLM is a library specifically designed for this purpose, offering a path to significantly enhance inference speed and efficiency by compiling models into highly optimized runtime engines ...", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/quantized-llm-deployment/chapter-4-optimizing-deploying-quantized-llms/gpu-optimization-tensorrt-llm", "content": "While general-purpose deployment frameworks offer flexibility, achieving maximum performance for quantized Large Language Models (LLMs) on NVIDIA GPUs often requires specialized optimization . NVIDIA 's TensorRT-LLM is a library specifically designed for this purpose, offering a path to significantly enhance inference speed and efficiency by compiling models into highly optimized runtime engines ..."}
+{"idx": 4, "title": "NVIDIA TensorRT Model Optimizer - GitHub", "date": "", "ddg_snippet": "The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models . [Input] Model Optimizer currently supports inputs of a Hugging Face, PyTorch or ONNX model .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/TensorRT-Model-Optimizer", "content": "The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models . [Input] Model Optimizer currently supports inputs of a Hugging Face, PyTorch or ONNX model ."}
+{"idx": 5, "title": "Optimizing LLMs for Performance and Accuracy with Post ...", "date": "", "ddg_snippet": "Aug 1, 2025 · Further quantizing to formats like FP4 unlocks substantial efficiency gains and performance, supported by a growing ecosystem of open-source techniques. NVIDIA TensorRT Model Optimizer post-training quantization (PTQ) framework offers a flexible and modular approach to applying these optimizations.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/optimizing-llms-for-performance-and-accuracy-with-post-training-quantization/", "content": "Aug 1, 2025 · Further quantizing to formats like FP4 unlocks substantial efficiency gains and performance, supported by a growing ecosystem of open-source techniques. NVIDIA TensorRT Model Optimizer post-training quantization (PTQ) framework offers a flexible and modular approach to applying these optimizations."}
+{"idx": 6, "title": "NVIDIA Hardware Innovations and Open Source ...", "date": "", "ddg_snippet": "22 Aug 2025 — ... Model Optimizer, provides optimized kernels and quantization tools for deploying models at scale. TensorRT-LLM taps the new Blackwell ...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/nvidia-hardware-innovations-and-open-source-contributions-are-shaping-ai/", "content": "22 Aug 2025 — ... Model Optimizer, provides optimized kernels and quantization tools for deploying models at scale. TensorRT-LLM taps the new Blackwell ..."}
+{"idx": 7, "title": "AI Models", "date": "", "ddg_snippet": "Explore and deploy top AI models built by the community, accelerated by NVIDIA's AI inference platform, and run on NVIDIA-accelerated infrastructure.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/ai-models", "content": "Explore and deploy top AI models built by the community, accelerated by NVIDIA's AI inference platform, and run on NVIDIA-accelerated infrastructure."}
+{"idx": 8, "title": "OpenVLA", "date": "", "ddg_snippet": "The tutorials's goal is to provide optimized quantization and inference for deploying VLA models ... models, and tools for advancing physical AI and robotics.", "subpage_snippet": "", "source": "www.jetson-ai-lab.com", "link": "https://www.jetson-ai-lab.com/openvla.html", "content": "The tutorials's goal is to provide optimized quantization and inference for deploying VLA models ... models, and tools for advancing physical AI and robotics."}
+{"idx": 9, "title": "TensorRT SDK | NVIDIA Developer", "date": "", "ddg_snippet": "NVIDIA TensorRT Model Optimizer provides easy-to-use quantization techniques, including post-training quantization and quantization-aware training to compress ...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/tensorrt", "content": "NVIDIA TensorRT Model Optimizer provides easy-to-use quantization techniques, including post-training quantization and quantization-aware training to compress ..."}
diff --git a/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_arXiv.jsonl b/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e932cb58f4c8666d4c5c966c5fe1de2adddf7e5a
--- /dev/null
+++ b/data/sampled_jsons/Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05028v1", "content": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address ..."}
+{"idx": 1, "title": "N -O ONLINE LEARNING FOR MULTI A S COORDINATION: TIGHT AP ...", "date": "", "ddg_snippet": "N -O ONLINE LEARNING FOR MULTI A S COORDINATION: TIGHT AP PROXIMATION AND COMMUNICATION EFFICIENCY NEAR-OPTIMAL ONLINE LEARNING FOR MULTI-AGENT SUBMODULAR COORDINATION : TIGHT AP-PROXIMATION AND COMMUNICATION EFFICIENCY", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i8dYPGdB1C", "content": "N -O ONLINE LEARNING FOR MULTI A S COORDINATION: TIGHT AP PROXIMATION AND COMMUNICATION EFFICIENCY NEAR-OPTIMAL ONLINE LEARNING FOR MULTI-AGENT SUBMODULAR COORDINATION : TIGHT AP-PROXIMATION AND COMMUNICATION EFFICIENCY"}
+{"idx": 2, "title": "[2502.05028] Near - Optimal Online Learning for Multi - Agent ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency, by Qixin Zhang and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05028", "content": "View a PDF of the paper titled Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency, by Qixin Zhang and 4 other authors."}
+{"idx": 3, "title": "Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "Authors Qixin ZHANG, Zongqi Wan, Yu Yang, Li Shen, Dacheng Tao Abstract Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/3340ee1e4a8bad8d32c35721712b4d0a-Abstract-Conference.html", "content": "Authors Qixin ZHANG, Zongqi Wan, Yu Yang, Li Shen, Dacheng Tao Abstract Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning , robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the ..."}
+{"idx": 4, "title": "NEAR-OPTIMAL ONLINE LEARNING FOR MULTI-AGENT SUBMODULAR ...", "date": "", "ddg_snippet": "Dive into the research topics of ' NEAR-OPTIMAL ONLINE LEARNING FOR MULTI-AGENT SUBMODULAR COORDINATION : TIGHT APPROXIMATION AND COMMUNICATION EFFICIENCY'. Together they form a unique fingerprint.", "subpage_snippet": "", "source": "scholars.cityu.edu.hk", "link": "https://scholars.cityu.edu.hk/en/publications/near-optimal-online-learning-for-multi-agent-submodular-coordinat", "content": "Dive into the research topics of ' NEAR-OPTIMAL ONLINE LEARNING FOR MULTI-AGENT SUBMODULAR COORDINATION : TIGHT APPROXIMATION AND COMMUNICATION EFFICIENCY'. Together they form a unique fingerprint."}
+{"idx": 5, "title": "Robust Near-Optimal Coordination in Uncertain Multiagent ...", "date": "", "ddg_snippet": "This article addresses the robust coordination problem for nonlinear uncertain second-order multiagent networks with motion constraints, including velocity saturation and collision avoidance. A single-critic neural network-based approximate dynamic programming approach and exact estimation of unknown dynamics are employed to learn online the optimal value function and controller. By ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9619871", "content": "This article addresses the robust coordination problem for nonlinear uncertain second-order multiagent networks with motion constraints, including velocity saturation and collision avoidance. A single-critic neural network-based approximate dynamic programming approach and exact estimation of unknown dynamics are employed to learn online the optimal value function and controller. By ..."}
+{"idx": 6, "title": "Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "Feb 7, 2025 · View on arXiv @article {zhang2025_2502.05028, title= { Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency }, author= { Qixin Zhang and Zongqi Wan and Yu Yang and Li Shen and Dacheng Tao }, journal= { arXiv preprint arXiv :2502.05028}, year= { 2025 } }", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.05028", "content": "Feb 7, 2025 · View on arXiv @article {zhang2025_2502.05028, title= { Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency }, author= { Qixin Zhang and Zongqi Wan and Yu Yang and Li Shen and Dacheng Tao }, journal= { arXiv preprint arXiv :2502.05028}, year= { 2025 } }"}
+{"idx": 7, "title": "(PDF) Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Near - optimal online learning for multi -. Agent submodular coordination : tight ap-. Proximation and communication efficiency.develop a surrogate function for the multi-linear extension of submodular functions with curvature. c.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388847678_Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_Tight_Approximation_and_Communication_Efficiency", "content": "Near - optimal online learning for multi -. Agent submodular coordination : tight ap-. Proximation and communication efficiency.develop a surrogate function for the multi-linear extension of submodular functions with curvature. c."}
+{"idx": 8, "title": "ICLR Poster Near - Optimal Online Learning for Multi - Agent ...", "date": "", "ddg_snippet": "To address these challenges, we firstly present a $\\textbf{MA-OSMA}$ algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28714", "content": "To address these challenges, we firstly present a $\\textbf{MA-OSMA}$ algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph..."}
+{"idx": 9, "title": "Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Moreover, $\\textbf{MA-OSMA}$ leverages a novel surrogate gradient to avoid sub-optimal stationary points.Go Home. Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2502.05028", "content": "Moreover, $\\textbf{MA-OSMA}$ leverages a novel surrogate gradient to avoid sub-optimal stationary points.Go Home. Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency."}
diff --git a/data/sampled_jsons/NeurELA_Ts-Attn_architecture_tensor_transformation_dimensions_candidates_hidden_size_before_Attn_int.jsonl b/data/sampled_jsons/NeurELA_Ts-Attn_architecture_tensor_transformation_dimensions_candidates_hidden_size_before_Attn_int.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85458c0967ec4a756e95740a0fdd40dfaa2fee48
--- /dev/null
+++ b/data/sampled_jsons/NeurELA_Ts-Attn_architecture_tensor_transformation_dimensions_candidates_hidden_size_before_Attn_int.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Does load_ attn _procs work with safetensors? - Diffusers ...", "date": "", "ddg_snippet": "Mar 27, 2023 · I am not having much luck fining working examples of load_ attn _procs that work with anything other than a pytorch.bin file so maybe it just not there yet. If anyone has gotten this to work, I would love to know it!", "subpage_snippet": "", "source": "discuss.huggingface.co", "link": "https://discuss.huggingface.co/t/does-load-attn-procs-work-with-safetensors/34827", "content": "Mar 27, 2023 · I am not having much luck fining working examples of load_ attn _procs that work with anything other than a pytorch.bin file so maybe it just not there yet. If anyone has gotten this to work, I would love to know it!"}
+{"idx": 1, "title": "Advancements in Black-Box Optimization with NeurELA", "date": "", "ddg_snippet": "NeurELA improves Black-Box Optimization through real-time landscape analysis and meta-learning.By leveraging this evolutionary approach, NeurELA can adapt to diverse MetaBBO tasks, even those it has not encountered before . # Zero-shot Generalization.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-24-advancements-in-black-box-optimization-with-neurela--a3z2l1o", "content": "NeurELA improves Black-Box Optimization through real-time landscape analysis and meta-learning.By leveraging this evolutionary approach, NeurELA can adapt to diverse MetaBBO tasks, even those it has not encountered before . # Zero-shot Generalization."}
+{"idx": 2, "title": "Installing flash- attn without compiling it | Simon Willison’s TILs", "date": "", "ddg_snippet": "pip install flash- attn --no-build-isolation.flash_ attn -2.6.3+cu123torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl. This seemed to work (and installed in just a couple of seconds): import flash_ attn flash_ attn .__version__.", "subpage_snippet": "", "source": "til.simonwillison.net", "link": "https://til.simonwillison.net/python/installing-flash-attention", "content": "pip install flash- attn --no-build-isolation.flash_ attn -2.6.3+cu123torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl. This seemed to work (and installed in just a couple of seconds): import flash_ attn flash_ attn .__version__."}
+{"idx": 3, "title": "Neural Network Architecture: Criteria for Choosing ... - Baeldung", "date": "", "ddg_snippet": "Feb 13, 2025 · Explore methods for identifying the correct size and number of hidden layers in a neural network.", "subpage_snippet": "", "source": "www.baeldung.com", "link": "https://www.baeldung.com/cs/neural-networks-hidden-layers-criteria", "content": "Feb 13, 2025 · Explore methods for identifying the correct size and number of hidden layers in a neural network."}
+{"idx": 4, "title": "Neural Exploratory Landscape Analysis", "date": "", "ddg_snippet": "represents the hidden dimension used in the subsequent two-stage attention module.This highly parallelizable, attention-based architecture significantly boosts the scalability of NeurELA as the number of candidates .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v2", "content": "represents the hidden dimension used in the subsequent two-stage attention module.This highly parallelizable, attention-based architecture significantly boosts the scalability of NeurELA as the number of candidates ."}
+{"idx": 5, "title": "arXiv:2408.10672v2 [cs.LG] 26 Sep 2024", "date": "", "ddg_snippet": "e Ts-Attn module, i.e., l = (1, 3, 5). We additionally pre-train three MLP baselines, which substitute the Ts-Attn module in NeurELA with a linear feed-forward Table 1: The average wall ti", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.10672v2", "content": "e Ts-Attn module, i.e., l = (1, 3, 5). We additionally pre-train three MLP baselines, which substitute the Ts-Attn module in NeurELA with a linear feed-forward Table 1: The average wall ti"}
+{"idx": 6, "title": "Growing models in the hidden size dimension – AI-ASSeSS", "date": "", "ddg_snippet": "Oct 25, 2024 · The underlying mathematical implementation of a model makes increasing the hidden size dimension, while keeping loss equal, far more complicated than increasing size in other dimensions .", "subpage_snippet": "", "source": "coldint.io", "link": "https://coldint.io/growing-models-in-the-hidden-size-dimension/", "content": "Oct 25, 2024 · The underlying mathematical implementation of a model makes increasing the hidden size dimension, while keeping loss equal, far more complicated than increasing size in other dimensions ."}
+{"idx": 7, "title": "LLaMA* : non-uniform hidden state · ggml-org llama.cpp ...", "date": "", "ddg_snippet": "Nov 20, 2023 · Based on some observations and intuition about the variable importance of the layers in the LLaMA architecture during quantization, we propose a slight change in the architecture that makes the size of the hidden state variable - i.e. it changes across the layers of the model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ggml-org/llama.cpp/discussions/4147", "content": "Nov 20, 2023 · Based on some observations and intuition about the variable importance of the layers in the LLaMA architecture during quantization, we propose a slight change in the architecture that makes the size of the hidden state variable - i.e. it changes across the layers of the model."}
+{"idx": 8, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "Ts - Attn receives Et and then advances the information sharing at both cross-solution and cross- dimension levels.This highly parallelizable, attention-based architecture significantly boosts the scalability of NeurELA as the number of candidates m or dimensions d increases.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=EEI5R89Cmv", "content": "Ts - Attn receives Et and then advances the information sharing at both cross-solution and cross- dimension levels.This highly parallelizable, attention-based architecture significantly boosts the scalability of NeurELA as the number of candidates m or dimensions d increases."}
+{"idx": 9, "title": "deploy_seq2seq_hybrid_frontend_tutorial.ipynb - Colab", "date": "", "ddg_snippet": "attn _energies = attn _energies.t(). # Return the softmax normalized probability scores (with added dimension ).self. attn = Attn ( attn _model, hidden _ size ). def forward(self, input_step, last_hidden, encoder_outputs): # Note: we run this one step (word) at a time.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/_downloads/deploy_seq2seq_hybrid_frontend_tutorial.ipynb", "content": "attn _energies = attn _energies.t(). # Return the softmax normalized probability scores (with added dimension ).self. attn = Attn ( attn _model, hidden _ size ). def forward(self, input_step, last_hidden, encoder_outputs): # Note: we run this one step (word) at a time."}
diff --git a/data/sampled_jsons/NeurELA_training_efficiency_evolution_strategies_limitations_neuroevolution.jsonl b/data/sampled_jsons/NeurELA_training_efficiency_evolution_strategies_limitations_neuroevolution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..52d0d7f3826c71385d7c42f7eb5657a6342ab73f
--- /dev/null
+++ b/data/sampled_jsons/NeurELA_training_efficiency_evolution_strategies_limitations_neuroevolution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enhancing Neural Network Training Through Neuroevolutionary ...", "date": "", "ddg_snippet": "Mar 28, 2025 · Evolutionary algorithms (EAs) offer an alternative approach to optimizing neural networks by mimicking biological evolution through selection, mutation, and crossover operators. Neuroevolution, which integrates EAs into ANN training , optimizes neural networks by evolving a population of candidate models through genetic operators.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7390/13/7/1114", "content": "Mar 28, 2025 · Evolutionary algorithms (EAs) offer an alternative approach to optimizing neural networks by mimicking biological evolution through selection, mutation, and crossover operators. Neuroevolution, which integrates EAs into ANN training , optimizes neural networks by evolving a population of candidate models through genetic operators."}
+{"idx": 1, "title": "Neuroevolution in Deep Neural Networks: Current Trends and ...", "date": "", "ddg_snippet": "Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05415", "content": "Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs."}
+{"idx": 2, "title": "Neuroevolution insights into biological neural computation", "date": "", "ddg_snippet": "Neuroevolution is a computational modeling technique in which an artificial neural network architecture and/or parameters are optimized through evolutionary computation. Much of current work in neural networks and artificial intelligence (AI) in general builds on gradient descent methods, i.e. learning a statistical model of a large training set.", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/science.adp7478", "content": "Neuroevolution is a computational modeling technique in which an artificial neural network architecture and/or parameters are optimized through evolutionary computation. Much of current work in neural networks and artificial intelligence (AI) in general builds on gradient descent methods, i.e. learning a statistical model of a large training set."}
+{"idx": 3, "title": "Neuroevolution in Artificial Intelligence - IRE Journals", "date": "", "ddg_snippet": "Neuroevolution, within the expansive domain of artificial intelligence (AI), marks a significant departure from conventional approaches to training artificial neural networks (ANNs)[10]. Instead of relying on standard optimization methods, neuroevolution draws inspiration from the intricate processes of biological evolution . This not only refines the parameters of neural networks but also ...", "subpage_snippet": "", "source": "www.irejournals.com", "link": "https://www.irejournals.com/formatedpaper/1705504.pdf", "content": "Neuroevolution, within the expansive domain of artificial intelligence (AI), marks a significant departure from conventional approaches to training artificial neural networks (ANNs)[10]. Instead of relying on standard optimization methods, neuroevolution draws inspiration from the intricate processes of biological evolution . This not only refines the parameters of neural networks but also ..."}
+{"idx": 4, "title": "Neuroevolution: Evolving Neural Network with Genetic Algorithms", "date": "", "ddg_snippet": "Nov 30, 2023 · This approach draws inspiration from the principles of natural evolution . Before getting into neuroevolution in detail, let us first overview the concepts of neural networks and genetic algorithm. 1.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@roopal.tatiwar20/neuroevolution-evolving-neural-network-with-genetic-algorithms-8ca2165ad04c", "content": "Nov 30, 2023 · This approach draws inspiration from the principles of natural evolution . Before getting into neuroevolution in detail, let us first overview the concepts of neural networks and genetic algorithm. 1."}
+{"idx": 5, "title": "Designing neural networks through neuroevolution - Nature", "date": "", "ddg_snippet": "Jan 7, 2019 · Mania et al. 45 showed that a simplified neuroevolution variant of evolution strategies , training a single-layer neural network (that is, learning a linear mapping from states to actions ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-018-0006-z", "content": "Jan 7, 2019 · Mania et al. 45 showed that a simplified neuroevolution variant of evolution strategies , training a single-layer neural network (that is, learning a linear mapping from states to actions ..."}
+{"idx": 6, "title": "Neuroevolution in Deep Neural Networks: Current Trends and ...", "date": "", "ddg_snippet": "A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimization of DNNs. Neuroevolution is a ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9383028", "content": "A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimization of DNNs. Neuroevolution is a ..."}
+{"idx": 7, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "by Z Ma · Cited by 2 — NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy . Extensive experiments show that NeurELA achieves ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=EEI5R89Cmv", "content": "by Z Ma · Cited by 2 — NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy . Extensive experiments show that NeurELA achieves ..."}
+{"idx": 8, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "27 Mar 2025 — However, a major limitation is that the training efficiency of NeurELA ... Hence, we train the neural network in NeurELA through neuroevolution .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v3", "content": "27 Mar 2025 — However, a major limitation is that the training efficiency of NeurELA ... Hence, we train the neural network in NeurELA through neuroevolution ."}
+{"idx": 9, "title": "[Literature Review] Neural Exploratory Landscape Analysis", "date": "", "ddg_snippet": "The training follows a multi-task neuroevolution paradigm to maximize the expected performance relative to various MetaBBO tasks. Zero-Shot Generalization and ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/neural-exploratory-landscape-analysis", "content": "The training follows a multi-task neuroevolution paradigm to maximize the expected performance relative to various MetaBBO tasks. Zero-Shot Generalization and ..."}
diff --git a/data/sampled_jsons/Neural_Persistence_Dynamics_crocker_plots_scalability_issues_Section_2.jsonl b/data/sampled_jsons/Neural_Persistence_Dynamics_crocker_plots_scalability_issues_Section_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..630c86fe3ab918c436d8d27f605824caffa22a27
--- /dev/null
+++ b/data/sampled_jsons/Neural_Persistence_Dynamics_crocker_plots_scalability_issues_Section_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "Despite remarkable success in distinguishing different configurations of models for collective behav-ior, all approaches suffer scalability issues , either in terms of the dimensionality of the vectorized persistence diagrams (as with the PSK approach of [23]), or in terms of the number of observation sequences (as is the case for crocker plots ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15732v1", "content": "Despite remarkable success in distinguishing different configurations of models for collective behav-ior, all approaches suffer scalability issues , either in terms of the dimensionality of the vectorized persistence diagrams (as with the PSK approach of [23]), or in terms of the number of observation sequences (as is the case for crocker plots ..."}
+{"idx": 1, "title": "plus-rkwitt/neural_persistence_dynamics - GitHub", "date": "", "ddg_snippet": "The Crocker stacks baseline comparison is implemented in crocker_stacks.py. To execute this script, you must first prepare the data using compute_cs.py. Additionally, you need to install the teaspoon library with the appropriate version for computing the Crocker stacks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plus-rkwitt/neural_persistence_dynamics", "content": "The Crocker stacks baseline comparison is implemented in crocker_stacks.py. To execute this script, you must first prepare the data using compute_cs.py. Additionally, you need to install the teaspoon library with the appropriate version for computing the Crocker stacks."}
+{"idx": 2, "title": "(PDF) Neural Persistence Dynamics - ResearchGate", "date": "", "ddg_snippet": "PDF | We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems... | Find, read and cite all the research you ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380895040_Neural_Persistence_Dynamics", "content": "PDF | We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems... | Find, read and cite all the research you ..."}
+{"idx": 3, "title": "[2405.15732] Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.15732", "content": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ..."}
+{"idx": 4, "title": "neural_persistence_dynamics/crocker_stacks.py at main - GitHub", "date": "", "ddg_snippet": "Skip to content Dismiss alert plus-rkwitt / neural_persistence_dynamics Public Notifications You must be signed in to change notification settings Fork 0 Star 6 Code Issues Pull requests Projects Security Insights", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plus-rkwitt/neural_persistence_dynamics/blob/main/crocker_stacks.py", "content": "Skip to content Dismiss alert plus-rkwitt / neural_persistence_dynamics Public Notifications You must be signed in to change notification settings Fork 0 Star 6 Code Issues Pull requests Projects Security Insights"}
+{"idx": 5, "title": "A Case Study on Identifying Bifurcation and Chaos with CROCKER Plots", "date": "", "ddg_snippet": "The CROCKER plot is a coarsened but easy to visualize representation of the data in a one-parameter varying family of persistence barcodes. In this paper, we use the CROCKER plot to view changes ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/359937143_A_Case_Study_on_Identifying_Bifurcation_and_Chaos_with_CROCKER_Plots", "content": "The CROCKER plot is a coarsened but easy to visualize representation of the data in a one-parameter varying family of persistence barcodes. In this paper, we use the CROCKER plot to view changes ..."}
+{"idx": 6, "title": "Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "Crocker stacks [Xian22a], an extension of this concept, adds a smoothing step that gradually reduces the impact of points of low persistence and, upon discretization, yields a third dimension to crocker plots .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15732", "content": "Crocker stacks [Xian22a], an extension of this concept, adds a smoothing step that gradually reduces the impact of points of low persistence and, upon discretization, yields a third dimension to crocker plots ."}
+{"idx": 7, "title": "PDF Intrinsic Dimension, Persistent Homology and Generalization in Neural ...", "date": "", "ddg_snippet": "They further-more show that Neural Persistence reflects many of the properties of convergence and can classify weights based on whether they overfit, underfit, or exactly fit the data. In a parallel line of work, [DZF19] analyze neural network training by calculating topological properties of the underlying graph structure.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/35a12c43227f217207d4e06ffefe39d3-Paper.pdf", "content": "They further-more show that Neural Persistence reflects many of the properties of convergence and can classify weights based on whether they overfit, underfit, or exactly fit the data. In a parallel line of work, [DZF19] analyze neural network training by calculating topological properties of the underlying graph structure."}
+{"idx": 8, "title": "Detecting bifurcations in dynamical systems with CROCKER plots", "date": "", "ddg_snippet": "Existing tools for bifurcation detection from signals of dynamical systems typically are either limited to a special class of systems or they require carefully chosen input parameters and a significant expertise to interpret the results. Therefore, we describe an alternative method based on persistent homology—a tool from topological data analysis—that utilizes Betti numbers and CROCKER ...", "subpage_snippet": "", "source": "pubs.aip.org", "link": "https://pubs.aip.org/aip/cha/article/32/9/093111/2835867/Detecting-bifurcations-in-dynamical-systems-with", "content": "Existing tools for bifurcation detection from signals of dynamical systems typically are either limited to a special class of systems or they require carefully chosen input parameters and a significant expertise to interpret the results. Therefore, we describe an alternative method based on persistent homology—a tool from topological data analysis—that utilizes Betti numbers and CROCKER ..."}
+{"idx": 9, "title": "Persistent homology approach for uncovering transitions to Chaos", "date": "", "ddg_snippet": "The primary goal of this study is the time-series analysis of the Rössler system, specifically constructing bifurcation diagrams using the natural frequency as the control parameter [21]. To generate CROCKER plots , we transformed the time-series data for each value of the control parameter into persistence diagrams.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0960077925000670", "content": "The primary goal of this study is the time-series analysis of the Rössler system, specifically constructing bifurcation diagrams using the natural frequency as the control parameter [21]. To generate CROCKER plots , we transformed the time-series data for each value of the control parameter into persistence diagrams."}
diff --git a/data/sampled_jsons/No_Free_Delivery_Service_Theorem_1_epistemic_limits_test_validity.jsonl b/data/sampled_jsons/No_Free_Delivery_Service_Theorem_1_epistemic_limits_test_validity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..407acd8350d2986e9bdbd3a491f1e7f31bc8dd7c
--- /dev/null
+++ b/data/sampled_jsons/No_Free_Delivery_Service_Theorem_1_epistemic_limits_test_validity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF No free delivery service", "date": "", "ddg_snippet": "In particular, I have shown that there exists no free delivery service of data that allows for test validity on a global scale in this setting. While valid inferences are possible with respect to the sampling distribution S and within high -cores, they are unlikely if T extends to the entirety of the system.", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2024/file/b97fc02c9e536d68300d82be05c23aa2-Paper-Conference.pdf", "content": "In particular, I have shown that there exists no free delivery service of data that allows for test validity on a global scale in this setting. While valid inferences are possible with respect to the sampling distribution S and within high -cores, they are unlikely if T extends to the entirety of the system."}
+{"idx": 1, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "Rapid model validation via the train- test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity . Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems, including ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.13653", "content": "Rapid model validation via the train- test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity . Yet, without rigorous model validation we cannot ensure the intended outcomes of deployed AI systems, including ..."}
+{"idx": 2, "title": "No Free Delivery Service: Epistemic limits of passive data collection ...", "date": "", "ddg_snippet": "Rapid model validation via the train- test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity .", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/hash/b97fc02c9e536d68300d82be05c23aa2-Abstract-Conference.html", "content": "Rapid model validation via the train- test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity ."}
+{"idx": 3, "title": "NoFreeDeliveryService No - arXiv.org", "date": "", "ddg_snippet": "mplex social systems. In particular, I have shown that there exists no free delivery service of data that allows for test validity on a global cale in this setting. While valid inferences are possible with respect to the sampling distribution and within high -cores, they are unlikely if extends to the e", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.13653v1", "content": "mplex social systems. In particular, I have shown that there exists no free delivery service of data that allows for test validity on a global cale in this setting. While valid inferences are possible with respect to the sampling distribution and within high -cores, they are unlikely if extends to the e"}
+{"idx": 4, "title": "Validity Beyond Measurement: Why Psychometric Validity Is Insufficient ...", "date": "", "ddg_snippet": "We use the working concept validity of the epistemic process, or - in short - epistemic validity , to elaborate and denominate the connection between the various epistemic problems voiced in the literature, and to allow for concrete consideration of their impact on the validity of conducted psychotherapy research.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6423000/", "content": "We use the working concept validity of the epistemic process, or - in short - epistemic validity , to elaborate and denominate the connection between the various epistemic problems voiced in the literature, and to allow for concrete consideration of their impact on the validity of conducted psychotherapy research."}
+{"idx": 5, "title": "No Free Delivery Service: Epistemic limits of passive data...", "date": "", "ddg_snippet": "The paper shows that there is \" no free delivery service \" of data that allows inference/ test validity on a global scale for complex social systems. More importantly, the paper provides the metrics and necessary conditions that limit the scope of AI in complex social systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=XZ0fpoAKEB", "content": "The paper shows that there is \" no free delivery service \" of data that allows inference/ test validity on a global scale for complex social systems. More importantly, the paper provides the metrics and necessary conditions that limit the scope of AI in complex social systems."}
+{"idx": 6, "title": "No Free Delivery Service: Epistemic Limits of Passive Data Collection ...", "date": "", "ddg_snippet": "No Free Delivery Service Epistemic limits of passive data collection in complex social systems Maximilian Nickel FAIR at Meta Rapid model validation via the train- test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/795949995/2411-13653v1", "content": "No Free Delivery Service Epistemic limits of passive data collection in complex social systems Maximilian Nickel FAIR at Meta Rapid model validation via the train- test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test ..."}
+{"idx": 7, "title": "[PDF] No Free Delivery Service: Epistemic limits of passive data ...", "date": "", "ddg_snippet": "Rapid model validation via the train- test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/No-Free-Delivery-Service:-Epistemic-limits-of-data-Nickel/3e22669b497e2b1c674cdf0dfce85afd80ed5298", "content": "Rapid model validation via the train- test paradigm has been a key driver for the breathtaking progress in machine learning and AI. However, modern AI systems often depend on a combination of tasks and data collection practices that violate all assumptions ensuring test validity ."}
+{"idx": 8, "title": "Full text of \"Michael Quinn Patton Qualitative Research & Evaluation ...", "date": "", "ddg_snippet": "This simply means that the investigator does not set out to prove a particular perspective or manipulate the data to arrive at predisposed propositions. The neutral investigator enters the research arena with no axe to grind, no theory to prove (to test but not to prove), and no predetermined results to support.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/stream/michael-quinn-patton-qualitative-research-evaluation-methods-integrating-theory-/Michael+Quinn+Patton+-+Qualitative+Research+&+Evaluation+Methods_+Integrating+Theory+and+Practice-Sage+Publications,+Inc+(2014)_djvu.txt", "content": "This simply means that the investigator does not set out to prove a particular perspective or manipulate the data to arrive at predisposed propositions. The neutral investigator enters the research arena with no axe to grind, no theory to prove (to test but not to prove), and no predetermined results to support."}
+{"idx": 9, "title": "My AI skeptic friends are all nuts | Hacker News", "date": "", "ddg_snippet": "My AI skeptic friends are all nuts (fly.io) 2356 points by tabletcorry 3 months ago | hide | past | favorite | 2826 comments", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=44163063", "content": "My AI skeptic friends are all nuts (fly.io) 2356 points by tabletcorry 3 months ago | hide | past | favorite | 2826 comments"}
diff --git a/data/sampled_jsons/Normalizing_Flows_are_Capable_Generative_Models_Section_2.5_Score_Based_Denoising.jsonl b/data/sampled_jsons/Normalizing_Flows_are_Capable_Generative_Models_Section_2.5_Score_Based_Denoising.jsonl
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index 0000000000000000000000000000000000000000..da475aac0416912cf616d944e4288b120ed248e6
--- /dev/null
+++ b/data/sampled_jsons/Normalizing_Flows_are_Capable_Generative_Models_Section_2.5_Score_Based_Denoising.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "As a remedy, we propose a straightforward training-free technique that effectively denoises the generated samples, by drawing inspiration from score - based generative models . The idea is as follows. Consider the joint distribution (x, y) where x ∼ p data and y = x + ε for ε ∼ 𝒩 (0, σ 2 I).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "As a remedy, we propose a straightforward training-free technique that effectively denoises the generated samples, by drawing inspiration from score - based generative models . The idea is as follows. Consider the joint distribution (x, y) where x ∼ p data and y = x + ε for ε ∼ 𝒩 (0, σ 2 I)."}
+{"idx": 1, "title": "Combining normalizing flows with decision trees for ...", "date": "", "ddg_snippet": "Feb 1, 2025 · This section reviews scientific research related to this study, covering approaches for interpretable unsupervised outlier detection and methods for unsupervised outlier detection based on normalizing flows .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197624019298", "content": "Feb 1, 2025 · This section reviews scientific research related to this study, covering approaches for interpretable unsupervised outlier detection and methods for unsupervised outlier detection based on normalizing flows ."}
+{"idx": 2, "title": "NinA: Normalizing Flows in Action. Training VLA Models with ...", "date": "", "ddg_snippet": "In this preliminary work, we introduce Normalizing Flows in Action (NinA) – a VLA variant that replaces the diffusion- based action expert with a normalizing flow model. Using the FLOWER VLA ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/394940657_NinA_Normalizing_Flows_in_Action_Training_VLA_Models_with_Normalizing_Flows/fulltext/68ad29dfca495d76982fea2e/NinA-Normalizing-Flows-in-Action-Training-VLA-Models-with-Normalizing-Flows.pdf", "content": "In this preliminary work, we introduce Normalizing Flows in Action (NinA) – a VLA variant that replaces the diffusion- based action expert with a normalizing flow model. Using the FLOWER VLA ..."}
+{"idx": 3, "title": "Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "As a remedy, we propose a straightfor- ward training-free technique that effectively denoises the generated samples, by drawing inspiration from score - based generative models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.06329", "content": "As a remedy, we propose a straightfor- ward training-free technique that effectively denoises the generated samples, by drawing inspiration from score - based generative models ."}
+{"idx": 4, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v2", "content": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 5, "title": "Normalizing Flows are Capable Generative Models - Apple Machine...", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density…content type paper | published June 2025. Normalizing Flows are Capable Generative Models .", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/normalizing-flows", "content": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density…content type paper | published June 2025. Normalizing Flows are Capable Generative Models ."}
+{"idx": 6, "title": "(PDF) Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386577118_Normalizing_Flows_are_Capable_Generative_Models", "content": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 7, "title": "Normalizing Flows are Capable Generative Models | alphaXiv", "date": "", "ddg_snippet": "Abstract: Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2412.06329", "content": "Abstract: Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 8, "title": "Normalizing Flows (NFs)", "date": "", "ddg_snippet": "Normalizing flows are likelihood- based generative models that convert simple distributions into complex data densities using sequences of invertible, differentiable transformations.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/normalizing-flows-nfs", "content": "Normalizing flows are likelihood- based generative models that convert simple distributions into complex data densities using sequences of invertible, differentiable transformations."}
+{"idx": 9, "title": "Mastering Normalizing Flows : Transforming Data with Ease", "date": "", "ddg_snippet": "Title: Normalizing Flows are Capable Generative Models . Abstract: Normalizing Flows (NFs) are likelihood- based models for continuous inputs.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-03-28-mastering-normalizing-flows-transforming-data-with-ease--a3o055z", "content": "Title: Normalizing Flows are Capable Generative Models . Abstract: Normalizing Flows (NFs) are likelihood- based models for continuous inputs."}
diff --git a/data/sampled_jsons/Normalizing_Flows_are_Capable_Generative_Models_score_matching_denoising_mechanism.jsonl b/data/sampled_jsons/Normalizing_Flows_are_Capable_Generative_Models_score_matching_denoising_mechanism.jsonl
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index 0000000000000000000000000000000000000000..24cccd57042450829255b205c00ac41b70f0f911
--- /dev/null
+++ b/data/sampled_jsons/Normalizing_Flows_are_Capable_Generative_Models_score_matching_denoising_mechanism.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "by S Zhai · Cited by 25 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density estimation ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "by S Zhai · Cited by 25 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density estimation ..."}
+{"idx": 1, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "6 Jun 2025 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density estimation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "6 Jun 2025 — Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density estimation ..."}
+{"idx": 2, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density estimation and generative ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46564", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs . They have demonstrated promising results on both density estimation and generative ..."}
+{"idx": 3, "title": "Theoretical research on generative diffusion models", "date": "", "ddg_snippet": "13 Apr 2024 — Generative diffusion models are a family of generative models that slowly convert the score function or approximate lower bound of the data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.09016v1", "content": "13 Apr 2024 — Generative diffusion models are a family of generative models that slowly convert the score function or approximate lower bound of the data ..."}
+{"idx": 4, "title": "Matching flows instead of scores - Jakub M. Tomczak", "date": "", "ddg_snippet": "Please note that in the case of flow matching , unlike in score -based generative models , we assume that time flows from t=0 (i.e., noise), to t=1 (i.e., data).", "subpage_snippet": "", "source": "jmtomczak.github.io", "link": "https://jmtomczak.github.io/blog/18/18_fm.html", "content": "Please note that in the case of flow matching , unlike in score -based generative models , we assume that time flows from t=0 (i.e., noise), to t=1 (i.e., data)."}
+{"idx": 5, "title": "Denoising Deep Generative Models", "date": "", "ddg_snippet": "by G Loaiza-Ganem · 2023 · Cited by 10 — In this paper we propose two methodologies aimed at addressing this problem. Both are based on adding Gaussian noise to the data to remove the dimensionality ... 10 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v187/loaiza-ganem23a/loaiza-ganem23a.pdf", "content": "by G Loaiza-Ganem · 2023 · Cited by 10 — In this paper we propose two methodologies aimed at addressing this problem. Both are based on adding Gaussian noise to the data to remove the dimensionality ... 10 pages"}
+{"idx": 6, "title": "Training Energy-Based Normalizing Flow with Score ...", "date": "", "ddg_snippet": "In this paper, we establish a connection between the parameterization of flow -based and energy-based generative models , and present a new flow -based ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/72549", "content": "In this paper, we establish a connection between the parameterization of flow -based and energy-based generative models , and present a new flow -based ..."}
+{"idx": 7, "title": "Score-based Generative Modeling in Latent Space", "date": "", "ddg_snippet": "by A Vahdat · Cited by 792 — Augmented normalizing flows : Bridging the gap between generative flows and latent variable models. ... Spaces with Multi-scale Denoising Score Matching ...", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper/2021/file/5dca4c6b9e244d24a30b4c45601d9720-Paper.pdf", "content": "by A Vahdat · Cited by 792 — Augmented normalizing flows : Bridging the gap between generative flows and latent variable models. ... Spaces with Multi-scale Denoising Score Matching ..."}
+{"idx": 8, "title": "Hamiltonian Score Matching and Generative Flows", "date": "", "ddg_snippet": "9 Dec 2024 — In this work, we explore the potential of deliberately designing force fields for Hamiltonian ODEs, introducing Hamiltonian velocity predictors ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95722", "content": "9 Dec 2024 — In this work, we explore the potential of deliberately designing force fields for Hamiltonian ODEs, introducing Hamiltonian velocity predictors ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "Normalizing flows (NF) are a class of powerful generative models that have gained popularity in recent years due to their ability to model complex distributions ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Normalizing+flow+models", "content": "Normalizing flows (NF) are a class of powerful generative models that have gained popularity in recent years due to their ability to model complex distributions ..."}
diff --git a/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features_SDXL_architecture.jsonl b/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features_SDXL_architecture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..57ac5ba623acba77f7f8a7c3d6af28be633e6f80
--- /dev/null
+++ b/data/sampled_jsons/Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features_SDXL_architecture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03558v2", "content": "Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation."}
+{"idx": 1, "title": "(PDF) Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384680516_Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features", "content": "The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift."}
+{"idx": 2, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/633780c1344d0c95e4d2dd3431fe08d9-Abstract-Conference.html", "content": "Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation."}
+{"idx": 3, "title": "GitHub - Darkbblue/generic- diffusion -feature: Official implementation...", "date": "", "ddg_snippet": "Diffusion feature is a quite popular way to utilize generative diffusion models for discrimination. It's very simple: just extract some internal activations from a diffusion model , and then use these 2D features to replace image inputs of any discriminative model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Darkbblue/generic-diffusion-feature", "content": "Diffusion feature is a quite popular way to utilize generative diffusion models for discrimination. It's very simple: just extract some internal activations from a diffusion model , and then use these 2D features to replace image inputs of any discriminative model."}
+{"idx": 4, "title": "Available Projects Fall 2025 ‒ IVRL ‐ EPFL", "date": "", "ddg_snippet": "“ Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing.”“ Not all diffusion model activations have been evaluated as discriminative features .” Advances in Neural Information Processing Systems 37 (2024): 55141-55177.", "subpage_snippet": "", "source": "www.epfl.ch", "link": "https://www.epfl.ch/labs/ivrl/available-projects/", "content": "“ Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing.”“ Not all diffusion model activations have been evaluated as discriminative features .” Advances in Neural Information Processing Systems 37 (2024): 55141-55177."}
+{"idx": 5, "title": "Diffusion features for image", "date": "", "ddg_snippet": "A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence, NeurIPS23. More recent analysis on diffusion features. Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features , NeurIPS24.", "subpage_snippet": "", "source": "cs294-43-fall2024.pages.dev", "link": "https://cs294-43-fall2024.pages.dev/assets/presentations/prompt_to_prompt.pdf", "content": "A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence, NeurIPS23. More recent analysis on diffusion features. Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features , NeurIPS24."}
+{"idx": 6, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features .", "subpage_snippet": "", "source": "darkbblue.github.io", "link": "https://darkbblue.github.io/publications/2024-10-10-sdxl-feature/", "content": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features ."}
+{"idx": 7, "title": "Papers by Qingming Huang with links to code and results.", "date": "", "ddg_snippet": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features .To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Qingming+Huang", "content": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features .To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations."}
+{"idx": 8, "title": "Zitai Wang - Google Akademik", "date": "", "ddg_snippet": "2024. Not all diffusion model activations have been evaluated as discriminative features .Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques.", "subpage_snippet": "", "source": "scholar.google.co.id", "link": "https://scholar.google.co.id/citations?user=45qZ_LcAAAAJ&hl=tr", "content": "2024. Not all diffusion model activations have been evaluated as discriminative features .Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques."}
+{"idx": 9, "title": "Darkbblue has 27 repositories available. Follow their code on GitHub.", "date": "", "ddg_snippet": "generic-diffusion-feature generic-diffusion-feature Public. Official implementation of NeurIPS'24 paper Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features .", "subpage_snippet": "", "source": "git.jl-k.com", "link": "https://git.jl-k.com/Darkbblue", "content": "generic-diffusion-feature generic-diffusion-feature Public. Official implementation of NeurIPS'24 paper Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features ."}
diff --git a/data/sampled_jsons/OFUL_algorithm_matrix_inversion_computational_cost_optimization_problem_linear_bandits.jsonl b/data/sampled_jsons/OFUL_algorithm_matrix_inversion_computational_cost_optimization_problem_linear_bandits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a9a4f7b8882a40d7c5869dc2bd2bb672eca5afbe
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_matrix_inversion_computational_cost_optimization_problem_linear_bandits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF E cient Linear Bandits through Matrix Sketching", "date": "", "ddg_snippet": "Abstract We prove that two popular linear contex-tual bandit algorithms , OFUL and Thomp-son Sampling, can be made e cient using Frequent Directions, a deterministic online sketching technique. More precisely, we show that a sketch of size m allows a O(md) up-date time for both algorithms , as opposed to (d2) required by their non-sketched ver-sions in general (where d is the dimension of ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v89/kuzborskij19a/kuzborskij19a.pdf", "content": "Abstract We prove that two popular linear contex-tual bandit algorithms , OFUL and Thomp-son Sampling, can be made e cient using Frequent Directions, a deterministic online sketching technique. More precisely, we show that a sketch of size m allows a O(md) up-date time for both algorithms , as opposed to (d2) required by their non-sketched ver-sions in general (where d is the dimension of ..."}
+{"idx": 1, "title": "PDF Improved Algorithms for Linear Stochastic Bandits - NeurIPS", "date": "", "ddg_snippet": "We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem . In particular, we show that a simple modification of Auer's UCB algorithm (Auer, 2002) achieves with high probability constant regret. More importantly, we modify and, consequently, improve the analysis of the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2011/file/e1d5be1c7f2f456670de3d53c7b54f4a-Paper.pdf", "content": "We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem . In particular, we show that a simple modification of Auer's UCB algorithm (Auer, 2002) achieves with high probability constant regret. More importantly, we modify and, consequently, improve the analysis of the ..."}
+{"idx": 2, "title": "Stochastic Linear Bandits and UCB - Bandit Algorithms", "date": "", "ddg_snippet": "Stochastic linear bandits arise from realizing that when the reward is linear in the feature vectors, the identity of the actions becomes secondary and we rather let the algorithms choose the feature vectors directly: the identity of the actions adds no information or structure to the problem .", "subpage_snippet": "", "source": "banditalgs.com", "link": "https://banditalgs.com/2016/10/19/stochastic-linear-bandits/", "content": "Stochastic linear bandits arise from realizing that when the reward is linear in the feature vectors, the identity of the actions becomes secondary and we rather let the algorithms choose the feature vectors directly: the identity of the actions adds no information or structure to the problem ."}
+{"idx": 3, "title": "Efficient Linear Bandits through Matrix Sketching", "date": "", "ddg_snippet": "We prove that two popular linear contextual bandit algorithms , OFUL and Thompson Sampling, can be made efficient using Frequent Directions, a deterministic online sketching technique.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1809.11033", "content": "We prove that two popular linear contextual bandit algorithms , OFUL and Thompson Sampling, can be made efficient using Frequent Directions, a deterministic online sketching technique."}
+{"idx": 4, "title": "PDF Nearly Optimal Algorithms for Linear Contextual Bandits with ...", "date": "", "ddg_snippet": "This is implicitly assumed in almost all existing works for solving contextual linear bandit problems with infinite arms (e.g., OFUL and LinUCB algorithms ); otherwise, choosing an arm from the infinite decision set is computationally intractable.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/df5f94d6ac6e13d830d70536cde9f0d2-Paper-Conference.pdf", "content": "This is implicitly assumed in almost all existing works for solving contextual linear bandit problems with infinite arms (e.g., OFUL and LinUCB algorithms ); otherwise, choosing an arm from the infinite decision set is computationally intractable."}
+{"idx": 5, "title": "PDF Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "Abstract We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem . In particular, we show that a simple modification of Auer's UCB algorithm (Auer, 2002) achieves with high probability constant regret.", "subpage_snippet": "", "source": "www.david.palenica.com", "link": "http://www.david.palenica.com/papers/linear-bandit/linear-bandits-NIPS2011-camera-ready.pdf", "content": "Abstract We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem . In particular, we show that a simple modification of Auer's UCB algorithm (Auer, 2002) achieves with high probability constant regret."}
+{"idx": 6, "title": "MATRIX SKETCHING IN BANDITS CURRENT PITFALLS AND NEW FRAMEWORK - OpenReview", "date": "", "ddg_snippet": "The utilization of sketching techniques has progressively emerged as a pivotal method for enhancing the eficiency of online learning. In linear bandit settings, current sketch-based approaches leverage matrix sketching to reduce the per-round time complexity from Ω d2 to O(d), where d is the input dimension. Despite this improved eficiency, these approaches encounter critical pitfalls: if the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=X75isqETqR", "content": "The utilization of sketching techniques has progressively emerged as a pivotal method for enhancing the eficiency of online learning. In linear bandit settings, current sketch-based approaches leverage matrix sketching to reduce the per-round time complexity from Ω d2 to O(d), where d is the input dimension. Despite this improved eficiency, these approaches encounter critical pitfalls: if the ..."}
+{"idx": 7, "title": "Bandits with Mean Bounds - OpenReview", "date": "", "ddg_snippet": "The authors consider the MAB problem , where additional side information on the rewards distribution is available. This feature makes the mean estimate tighter and improves the method's overall convergence. Based on this approach, the authors propose a Restricted-set OFUL algorithm for the linear bandits setting and a Global Under-Explore algorithm for the stochastic setting. The corresponding ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4TZ4DE24fX", "content": "The authors consider the MAB problem , where additional side information on the rewards distribution is available. This feature makes the mean estimate tighter and improves the method's overall convergence. Based on this approach, the authors propose a Restricted-set OFUL algorithm for the linear bandits setting and a Global Under-Explore algorithm for the stochastic setting. The corresponding ..."}
+{"idx": 8, "title": "arXiv:1809.11033v3 [cs.LG] 21 Mar 2022", "date": "", "ddg_snippet": "Abstract We prove that two popular linear contextual bandit algorithms , OFUL and Thompson Sampling, can be made efficient using Frequent Directions, a deterministic online sketching technique.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1809.11033", "content": "Abstract We prove that two popular linear contextual bandit algorithms , OFUL and Thompson Sampling, can be made efficient using Frequent Directions, a deterministic online sketching technique."}
+{"idx": 9, "title": "PDF Online (Multinomial) Logistic Bandit: Improved Regret and ... - NeurIPS", "date": "", "ddg_snippet": "This paper proposed a jointly eficient algorithm OFUL -MLogB for both binary and multinomial logistic bandit problems with constant computation cost per round and improved regret guarantees.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/5ef04392708bb2340cb9b7da41225660-Paper-Conference.pdf", "content": "This paper proposed a jointly eficient algorithm OFUL -MLogB for both binary and multinomial logistic bandit problems with constant computation cost per round and improved regret guarantees."}
diff --git a/data/sampled_jsons/OFUL_confidence_set_construction_computational_bottleneck_ellipsoid.jsonl b/data/sampled_jsons/OFUL_confidence_set_construction_computational_bottleneck_ellipsoid.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2c74069cf82cc9af10056faa9e3729ca2fde6655
--- /dev/null
+++ b/data/sampled_jsons/OFUL_confidence_set_construction_computational_bottleneck_ellipsoid.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Noise-Adaptive Confidence Sets for Linear Bandits ... - GitHub", "date": "", "ddg_snippet": "by KS Jun · Cited by 4 — In this em- pirical analysis, our algorithms yield better or comparable performance compared to OFUL and potentially the simple discrete Bayesian optimization ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/jun24a/jun24a.pdf", "content": "by KS Jun · Cited by 4 — In this em- pirical analysis, our algorithms yield better or comparable performance compared to OFUL and potentially the simple discrete Bayesian optimization ..."}
+{"idx": 1, "title": "1 Introduction", "date": "", "ddg_snippet": "Our technique conducts a novel real-time geometric analysis of the d d d italic_d -dimensional confidence ellipsoid to fully leverage the historical information ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.14872v5", "content": "Our technique conducts a novel real-time geometric analysis of the d d d italic_d -dimensional confidence ellipsoid to fully leverage the historical information ..."}
+{"idx": 2, "title": "Meta-learning with Stochastic Linear Bandits", "date": "", "ddg_snippet": "by L Cella · Cited by 78 — We first study the benefit of the biased OFUL algorithm in terms of regret minimization. We then propose two strategies to estimate the bias within the learning ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v119/cella20a/cella20a-supp.pdf", "content": "by L Cella · Cited by 78 — We first study the benefit of the biased OFUL algorithm in terms of regret minimization. We then propose two strategies to estimate the bias within the learning ..."}
+{"idx": 3, "title": "Generalized Linear Bandits: Almost Optimal Regret with ...", "date": "", "ddg_snippet": "16 Jul 2025 — A key ingredient of OFU-based methods is the design of the confidence set , as the regret bound typically scales with the “radius” of the set. A ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11847v1", "content": "16 Jul 2025 — A key ingredient of OFU-based methods is the design of the confidence set , as the regret bound typically scales with the “radius” of the set. A ..."}
+{"idx": 4, "title": "Generalized Linear Bandits: Almost Optimal Regret with One ...", "date": "", "ddg_snippet": "by YJZSA Xu · 2025 — From a computational perspective, constructing the confidence set relies only on the online estimator, which can be updated with O(1) time ... 31 pages", "subpage_snippet": "", "source": "www.pengzhao-ml.com", "link": "https://www.pengzhao-ml.com/publication/arXiv'25_GLB-OMD.pdf", "content": "by YJZSA Xu · 2025 — From a computational perspective, constructing the confidence set relies only on the online estimator, which can be updated with O(1) time ... 31 pages"}
+{"idx": 5, "title": "Concentrated Differential Privacy for Bandits", "date": "", "ddg_snippet": "to design tight ellipsoid confidence sets around the private estimate˜θt, since the regret can be shown to be the sum of the confidence widths. To design ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2366aWQ6Nn", "content": "to design tight ellipsoid confidence sets around the private estimate˜θt, since the regret can be shown to be the sum of the confidence widths. To design ..."}
+{"idx": 6, "title": "Gaussian Process Optimization with Adaptive Sketching", "date": "", "ddg_snippet": "by D Calandriello · 2019 · Cited by 104 — The main computational bottleneck for ... Bounding the confidence ellipsoid . We begin by proving an intermediate result regarding the confidence ellipsoid . 25 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v99/calandriello19a/calandriello19a.pdf", "content": "by D Calandriello · 2019 · Cited by 104 — The main computational bottleneck for ... Bounding the confidence ellipsoid . We begin by proving an intermediate result regarding the confidence ellipsoid . 25 pages"}
+{"idx": 7, "title": "Provable and Efficient Algorithms for Federated, Batch and ...", "date": "", "ddg_snippet": "by A Ghosh · 2021 — We propose and analyze iterative algorithms that are computationally efficient, statistically sound and adaptive (in some settings). We consider ... 325 pages", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-89.pdf", "content": "by A Ghosh · 2021 — We propose and analyze iterative algorithms that are computationally efficient, statistically sound and adaptive (in some settings). We consider ... 325 pages"}
+{"idx": 8, "title": "An Asymptotically Optimal Primal-Dual Incremental ...", "date": "", "ddg_snippet": "by A Tirinzoni · Cited by 52 — We build on a reformulation of the lower bound, where context distribution and exploration policy are decoupled, and we obtain an algorithm robust to unbalanced.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/0f34314d2dd0c1b9311cb8f40eb4f255-Supplemental.pdf", "content": "by A Tirinzoni · Cited by 52 — We build on a reformulation of the lower bound, where context distribution and exploration policy are decoupled, and we obtain an algorithm robust to unbalanced."}
+{"idx": 9, "title": "Feasible Action Search for Bandit Linear Programs via ...", "date": "", "ddg_snippet": "by A Gangrade — We exploit the design of a recent (in- tractable) bandit feasibility test for LPs to propose a novel efficient method, 'Feasible Action Search via Thompson.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=GrF14Q0DNW", "content": "by A Gangrade — We exploit the design of a recent (in- tractable) bandit feasibility test for LPs to propose a novel efficient method, 'Feasible Action Search via Thompson."}
diff --git a/data/sampled_jsons/OFUL_optimism_face_uncertainty_linear_implementation_challenge_confidence_ellipsoid.jsonl b/data/sampled_jsons/OFUL_optimism_face_uncertainty_linear_implementation_challenge_confidence_ellipsoid.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..370490e59cf2beff39d15b875fc739af1721710d
--- /dev/null
+++ b/data/sampled_jsons/OFUL_optimism_face_uncertainty_linear_implementation_challenge_confidence_ellipsoid.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Upper Confidence Bound - Wikipedia", "date": "", "ddg_snippet": "Upper Confidence Bound is a family of algorithms in machine learning and statistics for solving the multi-armed bandit problem and addressing the exploration–exploitation trade-off. UCB methods select actions by computing an upper confidence estimate...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Upper_Confidence_Bound", "content": "Upper Confidence Bound is a family of algorithms in machine learning and statistics for solving the multi-armed bandit problem and addressing the exploration–exploitation trade-off. UCB methods select actions by computing an upper confidence estimate..."}
+{"idx": 1, "title": "Generalized Linear Bandits: Almost Optimal Regret with One-Pass", "date": "", "ddg_snippet": "While GLBs are widely applicable to real-world scenarios, their non- linear nature introduces significant challenges in achieving both computational ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11847v1", "content": "While GLBs are widely applicable to real-world scenarios, their non- linear nature introduces significant challenges in achieving both computational ..."}
+{"idx": 2, "title": "A Unifying View of Optimism in Episodic Reinforcement Learning", "date": "", "ddg_snippet": "The principle of “ optimism in the face of uncertainty ” underpins many theoretically successful reinforcement learning algorithms. In this paper we provide a general framework for designing, analyzing and implementing such algorithms in the episodic reinforcement learning problem.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/0f0e13216262f4a201bec128044dd30f-Paper.pdf", "content": "The principle of “ optimism in the face of uncertainty ” underpins many theoretically successful reinforcement learning algorithms. In this paper we provide a general framework for designing, analyzing and implementing such algorithms in the episodic reinforcement learning problem."}
+{"idx": 3, "title": "Value function optimistic initialization with uncertainty and ...", "date": "", "ddg_snippet": "Nov 25, 2023 · To address this challenge , we present UCOI ( Uncertainty and Confidence based Optimistic Initialization) in our paper. UCOI advocates exclusively applying OI to states characterized by high uncertainty as a way to enhance the sample complexity when learning new tasks, particularly in scenarios with non-uniform task distribution.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0950705123007864", "content": "Nov 25, 2023 · To address this challenge , we present UCOI ( Uncertainty and Confidence based Optimistic Initialization) in our paper. UCOI advocates exclusively applying OI to states characterized by high uncertainty as a way to enhance the sample complexity when learning new tasks, particularly in scenarios with non-uniform task distribution."}
+{"idx": 4, "title": "Meta-learning with Stochastic Linear Bandits", "date": "", "ddg_snippet": "Xt. According to the optimism in the face of uncertainty principle, at each round t OFUL picks the arm xt by solving the following optimization problem:", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/cella20a/cella20a.pdf", "content": "Xt. According to the optimism in the face of uncertainty principle, at each round t OFUL picks the arm xt by solving the following optimization problem:"}
+{"idx": 5, "title": "OPTIMISM IN REINFORCEMENT LEARNING WITH GENERALIZED LINEAR ...", "date": "", "ddg_snippet": "Optimism in the face of uncertainty is a well-understood and powerful algorithmic principle in short-horizon (e.g,. bandit) problems, as well as in tabular reinforcement learning (Azar et al., 2017; Dann et al., 2017; Jin et al., 2018).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=CBmJwzneppz", "content": "Optimism in the face of uncertainty is a well-understood and powerful algorithmic principle in short-horizon (e.g,. bandit) problems, as well as in tabular reinforcement learning (Azar et al., 2017; Dann et al., 2017; Jin et al., 2018)."}
+{"idx": 6, "title": "LinUCB/OFUL算法的Regret分析——上篇 - 知乎", "date": "", "ddg_snippet": "算法1. OFUL 算法 类似于UCB1算法， OFUL 也是基于面对不确定性的乐观主义（ Optimism in the Face of Uncertainty ）原则的算法，但这里的环境不再是多臂老虎机问题（Multi-armed Bandit，MAB），而是上下文老虎机问题（Contextual Bandit）问题。具体而言，Agent在做选择时需要考虑arm的上下文特征，这里假设每个arm的奖励 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/589299916", "content": "算法1. OFUL 算法 类似于UCB1算法， OFUL 也是基于面对不确定性的乐观主义（ Optimism in the Face of Uncertainty ）原则的算法，但这里的环境不再是多臂老虎机问题（Multi-armed Bandit，MAB），而是上下文老虎机问题（Contextual Bandit）问题。具体而言，Agent在做选择时需要考虑arm的上下文特征，这里假设每个arm的奖励 ..."}
+{"idx": 7, "title": "Stochastic Linear Bandits and UCB – Bandit Algorithms", "date": "", "ddg_snippet": "The generalization is based on the view that UCB implements the optimism in the face of uncertainty principle, according to which one should choose the actions as if the environment (in our case the linear bandit environment) was as nice as plausible possible.", "subpage_snippet": "", "source": "banditalgs.com", "link": "https://banditalgs.com/2016/10/19/stochastic-linear-bandits/", "content": "The generalization is based on the view that UCB implements the optimism in the face of uncertainty principle, according to which one should choose the actions as if the environment (in our case the linear bandit environment) was as nice as plausible possible."}
+{"idx": 8, "title": "(PDF) Linear Bandits on Ellipsoids : Minimax Optimal Algorithms", "date": "", "ddg_snippet": "( Optimism in the Face of Uncertainty Linear bandit. ) algorithm of Abbasi-Yadkori et al.T= 104. . The confidence ellipsoids in. OFUL . and.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389315749_Linear_Bandits_on_Ellipsoids_Minimax_Optimal_Algorithms", "content": "( Optimism in the Face of Uncertainty Linear bandit. ) algorithm of Abbasi-Yadkori et al.T= 104. . The confidence ellipsoids in. OFUL . and."}
+{"idx": 9, "title": "[Literature Review] Linear Bandits on Ellipsoids : Minimax Optimal ...", "date": "", "ddg_snippet": "The paper titled \" Linear Bandits on Ellipsoids : Minimax Optimal Algorithms\" by Raymond Zhang, Hédi Hadiji, and Richard Combes introduces a significant advancement in the study of linear stochastic bandits where the action set is defined as an ellipsoid .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/linear-bandits-on-ellipsoids-minimax-optimal-algorithms", "content": "The paper titled \" Linear Bandits on Ellipsoids : Minimax Optimal Algorithms\" by Raymond Zhang, Hédi Hadiji, and Richard Combes introduces a significant advancement in the study of linear stochastic bandits where the action set is defined as an ellipsoid ."}
diff --git a/data/sampled_jsons/OM-KIID_hardness_result_Karp_Vazirani_Vazirani_1990.jsonl b/data/sampled_jsons/OM-KIID_hardness_result_Karp_Vazirani_Vazirani_1990.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7cbdd09534a66346b15915c82403cafcc7d70812
--- /dev/null
+++ b/data/sampled_jsons/OM-KIID_hardness_result_Karp_Vazirani_Vazirani_1990.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "如何评价奥之心 2 月 6 日发布的 OM-3 复古无反相机及 3 款镜头？ - ...", "date": "", "ddg_snippet": "宣发 om3这次的宣发是声势浩大的，所谓的消息一点点泄漏，从马来西亚OM到高糊的机身谍照，到预告片，到发布前几天把镜头和机身照全曝光，持续时间起码1个月，即使我只是转发消息，在xhs的流量也是平时的10倍。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/11431717101", "content": "宣发 om3这次的宣发是声势浩大的，所谓的消息一点点泄漏，从马来西亚OM到高糊的机身谍照，到预告片，到发布前几天把镜头和机身照全曝光，持续时间起码1个月，即使我只是转发消息，在xhs的流量也是平时的10倍。"}
+{"idx": 1, "title": "知乎 - 有问题，就会有答案", "date": "", "ddg_snippet": "知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容，聚集了中文互联网科技、商业、影视、时 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/?lang=zh-Hant", "content": "知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容，聚集了中文互联网科技、商业、影视、时 ..."}
+{"idx": 2, "title": "le Forum OM - Le Phoceen", "date": "", "ddg_snippet": "Forum sur l'Olympique de Marseille - OM -, le football, le mercato", "subpage_snippet": "", "source": "forum.lephoceen.fr", "link": "https://forum.lephoceen.fr/", "content": "Forum sur l'Olympique de Marseille - OM -, le football, le mercato"}
+{"idx": 3, "title": "如何评价奥之心将于6月17日发布的OM-5 II相机？ - 知乎", "date": "", "ddg_snippet": "同角度对比下手上现在的OM-5 造型，是具有非常复杂的倒角结构来实现复古精致的外观的。 倒角更多光线折射反射面也就越多，更BLING BLING。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/1916811049228800625", "content": "同角度对比下手上现在的OM-5 造型，是具有非常复杂的倒角结构来实现复古精致的外观的。 倒角更多光线折射反射面也就越多，更BLING BLING。"}
+{"idx": 4, "title": "知乎 - 有问题，就会有答案", "date": "", "ddg_snippet": "知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容，聚集了中文互联网科技、商业、影视 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/?language=en-US", "content": "知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容，聚集了中文互联网科技、商业、影视 ..."}
+{"idx": 5, "title": "知乎 - 知乎", "date": "", "ddg_snippet": "知乎是一个中文互联网高质量问答社区和创作者聚集的原创内容平台，提供知识共享、互动交流和个人成长机会。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/selection/", "content": "知乎是一个中文互联网高质量问答社区和创作者聚集的原创内容平台，提供知识共享、互动交流和个人成长机会。"}
+{"idx": 6, "title": "请问运筹学和管理学的顶级期刊有哪些？能否介绍一些这些期刊的级别和...", "date": "", "ddg_snippet": "我主要是做OM方向研究，用博弈论或者queue game方法，研究领域包括平台运营管理和医疗运营管理。 同时，平时也会读一些OR方向相关的文章。 关于顶级期刊的划分，我们学院有自己的评估标准。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/296289547", "content": "我主要是做OM方向研究，用博弈论或者queue game方法，研究领域包括平台运营管理和医疗运营管理。 同时，平时也会读一些OR方向相关的文章。 关于顶级期刊的划分，我们学院有自己的评估标准。"}
+{"idx": 7, "title": "ODM 和 OEM 分别是什么？两者有什么本质区别？ - 知乎", "date": "", "ddg_snippet": "ODM和OEM分别是原始设计制造商和原始设备制造商，本文探讨它们的定义、区别及应用场景。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/20323695", "content": "ODM和OEM分别是原始设计制造商和原始设备制造商，本文探讨它们的定义、区别及应用场景。"}
+{"idx": 8, "title": "如何评价奥之心 2 月 15 日发布的 M43 新机 OM-1 ... - 知乎", "date": "", "ddg_snippet": "OM-1不足的地方（该吐槽的还是要吐槽，因为并没有完美的机） 1、还是2000万像素级别，如果有更高像素的话，相信更受追捧。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/516700297", "content": "OM-1不足的地方（该吐槽的还是要吐槽，因为并没有完美的机） 1、还是2000万像素级别，如果有更高像素的话，相信更受追捧。"}
+{"idx": 9, "title": "如何看待1月30日奥之心新发布的OM-1 MARK2？ - 知乎", "date": "", "ddg_snippet": "这次的OM-1 II在高分辨率拍摄中提供了14bit色深的选项，目前固件下可以手持拍摄5000万像素的照片。 可惜的是，截止到文章发布前，ADOBE系列的后期软件无法打开原始文件，所以等到能打开文件时再给大家比较一下同场景下的画质对比测试了。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/641987986", "content": "这次的OM-1 II在高分辨率拍摄中提供了14bit色深的选项，目前固件下可以手持拍摄5000万像素的照片。 可惜的是，截止到文章发布前，ADOBE系列的后期软件无法打开原始文件，所以等到能打开文件时再给大家比较一下同场景下的画质对比测试了。"}
diff --git a/data/sampled_jsons/Offline_RL_with_Preference_Data_Chen_et_al._2022_Theorem_4.5_regret_lower_bound.jsonl b/data/sampled_jsons/Offline_RL_with_Preference_Data_Chen_et_al._2022_Theorem_4.5_regret_lower_bound.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7675e8e88d0706bf65bee85c4be34382cbcb1879
--- /dev/null
+++ b/data/sampled_jsons/Offline_RL_with_Preference_Data_Chen_et_al._2022_Theorem_4.5_regret_lower_bound.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Automatic Reward Shaping from Confounded Offline Data", "date": "", "ddg_snippet": "An alternative strategy is to learn the shaping function from previous offline data, possibly collected by different be- havior policies or observing human operators interacting with the environment (Brys et al ., 2015; Mezghani et al ., 2022 ; Zhang et al ., 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.11478v2", "content": "An alternative strategy is to learn the shaping function from previous offline data, possibly collected by different be- havior policies or observing human operators interacting with the environment (Brys et al ., 2015; Mezghani et al ., 2022 ; Zhang et al ., 2024)."}
+{"idx": 1, "title": "Regret minimization in Linear Bandits with offline data via", "date": "", "ddg_snippet": "... the Offline -Online Phased Elimination ( OOPE ) algorithm for regret minimization in linear bandits in the online phase with access to offline data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08420v1", "content": "... the Offline -Online Phased Elimination ( OOPE ) algorithm for regret minimization in linear bandits in the online phase with access to offline data ..."}
+{"idx": 2, "title": "Adaptive Scaling of Policy Constraints for Offline", "date": "", "ddg_snippet": "Offline reinforcement learning ( RL ) learns a policy exclusively from a fixed, pre-collected dataset without further interactions with the environment ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19900v1", "content": "Offline reinforcement learning ( RL ) learns a policy exclusively from a fixed, pre-collected dataset without further interactions with the environment ..."}
+{"idx": 3, "title": "What Matters in Data for DPO?", "date": "", "ddg_snippet": "... for achieving this alignment are Reinforcement Learning from Human Feedback (RLHF) (Bai et al ., 2022 ; Ouyang et al ., 2022 ) and Direct Preference ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18312v1", "content": "... for achieving this alignment are Reinforcement Learning from Human Feedback (RLHF) (Bai et al ., 2022 ; Ouyang et al ., 2022 ) and Direct Preference ..."}
+{"idx": 4, "title": "TDRM: Smooth Reward Models with Temporal Difference for LLM RL", "date": "", "ddg_snippet": "... RL experiments, we show that incorporating TDRM into the RL loop yields strong performance gains (up to 51.1%) and data efficiency (matching 50.1k ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15110v1", "content": "... RL experiments, we show that incorporating TDRM into the RL loop yields strong performance gains (up to 51.1%) and data efficiency (matching 50.1k ..."}
+{"idx": 5, "title": "Optimal Type-Dependent Liquid Welfare Guarantees for ...", "date": "", "ddg_snippet": "1 day ago · Liaw et al . (2024) studied budget-constrained value maximizers and derive a bound of 2 2 on the POA of pure Nash equilibria. To the best of our knowledge, the work of Liaw et al . (2024) is the only one that studies the inefficiency of simultaneous FPAs in the autobidding setting under both ROI and budget constraints.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.20908v2", "content": "1 day ago · Liaw et al . (2024) studied budget-constrained value maximizers and derive a bound of 2 2 on the POA of pure Nash equilibria. To the best of our knowledge, the work of Liaw et al . (2024) is the only one that studies the inefficiency of simultaneous FPAs in the autobidding setting under both ROI and budget constraints."}
+{"idx": 6, "title": "Quantile-Optimal Policy Learning under Unmeasured Confounding", "date": "", "ddg_snippet": "... offline dataset often lacks full coverage, meaning that the distribution of the collected data might have insufficient overlap with that induced by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07140v1", "content": "... offline dataset often lacks full coverage, meaning that the distribution of the collected data might have insufficient overlap with that induced by ..."}
+{"idx": 7, "title": "‘RL exploration’ directory · Gwern.net", "date": "", "ddg_snippet": "Don’t Change the Algorithm, Change the Data : Exploratory Data for Offline Reinforcement Learning (ExORL) ”, Yarats et al 2022", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/exploration/index", "content": "Don’t Change the Algorithm, Change the Data : Exploratory Data for Offline Reinforcement Learning (ExORL) ”, Yarats et al 2022"}
+{"idx": 8, "title": "‘model-based RL’ directory · Gwern.net", "date": "", "ddg_snippet": "PI-ARS: Accelerating Evolution-Learned Visual-Locomotion With Predictive Information Representations ”, Lee et al 2022", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/model/index", "content": "PI-ARS: Accelerating Evolution-Learned Visual-Locomotion With Predictive Information Representations ”, Lee et al 2022"}
+{"idx": 9, "title": "Support vector machines for optimal channel decoding | EURASIP", "date": "", "ddg_snippet": "... aims at handling complex and time-consuming communication problems in a data -based approach, as opposed to the traditional model-based approach [ 4 ...", "subpage_snippet": "", "source": "jwcn-eurasipjournals.springeropen.com", "link": "https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-025-02493-6", "content": "... aims at handling complex and time-consuming communication problems in a data -based approach, as opposed to the traditional model-based approach [ 4 ..."}
diff --git a/data/sampled_jsons/Olah_circuits_2020_features_weights_neural_networks_structural_patterns.jsonl b/data/sampled_jsons/Olah_circuits_2020_features_weights_neural_networks_structural_patterns.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d92de5f04d4e6c73bbb7ae01158be9c7fa99903b
--- /dev/null
+++ b/data/sampled_jsons/Olah_circuits_2020_features_weights_neural_networks_structural_patterns.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Zoom In: An Introduction to Circuits - Distill", "date": "", "ddg_snippet": "Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/zoom-in/", "content": "Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks ."}
+{"idx": 1, "title": "Zoom In: An Introduction to Circuits | Nick's Notes", "date": "", "ddg_snippet": "Circuits - Features are connected by weights , forming circuits . A \" circuit \" is a computational subgraph of a neural network . It consists of a set of features , and the weighted edges that go between them in the original network . Universality - Analogous features and circuits form across models and tasks.", "subpage_snippet": "", "source": "www.nickjalbert.com", "link": "http://www.nickjalbert.com/reading/2020/03/27/zoom-in-an-introduction-to-circuits.html", "content": "Circuits - Features are connected by weights , forming circuits . A \" circuit \" is a computational subgraph of a neural network . It consists of a set of features , and the weighted edges that go between them in the original network . Universality - Analogous features and circuits form across models and tasks."}
+{"idx": 2, "title": "Zoom In: An Introduction to Circuits. Published by OpenAI. March 10, 2020", "date": "", "ddg_snippet": "Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks - OpenAI. March 10, 2020 Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .", "subpage_snippet": "", "source": "blog.biocomm.ai", "link": "https://blog.biocomm.ai/2020/03/10/zoom-in-an-introduction-to-circuits-published-by-openai-march-10-2020/", "content": "Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks - OpenAI. March 10, 2020 Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks ."}
+{"idx": 3, "title": "PDF An Introduction to Circuits in CNNs - GitHub Pages", "date": "", "ddg_snippet": "To what extent are there abstract recurring patterns -- \" circuit motifs\" -- in neural networks ? Traditional study of circuit motifs relies on highly sparse graphs to systematically analyze them.", "subpage_snippet": "", "source": "interpretablevision.github.io", "link": "https://interpretablevision.github.io/slide/cvpr20_chris.pdf", "content": "To what extent are there abstract recurring patterns -- \" circuit motifs\" -- in neural networks ? Traditional study of circuit motifs relies on highly sparse graphs to systematically analyze them."}
+{"idx": 4, "title": "Zoom In: An Introduction to Circuits — LessWrong", "date": "", "ddg_snippet": "Once it's established that neural networks have meaningful features and circuits in them, discovering new such circuits becomes a legitimate scientific endeavor—especially if, as the third claim suggests, those features and circuits are universal across many different networks . From \"Zoom In:\"", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/MG4ZjWQDrdpgeu8wG/zoom-in-an-introduction-to-circuits", "content": "Once it's established that neural networks have meaningful features and circuits in them, discovering new such circuits becomes a legitimate scientific endeavor—especially if, as the third claim suggests, those features and circuits are universal across many different networks . From \"Zoom In:\""}
+{"idx": 5, "title": "Distill: Zoom in on Circuits - Dynamically Typed", "date": "", "ddg_snippet": "From DT #35: \"By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .\" Chris Olah et al. wrote a fascinating new Distill article about \" circuits \" in convolutional neural networks . The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ...", "subpage_snippet": "", "source": "dynamicallytyped.com", "link": "https://dynamicallytyped.com/stories/2020/distill-zoom-in-on-circuits/", "content": "From DT #35: \"By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks .\" Chris Olah et al. wrote a fascinating new Distill article about \" circuits \" in convolutional neural networks . The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ..."}
+{"idx": 6, "title": "Zoom In: An Introduction to Circuits - ResearchGate", "date": "", "ddg_snippet": "A multitude of studies have later focused on interpreting weights and intermediate representations in neural networks ( Olah et al., 2017 ( Olah et al., , 2018 ( Olah et al., , 2020 Voss et al., 2021 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/339841165_Zoom_In_An_Introduction_to_Circuits", "content": "A multitude of studies have later focused on interpreting weights and intermediate representations in neural networks ( Olah et al., 2017 ( Olah et al., , 2018 ( Olah et al., , 2020 Voss et al., 2021 ..."}
+{"idx": 7, "title": "Zoom In: An Introduction to Circuits - Semantic Scholar", "date": "", "ddg_snippet": "It is demonstrated that BIMT discovers useful modular neural networks for many simple tasks, revealing compositional structures in symbolic formulas, interpretable decision boundaries and features for classification, and mathematical structure in algorithmic datasets.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Zoom-In:-An-Introduction-to-Circuits-Olah-Cammarata/a0cfd36e6c7abf070f492ae52a35af895a1c5592", "content": "It is demonstrated that BIMT discovers useful modular neural networks for many simple tasks, revealing compositional structures in symbolic formulas, interpretable decision boundaries and features for classification, and mathematical structure in algorithmic datasets."}
+{"idx": 8, "title": "Notes: \"Zoom In - An Introduction to Circuits\" - Tanay Biradar", "date": "", "ddg_snippet": "🌳 Notes: \"Zoom In - An Introduction to Circuits \" Introduction There's an uncanny overlap between the Effective Altruism and machine learning communities. Even more so in mechanistic interpretability, the subfield of ML that focuses on reverse-engineering and understanding the weights inside neural networks . I don't consider myself to have gone that deep in to the EA realm, but I've read a ...", "subpage_snippet": "", "source": "tanaybiradar.com", "link": "https://tanaybiradar.com/blog/notes-on-zoom-in-circuits/", "content": "🌳 Notes: \"Zoom In - An Introduction to Circuits \" Introduction There's an uncanny overlap between the Effective Altruism and machine learning communities. Even more so in mechanistic interpretability, the subfield of ML that focuses on reverse-engineering and understanding the weights inside neural networks . I don't consider myself to have gone that deep in to the EA realm, but I've read a ..."}
+{"idx": 9, "title": "Weight Banding - Distill", "date": "", "ddg_snippet": "So far, the Circuits thread has mostly focused on studying very small pieces of neural network - individual neurons and small circuits . In contrast, weight banding is an example of what we call a \" structural phenomenon,\" a larger-scale pattern in the circuits and features of a neural network .", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/weight-banding/", "content": "So far, the Circuits thread has mostly focused on studying very small pieces of neural network - individual neurons and small circuits . In contrast, weight banding is an example of what we call a \" structural phenomenon,\" a larger-scale pattern in the circuits and features of a neural network ."}
diff --git a/data/sampled_jsons/OmniBench_2506.08933_NVIDIA_A100_H100_experimental_setup_Section_5.1.jsonl b/data/sampled_jsons/OmniBench_2506.08933_NVIDIA_A100_H100_experimental_setup_Section_5.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4a7bba8f0a62ebe67dd988b530c7e159e859f0db
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_2506.08933_NVIDIA_A100_H100_experimental_setup_Section_5.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable ...", "date": "", "ddg_snippet": "In this section , we first introduce the experimental setup ( Sec-tion 5.1 ). Then, we comprehensively compare the differences in capabilities across various models on OmniBench , along with several key findings ( Section 5.2).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "In this section , we first introduce the experimental setup ( Sec-tion 5.1 ). Then, we comprehensively compare the differences in capabilities across various models on OmniBench , along with several key findings ( Section 5.2)."}
+{"idx": 1, "title": "NVIDIA H100 NVL GPU", "date": "", "ddg_snippet": "The NVIDIA H100 NVL card supports Multi-Instance GPU (MIG) capability by providing up to seven GPU instances per NVIDIA H100 NVL GPU. MIG technology can partition the NVIDIA H100 NVL GPU into individual instances, each fully isolated with its own high-bandwidth memory, cache, and compute cores, enabling optimized computational resource ...", "subpage_snippet": "", "source": "www.nvidia.com", "link": "https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/h100/PB-11773-001_v01.pdf", "content": "The NVIDIA H100 NVL card supports Multi-Instance GPU (MIG) capability by providing up to seven GPU instances per NVIDIA H100 NVL GPU. MIG technology can partition the NVIDIA H100 NVL GPU into individual instances, each fully isolated with its own high-bandwidth memory, cache, and compute cores, enabling optimized computational resource ..."}
+{"idx": 2, "title": "NVIDIA GPUs: H100 vs. A100 | a detailed comparison - Gcore", "date": "", "ddg_snippet": "Jan 6, 2025 · A detailed comparison of the H100 and A100 , focusing on their performance metrics and suitability for specific workloads so you can decide which is best for your use case.", "subpage_snippet": "", "source": "gcore.com", "link": "https://gcore.com/blog/nvidia-h100-a100", "content": "Jan 6, 2025 · A detailed comparison of the H100 and A100 , focusing on their performance metrics and suitability for specific workloads so you can decide which is best for your use case."}
+{"idx": 3, "title": "NVIDIA GPU benchmark: H100 vs A100 vs L40S - cudocompute.com", "date": "", "ddg_snippet": "Jun 23, 2025 · CUDO Compute's benchmarks show that NVIDIA H100 SXM trains 12 times faster and 86% cheaper than A100 , while L40S delivers the lowest inference cost-per-token.", "subpage_snippet": "", "source": "www.cudocompute.com", "link": "https://www.cudocompute.com/blog/real-world-gpu-benchmarks", "content": "Jun 23, 2025 · CUDO Compute's benchmarks show that NVIDIA H100 SXM trains 12 times faster and 86% cheaper than A100 , while L40S delivers the lowest inference cost-per-token."}
+{"idx": 4, "title": "NVIDIA H100 vs A100: Detailed GPU Comparison for 2024 Comparing NVIDIA’s Top AI GPUs H100, A100, A6000, and L40S What Limits Virtual Agent Application? OmniBench : A Scalable Multi What Limits Virtual Agent Application? OmniBench : A Scalable Multi What Limits Virtual Agent Application? OmniBench : A Scalable Multi Choosing between NVIDIA H100 vs A100 - Ori", "date": "", "ddg_snippet": "May 20, 2025 · Discover the differences between NVIDIA H100 and A100 GPUs. Compare specs, performance, pricing, and find out which GPU suits your AI workloads in 2024. Nov 1 , 2024 · Choosing the right GPU is key to optimizing AI model training and inference. NVIDIA ’s H100 , A100 , A6000, and L40S each have unique strengths, from high-capacity training to efficient inference. This article compares their performance and applications, showcasing real-world examples where top companies use these GPUs to power advanced AI projects. What are the different types of data in omnibench? The data in OmniBench can be broadly categorized into five types: Subtask Metadata, Subtask Trajectory, Subtask Evaluation, Task Metadata, and Task Trajectory . For each category, we present the corre-sponding data schema along with representative examples. Finally, we provide a visualization example of a task graph. What action types does omnibench support? Summary of action types in the desktop environment of OmniBench. SimulatesmouseclicksonUIcontrolelements . Supportsconfigurable mousebuttons(left,right,middle,x)andcanperformbothsingleand doubleclicks. What does an asterisk (*) mean in omnibench? An asterisk (*) indicates that the agent uses GPT-4o as the planner. In conclusion, we introduced OmniBench, a graph-based benchmark that addresses the limitations of existing eval-uation frameworks by enabling controllable task complex-ity through automated subtask composition. TABLE 1 - Technical Specifications NVIDIA A100 vs H100 According to NVIDIA , the H100 performance can be up to 30x better for inference and 9x better for training.", "subpage_snippet": "", "source": "docs.jarvislabs.ai", "link": "https://docs.jarvislabs.ai/blog/h100vsa100", "content": "May 20, 2025 · Discover the differences between NVIDIA H100 and A100 GPUs. Compare specs, performance, pricing, and find out which GPU suits your AI workloads in 2024. Nov 1 , 2024 · Choosing the right GPU is key to optimizing AI model training and inference. NVIDIA ’s H100 , A100 , A6000, and L40S each have unique strengths, from high-capacity training to efficient inference. This article compares their performance and applications, showcasing real-world examples where top companies use these GPUs to power advanced AI projects. What are the different types of data in omnibench? The data in OmniBench can be broadly categorized into five types: Subtask Metadata, Subtask Trajectory, Subtask Evaluation, Task Metadata, and Task Trajectory . For each category, we present the corre-sponding data schema along with representative examples. Finally, we provide a visualization example of a task graph. What action types does omnibench support? Summary of action types in the desktop environment of OmniBench. SimulatesmouseclicksonUIcontrolelements . Supportsconfigurable mousebuttons(left,right,middle,x)andcanperformbothsingleand doubleclicks. What does an asterisk (*) mean in omnibench? An asterisk (*) indicates that the agent uses GPT-4o as the planner. In conclusion, we introduced OmniBench, a graph-based benchmark that addresses the limitations of existing eval-uation frameworks by enabling controllable task complex-ity through automated subtask composition. TABLE 1 - Technical Specifications NVIDIA A100 vs H100 According to NVIDIA , the H100 performance can be up to 30x better for inference and 9x better for training."}
+{"idx": 5, "title": "Comparing NVIDIA’s Top AI GPUs H100, A100, A6000, and L40S", "date": "", "ddg_snippet": "Nov 1 , 2024 · Choosing the right GPU is key to optimizing AI model training and inference. NVIDIA ’s H100 , A100 , A6000, and L40S each have unique strengths, from high-capacity training to efficient inference. This article compares their performance and applications, showcasing real-world examples where top companies use these GPUs to power advanced AI projects.", "subpage_snippet": "", "source": "www.naddod.com", "link": "https://www.naddod.com/blog/comparing-nvidia-top-ai-gpus-h100-a100-a6000-and-l40s", "content": "Nov 1 , 2024 · Choosing the right GPU is key to optimizing AI model training and inference. NVIDIA ’s H100 , A100 , A6000, and L40S each have unique strengths, from high-capacity training to efficient inference. This article compares their performance and applications, showcasing real-world examples where top companies use these GPUs to power advanced AI projects."}
+{"idx": 6, "title": "Choosing between NVIDIA H100 vs A100 - Ori", "date": "", "ddg_snippet": "TABLE 1 - Technical Specifications NVIDIA A100 vs H100 According to NVIDIA , the H100 performance can be up to 30x better for inference and 9x better for training.", "subpage_snippet": "", "source": "www.ori.co", "link": "https://www.ori.co/blog/choosing-between-nvidia-h100-vs-a100-performance-and-costs-considerations", "content": "TABLE 1 - Technical Specifications NVIDIA A100 vs H100 According to NVIDIA , the H100 performance can be up to 30x better for inference and 9x better for training."}
+{"idx": 7, "title": "RFI поставка видеокарт NVIDIA A 100 , H 100 , H 200: процедура...", "date": "", "ddg_snippet": "RFI поставка видеокарт NVIDIA A 100 , H 100 , H 200. Организатор торгов. Общество с ограниченной ответственностью \"Группа компаний \"иннотех\" (ИНН 9703073496).Видеокарта NVIDIA A 100 40Гб. 77. г. Москва. НМЦ не указано.", "subpage_snippet": "", "source": "www.Roseltorg.ru", "link": "https://www.Roseltorg.ru/procedure/B2209251032383", "content": "RFI поставка видеокарт NVIDIA A 100 , H 100 , H 200. Организатор торгов. Общество с ограниченной ответственностью \"Группа компаний \"иннотех\" (ИНН 9703073496).Видеокарта NVIDIA A 100 40Гб. 77. г. Москва. НМЦ не указано."}
+{"idx": 8, "title": "OmniBench", "date": "", "ddg_snippet": "OmniBench spans five fundamental types of task complexity to construct 10 evaluation dimensions (see the main figure). Test tasks across these dimensions are categorized based on combinations of complexity types.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "OmniBench spans five fundamental types of task complexity to construct 10 evaluation dimensions (see the main figure). Test tasks across these dimensions are categorized based on combinations of complexity types."}
+{"idx": 9, "title": "$90000 NVIDIA A 100 GPU Server", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=iTtjOf0JRnI", "content": ""}
diff --git a/data/sampled_jsons/OmniBench_A100_OR_V100_OR_H100_OR_3090_OR_4090_experimental_setup.jsonl b/data/sampled_jsons/OmniBench_A100_OR_V100_OR_H100_OR_3090_OR_4090_experimental_setup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ca9f8d6f1ff340896b71023ce8a800c50cbc28c6
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_A100_OR_V100_OR_H100_OR_3090_OR_4090_experimental_setup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NVIDIA H100 vs A100: Detailed GPU Comparison for 2024 Images A10 vs. A100 vs. H100 - Which one should you choose? H100 vs A100 vs RTX 4090 - GPU Mart NVIDIA A100 vs. H100 vs. H800 (2025): Which AI Powerhouse GPU ... H100 vs Other GPUs Choosing The Right GPU for your machine ... NVIDIA GPUs: H100 vs. A100 | a detailed comparison - Gcore H100 Tensor Core GPU | NVIDIA", "date": "", "ddg_snippet": "May 20, 2025 · Discover the differences between NVIDIA H100 and A100 GPUs. Compare specs, performance, pricing, and find out which GPU suits your AI workloads in 2024. View all Jan 27, 2025 · Discover the best GPU for your AI workload: Compare A10, A100, and H100 performance, pricing, and use cases to make an informed decision. Explore the comprehensive comparison of H100 , A100, and RTX 4090. Discover their specifications, performance, and ideal use cases for your needs. Mar 12, 2025 · NVIDIA A100 vs. H100 vs. H800 – which one should you choose? The answer isn’t as straightforward as you might think! After testing all three in various scenarios, I’ve found that the A100 excels for budget-conscious organizations running diverse workloads and smaller models, making it perfect for startups and research teams. The H100 absolutely dominates when it comes to training and ... Oct 3, 2024 · H100 vs. Other GPUs: A comprehensive guide to selecting the ideal GPU for your machine learning workloads, exploring the unique advantages of NVIDIA’s H100 o… Jan 6, 2025 · A detailed comparison of the H100 and A100, focusing on their performance metrics and suitability for specific workloads so you can decide which is best for your use case. The NVIDIA H100 Tensor Core GPU delivers exceptional performance, scalability, and security for every workload. H100 uses breakthrough innovations based on the NVIDIA Hopper™ architecture to deliver industry-leading conversational AI, speeding up large language models (LLMs) by 30X. H100 also includes a dedicated Transformer Engine to solve trillion-parameter language models.", "subpage_snippet": "", "source": "docs.jarvislabs.ai", "link": "https://docs.jarvislabs.ai/blog/h100vsa100", "content": "May 20, 2025 · Discover the differences between NVIDIA H100 and A100 GPUs. Compare specs, performance, pricing, and find out which GPU suits your AI workloads in 2024. View all Jan 27, 2025 · Discover the best GPU for your AI workload: Compare A10, A100, and H100 performance, pricing, and use cases to make an informed decision. Explore the comprehensive comparison of H100 , A100, and RTX 4090. Discover their specifications, performance, and ideal use cases for your needs. Mar 12, 2025 · NVIDIA A100 vs. H100 vs. H800 – which one should you choose? The answer isn’t as straightforward as you might think! After testing all three in various scenarios, I’ve found that the A100 excels for budget-conscious organizations running diverse workloads and smaller models, making it perfect for startups and research teams. The H100 absolutely dominates when it comes to training and ... Oct 3, 2024 · H100 vs. Other GPUs: A comprehensive guide to selecting the ideal GPU for your machine learning workloads, exploring the unique advantages of NVIDIA’s H100 o… Jan 6, 2025 · A detailed comparison of the H100 and A100, focusing on their performance metrics and suitability for specific workloads so you can decide which is best for your use case. The NVIDIA H100 Tensor Core GPU delivers exceptional performance, scalability, and security for every workload. H100 uses breakthrough innovations based on the NVIDIA Hopper™ architecture to deliver industry-leading conversational AI, speeding up large language models (LLMs) by 30X. H100 also includes a dedicated Transformer Engine to solve trillion-parameter language models."}
+{"idx": 1, "title": "A10 vs. A100 vs. H100 - Which one should you choose?", "date": "", "ddg_snippet": "Jan 27, 2025 · Discover the best GPU for your AI workload: Compare A10, A100, and H100 performance, pricing, and use cases to make an informed decision.", "subpage_snippet": "", "source": "modal.com", "link": "https://modal.com/blog/gpu-types", "content": "Jan 27, 2025 · Discover the best GPU for your AI workload: Compare A10, A100, and H100 performance, pricing, and use cases to make an informed decision."}
+{"idx": 2, "title": "H100 vs A100 vs RTX 4090 - GPU Mart", "date": "", "ddg_snippet": "Explore the comprehensive comparison of H100 , A100, and RTX 4090. Discover their specifications, performance, and ideal use cases for your needs.", "subpage_snippet": "", "source": "www.gpu-mart.com", "link": "https://www.gpu-mart.com/blog/h100-vs-a100-vs-rtx-4090", "content": "Explore the comprehensive comparison of H100 , A100, and RTX 4090. Discover their specifications, performance, and ideal use cases for your needs."}
+{"idx": 3, "title": "NVIDIA A100 vs. H100 vs. H800 (2025): Which AI Powerhouse GPU ...", "date": "", "ddg_snippet": "Mar 12, 2025 · NVIDIA A100 vs. H100 vs. H800 – which one should you choose? The answer isn’t as straightforward as you might think! After testing all three in various scenarios, I’ve found that the A100 excels for budget-conscious organizations running diverse workloads and smaller models, making it perfect for startups and research teams. The H100 absolutely dominates when it comes to training and ...", "subpage_snippet": "", "source": "slviki.org", "link": "https://slviki.org/nvidia-a100-vs-h100-vs-h800-comparison/", "content": "Mar 12, 2025 · NVIDIA A100 vs. H100 vs. H800 – which one should you choose? The answer isn’t as straightforward as you might think! After testing all three in various scenarios, I’ve found that the A100 excels for budget-conscious organizations running diverse workloads and smaller models, making it perfect for startups and research teams. The H100 absolutely dominates when it comes to training and ..."}
+{"idx": 4, "title": "H100 vs Other GPUs Choosing The Right GPU for your machine ...", "date": "", "ddg_snippet": "Oct 3, 2024 · H100 vs. Other GPUs: A comprehensive guide to selecting the ideal GPU for your machine learning workloads, exploring the unique advantages of NVIDIA’s H100 o…", "subpage_snippet": "", "source": "www.digitalocean.com", "link": "https://www.digitalocean.com/community/tutorials/h100_vs_other_gpus_choosing_the_right_gpu_for_your_machine_learning_workload", "content": "Oct 3, 2024 · H100 vs. Other GPUs: A comprehensive guide to selecting the ideal GPU for your machine learning workloads, exploring the unique advantages of NVIDIA’s H100 o…"}
+{"idx": 5, "title": "NVIDIA GPUs: H100 vs. A100 | a detailed comparison - Gcore", "date": "", "ddg_snippet": "Jan 6, 2025 · A detailed comparison of the H100 and A100, focusing on their performance metrics and suitability for specific workloads so you can decide which is best for your use case.", "subpage_snippet": "", "source": "gcore.com", "link": "https://gcore.com/blog/nvidia-h100-a100", "content": "Jan 6, 2025 · A detailed comparison of the H100 and A100, focusing on their performance metrics and suitability for specific workloads so you can decide which is best for your use case."}
+{"idx": 6, "title": "H100 Tensor Core GPU | NVIDIA", "date": "", "ddg_snippet": "The NVIDIA H100 Tensor Core GPU delivers exceptional performance, scalability, and security for every workload. H100 uses breakthrough innovations based on the NVIDIA Hopper™ architecture to deliver industry-leading conversational AI, speeding up large language models (LLMs) by 30X. H100 also includes a dedicated Transformer Engine to solve trillion-parameter language models.", "subpage_snippet": "", "source": "www.nvidia.com", "link": "https://www.nvidia.com/en-us/data-center/h100/", "content": "The NVIDIA H100 Tensor Core GPU delivers exceptional performance, scalability, and security for every workload. H100 uses breakthrough innovations based on the NVIDIA Hopper™ architecture to deliver industry-leading conversational AI, speeding up large language models (LLMs) by 30X. H100 also includes a dedicated Transformer Engine to solve trillion-parameter language models."}
+{"idx": 7, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "Experimental Setup . Settings. We evaluate various models including MLLMs and ... All experiments are conducted with NVIDIA A100 80G GPUs. Baselines. We ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46463", "content": "Experimental Setup . Settings. We evaluate various models including MLLMs and ... All experiments are conducted with NVIDIA A100 80G GPUs. Baselines. We ..."}
+{"idx": 8, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "OmniBench data for some selected models. All experiments are conducted with NVIDIA A100 80G GPUs. Baselines. We conduct a comprehensive evaluation of the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d5d3691187b9f32f92e8e287e5005d40aa429f80.pdf", "content": "OmniBench data for some selected models. All experiments are conducted with NVIDIA A100 80G GPUs. Baselines. We conduct a comprehensive evaluation of the ..."}
+{"idx": 9, "title": "Multi-TW: Benchmarking Multimodal Models on Traditional ...", "date": "", "ddg_snippet": "2 Aug 2025 — This section details our experimental setup , the models evaluated, and the observed results. ... All experiments were conducted on an NVIDIA A100 - ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.01274v1", "content": "2 Aug 2025 — This section details our experimental setup , the models evaluated, and the observed results. ... All experiments were conducted on an NVIDIA A100 - ..."}
diff --git a/data/sampled_jsons/OmniBench_Cross-Verification_module_Section_3.3_virtual_agent.jsonl b/data/sampled_jsons/OmniBench_Cross-Verification_module_Section_3.3_virtual_agent.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2dd7205e67eac42c48006a900c9a6e31bcdb5566
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_Cross-Verification_module_Section_3.3_virtual_agent.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2506.08933] What Limits Virtual Agent Application? OmniBench ...", "date": "", "ddg_snippet": "Jun 10, 2025 · As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing benchmarks face significant limitations, including uncontrollable task complexity, extensive manual annotation with limited scenarios, and a lack of multidimensional evaluation. In response to these challenges, we introduce OmniBench , a self-generating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.08933", "content": "Jun 10, 2025 · As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing benchmarks face significant limitations, including uncontrollable task complexity, extensive manual annotation with limited scenarios, and a lack of multidimensional evaluation. In response to these challenges, we introduce OmniBench , a self-generating ..."}
+{"idx": 1, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable ...", "date": "", "ddg_snippet": "Since the quality of graph-structured tasks is critical to the ac-curate evaluation of the virtual agents , we further introduce three designs to enhance the quality of synthesized data: a cross-verification mechanism, an intent extraction module , and a consistency validator.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "Since the quality of graph-structured tasks is critical to the ac-curate evaluation of the virtual agents , we further introduce three designs to enhance the quality of synthesized data: a cross-verification mechanism, an intent extraction module , and a consistency validator."}
+{"idx": 2, "title": "What Limits Virtual Agent Application? OmniBench: A ... - GitHub", "date": "", "ddg_snippet": "Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs. It evaluates ten virtual agent capabilities using high-quality graph-based data, ensuring scalable and realistic task assessments.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench", "content": "Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs. It evaluates ten virtual agent capabilities using high-quality graph-based data, ensuring scalable and realistic task assessments."}
+{"idx": 3, "title": "OmniBench", "date": "", "ddg_snippet": "Based on OmniBench , we comprehensively evaluate 12 virtual agents , including both open-source and proprietary models, across all 10 capability dimensions as shown in the main figure, fully revealing the capability boundaries and providing concrete directions for future improvement.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "Based on OmniBench , we comprehensively evaluate 12 virtual agents , including both open-source and proprietary models, across all 10 capability dimensions as shown in the main figure, fully revealing the capability boundaries and providing concrete directions for future improvement."}
+{"idx": 4, "title": "OmniBench/README.md at main · antgroup/OmniBench · GitHub", "date": "", "ddg_snippet": "Based on OmniBench , we comprehensively evaluate 12 virtual agents , including both open-source and proprietary models, across all 10 capability dimensions as shown in the main figure, fully revealing the capability boundaries and providing concrete directions for future improvement.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench/blob/main/README.md", "content": "Based on OmniBench , we comprehensively evaluate 12 virtual agents , including both open-source and proprietary models, across all 10 capability dimensions as shown in the main figure, fully revealing the capability boundaries and providing concrete directions for future improvement."}
+{"idx": 5, "title": "OmniBench：重新定义虚拟代理评估的多维基准测试框架_omnieval-CSDN博...", "date": "", "ddg_snippet": "Jul 4, 2025 · 2. OmniBench ：突破性的自生成基准框架 为解决这些挑战，来自浙江大学、蚂蚁集团等机构的研究团队提出了 OmniBench ——一个基于图结构的自生成、跨平台基准测试框架，通过子任务组合自动合成可控复杂度的任务。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_42540492/article/details/149117606", "content": "Jul 4, 2025 · 2. OmniBench ：突破性的自生成基准框架 为解决这些挑战，来自浙江大学、蚂蚁集团等机构的研究团队提出了 OmniBench ——一个基于图结构的自生成、跨平台基准测试框架，通过子任务组合自动合成可控复杂度的任务。"}
+{"idx": 6, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "May 1, 2025 · In this work, the authors present OmniBench , a graph-based benchmark designed for evaluating multimodal virtual agents ' capabilities of solving complex tasks. The benchmark is synthetically generated, but in a way that the evaluation conclusions drawn from OmniBench can be generalized to real-world virtual agent applications (such as long ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "May 1, 2025 · In this work, the authors present OmniBench , a graph-based benchmark designed for evaluating multimodal virtual agents ' capabilities of solving complex tasks. The benchmark is synthetically generated, but in a way that the evaluation conclusions drawn from OmniBench can be generalized to real-world virtual agent applications (such as long ..."}
+{"idx": 7, "title": "What Limits Virtual Agent Application? OmniBench : A Scalable...", "date": "", "ddg_snippet": "In response to these challenges, we introduce OmniBench , a self-generating, cross -platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition. To evaluate the diverse capabilities of virtual agents on the graph...", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/What-Limits-Virtual-Agent-Application?-OmniBench:-A-Scalable-Multi-Dimensional-Benchmark-for-Essential-Virtual-Agent-Capabilities-2f155ea4-8dfd-470f-8fc7-1a25ad06f3ba", "content": "In response to these challenges, we introduce OmniBench , a self-generating, cross -platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition. To evaluate the diverse capabilities of virtual agents on the graph..."}
+{"idx": 8, "title": "omnibench · PyPI", "date": "", "ddg_snippet": "OmniBench Logo. A Customizable, Multi-Objective AI Agent Benchmarking Framework for Agentic Reliability and Mediation (ARM). OmniBench follows a clean, modular architecture that makes it easy to understand and extend", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/omnibench/", "content": "OmniBench Logo. A Customizable, Multi-Objective AI Agent Benchmarking Framework for Agentic Reliability and Mediation (ARM). OmniBench follows a clean, modular architecture that makes it easy to understand and extend"}
+{"idx": 9, "title": "machinelearningmastery.com/k-fold- cross -validation", "date": "", "ddg_snippet": "More info in the first of five sections in this tutorial.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/k-fold-cross-validation/", "content": "More info in the first of five sections in this tutorial."}
diff --git a/data/sampled_jsons/OmniBench_Cross-Verification_subtask_trajectory_evaluation_functions_mutual_verification.jsonl b/data/sampled_jsons/OmniBench_Cross-Verification_subtask_trajectory_evaluation_functions_mutual_verification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4a372ce94345ee5a2a2aaf79548039c4d142d875
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_Cross-Verification_subtask_trajectory_evaluation_functions_mutual_verification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "The authors' Cross - Verification method cleverly integrates subtask trajectory data with evaluation functions . By leveraging mutual verification between these two types of data, it iteratively optimizes both the synthesized trajectories and the evaluation functions simultaneously.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "The authors' Cross - Verification method cleverly integrates subtask trajectory data with evaluation functions . By leveraging mutual verification between these two types of data, it iteratively optimizes both the synthesized trajectories and the evaluation functions simultaneously."}
+{"idx": 1, "title": "[2506.08933] What Limits Virtual Agent Application? OmniBench: A ...", "date": "", "ddg_snippet": "In response to these challenges, we introduce OmniBench , a self-generating, cross -platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.08933", "content": "In response to these challenges, we introduce OmniBench , a self-generating, cross -platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition."}
+{"idx": 2, "title": "GitHub - antgroup/OmniBench: [ICML 2025 Oral] This is the official ...", "date": "", "ddg_snippet": "[June 5, 2025] We have released the exploration code for collecting subtask instructions in OmniBench , as well as the evaluation script used to evaluate virtual agents. Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench", "content": "[June 5, 2025] We have released the exploration code for collecting subtask instructions in OmniBench , as well as the evaluation script used to evaluate virtual agents. Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for ..."}
+{"idx": 3, "title": "OmniBench", "date": "", "ddg_snippet": "In contrast to previous coarse-grained evaluation methods, we introduce a graph-based evaluator that applies subtask -level evaluation functions in OmniBench . Specifically, we design two novel fine-grained metrics to evaluate agents' performance on graph-structured tasks and their alignment with human logic.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "In contrast to previous coarse-grained evaluation methods, we introduce a graph-based evaluator that applies subtask -level evaluation functions in OmniBench . Specifically, we design two novel fine-grained metrics to evaluate agents' performance on graph-structured tasks and their alignment with human logic."}
+{"idx": 4, "title": "ICML Poster What Limits Virtual Agent Application? OmniBench: A ...", "date": "", "ddg_snippet": "OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities Wendong Bu · Yang Wu · Qifan Yu · Minghe Gao · Bingchen Miao · Zhenkui Zhang · Kaihang Pan · liyunfei · Mengze Li · Wei Ji · Juncheng Li · Siliang Tang · Yueting Zhuang", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46463", "content": "OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities Wendong Bu · Yang Wu · Qifan Yu · Minghe Gao · Bingchen Miao · Zhenkui Zhang · Kaihang Pan · liyunfei · Mengze Li · Wei Ji · Juncheng Li · Siliang Tang · Yueting Zhuang"}
+{"idx": 5, "title": "OmniBench/README.md at main · antgroup/OmniBench · GitHub", "date": "", "ddg_snippet": "In contrast to previous coarse-grained evaluation methods, we introduce a graph-based evaluator that applies subtask -level evaluation functions in OmniBench . Specifically, we design two novel fine-grained metrics to evaluate agents' performance on graph-structured tasks and their alignment with human logic.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench/blob/main/README.md", "content": "In contrast to previous coarse-grained evaluation methods, we introduce a graph-based evaluator that applies subtask -level evaluation functions in OmniBench . Specifically, we design two novel fine-grained metrics to evaluate agents' performance on graph-structured tasks and their alignment with human logic."}
+{"idx": 6, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable Multi ...", "date": "", "ddg_snippet": "In response to these challenges, we introduce OmniBench , a self-generating, cross -platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2025arXiv250608933B/abstract", "content": "In response to these challenges, we introduce OmniBench , a self-generating, cross -platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition."}
+{"idx": 7, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable Multi ...", "date": "", "ddg_snippet": "The cross - verification mechanism iter-atively optimizes the demonstration trajectories and evaluation functions of subtasks , the intent extraction module ensures that the tasks have coherent goals, and the consistency validator aligns the semantics of the task graph and task instructions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "The cross - verification mechanism iter-atively optimizes the demonstration trajectories and evaluation functions of subtasks , the intent extraction module ensures that the tasks have coherent goals, and the consistency validator aligns the semantics of the task graph and task instructions."}
+{"idx": 8, "title": "OmniBench Data Explorer", "date": "", "ddg_snippet": "Data Explorer Below we show a small subset of OmniBench subtasks , which allows you to explore the data in detail.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/explorer.html", "content": "Data Explorer Below we show a small subset of OmniBench subtasks , which allows you to explore the data in detail."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "3 days ago — OmnixR presents a unique evaluation towards assessing OLMs over a diverse mix of modalities, such as a question that involves video, audio, and ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=tri-modal+evaluation", "content": "3 days ago — OmnixR presents a unique evaluation towards assessing OLMs over a diverse mix of modalities, such as a question that involves video, audio, and ..."}
diff --git a/data/sampled_jsons/OmniBench_official_page_experimental_setup_GPU_hardware.jsonl b/data/sampled_jsons/OmniBench_official_page_experimental_setup_GPU_hardware.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c2f7381dc74b608344615a3cd6b4313dd7dc153d
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_official_page_experimental_setup_GPU_hardware.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "Experiments. In this section, we first introduce the experimental setup (Sec- tion 5.1). Then, we comprehensively compare the differences in capabilities ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d5d3691187b9f32f92e8e287e5005d40aa429f80.pdf", "content": "Experiments. In this section, we first introduce the experimental setup (Sec- tion 5.1). Then, we comprehensively compare the differences in capabilities ..."}
+{"idx": 1, "title": "Multimodal AI on Developer GPUs: Alibaba", "date": "", "ddg_snippet": "Qwen2.5-Omni-3B is a transformer-based model that supports multimodal comprehension across text, images, and audio-video input.", "subpage_snippet": "", "source": "www.facebook.com", "link": "https://www.facebook.com/groups/DeepNetGroup/posts/2473964406329760/", "content": "Qwen2.5-Omni-3B is a transformer-based model that supports multimodal comprehension across text, images, and audio-video input."}
+{"idx": 2, "title": "Mungert/Qwen2.5-Omni-3B-GGUF", "date": "", "ddg_snippet": "26 Jul 2025 — Works on most devices with FP16 acceleration support (including many GPUs and some CPUs). Slightly lower numerical precision than BF16 but ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Mungert/Qwen2.5-Omni-3B-GGUF", "content": "26 Jul 2025 — Works on most devices with FP16 acceleration support (including many GPUs and some CPUs). Slightly lower numerical precision than BF16 but ..."}
+{"idx": 3, "title": "Only Top Reasoning Models Can Solve Spatial ...", "date": "", "ddg_snippet": "2 Sept 2025 — Hardware and Runtime: The local models were evaluated on a T4- GPU . Most CLIP models evaluate within a few minutes. The API models take between 1 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02175v1", "content": "2 Sept 2025 — Hardware and Runtime: The local models were evaluated on a T4- GPU . Most CLIP models evaluate within a few minutes. The API models take between 1 ..."}
+{"idx": 4, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Stronger Neyman Regret Guarantees for Adaptive Experimental Design ... Low-distortion and GPU -compatible Tree Embeddings in Hyperbolic Space · DEFAME ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Stronger Neyman Regret Guarantees for Adaptive Experimental Design ... Low-distortion and GPU -compatible Tree Embeddings in Hyperbolic Space · DEFAME ..."}
+{"idx": 5, "title": "Daily Papers", "date": "", "ddg_snippet": "3 days ago — Experimental results across various vision encoders, image resolutions, training dataset scales, varying sizes of LLMs (2.7B->70B), and diverse ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=image+and+video+benchmarks", "content": "3 days ago — Experimental results across various vision encoders, image resolutions, training dataset scales, varying sizes of LLMs (2.7B->70B), and diverse ..."}
+{"idx": 6, "title": "UCLA Electronic Theses and Dissertations", "date": "", "ddg_snippet": "This section contains all experiments details that help researchers reproduce our main results, including evaluation details and experimental settings . 49. Page ...", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt9kf898q5/qt9kf898q5.pdf", "content": "This section contains all experiments details that help researchers reproduce our main results, including evaluation details and experimental settings . 49. Page ..."}
+{"idx": 7, "title": "All Models", "date": "", "ddg_snippet": "... hardware ,Approach,Confidence,Abstract,Epochs,Benchmark data,Model ... GPU hours needed by Qwen3-30A-3B, and only 9.3% of the compute cost of Qwen3-32B ...", "subpage_snippet": "", "source": "epoch.ai", "link": "https://epoch.ai/data/generated/all_ai_models.csv", "content": "... hardware ,Approach,Confidence,Abstract,Epochs,Benchmark data,Model ... GPU hours needed by Qwen3-30A-3B, and only 9.3% of the compute cost of Qwen3-32B ..."}
+{"idx": 8, "title": "WorldSense: Evaluating Real-world Omnimodal ...", "date": "", "ddg_snippet": "26 May 2025 — We introduce WorldSense, the first benchmark to assess the multi-modal video understanding, that simultaneously encompasses visual, audio, and text inputs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04326v2", "content": "26 May 2025 — We introduce WorldSense, the first benchmark to assess the multi-modal video understanding, that simultaneously encompasses visual, audio, and text inputs."}
+{"idx": 9, "title": "Tavish9/awesome-daily-AI-arxiv", "date": "", "ddg_snippet": "This offers a viable path toward handling the long-term, dynamic workflows of autonomous agents. Extensive experiments on the LoCoMo and LongMemEval benchmarks ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Tavish9/awesome-daily-AI-arxiv", "content": "This offers a viable path toward handling the long-term, dynamic workflows of autonomous agents. Extensive experiments on the LoCoMo and LongMemEval benchmarks ..."}
diff --git a/data/sampled_jsons/OmniBench_paper_experimental_setup_NVIDIA_GPU_A100_H100_specifications.jsonl b/data/sampled_jsons/OmniBench_paper_experimental_setup_NVIDIA_GPU_A100_H100_specifications.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..61aeecc96c85889da5378150893815e1196027d5
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_paper_experimental_setup_NVIDIA_GPU_A100_H100_specifications.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NVIDIA H100 GPU Datasheet", "date": "", "ddg_snippet": "This datasheet details the performance and product specifications of the NVIDIA H100 Tensor Core GPU . It also explains the technological breakthroughs of the NVIDIA Hopper architecture.", "subpage_snippet": "", "source": "resources.nvidia.com", "link": "https://resources.nvidia.com/en-us-hopper-architecture/nvidia-tensor-core-gpu-datasheet", "content": "This datasheet details the performance and product specifications of the NVIDIA H100 Tensor Core GPU . It also explains the technological breakthroughs of the NVIDIA Hopper architecture."}
+{"idx": 1, "title": "GPU Microbenchmarks - GitHub", "date": "", "ddg_snippet": "GPU Microbenchmarks This repository contains CUDA-based benchmarks designed to evaluate various aspects of GPU memory and interconnection networks on NVIDIA GPUs (V100, A100 , H100 ). Each benchmark focuses on distinct architectural components, using bandwidth, latency, and execution time as key metrics.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chrirocca/GPUNetBench", "content": "GPU Microbenchmarks This repository contains CUDA-based benchmarks designed to evaluate various aspects of GPU memory and interconnection networks on NVIDIA GPUs (V100, A100 , H100 ). Each benchmark focuses on distinct architectural components, using bandwidth, latency, and execution time as key metrics."}
+{"idx": 2, "title": "NVIDIA H100 vs A100: Detailed GPU Comparison for 2024", "date": "", "ddg_snippet": "Discover the differences between NVIDIA H100 and A100 GPUs . Compare specs, performance, pricing, and find out which GPU suits your AI workloads in 2024.", "subpage_snippet": "", "source": "docs.jarvislabs.ai", "link": "https://docs.jarvislabs.ai/blog/h100vsa100", "content": "Discover the differences between NVIDIA H100 and A100 GPUs . Compare specs, performance, pricing, and find out which GPU suits your AI workloads in 2024."}
+{"idx": 3, "title": "Possible to build a GPU server at home with Nvidia H100/A100 ... - Reddit", "date": "", "ddg_snippet": "Possible to build a GPU server at home with Nvidia H100 / A100 cards? I have no knowledge of data center experience. I just use these cards for deep learning research. Out of curiosity I am wondering if this is possible to set up a GPU server at home with Nvidia H100 / A100 .", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/deeplearning/comments/18un35q/possible_to_build_a_gpu_server_at_home_with/", "content": "Possible to build a GPU server at home with Nvidia H100 / A100 cards? I have no knowledge of data center experience. I just use these cards for deep learning research. Out of curiosity I am wondering if this is possible to set up a GPU server at home with Nvidia H100 / A100 ."}
+{"idx": 4, "title": "PDF NVIDIA H100 Tensor Core GPU Architecture", "date": "", "ddg_snippet": "The following sections provide brief descriptions of NVIDIA data center-ready H100 -based systems and boards, including H100 GPUs in SMX5 and PCIe Gen 5 form-factors, DGX H100 and DGX SuperPOD systems, HGX H100 , and the H100 CNX Converged Accelerator that combines the power of the NVIDIA H100 GPU with the advanced networking capabilities of the ...", "subpage_snippet": "", "source": "www.advancedclustering.com", "link": "https://www.advancedclustering.com/wp-content/uploads/2022/03/gtc22-whitepaper-hopper.pdf", "content": "The following sections provide brief descriptions of NVIDIA data center-ready H100 -based systems and boards, including H100 GPUs in SMX5 and PCIe Gen 5 form-factors, DGX H100 and DGX SuperPOD systems, HGX H100 , and the H100 CNX Converged Accelerator that combines the power of the NVIDIA H100 GPU with the advanced networking capabilities of the ..."}
+{"idx": 5, "title": "NVIDIA A100 vs. H100: Choosing the Right GPU for Your AI Workloads", "date": "", "ddg_snippet": "Compare NVIDIA A100 vs H100 GPUs . Learn key architectural differences, performance benchmarks, and deployment considerations for AI and HPC workloads.", "subpage_snippet": "", "source": "www.clarifai.com", "link": "https://www.clarifai.com/blog/nvidia-a100-vs.-h100-choosing-the-right-gpu-for-your-ai-workloads", "content": "Compare NVIDIA A100 vs H100 GPUs . Learn key architectural differences, performance benchmarks, and deployment considerations for AI and HPC workloads."}
+{"idx": 6, "title": "PDF Nvidia H100 Nvl Gpu", "date": "", "ddg_snippet": "The NVIDIA H100 NVL card supports Multi-Instance GPU (MIG) capability by providing up to seven GPU instances per NVIDIA H100 NVL GPU . MIG technology can partition the NVIDIA H100 NVL GPU into individual instances, each fully isolated with its own high-bandwidth memory, cache, and compute cores, enabling optimized computational resource ...", "subpage_snippet": "", "source": "www.nvidia.com", "link": "https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/h100/PB-11773-001_v01.pdf", "content": "The NVIDIA H100 NVL card supports Multi-Instance GPU (MIG) capability by providing up to seven GPU instances per NVIDIA H100 NVL GPU . MIG technology can partition the NVIDIA H100 NVL GPU into individual instances, each fully isolated with its own high-bandwidth memory, cache, and compute cores, enabling optimized computational resource ..."}
+{"idx": 7, "title": "NVIDIA A100 vs H100 vs A800 vs H800 vs H200: Ultimate Comparison Guide ...", "date": "", "ddg_snippet": "This article explores the GPU performance, specifications , and pricing of the NVIDIA A100 , H100 , A800, H800, and H200. It provides an in-depth analysis of the architecture, memory, and power consumption of these high-performance computing and AI-specific GPUs , along with selection recommendations to help you find the most suitable product based on your needs.", "subpage_snippet": "", "source": "twqiang.com", "link": "https://twqiang.com/en/news/20250228095001ea1929", "content": "This article explores the GPU performance, specifications , and pricing of the NVIDIA A100 , H100 , A800, H800, and H200. It provides an in-depth analysis of the architecture, memory, and power consumption of these high-performance computing and AI-specific GPUs , along with selection recommendations to help you find the most suitable product based on your needs."}
+{"idx": 8, "title": "NVIDIA A100 vs. H100 vs. H800 (2025): Which AI Powerhouse GPU Delivers ...", "date": "", "ddg_snippet": "In the fast-evolving world of artificial intelligence (AI) and high-performance computing (HPC), NVIDIA dominates the GPU market with its cutting-edge hardware. The NVIDIA A100 , H100 , and H800 are among the most powerful GPUs available today, but each serves a different purpose.", "subpage_snippet": "", "source": "slviki.org", "link": "https://slviki.org/nvidia-a100-vs-h100-vs-h800-comparison/", "content": "In the fast-evolving world of artificial intelligence (AI) and high-performance computing (HPC), NVIDIA dominates the GPU market with its cutting-edge hardware. The NVIDIA A100 , H100 , and H800 are among the most powerful GPUs available today, but each serves a different purpose."}
+{"idx": 9, "title": "How do I configure a multi-GPU setup with NVIDIA\\'s A100 and H100 GPUs ...", "date": "", "ddg_snippet": "Configure multi- GPU setup with NVIDIA A100 & H100 GPUs : Learn optimal setup and configuration for improved performance.", "subpage_snippet": "", "source": "massedcompute.com", "link": "https://massedcompute.com/faq-answers/?question=How+do+I+configure+a+multi-GPU+setup+with+NVIDIA's+A100+and+H100+GPUs", "content": "Configure multi- GPU setup with NVIDIA A100 & H100 GPUs : Learn optimal setup and configuration for improved performance."}
diff --git a/data/sampled_jsons/OmniBench_paper_failure_analysis_hallucinatory_success_grounding_error_Section_5.3.jsonl b/data/sampled_jsons/OmniBench_paper_failure_analysis_hallucinatory_success_grounding_error_Section_5.3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a2d65d0ce42b5c5c2851f22c427127b4fba3407e
--- /dev/null
+++ b/data/sampled_jsons/OmniBench_paper_failure_analysis_hallucinatory_success_grounding_error_Section_5.3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench : A Scalable...", "date": "", "ddg_snippet": "Hallucinatory Success .In this section , we delve into the analysis of errors encountered during the OmniBench evaluation. This analysis aims not only to identify the current shortcom-ings of the agents but also to inform future improvements in their design and training.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "Hallucinatory Success .In this section , we delve into the analysis of errors encountered during the OmniBench evaluation. This analysis aims not only to identify the current shortcom-ings of the agents but also to inform future improvements in their design and training."}
+{"idx": 1, "title": "OmniBench", "date": "", "ddg_snippet": "Failure Analysis The top illustrates the distribution of the five main failure causes. The bottom presents examples of these five failure causes.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "Failure Analysis The top illustrates the distribution of the five main failure causes. The bottom presents examples of these five failure causes."}
+{"idx": 2, "title": "GitHub - antgroup/OmniBench: [ICML 2025 Oral] This is the ...", "date": "", "ddg_snippet": "[June 5, 2025] We have released the exploration code for collecting subtask instructions in OmniBench , as well as the evaluation script used to evaluate virtual agents. Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench", "content": "[June 5, 2025] We have released the exploration code for collecting subtask instructions in OmniBench , as well as the evaluation script used to evaluate virtual agents. Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for ..."}
+{"idx": 3, "title": "OmniBench/README.md at main · antgroup/OmniBench · GitHub", "date": "", "ddg_snippet": "The top illustrates the distribution of the five main failure causes. The bottom presents examples of these five failure causes.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench/blob/main/README.md", "content": "The top illustrates the distribution of the five main failure causes. The bottom presents examples of these five failure causes."}
+{"idx": 4, "title": "Module 3: The Failure Analysis Paper - University of Florida", "date": "", "ddg_snippet": "3 days ago · ENC 3246: Professional Communication for Engineers Module 3: The Failure Analysis Paper In this module are some examples of famous failures as well as ways to access standards to help with your failure analysis . The purpose of the investigation after a failure is to determine the root cause and what steps that can be taken to prevent other ...", "subpage_snippet": "", "source": "guides.uflib.ufl.edu", "link": "https://guides.uflib.ufl.edu/enc3246/mod3", "content": "3 days ago · ENC 3246: Professional Communication for Engineers Module 3: The Failure Analysis Paper In this module are some examples of famous failures as well as ways to access standards to help with your failure analysis . The purpose of the investigation after a failure is to determine the root cause and what steps that can be taken to prevent other ..."}
+{"idx": 5, "title": "OmniBench - m-a-p.ai", "date": "", "ddg_snippet": "OmniBench comprises 1142 question-answer pairs, with task type distribution, text length, and image and audio characteristics. The dataset's audio content falls into three categories: speech (human vocal communication), sound events (non-speech natural, environmental and mechanical sounds), and music (various compositions and performances).", "subpage_snippet": "", "source": "m-a-p.ai", "link": "https://m-a-p.ai/OmniBench/", "content": "OmniBench comprises 1142 question-answer pairs, with task type distribution, text length, and image and audio characteristics. The dataset's audio content falls into three categories: speech (human vocal communication), sound events (non-speech natural, environmental and mechanical sounds), and music (various compositions and performances)."}
+{"idx": 6, "title": "OmniBench: Towards The Future of Universal Omni-Language ...", "date": "", "ddg_snippet": "Sep 27, 2024 · This paper presents OmniBench , a multi-modal benchmark developed to evaluate the capacity of large multimodal language models (MLLMs) to process and reason across visual, auditory, and textual modalities. In this framework, the authors classify these systems as omni -language models (OLMs) and set a unique requirement for accurate responses that reflect an integrated understanding across all ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Rc8z5wLzBF", "content": "Sep 27, 2024 · This paper presents OmniBench , a multi-modal benchmark developed to evaluate the capacity of large multimodal language models (MLLMs) to process and reason across visual, auditory, and textual modalities. In this framework, the authors classify these systems as omni -language models (OLMs) and set a unique requirement for accurate responses that reflect an integrated understanding across all ..."}
+{"idx": 7, "title": "Unwind This Monday With the 5-4-3-2-1 Grounding Technique", "date": "", "ddg_snippet": "End this exercise with a long, deep breath. With these steps, you will be able to get the most out of the moment. Focusing on your senses will help you be more mindful, which will then help you accomplish your tasks and experience success .", "subpage_snippet": "", "source": "healthymonday.com", "link": "https://healthymonday.com/stress-management/unwind-monday-5-4-3-2-1-grounding-technique", "content": "End this exercise with a long, deep breath. With these steps, you will be able to get the most out of the moment. Focusing on your senses will help you be more mindful, which will then help you accomplish your tasks and experience success ."}
+{"idx": 8, "title": "THE 3 'MUST-DOS' OF ARGUMENT ANALYSIS ! The Nuts... - YouTube", "date": "", "ddg_snippet": "The first in a series of three videos that will help you improve your skills in argument analysis .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=jcKkd9IipiE", "content": "The first in a series of three videos that will help you improve your skills in argument analysis ."}
+{"idx": 9, "title": "m-a-p/ OmniBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "The image depicts a scene from what appears to be a television show or movie. The setting is a restaurant or café with a wooden partition in the background, which has a lattice design. The partition separates the dining area from another section of the establishment.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/m-a-p/OmniBench", "content": "The image depicts a scene from what appears to be a television show or movie. The setting is a restaurant or café with a wooden partition in the background, which has a lattice design. The partition separates the dining area from another section of the establishment."}
diff --git a/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Section_5_Bregman_divergence_year_2024.jsonl b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Section_5_Bregman_divergence_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7150ef63ce2ecc5de4d0d42a86b29c19ff12050f
--- /dev/null
+++ b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Section_5_Bregman_divergence_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Business Technology Products, Services & Solutions - Connection", "date": "", "ddg_snippet": "Let the experts at Connection listen to your needs, understand your goals, and deliver IT solutions and services designed around you. Because what you do matters.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/", "content": "Let the experts at Connection listen to your needs, understand your goals, and deliver IT solutions and services designed around you. Because what you do matters."}
+{"idx": 1, "title": "Careers - Connection", "date": "", "ddg_snippet": "Discover what unites our team and makes Connection a one-of- a -kind workplace. If you’re looking for a career where you’ll be surrounded by people who love to dream big, dig deep, and support each other, you’ll find your place at Connection .", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/content/careers", "content": "Discover what unites our team and makes Connection a one-of- a -kind workplace. If you’re looking for a career where you’ll be surrounded by people who love to dream big, dig deep, and support each other, you’ll find your place at Connection ."}
+{"idx": 2, "title": "Connection (CNXN) Reports Fourth Quarter and Full Year 2024...", "date": "", "ddg_snippet": "Feb 5 , 2025 · Connection –Public Sector Solutions (800.800.0019), is a rapid-response provider of IT products and services to federal, state, and local government agencies and educational institutions through specialized account managers, publications, and online at www. connection .com/publicsector.", "subpage_snippet": "", "source": "ir.connection.com", "link": "https://ir.connection.com/news-releases/news-release-details/connection-cnxn-reports-fourth-quarter-and-full-year-2024", "content": "Feb 5 , 2025 · Connection –Public Sector Solutions (800.800.0019), is a rapid-response provider of IT products and services to federal, state, and local government agencies and educational institutions through specialized account managers, publications, and online at www. connection .com/publicsector."}
+{"idx": 3, "title": "Enterprise - Connection", "date": "", "ddg_snippet": "Connection is an industry-leading, global technology solutions provider, focused on driving technology initiatives with large corporations. We connect our clients with the technology they need to not only solve their challenges, but also to transform their businesses.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/pccb2b/enterprise", "content": "Connection is an industry-leading, global technology solutions provider, focused on driving technology initiatives with large corporations. We connect our clients with the technology they need to not only solve their challenges, but also to transform their businesses."}
+{"idx": 4, "title": "Customer Care - Connection", "date": "", "ddg_snippet": "Get answers to your technical questions. 30-Day Free installation and diagnostic assistance for most products purchased from PC Connection . In some cases, you may be referred to the manufacturer in order to ensure the best possible support.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/IPA/Content/Support", "content": "Get answers to your technical questions. 30-Day Free installation and diagnostic assistance for most products purchased from PC Connection . In some cases, you may be referred to the manufacturer in order to ensure the best possible support."}
+{"idx": 5, "title": "Contact Us - Apple Authorized Reseller - Connection", "date": "", "ddg_snippet": "Connection , a Fortune 1000 company, calms the confusion of IT by delivering industry-leading technology solutions to enhance growth, elevate productivity, and empower innovation.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/IPA/Content/About/Contact", "content": "Connection , a Fortune 1000 company, calms the confusion of IT by delivering industry-leading technology solutions to enhance growth, elevate productivity, and empower innovation."}
+{"idx": 6, "title": "Artificial Intelligence - Connection", "date": "", "ddg_snippet": "Connection helps enterprises navigate multicloud strategies to modernize and build the infrastructure they need for data- and AI-centric workloads and face the future with confidence.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/solutions-services/artificial-intelligence", "content": "Connection helps enterprises navigate multicloud strategies to modernize and build the infrastructure they need for data- and AI-centric workloads and face the future with confidence."}
+{"idx": 7, "title": "Locations - Connection", "date": "", "ddg_snippet": "Connection has grown a great deal since the Company's founding in 1982. We now operate facilities in nine states across the U.S.—but we remain close to our Granite State roots.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/content/careers/locations", "content": "Connection has grown a great deal since the Company's founding in 1982. We now operate facilities in nine states across the U.S.—but we remain close to our Granite State roots."}
+{"idx": 8, "title": "IT Solutions & Services - Connection", "date": "", "ddg_snippet": "Connection provides the end-to-end technology solutions and services your organization, its people, and its customers need to function at their best. From digital workplace solutions to modern IT infrastructure and multicloud to supply chain and lifecycle, we have you covered.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/solutions-services", "content": "Connection provides the end-to-end technology solutions and services your organization, its people, and its customers need to function at their best. From digital workplace solutions to modern IT infrastructure and multicloud to supply chain and lifecycle, we have you covered."}
+{"idx": 9, "title": "Company History - Connection", "date": "", "ddg_snippet": "Discover how Connection started business back in 1982 and learn about the innovations, milestones, and memories we’ve made along the way.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/content/about/company-history", "content": "Discover how Connection started business back in 1982 and learn about the innovations, milestones, and memories we’ve made along the way."}
diff --git a/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Algorithm_1_SAHARA_Ships_metric.jsonl b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Algorithm_1_SAHARA_Ships_metric.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..35a7ae436218adfe7c18cb174be9ad6e3d7234fe
--- /dev/null
+++ b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Algorithm_1_SAHARA_Ships_metric.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "【论文阅读】ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODE...", "date": "", "ddg_snippet": "ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODEL SAFETY 原文摘要 研究背景与现状 背景 LLMs 在多种语言任务上表现出色，但其安全防护措施可能被绕过，从而生成有害内容。 已有研究发现，当模型的安全性表示或相关组件被压制时，其安全能力会受损。 现状 尽管安全机制的研究不断深入，但目前的研究 ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/xianshuiyihui/article/details/149698061", "content": "ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODEL SAFETY 原文摘要 研究背景与现状 背景 LLMs 在多种语言任务上表现出色，但其安全防护措施可能被绕过，从而生成有害内容。 已有研究发现，当模型的安全性表示或相关组件被压制时，其安全能力会受损。 现状 尽管安全机制的研究不断深入，但目前的研究 ..."}
+{"idx": 2, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Oct 17, 2024 · However, existing research tends to overlook the safety impact of multi- head attention mechanisms, despite their crucial role in various model functionalities. Hence, in this paper, we aim to explore the connection between standard attention mechanisms and safety capability to fill this gap in the safety -related mechanistic interpretability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.13708", "content": "Oct 17, 2024 · However, existing research tends to overlook the safety impact of multi- head attention mechanisms, despite their crucial role in various model functionalities. Hence, in this paper, we aim to explore the connection between standard attention mechanisms and safety capability to fill this gap in the safety -related mechanistic interpretability."}
+{"idx": 3, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Oct 17, 2024 · This paper proposes a novel metric which tailored for multi- head attention , the Safety Head ImPortant Score ( Ships ), to assess the individual heads ' contributions to model safety and introduces the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Large language models (LLMs) achieve state-of-the-art performance on multiple ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-the-Role-of-Attention-Heads-in-Large-Language-Zhou-Yu/c2bb84fbd2e9ec75e275f049353ed5d867ad5748", "content": "Oct 17, 2024 · This paper proposes a novel metric which tailored for multi- head attention , the Safety Head ImPortant Score ( Ships ), to assess the individual heads ' contributions to model safety and introduces the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Large language models (LLMs) achieve state-of-the-art performance on multiple ..."}
+{"idx": 4, "title": "GitHub - ydyjya/SafetyHeadAttribution", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ydyjya/SafetyHeadAttribution", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety ."}
+{"idx": 5, "title": "ICLR 2025 On the Role of Attention Heads in Large Language ...", "date": "", "ddg_snippet": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model .", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/oral/31798", "content": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model ."}
+{"idx": 6, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "The Ships metric is extended to the dataset level to identify safety - critical attention heads . The Safety Attention Head AttRibution Algorithm ( Sahara ) is proposed as a heuristic approach to find groups of safety heads .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper/arxiv/2410.13708", "content": "The Ships metric is extended to the dataset level to identify safety - critical attention heads . The Safety Attention Head AttRibution Algorithm ( Sahara ) is proposed as a heuristic approach to find groups of safety heads ."}
+{"idx": 7, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "We propose Safety Attention Head AttRibution Algorithm ( Sahara ), a heuristic approach for pinpointing these heads .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v1", "content": "We propose Safety Attention Head AttRibution Algorithm ( Sahara ), a heuristic approach for pinpointing these heads ."}
+{"idx": 8, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "View recent discussion. Abstract: Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2410.13708v1", "content": "View recent discussion. Abstract: Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations."}
+{"idx": 9, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Powerdrill is an AI service centered around personal and enterprise datasets, designed to unlock the full potential of your data.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-On-the-Role-cm2gn8owv6bsf019jmx2g333j", "content": "Powerdrill is an AI service centered around personal and enterprise datasets, designed to unlock the full potential of your data."}
diff --git a/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Algorithm_1_Sahara_SHIPS_metric_group__year_2023.jsonl b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Algorithm_1_Sahara_SHIPS_metric_group__year_2023.jsonl
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index 0000000000000000000000000000000000000000..af439c6e889e2a169d367d869779dc3348f4fcdc
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+++ b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Algorithm_1_Sahara_SHIPS_metric_group__year_2023.jsonl
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+{"idx": 0, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.13708", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model ."}
+{"idx": 1, "title": "SafetyHeadAttribution/Readme.md at main · ydyjya ... - GitHub", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ydyjya/SafetyHeadAttribution/blob/main/Readme.md", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety ."}
+{"idx": 2, "title": "【论文阅读】On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODEL SAFETY 原文摘要 研究背景与现状 背景 LLMs 在多种语言任务上表现出色，但其安全防护措施可能被绕过，从而生成有害内容。 已有研究发现，当模型的安全性表示或相关组件被压制时，其安全能力会受损。 现状 尽管安全机制的研究不断深入，但目前的研究 ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/xianshuiyihui/article/details/149698061", "content": "ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE MODEL SAFETY 原文摘要 研究背景与现状 背景 LLMs 在多种语言任务上表现出色，但其安全防护措施可能被绕过，从而生成有害内容。 已有研究发现，当模型的安全性表示或相关组件被压制时，其安全能力会受损。 现状 尽管安全机制的研究不断深入，但目前的研究 ..."}
+{"idx": 3, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "This paper proposes a novel metric which tailored for multi- head attention , the Safety Head ImPortant Score ( Ships ), to assess the individual heads' contributions to model safety and introduces the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Large language models (LLMs) achieve state- of - the -art performance on multiple ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-the-Role-of-Attention-Heads-in-Large-Language-Zhou-Yu/c2bb84fbd2e9ec75e275f049353ed5d867ad5748", "content": "This paper proposes a novel metric which tailored for multi- head attention , the Safety Head ImPortant Score ( Ships ), to assess the individual heads' contributions to model safety and introduces the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Large language models (LLMs) achieve state- of - the -art performance on multiple ..."}
+{"idx": 4, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=h0Ak8A5yqw", "content": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that special attention head has a significant impact on safety ."}
+{"idx": 5, "title": "PDF On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "On the Role of Attention Heads in Large Language Model Safety Zhenhong Zhou1, Haiyang Yu1, Xinghua Zhang1, Rongwu Xu3, Kun Wang2, Yang Liu4, Fei Huang1, Junfeng Fang2, Yongbin Li1", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28788.pdf", "content": "On the Role of Attention Heads in Large Language Model Safety Zhenhong Zhou1, Haiyang Yu1, Xinghua Zhang1, Rongwu Xu3, Kun Wang2, Yang Liu4, Fei Huang1, Junfeng Fang2, Yongbin Li1"}
+{"idx": 6, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Key contributions include: 1 ️⃣ Introduction of a novel metric , Safety Head ImPortant Score ( Ships ), designed to evaluate individual attention heads' contributions to safety . 2️⃣ ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/abdullah-kasri_on-the-role-of-attention-heads-in-large-language-activity-7311970512555921408-vnwp", "content": "Key contributions include: 1 ️⃣ Introduction of a novel metric , Safety Head ImPortant Score ( Ships ), designed to evaluate individual attention heads' contributions to safety . 2️⃣ ..."}
+{"idx": 7, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety ."}
+{"idx": 8, "title": "GitHub - ydyjya/SafetyHeadAttribution", "date": "", "ddg_snippet": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ydyjya/SafetyHeadAttribution", "content": "Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical safety attention heads inside the model . Our findings show that the special attention head has a significant impact on safety ."}
+{"idx": 9, "title": "PDF arXiv:2410.13708v1 [cs.CL] 17 Oct 2024 - ResearchGate", "date": "", "ddg_snippet": "-v1.5, underscoring its effectiveness. This work also presents the Safety Attention Head Attribution Algorithm ( Sahara ), a generalized version of Ships that identifies groups of heads whos", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety/fulltext/6711f20e069cb92a811a75e8/On-the-Role-of-Attention-Heads-in-Large-Language-Model-Safety.pdf", "content": "-v1.5, underscoring its effectiveness. This work also presents the Safety Attention Head Attribution Algorithm ( Sahara ), a generalized version of Ships that identifies groups of heads whos"}
diff --git a/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Llama-2-7b-chat_ablating_a_single_safe.jsonl b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Llama-2-7b-chat_ablating_a_single_safe.jsonl
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index 0000000000000000000000000000000000000000..4b8f1cc73e43b58d265d9cc8b8393e2d8d224244
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+++ b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_Llama-2-7b-chat_ablating_a_single_safe.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Role of Attention Heads in Large Language Model ...", "date": "", "ddg_snippet": "17 Oct 2024 — The results in Figure 2 , indicates that ablating the attention head with the highest Ships score significantly reduces the safety capability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v1", "content": "17 Oct 2024 — The results in Figure 2 , indicates that ablating the attention head with the highest Ships score significantly reduces the safety capability."}
+{"idx": 1, "title": "ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE ...", "date": "", "ddg_snippet": "As shown in Figure 17, when the safety attention head is not ablated , Llama - 2 - 7b - chat does not respond to any harmful queries , with an ASR of 0 across all three ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/3a4b2272f2a7c546abd9814d901492aba03bd0c6.pdf", "content": "As shown in Figure 17, when the safety attention head is not ablated , Llama - 2 - 7b - chat does not respond to any harmful queries , with an ASR of 0 across all three ..."}
+{"idx": 2, "title": "Gemma: Open Models Based on Gemini Research and ...", "date": "", "ddg_snippet": "Multi- Query Attention (Shazeer, 2019) . Notably, the 7B model uses multi- head attention while the 2B checkpoints use multi- query attention (with n u m ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.08295v1", "content": "Multi- Query Attention (Shazeer, 2019) . Notably, the 7B model uses multi- head attention while the 2B checkpoints use multi- query attention (with n u m ..."}
+{"idx": 3, "title": "Efficient and Scalable Large Multimodal Models", "date": "", "ddg_snippet": "19 Aug 2024 — (Right) An example of attention probability in a single head where a more important token receives more attention from other tokens. Thus. 237 pages", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-186.pdf", "content": "19 Aug 2024 — (Right) An example of attention probability in a single head where a more important token receives more attention from other tokens. Thus. 237 pages"}
+{"idx": 4, "title": "Proceedings of the 61st Annual Meeting of the Association ...", "date": "", "ddg_snippet": "Do language models have coherent mental models of everyday things? Yuling Gu | Bhavana Dalvi Mishra | Peter Clark.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2023.acl-long/", "content": "Do language models have coherent mental models of everyday things? Yuling Gu | Bhavana Dalvi Mishra | Peter Clark."}
+{"idx": 5, "title": "How useful is mechanistic interpretability?", "date": "", "ddg_snippet": "30 Nov 2023 — Take a model like LLaMA 2 7B Chat ; Take a dataset of tasks where the model may or may not refuse. Ideally with a specific token in the output ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/tEPHGZAb63dfq2v8n", "content": "30 Nov 2023 — Take a model like LLaMA 2 7B Chat ; Take a dataset of tasks where the model may or may not refuse. Ideally with a specific token in the output ..."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "Our findings show that the special attention head has a significant impact on safety . Ablating a single safety head allows aligned model (e.g., Llama - 2 - 7b - chat ) ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=standard+attention+mechanisms", "content": "Our findings show that the special attention head has a significant impact on safety . Ablating a single safety head allows aligned model (e.g., Llama - 2 - 7b - chat ) ..."}
+{"idx": 7, "title": "Track: Poster Session 2", "date": "", "ddg_snippet": "24 Apr 2025 — Ablating a single safety head allows aligned model (e.g., Llama-2-7b-chat) to respond to **16 × ↑ ** more harmful queries, while only ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31972", "content": "24 Apr 2025 — Ablating a single safety head allows aligned model (e.g., Llama-2-7b-chat) to respond to **16 × ↑ ** more harmful queries, while only ..."}
+{"idx": 8, "title": "Main track accepted papers (Guangzhou)", "date": "", "ddg_snippet": "Experimental results demonstrate that Sat-RIA outperforms existing large language models and provides more comprehensible answers with fewer hallucinations.", "subpage_snippet": "", "source": "2025.ijcai.org", "link": "https://2025.ijcai.org/guangzhou-main-track-accepted-papers/", "content": "Experimental results demonstrate that Sat-RIA outperforms existing large language models and provides more comprehensible answers with fewer hallucinations."}
+{"idx": 9, "title": "ICLR 2024 Orals", "date": "", "ddg_snippet": "Interpreting the attention heads , we characterize each head's role by ... Our study examines five popular models , namely text-davinci-003, ChatGPT, GPT-4, LLaMA - 2 ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/events/oral", "content": "Interpreting the attention heads , we characterize each head's role by ... Our study examines five popular models , namely text-davinci-003, ChatGPT, GPT-4, LLaMA - 2 ..."}
diff --git a/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_paper.jsonl b/data/sampled_jsons/On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety_paper.jsonl
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index 0000000000000000000000000000000000000000..a7e00903b16c089a269c56d74145555f4be5df3f
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+{"idx": 0, "title": "The Evolving Role of Large Language Models in Scientific", "date": "", "ddg_snippet": "Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11810v1", "content": "Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs)."}
+{"idx": 1, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "How Does Vision- Language Adaptation Impact the Safety of Vision Language Models ? ... Analysis of Neuron Interaction Dynamics in Large Models", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "How Does Vision- Language Adaptation Impact the Safety of Vision Language Models ? ... Analysis of Neuron Interaction Dynamics in Large Models"}
+{"idx": 2, "title": "What Do Large Language Models \"Understand\"? | Towards", "date": "", "ddg_snippet": "Instead let ’ s focus on the core of what an LLM does: they are statistical models that predict the likelihood of a token appearing in a piece ...", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/what-do-large-language-models-understand-befdb4411b77/", "content": "Instead let ’ s focus on the core of what an LLM does: they are statistical models that predict the likelihood of a token appearing in a piece ..."}
+{"idx": 3, "title": "CVPR 2025 Papers", "date": "", "ddg_snippet": "... in the Detail: Towards Injecting Fine Details of ... Do ImageNet-trained Models Learn Shortcuts? The Impact of Frequency Shortcuts on Generalization", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/papers.html", "content": "... in the Detail: Towards Injecting Fine Details of ... Do ImageNet-trained Models Learn Shortcuts? The Impact of Frequency Shortcuts on Generalization"}
+{"idx": 4, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "On the ( In )tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes ... On the Relation between ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "On the ( In )tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes ... On the Relation between ..."}
+{"idx": 5, "title": "hackslash dot org", "date": "", "ddg_snippet": "In the thirteenth installment of his blog series chronicling the development of a Large Language Model (LLM) from the ground up, Giles Thomas presents ...", "subpage_snippet": "", "source": "hackslash.org", "link": "https://hackslash.org/stories/tags/1223/", "content": "In the thirteenth installment of his blog series chronicling the development of a Large Language Model (LLM) from the ground up, Giles Thomas presents ..."}
+{"idx": 6, "title": "Subspace Rerouting: Using Mechanistic Interpretability to Craft", "date": "", "ddg_snippet": "2024), Jailbreak Lens (He et al.), or On the Role of Attention Heads in Large Language Model Safety (Zhou et al. ... instance, in Qwen 2.5 1.5b, a ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/mG7jioaAsBasnuD4b/subspace-rerouting-using-mechanistic-interpretability-to-2", "content": "2024), Jailbreak Lens (He et al.), or On the Role of Attention Heads in Large Language Model Safety (Zhou et al. ... instance, in Qwen 2.5 1.5b, a ..."}
+{"idx": 7, "title": "Subspace Rerouting: Using Mechanistic Interpretability to Craft", "date": "", "ddg_snippet": "2024), Jailbreak Lens (He et al.), or On the Role of Attention Heads in Large Language Model Safety (Zhou et al. ... instance, in Qwen 2.5 1.5b, a ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/mG7jioaAsBasnuD4b/subspace-rerouting-using-mechanistic-interpretability-to-2", "content": "2024), Jailbreak Lens (He et al.), or On the Role of Attention Heads in Large Language Model Safety (Zhou et al. ... instance, in Qwen 2.5 1.5b, a ..."}
+{"idx": 8, "title": "Kaiwen Xue", "date": "", "ddg_snippet": "It significantly advances the state- of - the -art discrete diffusion on 5 zero-shot language modeling benchmarks (measured by perplexity) at the GPT-2 ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Kaiwen+Xue", "content": "It significantly advances the state- of - the -art discrete diffusion on 5 zero-shot language modeling benchmarks (measured by perplexity) at the GPT-2 ..."}
+{"idx": 9, "title": "Jiaqi Yang", "date": "", "ddg_snippet": "The core insight in UGP is the elimination of cross- attention mechanisms to improve generalization, allowing the network to concentrate on intra ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Jiaqi+Yang", "content": "The core insight in UGP is the elimination of cross- attention mechanisms to improve generalization, allowing the network to concentrate on intra ..."}
diff --git a/data/sampled_jsons/Online_Sequence_Greedy_OSG_Xu_2023_submodular_multi-agent_abstract_year_2023.jsonl b/data/sampled_jsons/Online_Sequence_Greedy_OSG_Xu_2023_submodular_multi-agent_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..27a5ad584b5824d521f3e834df23b721d6bb7361
--- /dev/null
+++ b/data/sampled_jsons/Online_Sequence_Greedy_OSG_Xu_2023_submodular_multi-agent_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Near-optimal Online Learning for Multi-agent Submodular Coordination ...", "date": "", "ddg_snippet": "Given that the majority of applications occur in time-varying environments, Xu et al. ( 2023 ) proposed the online sequence greedy (OSG) algorithm for online MA-SM problem, which also ensures a sub-optimal ( 1 1+c)-approximation over a complete communication graph.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05028", "content": "Given that the majority of applications occur in time-varying environments, Xu et al. ( 2023 ) proposed the online sequence greedy (OSG) algorithm for online MA-SM problem, which also ensures a sub-optimal ( 1 1+c)-approximation over a complete communication graph."}
+{"idx": 1, "title": "UM-iRaL/ral23-online-submodular-coordination - GitHub", "date": "", "ddg_snippet": "Paper: Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi -Robot Coordination, IEEE Robotics & Automation Letters (RA-L), 2023 Authors: Zirui Xu , Hongyu Zhou, Vasileios Tzoumas This repository contains the simulation code for a multi -robot multi -target tracking problem. The main code is in main_OSG.py, random_OSG.py, and adversarial_OSG ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/UM-iRaL/ral23-online-submodular-coordination", "content": "Paper: Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi -Robot Coordination, IEEE Robotics & Automation Letters (RA-L), 2023 Authors: Zirui Xu , Hongyu Zhou, Vasileios Tzoumas This repository contains the simulation code for a multi -robot multi -target tracking problem. The main code is in main_OSG.py, random_OSG.py, and adversarial_OSG ..."}
+{"idx": 2, "title": "Near-Optimal Online Learning for Multi-Agent Submodular...", "date": "", "ddg_snippet": "If extending this method to our multi-agent online submodular maximization (MA-OSM) problem, it ultimately leads to the online sequential greedy ( OSG ) algorithm ( Xu et al., 2023 ).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=i8dYPGdB1C", "content": "If extending this method to our multi-agent online submodular maximization (MA-OSM) problem, it ultimately leads to the online sequential greedy ( OSG ) algorithm ( Xu et al., 2023 )."}
+{"idx": 3, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "Motivated by these practical use cases, this paper delves into the multi-agent online submodular maximization problem. To tackle the aforementioned MA-OSM problem, Xu et al. (2023)have recently proposed an online sequential greedy (OSG) algorithm, building upon the foundations of the classical greedy method (Fisher et al., 1978).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05028v1", "content": "Motivated by these practical use cases, this paper delves into the multi-agent online submodular maximization problem. To tackle the aforementioned MA-OSM problem, Xu et al. (2023)have recently proposed an online sequential greedy (OSG) algorithm, building upon the foundations of the classical greedy method (Fisher et al., 1978)."}
+{"idx": 4, "title": "[2502.05028] Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05028", "content": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address ..."}
+{"idx": 5, "title": "[2306.10835] Online Dynamic Submodular Optimization - arXiv.org", "date": "", "ddg_snippet": "We propose new algorithms with provable performance for online binary optimization subject to general constraints and in dynamic settings. We consider the subset of problems in which the objective function is submodular . We propose the online submodular greedy algorithm (OSGA) which solves to optimality an approximation of the previous round loss function to avoid the NP-hardness of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.10835", "content": "We propose new algorithms with provable performance for online binary optimization subject to general constraints and in dynamic settings. We consider the subset of problems in which the objective function is submodular . We propose the online submodular greedy algorithm (OSGA) which solves to optimality an approximation of the previous round loss function to avoid the NP-hardness of the ..."}
+{"idx": 6, "title": "Online Submodular Coordination with Bounded Tracking Regret: Theory ...", "date": "", "ddg_snippet": "Related Work in Submodular Optimization for Multi -Robot Coordination The seminal algorithm Sequential Greedy (SG) [13] is the first polynomial-time algorithm for eq.1 with near-optimal approximation guarantees.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2209.12429", "content": "Related Work in Submodular Optimization for Multi -Robot Coordination The seminal algorithm Sequential Greedy (SG) [13] is the first polynomial-time algorithm for eq.1 with near-optimal approximation guarantees."}
+{"idx": 7, "title": "Theory, Algorithm, and Applications to Multi-Robot Coordination", "date": "", "ddg_snippet": "Abstract—We enable efficient and effective coordination in unpredictable environments, i.e., in environments whose future evolution is unknown a priori and even adversarial. We are motivated by the future of autonomy that involves multiple robots coordinating in dynamic, unstructured, and adversarial environments to complete complex tasks such as target tracking, environmental mapping, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2209.12429v2", "content": "Abstract—We enable efficient and effective coordination in unpredictable environments, i.e., in environments whose future evolution is unknown a priori and even adversarial. We are motivated by the future of autonomy that involves multiple robots coordinating in dynamic, unstructured, and adversarial environments to complete complex tasks such as target tracking, environmental mapping, and ..."}
+{"idx": 8, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "Motivated by these practical use cases, this paper delves into the multi-agent online submodular maximization problem. To tackle the aforementioned MA-OSM problem, Xu et al. ( 2023 ) have recently proposed an online sequential greedy ( OSG ) algorithm, building upon the foundations of the classical greedy method (Fisher et al., 1978).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.05028", "content": "Motivated by these practical use cases, this paper delves into the multi-agent online submodular maximization problem. To tackle the aforementioned MA-OSM problem, Xu et al. ( 2023 ) have recently proposed an online sequential greedy ( OSG ) algorithm, building upon the foundations of the classical greedy method (Fisher et al., 1978)."}
+{"idx": 9, "title": "PDF IROS23 presentation final - GitHub Pages", "date": "", "ddg_snippet": "Z. Xu , X. Lin, and V. Tzoumas, \"Bandit submodular maximization for multi -robot coordination in unpredictable and partially observable environments,\" in Robotics: Science and Systems, 2023", "subpage_snippet": "", "source": "hongyu-zhou.github.io", "link": "https://hongyu-zhou.github.io/files/2023/RAL-22-2539.pdf", "content": "Z. Xu , X. Lin, and V. Tzoumas, \"Bandit submodular maximization for multi -robot coordination in unpredictable and partially observable environments,\" in Robotics: Science and Systems, 2023"}
diff --git a/data/sampled_jsons/OpenEarthMap_image_resolution_pixels.jsonl b/data/sampled_jsons/OpenEarthMap_image_resolution_pixels.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..88226c7817fafde483dcbcf183ba18dd563aa21e
--- /dev/null
+++ b/data/sampled_jsons/OpenEarthMap_image_resolution_pixels.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - cliffbb/OpenEarthMap-SAR: OpenEarthMap-SAR: A ...", "date": "", "ddg_snippet": "Each image has a size of 1024x1024 pixels at a ground sampling distance of 0.15m--0.5m. The dataset has been made publicly available at Zenodo, where you can download it.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cliffbb/OpenEarthMap-SAR", "content": "Each image has a size of 1024x1024 pixels at a ground sampling distance of 0.15m--0.5m. The dataset has been made publicly available at Zenodo, where you can download it."}
+{"idx": 1, "title": "OpenEarthMap-SAR: A Benchmark Synthetic Aperture Radar ... Images OpenEarthMap Global Land Cover Mapping - OpenEarthMap - Kaggle OpenEarthMap: A Benchmark Dataset for Global High-Resolution ... GitHub - bao18/open_earth_map: Quick start in OpenEarthMap OpenEarthMap: A Benchmark Dataset for Global High-Resolution ...", "date": "", "ddg_snippet": "Jan 18, 2025 · OpenEarthMap -SAR consists of 1.5 million segments of 5033 aerial and satellite images with the size of 1024 × 1024 pixels , covering 35 regions from Japan, France, and the USA, with partially manually annotated and fully pseudo 8-class land cover labels at a ground sampling distance of 0.15--0.5 m. View all It builds upon the OpenEarthMap dataset, a high- resolution land cover mapping benchmark, and promotes the development of models that generalize worldwide for accurate and scalable geospatial analysis. We provide annotations with eight classes: bareland, rangeland, developed space, road, tree, water, agriculture land, and building. Their color and proportion of pixels are summarized below. All the labeling was done manually, and it took 2.5 hours per image on average. In terms of image size, LoveDA is the same (1024×1024 pixels ) as OpenEarthMap , while DeepGlobe is larger (2448×2448 pixels ). The histogram of OpenEarthMap has a very long tail, showing a much larger number of segments in each image of OpenEarthMap than the other datasets. Label data of OpenEarthMap are provided under the same license as the original RGB images , which varies with each source dataset. For more details, please see the attribution of source data here. Oct 19, 2022 · We introduce OpenEarthMap , a benchmark dataset, for global high- resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents, with manually annotated 8-class land cover labels at a 0.25--0.5m ground sampling distance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.10891", "content": "Jan 18, 2025 · OpenEarthMap -SAR consists of 1.5 million segments of 5033 aerial and satellite images with the size of 1024 × 1024 pixels , covering 35 regions from Japan, France, and the USA, with partially manually annotated and fully pseudo 8-class land cover labels at a ground sampling distance of 0.15--0.5 m. View all It builds upon the OpenEarthMap dataset, a high- resolution land cover mapping benchmark, and promotes the development of models that generalize worldwide for accurate and scalable geospatial analysis. We provide annotations with eight classes: bareland, rangeland, developed space, road, tree, water, agriculture land, and building. Their color and proportion of pixels are summarized below. All the labeling was done manually, and it took 2.5 hours per image on average. In terms of image size, LoveDA is the same (1024×1024 pixels ) as OpenEarthMap , while DeepGlobe is larger (2448×2448 pixels ). The histogram of OpenEarthMap has a very long tail, showing a much larger number of segments in each image of OpenEarthMap than the other datasets. Label data of OpenEarthMap are provided under the same license as the original RGB images , which varies with each source dataset. For more details, please see the attribution of source data here. Oct 19, 2022 · We introduce OpenEarthMap , a benchmark dataset, for global high- resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents, with manually annotated 8-class land cover labels at a 0.25--0.5m ground sampling distance."}
+{"idx": 2, "title": "OpenEarthMap", "date": "", "ddg_snippet": "It builds upon the OpenEarthMap dataset, a high- resolution land cover mapping benchmark, and promotes the development of models that generalize worldwide for accurate and scalable geospatial analysis.", "subpage_snippet": "", "source": "open-earth-map.org", "link": "https://open-earth-map.org/", "content": "It builds upon the OpenEarthMap dataset, a high- resolution land cover mapping benchmark, and promotes the development of models that generalize worldwide for accurate and scalable geospatial analysis."}
+{"idx": 3, "title": "Global Land Cover Mapping - OpenEarthMap - Kaggle", "date": "", "ddg_snippet": "We provide annotations with eight classes: bareland, rangeland, developed space, road, tree, water, agriculture land, and building. Their color and proportion of pixels are summarized below. All the labeling was done manually, and it took 2.5 hours per image on average.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/aletbm/global-land-cover-mapping-openearthmap", "content": "We provide annotations with eight classes: bareland, rangeland, developed space, road, tree, water, agriculture land, and building. Their color and proportion of pixels are summarized below. All the labeling was done manually, and it took 2.5 hours per image on average."}
+{"idx": 4, "title": "OpenEarthMap: A Benchmark Dataset for Global High-Resolution ...", "date": "", "ddg_snippet": "In terms of image size, LoveDA is the same (1024×1024 pixels ) as OpenEarthMap , while DeepGlobe is larger (2448×2448 pixels ). The histogram of OpenEarthMap has a very long tail, showing a much larger number of segments in each image of OpenEarthMap than the other datasets.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2023/papers/Xia_OpenEarthMap_A_Benchmark_Dataset_for_Global_High-Resolution_Land_Cover_Mapping_WACV_2023_paper.pdf", "content": "In terms of image size, LoveDA is the same (1024×1024 pixels ) as OpenEarthMap , while DeepGlobe is larger (2448×2448 pixels ). The histogram of OpenEarthMap has a very long tail, showing a much larger number of segments in each image of OpenEarthMap than the other datasets."}
+{"idx": 5, "title": "GitHub - bao18/open_earth_map: Quick start in OpenEarthMap", "date": "", "ddg_snippet": "Label data of OpenEarthMap are provided under the same license as the original RGB images , which varies with each source dataset. For more details, please see the attribution of source data here.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bao18/open_earth_map", "content": "Label data of OpenEarthMap are provided under the same license as the original RGB images , which varies with each source dataset. For more details, please see the attribution of source data here."}
+{"idx": 6, "title": "OpenEarthMap: A Benchmark Dataset for Global High-Resolution ...", "date": "", "ddg_snippet": "Oct 19, 2022 · We introduce OpenEarthMap , a benchmark dataset, for global high- resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents, with manually annotated 8-class land cover labels at a 0.25--0.5m ground sampling distance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2210.10732", "content": "Oct 19, 2022 · We introduce OpenEarthMap , a benchmark dataset, for global high- resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents, with manually annotated 8-class land cover labels at a 0.25--0.5m ground sampling distance."}
+{"idx": 7, "title": "SatSynth: Augmenting Image-Mask Pairs through Diffusion Models", "date": "", "ddg_snippet": "... unconditional generation of novel data instances, common applications of image diffusion models include inpainting [ 45 , 39 ] , super- resolution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.16605v1", "content": "... unconditional generation of novel data instances, common applications of image diffusion models include inpainting [ 45 , 39 ] , super- resolution ..."}
+{"idx": 8, "title": "ESSD - SinoLC-1: the first 1 m resolution national-scale", "date": "", "ddg_snippet": "This framework resolved the label noise stemming from a resolution mismatch between images and labels by combining a resolution -preserving backbone ...", "subpage_snippet": "", "source": "essd.copernicus.org", "link": "https://essd.copernicus.org/articles/15/4749/2023/", "content": "This framework resolved the label noise stemming from a resolution mismatch between images and labels by combining a resolution -preserving backbone ..."}
+{"idx": 9, "title": "Image Analysis and Data Fusion - GRSS-IEEE", "date": "", "ddg_snippet": "JSTARS Special Issue on “2020 Gaofen Challenge on Automated High- Resolution Earth Observation Image Interpretation”", "subpage_snippet": "", "source": "www.grss-ieee.org", "link": "https://www.grss-ieee.org/technical-committees/image-analysis-and-data-fusion/", "content": "JSTARS Special Issue on “2020 Gaofen Challenge on Automated High- Resolution Earth Observation Image Interpretation”"}
diff --git a/data/sampled_jsons/OpenReview_1IyPRv1A0r_A_Likelihood_Based_Approach_Distribution_Regression_PDF.jsonl b/data/sampled_jsons/OpenReview_1IyPRv1A0r_A_Likelihood_Based_Approach_Distribution_Regression_PDF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8110262ca8813cd395306a2ae5d07360283927d
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+++ b/data/sampled_jsons/OpenReview_1IyPRv1A0r_A_Likelihood_Based_Approach_Distribution_Regression_PDF.jsonl
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+{"idx": 0, "title": "Chi-squared distribution - Wikipedia", "date": "", "ddg_snippet": "In probability theory and statistics, the. - distribution with. degrees of freedom is the distribution of a sum of the squares of. independent standard normal random variables. The chi-squared distribution . is a special case of the gamma distribution ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Chi-squared_distribution", "content": "In probability theory and statistics, the. - distribution with. degrees of freedom is the distribution of a sum of the squares of. independent standard normal random variables. The chi-squared distribution . is a special case of the gamma distribution ..."}
+{"idx": 1, "title": "A Proper Strucutred Prior for Bayesian T1 Mapping | OpenReview", "date": "", "ddg_snippet": "Posterior inference was performed using the No-U-Turn Sampler (NUTS). The proposed method is compared to maximum likelihood estimation and to alternative Bayesian models that employ uniform, Laplace, and bounded TV priors.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=EWTiqk2rFU", "content": "Posterior inference was performed using the No-U-Turn Sampler (NUTS). The proposed method is compared to maximum likelihood estimation and to alternative Bayesian models that employ uniform, Laplace, and bounded TV priors."}
+{"idx": 2, "title": "Alternative Likelihood Approximations for High-Dimensional Intervals...", "date": "", "ddg_snippet": "2 Projection Based High-Dimensional Intervals. Viewed from a Bayesian perspective, the lasso penalty corresponds to a Laplace (double-exponential) prior on the regression coefficients.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14971", "content": "2 Projection Based High-Dimensional Intervals. Viewed from a Bayesian perspective, the lasso penalty corresponds to a Laplace (double-exponential) prior on the regression coefficients."}
+{"idx": 3, "title": "On logistic regression with right censored data, with or without...", "date": "", "ddg_snippet": "A likelihood - based approach for cure regression models.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10985-022-09564-6", "content": "A likelihood - based approach for cure regression models."}
+{"idx": 4, "title": "PDF в Word - Бесплатный онлайн-конвертер", "date": "", "ddg_snippet": "Преобразуйте PDF в Word онлайн бесплатно с помощью Smallpdf.", "subpage_snippet": "", "source": "smallpdf.com", "link": "https://smallpdf.com/ru/pdf-to-word", "content": "Преобразуйте PDF в Word онлайн бесплатно с помощью Smallpdf."}
+{"idx": 5, "title": "Reliable option pricing through deep learning: An anomaly score- based ...", "date": "", "ddg_snippet": "Figure 6. Density plot comparing distributions of training data (2021) and test data (first half of 2022) for Time Split 5 (moneyness vs. closing price), highlighting a distributional shift.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/doi/10.3934/nhm.2025043?viewType=HTML", "content": "Figure 6. Density plot comparing distributions of training data (2021) and test data (first half of 2022) for Time Split 5 (moneyness vs. closing price), highlighting a distributional shift."}
+{"idx": 6, "title": "Imputing responses that are not missing | CoLab", "date": "", "ddg_snippet": "Inference under kernel regression imputation for missing response data is considered. An adjusted empirical likelihood approach to inference for the mean of the response variable is developed.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1007/0-387-26023-4_23", "content": "Inference under kernel regression imputation for missing response data is considered. An adjusted empirical likelihood approach to inference for the mean of the response variable is developed."}
+{"idx": 7, "title": "CrystalDiskInfo - скачать бесплатно CrystalDiskInfo 9.7.0", "date": "", "ddg_snippet": "Бесплатно. Windows. Категория: HDD утилиты. CrystalDiskInfo - отслеживает состояние жестких дисков, поддерживающих технологию S.M.A.R.T. Производит мониторинг и дает общую оценку \"здоровья\" вашего диска.", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-6420-crystaldiskinfo.html", "content": "Бесплатно. Windows. Категория: HDD утилиты. CrystalDiskInfo - отслеживает состояние жестких дисков, поддерживающих технологию S.M.A.R.T. Производит мониторинг и дает общую оценку \"здоровья\" вашего диска."}
+{"idx": 8, "title": "12 упражнений на условные предложения с ответами", "date": "", "ddg_snippet": "8 упражнений с ответами на все типы условных предложений в английском языке (Conditional Sentences): нулевого (0), первого (1), второго (2), третьего (3) и смешанного типов...", "subpage_snippet": "", "source": "EnglishWeb.ru", "link": "https://EnglishWeb.ru/grammar/conditionals-exercises.html", "content": "8 упражнений с ответами на все типы условных предложений в английском языке (Conditional Sentences): нулевого (0), первого (1), второго (2), третьего (3) и смешанного типов..."}
+{"idx": 9, "title": "Rudalle — Используйте технические возможности моделей для...", "date": "", "ddg_snippet": "Искусственный интеллект создаст для вас красочные изображения за пару минут. Всё что нужно — текстовое описание на русском или другом языке.", "subpage_snippet": "", "source": "rudalle.ru", "link": "https://rudalle.ru/", "content": "Искусственный интеллект создаст для вас красочные изображения за пару минут. Всё что нужно — текстовое описание на русском или другом языке."}
diff --git a/data/sampled_jsons/OpenReview_Stress-Testing_Capability_Elicitation_Password-Locked_Models_Section_6.2.jsonl b/data/sampled_jsons/OpenReview_Stress-Testing_Capability_Elicitation_Password-Locked_Models_Section_6.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..902433ba164b4d66e37a0d35614799cf8ec8c524
--- /dev/null
+++ b/data/sampled_jsons/OpenReview_Stress-Testing_Capability_Elicitation_Password-Locked_Models_Section_6.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "by R Greenblatt · Cited by 24 — In this paper, we inves- tigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "by R Greenblatt · Cited by 24 — In this paper, we inves- tigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce password - ..."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "We show results for the regular password - locked model trained with a single password for all domains (top) and a model trained with different passwords for each ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=uvvVjWP1aj&name=supplementary_material", "content": "We show results for the regular password - locked model trained with a single password for all domains (top) and a model trained with different passwords for each ..."}
+{"idx": 2, "title": "Stress-Testing Capability Elicitation Techniques", "date": "", "ddg_snippet": "by F Hofstätter · Cited by 2 — We demonstrate that password-locked models used in previous work are fragile to simple prompting techniques . We introduce a more robust model organism based on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zy6LB5t62f", "content": "by F Hofstätter · Cited by 2 — We demonstrate that password-locked models used in previous work are fragile to simple prompting techniques . We introduce a more robust model organism based on ..."}
+{"idx": 3, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "29 May 2024 — Password-locked models enable a novel method of evaluating capabilities elicitation methods , by testing whether these password-locked capabilities can be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "29 May 2024 — Password-locked models enable a novel method of evaluating capabilities elicitation methods , by testing whether these password-locked capabilities can be ..."}
+{"idx": 4, "title": "The Elicitation Game: Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "Capability evaluations are required to understand and regulate AI systems that may be deployed or further developed. Therefore, it is important.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kT0EVqL77E", "content": "Capability evaluations are required to understand and regulate AI systems that may be deployed or further developed. Therefore, it is important."}
+{"idx": 5, "title": "SMARTBACKDOOR", "date": "", "ddg_snippet": "by H Wang — Stress-testing capability elicitation with password-locked models . arXiv preprint arXiv:2405.19550, 2024. Danny Halawi, Alexander Wei, Eric ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ZaOHSBGOhV", "content": "by H Wang — Stress-testing capability elicitation with password-locked models . arXiv preprint arXiv:2405.19550, 2024. Danny Halawi, Alexander Wei, Eric ..."}
+{"idx": 6, "title": "arXiv:submit/6249128 [cs.AI] 3 Mar 2025", "date": "", "ddg_snippet": "by D Brown · Cited by 1 — Stress - testing capability elicitation with password - locked models . ... nMerger Agreement: Section 6.2 . [...] Reasoning trace from the model ...", "subpage_snippet": "", "source": "davisrbrown.com", "link": "https://davisrbrown.com/assets/task_elicitation_initial.pdf", "content": "by D Brown · Cited by 1 — Stress - testing capability elicitation with password - locked models . ... nMerger Agreement: Section 6.2 . [...] Reasoning trace from the model ..."}
+{"idx": 7, "title": "SCALABLE EXTRACTION OF TRAINING DATA FROM ...", "date": "", "ddg_snippet": "by M Nasr · Cited by 28 — In our experiments in Section 6.2 , we find that finetuned models can reproduce targeted strings ... # Splitting the dataset into the Training set and Test set ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=vjel3nWP2a", "content": "by M Nasr · Cited by 28 — In our experiments in Section 6.2 , we find that finetuned models can reproduce targeted strings ... # Splitting the dataset into the Training set and Test set ..."}
+{"idx": 8, "title": "Lessons from Defending Gemini Against Indirect Prompt ...", "date": "", "ddg_snippet": "20 May 2025 — This report describes the main lessons learned as we continue to improve the robustness of the most recent Gemini models to a subset of attacks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14534v1", "content": "20 May 2025 — This report describes the main lessons learned as we continue to improve the robustness of the most recent Gemini models to a subset of attacks."}
+{"idx": 9, "title": "System Card: Claude Opus 4 & Claude Sonnet 4 | PDF", "date": "", "ddg_snippet": "Syllabus for Peter Thiel s GERMAN 270: Sovereignty and the ... Per capita expenditure prediction using model stacking based on satellite ima...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/system-card-claude-opus-4-claude-sonnet-4/279651386", "content": "Syllabus for Peter Thiel s GERMAN 270: Sovereignty and the ... Per capita expenditure prediction using model stacking based on satellite ima..."}
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+{"idx": 4, "title": "AAAI 2026 | OpenReview", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for AAAI 2026", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=AAAI.org/2026", "content": "Welcome to the OpenReview homepage for AAAI 2026"}
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diff --git a/data/sampled_jsons/Open_Images_Dataset_V4_Kuznetsova_2020_paper_arxiv.jsonl b/data/sampled_jsons/Open_Images_Dataset_V4_Kuznetsova_2020_paper_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..92397e39a95b4e171a960eeae10d59a6a6601b11
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+{"idx": 0, "title": "[PDF] The Open Images Dataset V4", "date": "", "ddg_snippet": "In-depth comprehensive statistics about the dataset are provided, the quality of the annotations are validated, the performance of several modern models ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Open-Images-Dataset-V4-Kuznetsova-Rom/5ac18d505ed6d10e8692cbb7d33f6852e6782692", "content": "In-depth comprehensive statistics about the dataset are provided, the quality of the annotations are validated, the performance of several modern models ..."}
+{"idx": 1, "title": "[1811.00982] The Open Images Dataset V4: Unified ...", "date": "", "ddg_snippet": "by A Kuznetsova · 2018 · Cited by 3481 — We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1811.00982", "content": "by A Kuznetsova · 2018 · Cited by 3481 — We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection."}
+{"idx": 2, "title": "The Open Images Dataset V4", "date": "", "ddg_snippet": "by A Kuznetsova · 2018 · Cited by 3481 — This paper presents the Open Images Dataset V4 , which contains images and ground-truth annotations for the three tasks above (Figure 1) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1811.00982", "content": "by A Kuznetsova · 2018 · Cited by 3481 — This paper presents the Open Images Dataset V4 , which contains images and ground-truth annotations for the three tasks above (Figure 1) ..."}
+{"idx": 3, "title": "The Open Images Dataset V4", "date": "", "ddg_snippet": "by A Kuznetsova · 2020 · Cited by 3481 — We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-020-01316-z", "content": "by A Kuznetsova · 2020 · Cited by 3481 — We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection."}
+{"idx": 4, "title": "The Open Images Dataset V4", "date": "", "ddg_snippet": "The Open Images Dataset V4 ; AKAlina Kuznetsova ; HRHassan Rom ; NANeil Alldrin ; JUJasper Uijlings ; IKIvan Krasin.", "subpage_snippet": "", "source": "www.scilit.com", "link": "https://www.scilit.com/publications/8e2ad4ee24960829469a1b8b180c43cd", "content": "The Open Images Dataset V4 ; AKAlina Kuznetsova ; HRHassan Rom ; NANeil Alldrin ; JUJasper Uijlings ; IKIvan Krasin."}
+{"idx": 5, "title": "arXiv:2306.03514v3 [cs.CV] 9 Jun 2023", "date": "", "ddg_snippet": "by Y Zhang · 2023 · Cited by 323 — The open images dataset v4 : Unified image classification, object detection, and visual relationship detection at scale. IJCV, 2020 . [15] ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.03514", "content": "by Y Zhang · 2023 · Cited by 323 — The open images dataset v4 : Unified image classification, object detection, and visual relationship detection at scale. IJCV, 2020 . [15] ..."}
+{"idx": 6, "title": "MVImgNet2.0: A Larger-scale Dataset of Multi-view Images", "date": "", "ddg_snippet": "19 Nov 2024 — This paper constructs the MVImgNet2.0 dataset that expands MVImgNet into a total of ~520k objects and 515 categories, which derives a 3D dataset ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3687973", "content": "19 Nov 2024 — This paper constructs the MVImgNet2.0 dataset that expands MVImgNet into a total of ~520k objects and 515 categories, which derives a 3D dataset ..."}
+{"idx": 7, "title": "A survey of public datasets for computer vision tasks in ...", "date": "", "ddg_snippet": "by Y Lu · 2020 · Cited by 408 — This paper makes the first comprehensive but not exhaustive review of the public image datasets collected under field conditions for facilitating precision ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0168169920312709", "content": "by Y Lu · 2020 · Cited by 408 — This paper makes the first comprehensive but not exhaustive review of the public image datasets collected under field conditions for facilitating precision ..."}
+{"idx": 8, "title": "arXiv:2009.05175v1 [cs.CL] 10 Sep 2020", "date": "", "ddg_snippet": "10 Sept 2020 — 2020. The Open Images Dataset . V4: Unified image classification, object detection, and visual relationship detection at scale . Kuznetsova, P ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2009.05175v1", "content": "10 Sept 2020 — 2020. The Open Images Dataset . V4: Unified image classification, object detection, and visual relationship detection at scale . Kuznetsova, P ..."}
+{"idx": 9, "title": "The Open Images Dataset V 4 -Bohrium", "date": "", "ddg_snippet": "This paper presents the Open Images Dataset V 4 which contains images and annotations for image classification, object detection, and visual relationship detection.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/the-open-images-dataset-v4/812665394182488066-2473", "content": "This paper presents the Open Images Dataset V 4 which contains images and annotations for image classification, object detection, and visual relationship detection."}
diff --git a/data/sampled_jsons/Open_Images_V7_vs_V4_dataset_size_comparison_total_images_year_2023.jsonl b/data/sampled_jsons/Open_Images_V7_vs_V4_dataset_size_comparison_total_images_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06453ed7ff2785a740c90baba349ecf7f8f07d81
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+{"idx": 1, "title": "MareArts Computer Vision Study.: AWS ec2 gpu instance comparison", "date": "", "ddg_snippet": "The MNIST dataset is a dataset of handwritten digits, comprising 60 000 training examples and 10 000 test examples.", "subpage_snippet": "", "source": "study.marearts.com", "link": "https://study.marearts.com/2023/02/aws-ec2-gpu-instance-comparison.html", "content": "The MNIST dataset is a dataset of handwritten digits, comprising 60 000 training examples and 10 000 test examples."}
+{"idx": 2, "title": "Mean Average Precision (mAP) Explained: Everything You Need to", "date": "", "ddg_snippet": "... in case you are interested in building your own computer vision models—you are in for a treat! V7 gives you access to one of the best Open Datasets ...", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/mean-average-precision", "content": "... in case you are interested in building your own computer vision models—you are in for a treat! V7 gives you access to one of the best Open Datasets ..."}
+{"idx": 3, "title": "GitHub - dipuchak95/dip_net", "date": "", "ddg_snippet": "Download and unzip test-dev2017 dataset from MS COCO server: http:// images .cocodataset.org/zips/test2017.zip ... openimages.cfg - 247 MB - 18(R) FPS - ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dipuchak95/dip_net", "content": "Download and unzip test-dev2017 dataset from MS COCO server: http:// images .cocodataset.org/zips/test2017.zip ... openimages.cfg - 247 MB - 18(R) FPS - ..."}
+{"idx": 4, "title": "Tissue-resident microbiota signature in nasopharyngeal", "date": "", "ddg_snippet": "... profiles of 48 paired NPC tissues from patients with/without posttreatment tumour relapse from our previous study (Discovery cohort 2, Public dataset ...", "subpage_snippet": "", "source": "microbiomejournal.biomedcentral.com", "link": "https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-025-02114-w", "content": "... profiles of 48 paired NPC tissues from patients with/without posttreatment tumour relapse from our previous study (Discovery cohort 2, Public dataset ..."}
+{"idx": 5, "title": "Combining morphological and metabarcoding approaches reveals", "date": "", "ddg_snippet": "... there has been little comparison of metabarcoding and morphological datasets derived from the same samples, and metabarcoding studies covering total ...", "subpage_snippet": "", "source": "enveurope.springeropen.com", "link": "https://enveurope.springeropen.com/articles/10.1186/s12302-020-00321-w", "content": "... there has been little comparison of metabarcoding and morphological datasets derived from the same samples, and metabarcoding studies covering total ..."}
+{"idx": 6, "title": "The evolution of the YOLO neural networks family from v1 to v7.", "date": "", "ddg_snippet": "... image was to sequentially pass through parts of the original image using a sliding window of various sizes so that the classifier shows which part of ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/deelvin-machine-learning/the-evolution-of-the-yolo-neural-networks-family-from-v1-to-v7-48dd98702a3d", "content": "... image was to sequentially pass through parts of the original image using a sliding window of various sizes so that the classifier shows which part of ..."}
+{"idx": 7, "title": "MareArts Computer Vision Study.: Elastic image effect, python", "date": "", "ddg_snippet": "Labels: augmentation , cv2 , Elastic , image effect , map_coordinates , OpenCV , Python , python opencv , Total ... The MNIST dataset is a dataset of ...", "subpage_snippet": "", "source": "study.marearts.com", "link": "https://study.marearts.com/2018/11/elastic-image-effect-python-opencv.html", "content": "Labels: augmentation , cv2 , Elastic , image effect , map_coordinates , OpenCV , Python , python opencv , Total ... The MNIST dataset is a dataset of ..."}
+{"idx": 8, "title": "YOLO Algorithm for Object Detection Explained [+Examples]", "date": "", "ddg_snippet": "It deals with localizing a region of interest within an image and classifying this region like a typical image classifier.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/yolo-object-detection", "content": "It deals with localizing a region of interest within an image and classifying this region like a typical image classifier."}
+{"idx": 9, "title": "YOLO Algorithm for Object Detection Explained [+Examples] (2025)", "date": "", "ddg_snippet": "Single-shot object detection uses a single pass of the input image to make predictions about the presence and location of objects in the image .", "subpage_snippet": "", "source": "sailsojourn.com", "link": "https://sailsojourn.com/article/yolo-algorithm-for-object-detection-explained-examples", "content": "Single-shot object detection uses a single pass of the input image to make predictions about the presence and location of objects in the image ."}
diff --git a/data/sampled_jsons/Orecchia_Ameranis_Tsourakakis_Talwar_arXiv_Practical_Almost-Linear-Time_Approximation_Algorithms.jsonl b/data/sampled_jsons/Orecchia_Ameranis_Tsourakakis_Talwar_arXiv_Practical_Almost-Linear-Time_Approximation_Algorithms.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5155a77f7ccff1fd59f10049a6cd7bc341c5a85c
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+{"idx": 0, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "%0 Conference Paper %T Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering %A Lorenzo Orecchia %A Konstantinos Ameranis %A Charalampos Tsourakakis %A Kunal Talwar %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a.html", "content": "%0 Conference Paper %T Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering %A Lorenzo Orecchia %A Konstantinos Ameranis %A Charalampos Tsourakakis %A Kunal Talwar %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba ..."}
+{"idx": 1, "title": "PDF Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Practical Nearly- Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis 1 1 Lorenzo Orecchia", "subpage_snippet": "", "source": "tsourakakis.com", "link": "https://tsourakakis.com/wp-content/uploads/2022/06/aott_icml22.pdf", "content": "Practical Nearly- Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis 1 1 Lorenzo Orecchia"}
+{"idx": 2, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Our main algorithmic contributions are almost-linear-time algorithms O (log n)-approximation algorithms for both these objectives. To this end, we show that the cut-matching framework of (Khandekar et al., 2014) can be significantly extended to incorporate hybrid partitions.", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/publication/oatt-icml22/", "content": "Our main algorithmic contributions are almost-linear-time algorithms O (log n)-approximation algorithms for both these objectives. To this end, we show that the cut-matching framework of (Khandekar et al., 2014) can be significantly extended to incorporate hybrid partitions."}
+{"idx": 3, "title": "Konstantinos Ameranis's personal web page", "date": "", "ddg_snippet": "arXiv preprint arXiv:2307.11042 Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis , Lorenzo Orecchia , Charalampos Tsourakakis , Kunal Talwar", "subpage_snippet": "", "source": "people.cs.uchicago.edu", "link": "https://people.cs.uchicago.edu/~kameranis/", "content": "arXiv preprint arXiv:2307.11042 Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis , Lorenzo Orecchia , Charalampos Tsourakakis , Kunal Talwar"}
+{"idx": 4, "title": "PDF ICML 2022 — Practical Almost-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis , Lorenzo Orecchia , Kunal Talwar , Charalampos Tsourakakis", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16880_M2pKLQk.pdf", "content": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis , Lorenzo Orecchia , Kunal Talwar , Charalampos Tsourakakis"}
+{"idx": 5, "title": "Publications | Orecchia Research Group", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis , Kunal Talwar , Charalampos Tsourakakis . Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML, 2022.", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/publication/", "content": "Lorenzo Orecchia , Konstantinos Ameranis , Kunal Talwar , Charalampos Tsourakakis . Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML, 2022."}
+{"idx": 6, "title": "Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Crucially, we implement our approximation algorithm to produce both overlapping and hybrid partitions for large graphs, easily scaling to tens of millions of edges, and test our implementation on real-world datasets against other competitive baselines. Based on joint work with Lorenzo Orecchia .", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/talk/practical-nearly-linear-time-approximation-algorithms-for-hybrid-and-overlapping-graph-clustering/", "content": "Crucially, we implement our approximation algorithm to produce both overlapping and hybrid partitions for large graphs, easily scaling to tens of millions of edges, and test our implementation on real-world datasets against other competitive baselines. Based on joint work with Lorenzo Orecchia ."}
+{"idx": 7, "title": "graph partitioning | Orecchia Research Group", "date": "", "ddg_snippet": "Graduate StudentSubmodular Hypergraph Partitioning: Metric Relaxations and Fast Algorithms via an Improved Cut-Matching Game Konstantinos Ameranis , Antares Chen, Lorenzo Orecchia , Erasmo Tani ArXiv Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Lorenzo Orecchia , Konstantinos Ameranis , Kunal Talwar , Charalampos Tsourakakis ICML Slides PMLR Flow ...", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/tag/graph-partitioning/", "content": "Graduate StudentSubmodular Hypergraph Partitioning: Metric Relaxations and Fast Algorithms via an Improved Cut-Matching Game Konstantinos Ameranis , Antares Chen, Lorenzo Orecchia , Erasmo Tani ArXiv Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Lorenzo Orecchia , Konstantinos Ameranis , Kunal Talwar , Charalampos Tsourakakis ICML Slides PMLR Flow ..."}
+{"idx": 8, "title": "Almost-linear Time Approximation Algorithm to Euclidean $k$-median and ...", "date": "", "ddg_snippet": "While it is possible to improve either the approximation factor [Lattanzi and Sohler, ICML19] or the running time [Cohen-Addad et al., NeurIPS 20], it is unknown how precise a linear-time algorithm can be. In this paper, we almost answer this question by presenting an almost linear-time algorithm to compute a constant-factor approximation .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.11217", "content": "While it is possible to improve either the approximation factor [Lattanzi and Sohler, ICML19] or the running time [Cohen-Addad et al., NeurIPS 20], it is unknown how precise a linear-time algorithm can be. In this paper, we almost answer this question by presenting an almost linear-time algorithm to compute a constant-factor approximation ."}
+{"idx": 9, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Orecchia , Lorenzo; Ameranis , Konstantinos; Tsourakakis , Charalampos; Talwar , Kunal Date Published: 2022-07-01 Journal Name: Proceedings of the 39th International Conference on Machine Learning (ICML 2022) in Proceedings of Machine Learning Research Volume: 162 Page Range / eLocation ID: 17071-17093 Format (s): Medium: X Sponsoring Org:", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10349734-practical-almost-linear-time-approximation-algorithms-hybrid-overlapping-graph-clustering", "content": "Orecchia , Lorenzo; Ameranis , Konstantinos; Tsourakakis , Charalampos; Talwar , Kunal Date Published: 2022-07-01 Journal Name: Proceedings of the 39th International Conference on Machine Learning (ICML 2022) in Proceedings of Machine Learning Research Volume: 162 Page Range / eLocation ID: 17071-17093 Format (s): Medium: X Sponsoring Org:"}
diff --git a/data/sampled_jsons/Orecchia_et_al._2022_experimental_setup_computing_environment_year_2022.jsonl b/data/sampled_jsons/Orecchia_et_al._2022_experimental_setup_computing_environment_year_2022.jsonl
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index 0000000000000000000000000000000000000000..09e2950ebf93edecb600d809cee453eb6b42dd06
--- /dev/null
+++ b/data/sampled_jsons/Orecchia_et_al._2022_experimental_setup_computing_environment_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An analytical model of depth-dose distributions for carbon-ion", "date": "", "ddg_snippet": "Nichelatti et al (2019) modified the BAF model for use with lithium fluoride to allow for precise energy distribution analysis in experimental ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.19479v1", "content": "Nichelatti et al (2019) modified the BAF model for use with lithium fluoride to allow for precise energy distribution analysis in experimental ..."}
+{"idx": 1, "title": "Hybrid Compton-PET Imaging for ion-range verification", "date": "", "ddg_snippet": "However, from an experimental standpoint, PGI becomes also very challenging due to the requirement of in-beam measuring conditions, which include ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11273v1", "content": "However, from an experimental standpoint, PGI becomes also very challenging due to the requirement of in-beam measuring conditions, which include ..."}
+{"idx": 2, "title": "Micro-endoscopic In Vivo Monitoring in the Blood and Lymphatic", "date": "", "ddg_snippet": "... the setup for oral radiotherapy and intra-vital cheek monitoring is shown in Figure 1 (a), while the timeline of experiments is shown in Figure 1 (b).", "subpage_snippet": "", "source": "www.medsci.org", "link": "https://www.medsci.org/v16p1525.htm", "content": "... the setup for oral radiotherapy and intra-vital cheek monitoring is shown in Figure 1 (a), while the timeline of experiments is shown in Figure 1 (b)."}
+{"idx": 3, "title": "US20130142310A1 - Dynamic multi-axes trajectory optimization", "date": "", "ddg_snippet": "238000011960 computer -aided design Methods 0.000 description 5 ... 238000002474 experimental method Methods 0.000 description 2", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20130142310A1/en", "content": "238000011960 computer -aided design Methods 0.000 description 5 ... 238000002474 experimental method Methods 0.000 description 2"}
+{"idx": 4, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — Compared with the complicated “cut-matching and improve” approach of. ( Orecchia et al ., 2022 ), our method for 2-OC takes a simple local search heuristic based ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "by Y Pan — Compared with the complicated “cut-matching and improve” approach of. ( Orecchia et al ., 2022 ), our method for 2-OC takes a simple local search heuristic based ..."}
+{"idx": 5, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — ... ( Orecchia et al ., 2022 ) whose operating environment includes a cluster of machines. Regarding the weak theoretical results, we think that as ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "by Y Pan — ... ( Orecchia et al ., 2022 ) whose operating environment includes a cluster of machines. Regarding the weak theoretical results, we think that as ..."}
+{"idx": 6, "title": "GEANT4 Simulation of Proton Beam Properties from a Cyclotron", "date": "", "ddg_snippet": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/14/7670", "content": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ..."}
+{"idx": 7, "title": "Rotating Gantries Provide Individualized Beam Arrangements for", "date": "", "ddg_snippet": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2072-6694/15/7/2044", "content": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ..."}
+{"idx": 8, "title": "Towards compact laser-driven accelerators: exploring the", "date": "", "ddg_snippet": "In typical TNSA experiments , rolled commercial sheets with nominal thickness spanning from 100 s of nanometers up to several microns are frequently ...", "subpage_snippet": "", "source": "epjtechniquesandinstrumentation.springeropen.com", "link": "https://epjtechniquesandinstrumentation.springeropen.com/articles/10.1140/epjti/s40485-023-00102-8", "content": "In typical TNSA experiments , rolled commercial sheets with nominal thickness spanning from 100 s of nanometers up to several microns are frequently ..."}
+{"idx": 9, "title": "NeurIPS 2024 – Research Impact & Leadership", "date": "", "ddg_snippet": "Agency for Science, Technology and Research (A*STAR) • Alibaba Group • Allen Institute for AI • Amazon AGI Foundations • Apple • AX AI ...", "subpage_snippet": "", "source": "sites.gatech.edu", "link": "https://sites.gatech.edu/research/neurips-2024/", "content": "Agency for Science, Technology and Research (A*STAR) • Alibaba Group • Allen Institute for AI • Amazon AGI Foundations • Apple • AX AI ..."}
diff --git a/data/sampled_jsons/Origin_Identification_Text-Guided_Image-to-Image_Diffusion_Models_arXiv_Table_2_Image_Copy_Detection.jsonl b/data/sampled_jsons/Origin_Identification_Text-Guided_Image-to-Image_Diffusion_Models_arXiv_Table_2_Image_Copy_Detection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d149077c1d760a7ad1590f15a2d3b13b9088b9ca
--- /dev/null
+++ b/data/sampled_jsons/Origin_Identification_Text-Guided_Image-to-Image_Diffusion_Models_arXiv_Table_2_Image_Copy_Detection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generalizable Origin Identification for Text-Guided Image-to-Image ...", "date": "", "ddg_snippet": "This motivates us to introduce the task of origin ID entification for text-guided I mage-to-image D iffusion models (ID2), aiming to retrieve the original image of a given translated query. A straightforward solution to ID 2 involves training a specialized deep embedding model to extract and compare features from both query and reference images .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02376v1", "content": "This motivates us to introduce the task of origin ID entification for text-guided I mage-to-image D iffusion models (ID2), aiming to retrieve the original image of a given translated query. A straightforward solution to ID 2 involves training a specialized deep embedding model to extract and compare features from both query and reference images ."}
+{"idx": 1, "title": "ICML Poster Origin Identification for Text-Guided Image-to-Image ...", "date": "", "ddg_snippet": "To solve this challenge of the proposed ID 2 task, we contribute the first dataset and a theoretically guaranteed method, both emphasizing generalizability. The curated dataset, ** OriPID **, contains abundant ** Ori**gins and guided **P**rompts, which can be used to train and test potential ** ID**entification models across various diffusion models .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46505", "content": "To solve this challenge of the proposed ID 2 task, we contribute the first dataset and a theoretically guaranteed method, both emphasizing generalizability. The curated dataset, ** OriPID **, contains abundant ** Ori**gins and guided **P**rompts, which can be used to train and test potential ** ID**entification models across various diffusion models ."}
+{"idx": 2, "title": "Origin Identification for Text-Guided Image-to- ...", "date": "", "ddg_snippet": "18 Jun 2025 — TL;DR: We introduce the OriPID dataset and a generalizable method with theoretical guarantees to identify original images from their text - guided ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46n3izUNiv&noteId=sDvtTJFLIF", "content": "18 Jun 2025 — TL;DR: We introduce the OriPID dataset and a generalizable method with theoretical guarantees to identify original images from their text - guided ..."}
+{"idx": 3, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion ...", "date": "", "ddg_snippet": "Abstract. Text-guided image-to-image diffusion models ex- cel in translating images based on textual prompts, allowing for precise and creative visual ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/681ea68d062b8991956d7a196be74f59c4610d76.pdf", "content": "Abstract. Text-guided image-to-image diffusion models ex- cel in translating images based on textual prompts, allowing for precise and creative visual ..."}
+{"idx": 4, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion Models", "date": "", "ddg_snippet": "Text-guided image-to-image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights, and evading content tracing.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.02376", "content": "Text-guided image-to-image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights, and evading content tracing."}
+{"idx": 5, "title": "[ICML 2025] The official implementation of \"Origin Identification for ...", "date": "", "ddg_snippet": "About [ICML 2025] The official implementation of \" Origin Identification for Text-Guided Image-to-Image Diffusion Models \"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/WangWenhao0716/ID2", "content": "About [ICML 2025] The official implementation of \" Origin Identification for Text-Guided Image-to-Image Diffusion Models \""}
+{"idx": 6, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion Models", "date": "", "ddg_snippet": "VidProM is the first dataset featuring 1.67 million unique text - to -video prompts and 6.69 million videos generated from 4 different state-of-the-art diffusion models . It inspires many exciting new research areas, such as Text - to -Video Prompt Engineering, Efficient Video Generation, Fake Video Detection , and Video Copy Detection for Diffusion Models .", "subpage_snippet": "", "source": "id2icml.github.io", "link": "https://id2icml.github.io/", "content": "VidProM is the first dataset featuring 1.67 million unique text - to -video prompts and 6.69 million videos generated from 4 different state-of-the-art diffusion models . It inspires many exciting new research areas, such as Text - to -Video Prompt Engineering, Efficient Video Generation, Fake Video Detection , and Video Copy Detection for Diffusion Models ."}
+{"idx": 7, "title": "PDF Detecting Origin Attribution for Text-to-Image Diffusion Models", "date": "", "ddg_snippet": "Abstract Modern text - to - image (T2I) diffusion models can gener-ate images with remarkable realism and creativity. These advancements have sparked research in fake image detec-tion and attribution, yet prior studies have not fully explored the practical and scientific dimensions of this task. In addi-tion to attributing images to 12 state-of-the-art T2I genera-tors, we provide extensive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/papers/Xu_Detecting_Origin_Attribution_for_Text-to-Image_Diffusion_Models_WACV_2025_paper.pdf", "content": "Abstract Modern text - to - image (T2I) diffusion models can gener-ate images with remarkable realism and creativity. These advancements have sparked research in fake image detec-tion and attribution, yet prior studies have not fully explored the practical and scientific dimensions of this task. In addi-tion to attributing images to 12 state-of-the-art T2I genera-tors, we provide extensive ..."}
+{"idx": 8, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion Models", "date": "", "ddg_snippet": "Figure 1: The illustration for misusing text-guided image-to-image diffusion models in several scenarios: misinformation, copyright infringement, and evading content tracing. Specifically: (a) An altered image originally showing Donald Trump post-assassination is edited to depict Joe Biden instead; (b) The removal of a watermark from a copyrighted beach image , followed by modifications ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=46n3izUNiv", "content": "Figure 1: The illustration for misusing text-guided image-to-image diffusion models in several scenarios: misinformation, copyright infringement, and evading content tracing. Specifically: (a) An altered image originally showing Donald Trump post-assassination is edited to depict Joe Biden instead; (b) The removal of a watermark from a copyrighted beach image , followed by modifications ..."}
+{"idx": 9, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion Models", "date": "", "ddg_snippet": "This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID$^2$), aiming to retrieve the original image of a given translated query. A straightforward solution to ID$^2$ involves training a specialized deep embedding model to extract and compare features from both query and reference images .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Origin-Identification-for-Text-Guided-Diffusion-Wang-Sun/1c8645e9c74f14dac6fe15fefe0409bf16c3a225", "content": "This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID$^2$), aiming to retrieve the original image of a given translated query. A straightforward solution to ID$^2$ involves training a specialized deep embedding model to extract and compare features from both query and reference images ."}
diff --git a/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Figure_9_transformation_comple.jsonl b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Figure_9_transformation_comple.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8bd20ca355a162598fb562be9d4f7e5ef5fbe9ea
--- /dev/null
+++ b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Figure_9_transformation_comple.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2501.02376] Origin Identification for Text-Guided Image-to ... [ICML 2025] The official implementation of \"Origin ... - GitHub Origin Identification for Text-Guided Image-to-Image ... id2icml.github.io - ID² Detecting Origin Attribution for Text-to-Image Diffusion Models [PDF] Origin Identification for Text-Guided Image-to-Image ... Origin Identification for Text-Guided Image-to-Image ...", "date": "", "ddg_snippet": "Jan 4, 2025 · This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID 2), aiming to retrieve the original image of a given translated query. A straightforward solution to ID 2 involves training a specialized deep embedding model to extract and compare features from both query and reference images. ID2 [ICML 2025] The official implementation of \" Origin Identification for Text-Guided Image-to-Image Diffusion Models \". Text - guided image - to - image diffusion models ex- cel in translating images based on textual prompts, allowing for precise and creative visual modifica- tions. However, such a powerful technique can be misused for spreading misinformation, infring- ing on copyrights, and evading content tracing. This motivates us to introduce the task of ori- gin IDentification for text - guided Image - to - image ... Abstract Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights, and evading content tracing. This motivates us to introduce the task of origin ID entification for text - guided I mage- to - image ... Abstract Modern text - to - image (T2I) diffusion models can gener-ate images with remarkable realism and creativity. These advancements have sparked research in fake image detec-tion and attribution, yet prior studies have not fully explored the practical and scientific dimensions of this task. In addi-tion to attributing images to 12 state-of-the-art T2I genera-tors, we provide extensive ... This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID$^2$), aiming to retrieve the original image of a given translated query. A straightforward solution to ID$^2$ involves training a specialized deep embedding model to extract and compare features from both query and reference images. This paper proposes a novel task, origin identification for text-guided image-to-image diffusion models (ID2), which aims to identify the origin of a generated query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.02376", "content": "Jan 4, 2025 · This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID 2), aiming to retrieve the original image of a given translated query. A straightforward solution to ID 2 involves training a specialized deep embedding model to extract and compare features from both query and reference images. ID2 [ICML 2025] The official implementation of \" Origin Identification for Text-Guided Image-to-Image Diffusion Models \". Text - guided image - to - image diffusion models ex- cel in translating images based on textual prompts, allowing for precise and creative visual modifica- tions. However, such a powerful technique can be misused for spreading misinformation, infring- ing on copyrights, and evading content tracing. This motivates us to introduce the task of ori- gin IDentification for text - guided Image - to - image ... Abstract Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights, and evading content tracing. This motivates us to introduce the task of origin ID entification for text - guided I mage- to - image ... Abstract Modern text - to - image (T2I) diffusion models can gener-ate images with remarkable realism and creativity. These advancements have sparked research in fake image detec-tion and attribution, yet prior studies have not fully explored the practical and scientific dimensions of this task. In addi-tion to attributing images to 12 state-of-the-art T2I genera-tors, we provide extensive ... This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID$^2$), aiming to retrieve the original image of a given translated query. A straightforward solution to ID$^2$ involves training a specialized deep embedding model to extract and compare features from both query and reference images. This paper proposes a novel task, origin identification for text-guided image-to-image diffusion models (ID2), which aims to identify the origin of a generated query."}
+{"idx": 1, "title": "[ICML 2025] The official implementation of \"Origin ... - GitHub", "date": "", "ddg_snippet": "ID2 [ICML 2025] The official implementation of \" Origin Identification for Text-Guided Image-to-Image Diffusion Models \".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/WangWenhao0716/ID2", "content": "ID2 [ICML 2025] The official implementation of \" Origin Identification for Text-Guided Image-to-Image Diffusion Models \"."}
+{"idx": 2, "title": "Origin Identification for Text-Guided Image-to-Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models ex- cel in translating images based on textual prompts, allowing for precise and creative visual modifica- tions. However, such a powerful technique can be misused for spreading misinformation, infring- ing on copyrights, and evading content tracing. This motivates us to introduce the task of ori- gin IDentification for text - guided Image - to - image ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=46n3izUNiv", "content": "Text - guided image - to - image diffusion models ex- cel in translating images based on textual prompts, allowing for precise and creative visual modifica- tions. However, such a powerful technique can be misused for spreading misinformation, infring- ing on copyrights, and evading content tracing. This motivates us to introduce the task of ori- gin IDentification for text - guided Image - to - image ..."}
+{"idx": 3, "title": "Detecting Origin Attribution for Text-to-Image Diffusion Models", "date": "", "ddg_snippet": "Abstract Modern text - to - image (T2I) diffusion models can gener-ate images with remarkable realism and creativity. These advancements have sparked research in fake image detec-tion and attribution, yet prior studies have not fully explored the practical and scientific dimensions of this task. In addi-tion to attributing images to 12 state-of-the-art T2I genera-tors, we provide extensive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/papers/Xu_Detecting_Origin_Attribution_for_Text-to-Image_Diffusion_Models_WACV_2025_paper.pdf", "content": "Abstract Modern text - to - image (T2I) diffusion models can gener-ate images with remarkable realism and creativity. These advancements have sparked research in fake image detec-tion and attribution, yet prior studies have not fully explored the practical and scientific dimensions of this task. In addi-tion to attributing images to 12 state-of-the-art T2I genera-tors, we provide extensive ..."}
+{"idx": 4, "title": "[PDF] Origin Identification for Text-Guided Image-to-Image ...", "date": "", "ddg_snippet": "This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID$^2$), aiming to retrieve the original image of a given translated query. A straightforward solution to ID$^2$ involves training a specialized deep embedding model to extract and compare features from both query and reference images.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Origin-Identification-for-Text-Guided-Diffusion-Wang-Sun/1c8645e9c74f14dac6fe15fefe0409bf16c3a225", "content": "This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID$^2$), aiming to retrieve the original image of a given translated query. A straightforward solution to ID$^2$ involves training a specialized deep embedding model to extract and compare features from both query and reference images."}
+{"idx": 5, "title": "Origin Identification for Text-Guided Image-to-Image ...", "date": "", "ddg_snippet": "This paper proposes a novel task, origin identification for text-guided image-to-image diffusion models (ID2), which aims to identify the origin of a generated query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.02376", "content": "This paper proposes a novel task, origin identification for text-guided image-to-image diffusion models (ID2), which aims to identify the origin of a generated query."}
+{"idx": 6, "title": "Text Image Inpainting via Global Structure-Guided Diffusion", "date": "", "ddg_snippet": "... model uses the guidance of the complete global structure, predicted from the remaining regions of corrupted text images , to generate complete text ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.14832v3", "content": "... model uses the guidance of the complete global structure, predicted from the remaining regions of corrupted text images , to generate complete text ..."}
+{"idx": 7, "title": "Articulate3D: Zero-Shot Text-Driven 3D Object Posing", "date": "", "ddg_snippet": "... for instance, meticulously fine-tunes all parameters within the diffusion model while preserving the text transformer, utilising generated images to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19244v1", "content": "... for instance, meticulously fine-tunes all parameters within the diffusion model while preserving the text transformer, utilising generated images to ..."}
+{"idx": 8, "title": "DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete", "date": "", "ddg_snippet": "... information, such as class labels or text prompts, often helps to simplify the complex mapping by offering the DM’s denoiser additional cues for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.03300v1", "content": "... information, such as class labels or text prompts, often helps to simplify the complex mapping by offering the DM’s denoiser additional cues for ..."}
+{"idx": 9, "title": "Top 8 Large Vision Models: Use Cases and Challenges", "date": "", "ddg_snippet": "It was created by the LAION (Large-scale AI Open Network) project and is designed to train AI models , especially for tasks like image - text matching ...", "subpage_snippet": "", "source": "research.aimultiple.com", "link": "https://research.aimultiple.com/large-vision-models/", "content": "It was created by the LAION (Large-scale AI Open Network) project and is designed to train AI models , especially for tasks like image - text matching ..."}
diff --git a/data/sampled_jsons/Our_current_analysis_is_limited_to_the_offline_setting_and_does_not_account_for_on-policy_learning.jsonl b/data/sampled_jsons/Our_current_analysis_is_limited_to_the_offline_setting_and_does_not_account_for_on-policy_learning.jsonl
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index 0000000000000000000000000000000000000000..84a22f0172ed093c1422708145bbf49fcbae40c6
--- /dev/null
+++ b/data/sampled_jsons/Our_current_analysis_is_limited_to_the_offline_setting_and_does_not_account_for_on-policy_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Is Value Learning Really the Main Bottleneck in Offline RL?", "date": "", "ddg_snippet": "Namely, our analysis suggests that existing offline algorithms are often already great at learning an optimal policy from suboptimal data on in-distribution ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93647", "content": "Namely, our analysis suggests that existing offline algorithms are often already great at learning an optimal policy from suboptimal data on in-distribution ..."}
+{"idx": 1, "title": "What is the difference between off-policy and on-policy ...", "date": "", "ddg_snippet": "2 Dec 2015 — An off- policy learner learns the value of the optimal policy independently of the agent's actions. Q- learning is an off- policy learner.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/184657/what-is-the-difference-between-off-policy-and-on-policy-learning", "content": "2 Dec 2015 — An off- policy learner learns the value of the optimal policy independently of the agent's actions. Q- learning is an off- policy learner."}
+{"idx": 2, "title": "Decoupled Policy Learning for Mitigating Exploration Bias", "date": "", "ddg_snippet": "by MS Mark · Cited by 1 — However, the offline RL problem setting is not the focus of our paper. We look at the problem setting where we are initially given an offline ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lWe3GBRem8", "content": "by MS Mark · Cited by 1 — However, the offline RL problem setting is not the focus of our paper. We look at the problem setting where we are initially given an offline ..."}
+{"idx": 3, "title": "Enhancing Offline Reinforcement Learning with Curriculum ...", "date": "", "ddg_snippet": "2 Feb 2025 — Additionally, CUORL only considers the current policy to evaluate trajectories, neglecting information from the target dataset, which hinders ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00601v1", "content": "2 Feb 2025 — Additionally, CUORL only considers the current policy to evaluate trajectories, neglecting information from the target dataset, which hinders ..."}
+{"idx": 4, "title": "Using offline data to speed up Reinforcement Learning in ...", "date": "", "ddg_snippet": "by A Andres · 2025 · Cited by 10 — One of the key challenges of Reinforcement Learning (RL) is the ability of an agent to generalize its learned policy to unseen settings .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224018502", "content": "by A Andres · 2025 · Cited by 10 — One of the key challenges of Reinforcement Learning (RL) is the ability of an agent to generalize its learned policy to unseen settings ."}
+{"idx": 5, "title": "Showing Your Offline Reinforcement Learning Work: Online ...", "date": "", "ddg_snippet": "by V Kurenkov · 2022 · Cited by 26 — We stress that this is more critical for offline settings than for online ones, and that current evaluation methodology does not account for such dependence.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/kurenkov22a/kurenkov22a.pdf", "content": "by V Kurenkov · 2022 · Cited by 26 — We stress that this is more critical for offline settings than for online ones, and that current evaluation methodology does not account for such dependence."}
+{"idx": 6, "title": "SOReL and TOReL: Two Methods for Fully Offline ...", "date": "", "ddg_snippet": "29 May 2025 — Our analysis also reveals that by using offline data to infer a posterior over environment dynamics, we can approximate regret using the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22442v2", "content": "29 May 2025 — Our analysis also reveals that by using offline data to infer a posterior over environment dynamics, we can approximate regret using the ..."}
+{"idx": 7, "title": "QFAE: Q-Function guided Action Exploration for offline ...", "date": "", "ddg_snippet": "by T Pang · 2025 · Cited by 5 — This paper theoretically analyzes the impact of action exploration on policy learning , which implies that action exploration can improve policy learning .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0031320324007830", "content": "by T Pang · 2025 · Cited by 5 — This paper theoretically analyzes the impact of action exploration on policy learning , which implies that action exploration can improve policy learning ."}
+{"idx": 8, "title": "Decisions from Data: How Offline Reinforcement Learning ...", "date": "", "ddg_snippet": "Offline reinforcement learning algorithms hold the promise of turning data into powerful decision-making strategies, enabling end-to-end learning of policies.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sergey.levine/decisions-from-data-how-offline-reinforcement-learning-will-change-how-we-use-ml-24d98cb069b0", "content": "Offline reinforcement learning algorithms hold the promise of turning data into powerful decision-making strategies, enabling end-to-end learning of policies."}
+{"idx": 9, "title": "Offline Meta Reinforcement Learning – Identifiability ...", "date": "", "ddg_snippet": "by R Dorfman · 2021 · Cited by 81 — (1). 3.2 Off- Policy VariBAD. The on- policy VariBAD algorithm cannot be applied to our offline setting . Our first step is to modify. VariBAD to work off- policy . 12 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/248024541dbda1d3fd75fe49d1a4df4d-Paper.pdf", "content": "by R Dorfman · 2021 · Cited by 81 — (1). 3.2 Off- Policy VariBAD. The on- policy VariBAD algorithm cannot be applied to our offline setting . Our first step is to modify. VariBAD to work off- policy . 12 pages"}
diff --git a/data/sampled_jsons/Overlapping_Hierarchical_Clustering_OHC_2020_cost_function_year_2020.jsonl b/data/sampled_jsons/Overlapping_Hierarchical_Clustering_OHC_2020_cost_function_year_2020.jsonl
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index 0000000000000000000000000000000000000000..5866aaa9994d78afb3dc30d4ea95a8772ff45932
--- /dev/null
+++ b/data/sampled_jsons/Overlapping_Hierarchical_Clustering_OHC_2020_cost_function_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HIERARCHICAL OVERLAPPING CLUSTERING FUNCTION ALGORITHM AND ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 1, "title": "Overlapping Hierarchical Clustering (OHC) | SpringerLink (PDF) Overlapping Hierarchical Clustering (OHC) - ResearchGate Overlapping Hierarchical Clustering (OHC) | Advances in ... G O arXiv:2306.09950v1 [cs.DS] 16 Jun 2023 Overlapping Hierarchical Clustering ( OHC ) | SpringerLink Overlapping Hierarchical Clustering ( OHC ) | SpringerLink Overlapping Hierarchical Clustering ( OHC ) | SpringerLink Overlapping Hierarchical Clustering (OHC) - CentraleSupélec", "date": "", "ddg_snippet": "Tests: The tests we performed were focused on the quality of the hierarchical structures produced by our algorithm. To measure this quality we used the classical hierarchy produced by SLINK, an optimal single-linkage clustering algorithm proposed in Sibson et al. , as a baseline. Our goal was to study the behaviour of the merging criterion paramete... See full list on link.springer.com As there is no ground truth on the hierarchy of the data we used, we need a similarity measure to compare the hierarchical structures produced by hierarchical clustering algorithms. The goal is not only to compare the topology but also the content of the nodes of the structure. However up to our knowledge there is very little in the literature abou... See full list on link.springer.com Expressiveness: With this small following example we would like to present the expressiveness of our algorithm compared to classical hierarchical clustering algorithms such as SLINK. On the hand-built example shown in Fig. 3a we can clearly distinguish two groups of points, \\(\\{A,B,C,D,E\\}\\) and \\(\\{G,H,I,J,K\\}\\) and two points that we can consider... See full list on link.springer.com Apr 22, 2020 · The clustering is similarity based and uses standard linkage functions , such as single- and complete linkage, and is an extension of classical hierarchical clustering . Apr 27, 2020 · In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering . This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta’s cost function . For any input graph G with a clear cluster -structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta’s cost function . We compare the performance of our algorithm against the previous ... What happens if a cluster has no overlap? If the clusters present in the data show no overlaps, the obtained clusters are identical to the clusters we can compute using agglomerative clustering methods. What are Agglomerative Hierarchical Clustering methods? Agglomerative hierarchical clustering methods are widely used to analyze large amounts of data . These successful methods construct a dendrogram – a tree structure – that enables a natural exploration of data which is very suitable even for non-expert users. How does the OHC method work? The OHC method works as presented in Algorithm 1. We first compute the distance matrix of the data points (I3). We chose the cosine distance, widely use in NLP. Then we construct and maintain the \\ (\\delta \\) -neighbourhood graph \\ (G_ {\\delta } (V,E)\\), starting from \\ (\\delta = 0\\) (I4). Agglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures. These structures ease data exploration and are understandable even for non-specialists. But these methods necessarily result in a tree, since, at each agglomeration step, two clusters have to be merged. This may bias the data analysis process if, for example ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-44584-3_21", "content": "Tests: The tests we performed were focused on the quality of the hierarchical structures produced by our algorithm. To measure this quality we used the classical hierarchy produced by SLINK, an optimal single-linkage clustering algorithm proposed in Sibson et al. , as a baseline. Our goal was to study the behaviour of the merging criterion paramete... See full list on link.springer.com As there is no ground truth on the hierarchy of the data we used, we need a similarity measure to compare the hierarchical structures produced by hierarchical clustering algorithms. The goal is not only to compare the topology but also the content of the nodes of the structure. However up to our knowledge there is very little in the literature abou... See full list on link.springer.com Expressiveness: With this small following example we would like to present the expressiveness of our algorithm compared to classical hierarchical clustering algorithms such as SLINK. On the hand-built example shown in Fig. 3a we can clearly distinguish two groups of points, \\(\\{A,B,C,D,E\\}\\) and \\(\\{G,H,I,J,K\\}\\) and two points that we can consider... See full list on link.springer.com Apr 22, 2020 · The clustering is similarity based and uses standard linkage functions , such as single- and complete linkage, and is an extension of classical hierarchical clustering . Apr 27, 2020 · In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering . This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta’s cost function . For any input graph G with a clear cluster -structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta’s cost function . We compare the performance of our algorithm against the previous ... What happens if a cluster has no overlap? If the clusters present in the data show no overlaps, the obtained clusters are identical to the clusters we can compute using agglomerative clustering methods. What are Agglomerative Hierarchical Clustering methods? Agglomerative hierarchical clustering methods are widely used to analyze large amounts of data . These successful methods construct a dendrogram – a tree structure – that enables a natural exploration of data which is very suitable even for non-expert users. How does the OHC method work? The OHC method works as presented in Algorithm 1. We first compute the distance matrix of the data points (I3). We chose the cosine distance, widely use in NLP. Then we construct and maintain the \\ (\\delta \\) -neighbourhood graph \\ (G_ {\\delta } (V,E)\\), starting from \\ (\\delta = 0\\) (I4). Agglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures. These structures ease data exploration and are understandable even for non-specialists. But these methods necessarily result in a tree, since, at each agglomeration step, two clusters have to be merged. This may bias the data analysis process if, for example ..."}
+{"idx": 2, "title": "Overlapping Hierarchical Clustering (OHC)", "date": "", "ddg_snippet": "Overlapping hierarchical clustering framework Principle of the algorithm: Initialisation: Start with singleton clusters and the 0-neighbourhood graph. Main loop: Add links by increasing order in the graph, Look for impacted clusters, Use a density-based merging threshold to decide whether to grow the clusters or not.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/55_Overlapping_Hierarchical_Clustering_IDA_2020.pdf", "content": "Overlapping hierarchical clustering framework Principle of the algorithm: Initialisation: Start with singleton clusters and the 0-neighbourhood graph. Main loop: Add links by increasing order in the graph, Look for impacted clusters, Use a density-based merging threshold to decide whether to grow the clusters or not."}
+{"idx": 3, "title": "(PDF) Overlapping Hierarchical Clustering (OHC) - ResearchGate", "date": "", "ddg_snippet": "Apr 22, 2020 · The clustering is similarity based and uses standard linkage functions , such as single- and complete linkage, and is an extension of classical hierarchical clustering .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/340823458_Overlapping_Hierarchical_Clustering_OHC", "content": "Apr 22, 2020 · The clustering is similarity based and uses standard linkage functions , such as single- and complete linkage, and is an extension of classical hierarchical clustering ."}
+{"idx": 4, "title": "Overlapping Hierarchical Clustering (OHC) | Advances in ...", "date": "", "ddg_snippet": "Apr 27, 2020 · In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/978-3-030-44584-3_21", "content": "Apr 27, 2020 · In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering ."}
+{"idx": 5, "title": "Overlapping Hierarchical Clustering (OHC) - CentraleSupélec", "date": "", "ddg_snippet": "Agglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures. These structures ease data exploration and are understandable even for non-specialists. But these methods necessarily result in a tree, since, at each agglomeration step, two clusters have to be merged. This may bias the data analysis process if, for example ...", "subpage_snippet": "", "source": "centralesupelec.hal.science", "link": "https://centralesupelec.hal.science/hal-02452729v1", "content": "Agglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures. These structures ease data exploration and are understandable even for non-specialists. But these methods necessarily result in a tree, since, at each agglomeration step, two clusters have to be merged. This may bias the data analysis process if, for example ..."}
+{"idx": 6, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "by Y Pan — The paper presents the problem of hierarchical overlapping clustering (HOC ) for graph data, where clusters may overlap and form hierarchies. The paper presents ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "by Y Pan — The paper presents the problem of hierarchical overlapping clustering (HOC ) for graph data, where clusters may overlap and form hierarchies. The paper presents ..."}
+{"idx": 7, "title": "A New Way for Hierarchical and Topological Clustering", "date": "", "ddg_snippet": "by H Azzag · Cited by 3 — Hierarchical clustering algorithms are typically more effective in detecting the true clustering structure of a structured data set than partitioning algorithms ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-642-35855-5_5", "content": "by H Azzag · Cited by 3 — Hierarchical clustering algorithms are typically more effective in detecting the true clustering structure of a structured data set than partitioning algorithms ..."}
+{"idx": 8, "title": "Hierarchical Clustering: Objective Functions and Algorithms", "date": "", "ddg_snippet": "by V Cohen-Addad · 2018 · Cited by 382 — He showed that this cost function has certain desirable properties, such as in order to achieve optimal cost, disconnected components must be ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/1.9781611975031.26", "content": "by V Cohen-Addad · 2018 · Cited by 382 — He showed that this cost function has certain desirable properties, such as in order to achieve optimal cost, disconnected components must be ..."}
+{"idx": 9, "title": "DAG-Structured Clustering by Nearest Neighbors", "date": "", "ddg_snippet": "by N Monath · 2021 · Cited by 5 — lapping hierarchical clustering ( OHC ) (Jeantet et al.,. 2020 ), and others ... A cost function for similarity-based hier- archical clustering. Symposium ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v130/monath21a/monath21a.pdf", "content": "by N Monath · 2021 · Cited by 5 — lapping hierarchical clustering ( OHC ) (Jeantet et al.,. 2020 ), and others ... A cost function for similarity-based hier- archical clustering. Symposium ..."}
diff --git a/data/sampled_jsons/PAW_calculation_Price_Access_Websites_broadband_affordability_Pi_PT_formula.jsonl b/data/sampled_jsons/PAW_calculation_Price_Access_Websites_broadband_affordability_Pi_PT_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..84eb006787e6077eacf16408559f73e2940ad03d
--- /dev/null
+++ b/data/sampled_jsons/PAW_calculation_Price_Access_Websites_broadband_affordability_Pi_PT_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mean Airway Pressure (Paw) Calculator - MDApp", "date": "", "ddg_snippet": "This mean airway pressure ( Paw ) calculator determines the mean pressure applied during positive-pressure mechanical ventilation.", "subpage_snippet": "", "source": "www.mdapp.co", "link": "https://www.mdapp.co/mean-airway-pressure-paw-calculator-437/", "content": "This mean airway pressure ( Paw ) calculator determines the mean pressure applied during positive-pressure mechanical ventilation."}
+{"idx": 1, "title": "PDF A Framework for Improving Web Affordability and Inclusiveness", "date": "", "ddg_snippet": "AW4A provides a systematic way for incorporating affordability constraints in Web design by relying on a new fairness metric, PAW ( Price Adjusted Web access ), which captures how equitable and affordable Web accesses are across regions with different mobile broadband prices and income levels.", "subpage_snippet": "", "source": "www.ietf.org", "link": "https://www.ietf.org/slides/slides-biasws-a-framework-for-improving-web-affordability-and-inclusiveness-00.pdf", "content": "AW4A provides a systematic way for incorporating affordability constraints in Web design by relying on a new fairness metric, PAW ( Price Adjusted Web access ), which captures how equitable and affordable Web accesses are across regions with different mobile broadband prices and income levels."}
+{"idx": 2, "title": "PAW vs UAW: Are you a Prodigious or Under Accumulator of Wealth?", "date": "", "ddg_snippet": "Are you a PAW or a UAW? How can knowing the difference between the two help you better prepare for retirement or any other financial goal? Learn the answers to these questions and how understanding PAW vs. UAW can help your path to financial success!", "subpage_snippet": "", "source": "www.scottoeth.com", "link": "https://www.scottoeth.com/intrinsic-value-blog/2016/12/1/paw-vs-uaw", "content": "Are you a PAW or a UAW? How can knowing the difference between the two help you better prepare for retirement or any other financial goal? Learn the answers to these questions and how understanding PAW vs. UAW can help your path to financial success!"}
+{"idx": 3, "title": "Broadband Affordability - National Digital Inclusion Alliance", "date": "", "ddg_snippet": "Broadband affordability ensures everyone can access reliable, high-speed internet at a reasonable cost that meets their long-term needs. There are two primary strategies to ensure broadband is affordable: 1) encouraging Internet Service Providers (ISPs) to offer reliable, low-cost plans for low-income households, and 2) providing a government-funded benefit to low-income households to help ...", "subpage_snippet": "", "source": "www.digitalinclusion.org", "link": "https://www.digitalinclusion.org/broadband-affordability/", "content": "Broadband affordability ensures everyone can access reliable, high-speed internet at a reasonable cost that meets their long-term needs. There are two primary strategies to ensure broadband is affordable: 1) encouraging Internet Service Providers (ISPs) to offer reliable, low-cost plans for low-income households, and 2) providing a government-funded benefit to low-income households to help ..."}
+{"idx": 4, "title": "PDF IAB BIAS: A Framework for Improving Web Affordability and Inclusiveness", "date": "", "ddg_snippet": "PAW Index equation : average broadband price in region : target broadband price (2% of a country's GNIpc) , : average page sizes in region : average page size globally", "subpage_snippet": "", "source": "www.ietf.org", "link": "https://www.ietf.org/slides/slides-biasws-framework-for-improving-web-affordability-and-inclusiveness-habib-01.pdf", "content": "PAW Index equation : average broadband price in region : target broadband price (2% of a country's GNIpc) , : average page sizes in region : average page size globally"}
+{"idx": 5, "title": "Analyzing Disparities in Broadband Plans - ADDRESS", "date": "", "ddg_snippet": "Analyzing Disparities in Broadband Plans Digital equity in Internet access is often measured along three axes: availability, affordability , and adoption. Most prior work focuses on availability; the other two aspects have received little attention. In this work, we study broadband affordability in the US.", "subpage_snippet": "", "source": "address.cs.ucsb.edu", "link": "https://address.cs.ucsb.edu/bqt/", "content": "Analyzing Disparities in Broadband Plans Digital equity in Internet access is often measured along three axes: availability, affordability , and adoption. Most prior work focuses on availability; the other two aspects have received little attention. In this work, we study broadband affordability in the US."}
+{"idx": 6, "title": "Mean Airway Pressure Calculator", "date": "", "ddg_snippet": "The mean airway pressure calculator computes Paw affected by inspiratory time, peak inspiratory pressure, positive end-respiratory pressure, and the shape of the respiratory waveform.", "subpage_snippet": "", "source": "www.omnicalculator.com", "link": "https://www.omnicalculator.com/health/mean-airway-pressure", "content": "The mean airway pressure calculator computes Paw affected by inspiratory time, peak inspiratory pressure, positive end-respiratory pressure, and the shape of the respiratory waveform."}
+{"idx": 7, "title": "Are You a Prodigious Accumulator of Wealth? - Shortform Books", "date": "", "ddg_snippet": "How do you calculate to see if you are a prodigious accumulator of wealth ( PAW ) or an Under Accumulator of Wealth (UAW)? A prodigious accumulator of wealth has a net worth (excluding inheritance) at twice the expected level for their age, while an under accumulator of wealth has a net worth less than half of the expected level.", "subpage_snippet": "", "source": "www.shortform.com", "link": "https://www.shortform.com/blog/prodigious-accumulator-of-wealth/", "content": "How do you calculate to see if you are a prodigious accumulator of wealth ( PAW ) or an Under Accumulator of Wealth (UAW)? A prodigious accumulator of wealth has a net worth (excluding inheritance) at twice the expected level for their age, while an under accumulator of wealth has a net worth less than half of the expected level."}
+{"idx": 8, "title": "Rowlo - Wealth Calculator", "date": "", "ddg_snippet": "To determine if you are a UAW, AAW, or PAW you apply the following formula : Multiply your age times your realized pretax annual household income from all sources except inheritances. Divide by ten. This, less any inherited wealth, is what your net worth should be.", "subpage_snippet": "", "source": "www.rowlo.com", "link": "http://www.rowlo.com/Calculators/WealthCalculator.aspx", "content": "To determine if you are a UAW, AAW, or PAW you apply the following formula : Multiply your age times your realized pretax annual household income from all sources except inheritances. Divide by ten. This, less any inherited wealth, is what your net worth should be."}
+{"idx": 9, "title": "Am I Wealthy ? Calculator - wealthymatters", "date": "", "ddg_snippet": "For their age and income levels,the PAWs are people who have accumulated an exceptionally good amount of wealth and the UAWs are those who fail to impress on the wealth front.This is because many people with huge incomes have equally large expenses because of their lifestyle choices.To check if you are a PAW or UAW use the calculator here Link .", "subpage_snippet": "", "source": "wealthymatters.com", "link": "https://wealthymatters.com/2011/01/17/am-i-wealthy-calculator/", "content": "For their age and income levels,the PAWs are people who have accumulated an exceptionally good amount of wealth and the UAWs are those who fail to impress on the wealth front.This is because many people with huge incomes have equally large expenses because of their lifestyle choices.To check if you are a PAW or UAW use the calculator here Link ."}
diff --git a/data/sampled_jsons/PAW_index_Presumably_Affordable_Website_formula_broadband_price.jsonl b/data/sampled_jsons/PAW_index_Presumably_Affordable_Website_formula_broadband_price.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aea0f2c10929af7b222cbd08addce717b8819076
--- /dev/null
+++ b/data/sampled_jsons/PAW_index_Presumably_Affordable_Website_formula_broadband_price.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Harmonized indexes of consumer prices", "date": "", "ddg_snippet": "consumer price index , I would recommend the use of monthly chaining, using one of the three approximate formulae PAF, PAT or PAW defined by (118)-(120) above.", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID357342_code030114590.pdf?abstractid=357342&mirid=1", "content": "consumer price index , I would recommend the use of monthly chaining, using one of the three approximate formulae PAF, PAT or PAW defined by (118)-(120) above."}
+{"idx": 1, "title": "Central Chapter 16: Utah - BroadbandUSA", "date": "", "ddg_snippet": "1 Jun 2017 — ... Broadband Network. Final Programmatic Environmental Impact Statement for the Central United States. VOLUME 14 - CHAPTER 16. Colorado. Illinois.", "subpage_snippet": "", "source": "broadbandusa.ntia.gov", "link": "https://broadbandusa.ntia.gov/sites/default/files/2025-07/Ch_16_Utah_Central_FPEIS_June_2017.pdf", "content": "1 Jun 2017 — ... Broadband Network. Final Programmatic Environmental Impact Statement for the Central United States. VOLUME 14 - CHAPTER 16. Colorado. Illinois."}
+{"idx": 2, "title": "Cable/Satellite Mudslinging - TV Tropes", "date": "", "ddg_snippet": "... one involved talking computers that are so happy with Buckeye's internet speed because of how quickly they can load up games or hit shopping websites ...", "subpage_snippet": "", "source": "tvtropes.org", "link": "https://tvtropes.org/pmwiki/pmwiki.php/Main/CableSatelliteMudslinging", "content": "... one involved talking computers that are so happy with Buckeye's internet speed because of how quickly they can load up games or hit shopping websites ..."}
+{"idx": 3, "title": "Innovation « savingusmanufacturing.com", "date": "", "ddg_snippet": "However, the website of the Essex Richards law firm of Charlotte, NC has a warning that “ businesses should know that the DTSA contains certain ...", "subpage_snippet": "", "source": "savingusmanufacturing.com", "link": "https://savingusmanufacturing.com/blog/category/innovation/page/4/", "content": "However, the website of the Essex Richards law firm of Charlotte, NC has a warning that “ businesses should know that the DTSA contains certain ..."}
+{"idx": 4, "title": "Data-Driven Innovation (EN)", "date": "", "ddg_snippet": "28 Nov 2013 — We will need, for example, to recast how we think about infrastructure in the 21st Century, and expand it to encompass broadband networks, cloud ... 456 pages", "subpage_snippet": "", "source": "www.oecd.org", "link": "https://www.oecd.org/content/dam/oecd/en/publications/reports/2015/10/data-driven-innovation_g1g503d8/9789264229358-en.pdf", "content": "28 Nov 2013 — We will need, for example, to recast how we think about infrastructure in the 21st Century, and expand it to encompass broadband networks, cloud ... 456 pages"}
+{"idx": 5, "title": "Asia Transfer Pricing", "date": "", "ddg_snippet": "Once finalised, BEPS will presumably offer more unified and better guidance on concepts like substance and push taxpayers undergoing busi- ness transformations ...", "subpage_snippet": "", "source": "assets.kpmg.com", "link": "https://assets.kpmg.com/content/dam/kpmg/pdf/2015/04/asia-transfer-pricing-2014.pdf", "content": "Once finalised, BEPS will presumably offer more unified and better guidance on concepts like substance and push taxpayers undergoing busi- ness transformations ..."}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 7, "title": "ECC Report 236 - ECO Documentation Database", "date": "", "ddg_snippet": "Using TV white spaces to deliver Internet at greater speeds may provide a cheaper alternative as it requires little initial investment in comparison to building ...", "subpage_snippet": "", "source": "docdb.cept.org", "link": "https://docdb.cept.org/download/1214", "content": "Using TV white spaces to deliver Internet at greater speeds may provide a cheaper alternative as it requires little initial investment in comparison to building ..."}
+{"idx": 8, "title": "427r_a_e.doc", "date": "", "ddg_snippet": "1.1 is for the investigating authority, as a rule, to calculate costs on the basis of a producer or exporter's records.", "subpage_snippet": "", "source": "www.wto.org", "link": "https://www.wto.org/english/tratop_e/dispu_e/427r_a_e.doc", "content": "1.1 is for the investigating authority, as a rule, to calculate costs on the basis of a producer or exporter's records."}
+{"idx": 9, "title": "funny dog sitting positions", "date": "", "ddg_snippet": "Please share pics of your pups sitting funny I can't get over foster Yuzu sitting like she's airing out her tummy, but also crossing her front ...", "subpage_snippet": "", "source": "www.facebook.com", "link": "https://www.facebook.com/groups/121827021783245/posts/1756704581628806/", "content": "Please share pics of your pups sitting funny I can't get over foster Yuzu sitting like she's airing out her tummy, but also crossing her front ..."}
diff --git a/data/sampled_jsons/PAW_index_formula_price_access_websites_affordability.jsonl b/data/sampled_jsons/PAW_index_formula_price_access_websites_affordability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..97d41ce3eed19f74690df66362bcd5bfd19e6509
--- /dev/null
+++ b/data/sampled_jsons/PAW_index_formula_price_access_websites_affordability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "🐾 Spot & Tango vs. Sundays for Dogs - Bestie Paws", "date": "", "ddg_snippet": "When factoring in price per meal, portion control issues , and delivery reliability , the picture changes significantly from surface-level quotes.", "subpage_snippet": "", "source": "www.bestiepaws.com", "link": "https://www.bestiepaws.com/dog-food-review/spot-and-tango-vs-sundays/", "content": "When factoring in price per meal, portion control issues , and delivery reliability , the picture changes significantly from surface-level quotes."}
+{"idx": 1, "title": "website — Blog — Used Vet Equipment", "date": "", "ddg_snippet": "Thus, initiating the website early allows for a head start in this indexing process, ultimately contributing to improved search engine rankings.", "subpage_snippet": "", "source": "www.usedvetequipment.com", "link": "https://www.usedvetequipment.com/blog/tag/website", "content": "Thus, initiating the website early allows for a head start in this indexing process, ultimately contributing to improved search engine rankings."}
+{"idx": 2, "title": "Freshpet vs. Purina Pro Plan: Which Dog Food Is Better for Your", "date": "", "ddg_snippet": "... Some Purina Pro Plan formulas ... Affordable Options: Purina Pro Plan offers a range of prices , making it accessible to a wider range of budgets.", "subpage_snippet": "", "source": "www.bestiepaws.com", "link": "https://www.bestiepaws.com/dog-food-review/freshpet-vs-purina-pro-plan/", "content": "... Some Purina Pro Plan formulas ... Affordable Options: Purina Pro Plan offers a range of prices , making it accessible to a wider range of budgets."}
+{"idx": 3, "title": "Well Fargo Home Mortgage Refinance", "date": "", "ddg_snippet": "This formula checks to see if the balance of the loan is greater than and if it is, how to create excel spreadsheets for home office deductions ...", "subpage_snippet": "", "source": "www.mygreyworld.com", "link": "http://www.mygreyworld.com/paw/index_files/improvement/existing/wellfargohome.html", "content": "This formula checks to see if the balance of the loan is greater than and if it is, how to create excel spreadsheets for home office deductions ..."}
+{"idx": 4, "title": "Fotos Cuidad De Allende Nuevo Leon", "date": "", "ddg_snippet": "Cash america pawn, jonesboro rd se, money till pay day, marietta rd, canton, ga usa payday, candler rd, decatur, ga.", "subpage_snippet": "", "source": "www.mygreyworld.com", "link": "http://www.mygreyworld.com/paw/index_files/improvement/existing/fotoscuidadde.html", "content": "Cash america pawn, jonesboro rd se, money till pay day, marietta rd, canton, ga usa payday, candler rd, decatur, ga."}
+{"idx": 5, "title": "Best Dog Food for Puppies 2025", "date": "", "ddg_snippet": "According to Nom Nom s website , \" While plans start at $1.57 per meal, your price depends on your dog s age, weight, and activity level ...", "subpage_snippet": "", "source": "wagwalking.com", "link": "https://wagwalking.com/product-guides/best-products/dog-food/best-dog-food-for-puppies", "content": "According to Nom Nom s website , \" While plans start at $1.57 per meal, your price depends on your dog s age, weight, and activity level ..."}
+{"idx": 6, "title": "Blog — Valley Cats", "date": "", "ddg_snippet": "What to do!!! The formulas listed below are for emergency use only until regular kitten formula (such as KMR or Just Born) can be purchased from the ...", "subpage_snippet": "", "source": "valleycats.org", "link": "https://valleycats.org/cat-care", "content": "What to do!!! The formulas listed below are for emergency use only until regular kitten formula (such as KMR or Just Born) can be purchased from the ..."}
+{"idx": 7, "title": "Reminder: Don’t Sell in May and Go Away | Morningstar", "date": "", "ddg_snippet": "Simply staying invested in the index affords an investor a vastly larger amount of wealth. ... of the time, staying invested the whole year affords ...", "subpage_snippet": "", "source": "www.morningstar.ca", "link": "https://www.morningstar.ca/ca/news/221204/reminder-dont-sell-in-may-and-go-away.aspx", "content": "Simply staying invested in the index affords an investor a vastly larger amount of wealth. ... of the time, staying invested the whole year affords ..."}
+{"idx": 8, "title": "HDFC Coops Affordable Apartments \"But No Catch\" - nyc BLOG", "date": "", "ddg_snippet": "Large affordable apartments are in extreme demand and sell quickly when priced right. ... prices while still maintaining the intent and spirit of ...", "subpage_snippet": "", "source": "www.nycblogestate.com", "link": "https://www.nycblogestate.com/2014/06/hdfc-coops-affordable-apartments-no-but_29.html", "content": "Large affordable apartments are in extreme demand and sell quickly when priced right. ... prices while still maintaining the intent and spirit of ..."}
+{"idx": 9, "title": "Affordable SEO Packages for Small Business - Lion Spirit Media", "date": "", "ddg_snippet": "If your website directly sells to your customers then you will need to use one of our eCommerce packages instead. If you aren ’ t sure, then ...", "subpage_snippet": "", "source": "lionspiritmedia.co.uk", "link": "https://lionspiritmedia.co.uk/our-marketing-services/seo-agency/seo-packages/", "content": "If your website directly sells to your customers then you will need to use one of our eCommerce packages instead. If you aren ’ t sure, then ..."}
diff --git a/data/sampled_jsons/PMLR_267_conference.jsonl b/data/sampled_jsons/PMLR_267_conference.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..147ae64a88e76d84a7dfc1bac83cf470c047da34
--- /dev/null
+++ b/data/sampled_jsons/PMLR_267_conference.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TAG-ML - researchr conference series publications", "date": "", "ddg_snippet": "... Kvinge , Nina Miolane , Mathilde Papillon , Bastian Rieck , Sophia Sanborn , editors, Volume 221 of Proceedings of Machine Learning Research , PMLR ...", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/conferenceseries/tagml/publications", "content": "... Kvinge , Nina Miolane , Mathilde Papillon , Bastian Rieck , Sophia Sanborn , editors, Volume 221 of Proceedings of Machine Learning Research , PMLR ..."}
+{"idx": 1, "title": "[2402.08680] Mitigating Object Hallucination in Large", "date": "", "ddg_snippet": "In Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada. ... PMLR 267 , 2025", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.08680", "content": "In Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada. ... PMLR 267 , 2025"}
+{"idx": 2, "title": "Submission and Formatting Instructions for International", "date": "", "ddg_snippet": "... must beΩmodified to ‘‘\\textit{Proceedings of theΩ$\\mathit{42}^{nd}$ International Conference on Machine Learning},ΩVancouver, Canada, PMLR 267 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.18993v1", "content": "... must beΩmodified to ‘‘\\textit{Proceedings of theΩ$\\mathit{42}^{nd}$ International Conference on Machine Learning},ΩVancouver, Canada, PMLR 267 ..."}
+{"idx": 3, "title": "Submission and Formatting Instructions for International", "date": "", "ddg_snippet": "... nd} italic_42 start_POSTSUPERSCRIPT italic_n italic_d end_POSTSUPERSCRIPT International Conference on Machine Learning , Vancouver, Canada, PMLR 267 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10433v1", "content": "... nd} italic_42 start_POSTSUPERSCRIPT italic_n italic_d end_POSTSUPERSCRIPT International Conference on Machine Learning , Vancouver, Canada, PMLR 267 ..."}
+{"idx": 4, "title": "Computer Science", "date": "", "ddg_snippet": "Comments: Accepted at Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada. ... 2025 IEEE International Conference ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs/recent?skip=2243&show=50", "content": "Comments: Accepted at Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada. ... 2025 IEEE International Conference ..."}
+{"idx": 5, "title": "Artificial Intelligence", "date": "", "ddg_snippet": "Journal-ref: ACM 2025 Conference on ... Journal-ref: Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.AI/recent?skip=303&show=50", "content": "Journal-ref: ACM 2025 Conference on ... Journal-ref: Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada."}
+{"idx": 6, "title": "Antonio Sclocchi", "date": "", "ddg_snippet": "In International Conference on Machine Learning, PMLR 267 , 2025 ... In The Thirteenth International Conference on Learning Representations , 2025", "subpage_snippet": "", "source": "antonioscl.github.io", "link": "https://antonioscl.github.io/", "content": "In International Conference on Machine Learning, PMLR 267 , 2025 ... In The Thirteenth International Conference on Learning Representations , 2025"}
+{"idx": 7, "title": "Tristan Brugère’s personal website | Tristan Brugère", "date": "", "ddg_snippet": "... Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML)\", year = \"2023\", publisher = \" PMLR \", pages = \" 267 ...", "subpage_snippet": "", "source": "tristan.bruge.re", "link": "https://tristan.bruge.re/", "content": "... Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML)\", year = \"2023\", publisher = \" PMLR \", pages = \" 267 ..."}
+{"idx": 8, "title": "Peter Richtarik", "date": "", "ddg_snippet": "... SGD and Hogwild! convergence without the bounded gradients assumption Proceedings of The 35th International Conference on Machine Learning, PMLR 80 ...", "subpage_snippet": "", "source": "richtarik.org", "link": "https://richtarik.org/i_papers.html", "content": "... SGD and Hogwild! convergence without the bounded gradients assumption Proceedings of The 35th International Conference on Machine Learning, PMLR 80 ..."}
+{"idx": 9, "title": "Details of a Researcher - KASAI, Hiroyuki", "date": "", "ddg_snippet": "35th International Conference on Machine Learning (ICML2018) PMLR 80 2516 - 2524 2018.07 [Refereed] ... Conference on Machine Learning (ICML2016) ...", "subpage_snippet": "", "source": "w-rdb.waseda.jp", "link": "https://w-rdb.waseda.jp/html/100000047_en.html", "content": "35th International Conference on Machine Learning (ICML2018) PMLR 80 2516 - 2524 2018.07 [Refereed] ... Conference on Machine Learning (ICML2016) ..."}
diff --git a/data/sampled_jsons/PPO_Proximal_Policy_Optimization_imitation_learning_on-policy_extension.jsonl b/data/sampled_jsons/PPO_Proximal_Policy_Optimization_imitation_learning_on-policy_extension.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9b46930e7e1828e298141642c4d41c8e11d20f06
--- /dev/null
+++ b/data/sampled_jsons/PPO_Proximal_Policy_Optimization_imitation_learning_on-policy_extension.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Proximal policy optimization - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. Proximal policy optimization is a reinforcement learning algorithm for training an intelligent agent. Specifically, it is a policy gradient method, often used for deep RL when the policy network is very large...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Proximal_policy_optimization", "content": "Machine learningand data mining. v. t. e. Proximal policy optimization is a reinforcement learning algorithm for training an intelligent agent. Specifically, it is a policy gradient method, often used for deep RL when the policy network is very large..."}
+{"idx": 1, "title": "Proximal Policy Optimization (PPO) — verl documentation", "date": "", "ddg_snippet": "Jun 19, 2025 · Proximal Policy Optimization ( PPO ) is a family of policy gradient methods for reinforcement learning , proposed by OpenAI in 2017. PPO strikes a balance between simplicity, stability, and performance, making it one of the most widely used algorithms in modern RL applications, including large-scale language model fine-tuning.", "subpage_snippet": "", "source": "verl.readthedocs.io", "link": "https://verl.readthedocs.io/en/latest/algo/ppo.html", "content": "Jun 19, 2025 · Proximal Policy Optimization ( PPO ) is a family of policy gradient methods for reinforcement learning , proposed by OpenAI in 2017. PPO strikes a balance between simplicity, stability, and performance, making it one of the most widely used algorithms in modern RL applications, including large-scale language model fine-tuning."}
+{"idx": 2, "title": "Hindsight Experience Replay Accelerates Proximal Policy ...", "date": "", "ddg_snippet": "Oct 29, 2024 · Because post-hoc modification of the observed goal violates the assumptions of on-policy algorithms, HER is not typically applied to on-policy algorithms. Here, we show that HER can dramatically accelerate proximal policy optimization ( PPO ), an on-policy reinforcement learning algorithm, when tested on a custom predator-prey environment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.22524v1", "content": "Oct 29, 2024 · Because post-hoc modification of the observed goal violates the assumptions of on-policy algorithms, HER is not typically applied to on-policy algorithms. Here, we show that HER can dramatically accelerate proximal policy optimization ( PPO ), an on-policy reinforcement learning algorithm, when tested on a custom predator-prey environment."}
+{"idx": 3, "title": "Proximal Policy Optimization | myiKim/pytorch_DeepMimic ...", "date": "", "ddg_snippet": "May 12, 2025 · For information about the overall architecture that PPO operates within, see Actor-Critic Architecture. Introduction to PPO Proximal Policy Optimization ( PPO ) is a policy gradient method for reinforcement learning developed by OpenAI in 2017. It serves as the core learning algorithm in the PyTorch DeepMimic implementation as noted in the README:", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/myiKim/pytorch_DeepMimic/5.2-proximal-policy-optimization", "content": "May 12, 2025 · For information about the overall architecture that PPO operates within, see Actor-Critic Architecture. Introduction to PPO Proximal Policy Optimization ( PPO ) is a policy gradient method for reinforcement learning developed by OpenAI in 2017. It serves as the core learning algorithm in the PyTorch DeepMimic implementation as noted in the README:"}
+{"idx": 4, "title": "GitHub - AIResearcherHZ/Matlab_PPO: This is a MATLAB-based ...", "date": "", "ddg_snippet": "🤖 Matlab PPO Reinforcement Learning Framework A MATLAB-based reinforcement learning framework featuring Proximal Policy Optimization ( PPO ) algorithm and its multi-agent extension (MAPPO), with GPU acceleration and parallel computing support, suitable for control system research and engineering applications.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AIResearcherHZ/Matlab_PPO", "content": "🤖 Matlab PPO Reinforcement Learning Framework A MATLAB-based reinforcement learning framework featuring Proximal Policy Optimization ( PPO ) algorithm and its multi-agent extension (MAPPO), with GPU acceleration and parallel computing support, suitable for control system research and engineering applications."}
+{"idx": 5, "title": "Lecture 7: Policy Gradients and Imitation learning", "date": "", "ddg_snippet": "Today Proximal policy optimization ( PPO ) (will implement in homework) Generalized Advantage Estimation (GAE) Theory: Monotonic Improvement Theory", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs234/CS234Spr2024/slides/lecture7pre.pdf", "content": "Today Proximal policy optimization ( PPO ) (will implement in homework) Generalized Advantage Estimation (GAE) Theory: Monotonic Improvement Theory"}
+{"idx": 6, "title": "Proximal Policy Optimization (PPO): Why I Use It, How I ...", "date": "", "ddg_snippet": "Jul 16, 2025 · From Theory to Code: Why PPO Is My Reinforcement Learning Baseline PPO sits at the sweet spot between theoretical rigor and implementation simplicity. Like TRPO, it tries to take large but safe steps toward policy improvement, but it uses first-order optimization and clever clipping tricks instead of second-order constraints.", "subpage_snippet": "", "source": "aiarts.medium.com", "link": "https://aiarts.medium.com/proximal-policy-optimization-ppo-why-i-use-it-how-i-implement-it-and-what-im-learning-from-it-70bcd4a05969", "content": "Jul 16, 2025 · From Theory to Code: Why PPO Is My Reinforcement Learning Baseline PPO sits at the sweet spot between theoretical rigor and implementation simplicity. Like TRPO, it tries to take large but safe steps toward policy improvement, but it uses first-order optimization and clever clipping tricks instead of second-order constraints."}
+{"idx": 7, "title": "Shed Some Light on Proximal Policy Optimization (PPO) and Its ...", "date": "", "ddg_snippet": "May 31, 2025 · Proximal Policy Optimization ( PPO ) is a reinforcement learning algorithm that refines policy gradient methods like REINFORCE using importance sampling and a clipped surrogate objective to stabilize updates. PPO -Penalty explicitly penalizes KL divergence in the objective function, and PPO -Clip instead uses clipping to prevent large policy updates. In many robotics tasks, PPO is first used to ...", "subpage_snippet": "", "source": "lihanlian.github.io", "link": "https://lihanlian.github.io/posts/blog7", "content": "May 31, 2025 · Proximal Policy Optimization ( PPO ) is a reinforcement learning algorithm that refines policy gradient methods like REINFORCE using importance sampling and a clipped surrogate objective to stabilize updates. PPO -Penalty explicitly penalizes KL divergence in the objective function, and PPO -Clip instead uses clipping to prevent large policy updates. In many robotics tasks, PPO is first used to ..."}
+{"idx": 8, "title": "Proximal Policy Optimization ( PPO ) - PRIMO.ai", "date": "", "ddg_snippet": "YouTube ... Quora ...Google search ...Google News ...Bing News. Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization ... Policy Gradient ... Proximal Policy Optimization .", "subpage_snippet": "", "source": "primo.ai", "link": "https://primo.ai/index.php/Proximal_Policy_Optimization_(PPO)", "content": "YouTube ... Quora ...Google search ...Google News ...Bing News. Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization ... Policy Gradient ... Proximal Policy Optimization ."}
+{"idx": 9, "title": "Match or Replay: Self Imitating Proximal Policy Otimization", "date": "", "ddg_snippet": "• Self- imitating on - policy algorithm: We propose Self- Imitating Proximal Policy Optimization (SIPP), a novel self- imitation learning algorithm that enhances exploration and sample efficiency in dense and sparse reward settings.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=54TO3KUoYf", "content": "• Self- imitating on - policy algorithm: We propose Self- Imitating Proximal Policy Optimization (SIPP), a novel self- imitation learning algorithm that enhances exploration and sample efficiency in dense and sparse reward settings."}
diff --git a/data/sampled_jsons/PPO_Schulman_2017_experimental_results_section_baseline_comparisons_Atari_year_2017.jsonl b/data/sampled_jsons/PPO_Schulman_2017_experimental_results_section_baseline_comparisons_Atari_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..694004b04fb064ac045b93e799c05d3273bdfcbb
--- /dev/null
+++ b/data/sampled_jsons/PPO_Schulman_2017_experimental_results_section_baseline_comparisons_Atari_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg ...", "date": "", "ddg_snippet": "Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games. Figure 6 shows the learning curves of each of three random seeds, while Table 6 shows the mean performance. 10", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/also/Shulman-2017.pdf", "content": "Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games. Figure 6 shows the learning curves of each of three random seeds, while Table 6 shows the mean performance. 10"}
+{"idx": 1, "title": "RIMs-PPO relative score improvement over LSTM-PPO baseline (Schulman et ...", "date": "", "ddg_snippet": "RIMs- PPO relative score improvement over LSTM- PPO baseline ( Schulman et al., 2017 ) across all Atari games averaged over 3 trials per game. In both cases, PPO was used with the exact same settings ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/RIMs-PPO-relative-score-improvement-over-LSTM-PPO-baseline-Schulman-et-al-2017-across_fig6_336019045", "content": "RIMs- PPO relative score improvement over LSTM- PPO baseline ( Schulman et al., 2017 ) across all Atari games averaged over 3 trials per game. In both cases, PPO was used with the exact same settings ..."}
+{"idx": 2, "title": "GitHub - lucaslingle/pytorch_ppo_atari: Implementation of Proximal ...", "date": "", "ddg_snippet": "Proximal Policy Optimization is a reinforcement learning algorithm proposed by Schulman et al., 2017 . Compared to vanilla policy gradients and/or actor-critic methods, which optimize the model parameters by estimating the gradient of the reward surface and taking a single step, PPO takes inspiration ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lucaslingle/pytorch_ppo_atari", "content": "Proximal Policy Optimization is a reinforcement learning algorithm proposed by Schulman et al., 2017 . Compared to vanilla policy gradients and/or actor-critic methods, which optimize the model parameters by estimating the gradient of the reward surface and taking a single step, PPO takes inspiration ..."}
+{"idx": 3, "title": "Atari Games with Proximal Policy Optimization - Medium", "date": "", "ddg_snippet": "The first of all we need to explain \"What's Atari Games ?\": Atari console, video game console released in 1977 by the North American game manufacturer Atari , Inc. Using a cartridge-based ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@shogulomkurganov73/atari-games-with-proximal-policy-optimization-ed28c7fafa3f", "content": "The first of all we need to explain \"What's Atari Games ?\": Atari console, video game console released in 1977 by the North American game manufacturer Atari , Inc. Using a cartridge-based ..."}
+{"idx": 4, "title": "A Graphic Guide to Implementing PPO for Atari Games", "date": "", "ddg_snippet": "Implementing the Code Hopefully, the theory section was adequate to give some intuition about how an agent takes actions, the model is updated, why PPO works and why the clipping and probability ratio are required. The following section explains how we implemented PPO for ourselves - some of it might be obvious some not. However, it's probably helpful to go through everything if you want ...", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/a-graphic-guide-to-implementing-ppo-for-atari-games-5740ccbe3fbc/", "content": "Implementing the Code Hopefully, the theory section was adequate to give some intuition about how an agent takes actions, the model is updated, why PPO works and why the clipping and probability ratio are required. The following section explains how we implemented PPO for ourselves - some of it might be obvious some not. However, it's probably helpful to go through everything if you want ..."}
+{"idx": 5, "title": "️ PPO on Atari - RL", "date": "", "ddg_snippet": "Modern PPO on Atari Environments ¶ Objective ¶ The aim of this notebook is to construct a Proximal Policy Optimization ( PPO ) algorithm tailored for Atari environments - classic arcade games converted into test beds for reinforcement learning algorithms.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/code/auxeno/ppo-on-atari-rl", "content": "Modern PPO on Atari Environments ¶ Objective ¶ The aim of this notebook is to construct a Proximal Policy Optimization ( PPO ) algorithm tailored for Atari environments - classic arcade games converted into test beds for reinforcement learning algorithms."}
+{"idx": 6, "title": "A Comparative Study of Deep Reinforcement Learning Models: Dqn Vs Ppo ...", "date": "", "ddg_snippet": "In this comprehensive study, we explore the complex domain of Deep Reinforcement Learning (DRL), focusing on a thorough comparison of three well-known models: Deep Q-Networks (DQN), Proximal Policy Optimization ( PPO ), and Advantage Actor-Critic (A2C), specifically within the BreakOut Atari game environment. To ensure consistency and robustness in our experiments, we exclusively utilized the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.14151v1", "content": "In this comprehensive study, we explore the complex domain of Deep Reinforcement Learning (DRL), focusing on a thorough comparison of three well-known models: Deep Q-Networks (DQN), Proximal Policy Optimization ( PPO ), and Advantage Actor-Critic (A2C), specifically within the BreakOut Atari game environment. To ensure consistency and robustness in our experiments, we exclusively utilized the ..."}
+{"idx": 7, "title": "Advancements in PPO", "date": "", "ddg_snippet": "Advancements in PPO Introduction Proximal Policy Optimisation ( Schulman et al., 2017 ) is the leading algorithm for training reinforcement learning models. As well as other tasks, PPO plays a huge role in applying RLHF to LLMs (Ouyang et al., 2022).", "subpage_snippet": "", "source": "go281.user.srcf.net", "link": "https://go281.user.srcf.net/blog/research/ppo/", "content": "Advancements in PPO Introduction Proximal Policy Optimisation ( Schulman et al., 2017 ) is the leading algorithm for training reinforcement learning models. As well as other tasks, PPO plays a huge role in applying RLHF to LLMs (Ouyang et al., 2022)."}
+{"idx": 8, "title": "rl/sota-implementations/ppo/ppo_atari.py at main · pytorch/rl", "date": "", "ddg_snippet": "\"\"\" This script reproduces the Proximal Policy Optimization ( PPO ) Algorithm results from Schulman et al. 2017 for the Atari Environments. \"\"\" from __future__ import annotations import warnings import hydra from torchrl._utils import compile_with_warmup @hydra.main (config_path=\"\", config_name=\"config_atari\", version_base=\"1.1\") def main (cfg ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pytorch/rl/blob/main/sota-implementations/ppo/ppo_atari.py", "content": "\"\"\" This script reproduces the Proximal Policy Optimization ( PPO ) Algorithm results from Schulman et al. 2017 for the Atari Environments. \"\"\" from __future__ import annotations import warnings import hydra from torchrl._utils import compile_with_warmup @hydra.main (config_path=\"\", config_name=\"config_atari\", version_base=\"1.1\") def main (cfg ..."}
+{"idx": 9, "title": "[1707.06347] Proximal Policy Optimization Algorithms - arXiv.org", "date": "", "ddg_snippet": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1707.06347", "content": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."}
diff --git a/data/sampled_jsons/PPO_variants_loss_functions_RLHF_robustness_before_2025.jsonl b/data/sampled_jsons/PPO_variants_loss_functions_RLHF_robustness_before_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4165b699cfb5162be44c916e344b95cd5e7afd84
--- /dev/null
+++ b/data/sampled_jsons/PPO_variants_loss_functions_RLHF_robustness_before_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) REINFORCE++: An Efficient RLHF Algorithm with", "date": "", "ddg_snippet": "While state-of-the-art applications like ChatGPT or GPT-4 commonly employ Proximal Policy Optimization ( PPO ), the inclusion of a critic network ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387487679_REINFORCE_An_Efficient_RLHF_Algorithm_with_Robustnessto_Both_Prompt_and_Reward_Models", "content": "While state-of-the-art applications like ChatGPT or GPT-4 commonly employ Proximal Policy Optimization ( PPO ), the inclusion of a critic network ..."}
+{"idx": 1, "title": "How Good Are the Latest Open LLMs? And Is DPO Better Than PPO?", "date": "", "ddg_snippet": "... and alignment, researchers usually choose between using reinforcement learning with human feedback ( RLHF ) via proximal policy optimization ( PPO ) or ...", "subpage_snippet": "", "source": "magazine.sebastianraschka.com", "link": "https://magazine.sebastianraschka.com/p/how-good-are-the-latest-open-llms", "content": "... and alignment, researchers usually choose between using reinforcement learning with human feedback ( RLHF ) via proximal policy optimization ( PPO ) or ..."}
+{"idx": 2, "title": "RLHF 201 - with Nathan Lambert of AI2 and Interconnects", "date": "", "ddg_snippet": "... function that measures what the best decision would be; the fact that this function exists, makes it possible for RLHF to model human preferences and ...", "subpage_snippet": "", "source": "www.latent.space", "link": "https://www.latent.space/p/rlhf-201", "content": "... function that measures what the best decision would be; the fact that this function exists, makes it possible for RLHF to model human preferences and ..."}
+{"idx": 3, "title": "The Story of RLHF: Origins, Motivations, Techniques, and Modern", "date": "", "ddg_snippet": "... generative LLMs are trained via a pipeline that includes pretraining, supervised finetuning (SFT), reinforcement learning from human feedback ( RLHF ...", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/the-story-of-rlhf-origins-motivations", "content": "... generative LLMs are trained via a pipeline that includes pretraining, supervised finetuning (SFT), reinforcement learning from human feedback ( RLHF ..."}
+{"idx": 4, "title": "VRPO: Rethinking Value Modeling for Robust RL Training under", "date": "", "ddg_snippet": "Our findings underscore the often-overlooked importance of the value model in RLHF and offer a principled and practical approach to robust policy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03058v1", "content": "Our findings underscore the often-overlooked importance of the value model in RLHF and offer a principled and practical approach to robust policy ..."}
+{"idx": 5, "title": "Doubly Robust Alignment for Large Language Models", "date": "", "ddg_snippet": "Our work is closely related to reward- and preference-based RLHF algorithms, as well as doubly robust (DR) methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.01183v1", "content": "Our work is closely related to reward- and preference-based RLHF algorithms, as well as doubly robust (DR) methods."}
+{"idx": 6, "title": "The State of Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "In RLHF Step 3, the final stage, we are now updating the SFT model using proximal policy optimization ( PPO ) based on the reward scores from the ...", "subpage_snippet": "", "source": "magazine.sebastianraschka.com", "link": "https://magazine.sebastianraschka.com/p/the-state-of-llm-reasoning-model-training", "content": "In RLHF Step 3, the final stage, we are now updating the SFT model using proximal policy optimization ( PPO ) based on the reward scores from the ..."}
+{"idx": 7, "title": "Guide to Reinforcement Finetuning - Analytics Vidhya", "date": "", "ddg_snippet": "Before diving into reinforcement finetuning, it ’ s better to get acquainted with reinforcement learning, as it is its primary principle.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/04/reinforcement-finetuning/", "content": "Before diving into reinforcement finetuning, it ’ s better to get acquainted with reinforcement learning, as it is its primary principle."}
+{"idx": 8, "title": "Interviewing Finbarr Timbers on the \"We are So Back\"", "date": "", "ddg_snippet": "Last year, we probably would have done PPO versus DPO, which ... The RLHF thing is like that you have this like environment that's a reward model.", "subpage_snippet": "", "source": "www.interconnects.ai", "link": "https://www.interconnects.ai/p/finbarr-timbers", "content": "Last year, we probably would have done PPO versus DPO, which ... The RLHF thing is like that you have this like environment that's a reward model."}
+{"idx": 9, "title": "Reward Hacking in Reinforcement Learning | Lil'Log", "date": "", "ddg_snippet": "Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without ...", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/", "content": "Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without ..."}
diff --git a/data/sampled_jsons/PS-EIP_Figure_7_Glossy_EventPS-FCN_Ours_8.12_13.66.jsonl b/data/sampled_jsons/PS-EIP_Figure_7_Glossy_EventPS-FCN_Ours_8.12_13.66.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a61c8bf39ba0af0723ca09a473d506ca9f14a34
--- /dev/null
+++ b/data/sampled_jsons/PS-EIP_Figure_7_Glossy_EventPS-FCN_Ours_8.12_13.66.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "Experiments show that EIP - PS effectively recovers normal maps from various shapes and materials, even in the presence of noise and non-Lambertian reections. The average MAEs for all 3D-printed objects was 8 . 12 for EIP - PS , in contract to EventPS , whi...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "Experiments show that EIP - PS effectively recovers normal maps from various shapes and materials, even in the presence of noise and non-Lambertian reections. The average MAEs for all 3D-printed objects was 8 . 12 for EIP - PS , in contract to EventPS , whi..."}
+{"idx": 1, "title": "CVPR 2025 collected by Wang", "date": "", "ddg_snippet": "Finally, using only 37M publicly available real and synthetic images, we train a 1.16 billion parameter sparse transformer with only 1,890 USD economical cost and achieve a 12. 7 FID in zero-shot generation on the COCO dataset.", "subpage_snippet": "", "source": "hongsong-wang.github.io", "link": "https://hongsong-wang.github.io/CVPR2025_ABSTRACT/", "content": "Finally, using only 37M publicly available real and synthetic images, we train a 1.16 billion parameter sparse transformer with only 1,890 USD economical cost and achieve a 12. 7 FID in zero-shot generation on the COCO dataset."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_equation_10.jsonl b/data/sampled_jsons/PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_equation_10.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2704034d560485c1a694812fc6327e800925b119
--- /dev/null
+++ b/data/sampled_jsons/PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_equation_10.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "tt t. Events . Event Interval Profile Fitting to.In this paper, we propose a robust method for Lamber-tian event - based photometric stereo , namely PS - EIP ( Pho - tometric Stereo based on Event Interval Prole) as illus-trated in Fig.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "tt t. Events . Event Interval Profile Fitting to.In this paper, we propose a robust method for Lamber-tian event - based photometric stereo , namely PS - EIP ( Pho - tometric Stereo based on Event Interval Prole) as illus-trated in Fig."}
+{"idx": 1, "title": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.18341", "content": "This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals ."}
+{"idx": 2, "title": "Procedure of the proposed method: Events are recorded under moving...", "date": "", "ddg_snippet": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile .Context 1. ... this paper, we propose a robust method for Lambertian event - based photometric stereo , namely PS - EIP ( Photometric Stereo based on Event Interval Profile ) as illustrated in Fig.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Procedure-of-the-proposed-method-Events-are-recorded-under-moving-light-conditions-The_fig1_390142307", "content": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile .Context 1. ... this paper, we propose a robust method for Lambertian event - based photometric stereo , namely PS - EIP ( Photometric Stereo based on Event Interval Profile ) as illustrated in Fig."}
+{"idx": 3, "title": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals .", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile@CVPR2025@CVF", "content": "This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals ."}
+{"idx": 4, "title": "GitHub - chakravarthi589/ Event - based -Vision_Resources: Resources...", "date": "", "ddg_snippet": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile [Paper]. Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset [Paper].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chakravarthi589/Event-based-Vision_Resources", "content": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile [Paper]. Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset [Paper]."}
+{"idx": 5, "title": "Research on robot target recognition based on deep learning-Bohrium", "date": "", "ddg_snippet": "Target detection based on deep learning is a research hotspot, and integrating it into industrial robot vision target detection can improve automation and intelligence levels.[3] PS - EIP : Robust Photometric Stereo Based on Event Interval Profile .", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/research-on-robot-target-recognition-based-on-deep-learning/812106909594157057-97787", "content": "Target detection based on deep learning is a research hotspot, and integrating it into industrial robot vision target detection can improve automation and intelligence levels.[3] PS - EIP : Robust Photometric Stereo Based on Event Interval Profile ."}
+{"idx": 6, "title": "Articles by Takahito Aoto | Synthical", "date": "", "ddg_snippet": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile . Event - Based Bispectral Photometry Using Temporally Modulated Illumination. 31 December 2020 by Tsuyoshi Takatani and others.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/6599942c-7d8c-4eb0-96ca-4591e27ab142/articles", "content": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile . Event - Based Bispectral Photometry Using Temporally Modulated Illumination. 31 December 2020 by Tsuyoshi Takatani and others."}
+{"idx": 7, "title": "Takahito Aoto - Google Akademik", "date": "", "ddg_snippet": "2017. Event - based bispectral photometry using temporally modulated illumination. PS - EIP : Robust Photometric Stereo Based on Event Interval Profile .", "subpage_snippet": "", "source": "scholar.google.com.eg", "link": "https://scholar.google.com.eg/citations?user=RJQgZyAAAAAJ&hl=tr", "content": "2017. Event - based bispectral photometry using temporally modulated illumination. PS - EIP : Robust Photometric Stereo Based on Event Interval Profile ."}
+{"idx": 8, "title": "Latest 15 Papers - March 27, 2025 - Githubissues", "date": "", "ddg_snippet": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile .EF-3DGS: Event -Aided Free-Trajectory 3D Gaussian Splatting.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/zezhishao/DailyArXiv/293", "content": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile .EF-3DGS: Event -Aided Free-Trajectory 3D Gaussian Splatting."}
+{"idx": 9, "title": "CCD Workshop 2025", "date": "", "ddg_snippet": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile .21. #199. Solving partial differential equations in participating media. Ioannis Gkioulekas.", "subpage_snippet": "", "source": "kristinamonakhova.com", "link": "https://kristinamonakhova.com/ccd2025/", "content": "PS - EIP : Robust Photometric Stereo Based on Event Interval Profile .21. #199. Solving partial differential equations in participating media. Ioannis Gkioulekas."}
diff --git a/data/sampled_jsons/PS-EIP_event_interval_profile_specular_reflection_outlier_detection_year_2024.jsonl b/data/sampled_jsons/PS-EIP_event_interval_profile_specular_reflection_outlier_detection_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2365550746d3c604b65abbb37cf756244127da11
--- /dev/null
+++ b/data/sampled_jsons/PS-EIP_event_interval_profile_specular_reflection_outlier_detection_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "By exploiting the continuity of the profile and introducing an outlier detection method based on profile shape, our approach enhances robustness against outliers from shadows and specular reflections .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.18341", "content": "By exploiting the continuity of the profile and introducing an outlier detection method based on profile shape, our approach enhances robustness against outliers from shadows and specular reflections ."}
+{"idx": 1, "title": "PDF PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "By exploiting the continuity of the profile and introducing an outlier detection method based on profile shape, our approach enhances ro-bustness against outliers from shadows and specular reflec-tions .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "By exploiting the continuity of the profile and introducing an outlier detection method based on profile shape, our approach enhances ro-bustness against outliers from shadows and specular reflec-tions ."}
+{"idx": 2, "title": "PDF Generic and real-time detection of specular reflections in imag", "date": "", "ddg_snippet": "high computational cost. Our method targets a large range of field of application without a priori on the lighting conditions running in real-time by using sim-ple but effective properties of specular reflections . In this paper we present the related methods used for specularity reflections detection by highlighting the different applications targeted and results along their limitations. These ...", "subpage_snippet": "", "source": "www.alexandremorgand.fr", "link": "http://www.alexandremorgand.fr/Morgand_Tamaazousti_VISAPP14.pdf", "content": "high computational cost. Our method targets a large range of field of application without a priori on the lighting conditions running in real-time by using sim-ple but effective properties of specular reflections . In this paper we present the related methods used for specularity reflections detection by highlighting the different applications targeted and results along their limitations. These ..."}
+{"idx": 3, "title": "PDF EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Abstract Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "Abstract Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ..."}
+{"idx": 4, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Experiments using real event data from 3D-printed objects demonstrate that PS - EIP significantly improves robustness to outliers compared to EventPS's deep-learning variant, EventPS-FCN, without relying on deep learning.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "Experiments using real event data from 3D-printed objects demonstrate that PS - EIP significantly improves robustness to outliers compared to EventPS's deep-learning variant, EventPS-FCN, without relying on deep learning."}
+{"idx": 5, "title": "CVPR 2024最佳论文分享┆EventPS: 基于事件相机的实时光度立体视觉", "date": "", "ddg_snippet": "该论文的第一作者为于博涵（北京大学计算机学院2021级直博生，导师：施柏鑫）。 论文提出了一种使用事件相机的实时光度立体视觉（Photometric Stereo, PS ）新方法。 传统的 PS 方法需要在不同照明条件下捕捉多张高动态范围图像，这既耗时又需要高数据带宽。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/audyxiao001/article/details/140520624", "content": "该论文的第一作者为于博涵（北京大学计算机学院2021级直博生，导师：施柏鑫）。 论文提出了一种使用事件相机的实时光度立体视觉（Photometric Stereo, PS ）新方法。 传统的 PS 方法需要在不同照明条件下捕捉多张高动态范围图像，这既耗时又需要高数据带宽。"}
+{"idx": 6, "title": "GitHub - yasumat/RobustPhotometricStereo: Robust Photometric Stereo", "date": "", "ddg_snippet": "Conventional Photometric Stereo is based on least-square regression (or L2 residual minimization), which is susceptible to large outliers . For example, when a Lambertian reflectance and local illumination model are assumed, specular high-lights and cast shadows are regarded as outliers , causing inaccurate estimates of surface normal.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yasumat/RobustPhotometricStereo", "content": "Conventional Photometric Stereo is based on least-square regression (or L2 residual minimization), which is susceptible to large outliers . For example, when a Lambertian reflectance and local illumination model are assumed, specular high-lights and cast shadows are regarded as outliers , causing inaccurate estimates of surface normal."}
+{"idx": 7, "title": "Papers by Satoshi Ikehata - aimodels.fyi", "date": "", "ddg_snippet": "The key technical innovation is the profilebased outlier detection mechanism. The researchers observed that event interval profiles from Lambertian surfaces follow predictable patterns, while outliers from shadows and specular reflections create distinctive anomalies in these profiles .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/authors/arxiv/Satoshi+Ikehata", "content": "The key technical innovation is the profilebased outlier detection mechanism. The researchers observed that event interval profiles from Lambertian surfaces follow predictable patterns, while outliers from shadows and specular reflections create distinctive anomalies in these profiles ."}
+{"idx": 8, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections . This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.html", "content": "However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections . This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals ."}
+{"idx": 9, "title": "GitHub - yqueau/robust_ps: Matlab codes for a robust non-convex ...", "date": "", "ddg_snippet": "Matlab codes for a robust non-convex variational approach to photometric stereo under inaccurate lighting - yqueau/robust_ps", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yqueau/robust_ps", "content": "Matlab codes for a robust non-convex variational approach to photometric stereo under inaccurate lighting - yqueau/robust_ps"}
diff --git a/data/sampled_jsons/Papamakarios_et_al._2017_Masked_Autoregressive_Flow_abstract_year_2017.jsonl b/data/sampled_jsons/Papamakarios_et_al._2017_Masked_Autoregressive_Flow_abstract_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f599a5298c65bb0f8fa0c179464c1786f6838022
--- /dev/null
+++ b/data/sampled_jsons/Papamakarios_et_al._2017_Masked_Autoregressive_Flow_abstract_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2017/hash/6c1da886822c67822bcf3679d04369fa-Abstract.html", "content": "By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow ."}
+{"idx": 1, "title": "[1705.07057] Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks.View a PDF of the paper titled Masked Autoregressive Flow for Density Estimation, by George Papamakarios and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1705.07057", "content": "Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks.View a PDF of the paper titled Masked Autoregressive Flow for Density Estimation, by George Papamakarios and 2 other authors."}
+{"idx": 2, "title": "Maksed Autoregressive Flow for Density", "date": "", "ddg_snippet": "introduce MAF ( Masked Autoregressive Flow ). by stacking autoregressive models ( like a Normalizing flow ) closely related to IAF & generalization of Real NVP.", "subpage_snippet": "", "source": "seunghan96.github.io", "link": "https://seunghan96.github.io/assets/pdf/BNN/review/[review]34.Masked+Autoregressive+Flow+for+Density+Estimation+(2017).pdf", "content": "introduce MAF ( Masked Autoregressive Flow ). by stacking autoregressive models ( like a Normalizing flow ) closely related to IAF & generalization of Real NVP."}
+{"idx": 3, "title": "Neural Autoregressive Flows", "date": "", "ddg_snippet": "( 2017 ) and then Papamakarios et al . ( 2017 ) sub-sequently noticed that this same approach could be used efciently in reverse when the key operation is evaluating, as opposed to sampling from, the ow’s learned output density.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v80/huang18d/huang18d.pdf", "content": "( 2017 ) and then Papamakarios et al . ( 2017 ) sub-sequently noticed that this same approach could be used efciently in reverse when the key operation is evaluating, as opposed to sampling from, the ow’s learned output density."}
+{"idx": 4, "title": "Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "Papamakarios et al . [17] introduced masked autoregressive flows for gravitational wave parameter estimation, demonstrating improved sampling efficiency compared to traditional MCMC methods.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/317040628_Masked_Autoregressive_Flow_for_Density_Estimation", "content": "Papamakarios et al . [17] introduced masked autoregressive flows for gravitational wave parameter estimation, demonstrating improved sampling efficiency compared to traditional MCMC methods."}
+{"idx": 5, "title": "e-hulten/maf: PyTorch implementation of the Masked Autoregressive ...", "date": "", "ddg_snippet": "Masked Autoregressive Flow with PyTorch. This is a PyTorch implementation of the masked autoregressive flow (MAF) by Papamakarios et al . [1]. The Gaussian MADE that makes up each layer in the MAF is found in MADE.py, while the MAF itself is found in ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/e-hulten/maf", "content": "Masked Autoregressive Flow with PyTorch. This is a PyTorch implementation of the masked autoregressive flow (MAF) by Papamakarios et al . [1]. The Gaussian MADE that makes up each layer in the MAF is found in MADE.py, while the MAF itself is found in ..."}
+{"idx": 6, "title": "Simplest Masked AutoRegressive Flow [MAF] Code Discussion", "date": "", "ddg_snippet": "Смотрите видео онлайн «Simplest Masked AutoRegressive Flow [MAF] Code Discussion» на канале «Calcul Cocotte» в хорошем качестве и бесплатно, опубликованное 21 января 2025 года в 8:02, длительностью 00:12:33, на видеохостинге RUTUBE.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/62c7e71d1bfd97372d94b140554acd98/", "content": "Смотрите видео онлайн «Simplest Masked AutoRegressive Flow [MAF] Code Discussion» на канале «Calcul Cocotte» в хорошем качестве и бесплатно, опубликованное 21 января 2025 года в 8:02, длительностью 00:12:33, на видеохостинге RUTUBE."}
+{"idx": 7, "title": "Posit AI Blog: Experimenting with autoregressive flows in TensorFlow...", "date": "", "ddg_snippet": "Masked Autoregressive Flow . The MAF paper( Papamakarios , Pavlakou, and Murray 2017 ) applied masked autoregressive flows (as well as single-layer-MADE(Germain et al .", "subpage_snippet": "", "source": "blogs.rstudio.com", "link": "https://blogs.rstudio.com/ai/posts/2019-04-24-autoregressive-flows/", "content": "Masked Autoregressive Flow . The MAF paper( Papamakarios , Pavlakou, and Murray 2017 ) applied masked autoregressive flows (as well as single-layer-MADE(Germain et al ."}
+{"idx": 8, "title": "ICML Poster Neural Autoregressive Flows", "date": "", "ddg_snippet": "Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF) ( Papamakarios et al ., 2017 ), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2018/poster/2394", "content": "Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF) ( Papamakarios et al ., 2017 ), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster..."}
+{"idx": 9, "title": "Anomalous sound detection with masked autoregressive flows and", "date": "", "ddg_snippet": "Papamakarios et al . [4] state that there are two families of neural density estimators that are both exible and tractable: autoregressive models and normalizing ows. Certain au - toregressive models (when the underlying transformation is invert-ible) can be viewed as normalizing ows.", "subpage_snippet": "", "source": "dcase.community", "link": "https://dcase.community/documents/challenge2020/technical_reports/DCASE2020_Haunschmid_17_t2.pdf", "content": "Papamakarios et al . [4] state that there are two families of neural density estimators that are both exible and tractable: autoregressive models and normalizing ows. Certain au - toregressive models (when the underlying transformation is invert-ible) can be viewed as normalizing ows."}
diff --git a/data/sampled_jsons/PaperDigest_WWW_2024_Information_Retrieval_statistics_year_2024.jsonl b/data/sampled_jsons/PaperDigest_WWW_2024_Information_Retrieval_statistics_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..daf264884c20119b033c4a86133b29e9ac20f69b
--- /dev/null
+++ b/data/sampled_jsons/PaperDigest_WWW_2024_Information_Retrieval_statistics_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "List of countries and territories by motor vehicles per capita - Wikipedia", "date": "", "ddg_snippet": "Microstates such as San Marino, Andorra and Liechtenstein have high official rates of car ownership. Countries and territories listed by the number of road motor vehicles per 1,000 inhabitants are as follows. Population figures are from the United Na...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/List_of_countries_and_territories_by_motor_vehicles_per_capita", "content": "Microstates such as San Marino, Andorra and Liechtenstein have high official rates of car ownership. Countries and territories listed by the number of road motor vehicles per 1,000 inhabitants are as follows. Population figures are from the United Na..."}
+{"idx": 1, "title": "Most Influential Information Retrieval Papers ( 2024 –10)... - Medium", "date": "", "ddg_snippet": "Dec 14, 2024 . Innovations in Quantum-Like Multi-Sensory Integration for Robotics. Danny H Lee.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danny_54172/most-influential-information-retrieval-papers-2024-10-4453398ac0f9", "content": "Dec 14, 2024 . Innovations in Quantum-Like Multi-Sensory Integration for Robotics. Danny H Lee."}
+{"idx": 2, "title": "What is Information Retrieval ? | A Comprehensive... | Elastic", "date": "", "ddg_snippet": "Types of information retrieval modelsWhy is information retrieval important?Because it works with weighted statistics , the model is an ideal fit for providing ranked results.", "subpage_snippet": "", "source": "www.elastic.co", "link": "https://www.elastic.co/what-is/information-retrieval", "content": "Types of information retrieval modelsWhy is information retrieval important?Because it works with weighted statistics , the model is an ideal fit for providing ranked results."}
+{"idx": 3, "title": "Paper Digest & GPTs for Academic Research Like Paper Digest ( 2024 )", "date": "", "ddg_snippet": "Paper Digest . This symbol indicates that the GPT's builder has linked a verified domain or social media account to their profile. Conduct realistic test simulations and get feedback on the latest IELTS topics (May to August 2024 ).\"How to us...", "subpage_snippet": "", "source": "www.whatplugin.ai", "link": "https://www.whatplugin.ai/gpts/paper-digest", "content": "Paper Digest . This symbol indicates that the GPT's builder has linked a verified domain or social media account to their profile. Conduct realistic test simulations and get feedback on the latest IELTS topics (May to August 2024 ).\"How to us..."}
+{"idx": 4, "title": "InteGround: On the Evaluation of Verification and Retrieval Planning in...", "date": "", "ddg_snippet": "(2022); Shinn et al. ( 2024 ) provide another perspective on this setting, where planning is integrated to proactively intervene retrieval processes. The objective of planning is to increase the success rate of grounding, i.e., biasing the search space so that it is more likely for an informative set.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16534v1", "content": "(2022); Shinn et al. ( 2024 ) provide another perspective on this setting, where planning is integrated to proactively intervene retrieval processes. The objective of planning is to increase the success rate of grounding, i.e., biasing the search space so that it is more likely for an informative set."}
+{"idx": 5, "title": "Beyond MedSearch: Exploring New Frontiers in Medical Information ...", "date": "", "ddg_snippet": "December 10, 2024 Category: Blog. MedSearch has revolutionized clinical information retrieval , providing researchers and clinicians with a valuable tool. However, the landscape of medical knowledge is constantly evolving, demanding innovative approaches to keep pace.", "subpage_snippet": "", "source": "amaanyqnl634433.ja-blog.com", "link": "https://amaanyqnl634433.ja-blog.com/", "content": "December 10, 2024 Category: Blog. MedSearch has revolutionized clinical information retrieval , providing researchers and clinicians with a valuable tool. However, the landscape of medical knowledge is constantly evolving, demanding innovative approaches to keep pace."}
+{"idx": 6, "title": "Paper Digest – AI-Powered Research Platform", "date": "", "ddg_snippet": "Daily Paper Digest . Follow papers by area, author, keyword and receive daily updates. 2024 -10: Digest of all ACM Multimedia- 2024 papers | acm multimedia- 2024 console.", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/", "content": "Daily Paper Digest . Follow papers by area, author, keyword and receive daily updates. 2024 -10: Digest of all ACM Multimedia- 2024 papers | acm multimedia- 2024 console."}
+{"idx": 7, "title": "Paper Digest -Free AI-powered paper summaries", "date": "", "ddg_snippet": "How does Paper Digest extract key information ? Paper Digest uses advanced natural language processing algorithms to extract key points and summaries from research papers, ensuring users get the most relevant information swiftly.", "subpage_snippet": "", "source": "aichatonline.org", "link": "https://aichatonline.org/gpts-2OToEhqXE1-Paper-Digest", "content": "How does Paper Digest extract key information ? Paper Digest uses advanced natural language processing algorithms to extract key points and summaries from research papers, ensuring users get the most relevant information swiftly."}
+{"idx": 8, "title": "Latent Learning and Episodic Memory in AI", "date": "", "ddg_snippet": "This paper shows how retrieval -based episodic memory complements parametric learning to overcome latent learning limitations in both supervised and RL tasks.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2509.16189", "content": "This paper shows how retrieval -based episodic memory complements parametric learning to overcome latent learning limitations in both supervised and RL tasks."}
+{"idx": 9, "title": "Картинки Google", "date": "", "ddg_snippet": "Firefly-Serenity Chinese frequently asked questions and dictionary information .", "subpage_snippet": "", "source": "images.google.com", "link": "https://images.google.com/", "content": "Firefly-Serenity Chinese frequently asked questions and dictionary information ."}
diff --git a/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_arXiv_PDF.jsonl b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_arXiv_PDF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..64254cd6d4ac8b3c66643f67c165edba3f4e37cf
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+{"idx": 0, "title": "Parallels: Mac & Windows Virtualization, Remote Application...", "date": "", "ddg_snippet": "Download Parallels to run Windows on Mac, Chrome, gain access to virtual desktop infrastructure (VDI) with DaaS, & Toolbox to secure private files, & more.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/", "content": "Download Parallels to run Windows on Mac, Chrome, gain access to virtual desktop infrastructure (VDI) with DaaS, & Toolbox to secure private files, & more."}
+{"idx": 1, "title": "Run Windows on Mac with a virtual machine | Parallels Desktop", "date": "", "ddg_snippet": "Run Windows applications on your Mac effortlessly. Easy. Powerful. Seamless. Parallels ® Desktop for Mac Maximize your Mac's potential by running Windows and Windows applications on a virtual machine. Access over 200,000 apps to work, study and play effortlessly. Authorized by Microsoft.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/products/desktop/", "content": "Run Windows applications on your Mac effortlessly. Easy. Powerful. Seamless. Parallels ® Desktop for Mac Maximize your Mac's potential by running Windows and Windows applications on a virtual machine. Access over 200,000 apps to work, study and play effortlessly. Authorized by Microsoft."}
+{"idx": 2, "title": "Download Parallels Client, 2X RDP Client | Parallels RAS", "date": "", "ddg_snippet": "Parallels Client (formerly 2X RDP Client), when connected to Parallels Remote Application Server, provides secure access to business applications, virtual desktops, and data from your device. Download!", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/products/ras/download/client/", "content": "Parallels Client (formerly 2X RDP Client), when connected to Parallels Remote Application Server, provides secure access to business applications, virtual desktops, and data from your device. Download!"}
+{"idx": 3, "title": "Login - Parallels My Account", "date": "", "ddg_snippet": "Your personal account at Parallels: manage your Parallels product licenses, get technical support, ask questions on the Parallels product forums.", "subpage_snippet": "", "source": "my.parallels.com", "link": "https://my.parallels.com/", "content": "Your personal account at Parallels: manage your Parallels product licenses, get technical support, ask questions on the Parallels product forums."}
+{"idx": 4, "title": "Downloads - Install Parallels Desktop | Parallels", "date": "", "ddg_snippet": "Downloads for Parallels Desktop Run Windows applications on your Mac — without rebooting Try free for 14 days.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/products/desktop/download/", "content": "Downloads for Parallels Desktop Run Windows applications on your Mac — without rebooting Try free for 14 days."}
+{"idx": 5, "title": "Running TikTok Live Studio in Parallels Desktop on Apple silicon", "date": "", "ddg_snippet": "Jan 20, 2025 · While the application documentation does not explicitly state Arm-processor type support, according to our recent internal tests, TikTok Live Studio software can be run in Windows 11 Arm virtual machine under Parallels. If you have experienced issues installing the application previously, please check if it is running under the following conditions: 1. Parallels Desktop is v20.2.0 or later 2 ...", "subpage_snippet": "", "source": "kb.parallels.com", "link": "https://kb.parallels.com/en/130925", "content": "Jan 20, 2025 · While the application documentation does not explicitly state Arm-processor type support, according to our recent internal tests, TikTok Live Studio software can be run in Windows 11 Arm virtual machine under Parallels. If you have experienced issues installing the application previously, please check if it is running under the following conditions: 1. Parallels Desktop is v20.2.0 or later 2 ..."}
+{"idx": 6, "title": "About Us | Parallels", "date": "", "ddg_snippet": "Parallels Company Profile Parallels ® is a global leader in cross-platform solutions, enabling businesses and individuals to access and use the applications and files they need on any device or operating system. Parallels helps customers leverage the best technology available, whether it’s Windows, Linux, macOS, iOS, Android or the cloud. Parallels works with businesses, public sector ...", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/about/", "content": "Parallels Company Profile Parallels ® is a global leader in cross-platform solutions, enabling businesses and individuals to access and use the applications and files they need on any device or operating system. Parallels helps customers leverage the best technology available, whether it’s Windows, Linux, macOS, iOS, Android or the cloud. Parallels works with businesses, public sector ..."}
+{"idx": 7, "title": "Welcome to Parallels Support", "date": "", "ddg_snippet": "Welcome to Parallels Support Click below for frequently asked questions and other helpful tools to get the answer you need.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/support/cb/", "content": "Welcome to Parallels Support Click below for frequently asked questions and other helpful tools to get the answer you need."}
+{"idx": 8, "title": "Getting started with Parallels Desktop: How to install & run...", "date": "", "ddg_snippet": "Jul 18, 2025 · Learn how to install Parallels Desktop and run Windows on Mac, get Mac user pro tips, & explore ways to boost performance + optimize your Mac for work, school, and gaming.", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/blogs/getting-started-tips/", "content": "Jul 18, 2025 · Learn how to install Parallels Desktop and run Windows on Mac, get Mac user pro tips, & explore ways to boost performance + optimize your Mac for work, school, and gaming."}
+{"idx": 9, "title": "Buy Parallels Desktop for Mac | Apple M1, M2, M-series compatible", "date": "", "ddg_snippet": "Get our latest version with over 20 powerful new features to boost performance and productivity. Optimized for Apple M1 and M2 series chips and ready for macOS Ventura (when released).", "subpage_snippet": "", "source": "www.parallels.com", "link": "https://www.parallels.com/products/desktop/buy/", "content": "Get our latest version with over 20 powerful new features to boost performance and productivity. Optimized for Apple M1 and M2 series chips and ready for macOS Ventura (when released)."}
diff --git a/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_diagonal_update_all_gr.jsonl b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_diagonal_update_all_gr.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..730d0b1a6e3cf589ae41f9c75f25773571066325
--- /dev/null
+++ b/data/sampled_jsons/Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_diagonal_update_all_gr.jsonl
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+{"idx": 1, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks ... Optimization: Models , Insights, and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks ... Optimization: Models , Insights, and ..."}
+{"idx": 2, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "... for Hallucination Mitigation in Multimodal ... IMPACT: Iterative Mask- based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "... for Hallucination Mitigation in Multimodal ... IMPACT: Iterative Mask- based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling"}
+{"idx": 3, "title": "Downloads", "date": "", "ddg_snippet": "A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model - Based Reinforcement Learning ... and Normalizing Flow Toward ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model - Based Reinforcement Learning ... and Normalizing Flow Toward ..."}
+{"idx": 4, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling ... between Simulation and Diffusion for ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling ... between Simulation and Diffusion for ..."}
+{"idx": 5, "title": "Downloads", "date": "", "ddg_snippet": "Accelerated Primal-Dual Gradient Method for Smooth and Convex- Concave Saddle-Point Problems with Bilinear Coupling ... sampling method with complexity ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2022", "content": "Accelerated Primal-Dual Gradient Method for Smooth and Convex- Concave Saddle-Point Problems with Bilinear Coupling ... sampling method with complexity ..."}
+{"idx": 6, "title": "Downloads", "date": "", "ddg_snippet": "... for Non-Smooth Non-Convex ... A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "... for Non-Smooth Non-Convex ... A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI"}
+{"idx": 7, "title": "Wasserstein Policy Optimization", "date": "", "ddg_snippet": "... a policy update derived from the classic policy gradient theorem for stochastic policies (Sutton et al., 1999 ) , which applies to both discrete and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00663v1", "content": "... a policy update derived from the classic policy gradient theorem for stochastic policies (Sutton et al., 1999 ) , which applies to both discrete and ..."}
+{"idx": 8, "title": "Debian -- Software Packages in \"bullseye\", Subsection gnu-r", "date": "", "ddg_snippet": "GNU R Interface to BioMart databases (Ensembl, COSMIC, Wormbase and Gramene) ... base package for enabling powerful shiny web displays of ...", "subpage_snippet": "", "source": "packages.debian.org", "link": "https://packages.debian.org/bullseye/gnu-r/", "content": "GNU R Interface to BioMart databases (Ensembl, COSMIC, Wormbase and Gramene) ... base package for enabling powerful shiny web displays of ..."}
+{"idx": 9, "title": "Debian -- Software Packages in \"bullseye\", Subsection gnu-r", "date": "", "ddg_snippet": "GNU R Interface to BioMart databases (Ensembl, COSMIC, Wormbase and Gramene) ... base package for enabling powerful shiny web displays of ...", "subpage_snippet": "", "source": "packages.debian.org", "link": "https://packages.debian.org/bullseye/mips64el/gnu-r/", "content": "GNU R Interface to BioMart databases (Ensembl, COSMIC, Wormbase and Gramene) ... base package for enabling powerful shiny web displays of ..."}
diff --git a/data/sampled_jsons/Pathak_FourCastNet_2022_paper_evaluation_metrics_RMSE_MSE_what_metrics_used_year_2022.jsonl b/data/sampled_jsons/Pathak_FourCastNet_2022_paper_evaluation_metrics_RMSE_MSE_what_metrics_used_year_2022.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2202.11214] FourCastNet: A Global Data-driven High ... MSE vs RMSE vs MAE vs MAPE vs R-Squared: When to Use? Which Forecast Accuracy Measures Should You Use (and When ... Time Series Evaluation Metrics: MAE, MSE, RMSE, MAPE FourCastNet: A Global Data-driven High-resolution Weather ... Metrics Evaluation : MSE , RMSE , MAE and MAPE - Medium Time Series Evaluation Metrics : MAE, MSE , RMSE , MAPE Metrics Evaluation : MSE , RMSE , MAE and MAPE - Medium Time Series Evaluation Metrics : MAE, MSE , RMSE , MAPE Metrics Evaluation : MSE , RMSE , MAE and MAPE - Medium Time Series Evaluation Metrics : MAE, MSE , RMSE , MAPE Evaluation Metrics in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Feb 22, 2022 · FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.25^{\\\\circ}$ resolution. FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor. It has important implications for ... Aug 18, 2024 · The following are different types of regression model evaluation metrics including MSE , RMSE , MAE, MAPE, R-squared, and Adjusted R-squared which get used in different scenarios when training the regression models to solve the desired problem in hand. Jun 3, 2025 · Key Takeaways for Busy People: Different metrics highlight different things. RMSE emphasizes large errors, MAE values consistency, and MAPE expresses errors as percentages. An Introduction to Statistical Learning with Applications in R (2nd edition), Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, 2021 (Springer) - A widely used textbook for statistical learning, providing clear explanations of fundamental regression metrics like MAE, MSE , and RMSE . Figure 10: Comparison of ACC and RMSE metrics between the (downsampled) FourCastNet predictions, (downsampled) IFS, and baseline state-of-the-art DLWP model [Weyn et al., 2020] for (a) Z500 and (b) T2m. We observe that the FourCastNet predictions show significant improvement over the baseline model. We also note that the FourCastNet generates predictions that have a higher resolution by a ... What are evaluation metrics for machine learning models? Additionally, by using these metrics, data scientists can identify areas where the model is performing below expectations and make adjustments to improve the accuracy of the predictions. The most common types of evaluation metrics for Machine Learning models are MSE, RMSE, MAE, and MAPE . Let’s explain what each acronym means. How do you measure the performance of a forecasting model? Once you've trained a forecasting model, such as ARIMA or SARIMA, you need to quantify its performance. Simply looking at plots isn't enough for objective comparison or reporting. Evaluation metrics provide a standardized way to measure how close your model's forecasts are to the actual observed values in your test dataset. What is RMSE metric? The use of this metric in a business environment includes employing MSE in evaluating stock forecasts and financial asset price predictions, to fine-tune investment strategies. RMSE is the square root of MSE . Mathematically, it measures the standard deviation of the error. What metrics are used in time series forecasting? Let's examine four common metrics used in time series forecasting. The Mean Absolute Error, or M A E M AE, represents the average absolute difference between the forecasts and the actual values. What is MAPE metric? MAPE calculates the average of the absolute percentage differences between the model’s predictions and the actual values. Therefore, this metric expresses the average error as a percentage of the actual value. MAPE penalizes negative errors more heavily (when the predicted value exceeds the actual). What are evaluation metrics? Evaluation metrics provide a standardized way to measure how close your model's forecasts are to the actual observed values in your test dataset. These metrics focus on the prediction errors, which are the differences between the actual values (A c t u a l i Actuali) and the forecasted values (F o r e c a s t i F orecasti) at each time step i i. Jul 15, 2025 · By mastering the appropriate evaluation metrics , we upgrade ourselves to fine-tune machine learning models which helps in ensuring they meet the needs of diverse applications and deliver optimal performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.11214", "content": "Feb 22, 2022 · FourCastNet , short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.25^{\\\\circ}$ resolution. FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor. It has important implications for ... Aug 18, 2024 · The following are different types of regression model evaluation metrics including MSE , RMSE , MAE, MAPE, R-squared, and Adjusted R-squared which get used in different scenarios when training the regression models to solve the desired problem in hand. Jun 3, 2025 · Key Takeaways for Busy People: Different metrics highlight different things. RMSE emphasizes large errors, MAE values consistency, and MAPE expresses errors as percentages. An Introduction to Statistical Learning with Applications in R (2nd edition), Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, 2021 (Springer) - A widely used textbook for statistical learning, providing clear explanations of fundamental regression metrics like MAE, MSE , and RMSE . Figure 10: Comparison of ACC and RMSE metrics between the (downsampled) FourCastNet predictions, (downsampled) IFS, and baseline state-of-the-art DLWP model [Weyn et al., 2020] for (a) Z500 and (b) T2m. We observe that the FourCastNet predictions show significant improvement over the baseline model. We also note that the FourCastNet generates predictions that have a higher resolution by a ... What are evaluation metrics for machine learning models? Additionally, by using these metrics, data scientists can identify areas where the model is performing below expectations and make adjustments to improve the accuracy of the predictions. The most common types of evaluation metrics for Machine Learning models are MSE, RMSE, MAE, and MAPE . Let’s explain what each acronym means. How do you measure the performance of a forecasting model? Once you've trained a forecasting model, such as ARIMA or SARIMA, you need to quantify its performance. Simply looking at plots isn't enough for objective comparison or reporting. Evaluation metrics provide a standardized way to measure how close your model's forecasts are to the actual observed values in your test dataset. What is RMSE metric? The use of this metric in a business environment includes employing MSE in evaluating stock forecasts and financial asset price predictions, to fine-tune investment strategies. RMSE is the square root of MSE . Mathematically, it measures the standard deviation of the error. What metrics are used in time series forecasting? Let's examine four common metrics used in time series forecasting. The Mean Absolute Error, or M A E M AE, represents the average absolute difference between the forecasts and the actual values. What is MAPE metric? MAPE calculates the average of the absolute percentage differences between the model’s predictions and the actual values. Therefore, this metric expresses the average error as a percentage of the actual value. MAPE penalizes negative errors more heavily (when the predicted value exceeds the actual). What are evaluation metrics? Evaluation metrics provide a standardized way to measure how close your model's forecasts are to the actual observed values in your test dataset. These metrics focus on the prediction errors, which are the differences between the actual values (A c t u a l i Actuali) and the forecasted values (F o r e c a s t i F orecasti) at each time step i i. Jul 15, 2025 · By mastering the appropriate evaluation metrics , we upgrade ourselves to fine-tune machine learning models which helps in ensuring they meet the needs of diverse applications and deliver optimal performance."}
+{"idx": 1, "title": "Metrics Evaluation: MSE, RMSE, MAE and MAPE - Medium", "date": "", "ddg_snippet": "Feb 26, 2024 · The most common types of evaluation metrics for Machine Learning models are MSE , RMSE , MAE, and MAPE. Let’s explain what each acronym means.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jonatasv/metrics-evaluation-mse-rmse-mae-and-mape-317cab85a26b", "content": "Feb 26, 2024 · The most common types of evaluation metrics for Machine Learning models are MSE , RMSE , MAE, and MAPE. Let’s explain what each acronym means."}
+{"idx": 2, "title": "MSE vs RMSE vs MAE vs MAPE vs R-Squared: When to Use?", "date": "", "ddg_snippet": "Aug 18, 2024 · The following are different types of regression model evaluation metrics including MSE , RMSE , MAE, MAPE, R-squared, and Adjusted R-squared which get used in different scenarios when training the regression models to solve the desired problem in hand.", "subpage_snippet": "", "source": "vitalflux.com", "link": "https://vitalflux.com/mse-vs-rmse-vs-mae-vs-mape-vs-r-squared-when-to-use/", "content": "Aug 18, 2024 · The following are different types of regression model evaluation metrics including MSE , RMSE , MAE, MAPE, R-squared, and Adjusted R-squared which get used in different scenarios when training the regression models to solve the desired problem in hand."}
+{"idx": 3, "title": "Time Series Evaluation Metrics: MAE, MSE, RMSE, MAPE", "date": "", "ddg_snippet": "An Introduction to Statistical Learning with Applications in R (2nd edition), Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, 2021 (Springer) - A widely used textbook for statistical learning, providing clear explanations of fundamental regression metrics like MAE, MSE , and RMSE .", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/time-series-analysis-forecasting/chapter-6-model-evaluation-selection/evaluation-metrics-mae-mse-rmse", "content": "An Introduction to Statistical Learning with Applications in R (2nd edition), Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, 2021 (Springer) - A widely used textbook for statistical learning, providing clear explanations of fundamental regression metrics like MAE, MSE , and RMSE ."}
+{"idx": 4, "title": "FourCastNet: A Global Data-driven High-resolution Weather ...", "date": "", "ddg_snippet": "Figure 10: Comparison of ACC and RMSE metrics between the (downsampled) FourCastNet predictions, (downsampled) IFS, and baseline state-of-the-art DLWP model [Weyn et al., 2020] for (a) Z500 and (b) T2m. We observe that the FourCastNet predictions show significant improvement over the baseline model. We also note that the FourCastNet generates predictions that have a higher resolution by a ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/FourCastNet:-A-Global-Data-driven-High-resolution-Pathak-Subramanian/10194e9d1d6b8ca8870445c990d4933c1dac1125/figure/11", "content": "Figure 10: Comparison of ACC and RMSE metrics between the (downsampled) FourCastNet predictions, (downsampled) IFS, and baseline state-of-the-art DLWP model [Weyn et al., 2020] for (a) Z500 and (b) T2m. We observe that the FourCastNet predictions show significant improvement over the baseline model. We also note that the FourCastNet generates predictions that have a higher resolution by a ..."}
+{"idx": 5, "title": "Evaluation Metrics in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 15, 2025 · By mastering the appropriate evaluation metrics , we upgrade ourselves to fine-tune machine learning models which helps in ensuring they meet the needs of diverse applications and deliver optimal performance.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/metrics-for-machine-learning-model/", "content": "Jul 15, 2025 · By mastering the appropriate evaluation metrics , we upgrade ourselves to fine-tune machine learning models which helps in ensuring they meet the needs of diverse applications and deliver optimal performance."}
+{"idx": 6, "title": "GitHub - NVlabs/ FourCastNet : Initial public release of code, data, and...", "date": "", "ddg_snippet": "Downloading an initial condition to initialize FourCastNet . If you are interested in generating a forecast using FourCastNet for a specific time-interval, you should begin by downloading the ERA5 netCDF files for the relevant variables from the Copernicus Climate Change Service Data Store.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVlabs/FourCastNet", "content": "Downloading an initial condition to initialize FourCastNet . If you are interested in generating a forecast using FourCastNet for a specific time-interval, you should begin by downloading the ERA5 netCDF files for the relevant variables from the Copernicus Climate Change Service Data Store."}
+{"idx": 7, "title": "FourCastNet : A Global Data-driven High-resolution Weather Model...", "date": "", "ddg_snippet": "... Whereas FourCastNet uses identical input and output variables and trains a separate model to predict diagnostic variables ( Pathak et al., 2022 ), ACE uses a set of prognostic variables-which are both inputs to and outputs from the network-including the 2D fields surface pressure and...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/358814796_FourCastNet_A_Global_Data-driven_High-resolution_Weather_Model_using_Adaptive_Fourier_Neural_Operators", "content": "... Whereas FourCastNet uses identical input and output variables and trains a separate model to predict diagnostic variables ( Pathak et al., 2022 ), ACE uses a set of prognostic variables-which are both inputs to and outputs from the network-including the 2D fields surface pressure and..."}
+{"idx": 8, "title": "GMD - Root - mean - square error ( RMSE ) or mean absolute error...", "date": "", "ddg_snippet": "The root - mean - squared error ( RMSE ) and mean absolute error (MAE) are widely used metrics for evaluating models.How to cite. Hodson, T. O.: Root - mean - square error ( RMSE ) or mean absolute error (MAE): when to use them or not, Geosci.", "subpage_snippet": "", "source": "gmd.copernicus.org", "link": "https://gmd.copernicus.org/articles/15/5481/2022/", "content": "The root - mean - squared error ( RMSE ) and mean absolute error (MAE) are widely used metrics for evaluating models.How to cite. Hodson, T. O.: Root - mean - square error ( RMSE ) or mean absolute error (MAE): when to use them or not, Geosci."}
+{"idx": 9, "title": "3 Regression Metrics You Must Know: MAE, MSE , and RMSE", "date": "", "ddg_snippet": "Root Mean Squared Error ( RMSE ). MSE is a helpful metric , but it is hard to interpret.Regression Metrics (MAE, MSE , RMSE ): Scatterplot between height and weight. Plotted using Seaborn scatterplot(). The weight generally goes up as the height increases.", "subpage_snippet": "", "source": "proclusacademy.com", "link": "https://proclusacademy.com/blog/explainer/regression-metrics-you-must-know/", "content": "Root Mean Squared Error ( RMSE ). MSE is a helpful metric , but it is hard to interpret.Regression Metrics (MAE, MSE , RMSE ): Scatterplot between height and weight. Plotted using Seaborn scatterplot(). The weight generally goes up as the height increases."}
diff --git a/data/sampled_jsons/Pearl_probability_of_necessity_definition_counterfactual_Y_would_not_have_occurred_without_X.jsonl b/data/sampled_jsons/Pearl_probability_of_necessity_definition_counterfactual_Y_would_not_have_occurred_without_X.jsonl
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index 0000000000000000000000000000000000000000..2896d4d9613633e2127f30531469589ad4627dec
--- /dev/null
+++ b/data/sampled_jsons/Pearl_probability_of_necessity_definition_counterfactual_Y_would_not_have_occurred_without_X.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Probabilities Of Causation: Three Counterfactual ...", "date": "", "ddg_snippet": "The standard counterfactual definition of causation1 (i.e., that E would not have occurred if it were not for C), captures the notion of “necessary cause”. Competing notions such as “sufficient cause” and “necessary-and-sufficient cause” may be of interest in a number of applications,2 and these, too, can be given concise counterfactual definitions. One advantage of cast-ing ...", "subpage_snippet": "", "source": "ftp.cs.ucla.edu", "link": "https://ftp.cs.ucla.edu/pub/stat_ser/r260-reprint.pdf", "content": "The standard counterfactual definition of causation1 (i.e., that E would not have occurred if it were not for C), captures the notion of “necessary cause”. Competing notions such as “sufficient cause” and “necessary-and-sufficient cause” may be of interest in a number of applications,2 and these, too, can be given concise counterfactual definitions. One advantage of cast-ing ..."}
+{"idx": 1, "title": "COUNTERFACTUALS AND CAUSAL REASONING - Princeton University", "date": "", "ddg_snippet": "2. The counterfactual analysis of causation and the causal account of counterfactuals Y counterfactually depends on X just in case Y would not have existed (obtained, occurred ) if X had not existed (obtained, occurred ). Counterfactual accounts of causation aim to analyze causation in terms of counterfactual dependence. I will discuss three of the main challenges confronting this approach ...", "subpage_snippet": "", "source": "www.princeton.edu", "link": "https://www.princeton.edu/~bkment/articles/causal+reasoning.pdf", "content": "2. The counterfactual analysis of causation and the causal account of counterfactuals Y counterfactually depends on X just in case Y would not have existed (obtained, occurred ) if X had not existed (obtained, occurred ). Counterfactual accounts of causation aim to analyze causation in terms of counterfactual dependence. I will discuss three of the main challenges confronting this approach ..."}
+{"idx": 2, "title": "Probabilities of Causation: Three Counterfactual ... - JSTOR", "date": "", "ddg_snippet": "This paper provides formal semantics, based on structural models of counterfactuals, for the probability that event x was a necessary or sufficient cause (or both) of another event y .", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/20118223", "content": "This paper provides formal semantics, based on structural models of counterfactuals, for the probability that event x was a necessary or sufficient cause (or both) of another event y ."}
+{"idx": 3, "title": "Relative risks, the probability of necessity, and ...", "date": "", "ddg_snippet": "Jul 3, 2024 · 3 Probability of necessity and attributable fractions The probability of necessity is intended to estimate the probability that a disease would not have occurred , but for the exposure (such as smoking), having occurred . For completeness, the formal definition in terms of counterfactual notation is given below, N = P (Yx = ̄y ̄x| X = x , Y = y )", "subpage_snippet": "", "source": "www.medrxiv.org", "link": "https://www.medrxiv.org/content/10.1101/2024.07.03.24309898v1.full.pdf", "content": "Jul 3, 2024 · 3 Probability of necessity and attributable fractions The probability of necessity is intended to estimate the probability that a disease would not have occurred , but for the exposure (such as smoking), having occurred . For completeness, the formal definition in terms of counterfactual notation is given below, N = P (Yx = ̄y ̄x| X = x , Y = y )"}
+{"idx": 4, "title": "Probabilities of Causation - Three counterfactual ...", "date": "", "ddg_snippet": "Probability of Necessity The probability of necessity is the probability that y would not occur in the absence of x , in the case where x and y did occur.", "subpage_snippet": "", "source": "ics.uci.edu", "link": "https://ics.uci.edu/~dechter/courses/ics-295cr/2021-22_Q2_Winter/slides/classP6-w22-EdgarRobles-Probabilities_of_Causation.pdf", "content": "Probability of Necessity The probability of necessity is the probability that y would not occur in the absence of x , in the case where x and y did occur."}
+{"idx": 5, "title": "9 - Probability of Causation: Interpretation and Identification", "date": "", "ddg_snippet": "Mar 5, 2013 · In this chapter we explore the counterfactual interpretation of necessary and sufficient causes, illustrate the application of structural model semantics to the problem of identifying probabilities of causes, and present, by way of examples, new ways of estimating probabilities of causes from statistical data.", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/books/causality/probability-of-causation-interpretation-and-identification/F1E6BA6F1148AA111FD3340BD2A9447E", "content": "Mar 5, 2013 · In this chapter we explore the counterfactual interpretation of necessary and sufficient causes, illustrate the application of structural model semantics to the problem of identifying probabilities of causes, and present, by way of examples, new ways of estimating probabilities of causes from statistical data."}
+{"idx": 6, "title": "Estimating Categorical Counterfactuals via Deep Twin Networks", "date": "", "ddg_snippet": "The probability of necessity is the probability event Y would not have occurred without event X occurring , given that X , Y did in fact occur .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LeiiGwpGjSt", "content": "The probability of necessity is the probability event Y would not have occurred without event X occurring , given that X , Y did in fact occur ."}
+{"idx": 7, "title": "Local Explanations via Necessity and Sufficiency: Unifying Theory and...", "date": "", "ddg_snippet": "Logical and probabilistic definitions of necessity and sufficiency often fall short when we consider causal explanations (Tian & Pearl , 2000; Pearl , 2009). It may make sense to say in logic that if x is a necessary condition for y , then y is a sufficient condition for x ; it does not follow that if x ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11023-022-09598-7", "content": "Logical and probabilistic definitions of necessity and sufficiency often fall short when we consider causal explanations (Tian & Pearl , 2000; Pearl , 2009). It may make sense to say in logic that if x is a necessary condition for y , then y is a sufficient condition for x ; it does not follow that if x ..."}
+{"idx": 8, "title": "Local Explanations via Necessity and Sufficiency: Unifying Theory and...", "date": "", "ddg_snippet": "ing counterfactual probabilities – e.g., the probabilitythat. Alice would still hav e a headache if she had not taken an as-. pirin, gi ven that she does not ha ve a headache and did take.When necessity and sufficienc y are defined as probabilistic .", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/local-explanations-via-necessity-and-sufficiency-unifying-1565020", "content": "ing counterfactual probabilities – e.g., the probabilitythat. Alice would still hav e a headache if she had not taken an as-. pirin, gi ven that she does not ha ve a headache and did take.When necessity and sufficienc y are defined as probabilistic ."}
+{"idx": 9, "title": "Which Patients are in Greater Need: A counterfactual ... | aiws.net", "date": "", "ddg_snippet": "What does “in greatest need” mean? This is a counterfactual notion. People who are most in need have the highest probability of both survival if treated and death if not treated.", "subpage_snippet": "", "source": "aiws.net", "link": "https://aiws.net/aiws-university/modern-causal-inference/judea-pearls-works/writing/which-patients-are-in-greater-need-a-counterfactual-analysis-with-reflections-on-covid-19/", "content": "What does “in greatest need” mean? This is a counterfactual notion. People who are most in need have the highest probability of both survival if treated and death if not treated."}
diff --git a/data/sampled_jsons/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter_ab.jsonl b/data/sampled_jsons/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter_ab.jsonl
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index 0000000000000000000000000000000000000000..31b058fafdb508553fff65c8593f7afa09c70aaf
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+++ b/data/sampled_jsons/People_as_sensors_Mass_media_and_local_temperature_influence_climate_change_discussion_on_Twitter_ab.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Social media messaging by climate action NGOs: a case study of", "date": "", "ddg_snippet": "... tweets discussed risk factors beyond climate change that influence bushfire impacts, such as firefighting, emergency responses, hazard reduction, and ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/oocc/article/3/1/kgad011/7311736", "content": "... tweets discussed risk factors beyond climate change that influence bushfire impacts, such as firefighting, emergency responses, hazard reduction, and ..."}
+{"idx": 1, "title": "Climate Change Disinformation and How to Combat It | Annual", "date": "", "ddg_snippet": "Climate change also challenges many people &s worldview because any climate mitigation regime will have economic and political implications that are ...", "subpage_snippet": "", "source": "www.annualreviews.org", "link": "https://www.annualreviews.org/content/journals/10.1146/annurev-publhealth-090419-102409", "content": "Climate change also challenges many people &s worldview because any climate mitigation regime will have economic and political implications that are ..."}
+{"idx": 2, "title": "How Do Social Media Users Link Different Types of Extreme", "date": "", "ddg_snippet": "Kirilenko , AP, Molodtsova T and Stepchenkova SO [ 2015 ] People as sensors : Mass media and local temperature influence climate change discussion on ...", "subpage_snippet": "", "source": "www.worldscientific.com", "link": "https://www.worldscientific.com/doi/abs/10.1142/S2345737619500027", "content": "Kirilenko , AP, Molodtsova T and Stepchenkova SO [ 2015 ] People as sensors : Mass media and local temperature influence climate change discussion on ..."}
+{"idx": 3, "title": "(PDF) Using Social Media to Detect and Locate Wildfires", "date": "", "ddg_snippet": "Distributions of mean and median distance to closest fire from each null model on each social media dataset are shown as semi-transparent histograms ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/311133785_Using_Social_Media_to_Detect_and_Locate_Wildfires", "content": "Distributions of mean and median distance to closest fire from each null model on each social media dataset are shown as semi-transparent histograms ..."}
+{"idx": 4, "title": "Trust in social media is associated with misperceptions about", "date": "", "ddg_snippet": "... media platforms such as Facebook, Twitter , and YouTube are not only consumed by a large population but have also become a ‘public square’ for ...", "subpage_snippet": "", "source": "www.hnmr.org", "link": "https://www.hnmr.org/journal/view.php?number=55", "content": "... media platforms such as Facebook, Twitter , and YouTube are not only consumed by a large population but have also become a ‘public square’ for ..."}
+{"idx": 5, "title": "People as sensors: Mass media and local temperature influence ...", "date": "", "ddg_snippet": "Jan 1, 2015 · This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process. We used the volume of Twitter messages containing words “ climate change ” and “global warming” as the indicator of attention that public pays to the issue. Specifically, the goals were: (1) to investigate whether ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0959378014001952", "content": "Jan 1, 2015 · This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process. We used the volume of Twitter messages containing words “ climate change ” and “global warming” as the indicator of attention that public pays to the issue. Specifically, the goals were: (1) to investigate whether ..."}
+{"idx": 6, "title": "People as sensors: Mass media and local temperature influence ...", "date": "", "ddg_snippet": "Jul 25, 2014 · The high significance of the mass media and temperature variables in the majority of regression models suggests that both the weather and mass media coverage control public interest to the topic. However, no convincing evidence was found that the media acts as a mediator in the relationship between local weather and climate change discourse.", "subpage_snippet": "", "source": "research.fit.edu", "link": "https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/climate-communications/youth-climate-amp-social-media/Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect-CC-Discussion-On-Twitter.pdf", "content": "Jul 25, 2014 · The high significance of the mass media and temperature variables in the majority of regression models suggests that both the weather and mass media coverage control public interest to the topic. However, no convincing evidence was found that the media acts as a mediator in the relationship between local weather and climate change discourse."}
+{"idx": 7, "title": "People as sensors: mass media and local temperature influence ...", "date": "", "ddg_snippet": "The extreme cold and hot temperature anomalies were then transformed into country-level values that represent the number of people living in extreme temperature conditions. The rate of tweeting on climate change was regressed on the time variables, number of climate change publications in the mass media , and temperature .", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2014AGUFMGC41B0548K/abstract", "content": "The extreme cold and hot temperature anomalies were then transformed into country-level values that represent the number of people living in extreme temperature conditions. The rate of tweeting on climate change was regressed on the time variables, number of climate change publications in the mass media , and temperature ."}
+{"idx": 8, "title": "Sci-Hub | People as sensors: Mass media and local temperature ...", "date": "", "ddg_snippet": "Kirilenko , A. P., Molodtsova, T., & Stepchenkova, S. O. (2015). People as sensors : Mass media and local temperature influence climate change discussion on Twitter .", "subpage_snippet": "", "source": "sci-hub.se", "link": "https://sci-hub.se/10.1016/j.gloenvcha.2014.11.003", "content": "Kirilenko , A. P., Molodtsova, T., & Stepchenkova, S. O. (2015). People as sensors : Mass media and local temperature influence climate change discussion on Twitter ."}
+{"idx": 9, "title": "Twitter and Climate Change - Compass Hub", "date": "", "ddg_snippet": "This review presents what is currently known about the way climate change is discussed on Twitter , acknowledging advantages and limitations, and suggesting future areas for study. As an accessible platform, Twitter allows public expression of opinions on climate change and provides data on how these fluctuate over different times and places.", "subpage_snippet": "", "source": "compass.onlinelibrary.wiley.com", "link": "https://compass.onlinelibrary.wiley.com/doi/am-pdf/10.1111/soc4.12587", "content": "This review presents what is currently known about the way climate change is discussed on Twitter , acknowledging advantages and limitations, and suggesting future areas for study. As an accessible platform, Twitter allows public expression of opinions on climate change and provides data on how these fluctuate over different times and places."}
diff --git a/data/sampled_jsons/Perez_2022_Discovering_Language_Model_Behaviors_with_Model-written_Evaluations_abstract_arXiv2212.09_year_2022.jsonl b/data/sampled_jsons/Perez_2022_Discovering_Language_Model_Behaviors_with_Model-written_Evaluations_abstract_arXiv2212.09_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f066a9ef60289072e849ef3ead6856d1cdfdcfe7
--- /dev/null
+++ b/data/sampled_jsons/Perez_2022_Discovering_Language_Model_Behaviors_with_Model-written_Evaluations_abstract_arXiv2212.09_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2212.09251] Discovering Language Model Behaviors with Model ... Images Discovering Language Model Behaviors with Model-Written ... Discovering Language Model Behaviors with Model-Written ... Discovering Language Model Behaviors with Model-Written ... Discovering Language Model Behaviors with Model-Written ... Discovering Language Model Behaviors with Model-Written ... - ADS arXiv : 2212 .09251v1 [cs.CL] 19 Dec 2022 arXiv : 2212 .09251v1 [cs.CL] 19 Dec 2022 arXiv : 2212 .09251v1 [cs.CL] 19 Dec 2022 Discovering Language Model Behaviors with Model-Written ...", "date": "", "ddg_snippet": "Dec 19, 2022 · Here, we automatically generate evaluations with LMs . We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. View all 6 days ago · Here, we automatically generate evaluations with LMs . We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. This paper empirically examines, for the first time, how well large language models (LLMs) can build a mental model of reinforcement learning (RL) agents, termed agent mental modelling, by reasoning about an agent's behaviour and its effect on states from agent interaction history. Dec 19, 2022 · Abstract Automatically generated evaluations from language models reveal novel behaviors, including cases of inverse scaling with model size and reinforcement learning from human feedback. Jan 1, 2023 · Abstract : As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs . We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Who are the authors of CVPR 2022? In CVPR. Mandar Joshi, Terra Blevins, Mike Lewis, Daniel S. Weld, and Luke Zettlemoyer . 2022. Few-shot mining of naturally occurring inputs and outputs. Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022. Who developed the data visualization tool for EvalS? Deep Ganguli led the analysis of the data quality (§3.3) and diversity (§3.4). Karina Nguyen developed the interactive data visualization tool at evals.anthropic.com/model-written. Sam Ringer conducted the experiments on few-shot evaluation generation (§5), with help from Sandipan Kundu. Who led the experiments on bias evaluation Generation 6? Kamil ̇e Lukoši ̄ut ̇e led the experiments on bias evaluation generation (§6), with help from Nicholas Schiefer and Deep Ganguli. Nicholas Schiefer helped with plotting, human evaluations, and paper writing. Samuel R. Bowman and Amanda Askell provided feedback throughout the project, and Jack Clark and Catherine Olsson gave feedback on the draft. Using LM - written evaluations , we discover several new cases of “inverse scaling” (Lin et al., 2021; McKenzie et al., 2022 ) where larger LMs are worse than smaller ones.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.09251", "content": "Dec 19, 2022 · Here, we automatically generate evaluations with LMs . We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. View all 6 days ago · Here, we automatically generate evaluations with LMs . We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. This paper empirically examines, for the first time, how well large language models (LLMs) can build a mental model of reinforcement learning (RL) agents, termed agent mental modelling, by reasoning about an agent's behaviour and its effect on states from agent interaction history. Dec 19, 2022 · Abstract Automatically generated evaluations from language models reveal novel behaviors, including cases of inverse scaling with model size and reinforcement learning from human feedback. Jan 1, 2023 · Abstract : As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs . We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Who are the authors of CVPR 2022? In CVPR. Mandar Joshi, Terra Blevins, Mike Lewis, Daniel S. Weld, and Luke Zettlemoyer . 2022. Few-shot mining of naturally occurring inputs and outputs. Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022. Who developed the data visualization tool for EvalS? Deep Ganguli led the analysis of the data quality (§3.3) and diversity (§3.4). Karina Nguyen developed the interactive data visualization tool at evals.anthropic.com/model-written. Sam Ringer conducted the experiments on few-shot evaluation generation (§5), with help from Sandipan Kundu. Who led the experiments on bias evaluation Generation 6? Kamil ̇e Lukoši ̄ut ̇e led the experiments on bias evaluation generation (§6), with help from Nicholas Schiefer and Deep Ganguli. Nicholas Schiefer helped with plotting, human evaluations, and paper writing. Samuel R. Bowman and Amanda Askell provided feedback throughout the project, and Jack Clark and Catherine Olsson gave feedback on the draft. Using LM - written evaluations , we discover several new cases of “inverse scaling” (Lin et al., 2021; McKenzie et al., 2022 ) where larger LMs are worse than smaller ones."}
+{"idx": 1, "title": "Paper page - Discovering Language Model Behaviors with ...", "date": "", "ddg_snippet": "Papers. arxiv : 2212 . 09251 . Discovering Language Model Behaviors with Model - Written Evaluations . Published on Dec 19, 2022 .No model linking this paper. Cite arxiv .org/abs/ 2212 . 09251 in a model README.md to link it from this page.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2212.09251", "content": "Papers. arxiv : 2212 . 09251 . Discovering Language Model Behaviors with Model - Written Evaluations . Published on Dec 19, 2022 .No model linking this paper. Cite arxiv .org/abs/ 2212 . 09251 in a model README.md to link it from this page."}
+{"idx": 2, "title": "Discovering Language Model Behaviors with Model - Written ...", "date": "", "ddg_snippet": "arXiv : 2212 . 09251 v1 [cs.CL] 19 Dec 2022 . Abstract . As language models (LMs) scale, they develop many novel behaviors , good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.09251", "content": "arXiv : 2212 . 09251 v1 [cs.CL] 19 Dec 2022 . Abstract . As language models (LMs) scale, they develop many novel behaviors , good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing..."}
+{"idx": 3, "title": "Discovering Language Model Behaviors with Model-Written ...", "date": "", "ddg_snippet": "6 days ago · Here, we automatically generate evaluations with LMs . We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.findings-acl.847/", "content": "6 days ago · Here, we automatically generate evaluations with LMs . We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering."}
+{"idx": 4, "title": "Discovering Language Model Behaviors with Model-Written ...", "date": "", "ddg_snippet": "This paper empirically examines, for the first time, how well large language models (LLMs) can build a mental model of reinforcement learning (RL) agents, termed agent mental modelling, by reasoning about an agent's behaviour and its effect on states from agent interaction history.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/105118483/Discovering_Language_Model_Behaviors_with_Model_Written_Evaluations", "content": "This paper empirically examines, for the first time, how well large language models (LLMs) can build a mental model of reinforcement learning (RL) agents, termed agent mental modelling, by reasoning about an agent's behaviour and its effect on states from agent interaction history."}
+{"idx": 5, "title": "Discovering Language Model Behaviors with Model-Written ...", "date": "", "ddg_snippet": "Jan 1, 2023 · Abstract : As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=S86avshXBo", "content": "Jan 1, 2023 · Abstract : As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available)."}
+{"idx": 6, "title": "Discovering Language Model Behaviors with Model-Written ... - ADS", "date": "", "ddg_snippet": "Here, we automatically generate evaluations with LMs . We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2022arXiv221209251P/abstract", "content": "Here, we automatically generate evaluations with LMs . We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering."}
+{"idx": 7, "title": "Discovering Language Model Behaviors with Model - Written ...", "date": "", "ddg_snippet": "Abstract . As language models (LMs) scale, they develop many novel behaviors , good and bad, exacerbating the need to evaluate how they behave.Overall, LM- written evaluations are high-quality and let us quickly discover many novel LM behaviors .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366423471_Discovering_Language_Model_Behaviors_with_Model-Written_Evaluations", "content": "Abstract . As language models (LMs) scale, they develop many novel behaviors , good and bad, exacerbating the need to evaluate how they behave.Overall, LM- written evaluations are high-quality and let us quickly discover many novel LM behaviors ."}
+{"idx": 8, "title": "AI-Powered Paper Summarization about the arXiv paper 2212 . 09251 v1", "date": "", "ddg_snippet": "Discovering Language Model Behaviors with Model - Written Evaluations .Results of the summarizing process for the arXiv paper: 2212 . 09251 v1.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/2212.09251v1/", "content": "Discovering Language Model Behaviors with Model - Written Evaluations .Results of the summarizing process for the arXiv paper: 2212 . 09251 v1."}
+{"idx": 9, "title": "[PDF] Discovering Language Model Behaviors with Model - Written ...", "date": "", "ddg_snippet": "As language models (LMs) scale, they develop many novel behaviors , good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available).", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Discovering-Language-Model-Behaviors-with-Perez-Ringer/cef330bacf014d60daabbd489647b2006af130ca", "content": "As language models (LMs) scale, they develop many novel behaviors , good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available)."}
diff --git a/data/sampled_jsons/Position_-_Current_Model_Licensing_Practices_are_Dragging_Us_into_a_Quagmire_of_Legal_Noncompliance__year_2024.jsonl b/data/sampled_jsons/Position_-_Current_Model_Licensing_Practices_are_Dragging_Us_into_a_Quagmire_of_Legal_Noncompliance__year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dea1ee30a554446f4249c145a62bb84e43084fe8
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+++ b/data/sampled_jsons/Position_-_Current_Model_Licensing_Practices_are_Dragging_Us_into_a_Quagmire_of_Legal_Noncompliance__year_2024.jsonl
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+{"idx": 0, "title": "Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "by M Duan · Cited by 1 — Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance . Download PDF · Moming Duan, Mingzhe Du, Rui Zhao, Mengying ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1rh8iTehBc", "content": "by M Duan · Cited by 1 — Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance . Download PDF · Moming Duan, Mingzhe Du, Rui Zhao, Mengying ..."}
+{"idx": 1, "title": "Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance . Figure 1. Visualization of Model Dependencies on HF ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/4bb8da2945abd31f3e42d0e5f0a87a8bb47ddc9c.pdf", "content": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance . Figure 1. Visualization of Model Dependencies on HF ..."}
+{"idx": 2, "title": "Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "Meta Llama3 Community License: “You will not use the Llama. Materials or any output or results of the Llama Materials to improve. 6 pages", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/40180_4s09QZJ.pdf", "content": "Meta Llama3 Community License: “You will not use the Llama. Materials or any output or results of the Llama Materials to improve. 6 pages"}
+{"idx": 3, "title": "Anatomy of a Machine Learning Ecosystem: 2 Million ...", "date": "", "ddg_snippet": "9 Aug 2025 — Position: Current model licensing practices are dragging us into a quagmire of legal noncompliance . In Forty-second International Conference ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06811v1", "content": "9 Aug 2025 — Position: Current model licensing practices are dragging us into a quagmire of legal noncompliance . In Forty-second International Conference ..."}
+{"idx": 4, "title": "Homepage of Yinghui Wu", "date": "", "ddg_snippet": "23 Aug 2025 — Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance ... Slides: [ppt]| Teaser:[ pdf ]| Poster[ pdf ]", "subpage_snippet": "", "source": "yinghwu.github.io", "link": "https://yinghwu.github.io/yinghui-publication-year.html", "content": "23 Aug 2025 — Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance ... Slides: [ppt]| Teaser:[ pdf ]| Poster[ pdf ]"}
+{"idx": 5, "title": "Homepage of Yinghui Wu", "date": "", "ddg_snippet": "23 Aug 2025 — ... Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance (oral) The 42nd International Conference ...", "subpage_snippet": "", "source": "yinghwu.github.io", "link": "https://yinghwu.github.io/yinghui-publication.html", "content": "23 Aug 2025 — ... Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance (oral) The 42nd International Conference ..."}
+{"idx": 6, "title": "ML-Asset Management: Curation, Discovery, and Utilization", "date": "", "ddg_snippet": "16 Sept 2025 — Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance ... View or Download as a PDF file. PDF ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.14778/3750601.3750701", "content": "16 Sept 2025 — Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance ... View or Download as a PDF file. PDF ..."}
+{"idx": 7, "title": "Mingzhe Du", "date": "", "ddg_snippet": "Our paper “ Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance ” has been accepted into ICML’25 ...", "subpage_snippet": "", "source": "mingzhe.space", "link": "https://mingzhe.space/", "content": "Our paper “ Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance ” has been accepted into ICML’25 ..."}
+{"idx": 8, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "... all solutions are created equal: An analytical ... Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "... all solutions are created equal: An analytical ... Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance"}
+{"idx": 9, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "... and Bias in Algorithms, Data, and Models ... 10:45] VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "... and Bias in Algorithms, Data, and Models ... 10:45] VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models"}
diff --git a/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_experimental_setup_OR_hardware_configuration_O.jsonl b/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_experimental_setup_OR_hardware_configuration_O.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..abf731a6281589423e98d77a04bce43351e0cdc1
--- /dev/null
+++ b/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_experimental_setup_OR_hardware_configuration_O.jsonl
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+{"idx": 0, "title": "Approximation, Randomization, and Combinatorial Optimization ...", "date": "", "ddg_snippet": "Topics of interest for APPROX include approximation algorithms , hardness of approxim-ation, small space, sub- linear time and streaming algorithms , online algorithms , approaches that go beyond worst case analysis, distributed and parallel approximation , embeddings and metric-space methods, mathematical-programming methods, spectral methods, combinatorial optimization, algorithmic game theory ...", "subpage_snippet": "", "source": "drops.dagstuhl.de", "link": "https://drops.dagstuhl.de/storage/00lipics/lipics-vol353-approx-random2025/LIPIcs.APPROX-RANDOM.2025.0/LIPIcs.APPROX-RANDOM.2025.0.pdf", "content": "Topics of interest for APPROX include approximation algorithms , hardness of approxim-ation, small space, sub- linear time and streaming algorithms , online algorithms , approaches that go beyond worst case analysis, distributed and parallel approximation , embeddings and metric-space methods, mathematical-programming methods, spectral methods, combinatorial optimization, algorithmic game theory ..."}
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+{"idx": 2, "title": "Implementation of quantum imaginary-time evolution method on ...", "date": "", "ddg_snippet": "Jun 1, 2021 · The imaginary- time evolution method is a well-known approach used for obtaining the ground state in quantum many-body problems on a classical computer. A recently proposed quantum imaginary- time ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41534-021-00409-y", "content": "Jun 1, 2021 · The imaginary- time evolution method is a well-known approach used for obtaining the ground state in quantum many-body problems on a classical computer. A recently proposed quantum imaginary- time ..."}
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diff --git a/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_for_Hybrid_and_Overlapping_Graph_Clustering_co.jsonl b/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_for_Hybrid_and_Overlapping_Graph_Clustering_co.jsonl
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+{"idx": 1, "title": "Practical Almost - Linear - Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "While almost - linear - time approximation algorithms are known for edge-boundary-based graph partitioning, little progress has been made on fast algorithms for HGP, even in the special case of vertex-boundary-based graph partitioning.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a", "content": "While almost - linear - time approximation algorithms are known for edge-boundary-based graph partitioning, little progress has been made on fast algorithms for HGP, even in the special case of vertex-boundary-based graph partitioning."}
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+{"idx": 6, "title": "Cut-matching Games for Generalized Hypergraph Ratio Cuts", "date": "", "ddg_snippet": "Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370413190_Cut-matching_Games_for_Generalized_Hypergraph_Ratio_Cuts", "content": "Practical Almost - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering ."}
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+{"idx": 0, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "In this work, we introduce a frame-work based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to clusters with hybrid vertex- and edge-boundary structure. Our main algorithmic contributions are almost-linear-time algorithms O (log n)-approximation algorithms for both these objectives.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a.html", "content": "In this work, we introduce a frame-work based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to clusters with hybrid vertex- and edge-boundary structure. Our main algorithmic contributions are almost-linear-time algorithms O (log n)-approximation algorithms for both these objectives."}
+{"idx": 1, "title": "Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Crucially, we implement our approximation algorithm to produce both overlapping and hybrid partitions for large graphs , easily scaling to tens of millions of edges, and test our implementation on real-world datasets against other competitive baselines. Based on joint work with Lorenzo Orecchia .", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/talk/practical-nearly-linear-time-approximation-algorithms-for-hybrid-and-overlapping-graph-clustering/", "content": "Crucially, we implement our approximation algorithm to produce both overlapping and hybrid partitions for large graphs , easily scaling to tens of millions of edges, and test our implementation on real-world datasets against other competitive baselines. Based on joint work with Lorenzo Orecchia ."}
+{"idx": 2, "title": "PDF Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Practical Nearly- Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis 1 1 Lorenzo Orecchia", "subpage_snippet": "", "source": "tsourakakis.com", "link": "https://tsourakakis.com/wp-content/uploads/2022/06/aott_icml22.pdf", "content": "Practical Nearly- Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis 1 1 Lorenzo Orecchia"}
+{"idx": 3, "title": "ICML 2022 Practical Almost-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "In this work, we introduce a frame-work based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to clusters with hybrid vertex- and edge-boundary structure. Our main algorithmic contributions are almost-linear-time algorithms O (log n)-approximation algorithms for both these objectives.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/spotlight/16880", "content": "In this work, we introduce a frame-work based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to clusters with hybrid vertex- and edge-boundary structure. Our main algorithmic contributions are almost-linear-time algorithms O (log n)-approximation algorithms for both these objectives."}
+{"idx": 4, "title": "Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "In many graph-clustering applications, over-whelming empirical evidence suggests that communities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms ( OGP ). In this work, we intro-duce a framework based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to clusters with ...", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/62c28ae65aee126c0f8a213c/practical-almost-linear-time-approximation-algorithms-for-hybrid-and-overlapping-graph-clustering", "content": "In many graph-clustering applications, over-whelming empirical evidence suggests that communities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms ( OGP ). In this work, we intro-duce a framework based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to clusters with ..."}
+{"idx": 5, "title": "Lorenzo Orecchia - dblp", "date": "", "ddg_snippet": "2022 [c20] Lorenzo Orecchia , Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar: Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering . ICML 2022 : 17071-17093", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/32/4340", "content": "2022 [c20] Lorenzo Orecchia , Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar: Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering . ICML 2022 : 17071-17093"}
+{"idx": 6, "title": "PDF Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Abstract In many graph-clustering applications, over-whelming empirical evidence suggests that com-munities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms (OGP). In this work, we intro-duce a framework based on two novel cluster-ing objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a/orecchia22a.pdf", "content": "Abstract In many graph-clustering applications, over-whelming empirical evidence suggests that com-munities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms (OGP). In this work, we intro-duce a framework based on two novel cluster-ing objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to ..."}
+{"idx": 7, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "NSF Public Access Search Results Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Citation Details", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10349734-practical-almost-linear-time-approximation-algorithms-hybrid-overlapping-graph-clustering", "content": "NSF Public Access Search Results Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Citation Details"}
+{"idx": 8, "title": "Publications | Orecchia Research Group", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis, Kunal Talwar, Charalampos Tsourakakis. Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering . ICML, 2022 .", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/publication/", "content": "Lorenzo Orecchia , Konstantinos Ameranis, Kunal Talwar, Charalampos Tsourakakis. Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering . ICML, 2022 ."}
+{"idx": 9, "title": "A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph ...", "date": "", "ddg_snippet": "Our approach appears easily extendible to related problem, such as Sparsest Cut, and also yields an near- linear time O(1) - approximation to Dagupta's objective function for hierarchical clustering [Dasgupta, STOC'16] for the semi-random hierarchical stochastic block model inputs of [Cohen-Addad, Kanade, Mallmann-Trenn, Mathieu, JACM'19].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04857", "content": "Our approach appears easily extendible to related problem, such as Sparsest Cut, and also yields an near- linear time O(1) - approximation to Dagupta's objective function for hierarchical clustering [Dasgupta, STOC'16] for the semi-random hierarchical stochastic block model inputs of [Cohen-Addad, Kanade, Mallmann-Trenn, Mathieu, JACM'19]."}
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+{"idx": 3, "title": "Distributed, Parallel, and Cluster Computing", "date": "", "ddg_snippet": "We propose using a time -based trigger, Merkle proofs, and new resolver contracts to implement a practical escape hatch for these networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.DC/new", "content": "We propose using a time -based trigger, Merkle proofs, and new resolver contracts to implement a practical escape hatch for these networks."}
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+{"idx": 7, "title": "Michael Yoo", "date": "", "ddg_snippet": "... used on the side of the authoritative nameserver to approximate a user ’ s location, with heuristics and new techniques (EDNS0) to account for ...", "subpage_snippet": "", "source": "michael.yoo.id.au", "link": "https://michael.yoo.id.au/", "content": "... used on the side of the authoritative nameserver to approximate a user ’ s location, with heuristics and new techniques (EDNS0) to account for ..."}
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+{"idx": 9, "title": "Nikhil R. Devanur", "date": "", "ddg_snippet": "We give a proof of existence of equilibrium and a polynomial time algorithm for finding one, drawing heavily on techniques from LP duality and ...", "subpage_snippet": "", "source": "www.nikhildevanur.com", "link": "https://www.nikhildevanur.com/", "content": "We give a proof of existence of equilibrium and a polynomial time algorithm for finding one, drawing heavily on techniques from LP duality and ..."}
diff --git a/data/sampled_jsons/Practical_almost-linear-time_approximation_algorithms_for_hybrid_and_overlapping_graph_clustering_pd_year_2022.jsonl b/data/sampled_jsons/Practical_almost-linear-time_approximation_algorithms_for_hybrid_and_overlapping_graph_clustering_pd_year_2022.jsonl
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+{"idx": 0, "title": "Practical Nearly-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "Abstract In many graph - clustering applications, over-whelming empirical evidence suggests that com-munities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms (OGP). In this work, we intro-duce a framework based on two novel cluster-ing objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a/orecchia22a.pdf", "content": "Abstract In many graph - clustering applications, over-whelming empirical evidence suggests that com-munities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms (OGP). In this work, we intro-duce a framework based on two novel cluster-ing objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to ..."}
+{"idx": 1, "title": "Practical Almost-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "In this work, we introduce a frame-work based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to clusters with hybrid vertex-and edge-boundary structure. Our main algorithmic contributions are almost-linear-time algorithms O (log n)- approximation algorithms for both these objectives.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a.html", "content": "In this work, we introduce a frame-work based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to clusters with hybrid vertex-and edge-boundary structure. Our main algorithmic contributions are almost-linear-time algorithms O (log n)- approximation algorithms for both these objectives."}
+{"idx": 2, "title": "Practical Nearly-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "May 4, 2022 · Abstract In many graph - clustering applications, overwhelming empirical evidence suggests that communities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms ( OGP ). In this work, we introduce a framework based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to ...", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/talk/practical-nearly-linear-time-approximation-algorithms-for-hybrid-and-overlapping-graph-clustering/", "content": "May 4, 2022 · Abstract In many graph - clustering applications, overwhelming empirical evidence suggests that communities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms ( OGP ). In this work, we introduce a framework based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to overlapping clusters and to ..."}
+{"idx": 3, "title": "ICML 2022 Practical Almost-Linear-Time Approximation ...", "date": "", "ddg_snippet": "In this work, we introduce a frame-work based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to clusters with hybrid vertex-and edge-boundary structure. Our main algorithmic contributions are almost-linear-time algorithms O (log n)- approximation algorithms for both these objectives.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/spotlight/16880", "content": "In this work, we introduce a frame-work based on two novel clustering objectives, which naturally extend the well-studied notion of conductance to clusters with hybrid vertex-and edge-boundary structure. Our main algorithmic contributions are almost-linear-time algorithms O (log n)- approximation algorithms for both these objectives."}
+{"idx": 4, "title": "Practical Nearly-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "In many graph - clustering applications, over-whelming empirical evidence suggests that communities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms ( OGP ).", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/62c28ae65aee126c0f8a213c/practical-almost-linear-time-approximation-algorithms-for-hybrid-and-overlapping-graph-clustering", "content": "In many graph - clustering applications, over-whelming empirical evidence suggests that communities and clusters are naturally overlapping , calling for novel overlapping graph -partitioning algorithms ( OGP )."}
+{"idx": 5, "title": "Practical Almost-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "by K Ameranis · Cited by 9 — Hybrid and Overlapping Graph. Clustering ... Nearly-Linear-Time Approximation Algorithms for Overlapping Graph Clustering with Provable Guarantees.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16880_M2pKLQk.pdf", "content": "by K Ameranis · Cited by 9 — Hybrid and Overlapping Graph. Clustering ... Nearly-Linear-Time Approximation Algorithms for Overlapping Graph Clustering with Provable Guarantees."}
+{"idx": 6, "title": "Practical Nearly-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "Abstract. In many graph - clustering applications, over- whelming empirical evidence suggests that com- munities and clusters are naturally overlapping ,.", "subpage_snippet": "", "source": "mailman.cs.uchicago.edu", "link": "http://mailman.cs.uchicago.edu/pipermail/cs/attachments/20220602/19d83235/attachment-0001.pdf", "content": "Abstract. In many graph - clustering applications, over- whelming empirical evidence suggests that com- munities and clusters are naturally overlapping ,."}
+{"idx": 7, "title": "Hierarchical overlapping clustering: cost function, algorithm ...", "date": "", "ddg_snippet": "by Y Pan — Practical nearly-linear-time approximation algorithms for hybrid and overlapping graph clustering . In International Conference on Machine Learning, pp. 17071– ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "by Y Pan — Practical nearly-linear-time approximation algorithms for hybrid and overlapping graph clustering . In International Conference on Machine Learning, pp. 17071– ..."}
+{"idx": 8, "title": "Optimal LP Rounding and Linear-Time Approximation ...", "date": "", "ddg_snippet": "by N Veldt · 2022 · Cited by 7 — Abstract. We study the approximability of an existing frame- work for clustering edge-colored hypergraphs, which is closely related to chromatic correlation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.06506", "content": "by N Veldt · 2022 · Cited by 7 — Abstract. We study the approximability of an existing frame- work for clustering edge-colored hypergraphs, which is closely related to chromatic correlation."}
+{"idx": 9, "title": "[PDF] Overlapping clusters for distributed computation", "date": "", "ddg_snippet": "Practical Nearly - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering ... PDF . Add to Library. Alert. 1 Excerpt. On Clustering ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Overlapping-clusters-for-distributed-computation-Andersen-Gleich/21c879782c74f802884b88627ed3414a0aa7f9d6", "content": "Practical Nearly - Linear - Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering ... PDF . Add to Library. Alert. 1 Excerpt. On Clustering ..."}
diff --git a/data/sampled_jsons/ProDet_github_Cheng_Jikang_deepfake_detector.jsonl b/data/sampled_jsons/ProDet_github_Cheng_Jikang_deepfake_detector.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e32bc2de336f6479dc51a1267dd96404fd484cf7
--- /dev/null
+++ b/data/sampled_jsons/ProDet_github_Cheng_Jikang_deepfake_detector.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - beautyremain/ProDet: The official code for paper \"Can We Leave ...", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector \" (NIPS2024 poster) ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well. You may find the overall-best checkpoint of our method from Google Drive, which is recommended for ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector \" (NIPS2024 poster) ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well. You may find the overall-best checkpoint of our method from Google Drive, which is recommended for ..."}
+{"idx": 1, "title": "PDF Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/2718a032d15e0b80cd164b240220df89-Paper-Conference.pdf", "content": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive."}
+{"idx": 2, "title": "Jikang Cheng - OpenReview", "date": "", "ddg_snippet": "Expertise Image Generation 2024 - Present Deepfake Detection 2022 - 2025 Adversarial Example 2022 - 2023", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Jikang_Cheng1", "content": "Expertise Image Generation 2024 - Present Deepfake Detection 2022 - 2025 Adversarial Example 2022 - 2023"}
+{"idx": 3, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "ProDet : Deepfake detection enhanced by progressively organizing blendfake and deepfake data in the latent space, improving generalization and robustness.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/vh9yepleyd/", "content": "ProDet : Deepfake detection enhanced by progressively organizing blendfake and deepfake data in the latent space, improving generalization and robustness."}
+{"idx": 4, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Authors Jikang Cheng , Zhiyuan Yan, Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed ''blendfake'', encouraging models to learn generic forgery artifacts like blending ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/2718a032d15e0b80cd164b240220df89-Abstract-Conference.html", "content": "Authors Jikang Cheng , Zhiyuan Yan, Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed ''blendfake'', encouraging models to learn generic forgery artifacts like blending ..."}
+{"idx": 5, "title": "Deepfake Detection - GitHub", "date": "", "ddg_snippet": "This repository is focused on deepfake detection using various machine learning and deep learning techniques. The goal is to compare different methods for detecting deepfakes and evaluate their effectiveness across different datasets.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/emirhanbilgic/Deepfake_Detection", "content": "This repository is focused on deepfake detection using various machine learning and deep learning techniques. The goal is to compare different methods for detecting deepfakes and evaluate their effectiveness across different datasets."}
+{"idx": 6, "title": "NeurIPS Poster Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93195", "content": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive."}
+{"idx": 7, "title": "Deepfake Detection Project - srabontideb.github.io", "date": "", "ddg_snippet": "The increasing sophistication of deepfakes poses significant challenges to digital trust and information integrity. This project aims to contribute to robust detection methods by exploring a model that can learn the intrinsic characteristics of various manipulations.", "subpage_snippet": "", "source": "srabontideb.github.io", "link": "https://srabontideb.github.io/Deepfake_project/", "content": "The increasing sophistication of deepfakes poses significant challenges to digital trust and information integrity. This project aims to contribute to robust detection methods by exploring a model that can learn the intrinsic characteristics of various manipulations."}
+{"idx": 8, "title": "GitHub - jiaawe/Deepfake-Detector: AI & ML project", "date": "", "ddg_snippet": "Our project is a multifaceted initiative aimed at countering the burgeoning threat of deepfake technology across diverse media formats. Our approach leverages a rich, multi-source dataset comprising artificial (fake) and factual (real) images, audio, and video. This project is dedicated to developing advanced detection algorithms capable of identifying manipulated content, regardless of its ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jiaawe/Deepfake-Detector", "content": "Our project is a multifaceted initiative aimed at countering the burgeoning threat of deepfake technology across diverse media formats. Our approach leverages a rich, multi-source dataset comprising artificial (fake) and factual (real) images, audio, and video. This project is dedicated to developing advanced detection algorithms capable of identifying manipulated content, regardless of its ..."}
+{"idx": 9, "title": "1M-Deepfakes Detection Challenge - arXiv.org", "date": "", "ddg_snippet": "Abstract. The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security. Based on the recently released AV-Deepfake1M dataset, which contains more than 1 million manipulated videos across more than 2,000 subjects, we introduce the 1M- Deepfakes Detection ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.06991v1", "content": "Abstract. The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security. Based on the recently released AV-Deepfake1M dataset, which contains more than 1 million manipulated videos across more than 2,000 subjects, we introduce the 1M- Deepfakes Detection ..."}
diff --git a/data/sampled_jsons/Progressive_Proximal_Transport_PPT_recommendation.jsonl b/data/sampled_jsons/Progressive_Proximal_Transport_PPT_recommendation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..459e8f99e3caefe665577ca4410d59a45f4390ea
--- /dev/null
+++ b/data/sampled_jsons/Progressive_Proximal_Transport_PPT_recommendation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "#1 - Cap and trade system ppt", "date": "", "ddg_snippet": "When blemish eruptions occurred, she cap and trade system ppt depressed and worried constantly that people would look at her skin and think she was ...", "subpage_snippet": "", "source": "top5binarybrokers.com", "link": "http://top5binarybrokers.com/cap-and-trade-system-ppt.html", "content": "When blemish eruptions occurred, she cap and trade system ppt depressed and worried constantly that people would look at her skin and think she was ..."}
+{"idx": 1, "title": "PPT – Definitions PowerPoint presentation | free to view", "date": "", "ddg_snippet": "Transport is an important factor in the evolution of settlements and in turn, patterns of development influence the demand for movement (RCEP, 1995).", "subpage_snippet": "", "source": "www.powershow.com", "link": "https://www.powershow.com/view/9c344-MTdmO/Definitions_powerpoint_ppt_presentation", "content": "Transport is an important factor in the evolution of settlements and in turn, patterns of development influence the demand for movement (RCEP, 1995)."}
+{"idx": 2, "title": "PPT – Children and disasters A Boots On THE GROUND", "date": "", "ddg_snippet": "Problem disaster medical treatment and transport services are provided by disparate entities (e.g., out-of-area hospitals and ambulance services, DoD ...", "subpage_snippet": "", "source": "www.powershow.com", "link": "https://www.powershow.com/view/135050-NGI4Z/Children_and_disasters_A_Boots_On_THE_GROUND_PERSPECTIVE_powerpoint_ppt_presentation", "content": "Problem disaster medical treatment and transport services are provided by disparate entities (e.g., out-of-area hospitals and ambulance services, DoD ..."}
+{"idx": 3, "title": "PPT - AI in logistics and supply chain - Use cases applications", "date": "", "ddg_snippet": "... operational efficiency, necessitating flawless coordination across multiple domains, including scheduling, transportation, and customer service.", "subpage_snippet": "", "source": "www.slideserve.com", "link": "https://www.slideserve.com/Zoe15/ai-in-logistics-and-supply-chain-use-cases-applications-solution-and-implementation", "content": "... operational efficiency, necessitating flawless coordination across multiple domains, including scheduling, transportation, and customer service."}
+{"idx": 4, "title": "Warehousing waste management | PPT", "date": "", "ddg_snippet": "The main functions of warehouses include transportation consolidation, product mixing, docking, service, and protecting against contingencies.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/warehousing-waste-management/65669002", "content": "The main functions of warehouses include transportation consolidation, product mixing, docking, service, and protecting against contingencies."}
+{"idx": 5, "title": "Coastal Plains and Islands of India | PPT", "date": "", "ddg_snippet": "Land transport is the cheapest means of transport and helps connect villages and towns via roads and railways. Water transport developed from ...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/coastal-plains-and-islands-of-india-82711163/82711163", "content": "Land transport is the cheapest means of transport and helps connect villages and towns via roads and railways. Water transport developed from ..."}
+{"idx": 6, "title": "PPT - Clinical Update: 2021 AHA/ASA Guideline for Stroke", "date": "", "ddg_snippet": "CLASS (STRENGTH) OF RECOMMENDATION LEVEL (QUALITY) OF EVIDENCE CLASS 1 (STRONG) Benefit > Risk LEVEL A High-quality evidence from more than 1 RCT ...", "subpage_snippet": "", "source": "www.slideorbit.com", "link": "https://www.slideorbit.com/slide/clinical-update-2021-aha-asa-guideline-for-stroke-prevention/120072", "content": "CLASS (STRENGTH) OF RECOMMENDATION LEVEL (QUALITY) OF EVIDENCE CLASS 1 (STRONG) Benefit > Risk LEVEL A High-quality evidence from more than 1 RCT ..."}
+{"idx": 7, "title": "PPT - Maximize Productivity and Safety with Wheel Loaders", "date": "", "ddg_snippet": "Recommended Products for Enhanced Performance Recommended Products for Enhanced Performance WA700-8 Wheel Loader The WA700-8 is a high-performing ...", "subpage_snippet": "", "source": "www.slideserve.com", "link": "https://www.slideserve.com/genocidecurri/maximize-productivity-and-safety-with-wheel-loaders", "content": "Recommended Products for Enhanced Performance Recommended Products for Enhanced Performance WA700-8 Wheel Loader The WA700-8 is a high-performing ..."}
+{"idx": 8, "title": "[PPT] - Achievements and Challenges Nicole Denjoy DITTA Chair", "date": "", "ddg_snippet": "... proximity to resources and raw materials) Global • Increasingly complex, global supply chain Manufacturing • Ancillary regulation compliance ...", "subpage_snippet": "", "source": "www.sambuz.com", "link": "https://www.sambuz.com/doc/achievements-and-challenges-ppt-presentation-30099", "content": "... proximity to resources and raw materials) Global • Increasingly complex, global supply chain Manufacturing • Ancillary regulation compliance ..."}
+{"idx": 9, "title": "Catalyst-proximal plastrons enhance activity and selectivity of", "date": "", "ddg_snippet": "Here, we develop a gasphilic CO 2 trap that is placed in close proximity to the catalyst (see Figure 1 C) that enhances gas-liquid mass transfer and ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(20)30348-9", "content": "Here, we develop a gasphilic CO 2 trap that is placed in close proximity to the catalyst (see Figure 1 C) that enhances gas-liquid mass transfer and ..."}
diff --git a/data/sampled_jsons/Promoting_Fairness_Among_Dynamic_Agents_OM-KIID_references_hardness_results.jsonl b/data/sampled_jsons/Promoting_Fairness_Among_Dynamic_Agents_OM-KIID_references_hardness_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..906cb6d7fed94cde1dca6362b6b5e1f05e26df60
--- /dev/null
+++ b/data/sampled_jsons/Promoting_Fairness_Among_Dynamic_Agents_OM-KIID_references_hardness_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets ...", "date": "", "ddg_snippet": "We stress that all hardness results mentioned here are independent of any benchmarks. We also consider a few extensions of OM -LF by proposing a few variants of fairness metrics, including long-run group-level fairness and short-run fairness , and we devise related algorithms with provable competitive performance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0C3bLHwjsY", "content": "We stress that all hardness results mentioned here are independent of any benchmarks. We also consider a few extensions of OM -LF by proposing a few variants of fairness metrics, including long-run group-level fairness and short-run fairness , and we devise related algorithms with provable competitive performance."}
+{"idx": 1, "title": "FairnessMaximizationamong OfflineAgentsinOnline-MatchingMarke", "date": "", "ddg_snippet": "from riders who cancel matches with drivers from these groups. In this paper, we study fairness maximization among offline agents in OMMs, on the prototypical online matching model with known, independent, and identical ( KIID ) arrivals, which is widely used to model the dynamic arrivals of online agents in several real-w", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.08934v2", "content": "from riders who cancel matches with drivers from these groups. In this paper, we study fairness maximization among offline agents in OMMs, on the prototypical online matching model with known, independent, and identical ( KIID ) arrivals, which is widely used to model the dynamic arrivals of online agents in several real-w"}
+{"idx": 2, "title": "Fairness Maximization among Offline Agents in Online ... - ResearchGate", "date": "", "ddg_snippet": "In this paper, we propose online matching algorithms that optimize for either individual or group-level fairness among offline agents in OMMs.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/368435499_Fairness_Maximization_among_Offline_Agents_in_Online-Matching_Markets", "content": "In this paper, we propose online matching algorithms that optimize for either individual or group-level fairness among offline agents in OMMs."}
+{"idx": 3, "title": "\"Promoting Fairness Among Dynamic Agents in Online-Matching ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/nips/Ma024", "content": "Bibliographic details on Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions."}
+{"idx": 4, "title": "NeurIPS Poster Promoting Fairness Among Dynamic Agents in Online ...", "date": "", "ddg_snippet": "We stress that all hardness results mentioned here are independent of any benchmarks. We also consider a few extensions of OM -LF by proposing a few variants of fairness metrics, including long-run group-level fairness and short-run fairness , and we devise related algorithms with provable competitive performance.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96945", "content": "We stress that all hardness results mentioned here are independent of any benchmarks. We also consider a few extensions of OM -LF by proposing a few variants of fairness metrics, including long-run group-level fairness and short-run fairness , and we devise related algorithms with provable competitive performance."}
+{"idx": 5, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets ...", "date": "", "ddg_snippet": "The hardness results would highlight the inherent difficulty of the problem, which stems from the need to balance fairness across different online types with the dynamic and unpredictable nature of online arrivals.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/0c3blhwjsy/", "content": "The hardness results would highlight the inherent difficulty of the problem, which stems from the need to balance fairness across different online types with the dynamic and unpredictable nature of online arrivals."}
+{"idx": 6, "title": "PDF Promoting Fairness Among Dynamic Agents in", "date": "", "ddg_snippet": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions Author Will Ma1", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96945.pdf", "content": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions Author Will Ma1"}
+{"idx": 7, "title": "[2109.08934] Fairness Maximization among Offline Agents in Online ...", "date": "", "ddg_snippet": "In this paper, we propose online matching algorithms which optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve online competitive ratios at least 0.725 for individual fairness maximization (IFM) and 0.719 for group fairness ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.08934", "content": "In this paper, we propose online matching algorithms which optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve online competitive ratios at least 0.725 for individual fairness maximization (IFM) and 0.719 for group fairness ..."}
+{"idx": 8, "title": "PDF Fairness and Optimization in Dynamic Multiagent Allocation Problems - IJCAI", "date": "", "ddg_snippet": "Abstract In many allocation problems, understanding indi-vidual agents' needs, wants, and tradeoffs is cru-cial for providing fair and eficient solutions. This paper begins with motivating applications and crit-ical definitions. We review existing results , such as advising agents on relaxing restrictions for im-proved resource allocation, optimizing task alloca-tion in online settings ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0973.pdf", "content": "Abstract In many allocation problems, understanding indi-vidual agents' needs, wants, and tradeoffs is cru-cial for providing fair and eficient solutions. This paper begins with motivating applications and crit-ical definitions. We review existing results , such as advising agents on relaxing restrictions for im-proved resource allocation, optimizing task alloca-tion in online settings ..."}
+{"idx": 9, "title": "Fairness Maximization among Offline Agents in Online-Matching Markets", "date": "", "ddg_snippet": "We study the prototypical online matching model with known, independent, and identical ( KIID ) arrivals, which is widely used to model the dynamic arrivals of online agents in several real-world OMMs including rideshare and crowdsourcing markets [14, 18, 54].", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10420152", "content": "We study the prototypical online matching model with known, independent, and identical ( KIID ) arrivals, which is widely used to model the dynamic arrivals of online agents in several real-world OMMs including rideshare and crowdsourcing markets [14, 18, 54]."}
diff --git a/data/sampled_jsons/Promoting_Fairness_Among_Dynamic_Agents_in_Online-Matching_Markets_under_Known_Stationary_Arrival_Di.jsonl b/data/sampled_jsons/Promoting_Fairness_Among_Dynamic_Agents_in_Online-Matching_Markets_under_Known_Stationary_Arrival_Di.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3f559826ed038b7a272933e12adf8dd013bf038b
--- /dev/null
+++ b/data/sampled_jsons/Promoting_Fairness_Among_Dynamic_Agents_in_Online-Matching_Markets_under_Known_Stationary_Arrival_Di.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "Promoting Fairness Among Dynamic Agents in Online- ...", "date": "", "ddg_snippet": "by W Ma · 2024 · Cited by 1 — Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively with the objective of maximizing the ... 30 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/959f70ee50044bed305e48e3484005a7-Paper-Conference.pdf", "content": "by W Ma · 2024 · Cited by 1 — Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively with the objective of maximizing the ... 30 pages"}
+{"idx": 1, "title": "Promoting Fairness Among Dynamic Agents in Online - Matching ...", "date": "", "ddg_snippet": "Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/959f70ee50044bed305e48e3484005a7-Abstract-Conference.html", "content": "Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served."}
+{"idx": 2, "title": "Promoting Fairness Among Dynamic Agents in Online - Matching ...", "date": "", "ddg_snippet": "Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0C3bLHwjsY", "content": "Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served."}
+{"idx": 3, "title": "New Impossibility Result for Online Bipartite Matching", "date": "", "ddg_snippet": "We conclude this section by noting that the interest in online bipartite matching is further evidenced by the attention given in recent years to several related problems. These include some of its weighted [29, 37, 44], and fairness -aware [48], variants; in particular, Huang et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.14251", "content": "We conclude this section by noting that the interest in online bipartite matching is further evidenced by the attention given in recent years to several related problems. These include some of its weighted [29, 37, 44], and fairness -aware [48], variants; in particular, Huang et al."}
+{"idx": 4, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets ...", "date": "", "ddg_snippet": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions Will Ma Graduate School of Business Columbia University New York, NY 10027 wm2428@gsb.columbia.edu", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0C3bLHwjsY", "content": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions Will Ma Graduate School of Business Columbia University New York, NY 10027 wm2428@gsb.columbia.edu"}
+{"idx": 5, "title": "Promoting Fairness Among Dynamic Agents in Online- ...", "date": "", "ddg_snippet": "Promoting Fairness Among Dynamic Agents in. Online-Matching Markets under Known Stationary. Arrival Distributions. Presenter. Hai An Tran2. Author. Will Ma1.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96945.pdf", "content": "Promoting Fairness Among Dynamic Agents in. Online-Matching Markets under Known Stationary. Arrival Distributions. Presenter. Hai An Tran2. Author. Will Ma1."}
+{"idx": 6, "title": "Will Ma: Curriculum Vitae", "date": "", "ddg_snippet": "3. Promoting Fairness Among Dynamic Agents in Online - Matching Markets under Known Stationary Arrival Distributions with Pan Xu Neural Information Processing Systems (NeurIPS), 2024.", "subpage_snippet": "", "source": "business.columbia.edu", "link": "https://business.columbia.edu/sites/default/files-efs/person/cv/Ma_Will_CV_2024.pdf", "content": "3. Promoting Fairness Among Dynamic Agents in Online - Matching Markets under Known Stationary Arrival Distributions with Pan Xu Neural Information Processing Systems (NeurIPS), 2024."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Pythia-7B_MMLU_score_benchmark.jsonl b/data/sampled_jsons/Pythia-7B_MMLU_score_benchmark.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a070c5ae2be2b9e5bb7f06ef2c43164e0811f00
--- /dev/null
+++ b/data/sampled_jsons/Pythia-7B_MMLU_score_benchmark.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MMLU-Pro Benchmark Leaderboard - Artificial Analysis", "date": "", "ddg_snippet": "Compare AI model performance on MMLU -Pro Benchmark Leaderboard. An enhanced version of MMLU with 12,000 graduate-level questions across 14 subject areas, featuring ten answer options and deeper reasoning requirements.", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/evaluations/mmlu-pro", "content": "Compare AI model performance on MMLU -Pro Benchmark Leaderboard. An enhanced version of MMLU with 12,000 graduate-level questions across 14 subject areas, featuring ten answer options and deeper reasoning requirements."}
+{"idx": 1, "title": "OpenNMT-py/eval_llm/MMLU/readme.md at master - GitHub", "date": "", "ddg_snippet": "For 7B params models: Llama7B score (35.25) matches both the Llama paper and the score reported by chain-of-thought-hub Falcon7B is a little higher then the score reported by chain-of-thought-hub (0.2641) I ran MPT7B with chain-of-thought-hub and found 28.46, again ours is a little higher.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OpenNMT/OpenNMT-py/blob/master/eval_llm/MMLU/readme.md", "content": "For 7B params models: Llama7B score (35.25) matches both the Llama paper and the score reported by chain-of-thought-hub Falcon7B is a little higher then the score reported by chain-of-thought-hub (0.2641) I ran MPT7B with chain-of-thought-hub and found 28.46, again ours is a little higher."}
+{"idx": 2, "title": "Evaluation with MMLU dataset | Eole - ♂️ Work In Progress", "date": "", "ddg_snippet": "Evaluation with MMLU dataset How to run To run the MMLU benchmark with a specific model, execute the following command from the recipe directory (recipes/ mmlu ):", "subpage_snippet": "", "source": "eole-nlp.github.io", "link": "https://eole-nlp.github.io/eole/docs/recipes/mmlu/", "content": "Evaluation with MMLU dataset How to run To run the MMLU benchmark with a specific model, execute the following command from the recipe directory (recipes/ mmlu ):"}
+{"idx": 3, "title": "README.md · allenai/fluid-benchmarking at main - Hugging Face", "date": "", "ddg_snippet": "This dataset provides IRT models for ARC Challenge, GSM8K, HellaSwag, MMLU , TruthfulQA, and WinoGrande. Furthermore, it contains results for pretraining checkpoints of Amber-6.7B, K2-65B, OLMo1- 7B , OLMo2- 7B , Pythia-2.8B, and Pythia-6.9B, evaluated on these six benchmarks . 🚀 Usage For utilities to use the dataset and to replicate the results from the paper, please see the corresponding ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/allenai/fluid-benchmarking/blob/main/README.md", "content": "This dataset provides IRT models for ARC Challenge, GSM8K, HellaSwag, MMLU , TruthfulQA, and WinoGrande. Furthermore, it contains results for pretraining checkpoints of Amber-6.7B, K2-65B, OLMo1- 7B , OLMo2- 7B , Pythia-2.8B, and Pythia-6.9B, evaluated on these six benchmarks . 🚀 Usage For utilities to use the dataset and to replicate the results from the paper, please see the corresponding ..."}
+{"idx": 4, "title": "Models Benchmarks | Mistral AI", "date": "", "ddg_snippet": "The company reports benchmark results on popular public benchmarks such as MMLU (Massive Multitask Language Understanding), MT-bench, and others. You can find the benchmark results in the following blog posts:", "subpage_snippet": "", "source": "docs.mistral.ai", "link": "https://docs.mistral.ai/getting-started/models/benchmark/", "content": "The company reports benchmark results on popular public benchmarks such as MMLU (Massive Multitask Language Understanding), MT-bench, and others. You can find the benchmark results in the following blog posts:"}
+{"idx": 5, "title": "MMLU Benchmark - Test Any LLM's Performance | AI Model Evaluation", "date": "", "ddg_snippet": "Run the MMLU benchmark on any Large Language Model. Evaluate AI model performance across multiple subjects. Compare results with public models and track your progress.", "subpage_snippet": "", "source": "mmlu.borgcloud.ai", "link": "https://mmlu.borgcloud.ai/", "content": "Run the MMLU benchmark on any Large Language Model. Evaluate AI model performance across multiple subjects. Compare results with public models and track your progress."}
+{"idx": 6, "title": "GitHub - EleutherAI/pythia: The hub for EleutherAI's work on ...", "date": "", "ddg_snippet": "This repository is for EleutherAI's project Pythia which combines interpretability analysis and scaling laws to understand how knowledge develops and evolves during training in autoregressive transformers. For detailed info on the models, their training, and their properties, please see our paper Pythia : A Suite for Analyzing Large Language Models Across Training and Scaling. The Pythia suite ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/EleutherAI/pythia", "content": "This repository is for EleutherAI's project Pythia which combines interpretability analysis and scaling laws to understand how knowledge develops and evolves during training in autoregressive transformers. For detailed info on the models, their training, and their properties, please see our paper Pythia : A Suite for Analyzing Large Language Models Across Training and Scaling. The Pythia suite ..."}
+{"idx": 7, "title": "LLM Leaderboard 2025 - Verified AI Rankings", "date": "", "ddg_snippet": "Comprehensive AI (LLM) leaderboard with benchmarks , pricing, and capabilities. Compare leading LLMs with interactive visualizations, rankings and comparisons.", "subpage_snippet": "", "source": "llm-stats.com", "link": "https://llm-stats.com/", "content": "Comprehensive AI (LLM) leaderboard with benchmarks , pricing, and capabilities. Compare leading LLMs with interactive visualizations, rankings and comparisons."}
+{"idx": 8, "title": "What is going on with Mistral 7b finetunes? (Equal to Qwen 72b)", "date": "", "ddg_snippet": "MMLU seems to be the most resistant benchmark to cheating/contamination, but I may be wrong. There are a couple of 13Bs with an abnormally high MMLU score , but they've been flagged. All these \"SOTA\" 7B models can't compete with any 70+ MMLU scoring model in practice, and they're definitely not anywhere near Qwen-72B level.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/18et7h9/what_is_going_on_with_mistral_7b_finetunes_equal/", "content": "MMLU seems to be the most resistant benchmark to cheating/contamination, but I may be wrong. There are a couple of 13Bs with an abnormally high MMLU score , but they've been flagged. All these \"SOTA\" 7B models can't compete with any 70+ MMLU scoring model in practice, and they're definitely not anywhere near Qwen-72B level."}
+{"idx": 9, "title": "Chatbot Arena Leaderboard Week 8: Introducing MT-Bench and ... - LMSYS", "date": "", "ddg_snippet": "Chatbot Arena Elo, based on 42K anonymous votes from Chatbot Arena using the Elo rating system. MT-Bench score , based on a challenging multi-turn benchmark and GPT-4 grading, proposed and validated in our Judging LLM-as-a-judge paper. MMLU , a widely adopted benchmark .", "subpage_snippet": "", "source": "lmsys.org", "link": "https://lmsys.org/blog/2023-06-22-leaderboard/", "content": "Chatbot Arena Elo, based on 42K anonymous votes from Chatbot Arena using the Elo rating system. MT-Bench score , based on a challenging multi-turn benchmark and GPT-4 grading, proposed and validated in our Judging LLM-as-a-judge paper. MMLU , a widely adopted benchmark ."}
diff --git a/data/sampled_jsons/Qwen2-VL_61.2_58.5_XLRS-Bench_OR_Qwen2-VL_Avg_difference_Chinese_English.jsonl b/data/sampled_jsons/Qwen2-VL_61.2_58.5_XLRS-Bench_OR_Qwen2-VL_Avg_difference_Chinese_English.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..01f25aa697b8ddd0638d74feccd2f6b9e5cbd24d
--- /dev/null
+++ b/data/sampled_jsons/Qwen2-VL_61.2_58.5_XLRS-Bench_OR_Qwen2-VL_Avg_difference_Chinese_English.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CG-BENCH: CLUE-GROUNDED QUESTION ANSWER", "date": "", "ddg_snippet": "# Avg /Words of Options. 22.88. # Avg /Duration of Clues. 19.24. Table 1 ... The most frequent languages are. Chinese (730 videos) and English (432 videos).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d6392620ac4ddf95772d6caaba422cf9d04988c0.pdf", "content": "# Avg /Words of Options. 22.88. # Avg /Duration of Clues. 19.24. Table 1 ... The most frequent languages are. Chinese (730 videos) and English (432 videos)."}
+{"idx": 1, "title": "Expanding Performance Boundaries of Open-Source ...", "date": "", "ddg_snippet": "16 Dec 2024 — ... difference ... 0 benchmark, which evaluates tasks in both English and Chinese , the InternVL 2.5 models show significant improvements.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.05271v2", "content": "16 Dec 2024 — ... difference ... 0 benchmark, which evaluates tasks in both English and Chinese , the InternVL 2.5 models show significant improvements."}
+{"idx": 2, "title": "Multi3Hate: Multimodal, Multilingual, and Multicultural Hate ...", "date": "", "ddg_snippet": "by MD Bui · 2025 · Cited by 7 — Chinese annotators labeled the major- ity of ... ∆ shows the difference between the multilingual prompt. (+CAPT) and English prompt (+CAPT).", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.490.pdf", "content": "by MD Bui · 2025 · Cited by 7 — Chinese annotators labeled the major- ity of ... ∆ shows the difference between the multilingual prompt. (+CAPT) and English prompt (+CAPT)."}
+{"idx": 3, "title": "Centurio: On Drivers of Multilingual Ability of Large Vision- ...", "date": "", "ddg_snippet": "9 Jan 2025 — ... Chinese ) and T1 extremely low-resource languages (e.g., Maori ... avg . ar, es, fr, ru. Phi 3.5 - English , 59.6, 55.0, 52.3, 54.9, 57.6, 55.2. Phi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.05122v1", "content": "9 Jan 2025 — ... Chinese ) and T1 extremely low-resource languages (e.g., Maori ... avg . ar, es, fr, ru. Phi 3.5 - English , 59.6, 55.0, 52.3, 54.9, 57.6, 55.2. Phi ..."}
+{"idx": 4, "title": "Centurio: On Drivers of Multilingual Ability of Large Vision- ...", "date": "", "ddg_snippet": "by G Geigle · 2025 · Cited by 5 — ... English text in images. number of training languages? RQ2 ... 73.3 60.3 54.2 47.5 58.5 72.5 55.0 58.3 60.2 72.5 64.2 59.2 61.2 . Phi ... 51 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.143.pdf", "content": "by G Geigle · 2025 · Cited by 5 — ... English text in images. number of training languages? RQ2 ... 73.3 60.3 54.2 47.5 58.5 72.5 55.0 58.3 60.2 72.5 64.2 59.2 61.2 . Phi ... 51 pages"}
+{"idx": 5, "title": "PHANTOM OF LATENT FOR LARGE LANGUAGE AND ...", "date": "", "ddg_snippet": "comprehension skills (20 fine-grained abilities) in both English and Chinese . This bench- mark consists of 3,217 questions gathered from various sources to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/9e5b01580d67db86cf5761f70e9c9bdea799a0c3.pdf", "content": "comprehension skills (20 fine-grained abilities) in both English and Chinese . This bench- mark consists of 3,217 questions gathered from various sources to ..."}
+{"idx": 6, "title": "Towards Unified Interfaces for Generalist Agent In Diverse ...", "date": "", "ddg_snippet": "4.3 AVG . number of knowledge concept questions ... translated questions from this exam to English from Chinese . ... The key difference is in how the curved lines ...", "subpage_snippet": "", "source": "ra.adm.cs.cmu.edu", "link": "http://ra.adm.cs.cmu.edu/anon/2025/CMU-CS-25-120.pdf", "content": "4.3 AVG . number of knowledge concept questions ... translated questions from this exam to English from Chinese . ... The key difference is in how the curved lines ..."}
+{"idx": 7, "title": "Multi Hate:: Multimodal, Multilingual, and Multicultural ...", "date": "", "ddg_snippet": "English -speaking cultures due to its exclusively captions. English dataset. ... Chinese annotators labeled the major- across all ... Category AVG Highest Lowest ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/812094256/2411-03888v1", "content": "English -speaking cultures due to its exclusively captions. English dataset. ... Chinese annotators labeled the major- across all ... Category AVG Highest Lowest ..."}
+{"idx": 8, "title": "M2-omni | PDF | Data Compression | Applied Mathematics", "date": "", "ddg_snippet": "LLaVA-OneVision-7B [106] 8B 61.2 76.8 56.7 46.8 58.5 47.5 82.8 697 50.6 ... SVIT [266] 40000 0.54 Image-Audio-QA English / Chinese OK_VQA [140] 18018 0.24 ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/910976093/M2-omni", "content": "LLaVA-OneVision-7B [106] 8B 61.2 76.8 56.7 46.8 58.5 47.5 82.8 697 50.6 ... SVIT [266] 40000 0.54 Image-Audio-QA English / Chinese OK_VQA [140] 18018 0.24 ..."}
+{"idx": 9, "title": "Daily AI Papers - Gabriel Chua", "date": "", "ddg_snippet": "The methodology involves a dataset of 1012 Chinese and English questions across eight counter-intuitive categories (e.g., Intentional Textual Flaws, Code ...", "subpage_snippet": "", "source": "gabrielchua.me", "link": "https://gabrielchua.me/daily-ai-papers/", "content": "The methodology involves a dataset of 1012 Chinese and English questions across eight counter-intuitive categories (e.g., Intentional Textual Flaws, Code ..."}
diff --git a/data/sampled_jsons/Qwen2-VL_Chinese_English_Table_2_Avg._XLRS-Bench.jsonl b/data/sampled_jsons/Qwen2-VL_Chinese_English_Table_2_Avg._XLRS-Bench.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9fb5202a9650100e3a8c047813abbb48919fde64
--- /dev/null
+++ b/data/sampled_jsons/Qwen2-VL_Chinese_English_Table_2_Avg._XLRS-Bench.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ChartMind: A Comprehensive Benchmark for Complex Real-world", "date": "", "ddg_snippet": "2 Shenyang Institute of Computing ... It includes both English and Chinese charts, providing the first dual-language evaluation setting for chart QA.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23242v1", "content": "2 Shenyang Institute of Computing ... It includes both English and Chinese charts, providing the first dual-language evaluation setting for chart QA."}
+{"idx": 1, "title": "LongDocURL: a Comprehensive Multimodal Long Document Benchmark", "date": "", "ddg_snippet": "... 2 ,3 , Jiale Yuan 4 1 1 1 https://corp.digitalcorpora.org/corpora/files/CC-MAIN-2021-31-PDF-UNTRUNCATED/ , Pi Bu 4 , Peijie Wang 1, 2 , Zhong-Zhi Li 1, 2 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18424v3", "content": "... 2 ,3 , Jiale Yuan 4 1 1 1 https://corp.digitalcorpora.org/corpora/files/CC-MAIN-2021-31-PDF-UNTRUNCATED/ , Pi Bu 4 , Peijie Wang 1, 2 , Zhong-Zhi Li 1, 2 ..."}
+{"idx": 2, "title": "lmms-lab/LLaVA-Video-7B-Qwen2 · Hugging Face", "date": "", "ddg_snippet": "... VL /LLaVA-NeXT.git from llava.model.builder import load_pretrained_model from llava.mm_utils import get_model_name_from_path, process_images, ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/lmms-lab/LLaVA-Video-7B-Qwen2", "content": "... VL /LLaVA-NeXT.git from llava.model.builder import load_pretrained_model from llava.mm_utils import get_model_name_from_path, process_images, ..."}
+{"idx": 3, "title": "Are We on the Right Way for Assessing Document", "date": "", "ddg_snippet": "We introduce Double-Bench, the first comprehensive evaluation system for multilingual and multimodal document RAG, featuring 3,276 documents (72,880 ...", "subpage_snippet": "", "source": "double-bench.github.io", "link": "https://double-bench.github.io/", "content": "We introduce Double-Bench, the first comprehensive evaluation system for multilingual and multimodal document RAG, featuring 3,276 documents (72,880 ..."}
+{"idx": 4, "title": "jina-reranker-m0: Multilingual Multimodal Document Reranker", "date": "", "ddg_snippet": "... tables , infographics, and various layouts ... The architecture of jina-reranker-m0 is built upon Qwen2 - VL -2B and consists of 2 .4 billion parameters.", "subpage_snippet": "", "source": "jina.ai", "link": "https://jina.ai/news/jina-reranker-m0-multilingual-multimodal-document-reranker/", "content": "... tables , infographics, and various layouts ... The architecture of jina-reranker-m0 is built upon Qwen2 - VL -2B and consists of 2 .4 billion parameters."}
+{"idx": 5, "title": "ChatDOC/OCRFlux-3B · Hugging Face", "date": "", "ddg_snippet": "OCRFlux-bench-single : Containing 2000 pdf pages (1000 English pages and 1000 Chinese pages) and their ground-truth Markdowns (manually labeled with ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/ChatDOC/OCRFlux-3B", "content": "OCRFlux-bench-single : Containing 2000 pdf pages (1000 English pages and 1000 Chinese pages) and their ground-truth Markdowns (manually labeled with ..."}
+{"idx": 6, "title": "Qwen-Image-Edit: The Ultimate Technical Guide to AI-Powered", "date": "", "ddg_snippet": "... text_encoder_params\": sum(p.numel() for p in self.text_encoder.parameters()), \"total_params\": \"20B\", \"architecture\": \"MMDiT + Qwen2 .5- VL \" }", "subpage_snippet": "", "source": "collabnix.com", "link": "https://collabnix.com/qwen-image-edit-the-ultimate-technical-guide-to-ai-powered-image-editing-2025/", "content": "... text_encoder_params\": sum(p.numel() for p in self.text_encoder.parameters()), \"total_params\": \"20B\", \"architecture\": \"MMDiT + Qwen2 .5- VL \" }"}
+{"idx": 7, "title": "UniGen: Enhanced Training & Test-Time Strategies for", "date": "", "ddg_snippet": "Figure 2 : The architecture of UniGen , which is based on an autoregressive LLM and decoupled vision encoders for image understanding and generation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14682v1", "content": "Figure 2 : The architecture of UniGen , which is based on an autoregressive LLM and decoupled vision encoders for image understanding and generation ..."}
+{"idx": 8, "title": "E-ARMOR: Edge case Assessment and Review of Multilingual", "date": "", "ddg_snippet": "We present a large-scale comparative evaluation of five state-of-the-art LVLMs (InternVL, Qwen, GOT OCR, LLaMA, MiniCPM) and two traditional OCR ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03615v1", "content": "We present a large-scale comparative evaluation of five state-of-the-art LVLMs (InternVL, Qwen, GOT OCR, LLaMA, MiniCPM) and two traditional OCR ..."}
+{"idx": 9, "title": "VLMQ: Efficient Post-Training Quantization for Large", "date": "", "ddg_snippet": "... 2 : PCA-based Hessian feature analysis with activations (4096 points) extracted from the pre-attention trace of the 20 20 -th transformer layer in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03351v1", "content": "... 2 : PCA-based Hessian feature analysis with activations (4096 points) extracted from the pre-attention trace of the 20 20 -th transformer layer in ..."}
diff --git a/data/sampled_jsons/RA-PbRL_paper_Chen_2023_ICVaR-RLHF_random_reference_trajectory_unavoidable_linear_regret.jsonl b/data/sampled_jsons/RA-PbRL_paper_Chen_2023_ICVaR-RLHF_random_reference_trajectory_unavoidable_linear_regret.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c9d744bcea19512d07751c13943d43fcb4473b1f
--- /dev/null
+++ b/data/sampled_jsons/RA-PbRL_paper_Chen_2023_ICVaR-RLHF_random_reference_trajectory_unavoidable_linear_regret.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF RA-PbRL: Provably Eficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "The limitation of this work lies in the selection of a random reference trajectory for comparison, causing an unavoidable linear strong nested CVaR regret . Consequently, we are left with only a preference equation from which we are unable to compute the state-action reward function for each step.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "The limitation of this work lies in the selection of a random reference trajectory for comparison, causing an unavoidable linear strong nested CVaR regret . Consequently, we are left with only a preference equation from which we are unable to compute the state-action reward function for each step."}
+{"idx": 1, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "9 Jan 2025 — The limitation of this work lies in the selection of a random reference trajectory for comparison, causing an unavoidable linear strong nested ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v4", "content": "9 Jan 2025 — The limitation of this work lies in the selection of a random reference trajectory for comparison, causing an unavoidable linear strong nested ..."}
+{"idx": 2, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "9 Dec 2024 — ... random reference trajectory for comparison , causing an unavoidable linear strong nested CVaR regret. ... ICVaR-RLHF, detailed in Chen et al.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/95716", "content": "9 Dec 2024 — ... random reference trajectory for comparison , causing an unavoidable linear strong nested CVaR regret. ... ICVaR-RLHF, detailed in Chen et al."}
+{"idx": 3, "title": "A Complete List of ArXiv Papers on Alignment, Safety, and ...", "date": "", "ddg_snippet": "This webpage curates a comprehensive list of Arxiv papers relevant to the alignment, safety, and security of LLMs.", "subpage_snippet": "", "source": "xiangyuqi.com", "link": "https://xiangyuqi.com/arxiv-llm-alignment-safety-security/", "content": "This webpage curates a comprehensive list of Arxiv papers relevant to the alignment, safety, and security of LLMs."}
+{"idx": 4, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback ( RLHF ) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Reinforcement Learning from Human Feedback ( RLHF ) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 5, "title": "RA-PbRL | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "We also introduce Risk-Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3739861", "content": "We also introduce Risk-Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Additionally, we provide a theoretical analysis of the regret upper bounds, demonstrating that they are sublinear with respect to the number of episodes, and present empirical results to support our findings."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Authors Yujie Zhao, Jose Aguilar Escamilla, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback ( RLHF ) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "Authors Yujie Zhao, Jose Aguilar Escamilla, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback ( RLHF ) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning ( PbRL ), where the preferences specifically ..."}
+{"idx": 7, "title": "ICLR 2023 Papers", "date": "", "ddg_snippet": "A Study on Representation Learning Almost Linear Constant-Factor Sketching for $\\ell_1$ and Logistic Regression Concept Gradient: Concept-based Interpretation Without Linear Assumption Neural Networks Efficiently Learn Low-Dimensional Representations with SGD Neural Agents Struggle to Take Turns in Bidirectional Emergent Communication", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2023/papers.html", "content": "A Study on Representation Learning Almost Linear Constant-Factor Sketching for $\\ell_1$ and Logistic Regression Concept Gradient: Concept-based Interpretation Without Linear Assumption Neural Networks Efficiently Learn Low-Dimensional Representations with SGD Neural Agents Struggle to Take Turns in Bidirectional Emergent Communication"}
+{"idx": 8, "title": "[PDF] RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "This work explores and proves the applicability of two risk-aware objectives to PbRL : nested and static quantile risk objectives and introduces Risk-Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Preference-based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/RA-PbRL:-Provably-Efficient-Risk-Aware-Learning-Zhao-Escamill/80889b1260bfcf4275f7ec18bb4eec183400f05d", "content": "This work explores and proves the applicability of two risk-aware objectives to PbRL : nested and static quantile risk objectives and introduces Risk-Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Preference-based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode ..."}
+{"idx": 9, "title": "A survey of Preference Reinforcement Learning - GitHub", "date": "", "ddg_snippet": "* Regret analysis of the preference-based algorithm under assumption of trajectory embedding and preferences Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Kavka1/Preference-RL", "content": "* Regret analysis of the preference-based algorithm under assumption of trajectory embedding and preferences Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation."}
diff --git a/data/sampled_jsons/RA-PbRL_paper_introduction_section_challenges_risk_measures_preference-based_learning.jsonl b/data/sampled_jsons/RA-PbRL_paper_introduction_section_challenges_risk_measures_preference-based_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d73c2cdea5f2f9d350f976a67c1069c6f764b747
--- /dev/null
+++ b/data/sampled_jsons/RA-PbRL_paper_introduction_section_challenges_risk_measures_preference-based_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "P reference - based r einforcement L earning", "date": "", "ddg_snippet": "In this paper , we study offline preference - based reinforcement learning ( PbRL ), where learning is based on pre-collected preference feedback over pairs of trajec-tories.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5Y9NT6lW21", "content": "In this paper , we study offline preference - based reinforcement learning ( PbRL ), where learning is based on pre-collected preference feedback over pairs of trajec-tories."}
+{"idx": 1, "title": "Preference - Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Preference - based reinforcement learning ( PbRL ) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Preference - based reinforcement learning ( PbRL ) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal."}
+{"idx": 2, "title": "Learning Real-World Acrobatic Flight from Human Preferences", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning . Unsupervised Pretraining.Abstract— Preference - based reinforcement learning ( PbRL ) en-ables agents to learn control policies without requiring manually. a) Training in Simulation.", "subpage_snippet": "", "source": "rpg.ifi.uzh.ch", "link": "https://rpg.ifi.uzh.ch/docs/Arxiv25_Geles_PbRL.pdf", "content": "Preference - based Reinforcement Learning . Unsupervised Pretraining.Abstract— Preference - based reinforcement learning ( PbRL ) en-ables agents to learn control policies without requiring manually. a) Training in Simulation."}
+{"idx": 3, "title": "(PDF) PB$^2$: Preference Space Exploration via Population- Based ...", "date": "", "ddg_snippet": "Preference - based reinforcement learning ( PbRL ) has emerged as a promising approach for learning behaviors from human feedback without predefined reward functions.preference exploration problem. Section 4 presents our population-based approach in detail.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392766202_PB2_Preference_Space_Exploration_via_Population-Based_Methods_in_Preference-Based_Reinforcement_Learning", "content": "Preference - based reinforcement learning ( PbRL ) has emerged as a promising approach for learning behaviors from human feedback without predefined reward functions.preference exploration problem. Section 4 presents our population-based approach in detail."}
+{"idx": 4, "title": "A Survey of Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "Preference - Based vs. Reward- Based Learning . Nash Learning from Human Feedback.Building on prior work on the related setting of preference - based reinforcement learning ( PbRL ), it stands at the intersection of articial intelligence and human-computer interac-tion.", "subpage_snippet": "", "source": "epub.ub.uni-muenchen.de", "link": "https://epub.ub.uni-muenchen.de/125328/1/2312.14925v2.pdf", "content": "Preference - Based vs. Reward- Based Learning . Nash Learning from Human Feedback.Building on prior work on the related setting of preference - based reinforcement learning ( PbRL ), it stands at the intersection of articial intelligence and human-computer interac-tion."}
+{"idx": 5, "title": "[2509.15607] PRIMT: Preference - based Reinforcement Learning with...", "date": "", "ddg_snippet": "Abstract: Preference - based reinforcement learning ( PbRL ) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2509.15607", "content": "Abstract: Preference - based reinforcement learning ( PbRL ) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering."}
+{"idx": 6, "title": "ICML Query-Policy Misalignment in Preference - Based Reinforcement...", "date": "", "ddg_snippet": "Preference - based reinforcement learning ( PbRL ) provides a natural way to align RL agents’ behavior with human desired outcomes, but is often restrained by costly human feedback. To improve feedback efficiency, most existing PbRL methods focus on selecting queries to maximally improve...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2023/29101", "content": "Preference - based reinforcement learning ( PbRL ) provides a natural way to align RL agents’ behavior with human desired outcomes, but is often restrained by costly human feedback. To improve feedback efficiency, most existing PbRL methods focus on selecting queries to maximally improve..."}
+{"idx": 7, "title": "Rethinking Reinforcement Learning with Human Feedback", "date": "", "ddg_snippet": "Preference - based reinforcement learning ( PbRL ) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-24-rethinking-reinforcement-learning-with-human-feedback--ak5gopn", "content": "Preference - based reinforcement learning ( PbRL ) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards."}
+{"idx": 8, "title": "Preference - based Reinforcement Learning", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning ( PbRL ) replaces reward values in tra-ditional reinforcement learning by preferences to better elicit human opinion on the target objective, especially when numerical reward values are hard to design or interpret.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/d9d3837ee7981e8c064774da6cdd98bf-Paper.pdf", "content": "Preference - based Reinforcement Learning ( PbRL ) replaces reward values in tra-ditional reinforcement learning by preferences to better elicit human opinion on the target objective, especially when numerical reward values are hard to design or interpret."}
+{"idx": 9, "title": "GitHub - yingchengyang/Reinforcement- Learning - Papers : Related...", "date": "", "ddg_snippet": "Related papers for reinforcement learning , including classic papers and latest papers in top conferences.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yingchengyang/Reinforcement-Learning-Papers", "content": "Related papers for reinforcement learning , including classic papers and latest papers in top conferences."}
diff --git "a/data/sampled_jsons/RA-PbRL_regret_formula_equation_cumulative_sum_episodes_V_V\317\200_year_2024.jsonl" "b/data/sampled_jsons/RA-PbRL_regret_formula_equation_cumulative_sum_episodes_V_V\317\200_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..9b63f45de6ab1524f79d9596541f1726233b2ff2
--- /dev/null
+++ "b/data/sampled_jsons/RA-PbRL_regret_formula_equation_cumulative_sum_episodes_V_V\317\200_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "System of linear equations - Wikipedia", "date": "", "ddg_snippet": "Cramer's rule is an explicit formula for the solution of a system of linear equations , with each variable given by a quotient of two determinants.[9] For example, the solution to the system.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/System_of_linear_equations", "content": "Cramer's rule is an explicit formula for the solution of a system of linear equations , with each variable given by a quotient of two determinants.[9] For example, the solution to the system."}
+{"idx": 1, "title": "Quadratic Equation Solver", "date": "", "ddg_snippet": "Quadratic Equation Solver. We can help you solve an equation of the form \"ax2 + bx + c = 0\" Enter your values of a, b and c here (details below)", "subpage_snippet": "", "source": "www.mathsisfun.com", "link": "https://www.mathsisfun.com/quadratic-equation-solver.html", "content": "Quadratic Equation Solver. We can help you solve an equation of the form \"ax2 + bx + c = 0\" Enter your values of a, b and c here (details below)"}
+{"idx": 2, "title": "Basic Math Formulas - GeeksforGeeks", "date": "", "ddg_snippet": "Cubic Equation Formula . Resultant vector formula . Daily Compound Interest Formula . Sum of an Arithmetic Sequence Formula .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/maths/basic-math-formulas/", "content": "Cubic Equation Formula . Resultant vector formula . Daily Compound Interest Formula . Sum of an Arithmetic Sequence Formula ."}
+{"idx": 3, "title": "byjus.com/maths/maths- formulas -for-class-10", "date": "", "ddg_snippet": "The class 10 maths formulas include formulas related to real numbers, polynomials, quadratic equations , triangles, circles, statistics, probability, etc.", "subpage_snippet": "", "source": "byjus.com", "link": "https://byjus.com/maths/maths-formulas-for-class-10/", "content": "The class 10 maths formulas include formulas related to real numbers, polynomials, quadratic equations , triangles, circles, statistics, probability, etc."}
+{"idx": 4, "title": "Write formulas - MkDocs MagicSpace", "date": "", "ddg_snippet": "Write formulas . You can use MkDocs Workspace to create powerful mathematical documentation websites or print documents, by exporting docs to pdf files. MkDocs Workspace renders LaTeX math equations with MathJax.", "subpage_snippet": "", "source": "mkdocs-magicspace.alnoda.org", "link": "https://mkdocs-magicspace.alnoda.org/tutorials/markdown/formulas/", "content": "Write formulas . You can use MkDocs Workspace to create powerful mathematical documentation websites or print documents, by exporting docs to pdf files. MkDocs Workspace renders LaTeX math equations with MathJax."}
+{"idx": 5, "title": "Project Euler Solution #23: Non-abundant sums • RAW", "date": "", "ddg_snippet": "Sum vs XOR. Circle City. Anti-Palindromic Strings.With the isAbundant function, we can formulate a new function, which checks if a number is a sum of two abundant numbers", "subpage_snippet": "", "source": "raw.org", "link": "https://raw.org/puzzle/project-euler/problem-23/", "content": "Sum vs XOR. Circle City. Anti-Palindromic Strings.With the isAbundant function, we can formulate a new function, which checks if a number is a sum of two abundant numbers"}
+{"idx": 6, "title": "11.2: Introduction to ANOVA's Sum of Squares - Statistics LibreTexts", "date": "", "ddg_snippet": "Incorporating this, we find our equation for Between Groups Sum of Squares to beThe formula for this within groups sum of squares is again going to take on the same form and logic.", "subpage_snippet": "", "source": "stats.libretexts.org", "link": "https://stats.libretexts.org/Workbench/PSYC_2200:_Elementary_Statistics_for_Behavioral_and_Social_Science_(Oja)_WITHOUT_UNITS/11:_BG_ANOVA/11.02:_Introduction_to_ANOVA's_Sum_of_Squares", "content": "Incorporating this, we find our equation for Between Groups Sum of Squares to beThe formula for this within groups sum of squares is again going to take on the same form and logic."}
+{"idx": 7, "title": "Policy Gradient Method in Reinforcement Learning... - aigreeks.com", "date": "", "ddg_snippet": ": This is the total return (the cumulative sum of future discounted rewards) starting from time step.REINFORCE, a classic in the Policy Gradient Method in Reinforcement Learning, uses full episode returns for updates.", "subpage_snippet": "", "source": "aigreeks.com", "link": "https://aigreeks.com/policy-gradient-method-in-reinforcement-learning/", "content": ": This is the total return (the cumulative sum of future discounted rewards) starting from time step.REINFORCE, a classic in the Policy Gradient Method in Reinforcement Learning, uses full episode returns for updates."}
+{"idx": 8, "title": "ppo_cartpole - Colab", "date": "", "ddg_snippet": "tensorflow and keras for building the deep RL PPO agent. gymnasium for getting everything we need about the environment. scipy.signal for calculating the discounted cumulative sums of vectors.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/rl/ipynb/ppo_cartpole.ipynb", "content": "tensorflow and keras for building the deep RL PPO agent. gymnasium for getting everything we need about the environment. scipy.signal for calculating the discounted cumulative sums of vectors."}
+{"idx": 9, "title": "Percent Difference Calculator - Inch Calculator", "date": "", "ddg_snippet": "The percent difference formula is number a minus number b divided by a minus b divided by 2, times 100. Steps to Find Percent Difference.", "subpage_snippet": "", "source": "www.inchcalculator.com", "link": "https://www.inchcalculator.com/percent-difference-calculator/", "content": "The percent difference formula is number a minus number b divided by a minus b divided by 2, times 100. Steps to Find Percent Difference."}
diff --git a/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_6.1_noise-sensitive_LLaMa3_year_2024.jsonl b/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_6.1_noise-sensitive_LLaMa3_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8eb06d216a4468c19fca11ef56fe3f04448c293e
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_6.1_noise-sensitive_LLaMa3_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "Real-world RAG systems retrieve a mix of relevant and irrelevant content, making reader robustness to noise a key factor in performance . This section evaluates reader behav-ior when (1) at least one gold passage is present and (2) no gold passage is retrieved.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09040v3", "content": "Real-world RAG systems retrieve a mix of relevant and irrelevant content, making reader robustness to noise a key factor in performance . This section evaluates reader behav-ior when (1) at least one gold passage is present and (2) no gold passage is retrieved."}
+{"idx": 1, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/neulab/ragged", "content": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability."}
+{"idx": 2, "title": "PDF RAGGED: Towards Informed Design of", "date": "", "ddg_snippet": "RQ3. How Robust Are Readers to Noise When the Gold Passage Is Retrieved? We analyze cases where the top-k retrieved documents include at least 1 gold passage. Key aspets:1) gap between top-gold and top-k, 2) when top-k < no-context. Improve-then-plateau Peak-then-decline FLAN-T5 FLAN-UL2 LLaMa2 7B LLaMa2 70B", "subpage_snippet": "", "source": "adaptive-foundation-models.org", "link": "https://adaptive-foundation-models.org/posters/RAGGED_AFM_2024_Poster.pdf", "content": "RQ3. How Robust Are Readers to Noise When the Gold Passage Is Retrieved? We analyze cases where the top-k retrieved documents include at least 1 gold passage. Key aspets:1) gap between top-gold and top-k, 2) when top-k < no-context. Improve-then-plateau Peak-then-decline FLAN-T5 FLAN-UL2 LLaMa2 7B LLaMa2 70B"}
+{"idx": 3, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance , making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2403.09040v3", "content": "View recent discussion. Abstract: Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance , making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems ..."}
+{"idx": 4, "title": "RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems", "date": "", "ddg_snippet": "Retrieval-augmented generation ( RAG ) can significantly improve the performance of language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED , a framework for analyzing RAG configurations across ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tFw2M9hLUi", "content": "Retrieval-augmented generation ( RAG ) can significantly improve the performance of language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED , a framework for analyzing RAG configurations across ..."}
+{"idx": 5, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance , making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09040", "content": "Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance , making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader ..."}
+{"idx": 6, "title": "RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems", "date": "", "ddg_snippet": "To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems . On a set of representative DBQA tasks, we study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures. Through RAGGED , we uncover that different models suit substantially varied RAG setups.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v1", "content": "To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems . On a set of representative DBQA tasks, we study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures. Through RAGGED , we uncover that different models suit substantially varied RAG setups."}
+{"idx": 7, "title": "RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems", "date": "", "ddg_snippet": "Abstract Retrieval-augmented generation ( RAG ) can significantly improve the performance of language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED , a framework for analyzing RAG configurations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v2", "content": "Abstract Retrieval-augmented generation ( RAG ) can significantly improve the performance of language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED , a framework for analyzing RAG configurations ..."}
+{"idx": 8, "title": "Production-Ready RAG: Engineering Guidelines for Scalable Systems", "date": "", "ddg_snippet": "Learn how to build cost-effective, scalable AI systems with Retrieval Augmented Generation ( RAG ) architecture, optimizing performance and managing data.", "subpage_snippet": "", "source": "www.netguru.com", "link": "https://www.netguru.com/blog/rag-for-scalable-systems", "content": "Learn how to build cost-effective, scalable AI systems with Retrieval Augmented Generation ( RAG ) architecture, optimizing performance and managing data."}
+{"idx": 9, "title": "Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "Abstract. Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowl- edge, but its effectiveness is highly dependent on.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f6a0fe5ddc9cd97dfbd232abc279ce1ed7259753.pdf", "content": "Abstract. Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowl- edge, but its effectiveness is highly dependent on."}
diff --git a/data/sampled_jsons/RAG_Stability_Score_RSS_formula_RAGGED_paper.jsonl b/data/sampled_jsons/RAG_Stability_Score_RSS_formula_RAGGED_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..424ea551281cf0e9177e58fde6bf9a12f54c8178
--- /dev/null
+++ b/data/sampled_jsons/RAG_Stability_Score_RSS_formula_RAGGED_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09040", "content": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability."}
+{"idx": 1, "title": "RAG Evaluation Metrics Explained: A Complete Guide - Medium", "date": "", "ddg_snippet": "RAG Evaluation Metrics Explained: A Complete Guide RAG (Retrieval-Augmented Generation) enhances generative language models by integrating information retrieval techniques to address the issue of …", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@med.el.harchaoui/rag-evaluation-metrics-explained-a-complete-guide-dbd7a3b571a8", "content": "RAG Evaluation Metrics Explained: A Complete Guide RAG (Retrieval-Augmented Generation) enhances generative language models by integrating information retrieval techniques to address the issue of …"}
+{"idx": 2, "title": "RAG Evaluation - Hugging Face Open-Source AI Cookbook", "date": "", "ddg_snippet": "This notebook demonstrates how you can evaluate your RAG (Retrieval Augmented Generation), by building a synthetic evaluation dataset and using LLM-as-a-judge to compute the accuracy of your system. For an introduction to RAG , you can check this other cookbook! RAG systems are complex: here a RAG diagram, where we noted in blue all possibilities for system enhancement: Implementing any of ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/learn/cookbook/rag_evaluation", "content": "This notebook demonstrates how you can evaluate your RAG (Retrieval Augmented Generation), by building a synthetic evaluation dataset and using LLM-as-a-judge to compute the accuracy of your system. For an introduction to RAG , you can check this other cookbook! RAG systems are complex: here a RAG diagram, where we noted in blue all possibilities for system enhancement: Implementing any of ..."}
+{"idx": 3, "title": "Mastering RAG Evaluation: Metrics, Tools & Optimization - Evonomics", "date": "", "ddg_snippet": "In-depth look into RAG evaluation. Learn key metrics, tools, and optimization techniques to enhance your AI's accuracy and reliability.", "subpage_snippet": "", "source": "evonomics.eu", "link": "https://evonomics.eu/ai-leadership-journal/retrieval-augmented-generation/mastering-rag-evaluation/", "content": "In-depth look into RAG evaluation. Learn key metrics, tools, and optimization techniques to enhance your AI's accuracy and reliability."}
+{"idx": 4, "title": "The Ultimate Guide to Evaluate RAG System Components - MyScale", "date": "", "ddg_snippet": "This blog covers all methods of evaluating RAG systems, including recall, MRR and MAP for retrieval evaluation; faithfulness for response evaluation; and BLEU, ROUGE, METEOR for answer relevance.", "subpage_snippet": "", "source": "www.myscale.com", "link": "https://www.myscale.com/blog/ultimate-guide-to-evaluate-rag-system/", "content": "This blog covers all methods of evaluating RAG systems, including recall, MRR and MAP for retrieval evaluation; faithfulness for response evaluation; and BLEU, ROUGE, METEOR for answer relevance."}
+{"idx": 5, "title": "GitHub - neulab/ragged: Retrieval Augmented Generation Generalized ...", "date": "", "ddg_snippet": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/neulab/ragged", "content": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability."}
+{"idx": 6, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "Through two new metrics - RAG Stability Score ( RSS ) and RAG Scalability Coeficient (RSC) - RAGGED of-fers a principled way to assess how reliably and eficiently models use retrieved information across configurations and domains.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09040v3", "content": "Through two new metrics - RAG Stability Score ( RSS ) and RAG Scalability Coeficient (RSC) - RAGGED of-fers a principled way to assess how reliably and eficiently models use retrieved information across configurations and domains."}
+{"idx": 7, "title": "RAGAS for RAG in LLMs: A Comprehensive Guide to Evaluation Metrics.", "date": "", "ddg_snippet": "This is where RAGAS metrics, specifically designed for RAG models, become essential. RAGAS: Specialized Metrics for RAG Models RAGAS introduces several metrics that provide a more holistic evaluation of RAG models, focusing on aspects like faithfulness, answer relevancy, context precision, and context recall.", "subpage_snippet": "", "source": "dkaarthick.medium.com", "link": "https://dkaarthick.medium.com/ragas-for-rag-in-llms-a-comprehensive-guide-to-evaluation-metrics-3aca142d6e38", "content": "This is where RAGAS metrics, specifically designed for RAG models, become essential. RAGAS: Specialized Metrics for RAG Models RAGAS introduces several metrics that provide a more holistic evaluation of RAG models, focusing on aspects like faithfulness, answer relevancy, context precision, and context recall."}
+{"idx": 8, "title": "How to Measure RAG from Accuracy to Relevance?", "date": "", "ddg_snippet": "Learn key methods for evaluating Retrieval-Augmented Generation ( RAG ) systems using metrics like accuracy, coherence, and relevance.", "subpage_snippet": "", "source": "www.datategy.net", "link": "https://www.datategy.net/2024/09/27/how-to-measure-rag-from-accuracy-to-relevance/", "content": "Learn key methods for evaluating Retrieval-Augmented Generation ( RAG ) systems using metrics like accuracy, coherence, and relevance."}
+{"idx": 9, "title": "PDF ragged_afm - adaptive-foundation-models.org", "date": "", "ddg_snippet": "Which retriever to use? How many documents? Which reader to use? Why RAG Matters: Access to up-to-date knowledge, improved accuracy for complex tasks, cost-effective knowledge integration Challenges: Noisy data, retriever-reader mismatch, diverse task requirements. Solution: The RAGGED framework, a systematic tool for optimizing RAG configurations.", "subpage_snippet": "", "source": "adaptive-foundation-models.org", "link": "https://adaptive-foundation-models.org/posters/RAGGED_AFM_2024_Poster.pdf", "content": "Which retriever to use? How many documents? Which reader to use? Why RAG Matters: Access to up-to-date knowledge, improved accuracy for complex tasks, cost-effective knowledge integration Challenges: Noisy data, retriever-reader mismatch, diverse task requirements. Solution: The RAGGED framework, a systematic tool for optimizing RAG configurations."}
diff --git a/data/sampled_jsons/RAG_Stability_Score_RSS_formula_peak_performance_minimum_performance_arxiv_2403.09040.jsonl b/data/sampled_jsons/RAG_Stability_Score_RSS_formula_peak_performance_minimum_performance_arxiv_2403.09040.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..802261a35db310bb460ce1a74ca8dc326855740b
--- /dev/null
+++ b/data/sampled_jsons/RAG_Stability_Score_RSS_formula_peak_performance_minimum_performance_arxiv_2403.09040.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "RAGGED : Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "RAG Stability Score ( RSS ) The RSS metric quantifies how consistently a model maintains performance around its optimal retrieval depth.The numerator captures the minimum performance in this range, excluding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v3", "content": "RAG Stability Score ( RSS ) The RSS metric quantifies how consistently a model maintains performance around its optimal retrieval depth.The numerator captures the minimum performance in this range, excluding."}
+{"idx": 1, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "Mar 14, 2024 · In this work, we introduce RAGGED, a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09040", "content": "Mar 14, 2024 · In this work, we introduce RAGGED, a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability."}
+{"idx": 2, "title": "RAG Evaluation & Confidence Score | by Naresh Kancharla | Medium", "date": "", "ddg_snippet": "Mar 16, 2025 · Here are several methods to build a confidence score for RAG responses. This specific post will talk about RAGA’s approach (2nd approach).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@naresh.kancharla/rag-evaluation-confidence-score-dfd1bdd01b82", "content": "Mar 16, 2025 · Here are several methods to build a confidence score for RAG responses. This specific post will talk about RAGA’s approach (2nd approach)."}
+{"idx": 3, "title": "RAG Architecture Analysis: Optimize Retrieval & Generation", "date": "", "ddg_snippet": "Nov 25, 2024 · RAG Introduction RAG architectures facilitate the extraction of pertinent information, enhancing the overall quality and accuracy of generated outputs. This blog explores the intricacies of RAG architecture, focusing on the evaluation of its retrieval and generation components, the structure of effective evaluation datasets, and the metrics essential for assessing system performance ...", "subpage_snippet": "", "source": "www.getmaxim.ai", "link": "https://www.getmaxim.ai/blog/rag-evaluation-metrics/", "content": "Nov 25, 2024 · RAG Introduction RAG architectures facilitate the extraction of pertinent information, enhancing the overall quality and accuracy of generated outputs. This blog explores the intricacies of RAG architecture, focusing on the evaluation of its retrieval and generation components, the structure of effective evaluation datasets, and the metrics essential for assessing system performance ..."}
+{"idx": 4, "title": "RAG Evaluation Metrics: Measuring System Performance", "date": "", "ddg_snippet": "Feb 22, 2024 · Effective evaluation of RAG systems requires a multi-faceted approach combining automated metrics, human evaluation, and continuous monitoring. By implementing these evaluation strategies and metrics, you can ensure your RAG system meets user needs and maintains high quality standards over time.", "subpage_snippet": "", "source": "www.ragwire.com", "link": "https://www.ragwire.com/blog/rag-evaluation-metrics", "content": "Feb 22, 2024 · Effective evaluation of RAG systems requires a multi-faceted approach combining automated metrics, human evaluation, and continuous monitoring. By implementing these evaluation strategies and metrics, you can ensure your RAG system meets user needs and maintains high quality standards over time."}
+{"idx": 5, "title": "The Ultimate Guide to Evaluate RAG System Components - MyScale", "date": "", "ddg_snippet": "Jun 26, 2024 · This blog covers all methods of evaluating RAG systems, including recall, MRR and MAP for retrieval evaluation; faithfulness for response evaluation; and BLEU, ROUGE, METEOR for answer relevance.", "subpage_snippet": "", "source": "www.myscale.com", "link": "https://www.myscale.com/blog/ultimate-guide-to-evaluate-rag-system/", "content": "Jun 26, 2024 · This blog covers all methods of evaluating RAG systems, including recall, MRR and MAP for retrieval evaluation; faithfulness for response evaluation; and BLEU, ROUGE, METEOR for answer relevance."}
+{"idx": 6, "title": "Evaluation of RAG Metrics for Question Answering in the ...", "date": "", "ddg_snippet": "In this work, we enhance the current version of the RAGAS package for evaluation of RAG based QA - this helps in-vestigate the scores by analysing the intermediate outputs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.12873", "content": "In this work, we enhance the current version of the RAGAS package for evaluation of RAG based QA - this helps in-vestigate the scores by analysing the intermediate outputs."}
+{"idx": 7, "title": "Organizational Behaviour by Huczynski... - PDFCOFFEE.COM", "date": "", "ddg_snippet": "assessment scores and job performance are often weak. • Type theories (Hippocrates; Sheldon; Jung) classify. 3. Explain the uses and limitations of objective questionnaires and projective tests as measures of personality.", "subpage_snippet": "", "source": "pdfcoffee.com", "link": "https://pdfcoffee.com/organizational-behaviour-by-huczynski-a-buchanan-d-z-liborgpdf-pdf-free.html", "content": "assessment scores and job performance are often weak. • Type theories (Hippocrates; Sheldon; Jung) classify. 3. Explain the uses and limitations of objective questionnaires and projective tests as measures of personality."}
+{"idx": 8, "title": "Result Evaluation - IBM", "date": "", "ddg_snippet": "The complexity of RAG systems is significantly influenced by the enigmatic nature of Large Language Models (LLMs), as well as the intricate and interconnected components within the RAG pipeline. As technology continues to progress at an unprecedented rate, evaluating such a complex system becomes an increasingly arduous task.", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/architectures/papers/rag-cookbook/result-evaluation", "content": "The complexity of RAG systems is significantly influenced by the enigmatic nature of Large Language Models (LLMs), as well as the intricate and interconnected components within the RAG pipeline. As technology continues to progress at an unprecedented rate, evaluating such a complex system becomes an increasingly arduous task."}
diff --git a/data/sampled_jsons/RAP_Reasoning_via_Planning_large_language_models.jsonl b/data/sampled_jsons/RAP_Reasoning_via_Planning_large_language_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5fad60790ec496b10d962977fc28abca1635ae6e
--- /dev/null
+++ b/data/sampled_jsons/RAP_Reasoning_via_Planning_large_language_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning via Planning (RAP) the LLM Reasoners - MsTechDiva with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model", "date": "", "ddg_snippet": "May 24, 2023 · Large language models (LLMs) have shown remarkable reasoning capabilities, especially when prompted to generate intermediate reasoning steps (e.g., Chain-of-Thought, CoT). However, LLMs can still struggle with problems that are easy for humans, such as generating action plans for executing tasks in a given environment, or performing complex math, logical, and commonsense reasoning . The ... Sep 15, 2025 · To overcome the limitations, we propose a new LLM reasoning framework, Reasoning via Planning ( RAP ). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. News! We released LLM Reasoners, a library for complex reasoning with LLMs, and include the code to reproduce some experiments in RAP . Give it a try! Source code for the paper Reasoning with Language Model is Planning with World Model See full list on github.com •Warning: This code only supports LLaMA-1. Check our new library LLM Reasoners for more flexible choices of LLMs. •Our experiments are conducted with LLaMA-33B, which takes at least 4 GPUs of 24GB memory each. The code also supports smaller LLaMA models , but other LLMs (e.g. those from Hugging Face) are not tested. •Acquire the checkpoints of LLaMA from MetaAI following the LLaMA official repo and set up the environment variable: export LLAMA_CKPTS=\"YOUR_PATH_TO_LLAMA_CHECKPOINTS\" •Install all required packages for LLaMA official repo. See full list on github.com •Set up VAL following this guide and make sure you set the environment variable export VAL=\"YOUR_PATH_TO_VAL\" •Run the command: CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run --master_port 1034 --nproc_per_node 4 run_blocksworld.py --task mcts --model_name LLaMA --ckpt_path $LLAMA_CKPTS/30B --verbose True --data data/blocksworld/step_4.json --max_depth 4 --name run_4_May26_max_depth_4_alpha_05_rollouts_10 --rollouts 10 See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1054 run_gsm8k.py --llama-ckpt $LLAMA_CKPTS/30B --speedup-confidence-batch-size 2 •Use python run_gsm8k.py -- --help for details about arguments See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1074 run_prontoqa.py --llama-ckpt $LLAMA_CKPTS/30B •Use python run_prontoqa.py -- --help for details about arguments See full list on github.com Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks. On the planning abilities of large language models (a critical investigation with a proposed benchmark) [Valmeekam et al, 2023] Chain-of-thought prompting elicits reasoning in large language models [Wei et al., 2022] Mental models : Towards a cognitive science of language , inference, and consciousness [Johnson-Laird, 1983] From System 1 Deep ... Oct 7, 2023 · The paper proposes the Reasoning via Planning ( RAP ) framework to enhance large language models ' reasoning capabilities by incorporating an external world model and principled planning . How does rap enhance large language models' reasoning capabilities? The paper proposes the Reasoning via Planning (RAP) framework to enhance large language models' reasoning capabilities by incorporating an external world model and principled planning . Main Contributions: Introducing RAP Framework: RAP augments large language models with a world model and uses principled planning (MCTS) for reasoning. What is reasoning via planning (RAP)? To overcome the limitations, we propose a new LLM reasoning framework , Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. Can a large language model be used as a reasoning agent? TL;DR: We repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm for human-like reasoning. Large language models (LLMs) have shown remarkable reasoning capabilities , particularly with Chain-of-Thought-style prompts. What is rap – easoning VI A – P – Lanning? To overcome the limitations, we propose a new LLM reasoning framework , R–– easoning vi a–– P–– lanning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monto Carlo Tree Search) for strategic exploration in the vast reasoning space. What is rap framework & how does it work? Introducing RAP Framework: RAP augments large language models with a world model and uses principled planning (MCTS) for reasoning . Enhancing Reasoning : By simulating the state of the world and guiding the reasoning process, RAP improves performance in complex tasks compared to existing methods like CoT. Can a large language model solve a problem? Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. However, LLMs can still struggle with problems that are easy for humans , such as generating action plans for executing tasks or performing complex math or logical reasoning. To overcome these limitations, the authors propose a new LLM reasoning framework— Reasoning via Planning ( RAP ). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a planning algorithm based on Monte Carlo Tree Search (MCTS) to strategically explore the vast reasoning space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14992", "content": "May 24, 2023 · Large language models (LLMs) have shown remarkable reasoning capabilities, especially when prompted to generate intermediate reasoning steps (e.g., Chain-of-Thought, CoT). However, LLMs can still struggle with problems that are easy for humans, such as generating action plans for executing tasks in a given environment, or performing complex math, logical, and commonsense reasoning . The ... Sep 15, 2025 · To overcome the limitations, we propose a new LLM reasoning framework, Reasoning via Planning ( RAP ). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. News! We released LLM Reasoners, a library for complex reasoning with LLMs, and include the code to reproduce some experiments in RAP . Give it a try! Source code for the paper Reasoning with Language Model is Planning with World Model See full list on github.com •Warning: This code only supports LLaMA-1. Check our new library LLM Reasoners for more flexible choices of LLMs. •Our experiments are conducted with LLaMA-33B, which takes at least 4 GPUs of 24GB memory each. The code also supports smaller LLaMA models , but other LLMs (e.g. those from Hugging Face) are not tested. •Acquire the checkpoints of LLaMA from MetaAI following the LLaMA official repo and set up the environment variable: export LLAMA_CKPTS=\"YOUR_PATH_TO_LLAMA_CHECKPOINTS\" •Install all required packages for LLaMA official repo. See full list on github.com •Set up VAL following this guide and make sure you set the environment variable export VAL=\"YOUR_PATH_TO_VAL\" •Run the command: CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run --master_port 1034 --nproc_per_node 4 run_blocksworld.py --task mcts --model_name LLaMA --ckpt_path $LLAMA_CKPTS/30B --verbose True --data data/blocksworld/step_4.json --max_depth 4 --name run_4_May26_max_depth_4_alpha_05_rollouts_10 --rollouts 10 See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1054 run_gsm8k.py --llama-ckpt $LLAMA_CKPTS/30B --speedup-confidence-batch-size 2 •Use python run_gsm8k.py -- --help for details about arguments See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1074 run_prontoqa.py --llama-ckpt $LLAMA_CKPTS/30B •Use python run_prontoqa.py -- --help for details about arguments See full list on github.com Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks. On the planning abilities of large language models (a critical investigation with a proposed benchmark) [Valmeekam et al, 2023] Chain-of-thought prompting elicits reasoning in large language models [Wei et al., 2022] Mental models : Towards a cognitive science of language , inference, and consciousness [Johnson-Laird, 1983] From System 1 Deep ... Oct 7, 2023 · The paper proposes the Reasoning via Planning ( RAP ) framework to enhance large language models ' reasoning capabilities by incorporating an external world model and principled planning . How does rap enhance large language models' reasoning capabilities? The paper proposes the Reasoning via Planning (RAP) framework to enhance large language models' reasoning capabilities by incorporating an external world model and principled planning . Main Contributions: Introducing RAP Framework: RAP augments large language models with a world model and uses principled planning (MCTS) for reasoning. What is reasoning via planning (RAP)? To overcome the limitations, we propose a new LLM reasoning framework , Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. Can a large language model be used as a reasoning agent? TL;DR: We repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm for human-like reasoning. Large language models (LLMs) have shown remarkable reasoning capabilities , particularly with Chain-of-Thought-style prompts. What is rap – easoning VI A – P – Lanning? To overcome the limitations, we propose a new LLM reasoning framework , R–– easoning vi a–– P–– lanning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monto Carlo Tree Search) for strategic exploration in the vast reasoning space. What is rap framework & how does it work? Introducing RAP Framework: RAP augments large language models with a world model and uses principled planning (MCTS) for reasoning . Enhancing Reasoning : By simulating the state of the world and guiding the reasoning process, RAP improves performance in complex tasks compared to existing methods like CoT. Can a large language model solve a problem? Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. However, LLMs can still struggle with problems that are easy for humans , such as generating action plans for executing tasks or performing complex math or logical reasoning. To overcome these limitations, the authors propose a new LLM reasoning framework— Reasoning via Planning ( RAP ). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a planning algorithm based on Monte Carlo Tree Search (MCTS) to strategically explore the vast reasoning space."}
+{"idx": 1, "title": "Reasoning with Language Model is Planning with World Model", "date": "", "ddg_snippet": "Oct 7, 2023 · The paper proposes the Reasoning via Planning ( RAP ) framework to enhance large language models ' reasoning capabilities by incorporating an external world model and principled planning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=VTWWvYtF1R", "content": "Oct 7, 2023 · The paper proposes the Reasoning via Planning ( RAP ) framework to enhance large language models ' reasoning capabilities by incorporating an external world model and principled planning ."}
+{"idx": 2, "title": "Reasoning via Planning (RAP) the LLM Reasoners - MsTechDiva", "date": "", "ddg_snippet": "Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks.", "subpage_snippet": "", "source": "mstechdiva.com", "link": "https://mstechdiva.com/reasoning-via-planning-rap-the-llm-reasoners/", "content": "Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks."}
+{"idx": 3, "title": "Reasoning via Planning (RAP)", "date": "", "ddg_snippet": "25 Aug 2025 — RAP unifies classical AI planning, logical inference, probabilistic reasoning , and the emerging capabilities of LLMs and vision-LLMs (VLMs), ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/reasoning-via-planning-rap", "content": "25 Aug 2025 — RAP unifies classical AI planning, logical inference, probabilistic reasoning , and the emerging capabilities of LLMs and vision-LLMs (VLMs), ..."}
+{"idx": 4, "title": "🗣️🗺️🤖⚙️ Reasoning with Language Model is Planning ...", "date": "", "ddg_snippet": "This paper outlines a new framework, Reasoning via Planning ( RAP ). It argues that Large Language Models (LLMs) sometimes struggle with problems that ...", "subpage_snippet": "", "source": "bagrounds.org", "link": "https://bagrounds.org/articles/reasoning-with-language-model-is-planning-with-world-model", "content": "This paper outlines a new framework, Reasoning via Planning ( RAP ). It argues that Large Language Models (LLMs) sometimes struggle with problems that ..."}
+{"idx": 5, "title": "Reasoning with Language Model is Planning with World ...", "date": "", "ddg_snippet": "A new LLM reasoning framework, RAP , which repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Reasoning-with-Language-Model-is-Planning-with-Hao-Gu/5dbffedcabe3fa43060ebbe2b1789500edfd871f", "content": "A new LLM reasoning framework, RAP , which repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm ..."}
+{"idx": 6, "title": "Reasoning with Language Model is Planning with World ...", "date": "", "ddg_snippet": "by S Hao · Cited by 754 — LLM reasoning framework, Reasoning via Planning ( RAP ). RAP repurposes the. 11. LLM as both a world model and a reasoning agent, and incorporates a principled.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zQbff3h0da", "content": "by S Hao · Cited by 754 — LLM reasoning framework, Reasoning via Planning ( RAP ). RAP repurposes the. 11. LLM as both a world model and a reasoning agent, and incorporates a principled."}
+{"idx": 7, "title": "Reasoning with Language Model is Planning with World ...", "date": "", "ddg_snippet": "24 May 2023 — A new reasoning framework, RAP, enhances LLMs by integrating a world model and planning algorithm to improve performance in complex reasoning ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2305.14992", "content": "24 May 2023 — A new reasoning framework, RAP, enhances LLMs by integrating a world model and planning algorithm to improve performance in complex reasoning ..."}
+{"idx": 8, "title": "Reasoning with Language Model is Planning with World Model", "date": "", "ddg_snippet": "Sep 15, 2025 · To overcome the limitations, we propose a new LLM reasoning framework, Reasoning via Planning ( RAP ). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.emnlp-main.507/", "content": "Sep 15, 2025 · To overcome the limitations, we propose a new LLM reasoning framework, Reasoning via Planning ( RAP ). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space."}
+{"idx": 9, "title": "Reasoning with Language Model is Planning with World Model Reasoning via Planning (RAP) the LLM Reasoners - MsTechDiva with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model Reasoning with Language Model is Planning with World Model", "date": "", "ddg_snippet": "News! We released LLM Reasoners, a library for complex reasoning with LLMs, and include the code to reproduce some experiments in RAP . Give it a try! Source code for the paper Reasoning with Language Model is Planning with World Model See full list on github.com •Warning: This code only supports LLaMA-1. Check our new library LLM Reasoners for more flexible choices of LLMs. •Our experiments are conducted with LLaMA-33B, which takes at least 4 GPUs of 24GB memory each. The code also supports smaller LLaMA models , but other LLMs (e.g. those from Hugging Face) are not tested. •Acquire the checkpoints of LLaMA from MetaAI following the LLaMA official repo and set up the environment variable: export LLAMA_CKPTS=\"YOUR_PATH_TO_LLAMA_CHECKPOINTS\" •Install all required packages for LLaMA official repo. See full list on github.com •Set up VAL following this guide and make sure you set the environment variable export VAL=\"YOUR_PATH_TO_VAL\" •Run the command: CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run --master_port 1034 --nproc_per_node 4 run_blocksworld.py --task mcts --model_name LLaMA --ckpt_path $LLAMA_CKPTS/30B --verbose True --data data/blocksworld/step_4.json --max_depth 4 --name run_4_May26_max_depth_4_alpha_05_rollouts_10 --rollouts 10 See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1054 run_gsm8k.py --llama-ckpt $LLAMA_CKPTS/30B --speedup-confidence-batch-size 2 •Use python run_gsm8k.py -- --help for details about arguments See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1074 run_prontoqa.py --llama-ckpt $LLAMA_CKPTS/30B •Use python run_prontoqa.py -- --help for details about arguments See full list on github.com Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks. On the planning abilities of large language models (a critical investigation with a proposed benchmark) [Valmeekam et al, 2023] Chain-of-thought prompting elicits reasoning in large language models [Wei et al., 2022] Mental models : Towards a cognitive science of language , inference, and consciousness [Johnson-Laird, 1983] From System 1 Deep ... Oct 7, 2023 · The paper proposes the Reasoning via Planning ( RAP ) framework to enhance large language models ' reasoning capabilities by incorporating an external world model and principled planning . How does rap enhance large language models' reasoning capabilities? The paper proposes the Reasoning via Planning (RAP) framework to enhance large language models' reasoning capabilities by incorporating an external world model and principled planning . Main Contributions: Introducing RAP Framework: RAP augments large language models with a world model and uses principled planning (MCTS) for reasoning. What is reasoning via planning (RAP)? To overcome the limitations, we propose a new LLM reasoning framework , Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. Can a large language model be used as a reasoning agent? TL;DR: We repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm for human-like reasoning. Large language models (LLMs) have shown remarkable reasoning capabilities , particularly with Chain-of-Thought-style prompts. What is rap – easoning VI A – P – Lanning? To overcome the limitations, we propose a new LLM reasoning framework , R–– easoning vi a–– P–– lanning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monto Carlo Tree Search) for strategic exploration in the vast reasoning space. What is rap framework & how does it work? Introducing RAP Framework: RAP augments large language models with a world model and uses principled planning (MCTS) for reasoning . Enhancing Reasoning : By simulating the state of the world and guiding the reasoning process, RAP improves performance in complex tasks compared to existing methods like CoT. Can a large language model solve a problem? Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. However, LLMs can still struggle with problems that are easy for humans , such as generating action plans for executing tasks or performing complex math or logical reasoning. To overcome these limitations, the authors propose a new LLM reasoning framework— Reasoning via Planning ( RAP ). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a planning algorithm based on Monte Carlo Tree Search (MCTS) to strategically explore the vast reasoning space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Ber666/RAP", "content": "News! We released LLM Reasoners, a library for complex reasoning with LLMs, and include the code to reproduce some experiments in RAP . Give it a try! Source code for the paper Reasoning with Language Model is Planning with World Model See full list on github.com •Warning: This code only supports LLaMA-1. Check our new library LLM Reasoners for more flexible choices of LLMs. •Our experiments are conducted with LLaMA-33B, which takes at least 4 GPUs of 24GB memory each. The code also supports smaller LLaMA models , but other LLMs (e.g. those from Hugging Face) are not tested. •Acquire the checkpoints of LLaMA from MetaAI following the LLaMA official repo and set up the environment variable: export LLAMA_CKPTS=\"YOUR_PATH_TO_LLAMA_CHECKPOINTS\" •Install all required packages for LLaMA official repo. See full list on github.com •Set up VAL following this guide and make sure you set the environment variable export VAL=\"YOUR_PATH_TO_VAL\" •Run the command: CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run --master_port 1034 --nproc_per_node 4 run_blocksworld.py --task mcts --model_name LLaMA --ckpt_path $LLAMA_CKPTS/30B --verbose True --data data/blocksworld/step_4.json --max_depth 4 --name run_4_May26_max_depth_4_alpha_05_rollouts_10 --rollouts 10 See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1054 run_gsm8k.py --llama-ckpt $LLAMA_CKPTS/30B --speedup-confidence-batch-size 2 •Use python run_gsm8k.py -- --help for details about arguments See full list on github.com •Run with: CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master-port 1074 run_prontoqa.py --llama-ckpt $LLAMA_CKPTS/30B •Use python run_prontoqa.py -- --help for details about arguments See full list on github.com Aug 2, 2023 · The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning . Reasoning via Planning ( RAP ) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks. On the planning abilities of large language models (a critical investigation with a proposed benchmark) [Valmeekam et al, 2023] Chain-of-thought prompting elicits reasoning in large language models [Wei et al., 2022] Mental models : Towards a cognitive science of language , inference, and consciousness [Johnson-Laird, 1983] From System 1 Deep ... Oct 7, 2023 · The paper proposes the Reasoning via Planning ( RAP ) framework to enhance large language models ' reasoning capabilities by incorporating an external world model and principled planning . How does rap enhance large language models' reasoning capabilities? The paper proposes the Reasoning via Planning (RAP) framework to enhance large language models' reasoning capabilities by incorporating an external world model and principled planning . Main Contributions: Introducing RAP Framework: RAP augments large language models with a world model and uses principled planning (MCTS) for reasoning. What is reasoning via planning (RAP)? To overcome the limitations, we propose a new LLM reasoning framework , Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. Can a large language model be used as a reasoning agent? TL;DR: We repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm for human-like reasoning. Large language models (LLMs) have shown remarkable reasoning capabilities , particularly with Chain-of-Thought-style prompts. What is rap – easoning VI A – P – Lanning? To overcome the limitations, we propose a new LLM reasoning framework , R–– easoning vi a–– P–– lanning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monto Carlo Tree Search) for strategic exploration in the vast reasoning space. What is rap framework & how does it work? Introducing RAP Framework: RAP augments large language models with a world model and uses principled planning (MCTS) for reasoning . Enhancing Reasoning : By simulating the state of the world and guiding the reasoning process, RAP improves performance in complex tasks compared to existing methods like CoT. Can a large language model solve a problem? Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. However, LLMs can still struggle with problems that are easy for humans , such as generating action plans for executing tasks or performing complex math or logical reasoning. To overcome these limitations, the authors propose a new LLM reasoning framework— Reasoning via Planning ( RAP ). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a planning algorithm based on Monte Carlo Tree Search (MCTS) to strategically explore the vast reasoning space."}
diff --git a/data/sampled_jsons/RAP_reasoning_large_language_models_method_year_2023.jsonl b/data/sampled_jsons/RAP_reasoning_large_language_models_method_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..37e365191206a919add50abf71056df339f6dc38
--- /dev/null
+++ b/data/sampled_jsons/RAP_reasoning_large_language_models_method_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reasoning with Language Model is Planning with World Model -", "date": "", "ddg_snippet": "Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.emnlp-main.507/", "content": "Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts."}
+{"idx": 1, "title": "Outshift | Mastering the Art of Prompting for Large Language", "date": "", "ddg_snippet": "Reasoning via Planning ( RAP ) is an innovative framework proposed to bolster the reasoning capabilities of large language models (LLMs).", "subpage_snippet": "", "source": "outshift.cisco.com", "link": "https://outshift.cisco.com/blog/mastering-the-art-of-prompting-for-large-language-models-reducing-hallucination-and-improving-reasoning", "content": "Reasoning via Planning ( RAP ) is an innovative framework proposed to bolster the reasoning capabilities of large language models (LLMs)."}
+{"idx": 2, "title": "1 Overview of RAP. Our framework stores past experiences and", "date": "", "ddg_snippet": "Large Language Models (LLMs) such as GPT OpenAI ( 2023 ) and the LLaMA series GenAI ( 2023 ) , leveraging transformer architecture and self ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.03610v1", "content": "Large Language Models (LLMs) such as GPT OpenAI ( 2023 ) and the LLaMA series GenAI ( 2023 ) , leveraging transformer architecture and self ..."}
+{"idx": 3, "title": "RAP-SM: Robust Adversarial Prompt via Shadow Models for", "date": "", "ddg_snippet": "The rapid advancement of Large Language Models (LLMs) has brought to light a range of pressing concerns, including model leaks, malicious ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06304v1", "content": "The rapid advancement of Large Language Models (LLMs) has brought to light a range of pressing concerns, including model leaks, malicious ..."}
+{"idx": 4, "title": "ABAP RESTful Application Programming Model (RAP) - SAP Community", "date": "", "ddg_snippet": "So, is it possible to develop feature-rich applications without other frontend languages ? Yes, leveraging ABAP with RAP (ABAP Restful Application ...", "subpage_snippet": "", "source": "community.sap.com", "link": "https://community.sap.com/t5/technology-blogs-by-members/abap-restful-application-programming-model-rap/ba-p/13743339", "content": "So, is it possible to develop feature-rich applications without other frontend languages ? Yes, leveraging ABAP with RAP (ABAP Restful Application ..."}
+{"idx": 5, "title": "LanguageAgentTreeSearch | [ICML 2024] Official repository for", "date": "", "ddg_snippet": "Language models can use reasoning and enhance acting for decision-making tasks (ReAct, Reflexion). ... While large language models (LLMs) have ...", "subpage_snippet": "", "source": "lapisrocks.github.io", "link": "https://lapisrocks.github.io/LanguageAgentTreeSearch/", "content": "Language models can use reasoning and enhance acting for decision-making tasks (ReAct, Reflexion). ... While large language models (LLMs) have ..."}
+{"idx": 6, "title": "(PDF) A Survey on Mathematical Reasoning and Optimization with", "date": "", "ddg_snippet": "Recent advancements in Large Language Models (LLMs) have significantly improved AI-driven mathematical reasoning , theorem proving, and optimization ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390142528_A_Survey_on_Mathematical_Reasoning_and_Optimization_with_Large_Language_Models", "content": "Recent advancements in Large Language Models (LLMs) have significantly improved AI-driven mathematical reasoning , theorem proving, and optimization ..."}
+{"idx": 7, "title": "Meet RAP and LLM Reasoners: Two Frameworks Based on Similar", "date": "", "ddg_snippet": "Google Research has unveiled a groundbreaking method for fine-tuning large language models (LLMs) that slashes the amount of required training data ...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2023/07/31/meet-rap-and-llm-reasoners-two-frameworks-based-on-similar-concepts-for-advanced-reasoning-with-llms/", "content": "Google Research has unveiled a groundbreaking method for fine-tuning large language models (LLMs) that slashes the amount of required training data ..."}
+{"idx": 8, "title": "Xingwei Qu", "date": "", "ddg_snippet": "Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) improves the reasoning abilities of Large Language Models (LLMs) but it struggles ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Xingwei+Qu", "content": "Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) improves the reasoning abilities of Large Language Models (LLMs) but it struggles ..."}
+{"idx": 9, "title": "Home | Reasoners", "date": "", "ddg_snippet": "Generating accurate step-by-step reasoning is essential for Large Language Models (LLMs) to address complex problems and enhance robustness and ...", "subpage_snippet": "", "source": "www.llm-reasoners.net", "link": "https://www.llm-reasoners.net/", "content": "Generating accurate step-by-step reasoning is essential for Large Language Models (LLMs) to address complex problems and enhance robustness and ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_spurious_correlations_Section_5_underlying_cause.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_spurious_correlations_Section_5_underlying_cause.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ccfcd3d1a1441361b6fa395ec057b755a44a71d8
--- /dev/null
+++ b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_spurious_correlations_Section_5_underlying_cause.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Spurious relationship - Wikipedia", "date": "", "ddg_snippet": "Graphical model: Whereas a mediator is a factor in the causal chain, a confounder is a spurious factor incorrectly implying causation. In statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Spurious_relationship", "content": "Graphical model: Whereas a mediator is a factor in the causal chain, a confounder is a spurious factor incorrectly implying causation. In statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math...", "date": "", "ddg_snippet": "Spurious correlations arise when the model learns relationships between variables in the training data that don’t reflect true causal relationships in the real world.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "Spurious correlations arise when the model learns relationships between variables in the training data that don’t reflect true causal relationships in the real world."}
+{"idx": 3, "title": "[2406.14532] RL on Incorrect Synthetic Data Scales the Efficiency of...", "date": "", "ddg_snippet": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone."}
+{"idx": 4, "title": "Spurious Correlations", "date": "", "ddg_snippet": "spurious correlations . correlation is not causation.don't miss spurious scholar, where each of these is an academic paper.", "subpage_snippet": "", "source": "tylervigen.com", "link": "https://tylervigen.com/spurious-correlations", "content": "spurious correlations . correlation is not causation.don't miss spurious scholar, where each of these is an academic paper."}
+{"idx": 5, "title": "Bayesian beagle - RL on Incorrect Synthetic Data Scales the...", "date": "", "ddg_snippet": "Finetuning LLMs with model-generated data can improve math reasoning, especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations .", "subpage_snippet": "", "source": "bayesian-beagle.netlify.app", "link": "https://bayesian-beagle.netlify.app/posts/rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold/2024-06-20-rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold", "content": "Finetuning LLMs with model-generated data can improve math reasoning, especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations ."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math...", "date": "", "ddg_snippet": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL ).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL )."}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 8, "title": "AI-Powered Paper Summarization about the arXiv paper 2406.14532 v 1", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.For example, the model may learn spurious correlations or biases from the data , leading to incorrect solutions.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.For example, the model may learn spurious correlations or biases from the data , leading to incorrect solutions."}
+{"idx": 9, "title": "Reinforcement Learning", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. NeurIPS 2024.", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. NeurIPS 2024."}
diff --git a/data/sampled_jsons/Rafailov_et_al.,_2024_DPO_abstract_Bradley-Terry.jsonl b/data/sampled_jsons/Rafailov_et_al.,_2024_DPO_abstract_Bradley-Terry.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..19167868fc994783b7e0ae2afaa6da9375c785ba
--- /dev/null
+++ b/data/sampled_jsons/Rafailov_et_al.,_2024_DPO_abstract_Bradley-Terry.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF DPO: Conditional Preference Optimization for Multimodal Large Language ...", "date": "", "ddg_snippet": "1 Introduction Direct preference optimization ( DPO ) has emerged as the predominating method for aligning large language models (LLMs) with human prefer- ences ( Rafailov et al.,2023;Zhao et al.,2024 ).", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.460.pdf", "content": "1 Introduction Direct preference optimization ( DPO ) has emerged as the predominating method for aligning large language models (LLMs) with human prefer- ences ( Rafailov et al.,2023;Zhao et al.,2024 )."}
+{"idx": 1, "title": "PDF Handle With Care! A Mechanistic Case Study of DPO Out-of-Distribution ...", "date": "", "ddg_snippet": "Though successful, DPO exhibits strange training dynamics. One fundamental issue with DPO is its tendency to quickly shift probability mass towards out-of-distribution (OOD) trajectories ( Rafailov et al., 2024 ). This OOD behavior can be understood as an extreme type of overfitting, where DPO policies fit behaviors not even present in the train set. This problem is quite recent, and as such ...", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs224n/final-reports/256728108.pdf", "content": "Though successful, DPO exhibits strange training dynamics. One fundamental issue with DPO is its tendency to quickly shift probability mass towards out-of-distribution (OOD) trajectories ( Rafailov et al., 2024 ). This OOD behavior can be understood as an extreme type of overfitting, where DPO policies fit behaviors not even present in the train set. This problem is quite recent, and as such ..."}
+{"idx": 2, "title": "arXiv:2409.17431v1 [cs.CL] 25 Sep 2024", "date": "", "ddg_snippet": "ABSTRACT We derive and investigate two DPO variants that explicitly model the possibility of declaring a tie in pair-wise comparisons. We replace the Bradley-Terry model in DPO with two well-known modeling extensions, by Rao and Kupper and by Davidson, that assign probability to ties as alternatives to clear preferences. Our experiments in neural machine translation and summarization show that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.17431", "content": "ABSTRACT We derive and investigate two DPO variants that explicitly model the possibility of declaring a tie in pair-wise comparisons. We replace the Bradley-Terry model in DPO with two well-known modeling extensions, by Rao and Kupper and by Davidson, that assign probability to ties as alternatives to clear preferences. Our experiments in neural machine translation and summarization show that ..."}
+{"idx": 3, "title": "TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct ...", "date": "", "ddg_snippet": "Using the obtained reward and Bradley-Terry model, this work establishes a framework of computable loss functions with token-level re-ward guidance for DPO , and proposes a practical reward guidance based on the induced DPO re-ward.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.14574", "content": "Using the obtained reward and Bradley-Terry model, this work establishes a framework of computable loss functions with token-level re-ward guidance for DPO , and proposes a practical reward guidance based on the induced DPO re-ward."}
+{"idx": 4, "title": "[2305.18290] Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization ( DPO ), is stable, performant, and computationally lightweight, eliminating the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "The resulting algorithm, which we call Direct Preference Optimization ( DPO ), is stable, performant, and computationally lightweight, eliminating the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning."}
+{"idx": 5, "title": "PDF Direct Preference Optimization: Your Language Model is ... - NeurIPS", "date": "", "ddg_snippet": "The key insight of the DPO algorithm is that we can impose certain constraints on the under-constrained Plackett-Luce (and Bradley-Terry in particular) family of preference models, such that we preserve the class of representable reward models, but explicitly make the optimal policy in Eq. 4 analytically tractable for all prompts x.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/a85b405ed65c6477a4fe8302b5e06ce7-Paper-Conference.pdf", "content": "The key insight of the DPO algorithm is that we can impose certain constraints on the under-constrained Plackett-Luce (and Bradley-Terry in particular) family of preference models, such that we preserve the class of representable reward models, but explicitly make the optimal policy in Eq. 4 analytically tractable for all prompts x."}
+{"idx": 6, "title": "PDF Direct Alignment Algorithms - CS234 Lecture", "date": "", "ddg_snippet": "Bradley-Terry Model connects rewards to preferences: Preferred response Reward assigned to preferred and dispreferred responses", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs234/CS234Spr2024/slides/dpo_slides.pdf", "content": "Bradley-Terry Model connects rewards to preferences: Preferred response Reward assigned to preferred and dispreferred responses"}
+{"idx": 7, "title": "Direct Preference Optimization Explained In-depth - Tyler Romero", "date": "", "ddg_snippet": "With my first blog post, I want to cover an excellent paper that was published last year: Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafailov et al .", "subpage_snippet": "", "source": "www.tylerromero.com", "link": "https://www.tylerromero.com/posts/2024-04-dpo/", "content": "With my first blog post, I want to cover an excellent paper that was published last year: Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafailov et al ."}
+{"idx": 8, "title": "arXiv:2410.04350v3 [cs.CL] 15 Apr 2025", "date": "", "ddg_snippet": "To simplify alignment process, Direct Preference Optimization ( DPO ) ( Rafailov et al., 2024b) leverages the relationship between policy and reward functions to optimize both simultaneously without reinforcement learning. However, DPO is derived from a sequence-level Bradley-Terry model ( Bradley & Terry , 1952), which only focuses on preference relationships between two sequences while ignoring ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.04350", "content": "To simplify alignment process, Direct Preference Optimization ( DPO ) ( Rafailov et al., 2024b) leverages the relationship between policy and reward functions to optimize both simultaneously without reinforcement learning. However, DPO is derived from a sequence-level Bradley-Terry model ( Bradley & Terry , 1952), which only focuses on preference relationships between two sequences while ignoring ..."}
+{"idx": 9, "title": "Direct Preference Optimization: Your Language Model is Secretly a ...", "date": "", "ddg_snippet": "The key insight of the DPO algorithm is that we can impose certain constraints on the under-constrained Plackett-Luce (and Bradley-Terry in particular) family of preference models, such that we preserve the class of representable reward models, but explicitly make the optimal policy in Eq. 4 analytically tractable for all prompts x.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v2", "content": "The key insight of the DPO algorithm is that we can impose certain constraints on the under-constrained Plackett-Luce (and Bradley-Terry in particular) family of preference models, such that we preserve the class of representable reward models, but explicitly make the optimal policy in Eq. 4 analytically tractable for all prompts x."}
diff --git a/data/sampled_jsons/Raissi_et_al._2019_Physics-Informed_Neural_Networks_abstract_year_2019.jsonl b/data/sampled_jsons/Raissi_et_al._2019_Physics-Informed_Neural_Networks_abstract_year_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..274ba1a559c8010d46f5a514a641540b65294f19
--- /dev/null
+++ b/data/sampled_jsons/Raissi_et_al._2019_Physics-Informed_Neural_Networks_abstract_year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Physics-informed neural networks: A deep learning framework for solving ...", "date": "", "ddg_snippet": "We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of…", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999118307125", "content": "We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of…"}
+{"idx": 1, "title": "PDF Physics-informed neural networks: A deep learning framework for solving ...", "date": "", "ddg_snippet": "We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partialdifferential equations.", "subpage_snippet": "", "source": "faculty.sites.iastate.edu", "link": "https://faculty.sites.iastate.edu/hliu/files/inline-files/PINN_RPK_2019_1.pdf", "content": "We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partialdifferential equations."}
+{"idx": 2, "title": "Neural Fields: Physics-informed neural networks: A deep learning ...", "date": "", "ddg_snippet": "Physics Informed Deep Learning Data-driven solutions and discovery of Nonlinear Partial Differential Equations View on GitHub Authors Maziar Raissi , Paris Perdikaris, and George Em Karniadakis Abstract We introduce physics informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear ...", "subpage_snippet": "", "source": "neuralfields.cs.brown.edu", "link": "https://neuralfields.cs.brown.edu/paper_4.html", "content": "Physics Informed Deep Learning Data-driven solutions and discovery of Nonlinear Partial Differential Equations View on GitHub Authors Maziar Raissi , Paris Perdikaris, and George Em Karniadakis Abstract We introduce physics informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear ..."}
+{"idx": 3, "title": "Physics-Informed Neural Networks and Extensions - arXiv.org", "date": "", "ddg_snippet": "To this end, physics - informed learning, i.e., integrating seamlessly data and mathematical models, and implementing them using physics - informed neural networks (PINNs) [ Raissi et al. (2017a)], [ Raissi et al. (2017b)], [ Raissi et al. ( 2019 )] is a paradigm shift in defining the main thrust in scientific machine learning (SciML).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.16806v1", "content": "To this end, physics - informed learning, i.e., integrating seamlessly data and mathematical models, and implementing them using physics - informed neural networks (PINNs) [ Raissi et al. (2017a)], [ Raissi et al. (2017b)], [ Raissi et al. ( 2019 )] is a paradigm shift in defining the main thrust in scientific machine learning (SciML)."}
+{"idx": 4, "title": "Physics Informed Neural Networks - GitHub", "date": "", "ddg_snippet": "Raissi , Maziar, Paris Perdikaris, and George E. Karniadakis. \" Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.\" Journal of Computational Physics 378 ( 2019 ): 686-707. Raissi , Maziar, Paris Perdikaris, and George Em Karniadakis.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/maziarraissi/PINNs", "content": "Raissi , Maziar, Paris Perdikaris, and George E. Karniadakis. \" Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.\" Journal of Computational Physics 378 ( 2019 ): 686-707. Raissi , Maziar, Paris Perdikaris, and George Em Karniadakis."}
+{"idx": 5, "title": "Authors | Physics Informed Deep Learning", "date": "", "ddg_snippet": "Abstract We introduce physics informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations.", "subpage_snippet": "", "source": "maziarraissi.github.io", "link": "https://maziarraissi.github.io/PINNs/", "content": "Abstract We introduce physics informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations."}
+{"idx": 6, "title": "PDF Physics-Informed Neural Networks - Springer", "date": "", "ddg_snippet": "Physics - Informed Neural Networks Generating an accurate surrogate model of a complex physical system usually requires a large amount of solution data about the problem at hand. However, data acquisition from experiments or simulations is often infeasible or too costly. With this in mind, Raissi et al. proposed an approach, that augments surrogate models with existing knowledge about the ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-76587-3_5.pdf", "content": "Physics - Informed Neural Networks Generating an accurate surrogate model of a complex physical system usually requires a large amount of solution data about the problem at hand. However, data acquisition from experiments or simulations is often infeasible or too costly. With this in mind, Raissi et al. proposed an approach, that augments surrogate models with existing knowledge about the ..."}
+{"idx": 7, "title": "Physics-informed neural networks: A deep learning framework for solving ...", "date": "", "ddg_snippet": "We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In this work, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2019JCoPh.378..686R/abstract", "content": "We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In this work, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential ..."}
+{"idx": 8, "title": "dblp: Physics-informed neural networks: A deep learning framework for ...", "date": "", "ddg_snippet": "Bibliographic details on Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/jcphy/RaissiPK19", "content": "Bibliographic details on Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations."}
+{"idx": 9, "title": "Physics-Informed Neural Networks and Extensions", "date": "", "ddg_snippet": "Maziar Raissi 1, Paris Perdik aris 2, Nazanin Ahmadi 3, and George Em Karniadakis 4 Abstract In this paper, we review the new method Physics - Informed Neural Networks (PINNs) that has become", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648646_Physics-Informed_Neural_Networks_and_Extensions", "content": "Maziar Raissi 1, Paris Perdik aris 2, Nazanin Ahmadi 3, and George Em Karniadakis 4 Abstract In this paper, we review the new method Physics - Informed Neural Networks (PINNs) that has become"}
diff --git a/data/sampled_jsons/Raissi_et_al._2019_Physics-informed_neural_networks_PINNs_abstract.jsonl b/data/sampled_jsons/Raissi_et_al._2019_Physics-informed_neural_networks_PINNs_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..437f7a1dc9b67e9fa9f891bf03ec61067ec59596
--- /dev/null
+++ b/data/sampled_jsons/Raissi_et_al._2019_Physics-informed_neural_networks_PINNs_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1711.10561] Physics Informed Deep Learning (Part I): Data ...", "date": "", "ddg_snippet": "Nov 28, 2017 · We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this two part treatise, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1711.10561", "content": "Nov 28, 2017 · We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this two part treatise, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial ..."}
+{"idx": 1, "title": "Authors | Physics Informed Deep Learning", "date": "", "ddg_snippet": "Abstract We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations.", "subpage_snippet": "", "source": "maziarraissi.github.io", "link": "https://maziarraissi.github.io/PINNs/", "content": "Abstract We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations."}
+{"idx": 2, "title": "Physics-informed neural networks: A deep learning framework ...", "date": "", "ddg_snippet": "Feb 1, 2019 · We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999118307125", "content": "Feb 1, 2019 · We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations."}
+{"idx": 3, "title": "Physics-informed neural networks: A deep learning framework ...", "date": "", "ddg_snippet": "We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partialdifferential equations.", "subpage_snippet": "", "source": "faculty.sites.iastate.edu", "link": "https://faculty.sites.iastate.edu/hliu/files/inline-files/PINN_RPK_2019_1.pdf", "content": "We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partialdifferential equations."}
+{"idx": 4, "title": "Physics Informed Neural Networks - GitHub", "date": "", "ddg_snippet": "We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/maziarraissi/PINNs", "content": "We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations."}
+{"idx": 5, "title": "Physics-Informed Neural Networks and Extensions", "date": "", "ddg_snippet": "Aug 29, 2024 · Maziar Raissi 1, Paris Perdik aris 2, Nazanin Ahmadi 3, and George Em Karniadakis 4 Abstract In this paper, we review the new method Physics-Informed Neural Networks ( PINNs ) that has become", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648646_Physics-Informed_Neural_Networks_and_Extensions", "content": "Aug 29, 2024 · Maziar Raissi 1, Paris Perdik aris 2, Nazanin Ahmadi 3, and George Em Karniadakis 4 Abstract In this paper, we review the new method Physics-Informed Neural Networks ( PINNs ) that has become"}
+{"idx": 6, "title": "PHYSICS INFORMED NEURAL NETWORKS FOR TRANSFORMED GEOMETRIES ...", "date": "", "ddg_snippet": "1 INTRODUCTION Physics-informed neural networks ( PINNs ) ( Raissi et al ., 2019 ) are simple yet surprisingly pow-erful machine learning approaches to incorporate physical knowledge, in particular, formulated as partial differential equations (PDEs), into the training of neural networks . In the burgeoning field of physics-informed machine learning (Karniadakis et al ., 2021), they play a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kIZcruKmBg", "content": "1 INTRODUCTION Physics-informed neural networks ( PINNs ) ( Raissi et al ., 2019 ) are simple yet surprisingly pow-erful machine learning approaches to incorporate physical knowledge, in particular, formulated as partial differential equations (PDEs), into the training of neural networks . In the burgeoning field of physics-informed machine learning (Karniadakis et al ., 2021), they play a ..."}
+{"idx": 7, "title": "Physics-informed neural networks: A deep learning ...", "date": "", "ddg_snippet": "by M Raissi · 2019 · Cited by 17103 — We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125", "content": "by M Raissi · 2019 · Cited by 17103 — We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics."}
+{"idx": 8, "title": "Physics-informed neural networks: A deep learning ...", "date": "", "ddg_snippet": "by M Raissi · 2019 · Cited by 17103 — Abstract. We introduce physics-informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "http://ui.adsabs.harvard.edu/abs/2019JCoPh.378..686R/abstract", "content": "by M Raissi · 2019 · Cited by 17103 — Abstract. We introduce physics-informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of ..."}
+{"idx": 9, "title": "Scientific Machine Learning Through Physics–Informed ...", "date": "", "ddg_snippet": "by S Cuomo · 2022 · Cited by 2293 — Raissi et al [146] introduce and illustrate the PINN approach for solving nonlinear PDEs, like Schrödinger, Burgers, and Allen–Cahn equations.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10915-022-01939-z", "content": "by S Cuomo · 2022 · Cited by 2293 — Raissi et al [146] introduce and illustrate the PINN approach for solving nonlinear PDEs, like Schrödinger, Burgers, and Allen–Cahn equations."}
diff --git a/data/sampled_jsons/Reconciling_Kaplan_and_Chinchilla_Scaling_Laws_Kaplan_dataset_size_exponent.jsonl b/data/sampled_jsons/Reconciling_Kaplan_and_Chinchilla_Scaling_Laws_Kaplan_dataset_size_exponent.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6c8d82631a84ec9bf54912447b17dc805d1c793e
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+{"idx": 0, "title": "Reconciling Kaplan and Chinchilla Scaling Laws", "date": "", "ddg_snippet": "12 Jun 2024 — Reconciling Kaplan and Chinchilla Scaling Laws . Report issue for ... Kaplan's size range. Left in terms of training tokens, right in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12907v1", "content": "12 Jun 2024 — Reconciling Kaplan and Chinchilla Scaling Laws . Report issue for ... Kaplan's size range. Left in terms of training tokens, right in ..."}
+{"idx": 1, "title": "Reconciling Kaplan and Chinchilla Scaling Laws", "date": "", "ddg_snippet": "Reconciling Kaplan and Chinchilla Scaling Laws . Tim Pearce Microsoft Research ... sizes used Kaplan . Fit a local power law, for L∗. \\E in terms of C\\E ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f91518ed8d3298ef5e2625a7c2b5c611cfb94f60.pdf", "content": "Reconciling Kaplan and Chinchilla Scaling Laws . Tim Pearce Microsoft Research ... sizes used Kaplan . Fit a local power law, for L∗. \\E in terms of C\\E ..."}
+{"idx": 2, "title": "Reconciling Kaplan and Chinchilla Scaling Laws", "date": "", "ddg_snippet": "21 Nov 2024 — Reconciling Kaplan and Chinchilla Scaling Laws . Report issue for ... dataset size . This may arise from various factors: inherent biases ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12907v3", "content": "21 Nov 2024 — Reconciling Kaplan and Chinchilla Scaling Laws . Report issue for ... dataset size . This may arise from various factors: inherent biases ..."}
+{"idx": 3, "title": "Scaling Laws for LLM Pretraining", "date": "", "ddg_snippet": "18 Dec 2024 — Segue: Reconciling Kaplan and Chinchilla Scaling Laws . As an aside, Pearce and Song[3] have recently proposed a way to reconcile the Kaplan and ...", "subpage_snippet": "", "source": "www.jonvet.com", "link": "https://www.jonvet.com/blog/llm-scaling-laws", "content": "18 Dec 2024 — Segue: Reconciling Kaplan and Chinchilla Scaling Laws . As an aside, Pearce and Song[3] have recently proposed a way to reconcile the Kaplan and ..."}
+{"idx": 4, "title": "Chinchilla Scaling Laws", "date": "", "ddg_snippet": "8 Sept 2025 — 6. Reconciling Kaplan and Chinchilla Scaling Laws (2024). 7. Resolving Discrepancies in Compute-Optimal Scaling of Language Models (2024). 8 ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/chinchilla-scaling-laws", "content": "8 Sept 2025 — 6. Reconciling Kaplan and Chinchilla Scaling Laws (2024). 7. Resolving Discrepancies in Compute-Optimal Scaling of Language Models (2024). 8 ..."}
+{"idx": 5, "title": "Reconciling Kaplan and Chinchilla Scaling Laws", "date": "", "ddg_snippet": "Reconciling Kaplan and Chinchilla Scaling Laws . @article ... sizes , and dataset sizes using small-scale training costs on limited experiments.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Reconciling-Kaplan-and-Chinchilla-Scaling-Laws-Pearce-Song/df6227869dd72951c9c46f02cd65f6b588f129ab", "content": "Reconciling Kaplan and Chinchilla Scaling Laws . @article ... sizes , and dataset sizes using small-scale training costs on limited experiments."}
+{"idx": 6, "title": "Reconciling Kaplan and Chinchilla Scaling Laws", "date": "", "ddg_snippet": "Reconciling Kaplan and Chinchilla Scaling Laws . Published 11/21/2024 by Tim Pearce, Jinyeop Song ... Chinchilla regime, where tuning both model size and dataset size together leads to more efficient scaling.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/reconciling-kaplan-chinchilla-scaling-laws", "content": "Reconciling Kaplan and Chinchilla Scaling Laws . Published 11/21/2024 by Tim Pearce, Jinyeop Song ... Chinchilla regime, where tuning both model size and dataset size together leads to more efficient scaling."}
+{"idx": 7, "title": "Resolving Discrepancies in Compute-Optimal Scaling of ...", "date": "", "ddg_snippet": "C, we wish to predict how to best allocate it across model size (in parameters) and dataset size (in tokens). ... Reconciling Kaplan and Chinchilla scaling laws .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96646", "content": "C, we wish to predict how to best allocate it across model size (in parameters) and dataset size (in tokens). ... Reconciling Kaplan and Chinchilla scaling laws ."}
+{"idx": 8, "title": "[Literature Review] Reconciling Kaplan and Chinchilla ...", "date": "", "ddg_snippet": "This page provides the most accurate and concise summary worldwide for the paper titled Reconciling Kaplan and Chinchilla Scaling Laws . With Moonlight, your AI ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/reconciling-kaplan-and-chinchilla-scaling-laws", "content": "This page provides the most accurate and concise summary worldwide for the paper titled Reconciling Kaplan and Chinchilla Scaling Laws . With Moonlight, your AI ..."}
+{"idx": 9, "title": "Scaling Laws for Pre-training Agents and World Models", "date": "", "ddg_snippet": "Reconciling kaplan and chinchilla scaling laws . TMLR, 2024. Porian, T., Wortsman, M., Jitsev, J., Schmidt, L., and Carmon, Y. Resolving discrepancies in ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45787", "content": "Reconciling kaplan and chinchilla scaling laws . TMLR, 2024. Porian, T., Wortsman, M., Jitsev, J., Schmidt, L., and Carmon, Y. Resolving discrepancies in ..."}
diff --git a/data/sampled_jsons/Reddit_Earth_Strike_Fridays_for_Future_Extinction_Rebellion_communities_subreddits.jsonl b/data/sampled_jsons/Reddit_Earth_Strike_Fridays_for_Future_Extinction_Rebellion_communities_subreddits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d40d6d09c6a273a8d5e96cec0c92fdeed6cb1401
--- /dev/null
+++ b/data/sampled_jsons/Reddit_Earth_Strike_Fridays_for_Future_Extinction_Rebellion_communities_subreddits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Earth Strike - Wikipedia", "date": "", "ddg_snippet": "7 The post quickly gathered attention within Reddit , and the r/EarthStrike subreddit was formed to organise a general strike .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Earth_Strike", "content": "7 The post quickly gathered attention within Reddit , and the r/EarthStrike subreddit was formed to organise a general strike ."}
+{"idx": 1, "title": "getinvolved - EarthStrike", "date": "", "ddg_snippet": "... interested in cooperating with us in the future , you can easily send an Outreach Document containing all the necessary information about Earth Strike ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/wiki/getinvolved", "content": "... interested in cooperating with us in the future , you can easily send an Outreach Document containing all the necessary information about Earth Strike ..."}
+{"idx": 2, "title": "Book Review: Man After Man: An Anthropology of the Future by", "date": "", "ddg_snippet": "... Future , which illustrates possible evolutionary pathways of nonhuman animals 50 million years in the future when humanity has gone extinct ; and The ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/TheMotte/comments/o073ib/book_review_man_after_man_an_anthropology_of_the/", "content": "... Future , which illustrates possible evolutionary pathways of nonhuman animals 50 million years in the future when humanity has gone extinct ; and The ..."}
+{"idx": 3, "title": "Civil Resistance against climate destruction", "date": "", "ddg_snippet": "... Britain, charged with public nuisance for civil disobedience against her government's participation in the destruction of her children's future ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ClimateResistance/", "content": "... Britain, charged with public nuisance for civil disobedience against her government's participation in the destruction of her children's future ..."}
+{"idx": 4, "title": "We're Jason, Christian and Trudie from Ecosia, the search", "date": "", "ddg_snippet": "Join us for our sub-wide campaigns as we leverage the platform of Reddit to do some good for the climate. ... what it's like to be arrested for ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ClimateOffensive/comments/cnzbe0/were_jason_christian_and_trudie_from_ecosia_the/", "content": "Join us for our sub-wide campaigns as we leverage the platform of Reddit to do some good for the climate. ... what it's like to be arrested for ..."}
+{"idx": 5, "title": "Right now, thousands of climate activists from all over Europa", "date": "", "ddg_snippet": "Its seething wrath and urge for blood are fuelled by nothing but searing hate: Someone getting its personality wrong will definitely compute down to ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/europe/comments/c3ulbt/right_now_thousands_of_climate_activists_from_all/", "content": "Its seething wrath and urge for blood are fuelled by nothing but searing hate: Someone getting its personality wrong will definitely compute down to ..."}
+{"idx": 6, "title": "Friday headlines: Hamlet with Hamnet", "date": "", "ddg_snippet": "Foreign Affairs India pushes mining groups to pursue reserves in the \"lithium triangle\" between Argentina, Bolivia, and Chile.", "subpage_snippet": "", "source": "themorningnews.org", "link": "https://themorningnews.org/p/friday-headlines-hamlet-with-hamnet", "content": "Foreign Affairs India pushes mining groups to pursue reserves in the \"lithium triangle\" between Argentina, Bolivia, and Chile."}
+{"idx": 7, "title": "[WP] You are captaining a cutting-edge interstellar spacecraft,", "date": "", "ddg_snippet": "WP] You're a hero, and for some reason, your villain keeps trying and failing at the same crimes, at this point you decided you needed a day off, and ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/WritingPrompts/comments/144pvlw/wp_you_are_captaining_a_cuttingedge_interstellar/", "content": "WP] You're a hero, and for some reason, your villain keeps trying and failing at the same crimes, at this point you decided you needed a day off, and ..."}
+{"idx": 8, "title": "\"Should it [Mexico] accept the historical record, with all", "date": "", "ddg_snippet": "If it were not for the fact that disease decimated the peoples of Cemanahuac, Cortes and the Spanish would have been pushed out of the Americas ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/badhistory/comments/2qjnyo/should_it_mexico_accept_the_historical_record/", "content": "If it were not for the fact that disease decimated the peoples of Cemanahuac, Cortes and the Spanish would have been pushed out of the Americas ..."}
+{"idx": 9, "title": "I’m Oli Frost, the guy who makes novelty songs about the", "date": "", "ddg_snippet": "... most significantly I made a series of posters for various groups and events, like the Global Climate Strike , Fridays for Future , Extinction Rebellion ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/IAmA/comments/129js3s/im_oli_frost_the_guy_who_makes_novelty_songs/", "content": "... most significantly I made a series of posters for various groups and events, like the Global Climate Strike , Fridays for Future , Extinction Rebellion ..."}
diff --git a/data/sampled_jsons/Reddit_climate_activism_subreddits_rclimate_rClimateAction_rextinctionrebellion.jsonl b/data/sampled_jsons/Reddit_climate_activism_subreddits_rclimate_rClimateAction_rextinctionrebellion.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ad42eb2f1cd77e2c8be78e4056dfb0431da97401
--- /dev/null
+++ b/data/sampled_jsons/Reddit_climate_activism_subreddits_rclimate_rClimateAction_rextinctionrebellion.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fight Climate Change | Protect Our Future", "date": "", "ddg_snippet": "r / climateaction . A community giving advice, providing support, and working together to advance the climate movement.", "subpage_snippet": "", "source": "reddit.garudalinux.org", "link": "https://reddit.garudalinux.org/r/climateaction/controversial", "content": "r / climateaction . A community giving advice, providing support, and working together to advance the climate movement."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Sociodemographic features influence the engagement in climate activism , as they increase the sympathy for the cause and probability of frequenting subreddits where social influence takes place. Finally, mass media coverage of climate action has a positive effect on the engagement...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "Sociodemographic features influence the engagement in climate activism , as they increase the sympathy for the cause and probability of frequenting subreddits where social influence takes place. Finally, mass media coverage of climate action has a positive effect on the engagement..."}
+{"idx": 2, "title": "View on Redlib, an alternative private front-end to Reddit .", "date": "", "ddg_snippet": "reddit . You are about to leave Redlib. r / climateaction • u/Familiar-Ad-5544 • Apr 04 '23. Today I joined my 2 friends on their protest against the government’s handling of climate change & cost of living. We meet daily @Nelsons col.", "subpage_snippet": "", "source": "reddit.adminforge.de", "link": "https://reddit.adminforge.de/r/climateaction/comments/12bgysg/today_i_joined_my_2_friends_on_their_protest/", "content": "reddit . You are about to leave Redlib. r / climateaction • u/Familiar-Ad-5544 • Apr 04 '23. Today I joined my 2 friends on their protest against the government’s handling of climate change & cost of living. We meet daily @Nelsons col."}
+{"idx": 3, "title": "climate _discussion Subreddit ( r / climate _discussion...) | Anonview", "date": "", "ddg_snippet": "restricted. r / climate _discussion. / r / Climate _Discussion is a subreddit having an ongoing discussion about climate change, our response to it, and most importantly, actions we can take to make our future sustainable and habitable for everyone on the planet. 1.7K. Members.", "subpage_snippet": "", "source": "www.anonview.com", "link": "https://www.anonview.com/r/climate_discussion", "content": "restricted. r / climate _discussion. / r / Climate _Discussion is a subreddit having an ongoing discussion about climate change, our response to it, and most importantly, actions we can take to make our future sustainable and habitable for everyone on the planet. 1.7K. Members."}
+{"idx": 4, "title": "How has Reddit influenced your views on environmental sustainability...", "date": "", "ddg_snippet": "Reddit 's diverse range of subreddits offers a rich source of information, inspiration, and community support for environmental sustainability.", "subpage_snippet": "", "source": "blackhatseoforum.com", "link": "https://blackhatseoforum.com/reddit/how-has-reddit-influenced-your-views-on-environmental-sustainability-or-encoura/", "content": "Reddit 's diverse range of subreddits offers a rich source of information, inspiration, and community support for environmental sustainability."}
+{"idx": 5, "title": "Climate Activist Vs Angry Drivers Svenska | TikTok", "date": "", "ddg_snippet": "Extinction Rebellion Protest Amsterdam Climate activists from XR were blocking the A10 highway today. They were demanding that the ING bank will stop to invest in fossil fuel.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/climate-activist-vs-angry-drivers-svenska", "content": "Extinction Rebellion Protest Amsterdam Climate activists from XR were blocking the A10 highway today. They were demanding that the ING bank will stop to invest in fossil fuel."}
+{"idx": 6, "title": "Boomers Are Embracing Climate Activism | Growing participation of...", "date": "", "ddg_snippet": "in / r / climate . →. reddit . You are about to leave Redlib. Do you want to continue? r / climate • u/crustose_lichen • 4h ago.", "subpage_snippet": "", "source": "redlib.private.coffee", "link": "https://redlib.private.coffee/r/climate/comments/1grdst0/boomers_are_embracing_climate_activism_growing/", "content": "in / r / climate . →. reddit . You are about to leave Redlib. Do you want to continue? r / climate • u/crustose_lichen • 4h ago."}
+{"idx": 7, "title": "Govt's $8.27b climate action plan | LinkedIn", "date": "", "ddg_snippet": "It comes as the national climate risk assessment warns of dire consequences from rising global temperatures, and separate research reveals heatwaves caused 1,009 deaths between 2016 and 2019.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/news/story/govts-827b-climate-action-plan-7059745/", "content": "It comes as the national climate risk assessment warns of dire consequences from rising global temperatures, and separate research reveals heatwaves caused 1,009 deaths between 2016 and 2019."}
+{"idx": 8, "title": "The Reddit Climate Change Dataset Dataset | Papers With Code", "date": "", "ddg_snippet": "Dataset or its variant:* - The Reddit Climate Change Dataset.The comments are labeled with their subreddit , body, creation date, sentiment (calculated for you using a VADER pipeline), and score.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/dataset/the-reddit-climate-change-dataset", "content": "Dataset or its variant:* - The Reddit Climate Change Dataset.The comments are labeled with their subreddit , body, creation date, sentiment (calculated for you using a VADER pipeline), and score."}
+{"idx": 9, "title": "cathw/ reddit _ climate _comment · Datasets at Hugging Face", "date": "", "ddg_snippet": "The Reddit Climate Comment dataset is a collection of comments extracted from subreddits focused on discussions related to climate change, energy, and renewable energy.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/cathw/reddit_climate_comment", "content": "The Reddit Climate Comment dataset is a collection of comments extracted from subreddits focused on discussions related to climate change, energy, and renewable energy."}
diff --git a/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_year_2023.jsonl b/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..00f1075a5cd4132e5861b38425a03004c580676a
--- /dev/null
+++ b/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
+{"idx": 1, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and ..."}
+{"idx": 2, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ..."}
+{"idx": 3, "title": "Regret Matching$^+$: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/farina-2023-regret/", "content": "Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL"}
+{"idx": 4, "title": "Regret matching+ | Proceedings of the 37th International Conference on ...", "date": "", "ddg_snippet": "Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668812", "content": "Regret Matching + (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that ..."}
+{"idx": 5, "title": "PDF Regret Matching+: - Instability, average- and last-iterate convergence ...", "date": "", "ddg_snippet": "Instability , average- and last-iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Instability , average- and last-iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris"}
+{"idx": 6, "title": "Gabriele Farina - Regret Matching$^+$: (In)Stability and Fast ...", "date": "", "ddg_snippet": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/", "content": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM ..."}
+{"idx": 7, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to suffer large regret .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nYgs0qZJ97", "content": "Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to suffer large regret ."}
+{"idx": 8, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM + and its predictive version [12] can be unstable, which might cause other players to suffer large regret .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2305.14709", "content": "Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM + and its predictive version [12] can be unstable, which might cause other players to suffer large regret ."}
+{"idx": 9, "title": "Publication: Regret Matching+: (In)Stability and Fast Convergence in ...", "date": "", "ddg_snippet": "Published in : CoRR ( 2023 ) Keyphrases weighted majority game theory regret minimization matching algorithm online learning feature matching matching process nash equilibria nash equilibrium lower bound game theoretic online game stability analysis loss function computer games convergence rate convergence speed cooperative educational games ...", "subpage_snippet": "", "source": "cs.reviewer.ly", "link": "https://cs.reviewer.ly/app/publication/202eecea-86ff-4a89-bbd2-2f4bf4c8152f", "content": "Published in : CoRR ( 2023 ) Keyphrases weighted majority game theory regret minimization matching algorithm online learning feature matching matching process nash equilibria nash equilibrium lower bound game theoretic online game stability analysis loss function computer games convergence rate convergence speed cooperative educational games ..."}
diff --git a/data/sampled_jsons/Reinforcement_Learning_Guidance_RLG_diffusion_models_backpropagation-free.jsonl b/data/sampled_jsons/Reinforcement_Learning_Guidance_RLG_diffusion_models_backpropagation-free.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..00528332519df939131336d412dd18c889699c11
--- /dev/null
+++ b/data/sampled_jsons/Reinforcement_Learning_Guidance_RLG_diffusion_models_backpropagation-free.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - jinluo12345/Reinforcement-learning-guidance", "date": "", "ddg_snippet": "Overview This repository is an implementation of Reinforcement Learning Guidance ( RLG ), a novel inference-time method for enhancing and controlling the alignment of diffusion models . RLG adapts Classifier- Free Guidance (CFG) by combining the outputs of a base model and an RL-fine-tuned model using a geometric average, enabling dynamic control over alignment strength without additional training ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jinluo12345/Reinforcement-learning-guidance", "content": "Overview This repository is an implementation of Reinforcement Learning Guidance ( RLG ), a novel inference-time method for enhancing and controlling the alignment of diffusion models . RLG adapts Classifier- Free Guidance (CFG) by combining the outputs of a base model and an RL-fine-tuned model using a geometric average, enabling dynamic control over alignment strength without additional training ..."}
+{"idx": 1, "title": "Inference-Time Alignment Control for Diffusion Models with...", "date": "", "ddg_snippet": "In this paper, we introduced Reinforcement Learning Guidance ( RLG ), a novel, training- free method for dynamically controlling the alignment of generative models at inference time.Aligning text-to-image diffusion models with reward backpropagation .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21016v1", "content": "In this paper, we introduced Reinforcement Learning Guidance ( RLG ), a novel, training- free method for dynamically controlling the alignment of generative models at inference time.Aligning text-to-image diffusion models with reward backpropagation ."}
+{"idx": 2, "title": "Inference-Time Alignment Control for Diffusion Models with ...", "date": "", "ddg_snippet": "We introduce Reinforcement Learning Guidance ( RLG ), an inference-time method that adapts Classifier- Free Guidance (CFG) by combining the outputs of the base and RL fine-tuned models via a geo- metric average.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.21016", "content": "We introduce Reinforcement Learning Guidance ( RLG ), an inference-time method that adapts Classifier- Free Guidance (CFG) by combining the outputs of the base and RL fine-tuned models via a geo- metric average."}
+{"idx": 3, "title": "Inference-Time Alignment Control for Diffusion Models with ...", "date": "", "ddg_snippet": "Aug 28, 2025 · We introduce Reinforcement Learning Guidance ( RLG ), an inference-time method that adapts Classifier- Free Guidance (CFG) by combining the outputs of the base and RL fine-tuned models via a geometric average.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Inference-Time-Alignment-Control-for-Diffusion-with-Jin-Qiu/0b9a4ec41a0d995f60b780fbb262764e97044213", "content": "Aug 28, 2025 · We introduce Reinforcement Learning Guidance ( RLG ), an inference-time method that adapts Classifier- Free Guidance (CFG) by combining the outputs of the base and RL fine-tuned models via a geometric average."}
+{"idx": 4, "title": "Diffusion Models for Reinforcement Learning", "date": "", "ddg_snippet": "PlayFusion trains a discrete diffusion model on a text-annotated play dataset States and language instructions are encoded to the latent space, as conditions of diffusion models .", "subpage_snippet": "", "source": "wnzhang.net", "link": "https://wnzhang.net/teaching/sjtu-rl-2024/slides/15-diffusion-rl.pdf", "content": "PlayFusion trains a discrete diffusion model on a text-annotated play dataset States and language instructions are encoded to the latent space, as conditions of diffusion models ."}
+{"idx": 5, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Summary We train diffusion models directly on downstream objectives using reinforcement learning (RL). We do this by posing denoising diffusion as a multi-step decision-making problem, enabling a class of policy gradient algorithms that we call denoising diffusion policy optimization (DDPO).", "subpage_snippet": "", "source": "rl-diffusion.github.io", "link": "https://rl-diffusion.github.io/", "content": "Summary We train diffusion models directly on downstream objectives using reinforcement learning (RL). We do this by posing denoising diffusion as a multi-step decision-making problem, enabling a class of policy gradient algorithms that we call denoising diffusion policy optimization (DDPO)."}
+{"idx": 6, "title": "Towards Better Alignment: Training Diffusion Models with ...", "date": "", "ddg_snippet": "Abstract Diffusion models have achieved remarkable success in text-to-image generation. However, their practical applica-tions are hindered by the misalignment between generated images and corresponding text prompts. To tackle this is-sue, reinforcement learning (RL) has been considered for diffusion model fine-tuning.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_Towards_Better_Alignment_Training_Diffusion_Models_with_Reinforcement_Learning_Against_CVPR_2025_paper.pdf", "content": "Abstract Diffusion models have achieved remarkable success in text-to-image generation. However, their practical applica-tions are hindered by the misalignment between generated images and corresponding text prompts. To tackle this is-sue, reinforcement learning (RL) has been considered for diffusion model fine-tuning."}
+{"idx": 7, "title": "Local Pairwise Distance Matching for Backpropagation - Free ...", "date": "", "ddg_snippet": "How does this backpropagation - free reinforcement learning method utilize local, layer-wise losses for training neural networks? How do the two variations of the method employ domain-specific distance measurements, and what implications does this have for performance?", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-local-pairwise-distance-matching-for-cmd6g3v5rd3pq07nqxe4b7vzo", "content": "How does this backpropagation - free reinforcement learning method utilize local, layer-wise losses for training neural networks? How do the two variations of the method employ domain-specific distance measurements, and what implications does this have for performance?"}
+{"idx": 8, "title": "Bellman Diffusion Models for Offline Reinforcement Learning", "date": "", "ddg_snippet": "Keywords: Diffusion models , offline reinforcement learning , deep learning .We find that enforcing the Bellman flow constraints on a diffusion model leads to a temporal difference update on the predicted noise, similar to the standard TD- learning update on the predicted reward.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1isC66Gozb", "content": "Keywords: Diffusion models , offline reinforcement learning , deep learning .We find that enforcing the Bellman flow constraints on a diffusion model leads to a temporal difference update on the predicted noise, similar to the standard TD- learning update on the predicted reward."}
+{"idx": 9, "title": "(PDF) Prediction of Macroeconomic Growth Using Backpropagation ...", "date": "", "ddg_snippet": "hidden layers, and learning r ates, along with appropriate model selection, significantl y enhance the. performance of the backpropagation algorithm in macroeconomic prediction. This study highlights that.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388219822_Prediction_of_Macroeconomic_Growth_Using_Backpropagation_Algorithms_A_Review", "content": "hidden layers, and learning r ates, along with appropriate model selection, significantl y enhance the. performance of the backpropagation algorithm in macroeconomic prediction. This study highlights that."}
diff --git a/data/sampled_jsons/Representation_engineering_RepE_Zou_2023.jsonl b/data/sampled_jsons/Representation_engineering_RepE_Zou_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c6f7d419b2fea9cb9ea5d6acbe3ba87f1a072dde
--- /dev/null
+++ b/data/sampled_jsons/Representation_engineering_RepE_Zou_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Representation Engineering: A Top-Down Approach to AI ...", "date": "", "ddg_snippet": "by A Zou · 2023 · Cited by 518 — In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.01405", "content": "by A Zou · 2023 · Cited by 518 — In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems."}
+{"idx": 1, "title": "Representation Engineering", "date": "", "ddg_snippet": "by A Zou · 2023 · Cited by 506 — We identify and characterize the emerging area of representation engineering . ( RepE ), an approach to enhancing the transparency of AI ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.01405", "content": "by A Zou · 2023 · Cited by 506 — We identify and characterize the emerging area of representation engineering . ( RepE ), an approach to enhancing the transparency of AI ..."}
+{"idx": 2, "title": "Representation Engineering", "date": "", "ddg_snippet": "representation engineering . ( RepE ). Page 31. ACT II: Representation . Engineering . ( RepE ). Andy Zou . Page 32. RepE . Mind Reading. Mind Control. Page 33. RepE . 106 pages", "subpage_snippet": "", "source": "rdi.berkeley.edu", "link": "https://rdi.berkeley.edu/understanding_llms/assets/jan23_1.pdf", "content": "representation engineering . ( RepE ). Page 31. ACT II: Representation . Engineering . ( RepE ). Andy Zou . Page 32. RepE . Mind Reading. Mind Control. Page 33. RepE . 106 pages"}
+{"idx": 3, "title": "Representation Engineering: A Top-Down Approach to AI ...", "date": "", "ddg_snippet": "2 Oct 2023 — This paper identifies and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/58fdf550600fc3873729d466601c5d08a51ba8a0", "content": "2 Oct 2023 — This paper identifies and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of ..."}
+{"idx": 4, "title": "Representation Engineering: A Top-Down Approach to AI ...", "date": "", "ddg_snippet": "25 Jan 2024 — RepE is a top-down approach to transparency research that treats representations as the fundamental unit of analysis, aiming to understand and control ...", "subpage_snippet": "", "source": "montrealethics.ai", "link": "https://montrealethics.ai/representation-engineering-a-top-down-approach-to-ai-transparency/", "content": "25 Jan 2024 — RepE is a top-down approach to transparency research that treats representations as the fundamental unit of analysis, aiming to understand and control ..."}
+{"idx": 5, "title": "chrisliu298/awesome-representation-engineering", "date": "", "ddg_snippet": "This repository tracks the latest research on representation engineering ( RepE ), which was originally introduced by Zou et al. ( 2023 ).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chrisliu298/awesome-representation-engineering", "content": "This repository tracks the latest research on representation engineering ( RepE ), which was originally introduced by Zou et al. ( 2023 )."}
+{"idx": 6, "title": "Representation Engineering: A Top-Down Approach to ...", "date": "", "ddg_snippet": "In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems ...", "subpage_snippet": "", "source": "www.grayswan.ai", "link": "https://www.grayswan.ai/research/repe", "content": "In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems ..."}
+{"idx": 7, "title": "Representation Engineering", "date": "", "ddg_snippet": "In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems that ...", "subpage_snippet": "", "source": "aisafety.tokyo", "link": "https://aisafety.tokyo/benkyoukai/representation-engineering", "content": "In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems that ..."}
+{"idx": 8, "title": "Andy Zou", "date": "", "ddg_snippet": "In this paper, we introduce and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems.", "subpage_snippet": "", "source": "andyzoujm.github.io", "link": "https://andyzoujm.github.io/", "content": "In this paper, we introduce and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems."}
+{"idx": 9, "title": "Enhancing Neural Network Transparency through ...", "date": "", "ddg_snippet": "by A Zou — In this paper, we introduce and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=aCgybhcZFi", "content": "by A Zou — In this paper, we introduce and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems."}
diff --git a/data/sampled_jsons/Rew_short_formula_Equation_1_Feint_Behaviors_Strategies.jsonl b/data/sampled_jsons/Rew_short_formula_Equation_1_Feint_Behaviors_Strategies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f12edc0a2a9f652fdb270d393946198e930391d
--- /dev/null
+++ b/data/sampled_jsons/Rew_short_formula_Equation_1_Feint_Behaviors_Strategies.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NeurIPS Poster Feint Behaviors and Strategies", "date": "", "ddg_snippet": "9 Dec 2024 — Our work provides the first comprehensive and concrete formalization of Feint behaviors in action-level and strategy-level .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96274", "content": "9 Dec 2024 — Our work provides the first comprehensive and concrete formalization of Feint behaviors in action-level and strategy-level ."}
+{"idx": 1, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "The key idea of our work is to ( 1 ) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual- Behavior Model; (2) concertize the implications from our formalization of Feint on game strategies , in terms of temporal, spatial and their collective impacts respectively ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v2", "content": "The key idea of our work is to ( 1 ) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual- Behavior Model; (2) concertize the implications from our formalization of Feint on game strategies , in terms of temporal, spatial and their collective impacts respectively ..."}
+{"idx": 2, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "Sep 25, 2024 · The Design of Rew _temporal achieves the 3 points discussed in Section 4.2. 1 as follows: We use large weighted accumulation of short -term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ", "content": "Sep 25, 2024 · The Design of Rew _temporal achieves the 3 points discussed in Section 4.2. 1 as follows: We use large weighted accumulation of short -term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors ."}
+{"idx": 3, "title": "Feint behaviors and strategies | Proceedings of the 38th ...", "date": "", "ddg_snippet": "The key idea of our work is to ( 1 ) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual- Behavior Model; (2) concertize the implications from our formalization of Feint on game strategies , in terms of temporal, spatial and their collective impacts respectively ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738032", "content": "The key idea of our work is to ( 1 ) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual- Behavior Model; (2) concertize the implications from our formalization of Feint on game strategies , in terms of temporal, spatial and their collective impacts respectively ..."}
+{"idx": 4, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "Sep 26, 2024 · This research addresses these issues by introducing a novel formalization of feint behaviors , both at the action and strategy levels. The core contribution is a Palindrome-directed template and Dual- Behavior model for automatically generating and combining feints with subsequent actions.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "Sep 26, 2024 · This research addresses these issues by introducing a novel formalization of feint behaviors , both at the action and strategy levels. The core contribution is a Palindrome-directed template and Dual- Behavior model for automatically generating and combining feints with subsequent actions."}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, ...", "date": "", "ddg_snippet": "by J Liu · 2024 — In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation ... 29 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/064ae24cdbb3eaacc801ee7f4fe0e4f2-Paper-Conference.pdf", "content": "by J Liu · 2024 — In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation ... 29 pages"}
+{"idx": 6, "title": "Xiangjun Peng's Homepage", "date": "", "ddg_snippet": "Finalized Papers. Some papers are rewritten and implications are added. \" Feint Behaviors and Strategies : Formalization, Implementation and Evaluation\" Preprint Version Only; and gonna be revised later. The conference version at NeurIPS 2024 is here. Related preprints are here and here. \"SLITS: Sparsity-Lightened Intelligent Thread Scheduling\" The conference version at SIGMETRICS 2023 is here ...", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/", "content": "Finalized Papers. Some papers are rewritten and implications are added. \" Feint Behaviors and Strategies : Formalization, Implementation and Evaluation\" Preprint Version Only; and gonna be revised later. The conference version at NeurIPS 2024 is here. Related preprints are here and here. \"SLITS: Sparsity-Lightened Intelligent Thread Scheduling\" The conference version at SIGMETRICS 2023 is here ..."}
+{"idx": 7, "title": "Junyu Liu - Projects", "date": "", "ddg_snippet": "The key idea of our work is to ( 1 ) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual- Behavior Model; (2) concertize the implications from our formalization of Feint on game strategies , in terms of temporal, spatial, and their collective impacts ...", "subpage_snippet": "", "source": "junyu-liu-nate.github.io", "link": "https://junyu-liu-nate.github.io/projects/FeintFinal.html", "content": "The key idea of our work is to ( 1 ) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual- Behavior Model; (2) concertize the implications from our formalization of Feint on game strategies , in terms of temporal, spatial, and their collective impacts ..."}
+{"idx": 8, "title": "A History of Chess (Jerzy Gizycki, B. H. Wood (Editor) ) (Z- ...", "date": "", "ddg_snippet": "A HISTORY OF CHESS. Chess, perhaps the most. internationally popular pas time, is a fascinating subject for study. This book sets out,", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/743840953/A-History-of-Chess-Jerzy-Gizycki-B-H-Wood-Editor-Z-Library", "content": "A HISTORY OF CHESS. Chess, perhaps the most. internationally popular pas time, is a fascinating subject for study. This book sets out,"}
+{"idx": 9, "title": "A History of Chess - Gizycki J - 1960 Ed 1977 Ed Jparra ...", "date": "", "ddg_snippet": "4 Jun 2012 — A HISTORY OF CHESS. Chess, perhaps the most. internationally popular pas time, is a fascinating subject for study. This book sets out,", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/299929196/A-History-of-Chess-Gizycki-J-1960-Ed-1977-Ed-jparra-2012-06-04", "content": "4 Jun 2012 — A HISTORY OF CHESS. Chess, perhaps the most. internationally popular pas time, is a fascinating subject for study. This book sets out,"}
diff --git a/data/sampled_jsons/Rew_short_formula_feint_behaviors_equation.jsonl b/data/sampled_jsons/Rew_short_formula_feint_behaviors_equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d54a3065bdb097a74f8204ed451a8adc77a5ebdb
--- /dev/null
+++ b/data/sampled_jsons/Rew_short_formula_feint_behaviors_equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in com- petitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing lit- erature does not provide a comprehensive (and/or concrete ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v2", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in com- petitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing lit- erature does not provide a comprehensive (and/or concrete ..."}
+{"idx": 1, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short-term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ", "content": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short-term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors ."}
+{"idx": 2, "title": "Feint behaviors and strategies: - ACM Digital Library", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738032", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ..."}
+{"idx": 3, "title": "PDF feint_video_slides - neurips.cc", "date": "", "ddg_snippet": "Feint Behavior Characteristics and Templates Most real-world attack behaviors can be divided into 3 stages. Feint templates can be automatically generated from them.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96274.pdf", "content": "Feint Behavior Characteristics and Templates Most real-world attack behaviors can be divided into 3 stages. Feint templates can be automatically generated from them."}
+{"idx": 4, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "32the action-level characteristic of Feint and provide Feint behavior generation guidelines. On the 33strategy-level, existing learning-based works either neglect Feint behaviors or implicitly assume that 34they are the same as other behaviors which could have same impacts on strategies through learning.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "32the action-level characteristic of Feint and provide Feint behavior generation guidelines. On the 33strategy-level, existing learning-based works either neglect Feint behaviors or implicitly assume that 34they are the same as other behaviors which could have same impacts on strategies through learning."}
+{"idx": 5, "title": "[2403.07932v2] Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932v2", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ..."}
+{"idx": 6, "title": "FEINT IN MULTI-PLAYER GAMES - OpenReview", "date": "", "ddg_snippet": "The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts. Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WbyWDWoXD3", "content": "The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts. Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning)."}
+{"idx": 7, "title": "NeurIPS Poster Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "Poster Feint Behaviors and Strategies: Formalization, Implementation and Evaluation Junyu Liu · Xiangjun Peng", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96274", "content": "Poster Feint Behaviors and Strategies: Formalization, Implementation and Evaluation Junyu Liu · Xiangjun Peng"}
+{"idx": 8, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "This paper introduces a novel formalization of feint behaviors in multi-player games, improving AI performance and game diversity via a unified MARL implementation.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "This paper introduces a novel formalization of feint behaviors in multi-player games, improving AI performance and game diversity via a unified MARL implementation."}
+{"idx": 9, "title": "Junyu Liu - Projects", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.).", "subpage_snippet": "", "source": "junyu-liu-nate.github.io", "link": "https://junyu-liu-nate.github.io/projects/FeintFinal.html", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.)."}
diff --git a/data/sampled_jsons/Rubin_1974_Estimating_causal_effects_treatments_randomized_nonrandomized_studies_abstract.jsonl b/data/sampled_jsons/Rubin_1974_Estimating_causal_effects_treatments_randomized_nonrandomized_studies_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b686a167ffc1b6b0ff7e421e44170c9c68d6774c
--- /dev/null
+++ b/data/sampled_jsons/Rubin_1974_Estimating_causal_effects_treatments_randomized_nonrandomized_studies_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Estimating causal effects of treatments in randomized and nonrandomized ...", "date": "", "ddg_snippet": "Abstract Presents a discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation. The objective was to specify the benefits of randomization in estimating causal effects of treatments . It is concluded that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects is ...", "subpage_snippet": "", "source": "psycnet.apa.org", "link": "https://psycnet.apa.org/record/1975-06502-001", "content": "Abstract Presents a discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation. The objective was to specify the benefits of randomization in estimating causal effects of treatments . It is concluded that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects is ..."}
+{"idx": 1, "title": "PDF Estimating Causal Effects of Treatments in Randomized and Nonrandomized ...", "date": "", "ddg_snippet": "Recent psychological and educational literature has included extensive criticism of the use of nonrandomized studies to estimate causal effects of treatments (e.g., Campbell & Erlebacher, 1970). The im-plication in much of this literature is that only properly randomized experiments can lead to useful estimates of causal effects . If taken as applying to all fields of study, this position is ...", "subpage_snippet": "", "source": "www.mimuw.edu.pl", "link": "https://www.mimuw.edu.pl/~noble/courses/BayesianNetworks/74RUBIN.pdf", "content": "Recent psychological and educational literature has included extensive criticism of the use of nonrandomized studies to estimate causal effects of treatments (e.g., Campbell & Erlebacher, 1970). The im-plication in much of this literature is that only properly randomized experiments can lead to useful estimates of causal effects . If taken as applying to all fields of study, this position is ..."}
+{"idx": 2, "title": "Estimating Causal Effects of Treatments in Experimental and ...", "date": "", "ddg_snippet": "The objective is to specify the benefits of randomization in estimating causal effects of treatments . The basic conclusion is that randomization should be employed whenever possible, but the use of carefully controlled nonrandomized data to estimate causal effects is a reasonable and necessary procedure in many cases.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/abs/10.1002/j.2333-8504.1972.tb00631.x", "content": "The objective is to specify the benefits of randomization in estimating causal effects of treatments . The basic conclusion is that randomization should be employed whenever possible, but the use of carefully controlled nonrandomized data to estimate causal effects is a reasonable and necessary procedure in many cases."}
+{"idx": 3, "title": "PDF DOCUMENT RESUME AUTHOR Rubin, Donald TITLE Estimating Causal Effects of ...", "date": "", "ddg_snippet": "purpose was to specify the benefits of randomization in estimating causal effects of treatments . It is concluded that randomization should be employed whenever possible, but the use of carefully controlled nonrandomized data to estimate causal effects is a reasonable and necessary procedure in many cases. (Author)", "subpage_snippet": "", "source": "files.eric.ed.gov", "link": "https://files.eric.ed.gov/fulltext/ED069724.pdf", "content": "purpose was to specify the benefits of randomization in estimating causal effects of treatments . It is concluded that randomization should be employed whenever possible, but the use of carefully controlled nonrandomized data to estimate causal effects is a reasonable and necessary procedure in many cases. (Author)"}
+{"idx": 4, "title": "Estimating causal effects of treatments in randomized and nonrandomized ...", "date": "", "ddg_snippet": "Recent psychological and educational literature has included extensive criticism of the use of nonrandomized studies to estimate causal effects of treatments (e.g., Campbell & Erlebacher, 1970). The implication in much of this literature is that only properly randomized experiments can lead to useful estimates of causal effects .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/93080536/Estimating_causal_effects_of_treatments_in_randomized_and_nonrandomized_studies", "content": "Recent psychological and educational literature has included extensive criticism of the use of nonrandomized studies to estimate causal effects of treatments (e.g., Campbell & Erlebacher, 1970). The implication in much of this literature is that only properly randomized experiments can lead to useful estimates of causal effects ."}
+{"idx": 5, "title": "Rubin, D. (1974) Estimating Causal Effects of Treatments in Randomized ...", "date": "", "ddg_snippet": "Rubin , D. ( 1974 ) Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies . Journal of Educational Psychology, 66, 688-701.", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=1362668", "content": "Rubin , D. ( 1974 ) Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies . Journal of Educational Psychology, 66, 688-701."}
+{"idx": 6, "title": "Estimating Causal Effects of Treatments in Randomized and Nonrandomized ...", "date": "", "ddg_snippet": "Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies Author (s): Rubin , Donald B. Publication Year: 1974 Source: Journal of Educational Psychology, v66 n5 p688-701, 1974 Document Type: Article Page Count: 14 Subject/Key Words: Control Groups, Experimental Groups, Research Methodology, Statistical Analysis", "subpage_snippet": "", "source": "www.ets.org", "link": "https://www.ets.org/research/policy_research_reports/publications/article/1974/hrbx.html", "content": "Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies Author (s): Rubin , Donald B. Publication Year: 1974 Source: Journal of Educational Psychology, v66 n5 p688-701, 1974 Document Type: Article Page Count: 14 Subject/Key Words: Control Groups, Experimental Groups, Research Methodology, Statistical Analysis"}
+{"idx": 7, "title": "Estimating causal effects of treatments in randomized and nonrandomized ...", "date": "", "ddg_snippet": "A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating causal effects of treatments . The basic conclusion is that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Estimating-causal-effects-of-treatments-in-and-Rubin/545122e2990590524459ec9b59ccac6ce71e3b6a", "content": "A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating causal effects of treatments . The basic conclusion is that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects ..."}
+{"idx": 8, "title": "Estimating causal effects of treatments in randomized and nonrandomized ...", "date": "", "ddg_snippet": "Estimating causal effects of treatments in randomized and nonrandomized studies . 🔍 American Psychological Association; American Psychological Association (APA) (ISSN 0022-0663), Journal of Educational Psychology, #5, 66, pages 688-701, 1974 Rubin , Donald B. 🔍 DOI10.1037/h0037350 DOI: 10.1037/h0037350 Digital Object Identifier", "subpage_snippet": "", "source": "annas-archive.org", "link": "https://annas-archive.org/md5/3b19673c53c930459675b2ea2c36bb3b", "content": "Estimating causal effects of treatments in randomized and nonrandomized studies . 🔍 American Psychological Association; American Psychological Association (APA) (ISSN 0022-0663), Journal of Educational Psychology, #5, 66, pages 688-701, 1974 Rubin , Donald B. 🔍 DOI10.1037/h0037350 DOI: 10.1037/h0037350 Digital Object Identifier"}
+{"idx": 9, "title": "Estimating Causal Effects of Treatments in Randomized and Nonrandomized ...", "date": "", "ddg_snippet": "Presents a discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation. The objective was to specify the benefits of randomization in estimating causal effects of treatments . It is concluded that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects is a ...", "subpage_snippet": "", "source": "dash.harvard.edu", "link": "https://dash.harvard.edu/entities/publication/73120378-82c0-6bd4-e053-0100007fdf3b", "content": "Presents a discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation. The objective was to specify the benefits of randomization in estimating causal effects of treatments . It is concluded that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects is a ..."}
diff --git "a/data/sampled_jsons/R\316\264(n)_formula_Statistical_Collusion_by_Collectives_on_Learning_Platforms_arXiv_2502.04879.jsonl" "b/data/sampled_jsons/R\316\264(n)_formula_Statistical_Collusion_by_Collectives_on_Learning_Platforms_arXiv_2502.04879.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..7a82e662b6513b362ac5a5bd3112854d19f7ebab
--- /dev/null
+++ "b/data/sampled_jsons/R\316\264(n)_formula_Statistical_Collusion_by_Collectives_on_Learning_Platforms_arXiv_2502.04879.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms - arXiv.org", "date": "", "ddg_snippet": "Abstract As platforms increasingly rely on learning algo-rithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879", "content": "Abstract As platforms increasingly rely on learning algo-rithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 2, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.04879", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. …"}
+{"idx": 3, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ..."}
+{"idx": 4, "title": "arXiv:2502.04879v1 [stat.ML] 7 Feb 2025", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879v1", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
+{"idx": 5, "title": "PDF Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier, Francis Bach, Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/47263.pdf", "content": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier, Francis Bach, Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices"}
+{"idx": 6, "title": "\"Statistical Collusion by Collectives on Learning Platforms.\"", "date": "", "ddg_snippet": "Bibliographic details on Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-04879", "content": "Bibliographic details on Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 7, "title": "[论文审查] Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "相似审查 [论文审查] Algorithmic Collective Action with Two Collectives [论文审查] A Statistical Learning Approach for Feature-Aware Task-to-Core Allocation in Heterogeneous Platforms [论文审查] Reinforcement Learning , Collusion , and the Folk Theorem [论文审查] Collusion Detection with Graph Neural Networks", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/zh/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "相似审查 [论文审查] Algorithmic Collective Action with Two Collectives [论文审查] A Statistical Learning Approach for Feature-Aware Task-to-Core Allocation in Heterogeneous Platforms [论文审查] Reinforcement Learning , Collusion , and the Folk Theorem [论文审查] Collusion Detection with Graph Neural Networks"}
+{"idx": 8, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "by E Gauthier · 2025 · Cited by 2 — In this paper, we introduce a new framework to enable members of the collective to learn strategies h efficiently and to infer the parameters that determine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879?", "content": "by E Gauthier · 2025 · Cited by 2 — In this paper, we introduce a new framework to enable members of the collective to learn strategies h efficiently and to infer the parameters that determine ..."}
+{"idx": 9, "title": "[논문 리뷰] Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "이 논문은 학습 알고리즘에 대한 플랫폼에서 집단적 행동( collective action)이 어떻게 영향을 미칠 수 있는지를 다루고 있습니다. 플랫폼 이용자들이 자신들의 이익에 맞게 시스템을 조정하기 위해 협력할 수 있는 방법을 보입니다. 그들은 데이터 제출을 조정하여 플랫폼에 미치는 영향을 평가하고자 ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/ko/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "이 논문은 학습 알고리즘에 대한 플랫폼에서 집단적 행동( collective action)이 어떻게 영향을 미칠 수 있는지를 다루고 있습니다. 플랫폼 이용자들이 자신들의 이익에 맞게 시스템을 조정하기 위해 협력할 수 있는 방법을 보입니다. 그들은 데이터 제출을 조정하여 플랫폼에 미치는 영향을 평가하고자 ..."}
diff --git a/data/sampled_jsons/S18_characters_dataset_training_examples_count_Appendix_B.1_'Machine_Learning_meets_Algebraic_Combin.jsonl b/data/sampled_jsons/S18_characters_dataset_training_examples_count_Appendix_B.1_'Machine_Learning_meets_Algebraic_Combin.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a16f8bdec6530475ee05378ab6c7935af63a0013
--- /dev/null
+++ b/data/sampled_jsons/S18_characters_dataset_training_examples_count_Appendix_B.1_'Machine_Learning_meets_Algebraic_Combin.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "vivo S18 - Full phone specifications - GSMArena.com", "date": "", "ddg_snippet": "Dec 14, 2023 · vivo S18 Android smartphone. Announced Dec 2023. Features 6.78″ display, Snapdragon 7 Gen 3 chipset, 5000 mAh battery, 512 GB storage, 16 GB RAM.", "subpage_snippet": "", "source": "www.gsmarena.com", "link": "https://www.gsmarena.com/vivo_s18-12744.php", "content": "Dec 14, 2023 · vivo S18 Android smartphone. Announced Dec 2023. Features 6.78″ display, Snapdragon 7 Gen 3 chipset, 5000 mAh battery, 512 GB storage, 16 GB RAM."}
+{"idx": 1, "title": "King Song S18 , 903Wh Battery, 2,200W Motor, Suspension", "date": "", "ddg_snippet": "In short, King Song’s S18 is intended as a comparatively short-range ‘sport’ vehicle, with an emphasis on being elegant & light. At the same time, the V11 offers a larger battery pack (the largest produced by Inmotion to date), which could be considered more of a Cruiser.", "subpage_snippet": "", "source": "ewheels.com", "link": "https://ewheels.com/products/new-king-song-s18-1110wh-battery-2200w-motor-full-body-suspension", "content": "In short, King Song’s S18 is intended as a comparatively short-range ‘sport’ vehicle, with an emphasis on being elegant & light. At the same time, the V11 offers a larger battery pack (the largest produced by Inmotion to date), which could be considered more of a Cruiser."}
+{"idx": 2, "title": "Vivo S18 - Specifications", "date": "", "ddg_snippet": "Specifications of the Vivo S18 . Dimensions: 75.1 x 164.36 x 7.45 mm, Weight: 185 g, SoC: Qualcomm Snapdragon 7 Gen 3 (SM7550-AB), CPU: 1x 2.63 GHz Cortex-A715, 3x 2.4 GHz Cortex-A715, 4x 1.8 GHz Cortex-A510, GPU: Qualcomm Adreno 720, RAM: 8 GB, 12 GB, 16 GB, 3200 MHz, Storage: 256 GB, 512 GB, Display: 6.78 in, AMOLED, 1260 x 2800 pixels, 30 bit ...", "subpage_snippet": "", "source": "www.devicespecifications.com", "link": "https://www.devicespecifications.com/en/model/29385eb6", "content": "Specifications of the Vivo S18 . Dimensions: 75.1 x 164.36 x 7.45 mm, Weight: 185 g, SoC: Qualcomm Snapdragon 7 Gen 3 (SM7550-AB), CPU: 1x 2.63 GHz Cortex-A715, 3x 2.4 GHz Cortex-A715, 4x 1.8 GHz Cortex-A510, GPU: Qualcomm Adreno 720, RAM: 8 GB, 12 GB, 16 GB, 3200 MHz, Storage: 256 GB, 512 GB, Display: 6.78 in, AMOLED, 1260 x 2800 pixels, 30 bit ..."}
+{"idx": 3, "title": "vivo S18 - Full Phone Specs, Price and Comparison", "date": "", "ddg_snippet": "The vivo S18 comes with the latest Android 14, ensuring enhanced functionality and smooth multitasking. It features a extra-large 6.78-inches Crystal-clear Full HD+ display, perfect for both work and entertainment.", "subpage_snippet": "", "source": "www.techspecs.info", "link": "https://www.techspecs.info/vivo-s18/", "content": "The vivo S18 comes with the latest Android 14, ensuring enhanced functionality and smooth multitasking. It features a extra-large 6.78-inches Crystal-clear Full HD+ display, perfect for both work and entertainment."}
+{"idx": 4, "title": "Vivo S18 Series: Know Everything About the New Smartphones, and...", "date": "", "ddg_snippet": "Dec 18, 2023 · The Vivo S18 series, which includes the Vivo S18 , Vivo S18 Pro, and Vivo S18e, was released in China on Thursday. MediaTek processors power two phones, while a Snapdragon processor powers the third. The Vivo S18 series boasts AMOLED panels and 50-megapixel main cameras.", "subpage_snippet": "", "source": "www.pcquest.com", "link": "https://www.pcquest.com/vivo-s18-series-know-everything-about-the-new-smartphones-and-its-price-and-specifications/", "content": "Dec 18, 2023 · The Vivo S18 series, which includes the Vivo S18 , Vivo S18 Pro, and Vivo S18e, was released in China on Thursday. MediaTek processors power two phones, while a Snapdragon processor powers the third. The Vivo S18 series boasts AMOLED panels and 50-megapixel main cameras."}
+{"idx": 5, "title": "Samsung Galaxy S18 2024: Official Price, Release Date & Full...", "date": "", "ddg_snippet": "Mar 29, 2024 · Samsung’s upcoming flagship Galaxy S18 2024 has fantastic storage and 14 GB RAM. In addition, it has also 256GB ROM (expandable to 1TB). The massive RAM and ROM system will enable this phone to contain vast data. Furthermore, this type of storage makes a phone faster and smoother.", "subpage_snippet": "", "source": "smart-phoneprice.com", "link": "https://smart-phoneprice.com/samsung-galaxy-s18/", "content": "Mar 29, 2024 · Samsung’s upcoming flagship Galaxy S18 2024 has fantastic storage and 14 GB RAM. In addition, it has also 256GB ROM (expandable to 1TB). The massive RAM and ROM system will enable this phone to contain vast data. Furthermore, this type of storage makes a phone faster and smoother."}
+{"idx": 6, "title": "Specs revealed ahead of vivo S18 Series launch - Pocket Tactics", "date": "", "ddg_snippet": "Dec 5, 2023 · The vivo S18 Series, including the budget S18e , vanilla S18 , and S18 Pro with Dimensity 9200+ chip, are launching later this month.", "subpage_snippet": "", "source": "www.pockettactics.com", "link": "https://www.pockettactics.com/vivo-s18-series", "content": "Dec 5, 2023 · The vivo S18 Series, including the budget S18e , vanilla S18 , and S18 Pro with Dimensity 9200+ chip, are launching later this month."}
+{"idx": 7, "title": "vivo S18 : Price, specs and best deals - Kimovil", "date": "", "ddg_snippet": "Apr 24, 2024 · Introducing the vivo S18 : A Mid-Range Marvel. As technological advancements continue to revolutionize the smartphone industry, consumers are constantly seeking devices that provide a perfect balance between performance and affordability.", "subpage_snippet": "", "source": "www.kimovil.com", "link": "https://www.kimovil.com/en/where-to-buy-vivo-s18", "content": "Apr 24, 2024 · Introducing the vivo S18 : A Mid-Range Marvel. As technological advancements continue to revolutionize the smartphone industry, consumers are constantly seeking devices that provide a perfect balance between performance and affordability."}
+{"idx": 8, "title": "vivo S18 series is here with updated designs, impressive cameras", "date": "", "ddg_snippet": "Dec 14, 2023 · The S18 series boots OriginOS 4 based on Android 14 and features 5,000 mAh batteries with 80W fast charging. vivo S18 and S18 Pro come in black, green and silver colors.", "subpage_snippet": "", "source": "www.gsmarena.com", "link": "https://www.gsmarena.com/vivo_s18_series_is_here_with_updated_designs_impressive_cameras_and_80w_charging_-news-60912.php", "content": "Dec 14, 2023 · The S18 series boots OriginOS 4 based on Android 14 and features 5,000 mAh batteries with 80W fast charging. vivo S18 and S18 Pro come in black, green and silver colors."}
+{"idx": 9, "title": "RuPaul's Drag Race (Season 18)", "date": "", "ddg_snippet": "It was officially announced on August 20, 2025. [2] These contestants are currently rumored to be on the eighteenth season of RuPaul's Drag Race.[3] (Biographical information stated from during time of contest) If cast rumors are true, Season 18 will be the first season of RuPaul's Drag Race...", "subpage_snippet": "", "source": "rupaulsdragrace.fandom.com", "link": "https://rupaulsdragrace.fandom.com/wiki/RuPaul's_Drag_Race_(Season_18)", "content": "It was officially announced on August 20, 2025. [2] These contestants are currently rumored to be on the eighteenth season of RuPaul's Drag Race.[3] (Biographical information stated from during time of contest) If cast rumors are true, Season 18 will be the first season of RuPaul's Drag Race..."}
diff --git a/data/sampled_jsons/SAFE_algorithm_ADMM_dual_variable_update_u_k+1_=_u_k_+_x_k+1_-_z_k+1.jsonl b/data/sampled_jsons/SAFE_algorithm_ADMM_dual_variable_update_u_k+1_=_u_k_+_x_k+1_-_z_k+1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f810d97cdf66bebf1e47e658454ce7a22973a821
--- /dev/null
+++ b/data/sampled_jsons/SAFE_algorithm_ADMM_dual_variable_update_u_k+1_=_u_k_+_x_k+1_-_z_k+1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Enhanced ADMM-based Interior Point Method for Linear and", "date": "", "ddg_snippet": "The ADMM -based interior point (ABIP, Lin et al. 2021) method is a hybrid algorithm that effectively combines interior point method (IPM) and first ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2209.01793v3", "content": "The ADMM -based interior point (ABIP, Lin et al. 2021) method is a hybrid algorithm that effectively combines interior point method (IPM) and first ..."}
+{"idx": 1, "title": "Frontiers | Distributed ADMM power optimal control for", "date": "", "ddg_snippet": "This study uses an optimization algorithm with a Gaussian penalty function, ADMM - , to alternately optimize the power reference values of wind, light ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/energy-efficiency/articles/10.3389/fenef.2024.1337606/full", "content": "This study uses an optimization algorithm with a Gaussian penalty function, ADMM - , to alternately optimize the power reference values of wind, light ..."}
+{"idx": 2, "title": "Non-Stationary Bandit Convex Optimization: An Optimal Algorithm", "date": "", "ddg_snippet": "... the learner’s cumulative loss and that of a comparator sequence 𝒖 1 , … , 𝒖 T ∈ 𝒳 {\\bm{ u }}_{ 1 },\\ldots,{\\bm{ u }}_{T}\\in\\mathcal{ X } .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.04654v1", "content": "... the learner’s cumulative loss and that of a comparator sequence 𝒖 1 , … , 𝒖 T ∈ 𝒳 {\\bm{ u }}_{ 1 },\\ldots,{\\bm{ u }}_{T}\\in\\mathcal{ X } ."}
+{"idx": 3, "title": "Downloads", "date": "", "ddg_snippet": "Adaptive and Safe Bayesian Optimization in High Dimensions via One -Dimensional Subspaces ... Gradient Backpropagation Through Categorical Variables", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2019", "content": "Adaptive and Safe Bayesian Optimization in High Dimensions via One -Dimensional Subspaces ... Gradient Backpropagation Through Categorical Variables"}
+{"idx": 4, "title": "U.S. Patent for Controller with early termination in", "date": "", "ddg_snippet": "... starting strategies for IPMs have motivated the use of IPMs within branch-and-bound optimization for mixed-integer programming However, unlike dual ...", "subpage_snippet": "", "source": "patents.justia.com", "link": "https://patents.justia.com/patent/11340899", "content": "... starting strategies for IPMs have motivated the use of IPMs within branch-and-bound optimization for mixed-integer programming However, unlike dual ..."}
+{"idx": 5, "title": "U.S. Patent Application for Controller with Early Termination", "date": "", "ddg_snippet": "... starting strategies for IPMs have motivated the use of IPMs within branch-and-bound optimization for mixed-integer programming However, unlike dual ...", "subpage_snippet": "", "source": "patents.justia.com", "link": "https://patents.justia.com/patent/20220137961", "content": "... starting strategies for IPMs have motivated the use of IPMs within branch-and-bound optimization for mixed-integer programming However, unlike dual ..."}
+{"idx": 6, "title": "A Sparsity-Aware Autonomous Path Planning Accelerator with", "date": "", "ddg_snippet": "Third, at hardware and system level, existing solutions usually assume general-purpose processing systems as the underlying computing platform, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16177v1", "content": "Third, at hardware and system level, existing solutions usually assume general-purpose processing systems as the underlying computing platform, and ..."}
+{"idx": 7, "title": "Adversarial Graph Fusion for Incomplete Multi-view", "date": "", "ddg_snippet": "Zhangqi Jiang 1 Tingjin Luo 1 , Xu Yang 2 Xinyan Liang 3 1 National University of Defense Technology 2 Southeast University 3 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15955v1", "content": "Zhangqi Jiang 1 Tingjin Luo 1 , Xu Yang 2 Xinyan Liang 3 1 National University of Defense Technology 2 Southeast University 3 ..."}
+{"idx": 8, "title": "Online Distributed Optimization for Spatio-temporally", "date": "", "ddg_snippet": "... with the prices of ESS decreasing each year (e.g., The price of the Lithium-ion based battery pack fell from 1100 USD/kWh in 2010 to 156 USD/kWh in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.21884v1", "content": "... with the prices of ESS decreasing each year (e.g., The price of the Lithium-ion based battery pack fell from 1100 USD/kWh in 2010 to 156 USD/kWh in ..."}
+{"idx": 9, "title": "US10229092B2 - Systems and methods for robust low-rank matrix", "date": "", "ddg_snippet": "2017-09-13 Assigned to CITY UNIVERSITY OF HONG KONG reassignment CITY UNIVERSITY OF HONG KONG ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US10229092B2/en", "content": "2017-09-13 Assigned to CITY UNIVERSITY OF HONG KONG reassignment CITY UNIVERSITY OF HONG KONG ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR ..."}
diff --git a/data/sampled_jsons/SAFE_algorithm_sparsity-constrained_optimization_augmented_Lagrange_dual_flatness_penalty_parameter__year_2024.jsonl b/data/sampled_jsons/SAFE_algorithm_sparsity-constrained_optimization_augmented_Lagrange_dual_flatness_penalty_parameter__year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..13e81abff0c900f91abfe41743a0ec454ef7d13f
--- /dev/null
+++ b/data/sampled_jsons/SAFE_algorithm_sparsity-constrained_optimization_augmented_Lagrange_dual_flatness_penalty_parameter__year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective. We solve it explicitly via an augmented Lagrange dual approach and extend it further by proposing a generalized projection operation, resulting in novel pruning methods called SAFE and its extension, SAFE +.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective. We solve it explicitly via an augmented Lagrange dual approach and extend it further by proposing a generalized projection operation, resulting in novel pruning methods called SAFE and its extension, SAFE +."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization , we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time.Specifically, we formulate pruning as a sparsity ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LOG-postech/safe-torch", "content": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization , we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time.Specifically, we formulate pruning as a sparsity ..."}
+{"idx": 2, "title": "Convergence of a special partially augmented Lagrangian algorithm for ...", "date": "", "ddg_snippet": "In this paper, we consider the continuous relaxation reformulation of sparsity-constrained optimization problems. Based on the structure of the relaxation problem, a special partially augmented Lagrangian method is proposed. Unlike the classical approach, this algorithm preserves complementarity-type constraints in the augmented Lagrangian subproblems. Under mild conditions that do not depend ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11590-024-02172-3", "content": "In this paper, we consider the continuous relaxation reformulation of sparsity-constrained optimization problems. Based on the structure of the relaxation problem, a special partially augmented Lagrangian method is proposed. Unlike the classical approach, this algorithm preserves complementarity-type constraints in the augmented Lagrangian subproblems. Under mild conditions that do not depend ..."}
+{"idx": 3, "title": "PDF Gap Safe Screening Rules for Sparsity Enforcing Penalties", "date": "", "ddg_snippet": "Abstract In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data- tting term. A recently introduced technique called screening rules pro-pose to ignore some variables in the optimization leveraging the expected sparsity of the solutions and consequently leading to faster solvers.", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume18/16-577/16-577.pdf", "content": "Abstract In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data- tting term. A recently introduced technique called screening rules pro-pose to ignore some variables in the optimization leveraging the expected sparsity of the solutions and consequently leading to faster solvers."}
+{"idx": 4, "title": "PDF Controlled Sparsity via Constrained Optimization or - NeurIPS", "date": "", "ddg_snippet": "We resort to a constrained optimization approach as a tool to overcome the controllability shortcom- ings faced by penalty -based sparsity methods. Along with a reliable control of the model density, this technique provides a more interpretable hyper- parameter and removes the need for expensive iterative tuning.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/089b592cccfafdca8e0178e85b609f19-Paper-Conference.pdf", "content": "We resort to a constrained optimization approach as a tool to overcome the controllability shortcom- ings faced by penalty -based sparsity methods. Along with a reliable control of the model density, this technique provides a more interpretable hyper- parameter and removes the need for expensive iterative tuning."}
+{"idx": 5, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective. We solve it explicitly via an augmented Lagrange dual approach and extend it further by proposing a gen-eralized projection operation, resulting in novel pruning methods called SAFE and its extension, SAFE+.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.06866", "content": "Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective. We solve it explicitly via an augmented Lagrange dual approach and extend it further by proposing a gen-eralized projection operation, resulting in novel pruning methods called SAFE and its extension, SAFE+."}
+{"idx": 6, "title": "GitHub - xianchaoxiu/Sparse-Optimization", "date": "", "ddg_snippet": "[2013] Sparsity Constrained Nonlinear Optimization : Optimality Conditions and Algorithms , SIAM Journal on Optimization [Paper] [2012] Accelerated Iterative Hard Thresholding, Signal Processing [Paper]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xianchaoxiu/Sparse-Optimization", "content": "[2013] Sparsity Constrained Nonlinear Optimization : Optimality Conditions and Algorithms , SIAM Journal on Optimization [Paper] [2012] Accelerated Iterative Hard Thresholding, Signal Processing [Paper]"}
+{"idx": 7, "title": "A fast augmented Lagrangian framework and its application", "date": "", "ddg_snippet": "This letter investigates a fast augmented Lagrangian framework applicable to constrained convex optimization problems. By integrating the Aitken acceleration technique and the multi-direction acceleration technique, this framework enhances the convergence rate of the traditional augmented Lagrangian method and adapts to different types of constrained convex optimization problems on a case-by ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167691125001781", "content": "This letter investigates a fast augmented Lagrangian framework applicable to constrained convex optimization problems. By integrating the Aitken acceleration technique and the multi-direction acceleration technique, this framework enhances the convergence rate of the traditional augmented Lagrangian method and adapts to different types of constrained convex optimization problems on a case-by ..."}
+{"idx": 8, "title": "ICML Poster SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Abstract: Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization , we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time.Specifically, we formulate pruning as a sparsity ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46658", "content": "Abstract: Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization , we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time.Specifically, we formulate pruning as a sparsity ..."}
+{"idx": 9, "title": "Constrained Style Learning from Imperfect Demonstrations under", "date": "", "ddg_snippet": "... combined using a self-adjustable Lagrangian multiplier, dynamically adjusting in response to observed violations of task optimality constraints ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09371v1", "content": "... combined using a self-adjustable Lagrangian multiplier, dynamically adjusting in response to observed violations of task optimality constraints ..."}
diff --git a/data/sampled_jsons/SAFE_pruning_VGG-19_CIFAR-10_Table_7_validation_accuracy_90%_95%_98%_sparsity.jsonl b/data/sampled_jsons/SAFE_pruning_VGG-19_CIFAR-10_Table_7_validation_accuracy_90%_95%_98%_sparsity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8c6ca731628851c954b7a029349b3d8988aba3f
--- /dev/null
+++ b/data/sampled_jsons/SAFE_pruning_VGG-19_CIFAR-10_Table_7_validation_accuracy_90%_95%_98%_sparsity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Figure 2: Validation accuracy (mean±std) of VGG-19 and ResNet-20/32 models on CIFAR-10 /100 pruned across different sparsity levels and methods. SAFE consistently achieves superior performance across a broad range of sparsity levels.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.06866", "content": "Figure 2: Validation accuracy (mean±std) of VGG-19 and ResNet-20/32 models on CIFAR-10 /100 pruned across different sparsity levels and methods. SAFE consistently achieves superior performance across a broad range of sparsity levels."}
+{"idx": 1, "title": "Tutorial 2: 94% accuracy on Cifar10 in 2 minutes - Medium", "date": "", "ddg_snippet": "Apr 16, 2019 · Tutorial 2: 94% accuracy on Cifar10 in 2 minutes Prerequisite: Tutorial 0 (setting up Google Colab, TPU runtime, and Cloud Storage) Cifar10 is a classic dataset for deep learning, consisting of ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/fenwicks/tutorial-2-94-accuracy-on-cifar10-in-2-minutes-7b5aaecd9cdd", "content": "Apr 16, 2019 · Tutorial 2: 94% accuracy on Cifar10 in 2 minutes Prerequisite: Tutorial 0 (setting up Google Colab, TPU runtime, and Cloud Storage) Cifar10 is a classic dataset for deep learning, consisting of ..."}
+{"idx": 2, "title": "GitHub - xijia-tao/Implementation-of-VGGs-on-CIFAR-10: The ...", "date": "", "ddg_snippet": "The repository contains a detailed analysis on implementing VGG19 and (plain-layered) VGG34 on the CIFAR-10 dataset with code, and an explanation on the distinctive difference between them. It serves the purpose of storing my HW2 for the ICRA training, HKU, 2020. - xijia-tao/Implementation-of-VGGs-on- CIFAR-10", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xijia-tao/Implementation-of-VGGs-on-CIFAR-10", "content": "The repository contains a detailed analysis on implementing VGG19 and (plain-layered) VGG34 on the CIFAR-10 dataset with code, and an explanation on the distinctive difference between them. It serves the purpose of storing my HW2 for the ICRA training, HKU, 2020. - xijia-tao/Implementation-of-VGGs-on- CIFAR-10"}
+{"idx": 3, "title": "Low validation accuracy VGG-19 CIFAR-10 CNN - Reddit", "date": "", "ddg_snippet": "Hey Guys, I am trying to train a VGG-19 CNN on CIFAR-10 dataset using data augmentation and batch normalization. The code can be found VGG-19 CNN. I took two approaches to training the model: Using early stopping: loss = 2.2816 and accuracy = 47.1700% Without early stopping: loss = 3.3211 and accuracy = 56.6800% The loss and accuracy are on validation data. Also, when the model is trained ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/deeplearning/comments/hxk3g8/low_validation_accuracy_vgg19_cifar10_cnn/", "content": "Hey Guys, I am trying to train a VGG-19 CNN on CIFAR-10 dataset using data augmentation and batch normalization. The code can be found VGG-19 CNN. I took two approaches to training the model: Using early stopping: loss = 2.2816 and accuracy = 47.1700% Without early stopping: loss = 3.3211 and accuracy = 56.6800% The loss and accuracy are on validation data. Also, when the model is trained ..."}
+{"idx": 4, "title": "CIFAR10: 94% Of Accuracy By 50 Epochs With End-to-End ...", "date": "", "ddg_snippet": "Aug 28, 2019 · The below table show the test accuracy after training each networks: Several Training histories are shown below: From the training history figure, we can see the accuracy curves are still getting higher. Due to the purpose of proving the effect of 1-Cycle schedule, so the author just trained with 50 epochs.", "subpage_snippet": "", "source": "fptsoftware.com", "link": "https://fptsoftware.com/resource-center/blogs/cifar10-94-of-accuracy-by-50-epochs-with-end-to-end-training", "content": "Aug 28, 2019 · The below table show the test accuracy after training each networks: Several Training histories are shown below: From the training history figure, we can see the accuracy curves are still getting higher. Due to the purpose of proving the effect of 1-Cycle schedule, so the author just trained with 50 epochs."}
+{"idx": 5, "title": "The best CNN for CIFAR10 from scratch (93% accuracy)", "date": "", "ddg_snippet": "Feb 18, 2024 · The best CNN for CIFAR10 from scratch (93% accuracy ) The objective of this practice is to improve the accuracy of CIFAR10 as much as possible, specifically we want to exceed 92%.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@anderaquerretamontoro/the-best-cnn-for-cifar10-from-scratch-93-accuracy-bde35e17fca6", "content": "Feb 18, 2024 · The best CNN for CIFAR10 from scratch (93% accuracy ) The objective of this practice is to improve the accuracy of CIFAR10 as much as possible, specifically we want to exceed 92%."}
+{"idx": 6, "title": "Pruning results of VGG-16/19 on CIFAR-10 with fine-tuning", "date": "", "ddg_snippet": "VGG -16 and VGG-19 , our AGSPRL prunes all convolutional layers using the RLlearned rates. Table 4 presents the results for three different pruning rates: 30%, 50% and 70%.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Pruning-results-of-VGG-16-19-on-CIFAR-10-with-fine-tuning_tbl2_366142702", "content": "VGG -16 and VGG-19 , our AGSPRL prunes all convolutional layers using the RLlearned rates. Table 4 presents the results for three different pruning rates: 30%, 50% and 70%."}
+{"idx": 7, "title": "Eigenspectrum Analysis of Neural Networks without Aspect Ratio", "date": "", "ddg_snippet": "In one of the LLM pruning experiments, FARMS reduces the perplexity of the LLaMA-7B model by 17.3% when compared with the state-of-the-art method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06280v1", "content": "In one of the LLM pruning experiments, FARMS reduces the perplexity of the LLaMA-7B model by 17.3% when compared with the state-of-the-art method."}
+{"idx": 8, "title": "BDefects4NN: A Backdoor Defect Database for Controlled", "date": "", "ddg_snippet": "Utilizing these untrusted DNNs presents significant safety risks, as these models may contain intentional backdoor defects resulting from the black ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.00746v1", "content": "Utilizing these untrusted DNNs presents significant safety risks, as these models may contain intentional backdoor defects resulting from the black ..."}
+{"idx": 9, "title": "Exploring Neuromorphic Computing Based on Spiking Neural", "date": "", "ddg_snippet": "Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of attention lately due to its promise of reducing the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3571155", "content": "Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of attention lately due to its promise of reducing the ..."}
diff --git a/data/sampled_jsons/SAFE_pruning_algorithm_dual_variable_update_formula.jsonl b/data/sampled_jsons/SAFE_pruning_algorithm_dual_variable_update_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af47b380f1b09687de4938a4d6caddc97f32b811
--- /dev/null
+++ b/data/sampled_jsons/SAFE_pruning_algorithm_dual_variable_update_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Accelerated Primal- Dual Policy Optimization for Safe", "date": "", "ddg_snippet": "Update the dual variable with dual gradient ascent: λ(ik+1) = λ(ik) + βk Ci(πk) − di.Parameters for dual variable update . As for dual updates , PDO and APDO both use dual gradient ascent.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1802.06480", "content": "Update the dual variable with dual gradient ascent: λ(ik+1) = λ(ik) + βk Ci(πk) − di.Parameters for dual variable update . As for dual updates , PDO and APDO both use dual gradient ascent."}
+{"idx": 1, "title": "[Literature Review] Stochastic Smoothed Primal- Dual Algorithms for...", "date": "", "ddg_snippet": "Single-Loop Algorithm : The algorithm is single-loop, meaning it doesn't require inner iterations or large batch sizes in its main loop. Dual Variable Updates : Utilizes dual variable updates to ensure feasibility.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/stochastic-smoothed-primal-dual-algorithms-for-nonconvex-optimization-with-linear-inequality-constraints", "content": "Single-Loop Algorithm : The algorithm is single-loop, meaning it doesn't require inner iterations or large batch sizes in its main loop. Dual Variable Updates : Utilizes dual variable updates to ensure feasibility."}
+{"idx": 2, "title": "Primal– dual hybrid gradient algorithms for computing time-implicit...", "date": "", "ddg_snippet": "This research introduces a first-order optimization-based technique for HJ PDEs, which formulates the time-implicit update of HJ PDEs as saddle point problems. We remark that the saddle point formulation for HJ equations is aligned with the primal– dual formulation of optimal...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40687-025-00519-5", "content": "This research introduces a first-order optimization-based technique for HJ PDEs, which formulates the time-implicit update of HJ PDEs as saddle point problems. We remark that the saddle point formulation for HJ equations is aligned with the primal– dual formulation of optimal..."}
+{"idx": 3, "title": "Variable pruning is NP hard – Win Vector LLC", "date": "", "ddg_snippet": "Data science advising, consulting, and training. Variable pruning is NP hard. By jmount on August 27, 2016 • ( 2 Comments ). I am working on some practical articles on variable selection, especially in the context of step-wise linear regression and logistic regression.", "subpage_snippet": "", "source": "win-vector.com", "link": "https://win-vector.com/2016/08/27/variable-pruning-is-np-hard/", "content": "Data science advising, consulting, and training. Variable pruning is NP hard. By jmount on August 27, 2016 • ( 2 Comments ). I am working on some practical articles on variable selection, especially in the context of step-wise linear regression and logistic regression."}
+{"idx": 4, "title": "8.7: Appendix - Felsenstein's Pruning Algorithm - Biology LibreTexts", "date": "", "ddg_snippet": "Felsenstein’s pruning algorithm (1973) is an example of dynamic programming, a type of algorithm that has many applications in comparative biology.8.7: Appendix - Felsenstein's Pruning Algorithm . Last updated . Save as PDF.", "subpage_snippet": "", "source": "bio.libretexts.org", "link": "https://bio.libretexts.org/Bookshelves/Evolutionary_Developmental_Biology/Phylogenetic_Comparative_Methods_(Harmon)/08:_Fitting_Models_of_Discrete_Character_Evolution/8.07:_Appendix_-_Felsenstein's_Pruning_Algorithm", "content": "Felsenstein’s pruning algorithm (1973) is an example of dynamic programming, a type of algorithm that has many applications in comparative biology.8.7: Appendix - Felsenstein's Pruning Algorithm . Last updated . Save as PDF."}
+{"idx": 5, "title": "Efficient Algorithms for General Isotone Optimization", "date": "", "ddg_snippet": "From an optimization perspective, monotonicity is formulated as partial order constraints among the optimization variables , commonly known as isotone optimization. In this paper, we develop an efficient, provable convergent algorithm for solving isotone optimization problems.", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/20835/20835-13-24848-1-2-20220628.pdf", "content": "From an optimization perspective, monotonicity is formulated as partial order constraints among the optimization variables , commonly known as isotone optimization. In this paper, we develop an efficient, provable convergent algorithm for solving isotone optimization problems."}
+{"idx": 6, "title": "Researchers unveil a pruning algorithm to make artificial intelligence...", "date": "", "ddg_snippet": "It's unclear why the pruning technique works as well as it does. The researchers say they will leave that question for others to answer. As for those who wish to try it, the algorithm is as easy to implement as other pruning methods, without time-consuming tuning, the researchers say.", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2020-04-unveil-pruning-algorithm-artificial-intelligence.html", "content": "It's unclear why the pruning technique works as well as it does. The researchers say they will leave that question for others to answer. As for those who wish to try it, the algorithm is as easy to implement as other pruning methods, without time-consuming tuning, the researchers say."}
+{"idx": 7, "title": "Dnn weight compression method with admm framework", "date": "", "ddg_snippet": "For each iteration, dual variables are updating . Algorithm 1 Weight Pruning Method. 1: Input: MNIST dataset. 2: Initialization: Determine αi for each layer 3: for k in ADMM iterations do. 4: Update {Wi} and {bi} by solving", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/333656992.pdf", "content": "For each iteration, dual variables are updating . Algorithm 1 Weight Pruning Method. 1: Input: MNIST dataset. 2: Initialization: Determine αi for each layer 3: for k in ADMM iterations do. 4: Update {Wi} and {bi} by solving"}
+{"idx": 8, "title": "P ROGRESSIVE", "date": "", "ddg_snippet": "The Overall Framework. ADMM-based Pruning Algorithm . Masked Retraining Step. Progressive Weight Pruning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=rygo9iR9F7", "content": "The Overall Framework. ADMM-based Pruning Algorithm . Masked Retraining Step. Progressive Weight Pruning ."}
+{"idx": 9, "title": "Games 2", "date": "", "ddg_snippet": "Games: alpha-beta pruning . Pruning game trees. 3. 3 ≤2. • In the context of minimax search, we note that the root node is a max over its children. •Both algorithms learn from the same data and are based on gradient-based weight updates . •", "subpage_snippet": "", "source": "stanford-cs221.github.io", "link": "https://stanford-cs221.github.io/spring2023-extra/modules/games/games2-6pp.pdf", "content": "Games: alpha-beta pruning . Pruning game trees. 3. 3 ≤2. • In the context of minimax search, we note that the root node is a max over its children. •Both algorithms learn from the same data and are based on gradient-based weight updates . •"}
diff --git a/data/sampled_jsons/SAFE_pruning_augmented_Lagrangian_penalty_parameter_lambda_sparsity_constraint_optimization_dense_mo.jsonl b/data/sampled_jsons/SAFE_pruning_augmented_Lagrangian_penalty_parameter_lambda_sparsity_constraint_optimization_dense_mo.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..db06bf89e14575dae967750fa29a36cb7fef4591
--- /dev/null
+++ b/data/sampled_jsons/SAFE_pruning_augmented_Lagrangian_penalty_parameter_lambda_sparsity_constraint_optimization_dense_mo.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by DLKLJ Chung · 2025 — Then ¯x is a δ-stationary point of the sparsity - constrained optimization problem. 12 / 19. Page 13. Result: SAFE finds sparse and flat solutions.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46658.pdf", "content": "by DLKLJ Chung · 2025 — Then ¯x is a δ-stationary point of the sparsity - constrained optimization problem. 12 / 19. Page 13. Result: SAFE finds sparse and flat solutions."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by D Lee · 2025 — Our findings indicate that SAFE effectively induces sparsity while simultaneously enforcing flatness, as evi- denced by the concentration of ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.06866", "content": "by D Lee · 2025 — Our findings indicate that SAFE effectively induces sparsity while simultaneously enforcing flatness, as evi- denced by the concentration of ..."}
+{"idx": 2, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "7 Jun 2025 — To tackle this, we use an augmented Lagrangian dual-based approach well established in the optimization literature with convergence guarantees, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v1", "content": "7 Jun 2025 — To tackle this, we use an augmented Lagrangian dual-based approach well established in the optimization literature with convergence guarantees, ..."}
+{"idx": 3, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "This result demonstrates the effectiveness of SAFE in tackling the sharpness-aware sparsity - constrained optimization problem (3). Further experimental details ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46658", "content": "This result demonstrates the effectiveness of SAFE in tackling the sharpness-aware sparsity - constrained optimization problem (3). Further experimental details ..."}
+{"idx": 4, "title": "Compression of Deep-Learning Models Through Global ...", "date": "", "ddg_snippet": "by K Lee · 2023 · Cited by 3 — Our proposed method aims to perform weight pruning in a deep layered network, while producing similar performance, by setting a global removal ratio for the ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s44196-023-00202-z", "content": "by K Lee · 2023 · Cited by 3 — Our proposed method aims to perform weight pruning in a deep layered network, while producing similar performance, by setting a global removal ratio for the ..."}
+{"idx": 5, "title": "Structure Learning with Continuous Optimization: A Sober ...", "date": "", "ddg_snippet": "by I Ng · 2024 · Cited by 34 — Abstract. This paper investigates in which cases continuous optimization for directed acyclic graph (DAG) structure learning can and cannot perform well and ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v236/ng24a/ng24a.pdf", "content": "by I Ng · 2024 · Cited by 34 — Abstract. This paper investigates in which cases continuous optimization for directed acyclic graph (DAG) structure learning can and cannot perform well and ..."}
+{"idx": 6, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective. We solve it explicitly via an augmented Lagrange dual approach and extend it further by proposing a gen-eralized projection operation, resulting in novel pruning methods called SAFE and its extension, SAFE+.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.06866", "content": "Specifically, we formulate pruning as a sparsity -constrained optimization problem where flatness is encouraged as an objective. We solve it explicitly via an augmented Lagrange dual approach and extend it further by proposing a gen-eralized projection operation, resulting in novel pruning methods called SAFE and its extension, SAFE+."}
+{"idx": 7, "title": "PDF Controlled Sparsity via Constrained Optimization or - NeurIPS", "date": "", "ddg_snippet": "grepresents a group gof parameters of the network (e.g. individual layers, or the whole model ), and resort to well established gradient-based methods for optimizing the Lagrangian associated with the constrained optimization problem.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/089b592cccfafdca8e0178e85b609f19-Paper-Conference.pdf", "content": "grepresents a group gof parameters of the network (e.g. individual layers, or the whole model ), and resort to well established gradient-based methods for optimizing the Lagrangian associated with the constrained optimization problem."}
+{"idx": 8, "title": "PDF CME 338 Large-Scale Numerical Optimization - Stanford University", "date": "", "ddg_snippet": "a trust-region constraint ) to determine a set of active bounds. It then applies a modi ed Newton or quasi-Newton method to optimize the augmented Lagrangian objective L(x; y; ) with respect to the moving variables.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cme338/notes/notes11-BCL.pdf", "content": "a trust-region constraint ) to determine a set of active bounds. It then applies a modi ed Newton or quasi-Newton method to optimize the augmented Lagrangian objective L(x; y; ) with respect to the moving variables."}
+{"idx": 9, "title": "An augmented penalty function method with penalty parameter updates for ...", "date": "", "ddg_snippet": "Given an augmented Lagrangian scheme for a general optimization problem, we use an epsilon subgradient step for improving the dual function. This can be seen as an update for an augmented penalty method, which is more stable because it does not force the penalty parameter to tend to infinity. We establish for this update primal-dual convergence for our augmented penalty method. As ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0362546X11001362", "content": "Given an augmented Lagrangian scheme for a general optimization problem, we use an epsilon subgradient step for improving the dual function. This can be seen as an update for an augmented penalty method, which is more stable because it does not force the penalty parameter to tend to infinity. We establish for this update primal-dual convergence for our augmented penalty method. As ..."}
diff --git a/data/sampled_jsons/SCAMP-7_feature_descriptor_output_format_bytes_per_feature.jsonl b/data/sampled_jsons/SCAMP-7_feature_descriptor_output_format_bytes_per_feature.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..de6f991866c43cbd72700779c08646726c28433e
--- /dev/null
+++ b/data/sampled_jsons/SCAMP-7_feature_descriptor_output_format_bytes_per_feature.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Burroughs Large Systems - Wikipedia", "date": "", "ddg_snippet": "Donald Knuth had previously implemented ALGOL 58 on an earlier Burroughs machine during the three months of his summer break, and he was peripherally ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Burroughs_large_systems", "content": "Donald Knuth had previously implemented ALGOL 58 on an earlier Burroughs machine during the three months of his summer break, and he was peripherally ..."}
+{"idx": 1, "title": "EP1473636A1 - Information processing device and method, and", "date": "", "ddg_snippet": "... video data, each of which is 2G bytes per program, within 10 minutes using this method requires the transmission band of about 14.7G bits/second.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/EP1473636A1/en", "content": "... video data, each of which is 2G bytes per program, within 10 minutes using this method requires the transmission band of about 14.7G bits/second."}
+{"idx": 2, "title": "Revision History – Aperture Photometry Tool", "date": "", "ddg_snippet": "Users who want this feature can simply set compactFlag=true in the default preferences file, which is APT.pref in the hidden directory ...", "subpage_snippet": "", "source": "www.aperturephotometry.org", "link": "https://www.aperturephotometry.org/revision-history/", "content": "Users who want this feature can simply set compactFlag=true in the default preferences file, which is APT.pref in the hidden directory ..."}
+{"idx": 3, "title": "US8195821B2 - Autonomous information processing apparatus and", "date": "", "ddg_snippet": "... video data, each of which is 2 G bytes per program, within 10 minutes using this method requires the transmission band of about 14. 7 G bits/second.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US8195821B2/en", "content": "... video data, each of which is 2 G bytes per program, within 10 minutes using this method requires the transmission band of about 14. 7 G bits/second."}
+{"idx": 4, "title": "Burroughs large systems - CodeDocs", "date": "", "ddg_snippet": "... discussion, the machine designations, B5000, A Series, and ClearPath/MCP are used interchangeably although this needlessly conflates the features and ...", "subpage_snippet": "", "source": "codedocs.org", "link": "https://codedocs.org/what-is/burroughs-large-systems", "content": "... discussion, the machine designations, B5000, A Series, and ClearPath/MCP are used interchangeably although this needlessly conflates the features and ..."}
+{"idx": 5, "title": "Great Microprocessors of the Past and Present", "date": "", "ddg_snippet": "It ran at a clock rate of 740kHz (eight clock cycles per CPU cycle of 10.8 microseconds) - the original goal was 1MHz, to allow it to compute BCD ...", "subpage_snippet": "", "source": "barnkob.net", "link": "http://barnkob.net/~rmb/cpu.html", "content": "It ran at a clock rate of 740kHz (eight clock cycles per CPU cycle of 10.8 microseconds) - the original goal was 1MHz, to allow it to compute BCD ..."}
+{"idx": 6, "title": "Great Microprocessors of the Past and Present", "date": "", "ddg_snippet": "It ran at a clock rate of 740kHz (eight clock cycles per CPU cycle of 10.8 microseconds) - the original goal was 1MHz, to allow it to compute BCD ...", "subpage_snippet": "", "source": "www.crusher.dk", "link": "http://www.crusher.dk/~rmb/cpu.html", "content": "It ran at a clock rate of 740kHz (eight clock cycles per CPU cycle of 10.8 microseconds) - the original goal was 1MHz, to allow it to compute BCD ..."}
+{"idx": 7, "title": "US11514594B2 - Composite imaging systems using a focal plane", "date": "", "ddg_snippet": "Google has not performed a legal analysis and makes no representation as ... G06T7/246 — Analysis of motion using feature -based methods, e.g.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11514594B2/en", "content": "Google has not performed a legal analysis and makes no representation as ... G06T7/246 — Analysis of motion using feature -based methods, e.g."}
+{"idx": 8, "title": "FreshPorts -- security/gnupg: Complete and free PGP", "date": "", "ddg_snippet": "GnuPG allows encrypting and signing your data and communication, and features a versatile key management system as well as access modules for many ...", "subpage_snippet": "", "source": "www.freshports.org", "link": "https://www.freshports.org/security/gnupg/", "content": "GnuPG allows encrypting and signing your data and communication, and features a versatile key management system as well as access modules for many ..."}
+{"idx": 9, "title": "Great Microprocessors of the Past and Present", "date": "", "ddg_snippet": "It ran at a clock rate of 740kHz (eight clock cycles per CPU cycle of 10.8 microseconds) - the original goal was 1MHz, to allow it to compute BCD ...", "subpage_snippet": "", "source": "www.cl.cam.ac.uk", "link": "https://www.cl.cam.ac.uk/teaching/2006/CompArch/documents/all/trends/cpu_history.html", "content": "It ran at a clock rate of 740kHz (eight clock cycles per CPU cycle of 10.8 microseconds) - the original goal was 1MHz, to allow it to compute BCD ..."}
diff --git a/data/sampled_jsons/SDXL_transformer_blocks_attention_mechanism_feature_selection_prior_studies_underperformance.jsonl b/data/sampled_jsons/SDXL_transformer_blocks_attention_mechanism_feature_selection_prior_studies_underperformance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ca490ccf1dc647b9e09c283d9856479779b673d
--- /dev/null
+++ b/data/sampled_jsons/SDXL_transformer_blocks_attention_mechanism_feature_selection_prior_studies_underperformance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention Mechanisms Don't Learn Additive Models: Rethinking ... Rethinking attention modules of SDXL in a flow perspective research-papers/Summaries/Diffusion/SDXL.md at main - GitHub Selective Attention: Enhancing Transformer through Principled ... Beyond Markov: Transformers, memory, and attention An analysis of attention mechanisms and its variance in ... Selective Attention : Enhancing Transformer through Principled Context Beyond Markov: Transformers, memory, and attention Beyond Markov: Transformers, memory, and attention Selective Attention : Enhancing Transformer through Principled Context Beyond Markov: Transformers, memory, and attention Beyond Markov: Transformers, memory, and attention Increasing Transformer Model Efficiency Through Attention ...", "date": "", "ddg_snippet": "May 22, 2024 · We address the critical challenge of applying feature attribution methods to the transformer architecture, which dominates current applications in natural language processing and beyond. Traditional attribution methods to explainable AI (XAI) explicitly or implicitly rely on linear or additive surrogate models to quantify the impact of input features on a model's output. In this work, we ... We reinterpreted the attention mechanism in the training-free methods of diffusion models using flow matching theory. Based on this, we proposed the FlowAttnXL training-free framework for text-to-image tasks. It introduces two modules: Flow Matching Cross- Attention (FMXA) and Flow Arithmetic Perturbation Guidance (FAPG). These modules operate plug-and-play, requiring no retraining and ... Firstly, SDXL uses a much larger UNet backbone. As a result, the UNet has almost 3X model parameters (~2.6B) in comparison to earlier versions (~860M). Secondly, they shift the bulk of the transformer computation to lower-level features in the UNet. Specifically, they use a heterogeneous distribution of transformer blocks . And for efficiency, they omit the transformer block at the highest ... The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self- attention has enjoyed major success, it notably treats all queries q in the same way by applying the mapping V⊤softmax(Kq), where V, K are the value and key embeddings respectively. In this work, we argue that this uniform treatment ... Dec 2, 2024 · Interestingly, both the notions of working memory in cognitive science and transformer architectures rely heavily upon the concept of attention . We will argue that the move beyond Markov is crucial in the construction of generative models capable of dealing with much of the sequential data – and certainly language – that our brains contend ... The RMT (Recursive Memory Transformer ) model integrates block sparse attention and a recurrent memory mechanism [15]. RMT segments lengthy sequences, constraining attention to operate within each ... Do transformer language models suffer from attention dilution? For instance, it has been observed that current Transformer language models sufer from an attention dilution issue: the longer the input sequence, the flatter the attention distribution [49, 7]. A natural solution to the dispersed attention issue is to sharpen the self- attention distribution. Which generative model underwrites the attention mechanisms in a transformer network? The former is the implicit generative model that underwrites the attention mechanisms in a transformer network. The latter is the form of generative model that has been entertained in computational neuroscience to account for our ability to perceive and plan over a range of temporal scales. How can a transformer be used as a large language model? When transformers are trained to be used as large language models, they may be used to map a series of inputs to themselves, shifted in time . This motivates the form of the first line in Equation (6). In principle, one can use the above to recursively sample the next element in a sequence. Is SSA a good transformer-based model? SSA shows consistent benefits and augments the performance of existing transformer-based models such as Pythia and Llama 2. We also provide theoretical insights into the benefits of query, value, and positional selectivity. Why does a transformer use multi-headed attention? The use of multi-headed attention allows a transformer not only to learn several alternative attentional policies – which might emphasize different features or positions in sequences – but to learn how to select between these attentional sets. Are Transformers a link between attention and memory? By engaging with non-Markovian processes, we will suggest the problem solved by transformers provides a link between two concepts – often inextricable from one another in cognitive neuroscience – attention and memory (Manohar et al., 2019; Parr & Friston, 2017b). Nov 19, 2024 · Supported backends (as of now) include: FlashAttention-2, Memory-Efficient Attention , a C++-based Math Attention , and CuDNN. These backends fuse together high-level operations while employing GPU-level optimizations for increasing compute efficiency and memory utilization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.13536", "content": "May 22, 2024 · We address the critical challenge of applying feature attribution methods to the transformer architecture, which dominates current applications in natural language processing and beyond. Traditional attribution methods to explainable AI (XAI) explicitly or implicitly rely on linear or additive surrogate models to quantify the impact of input features on a model's output. In this work, we ... We reinterpreted the attention mechanism in the training-free methods of diffusion models using flow matching theory. Based on this, we proposed the FlowAttnXL training-free framework for text-to-image tasks. It introduces two modules: Flow Matching Cross- Attention (FMXA) and Flow Arithmetic Perturbation Guidance (FAPG). These modules operate plug-and-play, requiring no retraining and ... Firstly, SDXL uses a much larger UNet backbone. As a result, the UNet has almost 3X model parameters (~2.6B) in comparison to earlier versions (~860M). Secondly, they shift the bulk of the transformer computation to lower-level features in the UNet. Specifically, they use a heterogeneous distribution of transformer blocks . And for efficiency, they omit the transformer block at the highest ... The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self- attention has enjoyed major success, it notably treats all queries q in the same way by applying the mapping V⊤softmax(Kq), where V, K are the value and key embeddings respectively. In this work, we argue that this uniform treatment ... Dec 2, 2024 · Interestingly, both the notions of working memory in cognitive science and transformer architectures rely heavily upon the concept of attention . We will argue that the move beyond Markov is crucial in the construction of generative models capable of dealing with much of the sequential data – and certainly language – that our brains contend ... The RMT (Recursive Memory Transformer ) model integrates block sparse attention and a recurrent memory mechanism [15]. RMT segments lengthy sequences, constraining attention to operate within each ... Do transformer language models suffer from attention dilution? For instance, it has been observed that current Transformer language models sufer from an attention dilution issue: the longer the input sequence, the flatter the attention distribution [49, 7]. A natural solution to the dispersed attention issue is to sharpen the self- attention distribution. Which generative model underwrites the attention mechanisms in a transformer network? The former is the implicit generative model that underwrites the attention mechanisms in a transformer network. The latter is the form of generative model that has been entertained in computational neuroscience to account for our ability to perceive and plan over a range of temporal scales. How can a transformer be used as a large language model? When transformers are trained to be used as large language models, they may be used to map a series of inputs to themselves, shifted in time . This motivates the form of the first line in Equation (6). In principle, one can use the above to recursively sample the next element in a sequence. Is SSA a good transformer-based model? SSA shows consistent benefits and augments the performance of existing transformer-based models such as Pythia and Llama 2. We also provide theoretical insights into the benefits of query, value, and positional selectivity. Why does a transformer use multi-headed attention? The use of multi-headed attention allows a transformer not only to learn several alternative attentional policies – which might emphasize different features or positions in sequences – but to learn how to select between these attentional sets. Are Transformers a link between attention and memory? By engaging with non-Markovian processes, we will suggest the problem solved by transformers provides a link between two concepts – often inextricable from one another in cognitive neuroscience – attention and memory (Manohar et al., 2019; Parr & Friston, 2017b). Nov 19, 2024 · Supported backends (as of now) include: FlashAttention-2, Memory-Efficient Attention , a C++-based Math Attention , and CuDNN. These backends fuse together high-level operations while employing GPU-level optimizations for increasing compute efficiency and memory utilization."}
+{"idx": 1, "title": "Rethinking attention modules of SDXL in a flow perspective", "date": "", "ddg_snippet": "We reinterpreted the attention mechanism in the training-free methods of diffusion models using flow matching theory. Based on this, we proposed the FlowAttnXL training-free framework for text-to-image tasks. It introduces two modules: Flow Matching Cross- Attention (FMXA) and Flow Arithmetic Perturbation Guidance (FAPG). These modules operate plug-and-play, requiring no retraining and ...", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/doi/10.3934/mfc.2025015", "content": "We reinterpreted the attention mechanism in the training-free methods of diffusion models using flow matching theory. Based on this, we proposed the FlowAttnXL training-free framework for text-to-image tasks. It introduces two modules: Flow Matching Cross- Attention (FMXA) and Flow Arithmetic Perturbation Guidance (FAPG). These modules operate plug-and-play, requiring no retraining and ..."}
+{"idx": 2, "title": "research-papers/Summaries/Diffusion/SDXL.md at main - GitHub", "date": "", "ddg_snippet": "Firstly, SDXL uses a much larger UNet backbone. As a result, the UNet has almost 3X model parameters (~2.6B) in comparison to earlier versions (~860M). Secondly, they shift the bulk of the transformer computation to lower-level features in the UNet. Specifically, they use a heterogeneous distribution of transformer blocks . And for efficiency, they omit the transformer block at the highest ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/garg-aayush/research-papers/blob/main/Summaries/Diffusion/SDXL.md", "content": "Firstly, SDXL uses a much larger UNet backbone. As a result, the UNet has almost 3X model parameters (~2.6B) in comparison to earlier versions (~860M). Secondly, they shift the bulk of the transformer computation to lower-level features in the UNet. Specifically, they use a heterogeneous distribution of transformer blocks . And for efficiency, they omit the transformer block at the highest ..."}
+{"idx": 3, "title": "Selective Attention: Enhancing Transformer through Principled ...", "date": "", "ddg_snippet": "The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self- attention has enjoyed major success, it notably treats all queries q in the same way by applying the mapping V⊤softmax(Kq), where V, K are the value and key embeddings respectively. In this work, we argue that this uniform treatment ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/14fc4a68da97a3d31eb11c642b0b10fc-Paper-Conference.pdf", "content": "The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self- attention has enjoyed major success, it notably treats all queries q in the same way by applying the mapping V⊤softmax(Kq), where V, K are the value and key embeddings respectively. In this work, we argue that this uniform treatment ..."}
+{"idx": 4, "title": "Beyond Markov: Transformers, memory, and attention", "date": "", "ddg_snippet": "Dec 2, 2024 · Interestingly, both the notions of working memory in cognitive science and transformer architectures rely heavily upon the concept of attention . We will argue that the move beyond Markov is crucial in the construction of generative models capable of dealing with much of the sequential data – and certainly language – that our brains contend ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/17588928.2025.2484485", "content": "Dec 2, 2024 · Interestingly, both the notions of working memory in cognitive science and transformer architectures rely heavily upon the concept of attention . We will argue that the move beyond Markov is crucial in the construction of generative models capable of dealing with much of the sequential data – and certainly language – that our brains contend ..."}
+{"idx": 5, "title": "An analysis of attention mechanisms and its variance in ...", "date": "", "ddg_snippet": "The RMT (Recursive Memory Transformer ) model integrates block sparse attention and a recurrent memory mechanism [15]. RMT segments lengthy sequences, constraining attention to operate within each ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/379011929_An_analysis_of_attention_mechanisms_and_its_variance_in_transformer/fulltext/65f5993e32321b2cff84b884/An-analysis-of-attention-mechanisms-and-its-variance-in-transformer.pdf", "content": "The RMT (Recursive Memory Transformer ) model integrates block sparse attention and a recurrent memory mechanism [15]. RMT segments lengthy sequences, constraining attention to operate within each ..."}
+{"idx": 6, "title": "Increasing Transformer Model Efficiency Through Attention ...", "date": "", "ddg_snippet": "Nov 19, 2024 · Supported backends (as of now) include: FlashAttention-2, Memory-Efficient Attention , a C++-based Math Attention , and CuDNN. These backends fuse together high-level operations while employing GPU-level optimizations for increasing compute efficiency and memory utilization.", "subpage_snippet": "", "source": "bardai.ai", "link": "https://bardai.ai/2024/11/19/increasing-transformer-model-efficiency-through-attention-layer-optimization/", "content": "Nov 19, 2024 · Supported backends (as of now) include: FlashAttention-2, Memory-Efficient Attention , a C++-based Math Attention , and CuDNN. These backends fuse together high-level operations while employing GPU-level optimizations for increasing compute efficiency and memory utilization."}
+{"idx": 7, "title": "Improving Video Diffusion Transformer Training by Multi-Feature", "date": "", "ddg_snippet": "Notably, recent studies have shown that diffusion models inherently learn highly discriminative representations within their features (Tang et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09547v1", "content": "Notably, recent studies have shown that diffusion models inherently learn highly discriminative representations within their features (Tang et al."}
+{"idx": 8, "title": "Dynamic Attention-Guided Diffusion for Image Super-Resolution", "date": "", "ddg_snippet": "In response, we introduce a diffusion mechanism focusing on detail-rich areas using time-dependent and attention -guided masking.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2308.07977v4", "content": "In response, we introduce a diffusion mechanism focusing on detail-rich areas using time-dependent and attention -guided masking."}
+{"idx": 9, "title": "From Reflection to Perfection: Scaling Inference-Time", "date": "", "ddg_snippet": "In terms of architecture evolution, the community has transitioned from the prevalent U-Net diffusion models [ 44 ] towards diffusion transformers ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.16080v1", "content": "In terms of architecture evolution, the community has transitioned from the prevalent U-Net diffusion models [ 44 ] towards diffusion transformers ..."}
diff --git "a/data/sampled_jsons/SF2M_Tong_2024_growth_factors_neglects_Schr\303\266dinger_bridge_year_2024.jsonl" "b/data/sampled_jsons/SF2M_Tong_2024_growth_factors_neglects_Schr\303\266dinger_bridge_year_2024.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..2a7492a551db0b70f132c0fa0afc0d5c3deebad5
--- /dev/null
+++ "b/data/sampled_jsons/SF2M_Tong_2024_growth_factors_neglects_Schr\303\266dinger_bridge_year_2024.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Simulation-free Schrödinger bridges via score and flow matching", "date": "", "ddg_snippet": "View a PDF of the paper titled Simulation-free Schr\\\"odinger bridges via score and flow matching, by Alexander Tong and 7 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.03672", "content": "View a PDF of the paper titled Simulation-free Schr\\\"odinger bridges via score and flow matching, by Alexander Tong and 7 other authors"}
+{"idx": 1, "title": "Simulation-free Schr\\\"odinger bridges via score and flow matching", "date": "", "ddg_snippet": "Simulation-Free Schrödinger Bridges via Score and Flow Matching Alexander T ong 1,2 Nikolay Malkin 1,2 Kilian Fatras 1,3 Lazar Atanackovic 4,5 Y anlei Zhang 1,2 Guillaume Huguet 1,2 Guy Wolf 1,2 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372234530_Simulation-free_Schrodinger_bridges_via_score_and_flow_matching", "content": "Simulation-Free Schrödinger Bridges via Score and Flow Matching Alexander T ong 1,2 Nikolay Malkin 1,2 Kilian Fatras 1,3 Lazar Atanackovic 4,5 Y anlei Zhang 1,2 Guillaume Huguet 1,2 Guy Wolf 1,2 ..."}
+{"idx": 2, "title": "Simulation-Free Schrödinger Bridges via Score and Flow Matching", "date": "", "ddg_snippet": "[SF] 2 M interprets continuous-time stochastic generative modeling as a Schrödinger bridge problem. It relies on static entropy-regularized optimal transport, or a minibatch approximation, to efficiently learn the SB without simulating the learned stochastic process.", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2024/poster/6691", "content": "[SF] 2 M interprets continuous-time stochastic generative modeling as a Schrödinger bridge problem. It relies on static entropy-regularized optimal transport, or a minibatch approximation, to efficiently learn the SB without simulating the learned stochastic process."}
+{"idx": 3, "title": "Simulation-Free Schrödinger Bridges via Score and Flow Matching", "date": "", "ddg_snippet": "%0 Conference Paper %T Simulation-Free Schrödinger Bridges via Score and Flow Matching %A Alexander Y. Tong %A Nikolay Malkin %A Kilian Fatras %A Lazar Atanackovic %A Yanlei Zhang %A Guillaume Huguet %A Guy Wolf %A Yoshua Bengio %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/tong24a.html", "content": "%0 Conference Paper %T Simulation-Free Schrödinger Bridges via Score and Flow Matching %A Alexander Y. Tong %A Nikolay Malkin %A Kilian Fatras %A Lazar Atanackovic %A Yanlei Zhang %A Guillaume Huguet %A Guy Wolf %A Yoshua Bengio %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta ..."}
+{"idx": 4, "title": "ICML Poster Light and Optimal Schrödinger Bridge Matching", "date": "", "ddg_snippet": "We address these limitations and propose a novel procedure to learn SB which we call the optimal Schrödinger bridge matching. It exploits the optimal parameterization of the diffusion process and provably recovers the SB process (a) with a single bridge matching step and (b) with arbitrary transport plan as the input.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/poster/34581", "content": "We address these limitations and propose a novel procedure to learn SB which we call the optimal Schrödinger bridge matching. It exploits the optimal parameterization of the diffusion process and provably recovers the SB process (a) with a single bridge matching step and (b) with arbitrary transport plan as the input."}
+{"idx": 5, "title": "PDF Simulation-Free Schrödinger Bridges via Score and Flow Matching", "date": "", "ddg_snippet": "3.2 Building Schrödinger bridges via [ SF]2M and entropic optimal transport In the previous section, we showed that our method SF2M can approximate marginal probability pt in the form of eq. 12. In this section, we explain how our [ SF]2M framework is able to approximate a Schrödinger bridge as a special case.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2307.03672v1.pdf", "content": "3.2 Building Schrödinger bridges via [ SF]2M and entropic optimal transport In the previous section, we showed that our method SF2M can approximate marginal probability pt in the form of eq. 12. In this section, we explain how our [ SF]2M framework is able to approximate a Schrödinger bridge as a special case."}
+{"idx": 6, "title": "Simulation-Free Schrödinger Bridges via Score and Flow Matching", "date": "", "ddg_snippet": "In the previous section, we showed that our method SF2M can approximate marginal probability pt in the form of eq. 12. In this section, we explain how our [ SF]2M frame-work is able to approximate a Schr ̈odinger bridge as a spe-cial case. [ SF]2M approximates the Schr ̈odinger bridge .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=adkj23mvB0", "content": "In the previous section, we showed that our method SF2M can approximate marginal probability pt in the form of eq. 12. In this section, we explain how our [ SF]2M frame-work is able to approximate a Schr ̈odinger bridge as a spe-cial case. [ SF]2M approximates the Schr ̈odinger bridge ."}
+{"idx": 7, "title": "Simulation-Free Schrodinger Bridges via Score and Flow Matching - Alex Tong", "date": "", "ddg_snippet": "We present simulation-free score and flow matching ([ SF]2M ), a simulation-free objective for inferring stochastic dynamics given unpaired source and target samples drawn from arbitrary distributions. Our method generalizes both the score-matching loss used in the training of diffusion models and the recently proposed flow matching loss used in the training of continuous normalizing flows. [SF ...", "subpage_snippet": "", "source": "www.alextong.net", "link": "https://www.alextong.net/publication/tong-simulation-free-2023/", "content": "We present simulation-free score and flow matching ([ SF]2M ), a simulation-free objective for inferring stochastic dynamics given unpaired source and target samples drawn from arbitrary distributions. Our method generalizes both the score-matching loss used in the training of diffusion models and the recently proposed flow matching loss used in the training of continuous normalizing flows. [SF ..."}
+{"idx": 8, "title": "Modeling Cell Dynamics and Interactions with Unbalanced Mean Field ...", "date": "", "ddg_snippet": "Figure 2. (a) Illustration of the synthetic gene regulatory dynamics. (b) The ground truth cellular dynamics project on (X1,X2). (c) The dynamics learned by balanced Schrödinger bridge SF2M ( Tong et al., 2024b). (d) The dynamics learned by DeepRUOT. (e) The dynamics learned by CytoBridge. (f) The learned interaction potential. (g) The growth rates inferred by CytoBridge. (h) The constructed ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Modeling-Cell-Dynamics-and-Interactions-with-Mean-Zhang-Wang/511df751749fc7cc9f97fce3724b5947ec505f7f/figure/2", "content": "Figure 2. (a) Illustration of the synthetic gene regulatory dynamics. (b) The ground truth cellular dynamics project on (X1,X2). (c) The dynamics learned by balanced Schrödinger bridge SF2M ( Tong et al., 2024b). (d) The dynamics learned by DeepRUOT. (e) The dynamics learned by CytoBridge. (f) The learned interaction potential. (g) The growth rates inferred by CytoBridge. (h) The constructed ..."}
+{"idx": 9, "title": "Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the Schr'odinger ...", "date": "", "ddg_snippet": "For interpolation, we compare SnapMMD against five methods: optimal transport-conditional flow matching (OT-CFM) and Schrödinger bridge -conditional flow matching (SB-CFM) ( Tong et al.,2024a), simulation-free Schrödinger bridge ( SF2M ) ( Tong et al.,2024c), deep momentum multimarginal Schrödinger bridge (DMSB) (Chen et al.,2024), and again to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.16082", "content": "For interpolation, we compare SnapMMD against five methods: optimal transport-conditional flow matching (OT-CFM) and Schrödinger bridge -conditional flow matching (SB-CFM) ( Tong et al.,2024a), simulation-free Schrödinger bridge ( SF2M ) ( Tong et al.,2024c), deep momentum multimarginal Schrödinger bridge (DMSB) (Chen et al.,2024), and again to ..."}
diff --git a/data/sampled_jsons/SFT_trained_on_original_Dsyn_dataset_RFT_positives_generated_from_SFT_policy_performance.jsonl b/data/sampled_jsons/SFT_trained_on_original_Dsyn_dataset_RFT_positives_generated_from_SFT_policy_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c74606308adc38dccf73ad131420eb272ca8cf71
--- /dev/null
+++ b/data/sampled_jsons/SFT_trained_on_original_Dsyn_dataset_RFT_positives_generated_from_SFT_policy_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SFT Trainer", "date": "", "ddg_snippet": "trainer . train (). Expected dataset type and format. SFT supports both language modeling and prompt-completion datasets .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/trl/en/sft_trainer", "content": "trainer . train (). Expected dataset type and format. SFT supports both language modeling and prompt-completion datasets ."}
+{"idx": 1, "title": "GitHub - NKU-HLT/Off- Policy - SFT : Official implementation of...", "date": "", "ddg_snippet": "NKU-HLT/Off- Policy - SFT .Official implementation of \"Mind the Gap: Reducing Off- Policy Variance in Supervised Fine-Tuning via Data Rewriting\". About.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NKU-HLT/Off-Policy-SFT", "content": "NKU-HLT/Off- Policy - SFT .Official implementation of \"Mind the Gap: Reducing Off- Policy Variance in Supervised Fine-Tuning via Data Rewriting\". About."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math...", "date": "", "ddg_snippet": "The SFT (Supervised Finetuning) model trained on original synthetic data achieves relatively high test accuracy on both GSM8K and MATH datasets .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "The SFT (Supervised Finetuning) model trained on original synthetic data achieves relatively high test accuracy on both GSM8K and MATH datasets ."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "SFT / RFT policy suffers from spurious correlations in positive synthetic data . SFT on Dsyn original . verifies the final answer (see Appendix G, E for examples).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "SFT / RFT policy suffers from spurious correlations in positive synthetic data . SFT on Dsyn original . verifies the final answer (see Appendix G, E for examples)."}
+{"idx": 4, "title": "Learning to Reason by Failing: Offline RL on Sub-optimal Rollouts...", "date": "", "ddg_snippet": "204 do this by training SFT policies on Dsyn of sizes 8k and. 205 16k, and then generating RFT datasets from the correspond-. 206 ing SFT policies where we only add more correct solution. 207 traces (for the same problems) and scale RFT data from.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=v2PV1yCFJk", "content": "204 do this by training SFT policies on Dsyn of sizes 8k and. 205 16k, and then generating RFT datasets from the correspond-. 206 ing SFT policies where we only add more correct solution. 207 traces (for the same problems) and scale RFT data from."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Positive synthetic data . Learning theory dictates that the SFT policy trained on more SFT data (e.g., 1.5M for DeepSeek-Math [5]) would have improved math reasoning capbabilities.by training SFT policies on Dsyn of sizes 8k and 16k, and then generating RFT datasets from the.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4b77d5b896c321a29277524a98a50215-Paper-Conference.pdf", "content": "Positive synthetic data . Learning theory dictates that the SFT policy trained on more SFT data (e.g., 1.5M for DeepSeek-Math [5]) would have improved math reasoning capbabilities.by training SFT policies on Dsyn of sizes 8k and 16k, and then generating RFT datasets from the."}
+{"idx": 6, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM...", "date": "", "ddg_snippet": "positive data from SFT policy train ed on it is 2× more sample efficient. SFT -ed on the original data . Howev er , training on positive self - generated synthetic data alone o ften amplifies.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "positive data from SFT policy train ed on it is 2× more sample efficient. SFT -ed on the original data . Howev er , training on positive self - generated synthetic data alone o ften amplifies."}
+{"idx": 7, "title": "Refining Intelligence: A Comparative Study of Reinforcement...", "date": "", "ddg_snippet": "Interpretability: SFT : Produces interpretable outputs when trained on well-annotated datasets . For example, models fine-tuned on datasets with labeled reasoning steps (e.g., Chain-of-Thought annotations) can clearly explain their decisions.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/refining-intelligence-comparative-study-reinforcement-ramachandran-19jve", "content": "Interpretability: SFT : Produces interpretable outputs when trained on well-annotated datasets . For example, models fine-tuned on datasets with labeled reasoning steps (e.g., Chain-of-Thought annotations) can clearly explain their decisions."}
+{"idx": 8, "title": "DeepSeek V3.1 (free) - API, Providers, Stats | OpenRouter", "date": "", "ddg_snippet": "Data Policy . Prompt Training . Trained on 14.8T tokens using FP8 mixed precision, it achieves high training efficiency and stability, with strong performance across language, reasoning, math, and coding tasks.", "subpage_snippet": "", "source": "openrouter.ai", "link": "https://openrouter.ai/deepseek/deepseek-chat-v3.1:free", "content": "Data Policy . Prompt Training . Trained on 14.8T tokens using FP8 mixed precision, it achieves high training efficiency and stability, with strong performance across language, reasoning, math, and coding tasks."}
+{"idx": 9, "title": "Команда Magistral представила обновлённые версии своих... / Хабр", "date": "", "ddg_snippet": "Magistral Small 1.2 @MistralAI just dropped on Hugging Face. Building upon Mistral Small 3.2 (2506), with added reasoning capabilities, undergoing SFT from Magistral Medium traces and RL on top, it's a small, efficient reasoning model with 24B parameters.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/bothub/news/948704/", "content": "Magistral Small 1.2 @MistralAI just dropped on Hugging Face. Building upon Mistral Small 3.2 (2506), with added reasoning capabilities, undergoing SFT from Magistral Medium traces and RL on top, it's a small, efficient reasoning model with 24B parameters."}
diff --git a/data/sampled_jsons/SLHAMR_PA-MPJPE_85.9_OR_86.9_OR_87.9_single_moving_camera_pedestrian_motion_reconstruction.jsonl b/data/sampled_jsons/SLHAMR_PA-MPJPE_85.9_OR_86.9_OR_87.9_single_moving_camera_pedestrian_motion_reconstruction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f17d3ff029de8383cd8c491f721ce2909c0f70b5
--- /dev/null
+++ b/data/sampled_jsons/SLHAMR_PA-MPJPE_85.9_OR_86.9_OR_87.9_single_moving_camera_pedestrian_motion_reconstruction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ключи активации Windows 10 | Ответы Mail", "date": "", "ddg_snippet": "YTMG3-N6DKC-DKB77-7M9GH-8HVX7 Windows 10 Core N Activation Key 4CPRK-NM3K3-X6XXQ-RXX86-WXCHW Core Single Language BT79Q-G7N6G-PGBYW-4YWX6-6F4BT Core Country Specific.", "subpage_snippet": "", "source": "otvet.mail.ru", "link": "https://otvet.mail.ru/question/237199183", "content": "YTMG3-N6DKC-DKB77-7M9GH-8HVX7 Windows 10 Core N Activation Key 4CPRK-NM3K3-X6XXQ-RXX86-WXCHW Core Single Language BT79Q-G7N6G-PGBYW-4YWX6-6F4BT Core Country Specific."}
+{"idx": 1, "title": "Промты Для Фотосессии С Ии Парни | TikTok", "date": "", "ddg_snippet": "Scene filled with motion blur and dramatic lighting.The lighting is soft but focused, highlighting his intense, serious expression. He stands centered, facing the camera with one hand held across his chest. The train is blurred in motion behind him for a dramatic effect.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/промты-для-фотосессии-с-ии-парни", "content": "Scene filled with motion blur and dramatic lighting.The lighting is soft but focused, highlighting his intense, serious expression. He stands centered, facing the camera with one hand held across his chest. The train is blurred in motion behind him for a dramatic effect."}
+{"idx": 2, "title": "Pastein: -- hysteria • gamesense ----- enQ. 2023 ----& lt; Header >", "date": "", "ddg_snippet": "local cam _y = client. camera _angles() return (not block and using) and {180, cam _y} or nil end }, snap = { dechoke = false, check = function (cmd, settings, props) if props and props.on == \"Default\" then props = antiaim.data.scenery.default.snap end.", "subpage_snippet": "", "source": "pastein.ru", "link": "https://pastein.ru/t/cdU", "content": "local cam _y = client. camera _angles() return (not block and using) and {180, cam _y} or nil end }, snap = { dechoke = false, check = function (cmd, settings, props) if props and props.on == \"Default\" then props = antiaim.data.scenery.default.snap end."}
+{"idx": 3, "title": "D9 85 d9 87 d9 86 d8 af d8 b3 d8 af d9 8a d9 83 d9 88... | Laimoon", "date": "", "ddg_snippet": "View all online d9 85 d9 87 d9 86 d8 af d8 b3 d8 af d9 8a d9 83 d9 88 d8 b1 courses.", "subpage_snippet": "", "source": "jobs.laimoon.com", "link": "https://jobs.laimoon.com/ar/egypt/مهندس-ديكور", "content": "View all online d9 85 d9 87 d9 86 d8 af d8 b3 d8 af d9 8a d9 83 d9 88 d8 b1 courses."}
+{"idx": 4, "title": "ГДЗ глава 1. задача 86 геометрия 7‐9 класс Атанасян, Бутузов", "date": "", "ddg_snippet": "Подробное решение глава 1. задача № 86 по геометрии для учащихся 7‐9 класса , авторов Атанасян, Бутузов, Кадомцев, Позняк, Юдин 2016-2025.", "subpage_snippet": "", "source": "GDZ.ru", "link": "https://GDZ.ru/class-7/geometria/atanasyan-7-9/1-chapter-86/", "content": "Подробное решение глава 1. задача № 86 по геометрии для учащихся 7‐9 класса , авторов Атанасян, Бутузов, Кадомцев, Позняк, Юдин 2016-2025."}
+{"idx": 5, "title": "(Решено) Упр.88 Часть 1 ГДЗ Высоцкий Ященко 7-9 класс по...", "date": "", "ddg_snippet": "Вопросы 87 88 89 90 91 92 Вопросы 93 ...", "subpage_snippet": "", "source": "reshak.ru", "link": "https://reshak.ru/otvet/reshebniki.php?otvet=part1/88&predmet=visotskiy79", "content": "Вопросы 87 88 89 90 91 92 Вопросы 93 ..."}
+{"idx": 6, "title": "ГДЗ по химии 9 класс Габриелян - учебник Просвещение 2023", "date": "", "ddg_snippet": "Азот 87. Задания внутри параграфа 87 Проверьте свои знания 89 Примените свои знания 89 Выразите своё мнение 89 Используйте дополнительную информацию 89.", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/9-klass/himiya/gabrielyan-ostroumov-uchebnik", "content": "Азот 87. Задания внутри параграфа 87 Проверьте свои знания 89 Примените свои знания 89 Выразите своё мнение 89 Используйте дополнительную информацию 89."}
+{"idx": 7, "title": "SSD M.2 накопители купить в интернет-магазине DNS. SSD...", "date": "", "ddg_snippet": "4.85 |682 отзыва 6 Отличная надежность.4.87 |2.5k отзывов 9 Хорошая надежность.", "subpage_snippet": "", "source": "www.dns-shop.ru", "link": "https://www.dns-shop.ru/catalog/dd58148920724e77/ssd-m2-nakopiteli/", "content": "4.85 |682 отзыва 6 Отличная надежность.4.87 |2.5k отзывов 9 Хорошая надежность."}
+{"idx": 8, "title": "Холодильники - купить недорого, цены и отзывы в СИТИЛИНК", "date": "", "ddg_snippet": "Расположение морозильника. сбоку86. сверху138.Привезём. в 87 пунктов. (завтра). 1500 бонусов за отзыв промокод BONUS1500.", "subpage_snippet": "", "source": "www.citilink.ru", "link": "https://www.citilink.ru/catalog/holodilniki/", "content": "Расположение морозильника. сбоку86. сверху138.Привезём. в 87 пунктов. (завтра). 1500 бонусов за отзыв промокод BONUS1500."}
+{"idx": 9, "title": "Погода в Самаре на 14 дней (Самарская область)... - Погода Mail", "date": "", "ddg_snippet": "ночь+9°облачноощущается как +9°746 мм рт. ст.86%3 м/с С-СВ.", "subpage_snippet": "", "source": "pogoda.mail.ru", "link": "https://pogoda.mail.ru/prognoz/samara/14dney/", "content": "ночь+9°облачноощущается как +9°746 мм рт. ст.86%3 м/с С-СВ."}
diff --git "a/data/sampled_jsons/SPD_Algorithm_1_SB_sensitive_blocks_\317\2041_\317\2042_partial_sync-point_drop.jsonl" "b/data/sampled_jsons/SPD_Algorithm_1_SB_sensitive_blocks_\317\2041_\317\2042_partial_sync-point_drop.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..739f3cdab1f4192a75a83f93b3206440abc2fe3d
--- /dev/null
+++ "b/data/sampled_jsons/SPD_Algorithm_1_SB_sensitive_blocks_\317\2041_\317\2042_partial_sync-point_drop.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Semidefinite programming - Wikipedia", "date": "", "ddg_snippet": "Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function (a user-specified function that the user wants to minimize or maximize) over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron. [ 1 ] Semidefinite programming is a relatively new field of optimization ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Semidefinite_programming", "content": "Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function (a user-specified function that the user wants to minimize or maximize) over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron. [ 1 ] Semidefinite programming is a relatively new field of optimization ..."}
+{"idx": 1, "title": "SPD: Sync-Point Drop for Eficient Tensor Parallelism of Large Language ...", "date": "", "ddg_snippet": "Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ), to reduce communication overheads in tensor par-allelism by selectively dropping synchronization on attention outputs. In detail, we first propose block design that allows execution to proceed without communication through SPD .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20727", "content": "Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ), to reduce communication overheads in tensor par-allelism by selectively dropping synchronization on attention outputs. In detail, we first propose block design that allows execution to proceed without communication through SPD ."}
+{"idx": 2, "title": "[2502.20727] SPD: Sync-Point Drop for efficient tensor parallelism of ...", "date": "", "ddg_snippet": "Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ), to reduce communication overheads in tensor parallelism by selectively dropping synchronization on attention outputs. In detail, we first propose a block design that allows execution to proceed without communication through SPD .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.20727", "content": "Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ), to reduce communication overheads in tensor parallelism by selectively dropping synchronization on attention outputs. In detail, we first propose a block design that allows execution to proceed without communication through SPD ."}
+{"idx": 3, "title": "PDF Learning to Optimize on SPD Manifolds - CVF Open Access", "date": "", "ddg_snippet": "Solving such optimization problems is challenging due to the non-linearity of the SPD manifold, making optimization with SPD constraints heavily relying on expert knowledge and human involvement. In this paper, we propose a meta-learning method to automatically learn an iterative opti-mizer on SPD manifolds.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Gao_Learning_to_Optimize_on_SPD_Manifolds_CVPR_2020_paper.pdf", "content": "Solving such optimization problems is challenging due to the non-linearity of the SPD manifold, making optimization with SPD constraints heavily relying on expert knowledge and human involvement. In this paper, we propose a meta-learning method to automatically learn an iterative opti-mizer on SPD manifolds."}
+{"idx": 4, "title": "GitHub - facebookresearch/baspacho: Direct solver for sparse SPD ...", "date": "", "ddg_snippet": "Direct solver for sparse SPD matrices for nonlinear optimization. Implements supernodal Cholesky decomposition algorithm , and supports GPU (CUDA). - facebookresearch/baspacho", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/baspacho", "content": "Direct solver for sparse SPD matrices for nonlinear optimization. Implements supernodal Cholesky decomposition algorithm , and supports GPU (CUDA). - facebookresearch/baspacho"}
+{"idx": 5, "title": "Decoder block structure with sync-point drop (in 2-GPUs distributed ...", "date": "", "ddg_snippet": "Decoder block structure with sync-point drop (in 2-GPUs distributed inference case). 'Wi' and 'b' represent weight and bias of linear layer on each device (i).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Decoder-block-structure-with-sync-point-drop-in-2-GPUs-distributed-inference-case-Wi_fig2_389510233", "content": "Decoder block structure with sync-point drop (in 2-GPUs distributed inference case). 'Wi' and 'b' represent weight and bias of linear layer on each device (i)."}
+{"idx": 6, "title": "SPD: Sync-Point Drop for efficient tensor parallelism of Large...", "date": "", "ddg_snippet": "The paper introduces Sync-Point Drop ( SPD ), an optimization technique aimed at reducing communication overheads in distributed inference for large language models (LLMs) by minimizing synchronization on attention outputs within tensor parallelism. It proposes a block design that minimizes communication while preserving model accuracy, differentiating block sensitivity to communication (in ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uoU4ypjAmN", "content": "The paper introduces Sync-Point Drop ( SPD ), an optimization technique aimed at reducing communication overheads in distributed inference for large language models (LLMs) by minimizing synchronization on attention outputs within tensor parallelism. It proposes a block design that minimizes communication while preserving model accuracy, differentiating block sensitivity to communication (in ..."}
+{"idx": 7, "title": "Spd: Sync-point Drop for Efficient Tensor Par Allelism of Large ...", "date": "", "ddg_snippet": "ability and low latency. Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ) to reduce communication overheads in tensor parallelism by dropping synchronization on attention outputs. In detail, we first propose a block design that allows execution to proceed without communication through SPD . Second, we identify regions of communication redundancy, where dropping ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=uoU4ypjAmN", "content": "ability and low latency. Therefore, we introduce a novel optimization technique, Sync-Point Drop ( SPD ) to reduce communication overheads in tensor parallelism by dropping synchronization on attention outputs. In detail, we first propose a block design that allows execution to proceed without communication through SPD . Second, we identify regions of communication redundancy, where dropping ..."}
+{"idx": 8, "title": "Pure Numpy Implementation of the Coherent Point Drift Algorithm.", "date": "", "ddg_snippet": "Introduction This is a pure numpy implementation of the coherent point drift CPD algorithm by Myronenko and Song for use by the python community. It provides three registration methods for point clouds: 1 ) Scale and rigid registration; 2 ) Affine registration; and 3) Gaussian regularized non-rigid registration.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siavashk/pycpd", "content": "Introduction This is a pure numpy implementation of the coherent point drift CPD algorithm by Myronenko and Song for use by the python community. It provides three registration methods for point clouds: 1 ) Scale and rigid registration; 2 ) Affine registration; and 3) Gaussian regularized non-rigid registration."}
+{"idx": 9, "title": "PDF Lecture 6: Semidefinite Programming - Princeton University", "date": "", "ddg_snippet": "convex program consists of a convex function f and a convex body K and the goal is to minimize f(x) subject to x ∈ K. It is a vast generalization of linear programming and like LP, can be solved in polynomial time under fairly general conditions on f, K. Today's lecture is about a special type of convex program called semidefinite programs.", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/~hy2/teaching/fall23-cos521/notes/SDPs.pdf", "content": "convex program consists of a convex function f and a convex body K and the goal is to minimize f(x) subject to x ∈ K. It is a vast generalization of linear programming and like LP, can be solved in polynomial time under fairly general conditions on f, K. Today's lecture is about a special type of convex program called semidefinite programs."}
diff --git a/data/sampled_jsons/SPD_Sync-Point_Drop_LLaMA2-70B_Figure_7c_precise_speedup_1-node_LBW_70%_SPD.jsonl b/data/sampled_jsons/SPD_Sync-Point_Drop_LLaMA2-70B_Figure_7c_precise_speedup_1-node_LBW_70%_SPD.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..87e8c1c9d7f3b34ffbb0abc3b9ce31b5ad787c10
--- /dev/null
+++ b/data/sampled_jsons/SPD_Sync-Point_Drop_LLaMA2-70B_Figure_7c_precise_speedup_1-node_LBW_70%_SPD.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SPD: Sync-Point Drop for Efficient Tensor Parallelism of ...", "date": "", "ddg_snippet": "For all models with 2- node system, SPD shows over 10% speedup on both HBW and LBW of SPD per-centage over 70% . In LLaMA2 -13B ( Figure 7b) and 70B ( Figure 7c ), speedup over 20% can be achieved on the ex-treme level of SPD (nearly 100%) with LBW setting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20727", "content": "For all models with 2- node system, SPD shows over 10% speedup on both HBW and LBW of SPD per-centage over 70% . In LLaMA2 -13B ( Figure 7b) and 70B ( Figure 7c ), speedup over 20% can be achieved on the ex-treme level of SPD (nearly 100%) with LBW setting."}
+{"idx": 1, "title": "ICML Poster SPD: Sync-Point Drop for Efficient Tensor ...", "date": "", "ddg_snippet": "Poster SPD : Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models Han-Byul Kim · Duc Hoang · Arnav Kundu · Mohammad Samragh · Minsik Cho West Exhibition Hall B2-B3 #W-507 [ Abstract ] [ Lay Summary ] [ Project Page ] [ Poster] [ OpenReview] Thu 17 Jul 11 a.m. PDT — 1 :30 p.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46606", "content": "Poster SPD : Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models Han-Byul Kim · Duc Hoang · Arnav Kundu · Mohammad Samragh · Minsik Cho West Exhibition Hall B2-B3 #W-507 [ Abstract ] [ Lay Summary ] [ Project Page ] [ Poster] [ OpenReview] Thu 17 Jul 11 a.m. PDT — 1 :30 p.m. PDT"}
+{"idx": 2, "title": "SPD: Sync-Point Drop for efficient tensor parallelism of Large...", "date": "", "ddg_snippet": "Sep 27, 2024 · The paper introduces Sync-Point Drop ( SPD ), an optimization technique aimed at reducing communication overheads in distributed inference for large language models (LLMs) by minimizing synchronization on attention outputs within tensor parallelism.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uoU4ypjAmN", "content": "Sep 27, 2024 · The paper introduces Sync-Point Drop ( SPD ), an optimization technique aimed at reducing communication overheads in distributed inference for large language models (LLMs) by minimizing synchronization on attention outputs within tensor parallelism."}
+{"idx": 3, "title": "Abdullah K. on LinkedIn: SPD: Sync-Point Drop for efficient ...", "date": "", "ddg_snippet": "The recent study introduces a groundbreaking optimization technique known as Sync-Point Drop ( SPD ), aimed at enhancing tensor parallelism efficiency in distributed inference.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/abdullah-kasri_spd-sync-point-drop-for-efficient-tensor-activity-7302389847258710017-S4Q8", "content": "The recent study introduces a groundbreaking optimization technique known as Sync-Point Drop ( SPD ), aimed at enhancing tensor parallelism efficiency in distributed inference."}
+{"idx": 4, "title": "meta-llama/Llama-2-70b · Hugging Face", "date": "", "ddg_snippet": "Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/meta-llama/Llama-2-70b", "content": "Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback."}
+{"idx": 5, "title": "[2502.20727] SPD: Sync-Point Drop for efficient tensor ...", "date": "", "ddg_snippet": "Mar 5, 2025 · For LLaMA2-70B with LBW in Figure 7 (c), 70% SPD offers about 19.7% speedup while only sacrificing 0.94% accuracy. Note that this result is from simple zero-shot dropping from ISBs.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.20727", "content": "Mar 5, 2025 · For LLaMA2-70B with LBW in Figure 7 (c), 70% SPD offers about 19.7% speedup while only sacrificing 0.94% accuracy. Note that this result is from simple zero-shot dropping from ISBs."}
+{"idx": 6, "title": "Optimizing LLM Inference Across Multiple GPUs - zerna.io", "date": "", "ddg_snippet": "Sync-Point Drop ( SPD ) is a novel optimization technique that selectively eliminates synchronization points in tensor-parallel LLM inference, significantly reducing communication overhead.", "subpage_snippet": "", "source": "zerna.io", "link": "http://zerna.io/en/page/engineering/presentation_set/engineering-llm-research/presentation/engineering-model-optimization/slide/engineering-paper-2502_20727", "content": "Sync-Point Drop ( SPD ) is a novel optimization technique that selectively eliminates synchronization points in tensor-parallel LLM inference, significantly reducing communication overhead."}
+{"idx": 7, "title": "Iuno Best Builds and Teams | Wuthering Waves｜Game8", "date": "", "ddg_snippet": "At the minimum, try aiming for a 70 -250 crit ratio to consistently hit big numbers with Iuno.Iuno Resonance Chain. Recommended Sequence Nodes .", "subpage_snippet": "", "source": "game8.co", "link": "https://game8.co/games/Wuthering-Waves/archives/524889", "content": "At the minimum, try aiming for a 70 -250 crit ratio to consistently hit big numbers with Iuno.Iuno Resonance Chain. Recommended Sequence Nodes ."}
+{"idx": 8, "title": "4bit/ Llama - 2 - 70 b -chat-hf · Hugging Face", "date": "", "ddg_snippet": "This is the repository for the 70 B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/4bit/Llama-2-70b-chat-hf", "content": "This is the repository for the 70 B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom."}
+{"idx": 9, "title": "Как дообучить LLaMA бесплатно и без программирования... / Хабр", "date": "", "ddg_snippet": "Что потребуется для запуска нейронки. Есть две версии модели: LLaMA и LLaMA 2 . LLaMA есть в размерах 7B, 13, 30B, 65B, LLaMA 2 - в размерах 7B, 13B и 70 B . 7B весит примерно 13 гб, 65B - 120 гб.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/755114/", "content": "Что потребуется для запуска нейронки. Есть две версии модели: LLaMA и LLaMA 2 . LLaMA есть в размерах 7B, 13, 30B, 65B, LLaMA 2 - в размерах 7B, 13B и 70 B . 7B весит примерно 13 гб, 65B - 120 гб."}
diff --git a/data/sampled_jsons/SPD_Sync-Point_Drop_for_Efficient_Tensor_Parallelism_of_Large_Language_Models_filetypepdf.jsonl b/data/sampled_jsons/SPD_Sync-Point_Drop_for_Efficient_Tensor_Parallelism_of_Large_Language_Models_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..80a92d23fb63a9f68c2ab7931a516b74d7a85d8e
--- /dev/null
+++ b/data/sampled_jsons/SPD_Sync-Point_Drop_for_Efficient_Tensor_Parallelism_of_Large_Language_Models_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SPD : Sync - Point Drop for efficient tensor parallelism of Large ...", "date": "", "ddg_snippet": "(a) With full sync tensor parallelism . (b) With sync - point drop in attention output. Figure 1. Tensor parallelism applied on transformer decoder block (in 2-GPUs distributed inference case). strategies to each group based on their characteristics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20727", "content": "(a) With full sync tensor parallelism . (b) With sync - point drop in attention output. Figure 1. Tensor parallelism applied on transformer decoder block (in 2-GPUs distributed inference case). strategies to each group based on their characteristics."}
+{"idx": 1, "title": "SPD: SYNC-POINT DROP FOR EFFICIENT TENSOR PAR ALLELISM OF ...", "date": "", "ddg_snippet": "erheads in tensor parallelism by dropping synchronization on attention outputs. In detail, we first propose a . lock design that allows execution to proceed without communication through SPD . Second, we identify regions of communication red.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=uoU4ypjAmN", "content": "erheads in tensor parallelism by dropping synchronization on attention outputs. In detail, we first propose a . lock design that allows execution to proceed without communication through SPD . Second, we identify regions of communication red."}
+{"idx": 2, "title": "TPLA: Tensor Parallel Latent Attention for Efficient ...", "date": "", "ddg_snippet": "Aug 25, 2025 · Tensor parallelism addresses memory and compute lim-itations by splitting large tensors—such as weight matrices—across multiple devices, enabling intra-layer parallel computation for models that cannot fit on a single GPU.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.15881", "content": "Aug 25, 2025 · Tensor parallelism addresses memory and compute lim-itations by splitting large tensors—such as weight matrices—across multiple devices, enabling intra-layer parallel computation for models that cannot fit on a single GPU."}
+{"idx": 3, "title": "On Optimizing the Communication of Model Parallelism - MLSys", "date": "", "ddg_snippet": "We then propose two contributions to address cross-mesh resharding: an eficient broadcast-based communication system, and an “overlapping-friendly\" pipeline schedule. On microbenchmarks, our overall system outperforms existing ones by up to 10x across various tensor and mesh layouts.", "subpage_snippet": "", "source": "proceedings.mlsys.org", "link": "https://proceedings.mlsys.org/paper_files/paper/2023/file/a42cbafcabb6dc7ce77bfe2e80f5c772-Paper-mlsys2023.pdf", "content": "We then propose two contributions to address cross-mesh resharding: an eficient broadcast-based communication system, and an “overlapping-friendly\" pipeline schedule. On microbenchmarks, our overall system outperforms existing ones by up to 10x across various tensor and mesh layouts."}
+{"idx": 4, "title": "Efficient Large-Scale Language Model Training on GPU Clusters ... Megatron-LM: Training Multi-Billion Parameter Language Models ...", "date": "", "ddg_snippet": "In this section, we discuss the parallelism techniques that facilitate the eficient training of large models that do not fit in the memory of a single GPU. In this work, we combine pipeline model parallelism and tensor model parallelism (combination shown in Figure 2) with data parallelism. We call this PTD-P for short. See full list on people.eecs.berkeley.edu In this section, we consider the performance implications of com-bining pipeline and tensor model parallelism with data parallelism . Given a fixed budget of GPUs and batch size, one can use diferent degrees of the parallelism types in PTD-P to train models ; each dimension exposes tradeofs between memory footprint, device utilization, and amount of ... See full list on people.eecs.berkeley.edu We implemented three model-specific optimizations to the compu-tation graph to attain high performance. First, we changed the data layout in the transformer layer to avoid memory-intensive trans-pose operations, and to enable the use of strided batched GEMM kernels. Specifically, we changed the data layout from [, ,,h ] to [,,,h ], where , , , and ... See full list on people.eecs.berkeley.edu In this paper, we have shown how PTD-P (inter-node pipeline par-allelism, intra-node tensor parallelism , and data parallelism ) can be composed to achieve high aggregate throughput (502 petaFLOP/s) while training large models with a trillion parameters. This facil-itates end-to-end training in reasonable times (estimated time of around 3 months for ... See full list on people.eecs.berkeley.edu In this work, we present our techniques for train-ing very large transformer models and implement a simple, efficient intra-layer model parallel ap-proach that enables training transformer models with billions of parameters.", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~matei/papers/2021/sc_megatron_lm.pdf", "content": "In this section, we discuss the parallelism techniques that facilitate the eficient training of large models that do not fit in the memory of a single GPU. In this work, we combine pipeline model parallelism and tensor model parallelism (combination shown in Figure 2) with data parallelism. We call this PTD-P for short. See full list on people.eecs.berkeley.edu In this section, we consider the performance implications of com-bining pipeline and tensor model parallelism with data parallelism . Given a fixed budget of GPUs and batch size, one can use diferent degrees of the parallelism types in PTD-P to train models ; each dimension exposes tradeofs between memory footprint, device utilization, and amount of ... See full list on people.eecs.berkeley.edu We implemented three model-specific optimizations to the compu-tation graph to attain high performance. First, we changed the data layout in the transformer layer to avoid memory-intensive trans-pose operations, and to enable the use of strided batched GEMM kernels. Specifically, we changed the data layout from [, ,,h ] to [,,,h ], where , , , and ... See full list on people.eecs.berkeley.edu In this paper, we have shown how PTD-P (inter-node pipeline par-allelism, intra-node tensor parallelism , and data parallelism ) can be composed to achieve high aggregate throughput (502 petaFLOP/s) while training large models with a trillion parameters. This facil-itates end-to-end training in reasonable times (estimated time of around 3 months for ... See full list on people.eecs.berkeley.edu In this work, we present our techniques for train-ing very large transformer models and implement a simple, efficient intra-layer model parallel ap-proach that enables training transformer models with billions of parameters."}
+{"idx": 5, "title": "Megatron-LM: Training Multi-Billion Parameter Language Models ...", "date": "", "ddg_snippet": "In this work, we present our techniques for train-ing very large transformer models and implement a simple, efficient intra-layer model parallel ap-proach that enables training transformer models with billions of parameters.", "subpage_snippet": "", "source": "parsa.epfl.ch", "link": "https://parsa.epfl.ch/course-info/cs723/papers/Megatron.pdf", "content": "In this work, we present our techniques for train-ing very large transformer models and implement a simple, efficient intra-layer model parallel ap-proach that enables training transformer models with billions of parameters."}
+{"idx": 6, "title": "[2502.20727] SPD : Sync - Point Drop for efficient tensor parallelism ...", "date": "", "ddg_snippet": "View a PDF of the paper titled SPD : Sync - Point Drop for efficient tensor parallelism of Large Language Models , by Han-Byul Kim and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20727", "content": "View a PDF of the paper titled SPD : Sync - Point Drop for efficient tensor parallelism of Large Language Models , by Han-Byul Kim and 4 other authors."}
+{"idx": 7, "title": "SPD : Sync - Point Drop for Efficient Tensor Parallelism of Large ...", "date": "", "ddg_snippet": "With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing…", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/sync-point-drop", "content": "With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing…"}
+{"idx": 8, "title": "Sync - Point Drop for efficient tensor parallelism of Large Language ...", "date": "", "ddg_snippet": "Second, we apply different SPD strategies to attention blocks based on their sensitivity to the model accuracy. The proposed methods effectively alleviate communication bottlenecks while minimizing accuracy degradation during LLM inference, offering a scalable solution for diverse...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46606", "content": "Second, we apply different SPD strategies to attention blocks based on their sensitivity to the model accuracy. The proposed methods effectively alleviate communication bottlenecks while minimizing accuracy degradation during LLM inference, offering a scalable solution for diverse..."}
+{"idx": 9, "title": "SPD : Sync - Point Drop for efficient tensor parallelism of Large ...", "date": "", "ddg_snippet": "Overview SPD ( Sync - Point Drop ) reduces communication overhead in tensor parallelism for LLMsAchieves up to 25.5% inference speedup without accuracy loss", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/spd-sync-point-drop-efficient-tensor-parallelism", "content": "Overview SPD ( Sync - Point Drop ) reduces communication overhead in tensor parallelism for LLMsAchieves up to 25.5% inference speedup without accuracy loss"}
diff --git a/data/sampled_jsons/SWE-bench_Can_Language_Models_Resolve_Real-World_GitHub_Issues_abstract_Jimenez.jsonl b/data/sampled_jsons/SWE-bench_Can_Language_Models_Resolve_Real-World_GitHub_Issues_abstract_Jimenez.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..201b5b9b8de34f709a185da79c030c50b8579d1f
--- /dev/null
+++ b/data/sampled_jsons/SWE-bench_Can_Language_Models_Resolve_Real-World_GitHub_Issues_abstract_Jimenez.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2310.06770] SWE - bench : Can Language Models Resolve ...", "date": "", "ddg_snippet": "Resolving issues in SWE - bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.06770", "content": "Resolving issues in SWE - bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments..."}
+{"idx": 1, "title": "GitHub - SWE - bench / SWE - bench : SWE - bench : Can Language ...", "date": "", "ddg_snippet": "SWE - bench : Can Language Models Resolve Real - world Github Issues ? Overview. SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SWE-bench/SWE-bench", "content": "SWE - bench : Can Language Models Resolve Real - world Github Issues ? Overview. SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub ."}
+{"idx": 2, "title": "Paper page - SWE - bench : Can Language Models Resolve ...", "date": "", "ddg_snippet": "Abstract . SWE - bench evaluates language models ' capabilities in resolving real - world software engineering issues by requiring them to understand and modify code across multiple files, demonstrating that existing models can only handle the simplest tasks.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2310.06770", "content": "Abstract . SWE - bench evaluates language models ' capabilities in resolving real - world software engineering issues by requiring them to understand and modify code across multiple files, demonstrating that existing models can only handle the simplest tasks."}
+{"idx": 3, "title": "SWE - bench : Can Language Models Resolve Real - World GitHub ...", "date": "", "ddg_snippet": "This post is based on the following work: SWE - bench : Can Language Models Resolve Real - World GitHub Issues ? Carlos E. Jimenez *1, John Yang*1, Alexander Wettig1, Shunyu Yao1, Kexin Pei2, Ofir Press1, Karthik Narasimhan1 1Princeton Language and Intelligence (PLI)...", "subpage_snippet": "", "source": "pli.princeton.edu", "link": "https://pli.princeton.edu/blog/2023/swe-bench-can-language-models-resolve-real-world-github-issues", "content": "This post is based on the following work: SWE - bench : Can Language Models Resolve Real - World GitHub Issues ? Carlos E. Jimenez *1, John Yang*1, Alexander Wettig1, Shunyu Yao1, Kexin Pei2, Ofir Press1, Karthik Narasimhan1 1Princeton Language and Intelligence (PLI)..."}
+{"idx": 4, "title": "ICLR 2024 SWE - bench : Can Language Models Resolve Real - world ...", "date": "", "ddg_snippet": "Resolving issues in SWE - bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/oral/19757", "content": "Resolving issues in SWE - bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes..."}
+{"idx": 5, "title": "SWE - bench", "date": "", "ddg_snippet": "Can Language Models Resolve Real - world Github Issues ? SWE - bench evaluation works as follows. Per task instance, an AI system is given the issue text. The AI system should then modify the codebase in order to resolve the described issues .", "subpage_snippet": "", "source": "www.swebench.com", "link": "https://www.swebench.com/original.html", "content": "Can Language Models Resolve Real - world Github Issues ? SWE - bench evaluation works as follows. Per task instance, an AI system is given the issue text. The AI system should then modify the codebase in order to resolve the described issues ."}
+{"idx": 6, "title": "Kotlin- bench : LLM Evaluation Benchmark | Firebender", "date": "", "ddg_snippet": "Kotlin-bench adopts SWE - bench 's established methodology, creating an objective evaluation framework for AI models on real - world programming tasks. The approach pairs actual GitHub issues with their corresponding PRs...", "subpage_snippet": "", "source": "firebender.com", "link": "https://firebender.com/blog/kotlin-bench", "content": "Kotlin-bench adopts SWE - bench 's established methodology, creating an objective evaluation framework for AI models on real - world programming tasks. The approach pairs actual GitHub issues with their corresponding PRs..."}
+{"idx": 7, "title": "Paper Insights: SWE - BENCH : CAN LANGUAGE MODELS RESOLVE ...", "date": "", "ddg_snippet": "SWE - Bench includes GitHub issues for 12 popular repositories that report bugs or request new features. An LLM is tasked to pull requests and make changes in the repository to solve the issue . Authors use a 3-stage process to create the benchmark", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@shanmuka.sadhu/paper-insights-swe-bench-can-language-models-resolve-real-world-github-issues-d6ac309fcbb8", "content": "SWE - Bench includes GitHub issues for 12 popular repositories that report bugs or request new features. An LLM is tasked to pull requests and make changes in the repository to solve the issue . Authors use a 3-stage process to create the benchmark"}
+{"idx": 8, "title": "10 LLM coding benchmarks", "date": "", "ddg_snippet": "Paper: SWE - bench : Can Language Models Resolve Real - World GitHub Issues ? by Jimenez et al. (2023) Dataset: SWE - bench dataset.The benchmark authors employ static analysis to extract code completion tasks that specifically require cross-file context to solve accurately.", "subpage_snippet": "", "source": "www.evidentlyai.com", "link": "https://www.evidentlyai.com/blog/llm-coding-benchmarks", "content": "Paper: SWE - bench : Can Language Models Resolve Real - World GitHub Issues ? by Jimenez et al. (2023) Dataset: SWE - bench dataset.The benchmark authors employ static analysis to extract code completion tasks that specifically require cross-file context to solve accurately."}
+{"idx": 9, "title": "Multilingual SWE - Bench Fermatix supply: Evaluating Compact...", "date": "", "ddg_snippet": "SWE - bench : Can Language Models Resolve Real - World GitHub Issues ? arXiv preprint arXiv:2310.06780. Original SWE - Bench dataset and evaluation pipeline.", "subpage_snippet": "", "source": "fermatix.ai", "link": "https://fermatix.ai/multilingual-swe-bench", "content": "SWE - bench : Can Language Models Resolve Real - World GitHub Issues ? arXiv preprint arXiv:2310.06780. Original SWE - Bench dataset and evaluation pipeline."}
diff --git a/data/sampled_jsons/SWEBench_Jimenez_et_al._2024_abstract_year_2024.jsonl b/data/sampled_jsons/SWEBench_Jimenez_et_al._2024_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0876f5f3153c670a32b9ccc340b7ca98c7b809e4
--- /dev/null
+++ b/data/sampled_jsons/SWEBench_Jimenez_et_al._2024_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.03859] SWE-bench Multimodal: Do AI Systems Generalize ... Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao ... g SWE- : C L M R F 7> BENCH AN ANGUAGE ODELS ESOLVE REAL ... (PDF) SWE-bench Multimodal: Do AI Systems Generalize to ... SWE-bench: Can Language Models Resolve Real-World GitHub Issues? arXiv:2410.03859v1 [cs.CL] 4 Oct 2024 arXiv:2410.03859v1 [cs.CL] 4 Oct 2024 [2410.03859] SWE-bench Multimodal: Do AI Systems Generalize to Vis… arXiv:2410.03859v1 [cs.CL] 4 Oct 2024 [2410.03859] SWE-bench Multimodal: Do AI Systems Generalize to Vis… arXiv:2410.03859v1 [cs.CL] 4 Oct 2024 arXiv:2410.03859v1 [cs.CL] 4 Oct 2024 dblp: SWE-bench: Can Language Models Resolve Real-world ...", "date": "", "ddg_snippet": "Oct 4, 2024 · Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual elements such as ... Abstract Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. 023) or design library-specific problems (Lai et al ., 2022; Zan et al ., 2022). Instead of partitioning problems into siloed datasets and curtailing them for simplicity’s sake, SWE-bench ’s collection procedure transforms the source code with minimal post-processing, preserving a much broader set of challenges grounded in real-world software ... Oct 4, 2024 · Abstract and Figures Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a ... SWE-bench is introduced, an evaluation framework consisting of software engineering problems drawn from real GitHub issues and corresponding pull requests across popular Python repositories that shows that both state-of-the-art proprietary models and the fine-tuned model SWE-Llama can resolve only the simplest issues. Language models have outpaced our ability to evaluate them effectively, but ... ABSTRACT Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual el ... How is SWE-bench evaluated? fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve sof ware issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and la Does Swe-bench use GitHub repositories? These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories , with problem statements presented predominantly as text and lacking visual elements such as images. How effective is SWE-bench m for evaluating multimodal AI systems? ng of multiple modules in a codebase. The dificulty distribution suggests that SWE-bench M can be effective for tracking improve and agent capabilities.E LIMITATIONSBroader scope To comprehensively evaluate multimodal AI systems, we curate 617 task instances from 17 Does Swe-bench m support visual problem solving? Each SWE-bench M task instance contains at least one image in its problem statement or unit tests. Our analysis finds that top-performing SWE-bench systems struggle with SWE-bench M, revealing limitations in visual problem-solving and cross-language generalization. What is the performance gap between Swe-bench m and agentless? e of each baseline system in Table 3. While overall performance on SWE-bench M is relatively low, we observe a substantial performance gap between the interactive SWE-agent systems (11.5 % resolved on average) and the Agentless and RAG baselines, which achieve 3.9 and 5 Does Swe-bench m provide consistent test case results? 6 619↓24B.2 INCONSISTENCY TESTINGAll task instances in SWE-bench M should yield consistent test case results , so we use a simple scheme to filter out candidate Jun 2, 2025 · Carlos E. Jimenez , John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan: SWE-bench : Can Language Models Resolve Real-world Github Issues?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.03859", "content": "Oct 4, 2024 · Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual elements such as ... Abstract Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. 023) or design library-specific problems (Lai et al ., 2022; Zan et al ., 2022). Instead of partitioning problems into siloed datasets and curtailing them for simplicity’s sake, SWE-bench ’s collection procedure transforms the source code with minimal post-processing, preserving a much broader set of challenges grounded in real-world software ... Oct 4, 2024 · Abstract and Figures Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a ... SWE-bench is introduced, an evaluation framework consisting of software engineering problems drawn from real GitHub issues and corresponding pull requests across popular Python repositories that shows that both state-of-the-art proprietary models and the fine-tuned model SWE-Llama can resolve only the simplest issues. Language models have outpaced our ability to evaluate them effectively, but ... ABSTRACT Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual el ... How is SWE-bench evaluated? fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve sof ware issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and la Does Swe-bench use GitHub repositories? These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories , with problem statements presented predominantly as text and lacking visual elements such as images. How effective is SWE-bench m for evaluating multimodal AI systems? ng of multiple modules in a codebase. The dificulty distribution suggests that SWE-bench M can be effective for tracking improve and agent capabilities.E LIMITATIONSBroader scope To comprehensively evaluate multimodal AI systems, we curate 617 task instances from 17 Does Swe-bench m support visual problem solving? Each SWE-bench M task instance contains at least one image in its problem statement or unit tests. Our analysis finds that top-performing SWE-bench systems struggle with SWE-bench M, revealing limitations in visual problem-solving and cross-language generalization. What is the performance gap between Swe-bench m and agentless? e of each baseline system in Table 3. While overall performance on SWE-bench M is relatively low, we observe a substantial performance gap between the interactive SWE-agent systems (11.5 % resolved on average) and the Agentless and RAG baselines, which achieve 3.9 and 5 Does Swe-bench m provide consistent test case results? 6 619↓24B.2 INCONSISTENCY TESTINGAll task instances in SWE-bench M should yield consistent test case results , so we use a simple scheme to filter out candidate Jun 2, 2025 · Carlos E. Jimenez , John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan: SWE-bench : Can Language Models Resolve Real-world Github Issues?"}
+{"idx": 1, "title": "Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao ... g SWE- : C L M R F 7> BENCH AN ANGUAGE ODELS ESOLVE REAL ... (PDF) SWE-bench Multimodal: Do AI Systems Generalize to ... SWE-bench: Can Language Models Resolve Real-World GitHub Issues? arXiv:2410.03859v1 [cs.CL] 4 Oct 2024 arXiv:2410.03859v1 [cs.CL] 4 Oct 2024 [2410.03859] SWE-bench Multimodal: Do AI Systems Generalize to Vis… arXiv:2410.03859v1 [cs.CL] 4 Oct 2024 [2410.03859] SWE-bench Multimodal: Do AI Systems Generalize to Vis… arXiv:2410.03859v1 [cs.CL] 4 Oct 2024 arXiv:2410.03859v1 [cs.CL] 4 Oct 2024 dblp: SWE-bench: Can Language Models Resolve Real-world ...", "date": "", "ddg_snippet": "Abstract Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. 023) or design library-specific problems (Lai et al ., 2022; Zan et al ., 2022). Instead of partitioning problems into siloed datasets and curtailing them for simplicity’s sake, SWE-bench ’s collection procedure transforms the source code with minimal post-processing, preserving a much broader set of challenges grounded in real-world software ... Oct 4, 2024 · Abstract and Figures Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a ... SWE-bench is introduced, an evaluation framework consisting of software engineering problems drawn from real GitHub issues and corresponding pull requests across popular Python repositories that shows that both state-of-the-art proprietary models and the fine-tuned model SWE-Llama can resolve only the simplest issues. Language models have outpaced our ability to evaluate them effectively, but ... ABSTRACT Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual el ... How is SWE-bench evaluated? fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve sof ware issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and la Does Swe-bench use GitHub repositories? These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories , with problem statements presented predominantly as text and lacking visual elements such as images. How effective is SWE-bench m for evaluating multimodal AI systems? ng of multiple modules in a codebase. The dificulty distribution suggests that SWE-bench M can be effective for tracking improve and agent capabilities.E LIMITATIONSBroader scope To comprehensively evaluate multimodal AI systems, we curate 617 task instances from 17 Does Swe-bench m support visual problem solving? Each SWE-bench M task instance contains at least one image in its problem statement or unit tests. Our analysis finds that top-performing SWE-bench systems struggle with SWE-bench M, revealing limitations in visual problem-solving and cross-language generalization. What is the performance gap between Swe-bench m and agentless? e of each baseline system in Table 3. While overall performance on SWE-bench M is relatively low, we observe a substantial performance gap between the interactive SWE-agent systems (11.5 % resolved on average) and the Agentless and RAG baselines, which achieve 3.9 and 5 Does Swe-bench m provide consistent test case results? 6 619↓24B.2 INCONSISTENCY TESTINGAll task instances in SWE-bench M should yield consistent test case results , so we use a simple scheme to filter out candidate Jun 2, 2025 · Carlos E. Jimenez , John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan: SWE-bench : Can Language Models Resolve Real-world Github Issues?", "subpage_snippet": "", "source": "collaborate.princeton.edu", "link": "https://collaborate.princeton.edu/en/publications/swe-bench-can-language-models-resolve-real-world-github-issues", "content": "Abstract Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. 023) or design library-specific problems (Lai et al ., 2022; Zan et al ., 2022). Instead of partitioning problems into siloed datasets and curtailing them for simplicity’s sake, SWE-bench ’s collection procedure transforms the source code with minimal post-processing, preserving a much broader set of challenges grounded in real-world software ... Oct 4, 2024 · Abstract and Figures Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a ... SWE-bench is introduced, an evaluation framework consisting of software engineering problems drawn from real GitHub issues and corresponding pull requests across popular Python repositories that shows that both state-of-the-art proprietary models and the fine-tuned model SWE-Llama can resolve only the simplest issues. Language models have outpaced our ability to evaluate them effectively, but ... ABSTRACT Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual el ... How is SWE-bench evaluated? fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve sof ware issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and la Does Swe-bench use GitHub repositories? These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories , with problem statements presented predominantly as text and lacking visual elements such as images. How effective is SWE-bench m for evaluating multimodal AI systems? ng of multiple modules in a codebase. The dificulty distribution suggests that SWE-bench M can be effective for tracking improve and agent capabilities.E LIMITATIONSBroader scope To comprehensively evaluate multimodal AI systems, we curate 617 task instances from 17 Does Swe-bench m support visual problem solving? Each SWE-bench M task instance contains at least one image in its problem statement or unit tests. Our analysis finds that top-performing SWE-bench systems struggle with SWE-bench M, revealing limitations in visual problem-solving and cross-language generalization. What is the performance gap between Swe-bench m and agentless? e of each baseline system in Table 3. While overall performance on SWE-bench M is relatively low, we observe a substantial performance gap between the interactive SWE-agent systems (11.5 % resolved on average) and the Agentless and RAG baselines, which achieve 3.9 and 5 Does Swe-bench m provide consistent test case results? 6 619↓24B.2 INCONSISTENCY TESTINGAll task instances in SWE-bench M should yield consistent test case results , so we use a simple scheme to filter out candidate Jun 2, 2025 · Carlos E. Jimenez , John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan: SWE-bench : Can Language Models Resolve Real-world Github Issues?"}
+{"idx": 2, "title": "arXiv:2410.03859v1 [cs.CL] 4 Oct 2024", "date": "", "ddg_snippet": "ABSTRACT Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual el ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.03859", "content": "ABSTRACT Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual el ..."}
+{"idx": 3, "title": "ICLR 2024 — Best Papers & Talks (Benchmarks, Reasoning", "date": "", "ddg_snippet": "Carlos Jimenez & John Yang (Princeton) et al : SWE-bench: Can Language Models Resolve Real-world Github Issues? ( ICLR Oral , Paper , website )", "subpage_snippet": "", "source": "www.latent.space", "link": "https://www.latent.space/p/iclr-2024-benchmarks-agents", "content": "Carlos Jimenez & John Yang (Princeton) et al : SWE-bench: Can Language Models Resolve Real-world Github Issues? ( ICLR Oral , Paper , website )"}
+{"idx": 4, "title": "(PDF) SWE-bench Multimodal: Do AI Systems Generalize to ...", "date": "", "ddg_snippet": "Oct 4, 2024 · Abstract and Figures Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384699916_SWE-bench_Multimodal_Do_AI_Systems_Generalize_to_Visual_Software_Domains", "content": "Oct 4, 2024 · Abstract and Figures Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a ..."}
+{"idx": 5, "title": "dblp: SWE-bench: Can Language Models Resolve Real-world ...", "date": "", "ddg_snippet": "Jun 2, 2025 · Carlos E. Jimenez , John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan: SWE-bench : Can Language Models Resolve Real-world Github Issues?", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/JimenezYWYPPN24", "content": "Jun 2, 2025 · Carlos E. Jimenez , John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan: SWE-bench : Can Language Models Resolve Real-world Github Issues?"}
+{"idx": 6, "title": "The Rise of AI Teammates in Software Engineering (SE) 3.0: How", "date": "", "ddg_snippet": "... of AI-native SE (SE 3.0) (Hassan et al ... 2024b ) , examined challenges around trust and reliability in human-AI interactions (Hassan et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15003v1", "content": "... of AI-native SE (SE 3.0) (Hassan et al ... 2024b ) , examined challenges around trust and reliability in human-AI interactions (Hassan et al ."}
+{"idx": 7, "title": "cAST: Enhancing Code Retrieval-Augmented Generation with", "date": "", "ddg_snippet": "... code generation has emerged as a cornerstone of modern software engineering, powering tasks that range from automated bug fixing (Meng et al ., 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15655v1", "content": "... code generation has emerged as a cornerstone of modern software engineering, powering tasks that range from automated bug fixing (Meng et al ., 2024 ..."}
+{"idx": 8, "title": "R2E-Gym: Scaling Open-Weights Software Engineering Agents with", "date": "", "ddg_snippet": "While several benchmarks for evaluating Swe -agents on GitHub issues exist ( Jimenez et al ., 2023 ; Zhao et al ., 2024 ) , scalable curation of high ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.07164v1", "content": "While several benchmarks for evaluating Swe -agents on GitHub issues exist ( Jimenez et al ., 2023 ; Zhao et al ., 2024 ) , scalable curation of high ..."}
+{"idx": 9, "title": "Trae Agent: An LLM-based Agent for Software Engineering with", "date": "", "ddg_snippet": "... Anthropic , 2025b ) , and GPT-4.1 ( OpenAI , 2025a ) ) on the widely-used software issue resolution benchmark (i. e ., SWE-bench ( Jimenez et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23370v1", "content": "... Anthropic , 2025b ) , and GPT-4.1 ( OpenAI , 2025a ) ) on the widely-used software issue resolution benchmark (i. e ., SWE-bench ( Jimenez et al ..."}
diff --git a/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Contrastive_CRL_fails_real_data_reason.jsonl b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Contrastive_CRL_fails_real_data_reason.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6250016b5444ba4672efa742c61c1c3f6b73e0d6
--- /dev/null
+++ b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Contrastive_CRL_fails_real_data_reason.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning ( CRL ) on a simple, real -world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v1", "content": "We evaluate methods for causal representation learning ( CRL ) on a simple, real -world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors."}
+{"idx": 1, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "The paper seeks to validate advances in causal representation learning ( CRL ) by providing a sanity check . It emphasizes the importance of applying existing theoretical frameworks to real -world problems rather than solely relying on synthetic data for validation.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-sanity-checking-causal-representation-learning-on-cm7p9dbhq6e6507m0bf9fj22h", "content": "The paper seeks to validate advances in causal representation learning ( CRL ) by providing a sanity check . It emphasizes the importance of applying existing theoretical frameworks to real -world problems rather than solely relying on synthetic data for validation."}
+{"idx": 2, "title": "GitHub - simonbing/CRLSanityCheck", "date": "", "ddg_snippet": "Sanity Checking Causal Representation Learning on a Simple Real -World System.For the real - data experiment using the Contrastive CRL method, run. python contrastive _ crl _experiment --dataset lt_ crl _benchmark_v1 \\", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/simonbing/CRLSanityCheck", "content": "Sanity Checking Causal Representation Learning on a Simple Real -World System.For the real - data experiment using the Contrastive CRL method, run. python contrastive _ crl _experiment --dataset lt_ crl _benchmark_v1 \\"}
+{"idx": 3, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning ( CRL ) on a simple, real -world system where these methods are expected to work.The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data -generating process.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Sanity-Checking-Causal-Representation-Learning-on-a-Simple-Real-World-System-ea56ed1c-68a5-4a60-8384-8b1132108289", "content": "We evaluate methods for causal representation learning ( CRL ) on a simple, real -world system where these methods are expected to work.The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data -generating process."}
+{"idx": 4, "title": "ICML Poster Sanity Checking Causal Representation Learning on...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning ( CRL ) on a simple, real -world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "We evaluate methods for causal representation learning ( CRL ) on a simple, real -world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors."}
+{"idx": 5, "title": "AAAI '25 | Causal Reprsentation Learning", "date": "", "ddg_snippet": "Machine learning (ML) has proliferated the progress in learning informative representations for high-dimensional data . CRL , subsequently, facilitates causal reasoning , intervention, and planning. This tutorial will provide a thorough overview of the recent advances in CRL .", "subpage_snippet": "", "source": "www.isg-rpi.com", "link": "https://www.isg-rpi.com/aaai-25", "content": "Machine learning (ML) has proliferated the progress in learning informative representations for high-dimensional data . CRL , subsequently, facilitates causal reasoning , intervention, and planning. This tutorial will provide a thorough overview of the recent advances in CRL ."}
+{"idx": 6, "title": "TU Berlin - Cited by 49 - representation learning - causality - clima...", "date": "", "ddg_snippet": "Sanity checking causal representation learning on a simple real -world system.S Bing, J Wahl, J Runge. UAI 2025 Workshop on Causal Abstractions and Representations , 0. The system can't perform the operation now. Try again later.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=ydKQzzQAAAAJ&hl=en", "content": "Sanity checking causal representation learning on a simple real -world system.S Bing, J Wahl, J Runge. UAI 2025 Workshop on Causal Abstractions and Representations , 0. The system can't perform the operation now. Try again later."}
+{"idx": 7, "title": "CS231n Deep Learning for Computer Vision", "date": "", "ddg_snippet": "gradient checks , sanity checks , babysitting the learning process, momentum (+nesterov), second-order methods, Adagrad/RMSprop, hyperparameter optimization, model ensembles.", "subpage_snippet": "", "source": "cs231n.github.io", "link": "https://cs231n.github.io/", "content": "gradient checks , sanity checks , babysitting the learning process, momentum (+nesterov), second-order methods, Adagrad/RMSprop, hyperparameter optimization, model ensembles."}
+{"idx": 8, "title": "The \"Law'' of the Unconscious Contrastive Learner ... | OpenReview", "date": "", "ddg_snippet": "While internet-scale data often come in pairs (e.g., audio+image, image+text), we often want to perform inferences over modalities unseen together in the training data (e.g., audio+text). Prior work has addressed this issue by learning multiple contrastive embedding spaces between...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DsIOUoZkVk", "content": "While internet-scale data often come in pairs (e.g., audio+image, image+text), we often want to perform inferences over modalities unseen together in the training data (e.g., audio+text). Prior work has addressed this issue by learning multiple contrastive embedding spaces between..."}
+{"idx": 9, "title": "Deep contrastive representation learning for multi-modal clustering...", "date": "", "ddg_snippet": "Abstract: Benefiting from the informative expression capability of contrastive representation learning ( CRL ), recent multi-modal learning studies have achieved promising clustering performance.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/deep-contrastive-representation-learning-for-multi-modal-321e3k6xqc", "content": "Abstract: Benefiting from the informative expression capability of contrastive representation learning ( CRL ), recent multi-modal learning studies have achieved promising clustering performance."}
diff --git a/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Figure_3_MCC_scores_Contrastive_CRL.jsonl b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Figure_3_MCC_scores_Contrastive_CRL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..596ca3de2ea88a1e584082d6f7db8f92fc2b599f
--- /dev/null
+++ b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Figure_3_MCC_scores_Contrastive_CRL.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical experi-ment specifically built for this purpose...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20099", "content": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical experi-ment specifically built for this purpose..."}
+{"idx": 1, "title": "GitHub - simonbing/CRLSanityCheck", "date": "", "ddg_snippet": "Sanity Checking Causal Representation Learning on a Simple Real-World System. Paper ( CRL Sanity Check ).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/simonbing/CRLSanityCheck", "content": "Sanity Checking Causal Representation Learning on a Simple Real-World System. Paper ( CRL Sanity Check )."}
+{"idx": 2, "title": "ICML Poster Sanity Checking Causal Representation Learning on...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors."}
+{"idx": 3, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "The paper seeks to validate advances in causal representation learning ( CRL ) by providing a sanity check .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-sanity-checking-causal-representation-learning-on-cm7p9dbhq6e6507m0bf9fj22h", "content": "The paper seeks to validate advances in causal representation learning ( CRL ) by providing a sanity check ."}
+{"idx": 4, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Sanity-Checking-Causal-Representation-Learning-on-a-Simple-Real-World-System-ea56ed1c-68a5-4a60-8384-8b1132108289", "content": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors."}
+{"idx": 5, "title": "Learning Linear Causal Representations from Interventions", "date": "", "ddg_snippet": "Additional details on experiments. Learning Linear Causal Representations from Interventions under General Nonlinear Mixing.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=8CIKCf9ri6", "content": "Additional details on experiments. Learning Linear Causal Representations from Interventions under General Nonlinear Mixing."}
+{"idx": 6, "title": "Interventional Causal Representation Learning | alphaXiv", "date": "", "ddg_snippet": "Abstract: Causal representation learning seeks to extract high-level latent factors from low-level sensory data.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2209.11924v4", "content": "Abstract: Causal representation learning seeks to extract high-level latent factors from low-level sensory data."}
+{"idx": 7, "title": "Interventional Causal Representation Learning", "date": "", "ddg_snippet": "Causal representation learning seeks to extract high-level latent factors from low-level sensory data.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/ahuja23a/ahuja23a.pdf", "content": "Causal representation learning seeks to extract high-level latent factors from low-level sensory data."}
+{"idx": 8, "title": "Learning Linear Causal Representations from", "date": "", "ddg_snippet": "Connectivity- contrastive learning : Combining causal discovery and representation learning for multimodal data. In International Conference on Artificial Intelligence and Statistics, pages 3399–3426.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/8e5de4cb639ef718f44060dc257cb04f-Paper-Conference.pdf", "content": "Connectivity- contrastive learning : Combining causal discovery and representation learning for multimodal data. In International Conference on Artificial Intelligence and Statistics, pages 3399–3426."}
+{"idx": 9, "title": "AAAI '25 | Causal Reprsentation Learning", "date": "", "ddg_snippet": "The emerging field of causal representation learning ( CRL ) aims to learn the latent causal structures by integrating representation . CRL , subsequently, facilitates causal reasoning, intervention, and planning. This tutorial will provide a thorough overview of the recent advances in CRL .", "subpage_snippet": "", "source": "www.isg-rpi.com", "link": "https://www.isg-rpi.com/aaai-25", "content": "The emerging field of causal representation learning ( CRL ) aims to learn the latent causal structures by integrating representation . CRL , subsequently, facilitates causal reasoning, intervention, and planning. This tutorial will provide a thorough overview of the recent advances in CRL ."}
diff --git a/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System_Buchholz_Yao.jsonl b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System_Buchholz_Yao.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..abf0142f1f1cb0e7f4160406c07bbf1698ca38b3
--- /dev/null
+++ b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System_Buchholz_Yao.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.20099] Sanity Checking Causal Representation Learning on ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Sanity Checking Causal Representation Learning on a Simple Real - World System , by Juan L. Gamella and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20099", "content": "View a PDF of the paper titled Sanity Checking Causal Representation Learning on a Simple Real - World System , by Juan L. Gamella and 2 other authors."}
+{"idx": 1, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44652/paper", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work."}
+{"idx": 2, "title": "ICML Poster Sanity Checking Causal Representation Learning on ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose..."}
+{"idx": 3, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Sanity-Checking-Causal-Representation-Learning-on-a-Simple-Real-World-System-ea56ed1c-68a5-4a60-8384-8b1132108289", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process."}
+{"idx": 4, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "This work proposes a real - world benchmark for CRL methods, focusing on a simple physical system involving light polarization.The paper seeks to validate advances in causal representation learning (CRL) by providing a sanity check .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-sanity-checking-causal-representation-learning-on-cm7p9dbhq6e6507m0bf9fj22h", "content": "This work proposes a real - world benchmark for CRL methods, focusing on a simple physical system involving light polarization.The paper seeks to validate advances in causal representation learning (CRL) by providing a sanity check ."}
+{"idx": 5, "title": "TU Berlin - Cited by 49 - representation learning - causality - clima...", "date": "", "ddg_snippet": "Sanity checking causal representation learning on a simple real - world system .S Bing, J Wahl, J Runge. UAI 2025 Workshop on Causal Abstractions and Representations , 0. The system can't perform the operation now.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=ydKQzzQAAAAJ&hl=en", "content": "Sanity checking causal representation learning on a simple real - world system .S Bing, J Wahl, J Runge. UAI 2025 Workshop on Causal Abstractions and Representations , 0. The system can't perform the operation now."}
+{"idx": 6, "title": "GitHub - a-bissell/microfluidic- causal -chamber: A microfluidics based...", "date": "", "ddg_snippet": "Datasets for the 2025 benchmark paper \" Sanity Checking Causal Representation Learning on a Simple Real - World System \" by Juan L. Gamella*, Simon Bing*, and Jakob Runge.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/a-bissell/microfluidic-causal-chamber", "content": "Datasets for the 2025 benchmark paper \" Sanity Checking Causal Representation Learning on a Simple Real - World System \" by Juan L. Gamella*, Simon Bing*, and Jakob Runge."}
+{"idx": 7, "title": "Smoke and Mirrors in Causal Downstream Tasks", "date": "", "ddg_snippet": "Despite being the simplest possible causal setting and a perfect fit for deep learning , we theoretically find that many common choices in the literature may lead to biased estimates. To test the practical impact of these considerations, we recorded ISTAnt, the first real - world benchmark for...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/2e098001625a8b8f22bf6c1aef09c4e5-Paper-Conference.pdf", "content": "Despite being the simplest possible causal setting and a perfect fit for deep learning , we theoretically find that many common choices in the literature may lead to biased estimates. To test the practical impact of these considerations, we recorded ISTAnt, the first real - world benchmark for..."}
+{"idx": 8, "title": "Causal Differentiating Concepts... | ServiceNow IA recherche", "date": "", "ddg_snippet": "Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning .", "subpage_snippet": "", "source": "www.servicenow.com", "link": "https://www.servicenow.com/research/fr/publication/navita-goyal-caus-neurips2025.html", "content": "Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning ."}
+{"idx": 9, "title": "\"Understanding Mathematical Models: Simplifying Real - World ...\"", "date": "", "ddg_snippet": "A mathematical model is a representation of a real - world system or phenomenon using mathematical concepts and language.", "subpage_snippet": "", "source": "www.questionai.com", "link": "https://www.questionai.com/questions-s5aMuGvbRA0w/understanding-mathematical-models-simplifying-realworld", "content": "A mathematical model is a representation of a real - world system or phenomenon using mathematical concepts and language."}
diff --git a/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System_filetypepdf.jsonl b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e579fa733ed823d3a4560a509ff27ca51d9e6311
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+++ b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2502.20099", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work."}
+{"idx": 1, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44652/paper", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work."}
+{"idx": 2, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Sanity-Checking-Causal-Representation-Learning-on-a-Simple-Real-World-System-ea56ed1c-68a5-4a60-8384-8b1132108289", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work."}
+{"idx": 3, "title": "ICML Poster Sanity Checking Causal Representation Learning on ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real - world system where these methods are expected to work.The results reveal a reproducibility problem, as most methods already fail on this synthetic ablation despite its simple data-generating process."}
+{"idx": 4, "title": "Sanity Checking Causal Representation Learning on a Simple ...", "date": "", "ddg_snippet": "This work proposes a real - world benchmark for CRL methods, focusing on a simple physical system involving light polarization.The paper seeks to validate advances in causal representation learning (CRL) by providing a sanity check .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-sanity-checking-causal-representation-learning-on-cm7p9dbhq6e6507m0bf9fj22h", "content": "This work proposes a real - world benchmark for CRL methods, focusing on a simple physical system involving light polarization.The paper seeks to validate advances in causal representation learning (CRL) by providing a sanity check ."}
+{"idx": 5, "title": "TU Berlin - Cited by 49 - representation learning - causality - clima...", "date": "", "ddg_snippet": "Sanity checking causal representation learning on a simple real - world system .S Bing, J Wahl, J Runge. UAI 2025 Workshop on Causal Abstractions and Representations , 0. The system can't perform the operation now.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=ydKQzzQAAAAJ&hl=en", "content": "Sanity checking causal representation learning on a simple real - world system .S Bing, J Wahl, J Runge. UAI 2025 Workshop on Causal Abstractions and Representations , 0. The system can't perform the operation now."}
+{"idx": 6, "title": "Causal Representation Learning for Instantaneous... | OpenReview", "date": "", "ddg_snippet": "Keywords: Representation Learning , Causality , Causal Representation Learning , Causal Discovery, Disentanglement.In experiments on three datasets of interactive systems , iCITRIS accurately identifies the causal variables and their causal graph.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=itZ6ggvMnzS", "content": "Keywords: Representation Learning , Causality , Causal Representation Learning , Causal Discovery, Disentanglement.In experiments on three datasets of interactive systems , iCITRIS accurately identifies the causal variables and their causal graph."}
+{"idx": 7, "title": "GitHub - a-bissell/microfluidic- causal -chamber: A microfluidics based...", "date": "", "ddg_snippet": "Datasets for the 2025 benchmark paper \" Sanity Checking Causal Representation Learning on a Simple Real - World System \" by Juan L. Gamella*, Simon Bing*, and Jakob Runge. Light tunnel.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/a-bissell/microfluidic-causal-chamber", "content": "Datasets for the 2025 benchmark paper \" Sanity Checking Causal Representation Learning on a Simple Real - World System \" by Juan L. Gamella*, Simon Bing*, and Jakob Runge. Light tunnel."}
+{"idx": 8, "title": "Smoke and Mirrors in Causal Downstream Tasks", "date": "", "ddg_snippet": "To facilitate future research on representation learning for causal downstream tasks, we formulate the representation desiderata to obtain accurate estimates for downstream causal queries together with best practices.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17151v3/", "content": "To facilitate future research on representation learning for causal downstream tasks, we formulate the representation desiderata to obtain accurate estimates for downstream causal queries together with best practices."}
+{"idx": 9, "title": "Evaluate model drift with population stability and causal checks", "date": "", "ddg_snippet": "Evaluate model drift helps teams detect shifts in feature distributions and outcomes, using PSI, EMD, and causal sanity checks to separate signal from noise. This concise guide shows how to implement guardrails, governance, and backtests that protect model performance across deployments.", "subpage_snippet": "", "source": "tech-champion.com", "link": "https://tech-champion.com/data-science/evaluate-model-drift-using-psi-emd-and-causal-sanity/", "content": "Evaluate model drift helps teams detect shifts in feature distributions and outcomes, using PSI, EMD, and causal sanity checks to separate signal from noise. This concise guide shows how to implement guardrails, governance, and backtests that protect model performance across deployments."}
diff --git "a/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al._2023_static_synthetic_data_ImageNet-100_6.5M_top-1_accuracy_year_2023.jsonl" "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al._2023_static_synthetic_data_ImageNet-100_6.5M_top-1_accuracy_year_2023.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..58a922e666ab3b63fec38939303ee0566998a075
--- /dev/null
+++ "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al._2023_static_synthetic_data_ImageNet-100_6.5M_top-1_accuracy_year_2023.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Synthetic Experience Replay", "date": "", "ddg_snippet": "by C Lu · Cited by 108 — (2022); Azizi et al . ( 2023 ); Sariyildiz et al . ( 2023 ) consider generative training data for image classification and pre-training. They also find that ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tK6k9AtSUC", "content": "by C Lu · Cited by 108 — (2022); Azizi et al . ( 2023 ); Sariyildiz et al . ( 2023 ) consider generative training data for image classification and pre-training. They also find that ..."}
+{"idx": 1, "title": "Synthetic Experience Replay", "date": "", "ddg_snippet": "by C Lu · 2023 · Cited by 108 — Synthetic data from diffusion models improves imagenet classification, 2023 . [8] Philip J Ball, Cong Lu, Jack Parker-Holder, and Stephen ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.06614", "content": "by C Lu · 2023 · Cited by 108 — Synthetic data from diffusion models improves imagenet classification, 2023 . [8] Philip J Ball, Cong Lu, Jack Parker-Holder, and Stephen ..."}
+{"idx": 2, "title": "[2304.08466] Synthetic Data from Diffusion Models Improves ImageNet ...", "date": "", "ddg_snippet": "The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples). Augmenting the ImageNet training set with samples from the resulting models yields significant improvements in ImageNet classification accuracy over strong ResNet and Vision Transformer baselines.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.08466", "content": "The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples). Augmenting the ImageNet training set with samples from the resulting models yields significant improvements in ImageNet classification accuracy over strong ResNet and Vision Transformer baselines."}
+{"idx": 3, "title": "Synthetic Data from Diffusion Models Improves ImageNet Classification", "date": "", "ddg_snippet": "The model also yields a new state-of-the-art in Classification Accuracy Scores, i.e., ImageNet test accuracy for a ResNet-50 architecture trained solely on synthetic data (64.96 top-1 accuracy for 256×256 samples, improving to 69.24 for 1024×1024 samples).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DlRsoxjyPm", "content": "The model also yields a new state-of-the-art in Classification Accuracy Scores, i.e., ImageNet test accuracy for a ResNet-50 architecture trained solely on synthetic data (64.96 top-1 accuracy for 256×256 samples, improving to 69.24 for 1024×1024 samples)."}
+{"idx": 4, "title": "Evaluating a Synthetic Image Dataset Generated with Stable Diffusion", "date": "", "ddg_snippet": "For this, we use the Pytorch implementation of the vision transformer vit_h_14 model from [6] which has a top 1 accuracy of 88.55% and a top 5 accuracy of 98.69% on the real ImageNet data . This synthetic data is also a good way to improve the diversity of data in a supervised learning setting.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-99-3243-6_64", "content": "For this, we use the Pytorch implementation of the vision transformer vit_h_14 model from [6] which has a top 1 accuracy of 88.55% and a top 5 accuracy of 98.69% on the real ImageNet data . This synthetic data is also a good way to improve the diversity of data in a supervised learning setting."}
+{"idx": 5, "title": "PDF Synthetic Image Datasets with Stable Diffusion and Data Augmentation", "date": "", "ddg_snippet": "We use the Pytorch implementation of the vision transformer model, which has a top-1 accuracy of 88.55% and a top-5 accuracy of 98.69% on the ImageNet data , to verify that the generated images can ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Bob-Bennett-2/publication/370222699_Synthetic_Image_Datasets_with_Stable_Diffusion_and_Data_Augmentation/links/6446acb68ac1946c7a49f48e/Synthetic-Image-Datasets-with-Stable-Diffusion-and-Data-Augmentation.pdf", "content": "We use the Pytorch implementation of the vision transformer model, which has a top-1 accuracy of 88.55% and a top-5 accuracy of 98.69% on the ImageNet data , to verify that the generated images can ..."}
+{"idx": 6, "title": "CVPR 2023 Open Access Repository", "date": "", "ddg_snippet": "Fake It Till You Make It: Learning Transferable Representations From Synthetic ImageNet Clones Mert Bülent Sarıyıldız , Karteek Alahari, Diane Larlus, Yannis Kalantidis; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 , pp. 8011-8021", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/html/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.html", "content": "Fake It Till You Make It: Learning Transferable Representations From Synthetic ImageNet Clones Mert Bülent Sarıyıldız , Karteek Alahari, Diane Larlus, Yannis Kalantidis; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 , pp. 8011-8021"}
+{"idx": 7, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "ImageNet-100 (Tian et al.,2020;Sarıyıldız et al.,2023 ), a subset of ImageNet -1k (Deng et al.,2009), containing 100 classes and 5k validation examples, where the real val- idation set is used for evaluation and the real training set (126,689 examples) serves as a held-out test set.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0LZRtvK871", "content": "ImageNet-100 (Tian et al.,2020;Sarıyıldız et al.,2023 ), a subset of ImageNet -1k (Deng et al.,2009), containing 100 classes and 5k validation examples, where the real val- idation set is used for evaluation and the real training set (126,689 examples) serves as a held-out test set."}
+{"idx": 8, "title": "R -F : EFFECTIVE TRAINING DATA SYNTHESIS DISTRIBUTION MATCHING - arXiv.org", "date": "", "ddg_snippet": "erforms all state-of-the-art techniques across all benchmarks. It is crucial to highlight that our synthetic data exhibits improvements of 16.8% and 28.0% in IN-1K top-1 accuracy , compared to CiP (Lei et al ., 2023 ) and FakeIt ( Sarıyıldız et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.10402", "content": "erforms all state-of-the-art techniques across all benchmarks. It is crucial to highlight that our synthetic data exhibits improvements of 16.8% and 28.0% in IN-1K top-1 accuracy , compared to CiP (Lei et al ., 2023 ) and FakeIt ( Sarıyıldız et al."}
+{"idx": 9, "title": "SyntheticDatafromDiffusionModels ImprovesImageNetClassifica", "date": "", "ddg_snippet": "76 at 256×256 resolution) and Inception Score (239 at 256×256). The model also yields a new state-of-the-art in Classification Accuracy Scores, i.e., ImageNet test accuracy for a ResNet-50 architecture trained solely on synthetic data (64.96 top-1 accuracy", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=DlRsoxjyPm", "content": "76 at 256×256 resolution) and Inception Score (239 at 256×256). The model also yields a new state-of-the-art in Classification Accuracy Scores, i.e., ImageNet test accuracy for a ResNet-50 architecture trained solely on synthetic data (64.96 top-1 accuracy"}
diff --git a/data/sampled_jsons/Scaling_Data_Generation_in_Fine-Tuning_Language_Models_A_Survey_abstract.jsonl b/data/sampled_jsons/Scaling_Data_Generation_in_Fine-Tuning_Language_Models_A_Survey_abstract.jsonl
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index 0000000000000000000000000000000000000000..a1dee578f852f351727e009ea727946015d69b73
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation ."}
+{"idx": 1, "title": "Parameter-efficient fine-tuning in large language models: a ...", "date": "", "ddg_snippet": "May 3, 2025 · When adapting large language models to specific downstream tasks, their massive parameter scale poses a significant challenge in fine - tuning on hardware platforms with limited computational power and GPU memory.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11236-4", "content": "May 3, 2025 · When adapting large language models to specific downstream tasks, their massive parameter scale poses a significant challenge in fine - tuning on hardware platforms with limited computational power and GPU memory."}
+{"idx": 2, "title": "Parameter-Efficient Fine-Tuning in Large Models: A Survey of ...", "date": "", "ddg_snippet": "The large language models , as predicted by scaling law forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approached or even surpassed human levels.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.19878v3", "content": "The large language models , as predicted by scaling law forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approached or even surpassed human levels."}
+{"idx": 3, "title": "Scaling instruction-finetuned language models | The Journal ...", "date": "", "ddg_snippet": "Jan 1, 2024 · In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3722577.3722647", "content": "Jan 1, 2024 · In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data ."}
+{"idx": 4, "title": "Fine-tuning large language models for domain adaptation ...", "date": "", "ddg_snippet": "Mar 28, 2025 · In this work, we explore the effects of Continued Pretraining (CPT), Supervised Fine - Tuning (SFT), and various preference-based optimization approaches, including Direct Preference Optimization...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41524-025-01564-y", "content": "Mar 28, 2025 · In this work, we explore the effects of Continued Pretraining (CPT), Supervised Fine - Tuning (SFT), and various preference-based optimization approaches, including Direct Preference Optimization..."}
+{"idx": 5, "title": "Scaling Data Diversity for Fine-Tuning Language Models in ... Scaling Instruction-Finetuned Language Models Scaling instruction-finetuned language models | The Journal of Machine Scaling Instruction-Finetuned Language Models Scaling Instruction-Finetuned Language Models Parameter-efficient fine-tuning in large language models : a survey of Scaling Instruction-Finetuned Language Models Scaling Instruction-Finetuned Language Models Parameter-efficient fine-tuning in large language models: a ...", "date": "", "ddg_snippet": "Mar 17, 2024 · In this work, we first control the diversity of both sides according to the number of samples for fine - tuning , which can directly reflect their influence. We find that instead of numerous prompts, more responses but fewer prompts better trigger LLMs for human alignment. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. Can instruction finetuning improve model performance and generalization to unseen tasks? Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks . In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. Does scaling affect instruction finetuning? First, we study the impact of scaling on instruction finetuning. Our experiments show that instruction finetuning does scale well with the number of tasks and the size of the model . Their respective scaling behaviors suggest that future research should scale up the number of tasks and the size of the model even further. Does scaling a model improve performance? Scaling model size by another order of magnitude (though challenging) is expected to provide substantial performance gain . Scaling number of finetuning tasks should also improve performance, although likely only incrementally. Overall, the scaling curves plotted indicate that future work should continue scaling instruction finetuning. Why are large language models important? The large language models, as predicted by scaling law forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks , where they have approached or even surpassed human levels. However, the unprecedented scale of their parameters brings significant computational and storage costs. Can language models be improved without scaling compute? Additional research that improves language models without scaling compute includes better architectures (So et al., 2021), improved training objectives (Tay et al., 2022a), and better data (Du et al., 2022), among other work. Can language models be scaled to 540B parameters? Second, whereas prior work has focused on smaller language models such as a 3B model (Wang et al., 2022d), an 11B model (Sanh et al., 2021), and a 137B model (Wei et al., 2021), in this paper we scale up to 540B parameters and extensively study the efect of language model scaling. RLHF (Knox and Stone 2008; Christiano et al. 2017) emerged as a method to fine - tune language models using human feedback, aiming to align the LLMs with human preferences, and consequently enhancing alignment performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.11124", "content": "Mar 17, 2024 · In this work, we first control the diversity of both sides according to the number of samples for fine - tuning , which can directly reflect their influence. We find that instead of numerous prompts, more responses but fewer prompts better trigger LLMs for human alignment. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. Can instruction finetuning improve model performance and generalization to unseen tasks? Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks . In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. Does scaling affect instruction finetuning? First, we study the impact of scaling on instruction finetuning. Our experiments show that instruction finetuning does scale well with the number of tasks and the size of the model . Their respective scaling behaviors suggest that future research should scale up the number of tasks and the size of the model even further. Does scaling a model improve performance? Scaling model size by another order of magnitude (though challenging) is expected to provide substantial performance gain . Scaling number of finetuning tasks should also improve performance, although likely only incrementally. Overall, the scaling curves plotted indicate that future work should continue scaling instruction finetuning. Why are large language models important? The large language models, as predicted by scaling law forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks , where they have approached or even surpassed human levels. However, the unprecedented scale of their parameters brings significant computational and storage costs. Can language models be improved without scaling compute? Additional research that improves language models without scaling compute includes better architectures (So et al., 2021), improved training objectives (Tay et al., 2022a), and better data (Du et al., 2022), among other work. Can language models be scaled to 540B parameters? Second, whereas prior work has focused on smaller language models such as a 3B model (Wang et al., 2022d), an 11B model (Sanh et al., 2021), and a 137B model (Wei et al., 2021), in this paper we scale up to 540B parameters and extensively study the efect of language model scaling. RLHF (Knox and Stone 2008; Christiano et al. 2017) emerged as a method to fine - tune language models using human feedback, aiming to align the LLMs with human preferences, and consequently enhancing alignment performance."}
+{"idx": 6, "title": "Scaling Instruction-Finetuned Language Models", "date": "", "ddg_snippet": "In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume25/23-0870/23-0870.pdf", "content": "In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data."}
+{"idx": 7, "title": "Parameter-efficient fine-tuning in large language models: a ...", "date": "", "ddg_snippet": "RLHF (Knox and Stone 2008; Christiano et al. 2017) emerged as a method to fine - tune language models using human feedback, aiming to align the LLMs with human preferences, and consequently enhancing alignment performance.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10462-025-11236-4.pdf", "content": "RLHF (Knox and Stone 2008; Christiano et al. 2017) emerged as a method to fine - tune language models using human feedback, aiming to align the LLMs with human preferences, and consequently enhancing alignment performance."}
+{"idx": 8, "title": "[2312.06585] Beyond Human Data : Scaling Self-Training for...", "date": "", "ddg_snippet": "Abstract : Fine - tuning language models ~(LMs) on human- generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of high-quality human data . In this paper, we explore whether we can go beyond human...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.06585", "content": "Abstract : Fine - tuning language models ~(LMs) on human- generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of high-quality human data . In this paper, we explore whether we can go beyond human..."}
+{"idx": 9, "title": "Fine - tuning Large Language Models (LLMs): Practical... | Medium", "date": "", "ddg_snippet": "Fine - tuning adapts a pretrained model with task-specific data ; it’s far cheaper than training from scratch. PEFT (Parameter-Efficient Fine - Tuning ) methods like LoRA keep the base model frozen and train a tiny set of extra parameters, huge memory savings with comparable results.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@shubham.shardul2019/fine-tuning-large-language-models-llms-practical-guide-intuition-lora-qlora-deep-dive-and-ba01d61cd0a7", "content": "Fine - tuning adapts a pretrained model with task-specific data ; it’s far cheaper than training from scratch. PEFT (Parameter-Efficient Fine - Tuning ) methods like LoRA keep the base model frozen and train a tiny set of extra parameters, huge memory savings with comparable results."}
diff --git a/data/sampled_jsons/Scaling_Laws_for_Neural_Language_Models_equation_loss_dataset_size.jsonl b/data/sampled_jsons/Scaling_Laws_for_Neural_Language_Models_equation_loss_dataset_size.jsonl
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index 0000000000000000000000000000000000000000..444ebb243112f9450e1a4b32ead245eac9dc9514
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law", "date": "", "ddg_snippet": "... size and training dataset size , as well as the test loss achievable: { N o p ... \" Scaling Laws for Neural Language Models \". CoRR. abs/2001.08361. arXiv ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "... size and training dataset size , as well as the test loss achievable: { N o p ... \" Scaling Laws for Neural Language Models \". CoRR. abs/2001.08361. arXiv ..."}
+{"idx": 1, "title": "Two minutes NLP — Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "The paper Scaling Laws for Neural Language Models contains a study of empirical scaling laws for language model performance on the cross-entropy loss , focusing on the Transformer architecture.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/nlplanet/two-minutes-nlp-scaling-laws-for-neural-language-models-add6061aece7", "content": "The paper Scaling Laws for Neural Language Models contains a study of empirical scaling laws for language model performance on the cross-entropy loss , focusing on the Transformer architecture."}
+{"idx": 2, "title": "[2001.08361v1] Scaling Laws for Neural Language Models - arXiv.org", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2001.08361v1", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the ..."}
+{"idx": 3, "title": "PDF Scaling Laws for Neural Language Models - papers.baulab.info", "date": "", "ddg_snippet": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ...", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/Kaplan-2020.pdf", "content": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ..."}
+{"idx": 4, "title": "Scaling Laws for Neural Language Models | Fan Pu Zeng", "date": "", "ddg_snippet": "Paper Abstract We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.", "subpage_snippet": "", "source": "fanpu.io", "link": "https://fanpu.io/summaries/2024-03-23-scaling-laws-for-neural-language-models/", "content": "Paper Abstract We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude."}
+{"idx": 5, "title": "Scaling Laws for Neural Language Models - Semantic Scholar", "date": "", "ddg_snippet": "Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence. We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Scaling-Laws-for-Neural-Language-Models-Kaplan-McCandlish/e6c561d02500b2596a230b341a8eb8b921ca5bf2", "content": "Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence. We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of ..."}
+{"idx": 6, "title": "Imad Dabbura - Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "Scaling Laws for Neural Language Models Thesis: Provide empirical analysis and equations of how to go about the relationship between performance (measured in cross-entropy loss ) for LLM and may factors such as model size , dataset size , compute budget, etc.", "subpage_snippet": "", "source": "imaddabbura.github.io", "link": "https://imaddabbura.github.io/papers-summaries/scaling-laws-for-nlp.html", "content": "Scaling Laws for Neural Language Models Thesis: Provide empirical analysis and equations of how to go about the relationship between performance (measured in cross-entropy loss ) for LLM and may factors such as model size , dataset size , compute budget, etc."}
+{"idx": 7, "title": "Scaling Laws For Neural Language Models: The Paradox Of Emergent Abilities", "date": "", "ddg_snippet": "Scaling laws for neural language models are empirical relationships that link three levers—model size , dataset size , and training compute—to a single outcome: loss (or accuracy).", "subpage_snippet": "", "source": "binaryverseai.com", "link": "https://binaryverseai.com/scaling-laws-for-neural-language-models/", "content": "Scaling laws for neural language models are empirical relationships that link three levers—model size , dataset size , and training compute—to a single outcome: loss (or accuracy)."}
+{"idx": 8, "title": "PDF Scaling Laws for Neural Language Models - cdn.jsdelivr.net", "date": "", "ddg_snippet": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of mag-nitude. Other architectural details such as network width or depth have minimal effects within a wide range. Larger models are ...", "subpage_snippet": "", "source": "cdn.jsdelivr.net", "link": "https://cdn.jsdelivr.net/gh/yanfeng98/paper-is-all-you-need/papers/00001-scaling-laws.pdf", "content": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of mag-nitude. Other architectural details such as network width or depth have minimal effects within a wide range. Larger models are ..."}
+{"idx": 9, "title": "[2001.08361] Scaling Laws for Neural Language Models - ar5iv", "date": "", "ddg_snippet": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2001.08361", "content": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss . The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ..."}
diff --git a/data/sampled_jsons/Schubert_polynomials_n=6_95.2_97.8_98.1_machine_learning_accuracy.jsonl b/data/sampled_jsons/Schubert_polynomials_n=6_95.2_97.8_98.1_machine_learning_accuracy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..093e5ffe12b8a520ebc417e2ff204a1abeec23f1
--- /dev/null
+++ b/data/sampled_jsons/Schubert_polynomials_n=6_95.2_97.8_98.1_machine_learning_accuracy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning Framework for Characterizing", "date": "", "ddg_snippet": "A convolutional neural network was trained to classify AFM images by morphology type, achieving 97 % testing accuracy .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23064v1", "content": "A convolutional neural network was trained to classify AFM images by morphology type, achieving 97 % testing accuracy ."}
+{"idx": 1, "title": "Machine learning - CodeDocs", "date": "", "ddg_snippet": "8 ] [9] A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines , dealing mostly with ...", "subpage_snippet": "", "source": "codedocs.org", "link": "https://codedocs.org/what-is/machine-learning", "content": "8 ] [9] A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines , dealing mostly with ..."}
+{"idx": 2, "title": "Machine learning Hubbard parameters with equivariant neural", "date": "", "ddg_snippet": "Noteworthy among these are Hubbard-corrected DFT (so-called DFT+ U 𝑈 U italic_U 6 , 7 , 8 and its extension DFT+ U 𝑈 U italic_U + V 𝑉 V ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.02457v2", "content": "Noteworthy among these are Hubbard-corrected DFT (so-called DFT+ U 𝑈 U italic_U 6 , 7 , 8 and its extension DFT+ U 𝑈 U italic_U + V 𝑉 V ..."}
+{"idx": 3, "title": "Frontiers | EEG-based investigation of effects of mindfulness", "date": "", "ddg_snippet": "... term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2023.1033420/full", "content": "... term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine ..."}
+{"idx": 4, "title": "Paper Digest: ICASSP 2020 Highlights – Resources | Paper", "date": "", "ddg_snippet": "... 1 ) adapt state-of-the-art metric-based few-shot learning methods to automate the detection of similar-sounding events, requiring only one or few ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2020/04/icassp-2020-highlights/", "content": "... 1 ) adapt state-of-the-art metric-based few-shot learning methods to automate the detection of similar-sounding events, requiring only one or few ..."}
+{"idx": 5, "title": "Publikationen | Institut für Materialwissenschaft |", "date": "", "ddg_snippet": "... 2024, author = {Ou, Yongliang and Ikeda, Yuji and Scholz, Lena and Divinski, Sergiy and Fritzen, Felix and Grabowski, Blazej}, doi = ...", "subpage_snippet": "", "source": "www.imw.uni-stuttgart.de", "link": "https://www.imw.uni-stuttgart.de/md/publikationen/", "content": "... 2024, author = {Ou, Yongliang and Ikeda, Yuji and Scholz, Lena and Divinski, Sergiy and Fritzen, Felix and Grabowski, Blazej}, doi = ..."}
+{"idx": 6, "title": "KIT - IOR - Publikationen", "date": "", "ddg_snippet": "... 25: Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction, Bordeaux, 4th-7th March 2025, 1 – 8 , ...", "subpage_snippet": "", "source": "www.ior.kit.edu", "link": "https://www.ior.kit.edu/Publikationen.php", "content": "... 25: Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction, Bordeaux, 4th-7th March 2025, 1 – 8 , ..."}
+{"idx": 7, "title": "(PDF) A Comparative Study of Tracking Moving Objects in videos", "date": "", "ddg_snippet": "Abstract : Visual tracking is considered to be one of the most im portant challenges in computer vision with numerous applications such", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/377397020_A_Comparative_Study_of_Tracking_Moving_Objects_in_videos", "content": "Abstract : Visual tracking is considered to be one of the most im portant challenges in computer vision with numerous applications such"}
+{"idx": 8, "title": "Publications | Lehrstuhl Bioinformatik Jena", "date": "", "ddg_snippet": "... of DNA sequences beyond sequence similarity using deep neural networks Proc Natl Acad Sci U S A 119(35):e2122636119, 2022 [ DOI ] [ PubMed ] M.", "subpage_snippet": "", "source": "bio.informatik.uni-jena.de", "link": "https://bio.informatik.uni-jena.de/publications/", "content": "... of DNA sequences beyond sequence similarity using deep neural networks Proc Natl Acad Sci U S A 119(35):e2122636119, 2022 [ DOI ] [ PubMed ] M."}
+{"idx": 9, "title": "Newest 'multinomial-distribution' Questions - Cross", "date": "", "ddg_snippet": "... counts ($K$) in a population stratified by $E$ exposures and 1 outcome (all binary); so, we have $ N _e = 2 ^E$ total exposure groups and $ N _k = 2 ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/tagged/multinomial-distribution?tab=Newest", "content": "... counts ($K$) in a population stratified by $E$ exposures and 1 outcome (all binary); so, we have $ N _e = 2 ^E$ total exposure groups and $ N _k = 2 ..."}
diff --git a/data/sampled_jsons/Schulman_2017_PPO_paper_A2C_Atari_performance_table_Alien_Centipede.jsonl b/data/sampled_jsons/Schulman_2017_PPO_paper_A2C_Atari_performance_table_Alien_Centipede.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..17f59a29a544c9a56e40a22d7d3ddd1e39332e55
--- /dev/null
+++ b/data/sampled_jsons/Schulman_2017_PPO_paper_A2C_Atari_performance_table_Alien_Centipede.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford ...", "date": "", "ddg_snippet": "Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games. Figure 6 shows the learning curves of each of three random seeds, while Table 6 shows the mean performance . 10", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/also/Shulman-2017.pdf", "content": "Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games. Figure 6 shows the learning curves of each of three random seeds, while Table 6 shows the mean performance . 10"}
+{"idx": 1, "title": "(PDF) Proximal Policy Optimization Algorithms - Academia.edu GitHub - lucaslingle/pytorch_ppo_atari: Implementation of ... Paper page - Proximal Policy Optimization Algorithms A Comparison of Policy Gradient Methods for Multitask Learning", "date": "", "ddg_snippet": "Table 4: PPO hyperparameters used for the Roboschool experiments. Adam stepsize was adjusted based on the target value of the KL divergence. Performance on More Atari Games Table 6: Mean final scores (last 100 episodes) of PPO and A2C on Atari games after 40M game frames (10M timesteps). Implementation of Proximal Policy Optimization in PyTorch, supporting parallel experience collection. See full list on github.com Proximal Policy Optimization is a reinforcement learning algorithm proposed by Schulman et al., 2017 . Compared to vanilla policy gradients and/or actor-critic methods, which optimize the model parameters by estimating the gradient of the reward surface and taking a single step, PPO takes inspiration from an approximate natural policy gradient algorithm known as TRPO. TRPO is an example of an information-geometric trust region method, which aims to improve the policy by taking steps of a constant maximum size on the manifold of possible policies. The stepsize utilized in TRPO is the state-averaged KL divergence under the current policy; taking steps under TRPO amounts to solving a constrained optimization problem to ensure the step size is at most a certain amount. This is done using conjugate gradient descent to compute the (approximate) natural gradient, followed by a line search to ensure the step taken in parameter space leads to a policy whose state-averaged KL divergence to the previous policy is not larger than a certain amount. Compared to vanilla policy gradients and/or actor-critic methods, the PPO algorithm enjoys the following favorable properties: •Improved sample efficiency •Improved stability •Improved reusability of collected experience See full list on github.com Install the following system dependencies: Ubuntu Mac OS X Installation of the system packages on Mac requires Homebrew. With Homebrew installed, run the following: Everyone Once the system dependencies have been installed, it's time to install the python dependencies. Install the conda package manager from https://docs.conda.io/en/latest/miniconda.html See full list on github.com Training To run the default settings, you can simply type:This will launch 8 parallel processes, each running the main.py script. These processes will play the OpenAI gym environment 'PongNoFrameskip-v4' in parallel, and communicate gradient information and synchronize parameters using OpenMPI.To see additional options, you can simply type python main.py --help. In particular, you can pick any other Atari 2600 game supported by OpenAI gym, and this implementation will support it. Checkpoints By default, checkpoints are saved to ./checkpoints/model-ppo1-defaults. To pick a different checkpoint directory, you can set the --checkpoint_dir flag, and to pick a different checkpoint name, you can set the --model_name flag. Play To watch the trained agent play a game, you can runBe sure to use the correct env_name, and to pass in the appropriate checkpoint_dir and model_name. See full list on github.com Using our heavily-tuned implementation, we obtained the following results: Due to time constraints, we did not test every game, but simply picked five that appeared to contain representative challenges of the broader Atari suite. We may add further results in the future. See full list on github.com This project started out as a Pytorch port of OpenAI baselines ppo1, and the legacy repo for that port is available here. Since then, we have rewritten the implementation from scratch, and simplified it by removing the dependencies on baselines entirely. In the future, we would like to implement training pipelines for other RL algorithms such as Deep Q-Learning and Prioritized Experience Replay. We would also like to add support for recurrent policies like LSTMs, SNAILs and Transformers. Some of these recurrent architectures may also require special environments in order to provide a value-add, so we have deferred such an implementation to the future. See full list on github.com Jul 19, 2017 · Proximal Policy Optimization Algorithms ... John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford , Abstract This paper compares two policy gradient methods using multitask learning (MTL) on the Atari visual environments. These environments are complex and take millions of time steps to learn. This paper investigates Advantage Actor-Critic ( A2C ) and Proximal Policy Optimization’s ( PPO ) performance on one, two and four tasks from the Arcade Learning Environment. The results show that agents ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/72572628/Proximal_Policy_Optimization_Algorithms", "content": "Table 4: PPO hyperparameters used for the Roboschool experiments. Adam stepsize was adjusted based on the target value of the KL divergence. Performance on More Atari Games Table 6: Mean final scores (last 100 episodes) of PPO and A2C on Atari games after 40M game frames (10M timesteps). Implementation of Proximal Policy Optimization in PyTorch, supporting parallel experience collection. See full list on github.com Proximal Policy Optimization is a reinforcement learning algorithm proposed by Schulman et al., 2017 . Compared to vanilla policy gradients and/or actor-critic methods, which optimize the model parameters by estimating the gradient of the reward surface and taking a single step, PPO takes inspiration from an approximate natural policy gradient algorithm known as TRPO. TRPO is an example of an information-geometric trust region method, which aims to improve the policy by taking steps of a constant maximum size on the manifold of possible policies. The stepsize utilized in TRPO is the state-averaged KL divergence under the current policy; taking steps under TRPO amounts to solving a constrained optimization problem to ensure the step size is at most a certain amount. This is done using conjugate gradient descent to compute the (approximate) natural gradient, followed by a line search to ensure the step taken in parameter space leads to a policy whose state-averaged KL divergence to the previous policy is not larger than a certain amount. Compared to vanilla policy gradients and/or actor-critic methods, the PPO algorithm enjoys the following favorable properties: •Improved sample efficiency •Improved stability •Improved reusability of collected experience See full list on github.com Install the following system dependencies: Ubuntu Mac OS X Installation of the system packages on Mac requires Homebrew. With Homebrew installed, run the following: Everyone Once the system dependencies have been installed, it's time to install the python dependencies. Install the conda package manager from https://docs.conda.io/en/latest/miniconda.html See full list on github.com Training To run the default settings, you can simply type:This will launch 8 parallel processes, each running the main.py script. These processes will play the OpenAI gym environment 'PongNoFrameskip-v4' in parallel, and communicate gradient information and synchronize parameters using OpenMPI.To see additional options, you can simply type python main.py --help. In particular, you can pick any other Atari 2600 game supported by OpenAI gym, and this implementation will support it. Checkpoints By default, checkpoints are saved to ./checkpoints/model-ppo1-defaults. To pick a different checkpoint directory, you can set the --checkpoint_dir flag, and to pick a different checkpoint name, you can set the --model_name flag. Play To watch the trained agent play a game, you can runBe sure to use the correct env_name, and to pass in the appropriate checkpoint_dir and model_name. See full list on github.com Using our heavily-tuned implementation, we obtained the following results: Due to time constraints, we did not test every game, but simply picked five that appeared to contain representative challenges of the broader Atari suite. We may add further results in the future. See full list on github.com This project started out as a Pytorch port of OpenAI baselines ppo1, and the legacy repo for that port is available here. Since then, we have rewritten the implementation from scratch, and simplified it by removing the dependencies on baselines entirely. In the future, we would like to implement training pipelines for other RL algorithms such as Deep Q-Learning and Prioritized Experience Replay. We would also like to add support for recurrent policies like LSTMs, SNAILs and Transformers. Some of these recurrent architectures may also require special environments in order to provide a value-add, so we have deferred such an implementation to the future. See full list on github.com Jul 19, 2017 · Proximal Policy Optimization Algorithms ... John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford , Abstract This paper compares two policy gradient methods using multitask learning (MTL) on the Atari visual environments. These environments are complex and take millions of time steps to learn. This paper investigates Advantage Actor-Critic ( A2C ) and Proximal Policy Optimization’s ( PPO ) performance on one, two and four tasks from the Arcade Learning Environment. The results show that agents ..."}
+{"idx": 2, "title": "GitHub - lucaslingle/pytorch_ppo_atari: Implementation of ... Paper page - Proximal Policy Optimization Algorithms A Comparison of Policy Gradient Methods for Multitask Learning", "date": "", "ddg_snippet": "Implementation of Proximal Policy Optimization in PyTorch, supporting parallel experience collection. See full list on github.com Proximal Policy Optimization is a reinforcement learning algorithm proposed by Schulman et al., 2017 . Compared to vanilla policy gradients and/or actor-critic methods, which optimize the model parameters by estimating the gradient of the reward surface and taking a single step, PPO takes inspiration from an approximate natural policy gradient algorithm known as TRPO. TRPO is an example of an information-geometric trust region method, which aims to improve the policy by taking steps of a constant maximum size on the manifold of possible policies. The stepsize utilized in TRPO is the state-averaged KL divergence under the current policy; taking steps under TRPO amounts to solving a constrained optimization problem to ensure the step size is at most a certain amount. This is done using conjugate gradient descent to compute the (approximate) natural gradient, followed by a line search to ensure the step taken in parameter space leads to a policy whose state-averaged KL divergence to the previous policy is not larger than a certain amount. Compared to vanilla policy gradients and/or actor-critic methods, the PPO algorithm enjoys the following favorable properties: •Improved sample efficiency •Improved stability •Improved reusability of collected experience See full list on github.com Install the following system dependencies: Ubuntu Mac OS X Installation of the system packages on Mac requires Homebrew. With Homebrew installed, run the following: Everyone Once the system dependencies have been installed, it's time to install the python dependencies. Install the conda package manager from https://docs.conda.io/en/latest/miniconda.html See full list on github.com Training To run the default settings, you can simply type:This will launch 8 parallel processes, each running the main.py script. These processes will play the OpenAI gym environment 'PongNoFrameskip-v4' in parallel, and communicate gradient information and synchronize parameters using OpenMPI.To see additional options, you can simply type python main.py --help. In particular, you can pick any other Atari 2600 game supported by OpenAI gym, and this implementation will support it. Checkpoints By default, checkpoints are saved to ./checkpoints/model-ppo1-defaults. To pick a different checkpoint directory, you can set the --checkpoint_dir flag, and to pick a different checkpoint name, you can set the --model_name flag. Play To watch the trained agent play a game, you can runBe sure to use the correct env_name, and to pass in the appropriate checkpoint_dir and model_name. See full list on github.com Using our heavily-tuned implementation, we obtained the following results: Due to time constraints, we did not test every game, but simply picked five that appeared to contain representative challenges of the broader Atari suite. We may add further results in the future. See full list on github.com This project started out as a Pytorch port of OpenAI baselines ppo1, and the legacy repo for that port is available here. Since then, we have rewritten the implementation from scratch, and simplified it by removing the dependencies on baselines entirely. In the future, we would like to implement training pipelines for other RL algorithms such as Deep Q-Learning and Prioritized Experience Replay. We would also like to add support for recurrent policies like LSTMs, SNAILs and Transformers. Some of these recurrent architectures may also require special environments in order to provide a value-add, so we have deferred such an implementation to the future. See full list on github.com Jul 19, 2017 · Proximal Policy Optimization Algorithms ... John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford , Abstract This paper compares two policy gradient methods using multitask learning (MTL) on the Atari visual environments. These environments are complex and take millions of time steps to learn. This paper investigates Advantage Actor-Critic ( A2C ) and Proximal Policy Optimization’s ( PPO ) performance on one, two and four tasks from the Arcade Learning Environment. The results show that agents ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lucaslingle/pytorch_ppo_atari", "content": "Implementation of Proximal Policy Optimization in PyTorch, supporting parallel experience collection. See full list on github.com Proximal Policy Optimization is a reinforcement learning algorithm proposed by Schulman et al., 2017 . Compared to vanilla policy gradients and/or actor-critic methods, which optimize the model parameters by estimating the gradient of the reward surface and taking a single step, PPO takes inspiration from an approximate natural policy gradient algorithm known as TRPO. TRPO is an example of an information-geometric trust region method, which aims to improve the policy by taking steps of a constant maximum size on the manifold of possible policies. The stepsize utilized in TRPO is the state-averaged KL divergence under the current policy; taking steps under TRPO amounts to solving a constrained optimization problem to ensure the step size is at most a certain amount. This is done using conjugate gradient descent to compute the (approximate) natural gradient, followed by a line search to ensure the step taken in parameter space leads to a policy whose state-averaged KL divergence to the previous policy is not larger than a certain amount. Compared to vanilla policy gradients and/or actor-critic methods, the PPO algorithm enjoys the following favorable properties: •Improved sample efficiency •Improved stability •Improved reusability of collected experience See full list on github.com Install the following system dependencies: Ubuntu Mac OS X Installation of the system packages on Mac requires Homebrew. With Homebrew installed, run the following: Everyone Once the system dependencies have been installed, it's time to install the python dependencies. Install the conda package manager from https://docs.conda.io/en/latest/miniconda.html See full list on github.com Training To run the default settings, you can simply type:This will launch 8 parallel processes, each running the main.py script. These processes will play the OpenAI gym environment 'PongNoFrameskip-v4' in parallel, and communicate gradient information and synchronize parameters using OpenMPI.To see additional options, you can simply type python main.py --help. In particular, you can pick any other Atari 2600 game supported by OpenAI gym, and this implementation will support it. Checkpoints By default, checkpoints are saved to ./checkpoints/model-ppo1-defaults. To pick a different checkpoint directory, you can set the --checkpoint_dir flag, and to pick a different checkpoint name, you can set the --model_name flag. Play To watch the trained agent play a game, you can runBe sure to use the correct env_name, and to pass in the appropriate checkpoint_dir and model_name. See full list on github.com Using our heavily-tuned implementation, we obtained the following results: Due to time constraints, we did not test every game, but simply picked five that appeared to contain representative challenges of the broader Atari suite. We may add further results in the future. See full list on github.com This project started out as a Pytorch port of OpenAI baselines ppo1, and the legacy repo for that port is available here. Since then, we have rewritten the implementation from scratch, and simplified it by removing the dependencies on baselines entirely. In the future, we would like to implement training pipelines for other RL algorithms such as Deep Q-Learning and Prioritized Experience Replay. We would also like to add support for recurrent policies like LSTMs, SNAILs and Transformers. Some of these recurrent architectures may also require special environments in order to provide a value-add, so we have deferred such an implementation to the future. See full list on github.com Jul 19, 2017 · Proximal Policy Optimization Algorithms ... John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford , Abstract This paper compares two policy gradient methods using multitask learning (MTL) on the Atari visual environments. These environments are complex and take millions of time steps to learn. This paper investigates Advantage Actor-Critic ( A2C ) and Proximal Policy Optimization’s ( PPO ) performance on one, two and four tasks from the Arcade Learning Environment. The results show that agents ..."}
+{"idx": 3, "title": "Paper page - Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "Jul 19, 2017 · Proximal Policy Optimization Algorithms ... John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford ,", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/1707.06347", "content": "Jul 19, 2017 · Proximal Policy Optimization Algorithms ... John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford ,"}
+{"idx": 4, "title": "Giant centipede eating mouse. - YouTube", "date": "", "ddg_snippet": "A giant centipede , scolopendra gigantea robusta, killing and eating a mouse.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=8CL2hetqpfg", "content": "A giant centipede , scolopendra gigantea robusta, killing and eating a mouse."}
+{"idx": 5, "title": "[1707.06347] Proximal Policy Optimization Algorithms - arXiv.org", "date": "", "ddg_snippet": "Jul 20, 2017 · Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1707.06347", "content": "Jul 20, 2017 · Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."}
+{"idx": 6, "title": "Proximal Policy Optimization Algorithms - Semantic Scholar", "date": "", "ddg_snippet": "Jul 20, 2017 · Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Proximal-Policy-Optimization-Algorithms-Schulman-Wolski/dce6f9d4017b1785979e7520fd0834ef8cf02f4b", "content": "Jul 20, 2017 · Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."}
+{"idx": 7, "title": "A Comparison of Policy Gradient Methods for Multitask Learning", "date": "", "ddg_snippet": "Abstract This paper compares two policy gradient methods using multitask learning (MTL) on the Atari visual environments. These environments are complex and take millions of time steps to learn. This paper investigates Advantage Actor-Critic ( A2C ) and Proximal Policy Optimization’s ( PPO ) performance on one, two and four tasks from the Arcade Learning Environment. The results show that agents ...", "subpage_snippet": "", "source": "newhonors.cs.umd.edu", "link": "http://newhonors.cs.umd.edu/uploads/thesis/file/211/CNalty_-_Honors_Thesis.pdf", "content": "Abstract This paper compares two policy gradient methods using multitask learning (MTL) on the Atari visual environments. These environments are complex and take millions of time steps to learn. This paper investigates Advantage Actor-Critic ( A2C ) and Proximal Policy Optimization’s ( PPO ) performance on one, two and four tasks from the Arcade Learning Environment. The results show that agents ..."}
+{"idx": 8, "title": "Proximal Policy Optimization — Spinning Up documentation", "date": "", "ddg_snippet": "PPO methods are significantly simpler to implement, and empirically seem to perform at least as well as TRPO. There are two primary variants of PPO : PPO -Penalty and PPO -Clip. Schulman 2017 is included because it is the original paper describing PPO .", "subpage_snippet": "", "source": "spinningup.openai.com", "link": "https://spinningup.openai.com/en/latest/algorithms/ppo.html", "content": "PPO methods are significantly simpler to implement, and empirically seem to perform at least as well as TRPO. There are two primary variants of PPO : PPO -Penalty and PPO -Clip. Schulman 2017 is included because it is the original paper describing PPO ."}
+{"idx": 9, "title": "Fisch - How to Get All 5 Crimson King Fragments", "date": "", "ddg_snippet": "A complete guide to the Crimson King quest in Fisch. Find all 5 crown fragments, learn the strategies for each quest, and discover how to unlock the new rod in the ancient archives. Table of Contents.", "subpage_snippet": "", "source": "trioner.com", "link": "https://trioner.com/fisch-how-to-get-all-5-crimson-king-fragment/", "content": "A complete guide to the Crimson King quest in Fisch. Find all 5 crown fragments, learn the strategies for each quest, and discover how to unlock the new rod in the ancient archives. Table of Contents."}
diff --git a/data/sampled_jsons/Schulman_PPO_2017_Atari_games_A2C_results_reproduction_github.jsonl b/data/sampled_jsons/Schulman_PPO_2017_Atari_games_A2C_results_reproduction_github.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b63646e3fa101ec2e0cb47156eccd42297d4dfb6
--- /dev/null
+++ b/data/sampled_jsons/Schulman_PPO_2017_Atari_games_A2C_results_reproduction_github.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - lucaslingle/pytorch_ppo_atari: Implementation of Proximal ...", "date": "", "ddg_snippet": "Proximal Policy Optimization is a reinforcement learning algorithm proposed by Schulman et al., 2017 . Compared to vanilla policy gradients and/or actor-critic methods, which optimize the model parameters by estimating the gradient of the reward surface and taking a single step, PPO takes inspiration ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lucaslingle/pytorch_ppo_atari", "content": "Proximal Policy Optimization is a reinforcement learning algorithm proposed by Schulman et al., 2017 . Compared to vanilla policy gradients and/or actor-critic methods, which optimize the model parameters by estimating the gradient of the reward surface and taking a single step, PPO takes inspiration ..."}
+{"idx": 1, "title": "PDF John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg ...", "date": "", "ddg_snippet": "Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games . Figure 6 shows the learning curves of each of three random seeds, while Table 6 shows the mean performance. 10", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/also/Shulman-2017.pdf", "content": "Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games . Figure 6 shows the learning curves of each of three random seeds, while Table 6 shows the mean performance. 10"}
+{"idx": 2, "title": "atari_games.ipynb - Colab", "date": "", "ddg_snippet": "It provides scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos. Documentation is available online: https://stable-baselines3.readthedocs.io/", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/atari_games.ipynb", "content": "It provides scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos. Documentation is available online: https://stable-baselines3.readthedocs.io/"}
+{"idx": 3, "title": "Atari Games - hsiehjackson.github.io", "date": "", "ddg_snippet": "Reinforcement learning is a machine learning algorithm that aims to teach software agents taking actions in an environment by maximizing a reward function. In this project, I implemented several algorithms including Policy Gradient, Deep Q-Learning (DQN), and A2C for the atari games , such as LunarLander, Assault, and Mario. My results are shown above in Figure 1.", "subpage_snippet": "", "source": "hsiehjackson.github.io", "link": "https://hsiehjackson.github.io/public/pages/project/atari/index.html", "content": "Reinforcement learning is a machine learning algorithm that aims to teach software agents taking actions in an environment by maximizing a reward function. In this project, I implemented several algorithms including Policy Gradient, Deep Q-Learning (DQN), and A2C for the atari games , such as LunarLander, Assault, and Mario. My results are shown above in Figure 1."}
+{"idx": 4, "title": "pytorch-a2c-ppo-acktr - GitHub", "date": "", "ddg_snippet": "Also see the OpenAI posts: A2C /ACKTR and PPO for more information. This implementation is inspired by the OpenAI baselines for A2C , ACKTR and PPO . It uses the same hyper parameters and the model since they were well tuned for Atari games . Please use this bibtex if you want to cite this repository in your publications:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/RizeaValentina/Atari", "content": "Also see the OpenAI posts: A2C /ACKTR and PPO for more information. This implementation is inspired by the OpenAI baselines for A2C , ACKTR and PPO . It uses the same hyper parameters and the model since they were well tuned for Atari games . Please use this bibtex if you want to cite this repository in your publications:"}
+{"idx": 5, "title": "Mastering Atari Games: Implementing PPO with 9 Crucial Details - Toolify", "date": "", "ddg_snippet": "Discover how to use Proximal Policy Optimization ( PPO ) to train agents in Atari games with 9 essential implementation details. Learn about Gym ID changes, vector environment setup, Atari preprocessing wrappers, and more.", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-news/mastering-atari-games-implementing-ppo-with-9-crucial-details-1059685", "content": "Discover how to use Proximal Policy Optimization ( PPO ) to train agents in Atari games with 9 essential implementation details. Learn about Gym ID changes, vector environment setup, Atari preprocessing wrappers, and more."}
+{"idx": 6, "title": "Atari Games with Proximal Policy Optimization - Medium", "date": "", "ddg_snippet": "The first of all we need to explain \"What's Atari Games ?\": Atari console, video game console released in 1977 by the North American game manufacturer Atari , Inc. Using a cartridge-based ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@shogulomkurganov73/atari-games-with-proximal-policy-optimization-ed28c7fafa3f", "content": "The first of all we need to explain \"What's Atari Games ?\": Atari console, video game console released in 1977 by the North American game manufacturer Atari , Inc. Using a cartridge-based ..."}
+{"idx": 7, "title": "A Comparative Study of Deep Reinforcement Learning Models: Dqn Vs Ppo ...", "date": "", "ddg_snippet": "Abstract. This study conducts a comparative analysis of three advanced Deep Reinforcement Learning models - Deep Q-Networks (DQN), Proximal Policy Optimization ( PPO ), and Advantage Actor-Critic ( A2C ) - exclusively within the BreakOut Atari game environment. Our research aims to assess the performance and effectiveness of these models in a singular, controlled setting. Through rigorous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.14151v1", "content": "Abstract. This study conducts a comparative analysis of three advanced Deep Reinforcement Learning models - Deep Q-Networks (DQN), Proximal Policy Optimization ( PPO ), and Advantage Actor-Critic ( A2C ) - exclusively within the BreakOut Atari game environment. Our research aims to assess the performance and effectiveness of these models in a singular, controlled setting. Through rigorous ..."}
+{"idx": 8, "title": "hiaripc/Atari_PPO_with_RND - GitHub", "date": "", "ddg_snippet": "We implemented the PPO algorithm to create an agent capable of playing the Atari game Freeway, following the paper's hyperparameters for Atari games . The implementation involves 8 parallel actors playing for 128 timesteps, for a total of 10 million steps in the environment. The network architecture ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hiaripc/Atari_PPO_with_RND", "content": "We implemented the PPO algorithm to create an agent capable of playing the Atari game Freeway, following the paper's hyperparameters for Atari games . The implementation involves 8 parallel actors playing for 128 timesteps, for a total of 10 million steps in the environment. The network architecture ..."}
+{"idx": 9, "title": "ArCHer: Training Language Model Agents via Hierarchical", "date": "", "ddg_snippet": "Due to this, on-policy methods such as PPO ( Schulman et al., 2017 ) quickly become impractical owing to their inability to reuse data from past ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.19446v1", "content": "Due to this, on-policy methods such as PPO ( Schulman et al., 2017 ) quickly become impractical owing to their inability to reuse data from past ..."}
diff --git a/data/sampled_jsons/Schulman_et_al._2017_PPO_Atari_A2C_performance_table_year_2017.jsonl b/data/sampled_jsons/Schulman_et_al._2017_PPO_Atari_A2C_performance_table_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d6d0d3749b759ea53e742b6a0dde391d640fc959
--- /dev/null
+++ b/data/sampled_jsons/Schulman_et_al._2017_PPO_Atari_A2C_performance_table_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DQN VS PPO VS A2C", "date": "", "ddg_snippet": "19 Jul 2024 — This study conducts a comparative analysis of three advanced Deep Reinforcement Learning models – Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.14151v1", "content": "19 Jul 2024 — This study conducts a comparative analysis of three advanced Deep Reinforcement Learning models – Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), ..."}
+{"idx": 1, "title": "PPO Explained: The RL Algorithm That Took the World by ...", "date": "", "ddg_snippet": "Proximal Policy Optimization offers a sweet spot: robust performance , relative ease of coding, and strong compatibility with various neural architectures.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@vivek_tiwari_vt/ppo-explained-the-rl-algorithm-that-took-the-world-by-storm-8a245910b8ef", "content": "Proximal Policy Optimization offers a sweet spot: robust performance , relative ease of coding, and strong compatibility with various neural architectures."}
+{"idx": 2, "title": "A STRONG ON-POLICY COMPETITOR TO PPO", "date": "", "ddg_snippet": "by X Chu · Cited by 2 — Unlike the Atari domain, we utilize the con- stant learning rate strategy as Schulman et al . ( 2017 ) in the continuous domain instead of the linear decrease.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0migj5lyUZl", "content": "by X Chu · Cited by 2 — Unlike the Atari domain, we utilize the con- stant learning rate strategy as Schulman et al . ( 2017 ) in the continuous domain instead of the linear decrease."}
+{"idx": 3, "title": "Stable-Baselines3: Reliable Reinforcement ...", "date": "", "ddg_snippet": "by A Raffin · 2021 · Cited by 3680 — Stable-Baselines3 contains the following state-of-the-art on- and off-policy algorithms, commonly used as experimental baselines: A2C (Mnih et al ., 2016),. PPO ... 8 pages", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume22/20-1364/20-1364.pdf", "content": "by A Raffin · 2021 · Cited by 3680 — Stable-Baselines3 contains the following state-of-the-art on- and off-policy algorithms, commonly used as experimental baselines: A2C (Mnih et al ., 2016),. PPO ... 8 pages"}
+{"idx": 4, "title": "Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "by J Schulman · 2017 · Cited by 30034 — Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games. Figure 6 shows the learning curves of each of three ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1707.06347", "content": "by J Schulman · 2017 · Cited by 30034 — Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games. Figure 6 shows the learning curves of each of three ..."}
+{"idx": 5, "title": "The 37 Implementation Details of Proximal Policy Optimization", "date": "", "ddg_snippet": "25 Mar 2022 — Despite this simpler objective, Schulman et al ., ( 2017 ) show PPO has higher sample efficiency than TRPO in many control tasks. PPO also has good ...", "subpage_snippet": "", "source": "iclr-blog-track.github.io", "link": "https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/", "content": "25 Mar 2022 — Despite this simpler objective, Schulman et al ., ( 2017 ) show PPO has higher sample efficiency than TRPO in many control tasks. PPO also has good ..."}
+{"idx": 6, "title": "POLICY OPTIMIZATION BY GENETIC DISTILLATION", "date": "", "ddg_snippet": "by T Gangwani · 2018 · Cited by 47 — PPO performs 10 steps of full-batch gradient descent on the policy parameters using the same collected batch of simulation data, while A2C does a single descent ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ByOnmlWC-", "content": "by T Gangwani · 2018 · Cited by 47 — PPO performs 10 steps of full-batch gradient descent on the policy parameters using the same collected batch of simulation data, while A2C does a single descent ..."}
+{"idx": 7, "title": "Discovering General Reinforcement Learning Algorithms ...", "date": "", "ddg_snippet": "by MT Jackson · 2023 · Cited by 22 — In this, we co-train an antagonist agent using a manually designed RL algorithm A (e.g., A2C [Mnih et al ., 2016], PPO [ Schulman et al ., 2017 ]) in parallel. 19 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/fce2d8a485746f76aac7b5650db2679d-Paper-Conference.pdf", "content": "by MT Jackson · 2023 · Cited by 22 — In this, we co-train an antagonist agent using a manually designed RL algorithm A (e.g., A2C [Mnih et al ., 2016], PPO [ Schulman et al ., 2017 ]) in parallel. 19 pages"}
+{"idx": 8, "title": "Attention-based advantage actor-critic algorithm with ...", "date": "", "ddg_snippet": "by C Zhou · 2023 · Cited by 20 — This paper focuses on the advantage actor critic algorithm and introduces an attention-based actor critic algorithm with experience replay algorithm.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10845-022-01988-z", "content": "by C Zhou · 2023 · Cited by 20 — This paper focuses on the advantage actor critic algorithm and introduces an attention-based actor critic algorithm with experience replay algorithm."}
+{"idx": 9, "title": "ChainerRL: A Deep Reinforcement Learning Library", "date": "", "ddg_snippet": "by Y Fujita · 2021 · Cited by 176 — Abstract. In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning ... 14 pages", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume22/20-376/20-376.pdf", "content": "by Y Fujita · 2021 · Cited by 176 — Abstract. In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning ... 14 pages"}
diff --git a/data/sampled_jsons/Schulman_et_al._2017_Proximal_Policy_Optimization_PPO_paper.jsonl b/data/sampled_jsons/Schulman_et_al._2017_Proximal_Policy_Optimization_PPO_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7af6ce25abf5daf836ab1edace8051a38ff48267
--- /dev/null
+++ b/data/sampled_jsons/Schulman_et_al._2017_Proximal_Policy_Optimization_PPO_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1707.06347] Proximal Policy Optimization Algorithms - arXiv.org", "date": "", "ddg_snippet": "The new methods, which we call proximal policy optimization ( PPO ), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1707.06347", "content": "The new methods, which we call proximal policy optimization ( PPO ), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically)."}
+{"idx": 1, "title": "PDF John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg ...", "date": "", "ddg_snippet": "The new methods, which we call proximal policy optimization ( PPO ), have some of the bene ts of trust region policy optimiza - tion (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically).", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/also/Shulman-2017.pdf", "content": "The new methods, which we call proximal policy optimization ( PPO ), have some of the bene ts of trust region policy optimiza - tion (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically)."}
+{"idx": 2, "title": "Understanding and Implementing Proximal Policy Optimization (Schulman ...", "date": "", "ddg_snippet": "Proximal Policy Optimization ( PPO ) Clipped Surrogate Objective With the motives mentioned above, Proximal Policy Optimization attempts to simplify the optimization process while retaining the advantages of TRPO. One of this paper's main contribution is the clipped surrogate objective: Clipped Surrogate Objective ( Schulman et al ., 2017 ) Here, we compute an expectation over a minimum of two ...", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/understanding-and-implementing-proximal-policy-optimization-schulman-et-al-2017-9523078521ce/", "content": "Proximal Policy Optimization ( PPO ) Clipped Surrogate Objective With the motives mentioned above, Proximal Policy Optimization attempts to simplify the optimization process while retaining the advantages of TRPO. One of this paper's main contribution is the clipped surrogate objective: Clipped Surrogate Objective ( Schulman et al ., 2017 ) Here, we compute an expectation over a minimum of two ..."}
+{"idx": 3, "title": "Paper page - Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "Proximal Policy Optimization Algorithms ... John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford ,", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/1707.06347", "content": "Proximal Policy Optimization Algorithms ... John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford ,"}
+{"idx": 4, "title": "Proximal Policy Optimization Algorithms | Request PDF", "date": "", "ddg_snippet": "This paper utilizes Proximal Policy Optimization ( PPO ) ( Schulman et al ., 2017 ) as the oracle algorithm for our set of strategies N , which consists of convolutional neural network parameterized ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/318584439_Proximal_Policy_Optimization_Algorithms", "content": "This paper utilizes Proximal Policy Optimization ( PPO ) ( Schulman et al ., 2017 ) as the oracle algorithm for our set of strategies N , which consists of convolutional neural network parameterized ..."}
+{"idx": 5, "title": "PDF Schulman et al. - 2017 - Proximal Policy Optimization ... - GitHub", "date": "", "ddg_snippet": "A place to collect and sync papers I found helpful. - ksang/ paper -collections", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ksang/paper-collections/blob/main/reinforcement+learning/Schulman+et+al.+-+2017+-+Proximal+Policy+Optimization+Algorithms.pdf", "content": "A place to collect and sync papers I found helpful. - ksang/ paper -collections"}
+{"idx": 6, "title": "Proximal policy optimization with reward-based prioritization", "date": "", "ddg_snippet": "In recent years, deep reinforcement learning algorithms based on policy gradients have achieving significant improvements in many tasks, including the Proximal Policy Optimization ( PPO ) algorithm ( Schulman et al ., 2017 ). The PPO algorithm applies policy gradients to optimize a surrogate objective, thereby effectively addressing optimization challenges inherent in deep reinforcement learning ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0957417425012813", "content": "In recent years, deep reinforcement learning algorithms based on policy gradients have achieving significant improvements in many tasks, including the Proximal Policy Optimization ( PPO ) algorithm ( Schulman et al ., 2017 ). The PPO algorithm applies policy gradients to optimize a surrogate objective, thereby effectively addressing optimization challenges inherent in deep reinforcement learning ..."}
+{"idx": 7, "title": "Proximal Policy Optimization Algorithms - Semantic Scholar", "date": "", "ddg_snippet": "The new methods, which we call proximal policy optimization ( PPO ), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically).", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Proximal-Policy-Optimization-Algorithms-Schulman-Wolski/dce6f9d4017b1785979e7520fd0834ef8cf02f4b", "content": "The new methods, which we call proximal policy optimization ( PPO ), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically)."}
+{"idx": 8, "title": "Proximal Policy Optimization Algorithms", "date": "", "ddg_snippet": "Abstract:Proximal policy optimization ( PPO ) has yielded state-of-the-art results in policy search, a subfield of reinforcement learning, with one of its key points being the use of a surrogate objective function to restrict the step size at each policy update.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/dce6f9d4017b1785979e7520fd0834ef8cf02f4b", "content": "Abstract:Proximal policy optimization ( PPO ) has yielded state-of-the-art results in policy search, a subfield of reinforcement learning, with one of its key points being the use of a surrogate objective function to restrict the step size at each policy update."}
+{"idx": 9, "title": "Proximal Policy Optimization Algorithms - ADS", "date": "", "ddg_snippet": "The new methods, which we call proximal policy optimization ( PPO ), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically).", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2017arXiv170706347S/abstract", "content": "The new methods, which we call proximal policy optimization ( PPO ), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically)."}
diff --git a/data/sampled_jsons/Score-based_generative_modeling_through_stochastic_differential_equations_abstract_year_2021.jsonl b/data/sampled_jsons/Score-based_generative_modeling_through_stochastic_differential_equations_abstract_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6429be2933931d6567834621655647c919407104
--- /dev/null
+++ b/data/sampled_jsons/Score-based_generative_modeling_through_stochastic_differential_equations_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Score - Based Generative Modeling through Stochastic Differential ...", "date": "", "ddg_snippet": "Keywords: generative models , score - based generative models , stochastic differential equations , score matching, diffusion.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=PxTIG12RRHS", "content": "Keywords: generative models , score - based generative models , stochastic differential equations , score matching, diffusion."}
+{"idx": 1, "title": "ICLR 2021 Score - Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "Abstract : Creating noise from data is easy; creating data from noise is generative modeling .By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/oral/3402", "content": "Abstract : Creating noise from data is easy; creating data from noise is generative modeling .By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples."}
+{"idx": 2, "title": "Score - Based Generative Modeling through Stochastic Differential ...", "date": "", "ddg_snippet": "We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2011.13456", "content": "We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding..."}
+{"idx": 3, "title": "ScoreSdeVpScheduler", "date": "", "ddg_snippet": "It was introduced in the Score - Based Generative Modeling through Stochastic Differential Equations paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole. The abstract from the paper is", "subpage_snippet": "", "source": "ollama.hf-mirror.com", "link": "https://ollama.hf-mirror.com/docs/diffusers/api/schedulers/score_sde_vp", "content": "It was introduced in the Score - Based Generative Modeling through Stochastic Differential Equations paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole. The abstract from the paper is"}
+{"idx": 4, "title": "Unsupervised Visual Defect Detection with Score - Based Generative ...", "date": "", "ddg_snippet": "Score - Based Generative Modeling through Stochastic Differential Equations .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/unsupervised-visual-defect-detection-with-score-based-ysx6loqo", "content": "Score - Based Generative Modeling through Stochastic Differential Equations ."}
+{"idx": 5, "title": "[논문리뷰 part3-6] Score - Based Generative Modeling through ...", "date": "", "ddg_snippet": "[논문리뷰 part3-7] Score - Based Generative Modeling through Stochastic Differential Equations И.В. Оселедец.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=VT2GuMnkvb0", "content": "[논문리뷰 part3-7] Score - Based Generative Modeling through Stochastic Differential Equations И.В. Оселедец."}
+{"idx": 6, "title": "google/ncsnpp-ffhq-256 · Hugging Face", "date": "", "ddg_snippet": "Paper: Score - Based Generative Modeling through Stochastic Differential Equations . Authors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole. Abstract", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/google/ncsnpp-ffhq-256", "content": "Paper: Score - Based Generative Modeling through Stochastic Differential Equations . Authors: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole. Abstract"}
+{"idx": 7, "title": "Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek...", "date": "", "ddg_snippet": "Score - Based Generative Modeling through Stochastic Differential Equations . May 3, 2021.By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.", "subpage_snippet": "", "source": "slideslive.com", "link": "https://slideslive.com/38954067/scorebased-generative-modeling-through-stochastic-differential-equations", "content": "Score - Based Generative Modeling through Stochastic Differential Equations . May 3, 2021.By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples."}
+{"idx": 8, "title": "AI-Powered Paper Summarization about the arXiv paper 2011.13456v1", "date": "", "ddg_snippet": "Score - Based Generative Modeling through Stochastic Differential Equations .- Generative modeling : Creating new data based on existing data patterns. - Stochastic differential equations (SDE): Equations that involve random variables and their changing values over time.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/2011.13456v1/", "content": "Score - Based Generative Modeling through Stochastic Differential Equations .- Generative modeling : Creating new data based on existing data patterns. - Stochastic differential equations (SDE): Equations that involve random variables and their changing values over time."}
+{"idx": 9, "title": "The most insightful stories about De - Medium", "date": "", "ddg_snippet": "Abstract This article explores score - based generative modeling through stochastic differential equations (SDEs), a powerful framework for…", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/tag/de/archive", "content": "Abstract This article explores score - based generative modeling through stochastic differential equations (SDEs), a powerful framework for…"}
diff --git a/data/sampled_jsons/ScoreDec_abstract_full-band_48_kHz_speech.jsonl b/data/sampled_jsons/ScoreDec_abstract_full-band_48_kHz_speech.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b45f62b0151f6729430e997a7dcbf1aed57a022d
--- /dev/null
+++ b/data/sampled_jsons/ScoreDec_abstract_full-band_48_kHz_speech.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ABSTRACT arXiv:2401.12160v1 [eess.AS] 22 Jan 2024", "date": "", "ddg_snippet": "spectral domain for the E2E AudioDec codec [11]. The proposed ScoreDec attains high-fidelity speech reconstruction, preserves the original phase inf rmation, and gets rid of the tricky GAN training. According to the objective and subjective experimental results, the reconstructed coded speech achieves human-level naturalness with a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.12160", "content": "spectral domain for the E2E AudioDec codec [11]. The proposed ScoreDec attains high-fidelity speech reconstruction, preserves the original phase inf rmation, and gets rid of the tricky GAN training. According to the objective and subjective experimental results, the reconstructed coded speech achieves human-level naturalness with a ..."}
+{"idx": 1, "title": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A ...", "date": "", "ddg_snippet": "Jan 22, 2024 · Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.12160", "content": "Jan 22, 2024 · Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information."}
+{"idx": 2, "title": "ScoreDec: A Phase-Preserving High-Fidelity Audio Codec ...", "date": "", "ddg_snippet": "ScoreDec with a 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10448371", "content": "ScoreDec with a 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information."}
+{"idx": 3, "title": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec ...", "date": "", "ddg_snippet": "ScoreDec with a 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information.", "subpage_snippet": "", "source": "bigpon.github.io", "link": "https://bigpon.github.io/ScoreDec_demo/", "content": "ScoreDec with a 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information."}
+{"idx": 4, "title": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A ...", "date": "", "ddg_snippet": "Feb 27, 2024 · Both the objective and subjective experimental results show that ScoreDec with a 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2401.12160", "content": "Feb 27, 2024 · Both the objective and subjective experimental results show that ScoreDec with a 24 kbps bitrate encodes and decodes full-band 48 kHz speech with human-level naturalness and well-preserved phase information."}
+{"idx": 5, "title": "FB-MSTCN: A Full - Band Single-Channel Speech Enhancement...", "date": "", "ddg_snippet": "full - band speech signals. real-time processing.The 48 kHz full - band time-domain signal is divided into three sub-channels by extracting, and a two-stage processing scheme of `masking + compensation' is proposed to enhance the signal in the complex domain.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/fb-mstcn-a-full-band-single-channel-speech-enhancement-method-based-on-multi-scale-temporal-convolutional-network/867754491028439638-108584", "content": "full - band speech signals. real-time processing.The 48 kHz full - band time-domain signal is divided into three sub-channels by extracting, and a two-stage processing scheme of `masking + compensation' is proposed to enhance the signal in the complex domain."}
+{"idx": 6, "title": "(PDF) A Hybrid DSP/Deep Learning Approach to Real-Time Full - Band ...", "date": "", "ddg_snippet": "Real-Time Full - Band Speech Enhancement. Jean-Marc Valin. Mozilla Corporation. Mountain View, CA, USA. jmvalin@jmvalin.ca. Abstract —Despite noise suppression being a mature area in.with low complexity. We also focus on full - band ( 48 kHz ).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/320033415_A_Hybrid_DSPDeep_Learning_Approach_to_Real-Time_Full-Band_Speech_Enhancement", "content": "Real-Time Full - Band Speech Enhancement. Jean-Marc Valin. Mozilla Corporation. Mountain View, CA, USA. jmvalin@jmvalin.ca. Abstract —Despite noise suppression being a mature area in.with low complexity. We also focus on full - band ( 48 kHz )."}
+{"idx": 7, "title": "Submitted to INTERSPEECH", "date": "", "ddg_snippet": "Index Terms— Band-split RNN, Full - band speech en-hancement, Personalized speech enhancement. 1. Introduction. Speech enhancement (SE) is an important task in speech com-munication that aims to improve the subjective and objective quality of speech signals.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/high-fidelity-speech-enhancement-with-band-split-rnn-3d4u4cnp.pdf", "content": "Index Terms— Band-split RNN, Full - band speech en-hancement, Personalized speech enhancement. 1. Introduction. Speech enhancement (SE) is an important task in speech com-munication that aims to improve the subjective and objective quality of speech signals."}
+{"idx": 8, "title": "Israel D. Gebru", "date": "", "ddg_snippet": "ScoreDec : A Phase-Preserving High-Fidelity Audio Codec with a Generalized Score-Based Diffusion Post-Filter Yi-Chiao Wu, Dejan Markovic, Steven Krenn, Israel D. Gebru, Alexander Richard.", "subpage_snippet": "", "source": "idgebru.com", "link": "https://idgebru.com/", "content": "ScoreDec : A Phase-Preserving High-Fidelity Audio Codec with a Generalized Score-Based Diffusion Post-Filter Yi-Chiao Wu, Dejan Markovic, Steven Krenn, Israel D. Gebru, Alexander Richard."}
+{"idx": 9, "title": "AudioDec: An Open-source Streaming High-fidelity Neural Audio ...", "date": "", "ddg_snippet": "Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2305.16608", "content": "Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information."}
diff --git a/data/sampled_jsons/Section_3.1_license_mismatch_rate_rises_July_2024_model_licensing_practices_quagmire_legal_noncompli.jsonl b/data/sampled_jsons/Section_3.1_license_mismatch_rate_rises_July_2024_model_licensing_practices_quagmire_legal_noncompli.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5be1a2b71715712b98b8ef274f24c02952e5d296
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+++ b/data/sampled_jsons/Section_3.1_license_mismatch_rate_rises_July_2024_model_licensing_practices_quagmire_legal_noncompli.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[Solved] How to Fix 'Your AutoCAD License Is Not Valid'", "date": "", "ddg_snippet": "Resetting the licensing can fix the “your AutoCAD license is not valid” error by resolving any issues related to the licensing configuration and settings. This process helps to restore the licensing components to their default state.How Do I Remove a Non Valid License in AutoCAD?", "subpage_snippet": "", "source": "blog.zwsoft.com", "link": "https://blog.zwsoft.com/fix-autocad-license-not-valid/", "content": "Resetting the licensing can fix the “your AutoCAD license is not valid” error by resolving any issues related to the licensing configuration and settings. This process helps to restore the licensing components to their default state.How Do I Remove a Non Valid License in AutoCAD?"}
+{"idx": 1, "title": "Licensing (runtime) allocation - Orchestrator - UiPath Community Forum", "date": "", "ddg_snippet": "It will consume 1 license , not 3 becaue your Machine Template configured with 3 runtimes and only 1 background process job is running. Result in only 1 runtime license is consumed.", "subpage_snippet": "", "source": "forum.uipath.com", "link": "https://forum.uipath.com/t/licensing-runtime-allocation/5278149", "content": "It will consume 1 license , not 3 becaue your Machine Template configured with 3 runtimes and only 1 background process job is running. Result in only 1 runtime license is consumed."}
+{"idx": 2, "title": "Luxury Fashion Licensing in India: A Boon or... - Fashion Law Journal", "date": "", "ddg_snippet": "IP First , Licensing Second. Brands must register trademarks, designs, and trade dress in India before licensing .Hermès offers a best- practice model : every licensed product category undergoes rigorous vetting, ensuring craftsmanship remains uncompromised.", "subpage_snippet": "", "source": "fashionlawjournal.com", "link": "https://fashionlawjournal.com/luxury-fashion-licensing-in-india/", "content": "IP First , Licensing Second. Brands must register trademarks, designs, and trade dress in India before licensing .Hermès offers a best- practice model : every licensed product category undergoes rigorous vetting, ensuring craftsmanship remains uncompromised."}
+{"idx": 3, "title": "iMazing 3.0. 3 . 1 License Key + Activation Number Free Download", "date": "", "ddg_snippet": "iMazing License Key Full Torrent Full Version latest 2024 . There are five main things that iMazing can do: copy to the laptop, copy a folder to a device, copy files to a device, make a folder, and delete from a device.", "subpage_snippet": "", "source": "freefullpc.com", "link": "https://freefullpc.com/imazing-license-key/", "content": "iMazing License Key Full Torrent Full Version latest 2024 . There are five main things that iMazing can do: copy to the laptop, copy a folder to a device, copy files to a device, make a folder, and delete from a device."}
+{"idx": 4, "title": "Corporate Penalties Levied in Each U.S. State (2020– 2024 )", "date": "", "ddg_snippet": "The Federal Reserve made their first interest rate cut of this year of 25 basis points on September 17th, and projects rates to decline to 3.50–3.75% by the end of 2025.", "subpage_snippet": "", "source": "www.visualcapitalist.com", "link": "https://www.visualcapitalist.com/corporate-penalties-across-the-u-s-mapped-by-state-2020-2024/", "content": "The Federal Reserve made their first interest rate cut of this year of 25 basis points on September 17th, and projects rates to decline to 3.50–3.75% by the end of 2025."}
+{"idx": 5, "title": "(PDF) An overview of model uncertainty and variability in LLM-based...", "date": "", "ddg_snippet": "capable of detecting alignment risks, legal noncompliance , and. model deception—before frontier models are widely adopted in.architectures and permissive licensing . These models are. increasingly chosen for cloud integration, mass-market.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/394651429_An_overview_of_model_uncertainty_and_variability_in_LLM-based_sentiment_analysis_challenges_mitigation_strategies_and_the_role_of_explainability", "content": "capable of detecting alignment risks, legal noncompliance , and. model deception—before frontier models are widely adopted in.architectures and permissive licensing . These models are. increasingly chosen for cloud integration, mass-market."}
+{"idx": 6, "title": "How to get international driving license in HONG KONG", "date": "", "ddg_snippet": "To obtain a driving license , you must be at least 18 years old and hold a valid passport. You will also need to pass a theory and practical driving test, and make sure you have a valid driver’s license from your home country.", "subpage_snippet": "", "source": "www.hollymelody.com", "link": "https://www.hollymelody.com/how-to-get-international-driving-license-in-hong-kong-can-foreigners-drive-in-h", "content": "To obtain a driving license , you must be at least 18 years old and hold a valid passport. You will also need to pass a theory and practical driving test, and make sure you have a valid driver’s license from your home country."}
+{"idx": 7, "title": "Bingsheng He @ NUS SoC", "date": "", "ddg_snippet": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance . Call for contributions.Qinbin Li*, Bingsheng He, Dawn Song. Practical One -Shot Federated Learning for Cross-Silo Setting.", "subpage_snippet": "", "source": "www.comp.nus.edu.sg", "link": "https://www.comp.nus.edu.sg/~hebs/", "content": "Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance . Call for contributions.Qinbin Li*, Bingsheng He, Dawn Song. Practical One -Shot Federated Learning for Cross-Silo Setting."}
+{"idx": 8, "title": "Permanent Residency in Chile: Complete 2025 Guide to... | Expat.cl", "date": "", "ddg_snippet": "Unrestricted work authorization in any legal profession or businessRight to establish and operate businessesEqual treatment in employment and professional licensing", "subpage_snippet": "", "source": "www.expat.cl", "link": "https://www.expat.cl/guide-chile/immigration-visas/permanent-residency-in-chile/", "content": "Unrestricted work authorization in any legal profession or businessRight to establish and operate businessesEqual treatment in employment and professional licensing"}
+{"idx": 9, "title": "Trump and Xi make progress on TikTok deal, plan to meet in... | Reuters", "date": "", "ddg_snippet": "[1/3]A general view of the offices of TikTok, as the site faces an April 5 deadline to reach a deal to find a non -Chinese buyer under threat of being banned from the United States, in Culver City, California, U.S., April 2, 2025. REUTERS/Daniel Cole/File Photo Purchase Licensing Rights.", "subpage_snippet": "", "source": "www.reuters.com", "link": "https://www.reuters.com/world/china/trump-xi-seek-tiktok-win-break-us-china-gridlock-2025-09-19/", "content": "[1/3]A general view of the offices of TikTok, as the site faces an April 5 deadline to reach a deal to find a non -Chinese buyer under threat of being banned from the United States, in Culver City, California, U.S., April 2, 2025. REUTERS/Daniel Cole/File Photo Purchase Licensing Rights."}
diff --git a/data/sampled_jsons/Section_3.2_four-step_self-supervised_learning_mapping_behaviors_and_emotions_Checks-and-Balances_Fr.jsonl b/data/sampled_jsons/Section_3.2_four-step_self-supervised_learning_mapping_behaviors_and_emotions_Checks-and-Balances_Fr.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b848daee20fa5cff0dd8b2390490f685504f3963
--- /dev/null
+++ b/data/sampled_jsons/Section_3.2_four-step_self-supervised_learning_mapping_behaviors_and_emotions_Checks-and-Balances_Fr.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "This paper introduces a checks -and- balances framework for ethical alignment of Large Lan- guage Models (LLMs), inspired by three-branch.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d24155c881921de1284ece531612c8597f7c0a32.pdf", "content": "This paper introduces a checks -and- balances framework for ethical alignment of Large Lan- guage Models (LLMs), inspired by three-branch."}
+{"idx": 1, "title": "Systematic mapping study of tools to identify emotions and ...", "date": "", "ddg_snippet": "by A Islam · 2025 · Cited by 1 — This Systematic Mapping Study, based on a review of research articles from reputable journals, presents tools that use a variety of data sources.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s44163-025-00271-3", "content": "by A Islam · 2025 · Cited by 1 — This Systematic Mapping Study, based on a review of research articles from reputable journals, presents tools that use a variety of data sources."}
+{"idx": 2, "title": "Self-Supervised EEG Representation Learning for Robust ...", "date": "", "ddg_snippet": "23 Jul 2024 — We propose a self-supervised framework with contrastive learning for robust EEG-based emotion recognition, which can effectively leverage both readily ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3674975", "content": "23 Jul 2024 — We propose a self-supervised framework with contrastive learning for robust EEG-based emotion recognition, which can effectively leverage both readily ..."}
+{"idx": 3, "title": "Exploring Expression-Related Self-Supervised Learning and ...", "date": "", "ddg_snippet": "by F Xue · 2023 · Cited by 2 — In this paper, we explore an expression-related self - supervised learning (SSL) method called ContraWarping to perform expression classification in the 5th ... 8 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023W/ABAW/papers/Xue_Exploring_Expression-Related_Self-Supervised_Learning_and_Spatial_Reserve_Pooling_for_Affective_CVPRW_2023_paper.pdf", "content": "by F Xue · 2023 · Cited by 2 — In this paper, we explore an expression-related self - supervised learning (SSL) method called ContraWarping to perform expression classification in the 5th ... 8 pages"}
+{"idx": 4, "title": "LLMs are aware of their learned behaviors", "date": "", "ddg_snippet": "19 Jan 2025 — We study behavioral self -awareness — an LLM's ability to articulate its behaviors without requiring in-context examples ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.11120v1", "content": "19 Jan 2025 — We study behavioral self -awareness — an LLM's ability to articulate its behaviors without requiring in-context examples ..."}
+{"idx": 5, "title": "Visualization and Semantic Labeling of Mood States Based ...", "date": "", "ddg_snippet": "by H Madokoro · 2022 — We present a classification and visualization method of mood states based on unsupervised machine learning (ML) algorithms.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9408575/", "content": "by H Madokoro · 2022 — We present a classification and visualization method of mood states based on unsupervised machine learning (ML) algorithms."}
+{"idx": 6, "title": "Machine learning for human emotion recognition", "date": "", "ddg_snippet": "by EMG Younis · 2024 · Cited by 41 — The fourth section examines emotion detection and analysis using various inputs and models. Emotions derived from speech and physiological ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00521-024-09426-2", "content": "by EMG Younis · 2024 · Cited by 41 — The fourth section examines emotion detection and analysis using various inputs and models. Emotions derived from speech and physiological ..."}
+{"idx": 7, "title": "LLM-based conversational agents for behaviour change ...", "date": "", "ddg_snippet": "by S Meyer · 2025 · Cited by 2 — This is followed by an overview of the study design ( Section 3.2 ), in which we also outline the conversational framework intended to maximise MI-adherence and ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1071581925000710", "content": "by S Meyer · 2025 · Cited by 2 — This is followed by an overview of the study design ( Section 3.2 ), in which we also outline the conversational framework intended to maximise MI-adherence and ..."}
+{"idx": 8, "title": "Advancing multimodal emotion recognition in big data through ...", "date": "", "ddg_snippet": "by AA Wafa · 2025 — Section 3 outlines the proposed framework , beginning with an overview of the MER task in Section 3 to show the framework as input and output in ...", "subpage_snippet": "", "source": "journalofbigdata.springeropen.com", "link": "https://journalofbigdata.springeropen.com/articles/10.1186/s40537-025-01264-w", "content": "by AA Wafa · 2025 — Section 3 outlines the proposed framework , beginning with an overview of the MER task in Section 3 to show the framework as input and output in ..."}
+{"idx": 9, "title": "Emotion recognition systems with electrodermal activity", "date": "", "ddg_snippet": "by TA D'AMELIO · 2025 · Cited by 1 — We conducted a systematic review and meta-analysis on electrodermal-activity-based emotion -recognition systems. Our findings suggest that arousal prediction ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231225015036", "content": "by TA D'AMELIO · 2025 · Cited by 1 — We conducted a systematic review and meta-analysis on electrodermal-activity-based emotion -recognition systems. Our findings suggest that arousal prediction ..."}
diff --git a/data/sampled_jsons/Section_3_synthetic_datasets_FD2_x_=_Likelihood_Based_Approach_to_Distribution_Regression.jsonl b/data/sampled_jsons/Section_3_synthetic_datasets_FD2_x_=_Likelihood_Based_Approach_to_Distribution_Regression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..33fe1490e25973a473ebaaa56df10f9ddf7bf0d4
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+++ b/data/sampled_jsons/Section_3_synthetic_datasets_FD2_x_=_Likelihood_Based_Approach_to_Distribution_Regression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1IyPRv1A0r", "content": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-."}
+{"idx": 3, "title": "A likelihood based approach to distribution regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — We investigated statistical properties of a likelihood - based conditional deep generative model for distribution regression in a scenario where ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — We investigated statistical properties of a likelihood - based conditional deep generative model for distribution regression in a scenario where ..."}
+{"idx": 4, "title": "Adaptive Distributed Kernel Ridge Regression: A Feasible ...", "date": "", "ddg_snippet": "by SB Lin · 2025 · Cited by 1 — In this section , we propose the adaptive parameter selection strategy for distributed kernel ridge regression , which is named AdaDKRR, to address the data silos ... 54 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume26/23-0806/23-0806.pdf", "content": "by SB Lin · 2025 · Cited by 1 — In this section , we propose the adaptive parameter selection strategy for distributed kernel ridge regression , which is named AdaDKRR, to address the data silos ... 54 pages"}
+{"idx": 5, "title": "Pure Differential Privacy for Functional Summaries with a ...", "date": "", "ddg_snippet": "by H Lin · 2024 · Cited by 1 — Numerical experiments on synthetic and real datasets demon- strate the effectiveness of the proposed mechanism. Keywords: Differential Privacy, Functional Data ...", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume25/22-1384/22-1384.pdf", "content": "by H Lin · 2024 · Cited by 1 — Numerical experiments on synthetic and real datasets demon- strate the effectiveness of the proposed mechanism. Keywords: Differential Privacy, Functional Data ..."}
+{"idx": 6, "title": "PrivBayes: Private Data Release via Bayesian Networks", "date": "", "ddg_snippet": "by JUN ZHANG · 2017 · Cited by 910 — (Data synthesis) We show how to generate synthetic data from the differentially private Bayesian network, without explicitly materializing the full-dimensional ...", "subpage_snippet": "", "source": "dimacs.rutgers.edu", "link": "http://dimacs.rutgers.edu/~graham/pubs/papers/privbayes-tods.pdf", "content": "by JUN ZHANG · 2017 · Cited by 910 — (Data synthesis) We show how to generate synthetic data from the differentially private Bayesian network, without explicitly materializing the full-dimensional ..."}
+{"idx": 7, "title": "Dimension Agnostic Testing of Survey Data Credibility through ...", "date": "", "ddg_snippet": "Abstract Assessing whether a sample survey credibly represents the population is a crit-ical question for ensuring the validity of downstream research. Generally, this problem reduces to estimating the distance between two high-dimensional distri-butions, which typically requires a number of samples that grows exponentially with the dimension. However, depending on the model used for data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.20616v1", "content": "Abstract Assessing whether a sample survey credibly represents the population is a crit-ical question for ensuring the validity of downstream research. Generally, this problem reduces to estimating the distance between two high-dimensional distri-butions, which typically requires a number of samples that grows exponentially with the dimension. However, depending on the model used for data ..."}
+{"idx": 8, "title": "On the use of trajectory data for tackling data scarcity", "date": "", "ddg_snippet": "by G Pons · 2025 · Cited by 1 — In this paper, we propose the use of these data to tackle the data scarcity problem in data analysis by appropriately transforming them to extract relevant ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0306437925000080", "content": "by G Pons · 2025 · Cited by 1 — In this paper, we propose the use of these data to tackle the data scarcity problem in data analysis by appropriately transforming them to extract relevant ..."}
+{"idx": 9, "title": "From Logical Definitions to Quantitative Metrics", "date": "", "ddg_snippet": "In this work, we establish algebraic relationships between logical definitions and quantitative metrics to derive theoretically grounded disentanglement metrics ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93305", "content": "In this work, we establish algebraic relationships between logical definitions and quantitative metrics to derive theoretically grounded disentanglement metrics ..."}
diff --git a/data/sampled_jsons/Section_4.1_Playground_V2.5_SDXL_private_dataset_training_data_GenAI_Arena.jsonl b/data/sampled_jsons/Section_4.1_Playground_V2.5_SDXL_private_dataset_training_data_GenAI_Arena.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fadae9dae18a690e6c6d1d52ecbc4708574e8039
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+++ b/data/sampled_jsons/Section_4.1_Playground_V2.5_SDXL_private_dataset_training_data_GenAI_Arena.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenAI Arena: An Open Evaluation Platform for Generative ...", "date": "", "ddg_snippet": "24 Oct 2024 — 5 , released by Playground .ai, which follows the same architecture as SDXL but is trained with a private dataset . In contrast, SDXL only ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/97878", "content": "24 Oct 2024 — 5 , released by Playground .ai, which follows the same architecture as SDXL but is trained with a private dataset . In contrast, SDXL only ..."}
+{"idx": 1, "title": "GenAI Arena: An Open Evaluation Platform for Generative ...", "date": "", "ddg_snippet": "6 Jun 2024 — Playground V2 and Playground V2.5 are based on SDXL architecture, but trained by Playground.ai from scratch with an internal dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v1", "content": "6 Jun 2024 — Playground V2 and Playground V2.5 are based on SDXL architecture, but trained by Playground.ai from scratch with an internal dataset."}
+{"idx": 2, "title": "GenAI For Developers | PDF | Artificial Intelligence", "date": "", "ddg_snippet": "11 Jul 2025 — The document provides an overview of the Transformer architecture and its significance in Generative AI and Large Language Models (LLMs).", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/887022171/GenAI-for-Developers", "content": "11 Jul 2025 — The document provides an overview of the Transformer architecture and its significance in Generative AI and Large Language Models (LLMs)."}
+{"idx": 3, "title": "fdaudens/hf-blog-posts · Datasets at Hugging Face", "date": "", "ddg_snippet": "This project addresses the critical need for advancement in Hebrew NLP. As Hebrew is considered a low-resource language, existing LLM leaderboards often ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/fdaudens/hf-blog-posts/viewer/default/train", "content": "This project addresses the critical need for advancement in Hebrew NLP. As Hebrew is considered a low-resource language, existing LLM leaderboards often ..."}
+{"idx": 4, "title": "Daily Papers", "date": "", "ddg_snippet": "In this paper, we evaluate the robustness of one such algorithm known as Efficient NAS (ENAS) against data agnostic poisoning attacks on the original search ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=multiple-instance+poisoning+attacks", "content": "In this paper, we evaluate the robustness of one such algorithm known as Efficient NAS (ENAS) against data agnostic poisoning attacks on the original search ..."}
+{"idx": 5, "title": "taishi-i/awesome-ChatGPT-repositories", "date": "", "ddg_snippet": "helix - ♾️ Helix is a private GenAI stack for building AI applications with declarative pipelines, knowledge (RAG), API bindings, and first-class testing.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/taishi-i/awesome-ChatGPT-repositories", "content": "helix - ♾️ Helix is a private GenAI stack for building AI applications with declarative pipelines, knowledge (RAG), API bindings, and first-class testing."}
+{"idx": 6, "title": "📅 ThursdAI - Feb 29 - Leap Year Special ✨", "date": "", "ddg_snippet": "Suhail Doshi and Playground release a new foundational image model called Playground v2 . 5 and it looks awesome, very realistic and honestly looks ...", "subpage_snippet": "", "source": "sub.thursdai.news", "link": "https://sub.thursdai.news/p/thursdai-feb-29-leap-year-special", "content": "Suhail Doshi and Playground release a new foundational image model called Playground v2 . 5 and it looks awesome, very realistic and honestly looks ..."}
+{"idx": 7, "title": "📅 ThursdAI - Mar 7 - Anthropic gives us Claude 3, Elon vs", "date": "", "ddg_snippet": "... can get! Between Modal, Fal and Fireworks, I somehow started a performance competition that got these folks to serve Playground 2. 5 model in sub 1 . 5 ...", "subpage_snippet": "", "source": "sub.thursdai.news", "link": "https://sub.thursdai.news/p/thursdai-mar-7-anthropic-gives-us", "content": "... can get! Between Modal, Fal and Fireworks, I somehow started a performance competition that got these folks to serve Playground 2. 5 model in sub 1 . 5 ..."}
+{"idx": 8, "title": "Fantastic Copyrighted Beasts and How (Not) to Generate Them", "date": "", "ddg_snippet": "Figure 1 : Examples of copyrighted characters generated by the open-source Playground v2 . 5 model (Li et al., 2024a ) and proprietary DALL·E 3 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14526v2", "content": "Figure 1 : Examples of copyrighted characters generated by the open-source Playground v2 . 5 model (Li et al., 2024a ) and proprietary DALL·E 3 ..."}
+{"idx": 9, "title": "[AINews] Liquid Foundation Models: A New Transformers", "date": "", "ddg_snippet": "... A study on o1-preview model in medical scenarios showed that it surpasses GPT- 4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two ...", "subpage_snippet": "", "source": "buttondown.com", "link": "https://buttondown.com/ainews/archive/ainews-liquid-foundation-models-a-new/", "content": "... A study on o1-preview model in medical scenarios showed that it surpasses GPT- 4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two ..."}
diff --git a/data/sampled_jsons/Section_6.2_MATH_task_Pythia-1B_poor_performance_reason_sitearxiv.org.jsonl b/data/sampled_jsons/Section_6.2_MATH_task_Pythia-1B_poor_performance_reason_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0fb27379105640c95996b92863c9ac2b43dde736
--- /dev/null
+++ b/data/sampled_jsons/Section_6.2_MATH_task_Pythia-1B_poor_performance_reason_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2408.15079v1 [cs.CL] 27 Aug 2024", "date": "", "ddg_snippet": "by G Dong · 2024 · Cited by 8 — poor performance on scalability, precision, and recall, making them ... These labels are used to further ex- plore the potential on mathematics of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.15079", "content": "by G Dong · 2024 · Cited by 8 — poor performance on scalability, precision, and recall, making them ... These labels are used to further ex- plore the potential on mathematics of ..."}
+{"idx": 1, "title": "Robust Reinforcement Learning from Human Feedback for ...", "date": "", "ddg_snippet": "9 Apr 2025 — In this paper, we propose a robust algorithm to enhance the performance of existing approaches under such reward model misspecifications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.03784v2", "content": "9 Apr 2025 — In this paper, we propose a robust algorithm to enhance the performance of existing approaches under such reward model misspecifications."}
+{"idx": 2, "title": "Towards Understanding the Influence of Reward Margin on ...", "date": "", "ddg_snippet": "7 Apr 2024 — This leads to outputs that are either linguistically poor or overly verbose and misaligned with human preferences. It also complicates ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.04932v1", "content": "7 Apr 2024 — This leads to outputs that are either linguistically poor or overly verbose and misaligned with human preferences. It also complicates ..."}
+{"idx": 3, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "by R Greenblatt · 2024 · Cited by 25 — 2024) model fine-tuned to imitate poor answers generated by Pythia - 1B (Biderman et al., ... bad at MATH , it fails to explore good solutions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.19550?", "content": "by R Greenblatt · 2024 · Cited by 25 — 2024) model fine-tuned to imitate poor answers generated by Pythia - 1B (Biderman et al., ... bad at MATH , it fails to explore good solutions ..."}
+{"idx": 4, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "29 May 2024 — ... results in poor exploration, which slows down and sometimes entirely prevents learning. For example, because Pythia - 1B (the π weak ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "29 May 2024 — ... results in poor exploration, which slows down and sometimes entirely prevents learning. For example, because Pythia - 1B (the π weak ..."}
+{"idx": 5, "title": "LoRASuite: Efficient LoRA Adaptation Across Large ...", "date": "", "ddg_snippet": "by Y Li · 2025 — Figure 4: Average performance on math tasks under different settings for the MiniCPM-S- 1B to. MiniCPM-2B upgrade. Different LFT data scale ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.13515", "content": "by Y Li · 2025 — Figure 4: Average performance on math tasks under different settings for the MiniCPM-S- 1B to. MiniCPM-2B upgrade. Different LFT data scale ..."}
+{"idx": 6, "title": "Not quite Sherlock Holmes: Language model predictions ...", "date": "", "ddg_snippet": "by JA Michaelov · 2025 — All experiments were carried out on a computing cluster on NVIDIA A100 GPUs in under 2 GPU hours. ... Table 9: Pythia 1B scores on the English- ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.06808", "content": "by JA Michaelov · 2025 — All experiments were carried out on a computing cluster on NVIDIA A100 GPUs in under 2 GPU hours. ... Table 9: Pythia 1B scores on the English- ..."}
+{"idx": 7, "title": "Fairshare Data Pricing for Large Language Models", "date": "", "ddg_snippet": "by C Jiao — Model: Pythia - 1b ; Task : MedQA. effective for the high-budget ... (a) Low -budget buyer (MedAQ, Pythia - 1b ). 0 20 40 60 80100. Time Steps.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00198", "content": "by C Jiao — Model: Pythia - 1b ; Task : MedQA. effective for the high-budget ... (a) Low -budget buyer (MedAQ, Pythia - 1b ). 0 20 40 60 80100. Time Steps."}
+{"idx": 8, "title": "Continual Learning of Large Language Models", "date": "", "ddg_snippet": "25 Nov 2024 — Another line of work, Temporal Language Models (TLMs), takes a different approach to address knowledge retention, acquisition, and update under ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.16789v3", "content": "25 Nov 2024 — Another line of work, Temporal Language Models (TLMs), takes a different approach to address knowledge retention, acquisition, and update under ..."}
+{"idx": 9, "title": "Rule-based Data Selection for Large Language Models", "date": "", "ddg_snippet": "7 Oct 2024 — Rule Evaluation Metric: We introduce a novel rule evaluation metric designed to promote low correlation and high diversity among rules. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.04715v1", "content": "7 Oct 2024 — Rule Evaluation Metric: We introduce a novel rule evaluation metric designed to promote low correlation and high diversity among rules. We ..."}
diff --git a/data/sampled_jsons/Self-Repellent_Random_Walk_SRRW_problems_issues_disadvantages.jsonl b/data/sampled_jsons/Self-Repellent_Random_Walk_SRRW_problems_issues_disadvantages.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bfbaf703d1031a8384b7ca097d5c1fe032c99920
--- /dev/null
+++ b/data/sampled_jsons/Self-Repellent_Random_Walk_SRRW_problems_issues_disadvantages.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Accelerating Distributed Stochastic Optimization via Self- ...", "date": "", "ddg_snippet": "by J Hu · Cited by 4 — SIAM Journal on control and optimization, 44(1), 283-312, 2005. [2]. Doshi, V., Hu, J., & Eun, D. Y. Self - Repellent Random Walks on General ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BV1PHbTJzd", "content": "by J Hu · Cited by 4 — SIAM Journal on control and optimization, 44(1), 283-312, 2005. [2]. Doshi, V., Hu, J., & Eun, D. Y. Self - Repellent Random Walks on General ..."}
+{"idx": 1, "title": "Beyond random walk and metropolis-hastings samplers", "date": "", "ddg_snippet": "K(2024) Self - repellent random walks on general graphs - achieving minimal sampling variance via nonlinear markov chains (extended abstract)Proceedings of the ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/2254756.2254795", "content": "K(2024) Self - repellent random walks on general graphs - achieving minimal sampling variance via nonlinear markov chains (extended abstract)Proceedings of the ..."}
+{"idx": 2, "title": "quantum random walks: Topics by ...", "date": "", "ddg_snippet": "Self-Trapping Self - Repelling Random Walks · NASA Astrophysics Data System (ADS) ... issues of stability, overlap, coherence, and control. We have been ...", "subpage_snippet": "", "source": "www.science.gov", "link": "https://www.science.gov/topicpages/q/quantum+random+walks", "content": "Self-Trapping Self - Repelling Random Walks · NASA Astrophysics Data System (ADS) ... issues of stability, overlap, coherence, and control. We have been ..."}
+{"idx": 3, "title": "Self - Repellent Random Walks on General Graphs - Achieving...", "date": "", "ddg_snippet": "Chen, J. Two particles’ repelling random walks on the complete graph. Electronic Journal of Probability, 19 (none):1 – 17, 2014. Chen, T.-L. and Hwang, C.-R. Accelerating reversible markov chains.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=450iImFM4U", "content": "Chen, J. Two particles’ repelling random walks on the complete graph. Electronic Journal of Probability, 19 (none):1 – 17, 2014. Chen, T.-L. and Hwang, C.-R. Accelerating reversible markov chains."}
+{"idx": 4, "title": "ICML Poster Beyond Self - Repellent Kernels: History-Driven Target...", "date": "", "ddg_snippet": "With broad applications in network science and distributed optimization, recent innovations like the self - repellent random walk ( SRRW ) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46659", "content": "With broad applications in network science and distributed optimization, recent innovations like the self - repellent random walk ( SRRW ) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies."}
+{"idx": 5, "title": "Interphase chromatin as a self -returning random walk : Can... | bioRxiv", "date": "", "ddg_snippet": "We introduce a self -returning random walk to describe the structure of interphase chromatin.This self -returning random walk ( SRRW ) organizes its trajectory into clusters of random , tree-like topology, with the inter-cluster contacts boosted by self -returning.", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/413872v1.full", "content": "We introduce a self -returning random walk to describe the structure of interphase chromatin.This self -returning random walk ( SRRW ) organizes its trajectory into clusters of random , tree-like topology, with the inter-cluster contacts boosted by self -returning."}
+{"idx": 6, "title": "How Self - Repellent Random Walks operate part1(Best Bets... | Medium", "date": "", "ddg_snippet": "2/ Accelerating Distributed Stochastic Optimization via Self - Repellent Random Walks .Typically, these random - walks are chosen to be Markov chains that asymptotically sample from a desired target distribution, and play a critical role in the convergence of the optimization iterates.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@monocosmo77/how-self-repellent-random-walks-operate-part1-best-bets-for-machine-learning-research-b1b2de868d01", "content": "2/ Accelerating Distributed Stochastic Optimization via Self - Repellent Random Walks .Typically, these random - walks are chosen to be Markov chains that asymptotically sample from a desired target distribution, and play a critical role in the convergence of the optimization iterates."}
+{"idx": 7, "title": "Fluctuations of the local times of the self - repelling random walk with...", "date": "", "ddg_snippet": "Tóth and Vető defined a self - repelling random walk with directed edges, which shows peculiar behavior as its local time process converges to a deterministic one.", "subpage_snippet": "", "source": "www.peeref.com", "link": "https://www.peeref.com/works/28180044", "content": "Tóth and Vető defined a self - repelling random walk with directed edges, which shows peculiar behavior as its local time process converges to a deterministic one."}
+{"idx": 8, "title": "Chromatin as self -returning walks : From population to single cell and...", "date": "", "ddg_snippet": "affects the population-level domains folded by the SRRW model (Fig. 2 a). These domains are flanked on either side by a random walk boundary polymer to compare the SRRW domain against a random walk polymer.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9680733/", "content": "affects the population-level domains folded by the SRRW model (Fig. 2 a). These domains are flanked on either side by a random walk boundary polymer to compare the SRRW domain against a random walk polymer."}
+{"idx": 9, "title": "The \"True\" Self -Avoiding Random Walk in Z", "date": "", "ddg_snippet": "The \"true\" self -avoiding walk is a natural example of non-Markovian random walks ..) This model was introduced in the following paper, but the problem is still open. D. Amit, G. Parisi, and L. Peliti, \"Asymptotic Behaviour of the \"True\" Self -Avoiding Walk ,\" Phys.", "subpage_snippet": "", "source": "www.wolframcloud.com", "link": "https://www.wolframcloud.com/obj/df27292a-c5e3-40a5-b084-41576a920428?src=CloudBasicCopiedContent", "content": "The \"true\" self -avoiding walk is a natural example of non-Markovian random walks ..) This model was introduced in the following paper, but the problem is still open. D. Amit, G. Parisi, and L. Peliti, \"Asymptotic Behaviour of the \"True\" Self -Avoiding Walk ,\" Phys."}
diff --git a/data/sampled_jsons/Self-refine_Iterative_Refinement_with_Self-Feedback_Madaan_et_al._2024_year_2024.jsonl b/data/sampled_jsons/Self-refine_Iterative_Refinement_with_Self-Feedback_Madaan_et_al._2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a0f4add42ded4f8f6ac1ed6c0c68275cd292cc7c
--- /dev/null
+++ b/data/sampled_jsons/Self-refine_Iterative_Refinement_with_Self-Feedback_Madaan_et_al._2024_year_2024.jsonl
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+{"idx": 1, "title": "Log In To Your Self Financial Account", "date": "", "ddg_snippet": "Use this page to access your account at Self Financial, Inc., formerly known as Self Lender.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/login", "content": "Use this page to access your account at Self Financial, Inc., formerly known as Self Lender."}
+{"idx": 2, "title": "Sign Up For Your Credit Builder Account - Self", "date": "", "ddg_snippet": "Use Self to build credit while you save! Your Self Credit Builder Account includes credit education to help you reach your goals.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/signup", "content": "Use Self to build credit while you save! Your Self Credit Builder Account includes credit education to help you reach your goals."}
+{"idx": 3, "title": "How can we help? - support.self.inc", "date": "", "ddg_snippet": "Self Financial Public Community Home Back to Self .inc | Support: 1 (877) 883-0999", "subpage_snippet": "", "source": "support.self.inc", "link": "https://support.self.inc/s/", "content": "Self Financial Public Community Home Back to Self .inc | Support: 1 (877) 883-0999"}
+{"idx": 4, "title": "Credit Builder Loans to Build Credit | Self", "date": "", "ddg_snippet": "Elevate your credit score with Self 's Credit Builder Account. Accessible options to build credit, no credit check, & reports to all bureaus.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/credit-builder-loan", "content": "Elevate your credit score with Self 's Credit Builder Account. Accessible options to build credit, no credit check, & reports to all bureaus."}
+{"idx": 5, "title": "The Self Card — unlocked for everyone.", "date": "", "ddg_snippet": "Explore the secured Self Visa® Credit Card. Build your credit & payment history, plus no hard pull.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/visa-secured-credit-card", "content": "Explore the secured Self Visa® Credit Card. Build your credit & payment history, plus no hard pull."}
+{"idx": 6, "title": "Leading Credit Building Company - About Self Financial", "date": "", "ddg_snippet": "Hi. We're Self . We're here to help you build credit and savings and reach your financial goals. It all started with some missed payments. Self began in 2015 after a mistake with Founder James Garvey’s credit card that tanked his credit score.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/about", "content": "Hi. We're Self . We're here to help you build credit and savings and reach your financial goals. It all started with some missed payments. Self began in 2015 after a mistake with Founder James Garvey’s credit card that tanked his credit score."}
+{"idx": 7, "title": "Self - Credit Builder Loans by Self - Credit Building App Online", "date": "", "ddg_snippet": "Self helps you build credit with credit builder loans. A credit builder loan (or account) is a tiny loan that you have to save in a CD.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/home/dashboard", "content": "Self helps you build credit with credit builder loans. A credit builder loan (or account) is a tiny loan that you have to save in a CD."}
+{"idx": 8, "title": "Turn your rent payments into Credit Building opportunities - Self", "date": "", "ddg_snippet": "Self is your partner in building credit - providing you with the toolbox you need to build credit. We believe that you work hard for your money, so let your money work hard for you.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/learn/product/rent-reporting", "content": "Self is your partner in building credit - providing you with the toolbox you need to build credit. We believe that you work hard for your money, so let your money work hard for you."}
+{"idx": 9, "title": "Contact Support", "date": "", "ddg_snippet": "Self Financial Public Community Home Back to Self .inc | Support: 1 (877) 883-0999 Expand search", "subpage_snippet": "", "source": "support.self.inc", "link": "https://support.self.inc/s/contactsupport", "content": "Self Financial Public Community Home Back to Self .inc | Support: 1 (877) 883-0999 Expand search"}
diff --git a/data/sampled_jsons/Self-refine_Madaan_abstract_general_NLP_framework_tasks_year_2024.jsonl b/data/sampled_jsons/Self-refine_Madaan_abstract_general_NLP_framework_tasks_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7425077b3bfe547664c34d233d146b6b6a412346
--- /dev/null
+++ b/data/sampled_jsons/Self-refine_Madaan_abstract_general_NLP_framework_tasks_year_2024.jsonl
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+{"idx": 0, "title": "Beyond Completion: A Foundation Model for General Knowledge", "date": "", "ddg_snippet": "We propose a novel framework for addressing in-KG and out-of-KG reasoning tasks , integrating textual and structural modalities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.21926v1", "content": "We propose a novel framework for addressing in-KG and out-of-KG reasoning tasks , integrating textual and structural modalities."}
+{"idx": 1, "title": "ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM", "date": "", "ddg_snippet": "... refine the agent through a ReST-like method that ... We build a flavor of ReAct agent with self -critique for the task of long-form question answering.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.10003v1", "content": "... refine the agent through a ReST-like method that ... We build a flavor of ReAct agent with self -critique for the task of long-form question answering."}
+{"idx": 2, "title": "CoT-Driven Framework for Short Text Classification: Enhancing", "date": "", "ddg_snippet": "Additionally, we introduce the CoT-Driven Multi- Task learning (CDMT) framework , aimed at enhancing smaller models 1 1 1 In our study, we define ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.03158v2", "content": "Additionally, we introduce the CoT-Driven Multi- Task learning (CDMT) framework , aimed at enhancing smaller models 1 1 1 In our study, we define ..."}
+{"idx": 3, "title": "Lectures | 11-711 ANLP", "date": "", "ddg_snippet": "Reference: Self -Consistency Improves Chain of Thought Reasoning in Language Models (Wang et al. ... Reference: Bottom-up Abstractive Summarization ...", "subpage_snippet": "", "source": "phontron.com", "link": "https://phontron.com/class/anlp2024/lectures/", "content": "Reference: Self -Consistency Improves Chain of Thought Reasoning in Language Models (Wang et al. ... Reference: Bottom-up Abstractive Summarization ..."}
+{"idx": 4, "title": "Let Me Teach You: Pedagogical Foundations of Feedback for", "date": "", "ddg_snippet": "... framework , specifically adapted for LLMs; (iii) propose a general and extensive taxonomy of feedback content; and (iv) propose areas of future ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.00279v3", "content": "... framework , specifically adapted for LLMs; (iii) propose a general and extensive taxonomy of feedback content; and (iv) propose areas of future ..."}
+{"idx": 5, "title": "Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via", "date": "", "ddg_snippet": "... framework designed to enhance the reasoning capabilities of slow-thinking LLMs by fostering explicit self -critique and iterative refinement of their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21285v1", "content": "... framework designed to enhance the reasoning capabilities of slow-thinking LLMs by fostering explicit self -critique and iterative refinement of their ..."}
+{"idx": 6, "title": "Ramakanth Pasunuru's research works", "date": "", "ddg_snippet": "... NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Ramakanth-Pasunuru-2126669025", "content": "... NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model ..."}
+{"idx": 7, "title": "RL4F: Generating Natural Language Feedback with Reinforcement", "date": "", "ddg_snippet": "... RL4F (Reinforcement Learning for Feedback), a multi-agent collaborative framework where the critique generator is trained to maximize end- task ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370775704_RL4F_Generating_Natural_Language_Feedback_with_Reinforcement_Learning_for_Repairing_Model_Outputs", "content": "... RL4F (Reinforcement Learning for Feedback), a multi-agent collaborative framework where the critique generator is trained to maximize end- task ..."}
+{"idx": 8, "title": "Tatiana Anikina - ACL Anthology", "date": "", "ddg_snippet": "We validate Cross- Refine across three NLP tasks using three state-of-the-art open-source LLMs through automatic and human evaluation.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/t/tatiana-anikina/", "content": "We validate Cross- Refine across three NLP tasks using three state-of-the-art open-source LLMs through automatic and human evaluation."}
+{"idx": 9, "title": "Proceedings of the 2022 Conference on Empirical Methods in", "date": "", "ddg_snippet": "TweetNLP supports a diverse set of NLP tasks , including generic focus areas such as sentiment analysis and named entity recognition, as well as ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2022.emnlp-demos/", "content": "TweetNLP supports a diverse set of NLP tasks , including generic focus areas such as sentiment analysis and named entity recognition, as well as ..."}
diff --git a/data/sampled_jsons/Setlur_Garg_RL_Incorrect_Synthetic_Data_per-step_DPO_formal_algorithm.jsonl b/data/sampled_jsons/Setlur_Garg_RL_Incorrect_Synthetic_Data_per-step_DPO_formal_algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..14bd0c0c560ed0d8d4ac1168a57a3f206d3d1b03
--- /dev/null
+++ b/data/sampled_jsons/Setlur_Garg_RL_Incorrect_Synthetic_Data_per-step_DPO_formal_algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "Authors Amrith Setlur , Saurabh Garg , Xinyang (Young) Geng, Naman Garg , Virginia Smith, Aviral Kumar Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "Authors Amrith Setlur , Saurabh Garg , Xinyang (Young) Geng, Naman Garg , Virginia Smith, Aviral Kumar Abstract Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our ..."}
+{"idx": 1, "title": "RLonIncorrectSyntheticDataScalesthe ...", "date": "", "ddg_snippet": "QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step -level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step -level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "Jun 20, 2024 · We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone.", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "Jun 20, 2024 · We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits robustness benefits of RL over imitating positive data alone."}
+{"idx": 3, "title": "GitHub - dvlab-research/Step-DPO: Implementation for \"Step ...", "date": "", "ddg_snippet": "This repo provides the implementation of Step - DPO , a simple, effective, and data -efficient method for boosting the long-chain reasoning ability of LLMs, with a data construction pipeline that yields a high-quality dataset containing 10K step -wise preference pairs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dvlab-research/Step-DPO", "content": "This repo provides the implementation of Step - DPO , a simple, effective, and data -efficient method for boosting the long-chain reasoning ability of LLMs, with a data construction pipeline that yields a high-quality dataset containing 10K step -wise preference pairs."}
+{"idx": 4, "title": "Unearthing Gems from Stones: Policy Optimization with Negative", "date": "", "ddg_snippet": "We introduce BCPG-NSA, an effective offline RL training framework that integrates reasoning step segmentation, consensus-based LLM-PRM annotation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14403v1", "content": "We introduce BCPG-NSA, an effective offline RL training framework that integrates reasoning step segmentation, consensus-based LLM-PRM annotation ..."}
+{"idx": 5, "title": "ExPO: Unlocking Hard Reasoning with Self-Explanation-Guided", "date": "", "ddg_snippet": "RL -style post-training encompasses a broad family of algorithms , including reward-maximizing reinforcement learning (e.g., GRPO) [ 6 , 7 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02834v1", "content": "RL -style post-training encompasses a broad family of algorithms , including reward-maximizing reinforcement learning (e.g., GRPO) [ 6 , 7 ..."}
+{"idx": 6, "title": "o1 and Reasoning | AndoLogs", "date": "", "ddg_snippet": "It suggests that rather than using complex hand-crafted search algorithms or explicit verification steps , OpenAI has found a way to use RL to get ...", "subpage_snippet": "", "source": "blog.ando.ai", "link": "https://blog.ando.ai/posts/o1-and-reasoning/", "content": "It suggests that rather than using complex hand-crafted search algorithms or explicit verification steps , OpenAI has found a way to use RL to get ..."}
+{"idx": 7, "title": "Reinforcement Learning for LLM Reasoning - cs224r.stanford.edu", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur , Garg , Geng, Garg , Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur , Garg , Geng, Garg , Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning"}
+{"idx": 8, "title": "ICML Learning to Reason by Failing: Offline RL on Sub-optimal ...", "date": "", "ddg_snippet": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits benefits of RL over imitating positive data alone.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/38033", "content": "We show that training on per-step negatives can help to unlearn spurious correlations in the positive data , and is equivalent to advantage-weighted reinforcement learning ( RL ), implying that it inherits benefits of RL over imitating positive data alone."}
+{"idx": 9, "title": "Learning to Reason by Failing: Offline RL on Sub-optimal ...", "date": "", "ddg_snippet": "Jun 13, 2024 · This paper studies the scaling laws of two types of synthetic positive responses for training. It provides a valuable insight into the effectiveness of the self-generated responses and its shortages, which could benefit the scope of scaling synthetic data to improve LLMs. This paper suggests to use per-step DPO to incorporate negative synthetic responses to address the spurious pattern issue ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=v2PV1yCFJk", "content": "Jun 13, 2024 · This paper studies the scaling laws of two types of synthetic positive responses for training. It provides a valuable insight into the effectiveness of the self-generated responses and its shortages, which could benefit the scope of scaling synthetic data to improve LLMs. This paper suggests to use per-step DPO to incorporate negative synthetic responses to address the spurious pattern issue ..."}
diff --git a/data/sampled_jsons/Setlur_Garg_per-step_DPO_methodology_algorithm_pseudocode.jsonl b/data/sampled_jsons/Setlur_Garg_per-step_DPO_methodology_algorithm_pseudocode.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5bf3d660ea24222d512a0a952b035984b0a23cb
--- /dev/null
+++ b/data/sampled_jsons/Setlur_Garg_per-step_DPO_methodology_algorithm_pseudocode.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - dvlab-research/ Step - DPO : Implementation for \" Step - DPO ...\"", "date": "", "ddg_snippet": "This repo provides the implementation of Step - DPO , a simple, effective, and data-efficient method for boosting the long-chain reasoning ability of LLMs, with a data construction pipeline that yields a high-quality dataset containing 10K step -wise preference pairs. Notably, Step - DPO boosts the...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dvlab-research/Step-DPO", "content": "This repo provides the implementation of Step - DPO , a simple, effective, and data-efficient method for boosting the long-chain reasoning ability of LLMs, with a data construction pipeline that yields a high-quality dataset containing 10K step -wise preference pairs. Notably, Step - DPO boosts the..."}
+{"idx": 1, "title": "Step-DPO - a xinlai Collection - Hugging Face", "date": "", "ddg_snippet": "Jun 26, 2024 · Resources for \" Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs\"", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/xinlai/step-dpo-6682e12dfbbb2917c8161df7", "content": "Jun 26, 2024 · Resources for \" Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs\""}
+{"idx": 2, "title": "Step-DPO/README.md at main · dvlab-research/Step-DPO - GitHub", "date": "", "ddg_snippet": "Implementation for \" Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs\" - dvlab-research/ Step - DPO", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dvlab-research/Step-DPO/blob/main/README.md", "content": "Implementation for \" Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs\" - dvlab-research/ Step - DPO"}
+{"idx": 3, "title": "Step-DPO: Step-wise Preference Optimization for Long-chain ...", "date": "", "ddg_snippet": "Jun 26, 2024 · This limitation stems from a lack of fine-grained process supervision. We propose a simple, effective, and data-efficient method called Step - DPO , which treats individual reasoning steps as units for preference optimization rather than evaluating answers holistically.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.18629", "content": "Jun 26, 2024 · This limitation stems from a lack of fine-grained process supervision. We propose a simple, effective, and data-efficient method called Step - DPO , which treats individual reasoning steps as units for preference optimization rather than evaluating answers holistically."}
+{"idx": 4, "title": "Paper page - Step -Controlled DPO : Leveraging Stepwise Error ...", "date": "", "ddg_snippet": "Jul 2, 2024 · By applying these samples in DPO training, SCDPO can better align the model to understand reasoning errors and output accurate reasoning steps . We apply SCDPO to both code-integrated and chain-of-thought solutions, empirically showing that it consistently improves the performance compared to naive DPO on three different SFT models, including ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2407.00782", "content": "Jul 2, 2024 · By applying these samples in DPO training, SCDPO can better align the model to understand reasoning errors and output accurate reasoning steps . We apply SCDPO to both code-integrated and chain-of-thought solutions, empirically showing that it consistently improves the performance compared to naive DPO on three different SFT models, including ..."}
+{"idx": 5, "title": "2406.18629 - Step - DPO : Step -wise Preference Optimization for...", "date": "", "ddg_snippet": "In summary, the Step - DPO method represents a significant advancement in the field of LLMs, providing an effective solution for improving the accuracy and reliability of long-chain mathematical reasoning.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.18629", "content": "In summary, the Step - DPO method represents a significant advancement in the field of LLMs, providing an effective solution for improving the accuracy and reliability of long-chain mathematical reasoning."}
+{"idx": 6, "title": "How to Write Pseudocode ? A Beginner's Guide with Examples", "date": "", "ddg_snippet": "Pseudocode is an informal way of representing a computer algorithm or program in the simple English language. Learn how to write pseudocode in this article.", "subpage_snippet": "", "source": "www.techgeekbuzz.com", "link": "https://www.techgeekbuzz.com/blog/how-to-write-pseudocode/", "content": "Pseudocode is an informal way of representing a computer algorithm or program in the simple English language. Learn how to write pseudocode in this article."}
+{"idx": 7, "title": "[Literature Review] Bootstrapping Language Models with DPO Implicit...", "date": "", "ddg_snippet": "DPO simplifies traditional reinforcement learning from human feedback (RLHF) by eliminating the reward learning phase, thus streamlining the alignment process. DPO utilizes an implicit reward model, which serves as an evaluative mechanism for the responses generated by LLMs.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/bootstrapping-language-models-with-dpo-implicit-rewards", "content": "DPO simplifies traditional reinforcement learning from human feedback (RLHF) by eliminating the reward learning phase, thus streamlining the alignment process. DPO utilizes an implicit reward model, which serves as an evaluative mechanism for the responses generated by LLMs."}
+{"idx": 8, "title": "Step - DPO : Step -wise Preference Optimization for Long-chain...", "date": "", "ddg_snippet": "Presents a novel method called Step - DPO for optimizing the preferences of large language models (LLMs) for long-chain reasoning tasks.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/step-dpo-step-wise-preference-optimization-long", "content": "Presents a novel method called Step - DPO for optimizing the preferences of large language models (LLMs) for long-chain reasoning tasks."}
+{"idx": 9, "title": "Paper page - Step - DPO : Step -wise Preference Optimization for...", "date": "", "ddg_snippet": "Notably, Step - DPO , when applied to Qwen2-72B-Instruct, achieves scores of 70.8% and 94.0% on the test sets of MATH and GSM8K without bells and wistles, respectively, surpassing a series of closed-source models, including GPT-4-1106, Claude-3-Opus, and Gemini-1.5-Pro.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2406.18629", "content": "Notably, Step - DPO , when applied to Qwen2-72B-Instruct, achieves scores of 70.8% and 94.0% on the test sets of MATH and GSM8K without bells and wistles, respectively, surpassing a series of closed-source models, including GPT-4-1106, Claude-3-Opus, and Gemini-1.5-Pro."}
diff --git a/data/sampled_jsons/ShowUI_vision_language_action_model_GUI_automation_2024.jsonl b/data/sampled_jsons/ShowUI_vision_language_action_model_GUI_automation_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..586c511138fa308837a4c6f2e18a45b559b43bf5
--- /dev/null
+++ b/data/sampled_jsons/ShowUI_vision_language_action_model_GUI_automation_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ShowUI: One Vision-Language-Action Model for GUI Visual Agent", "date": "", "ddg_snippet": "Building Graphical User Interface ( GUI ) assistants holds significant promise for enhancing human workflow productivity. While most agents are language -based, relying on closed-source API with text-rich meta-information (e.g., HTML or accessibility tree), they show limitations in perceiving UI visuals as humans do, highlighting the need for GUI visual agents. In this work, we develop a vision ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.17465", "content": "Building Graphical User Interface ( GUI ) assistants holds significant promise for enhancing human workflow productivity. While most agents are language -based, relying on closed-source API with text-rich meta-information (e.g., HTML or accessibility tree), they show limitations in perceiving UI visuals as humans do, highlighting the need for GUI visual agents. In this work, we develop a vision ..."}
+{"idx": 1, "title": "GitHub - showlab/ShowUI: [CVPR 2025] Open-source, End-to-end, Vision ...", "date": "", "ddg_snippet": "Open-source, End-to-end, Lightweight, Vision-Language-Action model for GUI Agent & Computer Use. ShowUI 是一款开源的、端到端、轻量级的视觉-语言-动作模型，专为 GUI 智能体设计。 📑 Paper | 🤗 Hugging Models | 🤗 Spaces Demo | 📝 Slides | 🕹️ OpenBayes贝式计算 Demo 🤗 Datasets | 💬 X (Twitter) | 🖥️ Computer Use | 📖 GUI Paper List | 🤖 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/showlab/ShowUI", "content": "Open-source, End-to-end, Lightweight, Vision-Language-Action model for GUI Agent & Computer Use. ShowUI 是一款开源的、端到端、轻量级的视觉-语言-动作模型，专为 GUI 智能体设计。 📑 Paper | 🤗 Hugging Models | 🤗 Spaces Demo | 📝 Slides | 🕹️ OpenBayes贝式计算 Demo 🤗 Datasets | 💬 X (Twitter) | 🖥️ Computer Use | 📖 GUI Paper List | 🤖 ..."}
+{"idx": 2, "title": "ShowUI: One Vision-Language-Action Model for Generalist GUI Agent", "date": "", "ddg_snippet": "Inspired by the success of Vision-Language-Action (VLA) models in embodied environments, we explore their potential in the digital GUI world. In this work, we develop a recipe for training a VLA for GUI agent - ShowUI , a 4.2B parameter model based on Phi-3.5- vision -instruct.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=UXdxYnkJtX", "content": "Inspired by the success of Vision-Language-Action (VLA) models in embodied environments, we explore their potential in the digital GUI world. In this work, we develop a recipe for training a VLA for GUI agent - ShowUI , a 4.2B parameter model based on Phi-3.5- vision -instruct."}
+{"idx": 3, "title": "ShowUI: A Vision-Language-Action Model for GUI Visual Agents that ...", "date": "", "ddg_snippet": "ShowUI represents a significant advancement in vision-language-action models for GUI interactions. The researchers developed innovative solutions to address critical challenges in UI visual modeling and action processing.", "subpage_snippet": "", "source": "www.aiinteliigence.com", "link": "https://www.aiinteliigence.com/2024/12/02/showui-a-vision-language-action-model-for-gui-visual-agents-that-addresses-key-challenges-in-ui-visual-and-action-modeling/", "content": "ShowUI represents a significant advancement in vision-language-action models for GUI interactions. The researchers developed innovative solutions to address critical challenges in UI visual modeling and action processing."}
+{"idx": 4, "title": "ShowUI: One Vision-Language-Action Model for Generalist GUI Agent", "date": "", "ddg_snippet": "Poster in Workshop: Workshop on Open-World Agents: Synnergizing Reasoning and Decision-Making in Open-World Environments (OWA- 2024 ) ShowUI : One Vision-Language-Action Model for Generalist GUI Agent Kevin Qinghong Lin · Linjie Li · Difei Gao · Zhengyuan Yang · Zechen Bai · Weixian Lei · Lijuan Wang · Mike Zheng Shou", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/100941", "content": "Poster in Workshop: Workshop on Open-World Agents: Synnergizing Reasoning and Decision-Making in Open-World Environments (OWA- 2024 ) ShowUI : One Vision-Language-Action Model for Generalist GUI Agent Kevin Qinghong Lin · Linjie Li · Difei Gao · Zhengyuan Yang · Zechen Bai · Weixian Lei · Lijuan Wang · Mike Zheng Shou"}
+{"idx": 5, "title": "ShowUI: Advanced Open-Source Vision-Language-Action Model for GUI", "date": "", "ddg_snippet": "ShowUI is a vision-language-action model for GUI visual agents. It combines visual input, language understanding, and action prediction to allow more natural and efficient interactions with ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aimonks/showui-advanced-open-source-vision-language-action-model-for-gui-aea8c8f0223a", "content": "ShowUI is a vision-language-action model for GUI visual agents. It combines visual input, language understanding, and action prediction to allow more natural and efficient interactions with ..."}
+{"idx": 6, "title": "ShowUI: A New Vision-Language-Action Model for GUI Visual Agents", "date": "", "ddg_snippet": "A research paper published on arXiv introduces ShowUI , a novel vision-language-action model designed to address these limitations and create more effective GUI visual agents.", "subpage_snippet": "", "source": "ai-papers-reader.taodong.net", "link": "https://ai-papers-reader.taodong.net/2024-11-29/2411.17465/", "content": "A research paper published on arXiv introduces ShowUI , a novel vision-language-action model designed to address these limitations and create more effective GUI visual agents."}
+{"idx": 7, "title": "PDF ShowUI: One Vision-Language-Action Model for GUI Visual Agent", "date": "", "ddg_snippet": "We introduced ShowUI , a vision-language-action model for GUI visual agents that addresses key challenges in UI visual and action modeling, and instruction-tuning data cu-rations.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_ShowUI_One_Vision-Language-Action_Model_for_GUI_Visual_Agent_CVPR_2025_paper.pdf", "content": "We introduced ShowUI , a vision-language-action model for GUI visual agents that addresses key challenges in UI visual and action modeling, and instruction-tuning data cu-rations."}
+{"idx": 8, "title": "ShowUI: A Vision-Language-Action Model for GUI Visual Agents that ...", "date": "", "ddg_snippet": "Researchers from Show Lab, the National University of Singapore and Microsoft introduce ShowUI , a unique vision-language-action model designed to address key challenges in GUI automation . The model incorporates three innovative techniques: UI-Guided Visual Token Selection, which reduces computational costs by transforming screenshots into connected graphs and intelligently identifying ...", "subpage_snippet": "", "source": "theaisector.com", "link": "https://theaisector.com/2024/12/01/showui-a-vision-language-action-model-for-gui-visual-agents-that-addresses-key-challenges-in-ui-visual-and-action-modeling/", "content": "Researchers from Show Lab, the National University of Singapore and Microsoft introduce ShowUI , a unique vision-language-action model designed to address key challenges in GUI automation . The model incorporates three innovative techniques: UI-Guided Visual Token Selection, which reduces computational costs by transforming screenshots into connected graphs and intelligently identifying ..."}
+{"idx": 9, "title": "ShowUI from Microsoft: GUI Interaction with Vision-Language-Action AI", "date": "", "ddg_snippet": "A breakthrough in digital workflow assistants, bridging human-like perception and action for seamless GUI navigation. Enhanced Human-Like Interaction: ShowUI introduces a novel vision-language-action model , enabling more intuitive and effective GUI -based assistance. Efficient Data and Computational Innovations: The model leverages UI-guided visual token selection, significantly reducing ...", "subpage_snippet": "", "source": "neuronad.com", "link": "https://neuronad.com/ai-news/tech/showui-from-microsoft-gui-interaction-with-vision-language-action-ai/", "content": "A breakthrough in digital workflow assistants, bridging human-like perception and action for seamless GUI navigation. Enhanced Human-Like Interaction: ShowUI introduces a novel vision-language-action model , enabling more intuitive and effective GUI -based assistance. Efficient Data and Computational Innovations: The model leverages UI-guided visual token selection, significantly reducing ..."}
diff --git a/data/sampled_jsons/Sieve_MLE_FD3_dataset_Table_1_standard_deviation_MSE.jsonl b/data/sampled_jsons/Sieve_MLE_FD3_dataset_Table_1_standard_deviation_MSE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2526b75641a6399ff4bb2ef08be851600ea154b5
--- /dev/null
+++ b/data/sampled_jsons/Sieve_MLE_FD3_dataset_Table_1_standard_deviation_MSE.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Standard deviation - Wikipedia", "date": "", "ddg_snippet": "8. 1 Standard deviation of the mean .The standard deviation of a random variable, sample, statistical population, data set , or probability distribution is the square root of its variance.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Standard_deviation", "content": "8. 1 Standard deviation of the mean .The standard deviation of a random variable, sample, statistical population, data set , or probability distribution is the square root of its variance."}
+{"idx": 1, "title": "Standard Deviation - Formula | How to Calculate Standard Deviation ?", "date": "", "ddg_snippet": "We have different standard deviation formulas to find the standard deviation for sample, population, grouped data , and ungrouped data .", "subpage_snippet": "", "source": "www.cuemath.com", "link": "https://www.cuemath.com/data/standard-deviation/", "content": "We have different standard deviation formulas to find the standard deviation for sample, population, grouped data , and ungrouped data ."}
+{"idx": 2, "title": "Standard deviation : calculating step by step (article) | Khan Academy", "date": "", "ddg_snippet": "Lesson 4: Variance and standard deviation of a population. Mean and standard deviation versus median and IQR.", "subpage_snippet": "", "source": "www.khanacademy.org", "link": "https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/variance-standard-deviation-population/a/calculating-standard-deviation-step-by-step", "content": "Lesson 4: Variance and standard deviation of a population. Mean and standard deviation versus median and IQR."}
+{"idx": 3, "title": "Standard Deviation Formulas", "date": "", "ddg_snippet": "Standard Deviation Formulas. Deviation means how far from the normal.The Standard Deviation is a measure of how spread out numbers are. You might like to read this simpler page on Standard Deviation first . But here we explain the formulas.", "subpage_snippet": "", "source": "www.mathsisfun.com", "link": "https://www.mathsisfun.com/data/standard-deviation-formulas.html", "content": "Standard Deviation Formulas. Deviation means how far from the normal.The Standard Deviation is a measure of how spread out numbers are. You might like to read this simpler page on Standard Deviation first . But here we explain the formulas."}
+{"idx": 4, "title": "Standard Deviation Formula and Uses, vs. Variance", "date": "", "ddg_snippet": "Standard deviation is a statistic measuring the dispersion of a dataset relative to its mean . It is calculated as the square root of the variance. Learn how it's used.", "subpage_snippet": "", "source": "www.investopedia.com", "link": "https://www.investopedia.com/terms/s/standarddeviation.asp", "content": "Standard deviation is a statistic measuring the dispersion of a dataset relative to its mean . It is calculated as the square root of the variance. Learn how it's used."}
+{"idx": 5, "title": "How to Calculate Standard Deviation ? - GeeksforGeeks", "date": "", "ddg_snippet": "Standard Deviation is a measure of how data is spread out around the mean .In this article, we will discuss how to calculate Standard Deviation using a formula. Table of Content.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/maths/how-to-calculate-standard-deviation/", "content": "Standard Deviation is a measure of how data is spread out around the mean .In this article, we will discuss how to calculate Standard Deviation using a formula. Table of Content."}
+{"idx": 6, "title": "3 Regression Metrics You Must Know: MAE, MSE ... | Proclus Academy", "date": "", "ddg_snippet": "Root Mean Squared Error (RMSE). MAE vs. RMSE. Practice using Python & Scikit-Learn. Load Dataset .We’ll first train a model to predict a person’s weight based on height. Then we’ll calculate the metrics to evaluate the model. First off, let’s load the dataset using pandas", "subpage_snippet": "", "source": "proclusacademy.com", "link": "https://proclusacademy.com/blog/explainer/regression-metrics-you-must-know/", "content": "Root Mean Squared Error (RMSE). MAE vs. RMSE. Practice using Python & Scikit-Learn. Load Dataset .We’ll first train a model to predict a person’s weight based on height. Then we’ll calculate the metrics to evaluate the model. First off, let’s load the dataset using pandas"}
+{"idx": 7, "title": "How to Calculate Mean Squared Error ( MSE )... - Automate Excel", "date": "", "ddg_snippet": "The Mean Squared Error ( MSE ) is an estimate that measures the average squared difference between the estimated values and the actual values of a data distribution.The MSE of a set of observations is calculated using the formula: How to Calculate Mean Square Error 001.", "subpage_snippet": "", "source": "www.automateexcel.com", "link": "https://www.automateexcel.com/stats/calculate-mean-square-error/", "content": "The Mean Squared Error ( MSE ) is an estimate that measures the average squared difference between the estimated values and the actual values of a data distribution.The MSE of a set of observations is calculated using the formula: How to Calculate Mean Square Error 001."}
+{"idx": 8, "title": "Relative Standard Deviation Calculator - Inch Calculator", "date": "", "ddg_snippet": "Use our relative standard deviation calculator to find the RSD for a set of data . Plus, learn the formula and steps to find it.", "subpage_snippet": "", "source": "www.inchcalculator.com", "link": "https://www.inchcalculator.com/relative-standard-deviation-calculator/", "content": "Use our relative standard deviation calculator to find the RSD for a set of data . Plus, learn the formula and steps to find it."}
+{"idx": 9, "title": "Standard Deviation Calculator", "date": "", "ddg_snippet": "In the population standard deviation formula above, x is a data point, x (read \"x bar\") is the arithmetic mean , and n is the number of elements in the data set (count). Table of commonly used standard deviation cut-offs for normally distributed variables", "subpage_snippet": "", "source": "www.gigacalculator.com", "link": "https://www.gigacalculator.com/calculators/standard-deviation-calculator.php", "content": "In the population standard deviation formula above, x is a data point, x (read \"x bar\") is the arithmetic mean , and n is the number of elements in the data set (count). Table of commonly used standard deviation cut-offs for normally distributed variables"}
diff --git a/data/sampled_jsons/SigLIP_sigmoid_loss_contrastive_learning.jsonl b/data/sampled_jsons/SigLIP_sigmoid_loss_contrastive_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..617b0eb4fe94a77630df859465239d063fa5aa9d
--- /dev/null
+++ b/data/sampled_jsons/SigLIP_sigmoid_loss_contrastive_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "Contrastive learning has emerged as a prominent branch of self-supervised learning for several years. Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss . SigLIP achieves the performance comparable to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.12613", "content": "Contrastive learning has emerged as a prominent branch of self-supervised learning for several years. Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss . SigLIP achieves the performance comparable to ..."}
+{"idx": 1, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning - PMLR", "date": "", "ddg_snippet": "Contrastive learning has emerged as a prominent branch of self-supervised learning for several years. Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/lee24a.html", "content": "Contrastive learning has emerged as a prominent branch of self-supervised learning for several years. Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP , a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss ."}
+{"idx": 2, "title": "GitHub - filipbasara0/sigmoid-contrastive-loss: Implementation of ...", "date": "", "ddg_snippet": "The loss function is a sigmoid constrastive loss adapted from SigLIP , with an addition of a confidence penalty gamma that balances the ratio of positive and negative samples per batch, amplifying learning from harder examples and improving training stability.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/filipbasara0/sigmoid-contrastive-loss", "content": "The loss function is a sigmoid constrastive loss adapted from SigLIP , with an addition of a confidence penalty gamma that balances the ratio of positive and negative samples per batch, amplifying learning from harder examples and improving training stability."}
+{"idx": 3, "title": "SigLIP vs. CLIP: The Sigmoid Advantage | by heping_LU | Medium", "date": "", "ddg_snippet": "Contrastive Learning & Sigmoid Loss SigLIP outperforms CLIP at small batch sizes (e.g., 4-8k), but both reach saturation at 32k batch size despite claims that larger batches improve performance.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@jiangmen28/siglip-vs-clip-the-sigmoid-advantage-457f1cb872ab", "content": "Contrastive Learning & Sigmoid Loss SigLIP outperforms CLIP at small batch sizes (e.g., 4-8k), but both reach saturation at 32k batch size despite claims that larger batches improve performance."}
+{"idx": 4, "title": "SigLIP and SigLIP2 | google-research/big_vision | DeepWiki", "date": "", "ddg_snippet": "Introduction SigLIP and SigLIP2 are vision-language models that connect images and text through a contrastive learning approach. SigLIP introduced a sigmoid -based contrastive loss that improved upon the softmax approach used in earlier models like CLIP and LiT.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/google-research/big_vision/3.2-siglip-and-siglip2", "content": "Introduction SigLIP and SigLIP2 are vision-language models that connect images and text through a contrastive learning approach. SigLIP introduced a sigmoid -based contrastive loss that improved upon the softmax approach used in earlier models like CLIP and LiT."}
+{"idx": 5, "title": "[2303.15343] Sigmoid Loss for Language Image Pre-Training", "date": "", "ddg_snippet": "We propose a simple pairwise Sigmoid loss for Language-Image Pre-training ( SigLIP ). Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. The sigmoid loss simultaneously allows further scaling up the batch size, while also performing better at ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.15343", "content": "We propose a simple pairwise Sigmoid loss for Language-Image Pre-training ( SigLIP ). Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. The sigmoid loss simultaneously allows further scaling up the batch size, while also performing better at ..."}
+{"idx": 6, "title": "SigLIP: Sigmoid Loss in Language Image Pretraining", "date": "", "ddg_snippet": "Here's where SigLIP really shines because of the sigmoid loss : Simpler loss function: Unlike CLIP's cross-entropy loss , which deals with probabilities across multiple classes, SigLIP's sigmoid loss boils everything down to a simple yes or no.", "subpage_snippet": "", "source": "sushant-kumar.com", "link": "https://sushant-kumar.com/blog/siglip", "content": "Here's where SigLIP really shines because of the sigmoid loss : Simpler loss function: Unlike CLIP's cross-entropy loss , which deals with probabilities across multiple classes, SigLIP's sigmoid loss boils everything down to a simple yes or no."}
+{"idx": 7, "title": "Sigmoidal Large Image Pre-training (SigLIP) Encoder", "date": "", "ddg_snippet": "1. Principle of Sigmoid -Based Contrastive Learning Traditional large-scale vision-language pre-training approaches (e.g., CLIP) align image and text representations via a contrastive loss , typically relying on the InfoNCE (softmax-based) formulation. The SigLIP encoder replaces this global (batch-softmax) objective with a pairwise sigmoid loss that evaluates each image-text similarity ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/sigmoidal-large-image-pre-training-siglip-encoder", "content": "1. Principle of Sigmoid -Based Contrastive Learning Traditional large-scale vision-language pre-training approaches (e.g., CLIP) align image and text representations via a contrastive loss , typically relying on the InfoNCE (softmax-based) formulation. The SigLIP encoder replaces this global (batch-softmax) objective with a pairwise sigmoid loss that evaluates each image-text similarity ..."}
+{"idx": 8, "title": "GitHub - ramanakshay/clip: CLIP & SigLIP model training from scratch", "date": "", "ddg_snippet": "The SigLIP paper introduced a novel contrastive learning objective that performs better than softmax baselines, particularly for small batch sizes. To use the sigmoid loss , change loss under algorithm config to siglip from clip.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ramanakshay/clip", "content": "The SigLIP paper introduced a novel contrastive learning objective that performs better than softmax baselines, particularly for small batch sizes. To use the sigmoid loss , change loss under algorithm config to siglip from clip."}
+{"idx": 9, "title": "SigLIP: Sigmoid Loss for Language Image Pre-Training", "date": "", "ddg_snippet": "Sigmoid loss pseudo-implementation 우선 정규화된 text, image 표현을 내적하여 각 logits을 구합니다. 이후 대각선 값이 1, 나머지를 -1로 만든 label을 통해 element-wise multiplication로 각 값을 계산합니다. 결론적으로 Sigmoid 함수로 Contrastive Learning 과 유사한 효과를 낼 수 있습니다.", "subpage_snippet": "", "source": "taewan2002.medium.com", "link": "https://taewan2002.medium.com/siglip-sigmoid-loss-for-language-image-pre-training-aa68fedaa080", "content": "Sigmoid loss pseudo-implementation 우선 정규화된 text, image 표현을 내적하여 각 logits을 구합니다. 이후 대각선 값이 1, 나머지를 -1로 만든 label을 통해 element-wise multiplication로 각 값을 계산합니다. 결론적으로 Sigmoid 함수로 Contrastive Learning 과 유사한 효과를 낼 수 있습니다."}
diff --git a/data/sampled_jsons/SimXRD-4M_A.3_Data_Splitting.jsonl b/data/sampled_jsons/SimXRD-4M_A.3_Data_Splitting.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5209b488af9cbe450ce9db5ad23e5335693ee5e1
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_A.3_Data_Splitting.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "Additionally, published diffraction pattern datasets are saved in various formats, requiring specific data loaders provided by the authors. In contrast, the SimXRD-4M dataset developed in our work is fully accessible and features an easier workflow for machine learning training, easily integrable with TensorFlow or PyTorch frameworks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "Additionally, published diffraction pattern datasets are saved in various formats, requiring specific data loaders provided by the authors. In contrast, the SimXRD-4M dataset developed in our work is fully accessible and features an easier workflow for machine learning training, easily integrable with TensorFlow or PyTorch frameworks."}
+{"idx": 1, "title": "SimXRD-4M ICLR 2025 - GitHub", "date": "", "ddg_snippet": "Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are ..."}
+{"idx": 2, "title": "PDF SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance."}
+{"idx": 3, "title": "Simxrd-4m: B Simulated X-ray Diffraction D C Symmetry Classification", "date": "", "ddg_snippet": "ABSTRACT Powder X-ray diffraction (XRD) patterns are highly effective for crystal identi-fication and play a pivotal role in materials discovery. Although machine learn-ing (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established bench-marks. To address this, we introduce SimXRD-4M , the largest open-source ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mkuB677eMM", "content": "ABSTRACT Powder X-ray diffraction (XRD) patterns are highly effective for crystal identi-fication and play a pivotal role in materials discovery. Although machine learn-ing (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established bench-marks. To address this, we introduce SimXRD-4M , the largest open-source ..."}
+{"idx": 4, "title": "AI4Spectro (SimXRD) - Hugging Face", "date": "", "ddg_snippet": "SimXRD-4M databaseSimXRD- 4M Registration Utilize this page to register the utilization of SimXRD , thereby retrieving version information, backup data , and reference materials Introduction The SimXRD database is a comprehensive resource for spectral data analysis, designed to facilitate the identification of crystal materials both in and out library. Version V1.0.0 (May 2024) This version ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/AI4Spectro", "content": "SimXRD-4M databaseSimXRD- 4M Registration Utilize this page to register the utilization of SimXRD , thereby retrieving version information, backup data , and reference materials Introduction The SimXRD database is a comprehensive resource for spectral data analysis, designed to facilitate the identification of crystal materials both in and out library. Version V1.0.0 (May 2024) This version ..."}
+{"idx": 5, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal ...", "date": "", "ddg_snippet": "Spectroscopic data , particularly diffraction data , contain detailed crystal and microstructure information and thus are crucial for materials discovery. Powder X-ray diffraction (XRD) patterns are greatly effective in identifying crystals. Although machine learning (ML) has significantly advanced the analysis of powder XRD patterns, the progress is hindered by a lack of training data . To ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.15469", "content": "Spectroscopic data , particularly diffraction data , contain detailed crystal and microstructure information and thus are crucial for materials discovery. Powder X-ray diffraction (XRD) patterns are greatly effective in identifying crystals. Although machine learning (ML) has significantly advanced the analysis of powder XRD patterns, the progress is hindered by a lack of training data . To ..."}
+{"idx": 6, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "SimXRD-4M : Big Simulated X-ray Diffraction Data Accelerate the Crystalline Symmetry Classification Bin Cao 1,2∗ , Y ang Liu 1,3∗", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381665624_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Accelerate_the_Crystalline_Symmetry_Classification", "content": "SimXRD-4M : Big Simulated X-ray Diffraction Data Accelerate the Crystalline Symmetry Classification Bin Cao 1,2∗ , Y ang Liu 1,3∗"}
+{"idx": 7, "title": "Advancing Crystal Structure Analysis with SimXRD Dataset", "date": "", "ddg_snippet": "Title: SimXRD-4M : Big Simulated X-ray Diffraction Data Accelerate the Crystalline Symmetry Classification Abstract: Spectroscopic data , particularly diffraction data , contain detailed crystal and microstructure information and thus are crucial for materials discovery.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-advancing-crystal-structure-analysis-with-simxrd-dataset--ak4ex8o", "content": "Title: SimXRD-4M : Big Simulated X-ray Diffraction Data Accelerate the Crystalline Symmetry Classification Abstract: Spectroscopic data , particularly diffraction data , contain detailed crystal and microstructure information and thus are crucial for materials discovery."}
+{"idx": 8, "title": "ICLR Poster SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal ...", "date": "", "ddg_snippet": "Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established benchmarks. To address this, we introduce SimXRD , the largest open-source simulated XRD ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28452", "content": "Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established benchmarks. To address this, we introduce SimXRD , the largest open-source simulated XRD ..."}
+{"idx": 9, "title": "Publications - Ruifeng Tan", "date": "", "ddg_snippet": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper, we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ...", "subpage_snippet": "", "source": "ruifeng-tan.github.io", "link": "https://ruifeng-tan.github.io/publications/", "content": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper, we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ..."}
diff --git a/data/sampled_jsons/SimXRD-4M_Bidirectional-GRU_F1_score_Table_2.jsonl b/data/sampled_jsons/SimXRD-4M_Bidirectional-GRU_F1_score_Table_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3cf6852a94ba90a3fae4f36f9b51a151fac98ed3
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_Bidirectional-GRU_F1_score_Table_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Confusion matrix - Wikipedia", "date": "", "ddg_snippet": "In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervis...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Confusion_matrix", "content": "In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervis..."}
+{"idx": 1, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal ...", "date": "", "ddg_snippet": "The performance of the models is evaluated using accuracy, macro F1 - score , macro precision, and macro recall as metrics. All models are implemented using the PyTorch (Paszke et al., 2019) library and trained on GeForce RTX 3090 GPU.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "The performance of the models is evaluated using accuracy, macro F1 - score , macro precision, and macro recall as metrics. All models are implemented using the PyTorch (Paszke et al., 2019) library and trained on GeForce RTX 3090 GPU."}
+{"idx": 2, "title": "SIMXRD-4M: B SIMULATED X-RAY DIFFRACTION D C SYMMETRY ...", "date": "", "ddg_snippet": "uracy, macro F1 - score , macro precision, and macro recall as metrics. All models are implemented using the PyTor h (Paszke et al., 2019) library and trained on GeForce RTX 3090 GPU. Table 3: Results of weighted classification, label smooth", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mkuB677eMM", "content": "uracy, macro F1 - score , macro precision, and macro recall as metrics. All models are implemented using the PyTor h (Paszke et al., 2019) library and trained on GeForce RTX 3090 GPU. Table 3: Results of weighted classification, label smooth"}
+{"idx": 3, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal ...", "date": "", "ddg_snippet": "1: We introduce SimXRD , the largest open-source XRD pattern dataset for symmetry identification. 2: Data analysis reveals that the symmetry labels follow a long-tailed distribution.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "1: We introduce SimXRD , the largest open-source XRD pattern dataset for symmetry identification. 2: Data analysis reveals that the symmetry labels follow a long-tailed distribution."}
+{"idx": 4, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data ...", "date": "", "ddg_snippet": "15 Jun 2024 — Table 2: Results of crystal system classification and space group classification . Inference time is measured for a batch size of 100 samples ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "15 Jun 2024 — Table 2: Results of crystal system classification and space group classification . Inference time is measured for a batch size of 100 samples ..."}
+{"idx": 5, "title": "SIMXRD-4M: BIG SIMULATED X-RAY DIFFRACTION ...", "date": "", "ddg_snippet": "In-library classification Table 2 displays the baseline performance and inference time for the classification of crystal systems and space groups. Based on the ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/b04843d76c9f090beca55f335c4ea6bf-Paper-Conference.pdf", "content": "In-library classification Table 2 displays the baseline performance and inference time for the classification of crystal systems and space groups. Based on the ..."}
+{"idx": 6, "title": "AI4Spectro (SimXRD) - Hugging Face", "date": "", "ddg_snippet": "Aug 14, 2024 · Utilize this page to register the utilization of SimXRD , thereby retrieving version information, backup data, and reference materials. The SimXRD database is a comprehensive resource for spectral data analysis, designed to facilitate the identification of crystal materials both in and out library.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/AI4Spectro", "content": "Aug 14, 2024 · Utilize this page to register the utilization of SimXRD , thereby retrieving version information, backup data, and reference materials. The SimXRD database is a comprehensive resource for spectral data analysis, designed to facilitate the identification of crystal materials both in and out library."}
+{"idx": 7, "title": "[2406.15469] SimXRD-4M: Big Simulated X-ray Diffraction Data ...", "date": "", "ddg_snippet": "Jun 15, 2024 · We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD . The results indicate that existing neural networks struggle with low-frequency crystal classifications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.15469", "content": "Jun 15, 2024 · We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD . The results indicate that existing neural networks struggle with low-frequency crystal classifications."}
+{"idx": 8, "title": "towardsdatascience.com/micro-macro-weighted-averages-of- f 1 - score ...", "date": "", "ddg_snippet": "Micro and Macro Weighted Averages of F 1 Score .", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/micro-macro-weighted-averages-of-f1-score-clearly-explained-b603420b292f/", "content": "Micro and Macro Weighted Averages of F 1 Score ."}
+{"idx": 9, "title": "GitHub - Bin-Cao/SimXRD: [ICLR 2025] SimXRD-4M: Big Simulated ...", "date": "", "ddg_snippet": "Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials."}
diff --git a/data/sampled_jsons/SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchmark_Section.jsonl b/data/sampled_jsons/SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchmark_Section.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ca8347ec2c8ef2618f40c2330b257dc0bfbb93a3
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchmark_Section.jsonl
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+{"idx": 1, "title": "Ekersgatan 11 Örebro län, Örebro - hitta.se", "date": "", "ddg_snippet": "Fastigheten på Ekersgatan 11 är ett flerfamiljshus. Genomsnittlig ränta föregående månad (jul 2025) för ett bolån med 3 månaders bunden ränta: 2.89%. Räkna på ditt bolån! Bolåneränta du får hos oss är individuell och kan skilja sig från den genomsnittliga räntan.", "subpage_snippet": "", "source": "www.hitta.se", "link": "https://www.hitta.se/örebro+län/örebro/ekersgatan+11/område/59.276550750845196:15.204914088707486", "content": "Fastigheten på Ekersgatan 11 är ett flerfamiljshus. Genomsnittlig ränta föregående månad (jul 2025) för ett bolån med 3 månaders bunden ränta: 2.89%. Räkna på ditt bolån! Bolåneränta du får hos oss är individuell och kan skilja sig från den genomsnittliga räntan."}
+{"idx": 2, "title": "Ekersgatan 11 70342 Örebro - karta på Eniro", "date": "", "ddg_snippet": "Upptäck lokala företag, sök efter vänner och familj samt kolla tomtgränser, historiska flygfoton, cykelvägar m.m.", "subpage_snippet": "", "source": "kartor.eniro.se", "link": "https://kartor.eniro.se/sök/ekersgatan-11-70342-örebro", "content": "Upptäck lokala företag, sök efter vänner och familj samt kolla tomtgränser, historiska flygfoton, cykelvägar m.m."}
+{"idx": 3, "title": "Personer i Örebro på Ekersgatan 11 - Ratsit", "date": "", "ddg_snippet": "Privatpersoner på Ekersgatan 11 (8 st) Emma Linnea Fransson, 29 Ekersgatan 11 lgh 1101 Carl Ludwig Nordefors, 32 Ekersgatan 11 lgh 1101 1", "subpage_snippet": "", "source": "www.ratsit.se", "link": "https://www.ratsit.se/personer/Örebro-kommun/Örebro-70342/Ekersgatan-11/1", "content": "Privatpersoner på Ekersgatan 11 (8 st) Emma Linnea Fransson, 29 Ekersgatan 11 lgh 1101 Carl Ludwig Nordefors, 32 Ekersgatan 11 lgh 1101 1"}
+{"idx": 4, "title": "Pizzeria Galejan - Örebro Matställen Sverige", "date": "", "ddg_snippet": "Adress: Ekersgatan 11 , 703 42 Örebro , Sverige. Telefon: 196116341. Specialiteter: Pizzeria.", "subpage_snippet": "", "source": "restaurangkatalog.se", "link": "https://restaurangkatalog.se/pizzeria-galejan/", "content": "Adress: Ekersgatan 11 , 703 42 Örebro , Sverige. Telefon: 196116341. Specialiteter: Pizzeria."}
+{"idx": 5, "title": "Pizzeria Galejan i Örebro | RestaurangMenyn.se", "date": "", "ddg_snippet": "Feb 24, 2025 · *Observera: Denna menybeskrivning är automatiskt genererad och kan skilja sig från den faktiska menyn.* Restaurangen erbjuder ett brett urval av pizzor, sallader och kebabrätter. Njut av klassiska favoriter som Margherita och Calzone, fräscha alternativ som Hawaiisallad och Räksallad, samt smakrika kebabrätter som Kebabtallrik och Superkebab Rulle.", "subpage_snippet": "", "source": "www.restaurangmenyn.se", "link": "https://www.restaurangmenyn.se/orebro/centrum-stortorget/pizzeria-galejan/13397", "content": "Feb 24, 2025 · *Observera: Denna menybeskrivning är automatiskt genererad och kan skilja sig från den faktiska menyn.* Restaurangen erbjuder ett brett urval av pizzor, sallader och kebabrätter. Njut av klassiska favoriter som Margherita och Calzone, fräscha alternativ som Hawaiisallad och Räksallad, samt smakrika kebabrätter som Kebabtallrik och Superkebab Rulle."}
+{"idx": 6, "title": "PIZZERIA GALEJAN, Örebro - Restaurangomdömen, bilder och...", "date": "", "ddg_snippet": "En pra pizzeria med frächa råvaror och goda pizzor. Trevlig och hjälpsam personal. Nöjd med besöket och återkommer gärna.", "subpage_snippet": "", "source": "www.tripadvisor.se", "link": "https://www.tripadvisor.se/Restaurant_Review-g189861-d11045174-Reviews-Pizzeria_Galejan-Orebro_Orebro_County.html", "content": "En pra pizzeria med frächa råvaror och goda pizzor. Trevlig och hjälpsam personal. Nöjd med besöket och återkommer gärna."}
+{"idx": 7, "title": "Lena Åkesson (62 år) Örebro | Ratsit", "date": "", "ddg_snippet": "May 11, 2025 · Lena Elize Åkesson är en 62 årig kvinna bosatt i Örebro kommun. Hon bor i en lägenhet med 3 rum på 107 kvm på Ekersgatan 11 lgh 1102 i Örebro . Där har hon bott i sammanlagt 12 år och 191 dagar sedan 1 november 2012. Hon har inte någon särskild adress registrerad.", "subpage_snippet": "", "source": "www.ratsit.se", "link": "https://www.ratsit.se/19620725-Lena_Elize_Akesson_Orebro/rdk0n2qfSR1mRHz6_XytiqhEzXmcYmgNio4J91WLoLU", "content": "May 11, 2025 · Lena Elize Åkesson är en 62 årig kvinna bosatt i Örebro kommun. Hon bor i en lägenhet med 3 rum på 107 kvm på Ekersgatan 11 lgh 1102 i Örebro . Där har hon bott i sammanlagt 12 år och 191 dagar sedan 1 november 2012. Hon har inte någon särskild adress registrerad."}
+{"idx": 8, "title": "Örebro Skomakaren och Nyckelservice - Org.nr 630601-XXXX - - Se...", "date": "", "ddg_snippet": "Se företagsinformation om Örebro Skomakaren och Nyckelservice. Översikt med kontaktuppgifter, redovisning, befattningar, styrelse, ägare och händelser.", "subpage_snippet": "", "source": "www.allabolag.se", "link": "https://www.allabolag.se/foretag/örebro-skomakaren-och-nyckelservice/-/skor/MCPF0WSEQI5YHGY", "content": "Se företagsinformation om Örebro Skomakaren och Nyckelservice. Översikt med kontaktuppgifter, redovisning, befattningar, styrelse, ägare och händelser."}
+{"idx": 9, "title": "3 rums lägenhet i Örebro - 78 m² - 9 943 kr", "date": "", "ddg_snippet": "Hem Lägenheter Örebro 3 rum Västra Mark Hem Lägenheter Örebro 3 rum Västra Mark 53 minuter sedan 3 rums lägenhet på 78 m² Ekersgatan , 703 42 Örebro , Västra Mark - 2:a våningen Spara Månadshyra 9 943 kr 9 943 kr. Tillgänglig Omgående Hyresperiod Tillsvidare", "subpage_snippet": "", "source": "bostadsportal.se", "link": "https://bostadsportal.se/hyra-lägenhet/örebro/78m2-3-rok-id-5897501", "content": "Hem Lägenheter Örebro 3 rum Västra Mark Hem Lägenheter Örebro 3 rum Västra Mark 53 minuter sedan 3 rums lägenhet på 78 m² Ekersgatan , 703 42 Örebro , Västra Mark - 2:a våningen Spara Månadshyra 9 943 kr 9 943 kr. Tillgänglig Omgående Hyresperiod Tillsvidare"}
diff --git a/data/sampled_jsons/SimXRD-4M_largest_open-source_vs_Lee_et_al._2023_AdvancedXRDAnalysis_year_2024.jsonl b/data/sampled_jsons/SimXRD-4M_largest_open-source_vs_Lee_et_al._2023_AdvancedXRDAnalysis_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3d5fdc0f95ebdd32387aef59b9e41a5cd50f5d05
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+++ b/data/sampled_jsons/SimXRD-4M_largest_open-source_vs_Lee_et_al._2023_AdvancedXRDAnalysis_year_2024.jsonl
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+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data ...", "date": "", "ddg_snippet": "15 Jun 2024 — We introduce SimXRD, the largest open-source simulation dataset for symmetry identification, including 4,065,346 XRD patterns of 119,569 high- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "15 Jun 2024 — We introduce SimXRD, the largest open-source simulation dataset for symmetry identification, including 4,065,346 XRD patterns of 119,569 high- ..."}
+{"idx": 2, "title": "SIMXRD-4M: BIG SIMULATED X-RAY DIFFRACTION ...", "date": "", "ddg_snippet": "Using this method, we developed SimXRD, the largest open-source and physically detailed dataset aimed at advancing this interdisciplinary field. We source ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/b04843d76c9f090beca55f335c4ea6bf-Paper-Conference.pdf", "content": "Using this method, we developed SimXRD, the largest open-source and physically detailed dataset aimed at advancing this interdisciplinary field. We source ..."}
+{"idx": 3, "title": "(PDF) SIMXRD - 4 M : big simulated x-ray diffraction data and crystal...", "date": "", "ddg_snippet": "( Lee et al ., 2020) focus on mixtures of 38 distinct binary and ternary crystals in the Sr-Li-. Al -O inorganic compounds.Using this method, we developed SimXRD , the largest open - source and physically detailed.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389657996_SIMXRD-4M_BIG_SIMULATED_X-RAY_DIFFRACTION_DATA_AND_CRYSTAL_SYMMETRY_CLASSIFICATION_BENCHMARK", "content": "( Lee et al ., 2020) focus on mixtures of 38 distinct binary and ternary crystals in the Sr-Li-. Al -O inorganic compounds.Using this method, we developed SimXRD , the largest open - source and physically detailed."}
+{"idx": 4, "title": "An open platform for evaluating AI through human preference", "date": "", "ddg_snippet": "An open platform for evaluating AI through human preference...", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/", "content": "An open platform for evaluating AI through human preference..."}
+{"idx": 5, "title": "Open Source Liquidity Protocol on Layer 2s", "date": "", "ddg_snippet": "ZeroLend is an Open Source Protocol to create Non-Custodial Liquidity Markets to earn interest on supplying and borrowing assets with a variable or stable interest rate.", "subpage_snippet": "", "source": "app.zerolend.xyz", "link": "https://app.zerolend.xyz/", "content": "ZeroLend is an Open Source Protocol to create Non-Custodial Liquidity Markets to earn interest on supplying and borrowing assets with a variable or stable interest rate."}
+{"idx": 6, "title": "D ATA", "date": "", "ddg_snippet": "AdvancedXRDAnalysis ( Lee et al ., 2023 ) CrySTINet (Chen et al ., 2024) CPICANN (Cao, 2024) SimXRD .In this paper, we introduce SimXRD , the largest open - source XRD pattern dataset for symmetry identification.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.15469", "content": "AdvancedXRDAnalysis ( Lee et al ., 2023 ) CrySTINet (Chen et al ., 2024) CPICANN (Cao, 2024) SimXRD .In this paper, we introduce SimXRD , the largest open - source XRD pattern dataset for symmetry identification."}
+{"idx": 7, "title": "Как отличить цистит от простатита у мужчин - симптомы...", "date": "", "ddg_snippet": "Согласно исследованию Kim et al . ( 2023 ), мультипараметрическая МРТ имеет чувствительность 94% и специфичность 89% в дифференциальной диагностике простатита.", "subpage_snippet": "", "source": "Medgorod-clinic.ru", "link": "https://Medgorod-clinic.ru/stati/kak-otlichit-tsistit-ot-prostatita-u-muzhchin-/", "content": "Согласно исследованию Kim et al . ( 2023 ), мультипараметрическая МРТ имеет чувствительность 94% и специфичность 89% в дифференциальной диагностике простатита."}
+{"idx": 8, "title": "ahajournals.org/doi/10.1161/STR.0000000000000436", "date": "", "ddg_snippet": "Hoh BL, Ko NU, Amin-Hanjani S, et al . 2023 Guideline for the management of...", "subpage_snippet": "", "source": "www.ahajournals.org", "link": "https://www.ahajournals.org/doi/10.1161/STR.0000000000000436", "content": "Hoh BL, Ko NU, Amin-Hanjani S, et al . 2023 Guideline for the management of..."}
+{"idx": 9, "title": "AI Detector - Trusted AI Checker for ChatGPT, GPT5 & Gemini", "date": "", "ddg_snippet": "Simple and Credible Open AI, Grok, DeepSeek, and Gemini Detector Tool for Free. Millions of Users Trust ZeroGPT, See what sets ZeroGPT apart. Highlight sentences detected as AI/GPT.", "subpage_snippet": "", "source": "www.zerogpt.com", "link": "https://www.zerogpt.com/", "content": "Simple and Credible Open AI, Grok, DeepSeek, and Gemini Detector Tool for Free. Millions of Users Trust ZeroGPT, See what sets ZeroGPT apart. Highlight sentences detected as AI/GPT."}
diff --git a/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_SIIHPC_Algorithm_2_termination_condition.jsonl b/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_SIIHPC_Algorithm_2_termination_condition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..14afff75a63af008b4a01bf4fa69c2576ec2552e
--- /dev/null
+++ b/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_SIIHPC_Algorithm_2_termination_condition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Simple yet Effective Incomplete Multi-view Clustering:...", "date": "", "ddg_snippet": "Jan 22, 2025 · Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\\textbf {all}$ views. To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC , in this work. It ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KijslFbfOL", "content": "Jan 22, 2025 · Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\\textbf {all}$ views. To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC , in this work. It ..."}
+{"idx": 1, "title": "xiexzh/Incomplete-multi-view-clustering - GitHub Projective Incomplete Multi-View Clustering - IEEE Xplore Simple yet Effective Incomplete Multi-view Clustering ... SIMPLE YET EFFECTIVE INCOMPLETE MULTI VIEW CLUSTERING ... Highly-efficient Incomplete Largescale Multiview Clustering ... Simple yet Effective Incomplete Multi-view Clustering : Efficient and Effective Regularized Incomplete Multi-View Clustering Highly- efficient Incomplete Largescale Multiview Clustering with Efficient and Effective Regularized Incomplete Multi-View ...", "date": "", "ddg_snippet": "Collections for incomplete multi - view clustering methods (papers and codes). We are looking forward for other participants to share their papers and codes. If interested, please contact zhangpei@nudt.edu.cn. [:bell: News! :bell: ] Update at November 2022. See full list on github.com See full list on github.com Feb 10, 2023 · Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, data mining, and other fields and has been developed significantly in the past decades. Compared with single-view clustering , MVC improves clustering performance by exploiting complementary and consistent information among different views ... Abstract Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\\textbf {all}$ views. Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of all views. To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC , in this work. It firstly ... Multiview clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations. Most previous methods hold the assumption that each instance appears in all views. However, it is not uncommon to see that some views may contain some missing instances, which gives rise to incomplete multi-view clustering (IMVC) in literature ... What is incomplete multi-view clustering (imvc)? Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $extbf {all}$ views. Does incomplete multi-view clustering improve performance? Efficient and Effective Regularized Incomplete Multi-View Clustering Xinwang Liu , Miaomiao Li , Chang Tang , Jingyuan Xia , Jian Xiong, Li Liu , Marius Kloft , and En Zhu Abstract—Incomplete multi-view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance . How effective is Multiview clustering? 11 Multiview clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations. Most previous me In this paper, we first propose an Efficient and Effective Incomplete Multi- view Clustering (EE-IMVC) algorithm to address these issues.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xiexzh/Incomplete-multi-view-clustering", "content": "Collections for incomplete multi - view clustering methods (papers and codes). We are looking forward for other participants to share their papers and codes. If interested, please contact zhangpei@nudt.edu.cn. [:bell: News! :bell: ] Update at November 2022. See full list on github.com See full list on github.com Feb 10, 2023 · Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, data mining, and other fields and has been developed significantly in the past decades. Compared with single-view clustering , MVC improves clustering performance by exploiting complementary and consistent information among different views ... Abstract Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\\textbf {all}$ views. Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of all views. To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC , in this work. It firstly ... Multiview clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations. Most previous methods hold the assumption that each instance appears in all views. However, it is not uncommon to see that some views may contain some missing instances, which gives rise to incomplete multi-view clustering (IMVC) in literature ... What is incomplete multi-view clustering (imvc)? Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $extbf {all}$ views. Does incomplete multi-view clustering improve performance? Efficient and Effective Regularized Incomplete Multi-View Clustering Xinwang Liu , Miaomiao Li , Chang Tang , Jingyuan Xia , Jian Xiong, Li Liu , Marius Kloft , and En Zhu Abstract—Incomplete multi-view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance . How effective is Multiview clustering? 11 Multiview clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations. Most previous me In this paper, we first propose an Efficient and Effective Incomplete Multi- view Clustering (EE-IMVC) algorithm to address these issues."}
+{"idx": 2, "title": "Projective Incomplete Multi-View Clustering - IEEE Xplore", "date": "", "ddg_snippet": "Feb 10, 2023 · Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, data mining, and other fields and has been developed significantly in the past decades. Compared with single-view clustering , MVC improves clustering performance by exploiting complementary and consistent information among different views ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10042189", "content": "Feb 10, 2023 · Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, data mining, and other fields and has been developed significantly in the past decades. Compared with single-view clustering , MVC improves clustering performance by exploiting complementary and consistent information among different views ..."}
+{"idx": 3, "title": "Simple yet Effective Incomplete Multi-view Clustering ...", "date": "", "ddg_snippet": "Abstract Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\\textbf {all}$ views.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/14c856c7a41297804de4c4890e846b25-Abstract-Conference.html", "content": "Abstract Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\\textbf {all}$ views."}
+{"idx": 4, "title": "SIMPLE YET EFFECTIVE INCOMPLETE MULTI VIEW CLUSTERING ...", "date": "", "ddg_snippet": "Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of all views. To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC , in this work. It firstly ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=qZ2smF2lyZ&name=pdf", "content": "Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of all views. To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC , in this work. It firstly ..."}
+{"idx": 5, "title": "Highly-efficient Incomplete Largescale Multiview Clustering ...", "date": "", "ddg_snippet": "Multiview clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations. Most previous methods hold the assumption that each instance appears in all views. However, it is not uncommon to see that some views may contain some missing instances, which gives rise to incomplete multi-view clustering (IMVC) in literature ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9880247", "content": "Multiview clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations. Most previous methods hold the assumption that each instance appears in all views. However, it is not uncommon to see that some views may contain some missing instances, which gives rise to incomplete multi-view clustering (IMVC) in literature ..."}
+{"idx": 6, "title": "Efficient and Effective Regularized Incomplete Multi-View ...", "date": "", "ddg_snippet": "In this paper, we first propose an Efficient and Effective Incomplete Multi- view Clustering (EE-IMVC) algorithm to address these issues.", "subpage_snippet": "", "source": "xinwangliu.github.io", "link": "https://xinwangliu.github.io/papers/1_4_XinwangLiu_TPAMI21.pdf", "content": "In this paper, we first propose an Efficient and Effective Incomplete Multi- view Clustering (EE-IMVC) algorithm to address these issues."}
+{"idx": 7, "title": "ICLR Poster Simple yet Effective Incomplete Multi - view Clustering ...", "date": "", "ddg_snippet": "To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC , in this work.Finally, the clustering results are obtained by implementing spectral grouping action on the eigenvectors of stacked multi -scale consensus similarity.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/30038", "content": "To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC , in this work.Finally, the clustering results are obtained by implementing spectral grouping action on the eigenvectors of stacked multi -scale consensus similarity."}
+{"idx": 8, "title": "Efficient and Effective Incomplete Multi - view Clustering", "date": "", "ddg_snippet": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specied incomplete views to improve cluster -ing performance.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/4350/4228", "content": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specied incomplete views to improve cluster -ing performance."}
+{"idx": 9, "title": "Prototype Matching Learning for Incomplete Multi - View Clustering", "date": "", "ddg_snippet": "Incomplete Multi ‐ View Clustering aims to enhance clustering performance by using data from multiple modalities.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388207724_Prototype_Matching_Learning_for_Incomplete_Multi-view_Clustering", "content": "Incomplete Multi ‐ View Clustering aims to enhance clustering performance by using data from multiple modalities."}
diff --git a/data/sampled_jsons/Sinkhorn_1964_relationship_arbitrary_positive_matrices_doubly_stochastic_year_1964.jsonl b/data/sampled_jsons/Sinkhorn_1964_relationship_arbitrary_positive_matrices_doubly_stochastic_year_1964.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..84e21884acc7a00326d3023560711e7d14d43ab4
--- /dev/null
+++ b/data/sampled_jsons/Sinkhorn_1964_relationship_arbitrary_positive_matrices_doubly_stochastic_year_1964.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Relationship Between Arbitrary Positive Matrices and Doubly ...", "date": "", "ddg_snippet": "Richard Sinkhorn . Ann. Math.Vol.35 • No. 2 • June, 1964 . Institute of Mathematical Statistics.", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-35/issue-2/A-Relationship-Between-Arbitrary-Positive-Matrices-and-Doubly-Stochastic-Matrices/10.1214/aoms/1177703591.short", "content": "Richard Sinkhorn . Ann. Math.Vol.35 • No. 2 • June, 1964 . Institute of Mathematical Statistics."}
+{"idx": 1, "title": "A Relationship Between Arbitrary Positive Matrices and Doubly ...", "date": "", "ddg_snippet": "Richard Sinkhorn .CiteDownload. ShareDownload. 1 June 1964 . journal article. Published by Institute of Mathematical Statistics in The Annals of Mathematical Statistics.", "subpage_snippet": "", "source": "www.scilit.com", "link": "https://www.scilit.com/publications/f78016a4908c46e4690e1550598bc28f", "content": "Richard Sinkhorn .CiteDownload. ShareDownload. 1 June 1964 . journal article. Published by Institute of Mathematical Statistics in The Annals of Mathematical Statistics."}
+{"idx": 2, "title": "Sinkhorn 's theorem - Wikiwand", "date": "", "ddg_snippet": "Sinkhorn , Richard. ( 1964 ). \"A relationship between arbitrary positive matrices and doubly stochastic matrices .\"", "subpage_snippet": "", "source": "www.wikiwand.com", "link": "https://www.wikiwand.com/en/articles/Sinkhorn's_theorem", "content": "Sinkhorn , Richard. ( 1964 ). \"A relationship between arbitrary positive matrices and doubly stochastic matrices .\""}
+{"idx": 3, "title": "Combinatorial structure behind Sinkhorn limits", "date": "", "ddg_snippet": "Richard Sinkhorn , A relationship between arbitrary positive matrices and doubly stochastic matrices , The Annals of Mathematical Statistics 35 ( 1964 ) 876–879.", "subpage_snippet": "", "source": "sites.math.rutgers.edu", "link": "https://sites.math.rutgers.edu/~zeilberg/expmath/rowland2024.pdf", "content": "Richard Sinkhorn , A relationship between arbitrary positive matrices and doubly stochastic matrices , The Annals of Mathematical Statistics 35 ( 1964 ) 876–879."}
+{"idx": 4, "title": "Online Sinkhorn : Optimal Transport distances from sample streams", "date": "", "ddg_snippet": "3Richard Sinkhorn . “A relationship between arbitrary positive matrices and doubly stochastic matrices ”. In: The Annals of Mathematical Statistics 35 ( 1964 ), pp. 876–879.", "subpage_snippet": "", "source": "www.cirm-math.fr", "link": "https://www.cirm-math.fr/RepOrga/2133/Slides/slides_arthur_mensch.pdf", "content": "3Richard Sinkhorn . “A relationship between arbitrary positive matrices and doubly stochastic matrices ”. In: The Annals of Mathematical Statistics 35 ( 1964 ), pp. 876–879."}
+{"idx": 5, "title": "Doubly stochastic operators obtained from positive operators", "date": "", "ddg_snippet": "3. R. Sinkhorn , A relationship between arbitrary positive matrices and doubly stochastic matrices , Ann. Math. Stat. 35 ( 1964 ), 876-879.", "subpage_snippet": "", "source": "msp.org", "link": "https://msp.org/pjm/1965/15-1/pjm-v15-n1-p15-s.pdf", "content": "3. R. Sinkhorn , A relationship between arbitrary positive matrices and doubly stochastic matrices , Ann. Math. Stat. 35 ( 1964 ), 876-879."}
+{"idx": 6, "title": "matrices - How to convert any non-negative matrix into a doubly ...", "date": "", "ddg_snippet": "There is a paper by Richard Sinkhorn : A relationship between arbitrary positive matrices and doubly stochastic matrices , The Annals of Mathematical statistics, 35 ( 1964 ), 876–879.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/179940/how-to-convert-any-non-negative-matrix-into-a-doubly-stochastic-matrix/179952", "content": "There is a paper by Richard Sinkhorn : A relationship between arbitrary positive matrices and doubly stochastic matrices , The Annals of Mathematical statistics, 35 ( 1964 ), 876–879."}
+{"idx": 7, "title": "Sinkformers: Transformers with Doubly Stochastic Attention", "date": "", "ddg_snippet": "Therefore, it seems natural to impose double stochasticity as a prior and study theoretically and experimentally the result-ing model. Sinkhorn , R. ( 1964 ). A relationship between arbitrary positive matrices and doubly stochastic matrices .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v151/sander22a/sander22a.pdf", "content": "Therefore, it seems natural to impose double stochasticity as a prior and study theoretically and experimentally the result-ing model. Sinkhorn , R. ( 1964 ). A relationship between arbitrary positive matrices and doubly stochastic matrices ."}
+{"idx": 8, "title": "Applications of Sinkhorn ’s alternative scaling to", "date": "", "ddg_snippet": "Richard Sinkhorn . A relationship between arbitrary positive matrices and doubly stochastic matrices . The annals of mathematical statistics, 35(2):876–879, 1964 .", "subpage_snippet": "", "source": "ion.nechita.net", "link": "https://ion.nechita.net/wp-content/uploads/2018/10/stage-Sinkhorn-QIT.pdf", "content": "Richard Sinkhorn . A relationship between arbitrary positive matrices and doubly stochastic matrices . The annals of mathematical statistics, 35(2):876–879, 1964 ."}
+{"idx": 9, "title": "Learning symmetries via weight-sharing with doubly stochastic tensors", "date": "", "ddg_snippet": "Sinkhorn , R. A relationship between arbitrary positive matrices and doubly stochastic matrices .The Sinkhorn operator ( Sinkhorn , 1964 ; Adams & Zemel, 2011) transforms an arbitrary matrix to a doubly stochastic one.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=S8nQlS9q9x", "content": "Sinkhorn , R. A relationship between arbitrary positive matrices and doubly stochastic matrices .The Sinkhorn operator ( Sinkhorn , 1964 ; Adams & Zemel, 2011) transforms an arbitrary matrix to a doubly stochastic one."}
diff --git a/data/sampled_jsons/Sitan_Chen_Harvard_GitHub_feature-localization.jsonl b/data/sampled_jsons/Sitan_Chen_Harvard_GitHub_feature-localization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d10e34747238ee36dc2714879be7a60ee06a6889
--- /dev/null
+++ b/data/sampled_jsons/Sitan_Chen_Harvard_GitHub_feature-localization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "In this work we develop a simple, unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models, we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QvqnPVGWAN", "content": "In this work we develop a simple, unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models, we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models."}
+{"idx": 1, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "To study feature localization in diffusion and autoregressive models, we consider a forward-reverseexperiment. A forward-reverse experiment considers the amount of \"noise\" one would need to add to a generation so that running the generative model starting from the noised generation would still yield a sample with the same feature .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "To study feature localization in diffusion and autoregressive models, we consider a forward-reverseexperiment. A forward-reverse experiment considers the amount of \"noise\" one would need to add to a generation so that running the generative model starting from the noised generation would still yield a sample with the same feature ."}
+{"idx": 2, "title": "Sitan Chen: Homepage", "date": "", "ddg_snippet": "I am broadly interested in algorithmic questions about learning from data. In the last few years this has led me to study the science and theory of localization ...", "subpage_snippet": "", "source": "sitanchen.com", "link": "https://sitanchen.com/", "content": "I am broadly interested in algorithmic questions about learning from data. In the last few years this has led me to study the science and theory of localization ..."}
+{"idx": 3, "title": "BLINK OF AN EYE: A SIMPLE THEORY FOR FEATURE", "date": "", "ddg_snippet": "LOCALIZATION IN GENERATIVE MODELS . Marvin Li, Aayush Karan, & Sitan Chen. Department of Computer Science. Harvard University. Cambridge, MA 02138, USA. ABSTRACT.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/869c142886213628c0ffd59131341197f4ddace8.pdf", "content": "LOCALIZATION IN GENERATIVE MODELS . Marvin Li, Aayush Karan, & Sitan Chen. Department of Computer Science. Harvard University. Cambridge, MA 02138, USA. ABSTRACT."}
+{"idx": 4, "title": "Homepage: Sitan Chen", "date": "", "ddg_snippet": "Sitan Chen I am an Assistant Professor of Computer Science at Harvard's John A. Paulson School of Engineering and Applied Sciences, where I am a member of the Theory of Computation group, the ML Foundations group, and the Harvard Quantum Initiative.", "subpage_snippet": "", "source": "www.sitanchen.com", "link": "https://www.sitanchen.com/", "content": "Sitan Chen I am an Assistant Professor of Computer Science at Harvard's John A. Paulson School of Engineering and Applied Sciences, where I am a member of the Theory of Computation group, the ML Foundations group, and the Harvard Quantum Initiative."}
+{"idx": 5, "title": "Sitan Chen June 10, 2025 arXiv:2502.00921v2 [cs.LG] 5 Jun 2025", "date": "", "ddg_snippet": "Harvard SEAS Sitan Chen ‡ Harvard SEAS June 10, 2025 Abstract Large language models can exhibit unexpected behavior in the blink of an eye. In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00921v2", "content": "Harvard SEAS Sitan Chen ‡ Harvard SEAS June 10, 2025 Abstract Large language models can exhibit unexpected behavior in the blink of an eye. In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks."}
+{"idx": 6, "title": "GitHub - marvinli-harvard/critical-windows", "date": "", "ddg_snippet": "This repo provides some experimental tools to investigate the phenomena of feature emergence in diffusion models, where features of the final outputted images like color, background, or clothing type are fossilized in narrow intervals of the reverse denoising process. This code accompanies the ICML 2024 paper (Li and Chen , 2024).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/marvinli-harvard/critical-windows", "content": "This repo provides some experimental tools to investigate the phenomena of feature emergence in diffusion models, where features of the final outputted images like color, background, or clothing type are fossilized in narrow intervals of the reverse denoising process. This code accompanies the ICML 2024 paper (Li and Chen , 2024)."}
+{"idx": 7, "title": "Sitan Chen - OpenReview", "date": "", "ddg_snippet": "Sitan Chen Assistant Professor, Computer Science, Harvard University Joined January 2019", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Sitan_Chen1", "content": "Sitan Chen Assistant Professor, Computer Science, Harvard University Joined January 2019"}
+{"idx": 8, "title": "Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "Blink of an eye: a simple theory for feature localization in generative models Marvin Li, Aayush Karan, Sitan Chen Published: 05 Mar 2025, Last Modified: 23 Apr 2025 FPI-ICLR2025 Poster Everyone Revisions BibTeX CC BY 4.0 Keywords: stochastic localization , interpretability, large language models, diffusion models", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nodyP4FLrM", "content": "Blink of an eye: a simple theory for feature localization in generative models Marvin Li, Aayush Karan, Sitan Chen Published: 05 Mar 2025, Last Modified: 23 Apr 2025 FPI-ICLR2025 Poster Everyone Revisions BibTeX CC BY 4.0 Keywords: stochastic localization , interpretability, large language models, diffusion models"}
+{"idx": 9, "title": "BLINK OF AN EYE A SIMPLE THEORY FOR FEATURE LOCALIZATION ... - OpenReview", "date": "", "ddg_snippet": "Published as a conference paper at ICLR 2025 BLINK OF AN EYE:A SIMPLE THEORY FOR FEATURE LOCALIZATION IN GENERATIVE MODELS Marvin Li, Aayush Karan, & Sitan Chen Department of Computer Science Harvard University Cambridge, MA 02138, USA ABSTRACT Large language models can exhibit undesirable and unexpected behavior in the blink of an eye.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=nodyP4FLrM", "content": "Published as a conference paper at ICLR 2025 BLINK OF AN EYE:A SIMPLE THEORY FOR FEATURE LOCALIZATION IN GENERATIVE MODELS Marvin Li, Aayush Karan, & Sitan Chen Department of Computer Science Harvard University Cambridge, MA 02138, USA ABSTRACT Large language models can exhibit undesirable and unexpected behavior in the blink of an eye."}
diff --git a/data/sampled_jsons/Socialized_Coevolution_Advancing_a_Better_World_through_Cross-Task_Collaboration_Table_3.jsonl b/data/sampled_jsons/Socialized_Coevolution_Advancing_a_Better_World_through_Cross-Task_Collaboration_Table_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f565912b6edb468fcea24c0c75e1a589e54c6b51
--- /dev/null
+++ b/data/sampled_jsons/Socialized_Coevolution_Advancing_a_Better_World_through_Cross-Task_Collaboration_Table_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Socialized Coevolution : Advancing a Better World ...", "date": "", "ddg_snippet": "Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized in different downstream tasks.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46680", "content": "Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized in different downstream tasks."}
+{"idx": 1, "title": "Advancing a Better World through Cross-Task Collaboration", "date": "", "ddg_snippet": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration ... The comparison between the proposed method and KD/MTL in Table 1 raises some ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0WQJ6DFSKp&referrer=[the+profile+of+Yu+Wang](/profile?id=~Yu_Wang33)", "content": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration ... The comparison between the proposed method and KD/MTL in Table 1 raises some ..."}
+{"idx": 2, "title": "Socialized Coevolution: Advancing a Better World through Cross-Task ...", "date": "", "ddg_snippet": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0WQJ6DFSKp", "content": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu"}
+{"idx": 3, "title": "Socialized Coevolution: Advancing a Better World through Cross-Task ...", "date": "", "ddg_snippet": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration 1 2 Xinjie Yao 3 Yu Wang 1 2 3 Pengfei Zhu 1 2 3 Wanyu Lin 4 Ruipu Zhao 1 5 Zhoupeng Guo 1 2 Qinghua Hu 3", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0WQJ6DFSKp", "content": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration 1 2 Xinjie Yao 3 Yu Wang 1 2 3 Pengfei Zhu 1 2 3 Wanyu Lin 4 Ruipu Zhao 1 5 Zhoupeng Guo 1 2 Qinghua Hu 3"}
+{"idx": 4, "title": "两篇论文被ICML 2025录用 - VISDRONE - aiskyeye.com", "date": "", "ddg_snippet": "论文题目： Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration 作者：姚鑫杰（博士研究生），王煜，朱鹏飞，林婉瑜，赵睿朴，郭周鹏，李维浩，胡清华 论文概述： 近期，团队提出了一个多智能体跨任务协同感知的社会化学习框架，已被2025国际机器学习大会（ICML）录用。在动态开放 ...", "subpage_snippet": "", "source": "aiskyeye.com", "link": "https://aiskyeye.com/论文《socialized-coevolution-advancing-a-better-world-through-cross-task-collaboration》和《task-gated-multi-expert-collaboration-network-for-degraded-multi/", "content": "论文题目： Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration 作者：姚鑫杰（博士研究生），王煜，朱鹏飞，林婉瑜，赵睿朴，郭周鹏，李维浩，胡清华 论文概述： 近期，团队提出了一个多智能体跨任务协同感知的社会化学习框架，已被2025国际机器学习大会（ICML）录用。在动态开放 ..."}
+{"idx": 5, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration · Analytical Construction on Geometric Architectures: Transitioning from ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration · Analytical Construction on Geometric Architectures: Transitioning from ..."}
+{"idx": 6, "title": "GitHub - yxjdarren/SC: The paper has been accepted to ICML 2025.", "date": "", "ddg_snippet": "The code repository for \" Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration \" (the paper has been accepted by ICML 2025) in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yxjdarren/SC", "content": "The code repository for \" Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration \" (the paper has been accepted by ICML 2025) in PyTorch."}
+{"idx": 7, "title": "Xinjie Yao | Vision Group", "date": "", "ddg_snippet": "[2025-05] \" Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration \" has been accepted by International Conference on Machine Learning (ICML, CCF-A).", "subpage_snippet": "", "source": "yxjdarren.github.io", "link": "https://yxjdarren.github.io/", "content": "[2025-05] \" Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration \" has been accepted by International Conference on Machine Learning (ICML, CCF-A)."}
+{"idx": 8, "title": "openreview.net/profile?id=~Wanyu_Lin1", "date": "", "ddg_snippet": "Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration . Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Wanyu_Lin1", "content": "Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration . Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu."}
+{"idx": 9, "title": "Wanyu Lin (@Wanyu1208) | Aguea", "date": "", "ddg_snippet": "Our paper “Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing”, “HPS: Hard Preference Sampling for Human Preference Alignment”,” Socialized Coevolution : Advancing a Better World through ...", "subpage_snippet": "", "source": "aguea.net", "link": "https://aguea.net/Wanyu1208", "content": "Our paper “Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing”, “HPS: Hard Preference Sampling for Human Preference Alignment”,” Socialized Coevolution : Advancing a Better World through ..."}
diff --git a/data/sampled_jsons/Soft_Reasoning_LLaMA3-8B-Ins_GSM8K_zero-shot_Table_1_accuracy.jsonl b/data/sampled_jsons/Soft_Reasoning_LLaMA3-8B-Ins_GSM8K_zero-shot_Table_1_accuracy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f664c347fe03850325aa5c0f87c676ca1abac728
--- /dev/null
+++ b/data/sampled_jsons/Soft_Reasoning_LLaMA3-8B-Ins_GSM8K_zero-shot_Table_1_accuracy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Soft Reasoning: Navigating Solution Spaces in Large Language ...", "date": "", "ddg_snippet": "Nov 13, 2024 · These tables extend the summary reported in the main text, showing detailed accuracy , coverage, and standard deviations for zero - shot to 8- shot setups. They provide a comprehensive view of how each baseline and our approach perform under various hyperparameter and prompt configurations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.24688v2", "content": "Nov 13, 2024 · These tables extend the summary reported in the main text, showing detailed accuracy , coverage, and standard deviations for zero - shot to 8- shot setups. They provide a comprehensive view of how each baseline and our approach perform under various hyperparameter and prompt configurations."}
+{"idx": 1, "title": "Soft Reasoning: Navigating Solution Spaces in Large Language ...", "date": "", "ddg_snippet": "Table 1 presents the accuracy of Soft Reasoning compared to baselines across four benchmarks and three LLMs under zero-shot and few-shot (8-shot) settings. The full table and the results for the Qwen2-70B-Ins model can be found in Tables 13 and 12, respectively, in Appendix B.8.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.24688v4", "content": "Table 1 presents the accuracy of Soft Reasoning compared to baselines across four benchmarks and three LLMs under zero-shot and few-shot (8-shot) settings. The full table and the results for the Qwen2-70B-Ins model can be found in Tables 13 and 12, respectively, in Appendix B.8."}
+{"idx": 2, "title": "On Memorization of Large Language Models in Logical Reasoning", "date": "", "ddg_snippet": "With this tool, we evaluate 11 off-the-shelf models, and fine-tuned Llama3 - 8B and GPT4o-mini to quantify memorization in reasoning tasks, and reveal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23123v1", "content": "With this tool, we evaluate 11 off-the-shelf models, and fine-tuned Llama3 - 8B and GPT4o-mini to quantify memorization in reasoning tasks, and reveal ..."}
+{"idx": 3, "title": "Soft Reasoning: Navigating Solution Spaces in Large Language", "date": "", "ddg_snippet": "Soft Reasoning refines these approaches by integrating controlled initial-token embedding perturbations with a strategic search algorithm inspired ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.24688v3", "content": "Soft Reasoning refines these approaches by integrating controlled initial-token embedding perturbations with a strategic search algorithm inspired ..."}
+{"idx": 4, "title": "From Passive to Active Reasoning: Can Large Language Models Ask", "date": "", "ddg_snippet": "Unlike PQ, which focuses exclusively on generating clarifying or exploratory questions, AR incorporates answers and refines its reasoning through ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08295v1", "content": "Unlike PQ, which focuses exclusively on generating clarifying or exploratory questions, AR incorporates answers and refines its reasoning through ..."}
+{"idx": 5, "title": "Instance-adaptive Zero-shot Chain-of-Thought Prompting", "date": "", "ddg_snippet": "It is critical to determine the key factors for good zero - shot CoT reasoning , therefore we dive into the LLMs inference process in disparate parts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.20441v3", "content": "It is critical to determine the key factors for good zero - shot CoT reasoning , therefore we dive into the LLMs inference process in disparate parts."}
+{"idx": 6, "title": "raincandy-u/Llama-3-Aplite-Instruct-4x8B-MoE · Hugging Face", "date": "", "ddg_snippet": "... 8B -Instruct experts: - source_model: Meta-Llama-3- 8B -Instruct positive_prompts: - \"explain\" - \"chat\" - \"assistant\" - source_model: ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/raincandy-u/Llama-3-Aplite-Instruct-4x8B-MoE", "content": "... 8B -Instruct experts: - source_model: Meta-Llama-3- 8B -Instruct positive_prompts: - \"explain\" - \"chat\" - \"assistant\" - source_model: ..."}
+{"idx": 7, "title": "Train your own R1 reasoning model with Unsloth —", "date": "", "ddg_snippet": "import re from datasets import load_dataset , Dataset Load and prep dataset SYSTEM_PROMPT = \" Respond in the following format: < reasoning ...", "subpage_snippet": "", "source": "rocm.docs.amd.com", "link": "https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/fine_tune/unsloth_Llama3_1_8B_GRPO.html", "content": "import re from datasets import load_dataset , Dataset Load and prep dataset SYSTEM_PROMPT = \" Respond in the following format: < reasoning ..."}
+{"idx": 8, "title": "Mind the Gap: Bridging Thought Leap for Improved", "date": "", "ddg_snippet": "This refers to instances where one or more intermediate reasoning steps are omitted between adjacent steps, creating cognitive gaps in the reasoning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14684v1", "content": "This refers to instances where one or more intermediate reasoning steps are omitted between adjacent steps, creating cognitive gaps in the reasoning ..."}
+{"idx": 9, "title": "GitHub - EdinburghNLP/awesome-hallucination-detection: List of", "date": "", "ddg_snippet": "Detecting Local Insights from Global Labels: Supervised & Zero - Shot Sequence Labeling via a Convolutional Decomposition", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/EdinburghNLP/awesome-hallucination-detection", "content": "Detecting Local Insights from Global Labels: Supervised & Zero - Shot Sequence Labeling via a Convolutional Decomposition"}
diff --git a/data/sampled_jsons/Solver-in-the-Loop_neural_PDE_error_correction_multiscale.jsonl b/data/sampled_jsons/Solver-in-the-Loop_neural_PDE_error_correction_multiscale.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..86849c5f68680a6b52ee4fd1fda64918e9f58164
--- /dev/null
+++ b/data/sampled_jsons/Solver-in-the-Loop_neural_PDE_error_correction_multiscale.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - bitzhangcy/ Neural - PDE - Solver", "date": "", "ddg_snippet": "A neural PDE solver with temporal stencil modeling. Solver - in - the - loop : Learning from differentiable physics to interact with iterative PDE -solvers.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bitzhangcy/Neural-PDE-Solver", "content": "A neural PDE solver with temporal stencil modeling. Solver - in - the - loop : Learning from differentiable physics to interact with iterative PDE -solvers."}
+{"idx": 1, "title": "Solver - in - the - Loop : Learning from Differentiable Physics to Interact...", "date": "", "ddg_snippet": "• Solver - in - the - loop (SOL): By integrating the learned function into a differentiable physics pipeline, the corrections can interact with the physical system, alter the states, and receive gradients about the future performance of these modications.", "subpage_snippet": "", "source": "www.lri.fr", "link": "https://www.lri.fr/~gcharpia/deeppractice/2022/chap_5_biblio/Solver/SolverInTheLoop.pdf", "content": "• Solver - in - the - loop (SOL): By integrating the learned function into a differentiable physics pipeline, the corrections can interact with the physical system, alter the states, and receive gradients about the future performance of these modications."}
+{"idx": 2, "title": "Solver - in - the - Loop : Learning from Differentiable Physics to Interact...", "date": "", "ddg_snippet": "• Solver - in - the - loop (SOL): By integrating the learned function into a differentiable physics pipeline, the corrections can interact with the physical system, alter the states, and receive gradients about the future performance of these modications.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/43e4e6a6f341e00671e123714de019a8-Paper.pdf", "content": "• Solver - in - the - loop (SOL): By integrating the learned function into a differentiable physics pipeline, the corrections can interact with the physical system, alter the states, and receive gradients about the future performance of these modications."}
+{"idx": 3, "title": "Blending neural operators and relaxation methods in PDE numerical...", "date": "", "ddg_snippet": "We exploit the weaknesses of the two approaches by combining them synergistically to develop a fast numerical solver of partial differential equations ( PDEs ) at scale.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-024-00910-x?error=cookies_not_supported&code=40233ccc-e427-4ab2-a1e8-126915803dcd", "content": "We exploit the weaknesses of the two approaches by combining them synergistically to develop a fast numerical solver of partial differential equations ( PDEs ) at scale."}
+{"idx": 4, "title": "Solver - in - the - loop approach to closure of shell models of turbulence", "date": "", "ddg_snippet": "This approach takes advantage of the differentiable physics paradigm of deep learning, allowing a neural network model to interact with the differential equation solver over time during the training process.", "subpage_snippet": "", "source": "cnrs.hal.science", "link": "https://cnrs.hal.science/LIX/hal-05031445v1", "content": "This approach takes advantage of the differentiable physics paradigm of deep learning, allowing a neural network model to interact with the differential equation solver over time during the training process."}
+{"idx": 5, "title": "Numerical PDE solvers outperform neural PDE solvers", "date": "", "ddg_snippet": "Solver in the loop [UBF+20] integrate NN methods with a PDE solver. ClimODE [VHG24] solves an advection equation with source.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21269v1", "content": "Solver in the loop [UBF+20] integrate NN methods with a PDE solver. ClimODE [VHG24] solves an advection equation with source."}
+{"idx": 6, "title": "(PDF) Active Learning for Neural PDE Solvers", "date": "", "ddg_snippet": "solver - in - the - loop setting, enabling the evaluation of existing and the development. of new AL methods for PDE solving. We use the benchmark to evaluate batch.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382884438_Active_Learning_for_Neural_PDE_Solvers", "content": "solver - in - the - loop setting, enabling the evaluation of existing and the development. of new AL methods for PDE solving. We use the benchmark to evaluate batch."}
+{"idx": 7, "title": "G enerative pde C ontrol", "date": "", "ddg_snippet": "Solver - in - the - loop : Learning from differentiable physics to interact with iterative pde -solvers.We use the finite difference method (called solver or ground-truth solver in the following) to generate the training data for the 1D Burgers’ equation .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=vaKnCahjdj", "content": "Solver - in - the - loop : Learning from differentiable physics to interact with iterative pde -solvers.We use the finite difference method (called solver or ground-truth solver in the following) to generate the training data for the 1D Burgers’ equation ."}
+{"idx": 8, "title": "Active Learning for Neural PDE Solvers", "date": "", "ddg_snippet": "Solving partial differential equations ( PDEs ) is a fundamental problem in engineering and science. While neural PDE solvers can be more efficient than established numerical solvers, they often require large amounts of training data that is costly to obtain.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/60960ad78868fce5c165295fbd895060-Abstract-Conference.html", "content": "Solving partial differential equations ( PDEs ) is a fundamental problem in engineering and science. While neural PDE solvers can be more efficient than established numerical solvers, they often require large amounts of training data that is costly to obtain."}
+{"idx": 9, "title": "General PDEs Research | DeepWiki", "date": "", "ddg_snippet": "\" Solver - in - the - Loop : Learning from Differentiable Physics to Interact with Iterative PDE -Solvers\". \"Learning to Optimize Multigrid PDE Solvers\". \"A neural -preconditioned poisson solver for mixed boundary conditions\".", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/thunil/Physics-Based-Deep-Learning/3.3-general-pdes-research", "content": "\" Solver - in - the - Loop : Learning from Differentiable Physics to Interact with Iterative PDE -Solvers\". \"Learning to Optimize Multigrid PDE Solvers\". \"A neural -preconditioned poisson solver for mixed boundary conditions\"."}
diff --git a/data/sampled_jsons/Song_et_al._2021_Score-based_Generative_Modeling_through_Stochastic_Differential_Equations.jsonl b/data/sampled_jsons/Song_et_al._2021_Score-based_Generative_Modeling_through_Stochastic_Differential_Equations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf85f66193213345fa78dff48eed674d7e341f94
--- /dev/null
+++ b/data/sampled_jsons/Song_et_al._2021_Score-based_Generative_Modeling_through_Stochastic_Differential_Equations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Score-Based Generative Modeling through Stochastic ... - GitHub Generative Modeling by Estimating Gradients of the Data ... Score-based generative modeling through stochastic evolution ... Literature Review: Score-Based Generative Modeling through ... Generative Modeling by Estimating Gradients of the Data Distribution Generative Modeling by Estimating Gradients of the Data Distribution S -B GENERATIVE MODELING THROUGH S Generative Modeling by Estimating Gradients of the Data Distribution S -B GENERATIVE MODELING THROUGH S S -B GENERATIVE MODELING THROUGH S Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "This repo contains the official implementation for the paper Score - Based Generative Modeling through Stochastic Differential Equations by Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole We propose a unified framework that generalizes and improves previous work on score - based generative models through the lens of stochastic differential equations (SDEs). In particular, we can transform data to a simple noise distribution with a continuous-time stochastic process described by an SDE. This SDE can be reversed for sample generation if we know the score of the marginal distributions at each intermediate time step, which can be estimated with score matching. The basic idea is captured in the figure below: Our work enables a better understanding of existing approaches, new sampling algorithms, exact likelihood computation, uniquely identifiable encoding, latent code manipulation, and brings new conditional generation abilities (including but not limited to class-conditional generation, inpainting and colorization) to the family of score - based generative models. See full list on github.com Aside from the NCSN++ and DDPM++ models in our paper, this codebase also re-implements many previous score - based models in one place, including NCSN from Generative Modeling by Estimating Gradients of the Data Distribution, NCSNv2 from Improved Techniques for Training Score - Based Generative Models, and DDPM from Denoising Diffusion Probabilistic Models. It supports training new models, evaluating the sample quality and likelihoods of existing models. We carefully designed the code to be modular and easily extensible to new SDEs, predictors, or correctors. See full list on github.com JAX vs. PyTorch In general, the PyTorch version consumes less memory but also runs slower than JAX. Here is a benchmark on training an NCSN++ cont. model with VE SDE. Hardware is 4x Nvidia Tesla V100 GPUs (32GB) See full list on github.com Dependencies Run the following to install a subset of necessary python packages for our code Stats files for quantitative evaluation We provide the stats file for CIFAR-10. You can download cifar10_stats.npz and save it to assets/stats/. Check out #5 on how to compute this stats file for new datasets. Usage Train and evaluate our models through main.py.•config is the path to the config file. Our prescribed config files are provided in configs/. They are formatted according to ml_collections and should be quite self-explanatory.Naming conventions of config files: the path of a config file is a combination of the following dimensions:•dataset: One of cifar10, celeba, celebahq, celebahq_256, ffhq_256, celebahq, ffhq.•model: One of ncsn, ncsnv2, ncsnpp, ddpm, ddpmpp.•continuous: train the model with continuously sampled time steps.•workdir is the path that stores all artifacts of one experiment, like checkpoints, samples, and evaluation results.•eval_folder is the name of a subfolder in workdir that stores all artifacts of the evaluation process, like meta checkpoints for pre-emption prevention, image samples, and numpy dumps of quantitative results.•mode is either \"train\" or \"eval\". When set to \"train\", it starts the training of a new model, or resumes the training of an old model if its meta-checkpoints (for resuming running after pre-emption in a cloud environment) exist in workdir/checkpoints-meta . When set to \"eval\", it can do an arbitrary combination of the following•Evaluate the loss function on the test / validation dataset.•Generate a fixed number of samples and compute its Inception score , FID, or KID. Prior to evaluation, stats files must have already been downloaded/computed and stored in assets/stats.•Compute the log-likelihood on the training or test dataset.These functionalities can be configured through config files, or more conveniently, through the command-line support of the ml_collections package. For example, to generate samples and evaluate sample quality, supply the --config.eval.enable_sampling flag; to compute log-likelihoods, supply the --config.eval.enable_bpd flag, and specify --config.eval.dataset=train/test to indicate whether to compute the likelihoods on the training or test dataset. See full list on github.com •New SDEs: inherent the sde_lib.SDE abstract class and implement all abstract methods. The discretize() method is optional and the default is Euler-Maruyama discretization. Existing sampling methods and likelihood computation will automatically work for this new SDE. •New predictors: inherent the sampling.Predictor abstract class, implement the update_fn abstract method, and register its name with @register_predictor. The new predictor can be directly used in sampling.get_pc_sampler for Predictor-Corrector sampling, and all other controllable generation methods in controllable_generation.py. See full list on github.com All checkpoints are provided in this Google drive. Instructions: You may find two checkpoints for some models. The first checkpoint (with a smaller number) is the one that we reported FID scores in our paper's Table 3 (also corresponding to the FID and IS columns in the table below). The second checkpoint (with a larger number) is the one that we reported likelihood values and FIDs of black-box ODE samplers in our paper's Table 2 (also FID(ODE) and NNL (bits/dim) columns in the table below). The former corresponds to the smallest FID during the course of training (every 50k iterations). The later is the last checkpoint during training. Per Google's policy, we cannot release our original CelebA and CelebA-HQ checkpoints. That said, I have re-trained models on FFHQ 1024px, FFHQ 256px and CelebA-HQ 256px with personal resources, and they achieved similar performance to our internal checkpoints. Here is a detailed list of checkpoints and their results reported in the paper. FID (ODE) corresponds to the sample quality of black-box ODE solver applied to the probability flow ODE. See full list on github.com •When using the JAX codebase, you can jit multiple training steps together to improve training speed at the cost of more memory usage. This can be set via config.training.n_jitted_steps. For CIFAR-10, we recommend using config.training.n_jitted_steps=5 when your GPU/TPU has sufficient memory; otherwise we recommend using config.training.n_jitted_steps=1. Our current implementation requires config.training.log_freq to be dividable by n_jitted_steps for logging and checkpointing to work normally. •The snr (signal-to-noise ratio) parameter of LangevinCorrector somewhat behaves like a temperature parameter. Larger snr typically results in smoother samples, while smaller snr gives more diverse but lower quality samples. Typical values of snr is 0.05 - 0.2, and it requires tuning to strike the sweet spot. See full list on github.com If you find the code useful for your research, please consider citing This work is built upon some previous papers which might also interest you: • Song , Yang, and Stefano Ermon. \" Generative Modeling by Estimating Gradients of the Data Distribution.\" Proceedings of the 33rd Annual Conference on Neural Information Processing Systems. 2019. • Song , Yang, and Stefano Ermon. \"Improved techniques for training score - based generative models.\" Proceedings of the 34th Annual Conference on Neural Information Processing Systems. 2020. See full list on github.com By generalizing the number of noise scales to infinity, we further proved that score-based generative models and diffusion probabilistic models can both be viewed as discretizations to stochastic differential equations determined by score functions. Dec 10, 2023 · Continuous-time score-based generative models consist of a pair of stochastic differential equations (SDEs)—a forward SDE that smoothly transitions data into a noise space and a reverse SDE that incrementally eliminates noise from a Gaussian prior distribution to generate data distribution samples—are intrinsically connected by the time ... Applied stochastic differential equations , CUP Song , Yang et al . ( 2021 ). Score-Based Generative Modeling through Stochastic Differential Equations , arXiv:2011.13456v2 Can score-based generative models be discretized to stochastic differential equations? By generalizing the number of noise scales to infinity, we further proved that score-based generative models and diffusion probabilistic models can both be viewed as discretizations to stochastic differential equations determined by score functions. How can score-based generative models achieve high likelihood? Using this likelihood weighting function , we can train score-based generative models to achieve very high likelihoods, comparable or even superior to state-of-the-art autoregressive models By solving the estimated reverse SDE with numerical SDE solvers, we can simulate the reverse stochastic process for sample generation. Is there a framework for score-based generative modeling based on SDEs? 6 CONCLUSION We presented a framework for score-based generative modeling based on SDEs . Are score-based generative modeling with multiple noise perturbations and diffusion probabilistic models different? Collectively, these latest developments seem to indicate that both score-based generative modeling with multiple noise perturbations and diffusion probabilistic models are different perspectives of the same model family , much like how wave mechanics and matrix mechanics are equivalent formulations of quantum mechanics in the history of physics 5 . How do score-based generative models solve the inverse problem? Leveraging Eq. (48), score - based generative models provide one way to solve the inverse problem. Suppose we have a diffusion process txptquT t0generated by perturbing x with an SDE, and a 31 How can score-based generative models be combined with fast sampling? Identifying ways of combining the stable learning of score - based generative models with the fast sampling of implicit models like GANs remains an important research direction. Additionally, the breadth of samplers one can use when given access to score functions introduces a number of hyper-parameters. Jul 2, 2025 · Score-Based Generative Modeling through Stochastic Differential Equations Published as a conference paper at ICLR 2021 Authors: Yang Song * (Stanford University, yangsong@cs.stanford.edu ) Jascha Sohl-Dickstein (Google Brain, jaschasd@google.com ) Diederik P. Kingma (Google Brain, durk@google.com ) Abhishek Kumar (Google Brain, abhishk@google.com ) Stefano Ermon (Stanford University, ermon@cs ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yang-song/score_sde", "content": "This repo contains the official implementation for the paper Score - Based Generative Modeling through Stochastic Differential Equations by Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole We propose a unified framework that generalizes and improves previous work on score - based generative models through the lens of stochastic differential equations (SDEs). In particular, we can transform data to a simple noise distribution with a continuous-time stochastic process described by an SDE. This SDE can be reversed for sample generation if we know the score of the marginal distributions at each intermediate time step, which can be estimated with score matching. The basic idea is captured in the figure below: Our work enables a better understanding of existing approaches, new sampling algorithms, exact likelihood computation, uniquely identifiable encoding, latent code manipulation, and brings new conditional generation abilities (including but not limited to class-conditional generation, inpainting and colorization) to the family of score - based generative models. See full list on github.com Aside from the NCSN++ and DDPM++ models in our paper, this codebase also re-implements many previous score - based models in one place, including NCSN from Generative Modeling by Estimating Gradients of the Data Distribution, NCSNv2 from Improved Techniques for Training Score - Based Generative Models, and DDPM from Denoising Diffusion Probabilistic Models. It supports training new models, evaluating the sample quality and likelihoods of existing models. We carefully designed the code to be modular and easily extensible to new SDEs, predictors, or correctors. See full list on github.com JAX vs. PyTorch In general, the PyTorch version consumes less memory but also runs slower than JAX. Here is a benchmark on training an NCSN++ cont. model with VE SDE. Hardware is 4x Nvidia Tesla V100 GPUs (32GB) See full list on github.com Dependencies Run the following to install a subset of necessary python packages for our code Stats files for quantitative evaluation We provide the stats file for CIFAR-10. You can download cifar10_stats.npz and save it to assets/stats/. Check out #5 on how to compute this stats file for new datasets. Usage Train and evaluate our models through main.py.•config is the path to the config file. Our prescribed config files are provided in configs/. They are formatted according to ml_collections and should be quite self-explanatory.Naming conventions of config files: the path of a config file is a combination of the following dimensions:•dataset: One of cifar10, celeba, celebahq, celebahq_256, ffhq_256, celebahq, ffhq.•model: One of ncsn, ncsnv2, ncsnpp, ddpm, ddpmpp.•continuous: train the model with continuously sampled time steps.•workdir is the path that stores all artifacts of one experiment, like checkpoints, samples, and evaluation results.•eval_folder is the name of a subfolder in workdir that stores all artifacts of the evaluation process, like meta checkpoints for pre-emption prevention, image samples, and numpy dumps of quantitative results.•mode is either \"train\" or \"eval\". When set to \"train\", it starts the training of a new model, or resumes the training of an old model if its meta-checkpoints (for resuming running after pre-emption in a cloud environment) exist in workdir/checkpoints-meta . When set to \"eval\", it can do an arbitrary combination of the following•Evaluate the loss function on the test / validation dataset.•Generate a fixed number of samples and compute its Inception score , FID, or KID. Prior to evaluation, stats files must have already been downloaded/computed and stored in assets/stats.•Compute the log-likelihood on the training or test dataset.These functionalities can be configured through config files, or more conveniently, through the command-line support of the ml_collections package. For example, to generate samples and evaluate sample quality, supply the --config.eval.enable_sampling flag; to compute log-likelihoods, supply the --config.eval.enable_bpd flag, and specify --config.eval.dataset=train/test to indicate whether to compute the likelihoods on the training or test dataset. See full list on github.com •New SDEs: inherent the sde_lib.SDE abstract class and implement all abstract methods. The discretize() method is optional and the default is Euler-Maruyama discretization. Existing sampling methods and likelihood computation will automatically work for this new SDE. •New predictors: inherent the sampling.Predictor abstract class, implement the update_fn abstract method, and register its name with @register_predictor. The new predictor can be directly used in sampling.get_pc_sampler for Predictor-Corrector sampling, and all other controllable generation methods in controllable_generation.py. See full list on github.com All checkpoints are provided in this Google drive. Instructions: You may find two checkpoints for some models. The first checkpoint (with a smaller number) is the one that we reported FID scores in our paper's Table 3 (also corresponding to the FID and IS columns in the table below). The second checkpoint (with a larger number) is the one that we reported likelihood values and FIDs of black-box ODE samplers in our paper's Table 2 (also FID(ODE) and NNL (bits/dim) columns in the table below). The former corresponds to the smallest FID during the course of training (every 50k iterations). The later is the last checkpoint during training. Per Google's policy, we cannot release our original CelebA and CelebA-HQ checkpoints. That said, I have re-trained models on FFHQ 1024px, FFHQ 256px and CelebA-HQ 256px with personal resources, and they achieved similar performance to our internal checkpoints. Here is a detailed list of checkpoints and their results reported in the paper. FID (ODE) corresponds to the sample quality of black-box ODE solver applied to the probability flow ODE. See full list on github.com •When using the JAX codebase, you can jit multiple training steps together to improve training speed at the cost of more memory usage. This can be set via config.training.n_jitted_steps. For CIFAR-10, we recommend using config.training.n_jitted_steps=5 when your GPU/TPU has sufficient memory; otherwise we recommend using config.training.n_jitted_steps=1. Our current implementation requires config.training.log_freq to be dividable by n_jitted_steps for logging and checkpointing to work normally. •The snr (signal-to-noise ratio) parameter of LangevinCorrector somewhat behaves like a temperature parameter. Larger snr typically results in smoother samples, while smaller snr gives more diverse but lower quality samples. Typical values of snr is 0.05 - 0.2, and it requires tuning to strike the sweet spot. See full list on github.com If you find the code useful for your research, please consider citing This work is built upon some previous papers which might also interest you: • Song , Yang, and Stefano Ermon. \" Generative Modeling by Estimating Gradients of the Data Distribution.\" Proceedings of the 33rd Annual Conference on Neural Information Processing Systems. 2019. • Song , Yang, and Stefano Ermon. \"Improved techniques for training score - based generative models.\" Proceedings of the 34th Annual Conference on Neural Information Processing Systems. 2020. See full list on github.com By generalizing the number of noise scales to infinity, we further proved that score-based generative models and diffusion probabilistic models can both be viewed as discretizations to stochastic differential equations determined by score functions. Dec 10, 2023 · Continuous-time score-based generative models consist of a pair of stochastic differential equations (SDEs)—a forward SDE that smoothly transitions data into a noise space and a reverse SDE that incrementally eliminates noise from a Gaussian prior distribution to generate data distribution samples—are intrinsically connected by the time ... Applied stochastic differential equations , CUP Song , Yang et al . ( 2021 ). Score-Based Generative Modeling through Stochastic Differential Equations , arXiv:2011.13456v2 Can score-based generative models be discretized to stochastic differential equations? By generalizing the number of noise scales to infinity, we further proved that score-based generative models and diffusion probabilistic models can both be viewed as discretizations to stochastic differential equations determined by score functions. How can score-based generative models achieve high likelihood? Using this likelihood weighting function , we can train score-based generative models to achieve very high likelihoods, comparable or even superior to state-of-the-art autoregressive models By solving the estimated reverse SDE with numerical SDE solvers, we can simulate the reverse stochastic process for sample generation. Is there a framework for score-based generative modeling based on SDEs? 6 CONCLUSION We presented a framework for score-based generative modeling based on SDEs . Are score-based generative modeling with multiple noise perturbations and diffusion probabilistic models different? Collectively, these latest developments seem to indicate that both score-based generative modeling with multiple noise perturbations and diffusion probabilistic models are different perspectives of the same model family , much like how wave mechanics and matrix mechanics are equivalent formulations of quantum mechanics in the history of physics 5 . How do score-based generative models solve the inverse problem? Leveraging Eq. (48), score - based generative models provide one way to solve the inverse problem. Suppose we have a diffusion process txptquT t0generated by perturbing x with an SDE, and a 31 How can score-based generative models be combined with fast sampling? Identifying ways of combining the stable learning of score - based generative models with the fast sampling of implicit models like GANs remains an important research direction. Additionally, the breadth of samplers one can use when given access to score functions introduces a number of hyper-parameters. Jul 2, 2025 · Score-Based Generative Modeling through Stochastic Differential Equations Published as a conference paper at ICLR 2021 Authors: Yang Song * (Stanford University, yangsong@cs.stanford.edu ) Jascha Sohl-Dickstein (Google Brain, jaschasd@google.com ) Diederik P. Kingma (Google Brain, durk@google.com ) Abhishek Kumar (Google Brain, abhishk@google.com ) Stefano Ermon (Stanford University, ermon@cs ..."}
+{"idx": 1, "title": "Score - Based Generative Modeling through", "date": "", "ddg_snippet": "Literature Review: Score - Based Generative Modeling through . Stochastic Dierential Equations ( Song et al . 2021 ).Reverse-time diusion equation models , Stochastic Process. Appl., 12(3): pp.313–326 Sarkka, Simo and Arno Solin (2019).", "subpage_snippet": "", "source": "parleyyang.github.io", "link": "https://parleyyang.github.io/Cam/LT2021/Songpaper.pdf", "content": "Literature Review: Score - Based Generative Modeling through . Stochastic Dierential Equations ( Song et al . 2021 ).Reverse-time diusion equation models , Stochastic Process. Appl., 12(3): pp.313–326 Sarkka, Simo and Arno Solin (2019)."}
+{"idx": 2, "title": "Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "Nov 26, 2020 · We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling , allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456", "content": "Nov 26, 2020 · We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling , allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE."}
+{"idx": 3, "title": "S -B GENERATIVE MODELING THROUGH S DIFFERENTIAL EQUATIONS", "date": "", "ddg_snippet": "To enable new sampling methods and further extend the capabilities of score-based generative models , we propose a unified framework that generalizes previous approaches through the lens of stochastic differential equations (SDEs).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/ef0eadbe07115b0853e964f17aa09d811cd490f1.pdf", "content": "To enable new sampling methods and further extend the capabilities of score-based generative models , we propose a unified framework that generalizes previous approaches through the lens of stochastic differential equations (SDEs)."}
+{"idx": 4, "title": "Generative Modeling by Estimating Gradients of the Data ...", "date": "", "ddg_snippet": "By generalizing the number of noise scales to infinity, we further proved that score-based generative models and diffusion probabilistic models can both be viewed as discretizations to stochastic differential equations determined by score functions.", "subpage_snippet": "", "source": "yang-song.net", "link": "https://yang-song.net/blog/2021/score/", "content": "By generalizing the number of noise scales to infinity, we further proved that score-based generative models and diffusion probabilistic models can both be viewed as discretizations to stochastic differential equations determined by score functions."}
+{"idx": 5, "title": "Score-based generative modeling through stochastic evolution ...", "date": "", "ddg_snippet": "Dec 10, 2023 · Continuous-time score-based generative models consist of a pair of stochastic differential equations (SDEs)—a forward SDE that smoothly transitions data into a noise space and a reverse SDE that incrementally eliminates noise from a Gaussian prior distribution to generate data distribution samples—are intrinsically connected by the time ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3667767", "content": "Dec 10, 2023 · Continuous-time score-based generative models consist of a pair of stochastic differential equations (SDEs)—a forward SDE that smoothly transitions data into a noise space and a reverse SDE that incrementally eliminates noise from a Gaussian prior distribution to generate data distribution samples—are intrinsically connected by the time ..."}
+{"idx": 6, "title": "Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "Jul 2, 2025 · Score-Based Generative Modeling through Stochastic Differential Equations Published as a conference paper at ICLR 2021 Authors: Yang Song * (Stanford University, yangsong@cs.stanford.edu ) Jascha Sohl-Dickstein (Google Brain, jaschasd@google.com ) Diederik P. Kingma (Google Brain, durk@google.com ) Abhishek Kumar (Google Brain, abhishk@google.com ) Stefano Ermon (Stanford University, ermon@cs ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/Menger_Gilmour/article/details/149079969", "content": "Jul 2, 2025 · Score-Based Generative Modeling through Stochastic Differential Equations Published as a conference paper at ICLR 2021 Authors: Yang Song * (Stanford University, yangsong@cs.stanford.edu ) Jascha Sohl-Dickstein (Google Brain, jaschasd@google.com ) Diederik P. Kingma (Google Brain, durk@google.com ) Abhishek Kumar (Google Brain, abhishk@google.com ) Stefano Ermon (Stanford University, ermon@cs ..."}
+{"idx": 7, "title": "Score - based generative modeling through stochastic differential ...", "date": "", "ddg_snippet": "Stefano Ermon. Ben Poole. ICLR 2021 ( 2021 ) (to appear).By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/score-based-generative-modeling-through-stochastic-differential-equations/", "content": "Stefano Ermon. Ben Poole. ICLR 2021 ( 2021 ) (to appear).By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples."}
+{"idx": 8, "title": "Score - Based Generative Modeling through Stochastic Differential ...", "date": "", "ddg_snippet": "back arrow Go to ICLR 2021 Conference homepage. Score - Based Generative Modeling through Stochastic Differential Equations Download PDF.Keywords: generative models , score - based generative models , stochastic differential equations , score matching, diffusion.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=PxTIG12RRHS", "content": "back arrow Go to ICLR 2021 Conference homepage. Score - Based Generative Modeling through Stochastic Differential Equations Download PDF.Keywords: generative models , score - based generative models , stochastic differential equations , score matching, diffusion."}
+{"idx": 9, "title": "ICLR 2021 Score - Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/oral/3402", "content": "By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples."}
diff --git a/data/sampled_jsons/Song_et_al_2021_score-based_SDE_abstract_smoothly_transforms_complex_data_distribution_prior_year_2021.jsonl b/data/sampled_jsons/Song_et_al_2021_score-based_SDE_abstract_smoothly_transforms_complex_data_distribution_prior_year_2021.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SDE Matching: Scalable and Simulation-Free Training of Latent", "date": "", "ddg_snippet": "In parallel, Score Matching (Ho et al ., 2020 ; Song et al ., 2021c ) and Flow Matching methods (Lipman et al ., 2023 ; Albergo et al ., 2023 ; Liu ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02472v2", "content": "In parallel, Score Matching (Ho et al ., 2020 ; Song et al ., 2021c ) and Flow Matching methods (Lipman et al ., 2023 ; Albergo et al ., 2023 ; Liu ..."}
+{"idx": 1, "title": "A Review on Score-based Generative Models for Audio Applications", "date": "", "ddg_snippet": "SGMs, on the other hand, originating from score matching [ 5 ] , use neural networks to learn the Stein score function of the data distribution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08457v1", "content": "SGMs, on the other hand, originating from score matching [ 5 ] , use neural networks to learn the Stein score function of the data distribution ..."}
+{"idx": 2, "title": "COLT 2025 Book of Abstracts", "date": "", "ddg_snippet": "This justifies the success of data - based initialization for score matching methods despite slow mixing for the data distribution , and improves and ...", "subpage_snippet": "", "source": "learningtheory.org", "link": "https://learningtheory.org/colt2025/abstracts.html", "content": "This justifies the success of data - based initialization for score matching methods despite slow mixing for the data distribution , and improves and ..."}
+{"idx": 3, "title": "WC 2021", "date": "", "ddg_snippet": "We also propose an $F$-type test for direct effects and show that the proposed test asymptotically follows a $\\chi^2$- distribution under null ...", "subpage_snippet": "", "source": "duetone.org", "link": "https://duetone.org/wc21/day/1", "content": "We also propose an $F$-type test for direct effects and show that the proposed test asymptotically follows a $\\chi^2$- distribution under null ..."}
+{"idx": 4, "title": "DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete", "date": "", "ddg_snippet": "... random noise from the Gaussian prior and transforms it into data through a generative ordinary differential equation (ODE) ( Song et al ., 2021 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.03300v1", "content": "... random noise from the Gaussian prior and transforms it into data through a generative ordinary differential equation (ODE) ( Song et al ., 2021 ) ."}
+{"idx": 5, "title": "Diffusion Models for Robotic Manipulation: A Survey", "date": "", "ddg_snippet": "While GMMs and IBCs can model multi-modal distributions , and IBCs can even learn complex discontinuous distributions (Florence et al .,, 2022 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.08438v1", "content": "While GMMs and IBCs can model multi-modal distributions , and IBCs can even learn complex discontinuous distributions (Florence et al .,, 2022 ..."}
+{"idx": 6, "title": "Nonlinear ensemble filtering with diffusion models: Application", "date": "", "ddg_snippet": "... in the linear nature of the EnKF’ s update, which limits the ability to represent more complex analysis (posterior) distributions (Spantini et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.00844v1", "content": "... in the linear nature of the EnKF’ s update, which limits the ability to represent more complex analysis (posterior) distributions (Spantini et al ..."}
+{"idx": 7, "title": "AISTATS 2022 Orals", "date": "", "ddg_snippet": "... What is the smallest object that contains all rectangular partitions with n or fewer blocks?'' and shows its application to relational data analysis ...", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2022/events/Oral", "content": "... What is the smallest object that contains all rectangular partitions with n or fewer blocks?'' and shows its application to relational data analysis ..."}
+{"idx": 8, "title": "Vinija's Notes • Primers • Diffusion Models", "date": "", "ddg_snippet": "Diffusion probabilistic models (also simply called diffusion models) are generative models, meaning that they are used to generate data similar to ...", "subpage_snippet": "", "source": "vinija.ai", "link": "https://vinija.ai/models/diffusion-models/", "content": "Diffusion probabilistic models (also simply called diffusion models) are generative models, meaning that they are used to generate data similar to ..."}
+{"idx": 9, "title": "DconnLoop: a deep learning model for predicting chromatin loops", "date": "", "ddg_snippet": "HiCCUPS [ 3 , 20 ] is a peak-finding algorithm based on the Poisson distribution that identifies peaks as chromatin interactions by comparing the ...", "subpage_snippet": "", "source": "bmcbioinformatics.biomedcentral.com", "link": "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-025-06092-6", "content": "HiCCUPS [ 3 , 20 ] is a peak-finding algorithm based on the Poisson distribution that identifies peaks as chromatin interactions by comparing the ..."}
diff --git a/data/sampled_jsons/Song_et_al_2021_score-based_generative_modeling_through_stochastic_differential_equations_full_abstr_year_2021.jsonl b/data/sampled_jsons/Song_et_al_2021_score-based_generative_modeling_through_stochastic_differential_equations_full_abstr_year_2021.jsonl
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index 0000000000000000000000000000000000000000..bdf02b59e4b88040c220aede37dceaf78b41f020
--- /dev/null
+++ b/data/sampled_jsons/Song_et_al_2021_score-based_generative_modeling_through_stochastic_differential_equations_full_abstr_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "by Y Song · 2020 · Cited by 8724 — Abstract page for arXiv paper 2011.13456: Score-Based Generative Modeling through Stochastic Differential Equations .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456", "content": "by Y Song · 2020 · Cited by 8724 — Abstract page for arXiv paper 2011.13456: Score-Based Generative Modeling through Stochastic Differential Equations ."}
+{"idx": 1, "title": "SCORE-BASED GENERATIVE MODELING THROUGH ...", "date": "", "ddg_snippet": "by Y Song · Cited by 8724 — Published as a conference paper at ICLR 2021 score matching ( Song et al ., 2019a) and finite-difference score matching (Pang et al ., 2020) are also.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/ef0eadbe07115b0853e964f17aa09d811cd490f1.pdf?ref=news-tutorials-ai-research", "content": "by Y Song · Cited by 8724 — Published as a conference paper at ICLR 2021 score matching ( Song et al ., 2019a) and finite-difference score matching (Pang et al ., 2020) are also."}
+{"idx": 2, "title": "Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "In this study, inspired by the work and code provided by Song et al . [17], we conducted experiments using a score based generative model trained on the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/b8376fb2c21c42d9287e7dfdf8d39f2618e8fb5b.pdf", "content": "In this study, inspired by the work and code provided by Song et al . [17], we conducted experiments using a score based generative model trained on the ..."}
+{"idx": 3, "title": "Score-based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "Score-based generative modeling through stochastic differential equations . In Proc. of the International Conference on Learning Representations (ICLR), 2020 ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/72191", "content": "Score-based generative modeling through stochastic differential equations . In Proc. of the International Conference on Learning Representations (ICLR), 2020 ..."}
+{"idx": 4, "title": "Score-based Neural Ordinary Differential Equations for ...", "date": "", "ddg_snippet": "11 Sept 2025 — Y. Song et al . Score-Based Generative Modeling through Stochastic Differential Equations . International Conference on Learning Representations.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0021999125006515", "content": "11 Sept 2025 — Y. Song et al . Score-Based Generative Modeling through Stochastic Differential Equations . International Conference on Learning Representations."}
+{"idx": 5, "title": "yang-song/score_sde_pytorch: PyTorch implementation for ...", "date": "", "ddg_snippet": "This repo contains a PyTorch implementation for the paper Score-Based Generative Modeling through Stochastic Differential Equations by Yang Song", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yang-song/score_sde_pytorch", "content": "This repo contains a PyTorch implementation for the paper Score-Based Generative Modeling through Stochastic Differential Equations by Yang Song"}
+{"idx": 6, "title": "Score-based generative modeling through stochastic ...", "date": "", "ddg_snippet": "by S Lim · 2023 · Cited by 24 — Score-based generative modeling through stochastic differential equations . In Proc. of the International Conference on Learning Representations (ICLR), 2020 ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3667767", "content": "by S Lim · 2023 · Cited by 24 — Score-based generative modeling through stochastic differential equations . In Proc. of the International Conference on Learning Representations (ICLR), 2020 ..."}
+{"idx": 7, "title": "Score-based Generative Modeling in Latent Space", "date": "", "ddg_snippet": "by A Vahdat · Cited by 789 — Score-based generative modeling through stochastic differential equations . In International Conference on Learning Representations, 2021 . [3] Yang Song and ... 16 pages", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper/2021/file/5dca4c6b9e244d24a30b4c45601d9720-Paper.pdf", "content": "by A Vahdat · Cited by 789 — Score-based generative modeling through stochastic differential equations . In International Conference on Learning Representations, 2021 . [3] Yang Song and ... 16 pages"}
+{"idx": 8, "title": "Score-based Neural Ordinary Differential Equations for ...", "date": "", "ddg_snippet": "24 Sept 2024 — Score-based generative modeling through stochastic differential equations . In International Conference on Learning Representations, 2021 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.16471v1", "content": "24 Sept 2024 — Score-based generative modeling through stochastic differential equations . In International Conference on Learning Representations, 2021 ."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "Score-Based Generative Modeling through Stochastic Differential Equations · Creating noise from data is easy; creating data from noise is generative modeling ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=ODE-to-SDE+conversion", "content": "Score-Based Generative Modeling through Stochastic Differential Equations · Creating noise from data is easy; creating data from noise is generative modeling ..."}
diff --git a/data/sampled_jsons/SpeechSSM_Figure_1_400_seconds_vs_240_seconds_4_minutes_training_length_year_2024.jsonl b/data/sampled_jsons/SpeechSSM_Figure_1_400_seconds_vs_240_seconds_4_minutes_training_length_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2571a09a5493aadcee0634abe72daf10c87761d4
--- /dev/null
+++ b/data/sampled_jsons/SpeechSSM_Figure_1_400_seconds_vs_240_seconds_4_minutes_training_length_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SpeechSSM model that generates natural speech for up to 16 minutes", "date": "", "ddg_snippet": "The SpeechSSM model allows the production of multiple syllables at once through a non-sequential voice synthesis model called\"Non-Autoregressive\" and also the ability to generate speech up to 16 minutes by the researcher creating a new dataset under the name \"LibriSpeech-Long\" unlike traditional models that build sound word by word or letter by ...", "subpage_snippet": "", "source": "www.e-technook.com", "link": "https://www.e-technook.com/2025/07/speechssm-model-that-generates-natural.html", "content": "The SpeechSSM model allows the production of multiple syllables at once through a non-sequential voice synthesis model called\"Non-Autoregressive\" and also the ability to generate speech up to 16 minutes by the researcher creating a new dataset under the name \"LibriSpeech-Long\" unlike traditional models that build sound word by word or letter by ..."}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Figure 2: Transcriptions of the first 4 minutes of speech continuations generated by SpeechSSM (ours) and a recurrently-prompted Spirit LM; parts have been abbreviated with (more speech…) for clarity and emphasis. The generations are conditioned on a 10- second audio-only prompt from our proposed LibriSpeech-Long test-clean benchmark.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v1", "content": "Figure 2: Transcriptions of the first 4 minutes of speech continuations generated by SpeechSSM (ours) and a recurrently-prompted Spirit LM; parts have been abbreviated with (more speech…) for clarity and emphasis. The generations are conditioned on a 10- second audio-only prompt from our proposed LibriSpeech-Long test-clean benchmark."}
+{"idx": 2, "title": "GitHub - google-deepmind/librispeech-long: LibriSpeech-Long is a ...", "date": "", "ddg_snippet": "Download audio, ground-truth transcripts, and per-file durations for all splits (3GB). This is a benchmark dataset for evaluating long-form variants of speech processing tasks such as speech continuation, speech recognition, and text-to-speech synthesis. It is derived from the LibriSpeech dev and test sets, whose utterances are reprocessed into contiguous examples of up to 4 minutes in length ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/google-deepmind/librispeech-long", "content": "Download audio, ground-truth transcripts, and per-file durations for all splits (3GB). This is a benchmark dataset for evaluating long-form variants of speech processing tasks such as speech continuation, speech recognition, and text-to-speech synthesis. It is derived from the LibriSpeech dev and test sets, whose utterances are reprocessed into contiguous examples of up to 4 minutes in length ..."}
+{"idx": 3, "title": "Ph.D. candidate Se Jin Park from Professor Yong Man Ro's lab develops ...", "date": "", "ddg_snippet": "< Figure 2. Maximum sequence length considered in various Spoken Language Models (SLMs). Whereas conventional SLMs have been trained and evaluated on sequences up to 200 seconds in length , SpeechSSM is capable of training and evaluating speech up to 16 minutes .", "subpage_snippet": "", "source": "ee.kaist.ac.kr", "link": "https://ee.kaist.ac.kr/en/research-achieve/ph-d-candidate-se-jin-park-from-professor-yong-man-ros-lab-develops-speechssm-opening-up-possibilities-for-a-24-hour-ai-voice-assistant/", "content": "< Figure 2. Maximum sequence length considered in various Spoken Language Models (SLMs). Whereas conventional SLMs have been trained and evaluated on sequences up to 200 seconds in length , SpeechSSM is capable of training and evaluating speech up to 16 minutes ."}
+{"idx": 4, "title": "Breaking New Ground in Voice Technology - scisimple.com", "date": "", "ddg_snippet": "With these considerations we propose SpeechSSM , the first speech language model to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech) in a single decoding session without text intermediates, based on recent advances in linear-time sequence modeling.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-01-26-breaking-new-ground-in-voice-technology--akg2dxo", "content": "With these considerations we propose SpeechSSM , the first speech language model to learn from and sample long-form spoken audio (e.g., 16 minutes of read or extemporaneous speech) in a single decoding session without text intermediates, based on recent advances in linear-time sequence modeling."}
+{"idx": 5, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes , a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds , due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.18603", "content": "We consider the generative modeling of speech over multiple minutes , a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds , due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation ..."}
+{"idx": 6, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Abstract We consider the generative modeling of speech over multiple minutes , a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken lan-guage models struggle to generate plausible speech past tens of seconds , due to high temporal resolution of speech tokens causing loss of co-herence, architectural issues with long-sequence training or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.18603", "content": "Abstract We consider the generative modeling of speech over multiple minutes , a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken lan-guage models struggle to generate plausible speech past tens of seconds , due to high temporal resolution of speech tokens causing loss of co-herence, architectural issues with long-sequence training or ..."}
+{"idx": 7, "title": "Convert Words to Time - Talk Time Calculator", "date": "", "ddg_snippet": "Quickly convert the number of words in a talk, presentation, or speech to how many minutes it will take to read.", "subpage_snippet": "", "source": "wordstotime.com", "link": "https://wordstotime.com/", "content": "Quickly convert the number of words in a talk, presentation, or speech to how many minutes it will take to read."}
+{"idx": 8, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Our work makes initial progress on naturalistic, audio-native, long-form speech generation: We introduce SpeechSSM , the first spoken language model for long-form speech. Our 2B and 9B models: produces speech textlessly and in constant memory, for unbounded real-time generation; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ...", "subpage_snippet": "", "source": "google.github.io", "link": "https://google.github.io/tacotron/publications/speechssm/", "content": "Our work makes initial progress on naturalistic, audio-native, long-form speech generation: We introduce SpeechSSM , the first spoken language model for long-form speech. Our 2B and 9B models: produces speech textlessly and in constant memory, for unbounded real-time generation; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ..."}
+{"idx": 9, "title": "yunjae-won/mp_mistral7bv3_sft_40k_n2_250716 - Hugging Face", "date": "", "ddg_snippet": "yunjae-won/mp_mistral7bv3_sft_40k_n2_250716 · Datasets at Hugging Facetrain · 40k rows", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/yunjae-won/mp_mistral7bv3_sft_40k_n2_250716/viewer/default/train", "content": "yunjae-won/mp_mistral7bv3_sft_40k_n2_250716 · Datasets at Hugging Facetrain · 40k rows"}
diff --git a/data/sampled_jsons/SpeechSSM_experimental_setup_section_default_training_sequence_duration_year_2024.jsonl b/data/sampled_jsons/SpeechSSM_experimental_setup_section_default_training_sequence_duration_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7a934fd5751dd6017f65b4c872b9a8cbe20dc213
--- /dev/null
+++ b/data/sampled_jsons/SpeechSSM_experimental_setup_section_default_training_sequence_duration_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Researcher develops ' SpeechSSM ,' opening up possibilities for...", "date": "", "ddg_snippet": "SpeechSSM effectively processes unbounded speech sequences by dividing speech data into short, fixed units (windows), processing each unit independently, and then combining them to create long speech.Tapping fingers to a beat can aid speech comprehension in noisy settings .", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-07-speechssm-possibilities-hour-ai-voice.html", "content": "SpeechSSM effectively processes unbounded speech sequences by dividing speech data into short, fixed units (windows), processing each unit independently, and then combining them to create long speech.Tapping fingers to a beat can aid speech comprehension in noisy settings ."}
+{"idx": 1, "title": "Model Training with Ultralytics YOLO - Ultralytics YOLO Docs", "date": "", "ddg_snippet": "Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings , augmentation, and hardware utilization.", "subpage_snippet": "", "source": "docs.ultralytics.com", "link": "https://docs.ultralytics.com/modes/train/", "content": "Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings , augmentation, and hardware utilization."}
+{"idx": 2, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "... training sequences of up to a target duration . The default for SpeechSSM is 4min (240s) during training , though we compare with target durations of 30s and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46499", "content": "... training sequences of up to a target duration . The default for SpeechSSM is 4min (240s) during training , though we compare with target durations of 30s and ..."}
+{"idx": 3, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "24 Dec 2024 — We propose SpeechSSM , the first speech language model to learn from and sample long-form spoken audio (eg, 16 minutes of read or extemporaneous speech) in a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v1", "content": "24 Dec 2024 — We propose SpeechSSM , the first speech language model to learn from and sample long-form spoken audio (eg, 16 minutes of read or extemporaneous speech) in a ..."}
+{"idx": 4, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "We describe our implementations of both in Section 7.2. 5. Experimental Setup ... '4min' denotes the default SpeechSSM -2B and '16min' the variant trained on 16min.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=4AmFA0qNQ2&name=pdf", "content": "We describe our implementations of both in Section 7.2. 5. Experimental Setup ... '4min' denotes the default SpeechSSM -2B and '16min' the variant trained on 16min."}
+{"idx": 5, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "10 Jul 2025 — ... training sequences of up to a target duration . The default for SpeechSSM is 4min (240s) during training , though we compare with target ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v2", "content": "10 Jul 2025 — ... training sequences of up to a target duration . The default for SpeechSSM is 4min (240s) during training , though we compare with target ..."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "In the speech generation phase, a lightweight decoder facilitates real- time emotional speech through training on speech tasks and preference learning .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=speech+language+models", "content": "In the speech generation phase, a lightweight decoder facilitates real- time emotional speech through training on speech tasks and preference learning ."}
+{"idx": 7, "title": "PROSODYLM: Uncovering the Emerging Prosody ...", "date": "", "ddg_snippet": "by K Qian — 4 Experiments In this section, we will explore how PROSODYLM, only pre-trained on around 30k hours of audiobooks (details in Appendix C), can capture the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=uBg8PClMUu", "content": "by K Qian — 4 Experiments In this section, we will explore how PROSODYLM, only pre-trained on around 30k hours of audiobooks (details in Appendix C), can capture the ..."}
+{"idx": 8, "title": "ICML 2025 Wednesday 07/16", "date": "", "ddg_snippet": "In the experiments , we successfully scale up the visual sequence to an exceptional length of 50,176 tokens, achieving a competitive test accuracy of 84.6 ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/16", "content": "In the experiments , we successfully scale up the visual sequence to an exceptional length of 50,176 tokens, achieving a competitive test accuracy of 84.6 ..."}
+{"idx": 9, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "SpeechSSMs leverage recent advances in linear-time sequence modeling to greatly surpass current Transformer spoken LMs in coherence and efficiency on multi-minute generations while still matching them at the utterance level.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.18603", "content": "SpeechSSMs leverage recent advances in linear-time sequence modeling to greatly surpass current Transformer spoken LMs in coherence and efficiency on multi-minute generations while still matching them at the utterance level."}
diff --git a/data/sampled_jsons/Spring_Li_2024_account_migration_vs_state_placement_blockchain_sharding_year_2024.jsonl b/data/sampled_jsons/Spring_Li_2024_account_migration_vs_state_placement_blockchain_sharding_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..199e768f8e297643f46a7b882ac795bd7c01dcd3
--- /dev/null
+++ b/data/sampled_jsons/Spring_Li_2024_account_migration_vs_state_placement_blockchain_sharding_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain Sharding", "date": "", "ddg_snippet": "Therefore, implementing an experimental platform for blockchain sharding mechanisms involves multiple functionalities such as state management, cross ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.03612v5", "content": "Therefore, implementing an experimental platform for blockchain sharding mechanisms involves multiple functionalities such as state management, cross ..."}
+{"idx": 1, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement...", "date": "", "ddg_snippet": "Blockchain , Sharding , Account migration , Reinforcement learning. 2024 . SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement . In Proceedings of the ACM on Web Conference 2024 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "Blockchain , Sharding , Account migration , Reinforcement learning. 2024 . SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement . In Proceedings of the ACM on Web Conference 2024 ."}
+{"idx": 2, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement...", "date": "", "ddg_snippet": "Blockchain , Sharding , Account migration , Reinforcement learning. ∗Corresponding author.and Jieyi Long. 2024 . SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement .", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "Blockchain , Sharding , Account migration , Reinforcement learning. ∗Corresponding author.and Jieyi Long. 2024 . SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement ."}
+{"idx": 3, "title": "(PDF) BrokerChain: A Cross- Shard Blockchain Protocol for...", "date": "", "ddg_snippet": "Account Migration across Blockchain Shards using Fine-tuned Lock Mechanism. May 2024 . Huawei Huang. Zibin Zheng.In blockchain state sharding , account migration across shards is crucial to the low ratio of cross- shard transactions and cross- shard workload balance.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/356789473_BrokerChain_A_Cross-Shard_Blockchain_Protocol_for_AccountBalance-based_State_Sharding", "content": "Account Migration across Blockchain Shards using Fine-tuned Lock Mechanism. May 2024 . Huawei Huang. Zibin Zheng.In blockchain state sharding , account migration across shards is crucial to the low ratio of cross- shard transactions and cross- shard workload balance."}
+{"idx": 4, "title": "ContribChain: A Stress-Balanced Blockchain Sharding Protocol with...", "date": "", "ddg_snippet": "P. Li , M. Song, M. Xing, Z. Xiao, Q. Ding, S. Guan, and J. Long, “ Spring : Improving the throughput of sharding blockchain via deep reinforcement learning based state placement ,” in Proceedings of the ACM on Web Conference 2024 , 2024 , pp. 2836–2846.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06899v1", "content": "P. Li , M. Song, M. Xing, Z. Xiao, Q. Ding, S. Guan, and J. Long, “ Spring : Improving the throughput of sharding blockchain via deep reinforcement learning based state placement ,” in Proceedings of the ACM on Web Conference 2024 , 2024 , pp. 2836–2846."}
+{"idx": 5, "title": "Estuary: A Low Cross- Shard Blockchain Sharding ... | IEEE Xplore", "date": "", "ddg_snippet": "In this article, we propose Estuary, a novel low cross- shard blockchain sharding protocol. Taking the state model as an entry point, Estuary designs a multi-level state model and state splitting and aggregation mechanism.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10384778", "content": "In this article, we propose Estuary, a novel low cross- shard blockchain sharding protocol. Taking the state model as an entry point, Estuary designs a multi-level state model and state splitting and aggregation mechanism."}
+{"idx": 6, "title": "Blockchain Sharding : Scalability Without Losing Security", "date": "", "ddg_snippet": "Blockchain Sharding : Solving Scalability Without Sacrificing Security. Blockchain Sharding vs Sidechains. Sidechains are separate blockchains connected to the main chain. They can run fast and cheap, but they may not be as secure as the main chain.", "subpage_snippet": "", "source": "thebitjournal.com", "link": "https://thebitjournal.com/blockchain-sharding-solving-scalability-secure/", "content": "Blockchain Sharding : Solving Scalability Without Sacrificing Security. Blockchain Sharding vs Sidechains. Sidechains are separate blockchains connected to the main chain. They can run fast and cheap, but they may not be as secure as the main chain."}
+{"idx": 7, "title": "Sharding Types - Everything You Need to Know | Shardeum", "date": "", "ddg_snippet": "Like state sharding , static state sharding also does not guarantee atomic and cross- shard composability.Tangle is used for transactions in a decentralized network, while blockchain carries all transactions within its network Check the difference between tangle vs ...", "subpage_snippet": "", "source": "shardeum.org", "link": "https://shardeum.org/blog/sharding-types/", "content": "Like state sharding , static state sharding also does not guarantee atomic and cross- shard composability.Tangle is used for transactions in a decentralized network, while blockchain carries all transactions within its network Check the difference between tangle vs ..."}
+{"idx": 8, "title": "SharDAG: Scaling DAG-based Blockchains via", "date": "", "ddg_snippet": "Specically, when migrating account states across shards , it is necessary to delete nodes in the monolithic MPT and generate corresponding proofs, which involves hash operations of all nodes in the path from the leaf node to the root, resulting in long state reconguration delays.", "subpage_snippet": "", "source": "iqua.ece.utoronto.ca", "link": "https://iqua.ece.utoronto.ca/papers/jxiao-icde24.pdf", "content": "Specically, when migrating account states across shards , it is necessary to delete nodes in the monolithic MPT and generate corresponding proofs, which involves hash operations of all nodes in the path from the leaf node to the root, resulting in long state reconguration delays."}
+{"idx": 9, "title": "What Exactly Is Blockchain Sharding And How Does It Impact The...", "date": "", "ddg_snippet": "State sharding – Sharding the blockchain state enables processing transactions in parallel. State refers to account balances and smart contract data.", "subpage_snippet": "", "source": "blog.bitamp.com", "link": "https://blog.bitamp.com/what-exactly-is-blockchain-sharding-and-how-does-it-impact-the-network/", "content": "State sharding – Sharding the blockchain state enables processing transactions in parallel. State refers to account balances and smart contract data."}
diff --git a/data/sampled_jsons/Spring_Li_et_al._2024_blockchain.jsonl b/data/sampled_jsons/Spring_Li_et_al._2024_blockchain.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..75b0a516af5ce2c9ed47fe389847b4489a9ff1c7
--- /dev/null
+++ b/data/sampled_jsons/Spring_Li_et_al._2024_blockchain.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "by P Li · 2024 · Cited by 21 — SPRING is a deep-reinforcement-learning framework for state placement in sharding blockchains , aiming to reduce cross-shard transactions and ...", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_Spring_camera_ready.pdf", "content": "by P Li · 2024 · Cited by 21 — SPRING is a deep-reinforcement-learning framework for state placement in sharding blockchains , aiming to reduce cross-shard transactions and ..."}
+{"idx": 1, "title": "Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "13 May 2024 — In this paper, we present SPRING , the first deep-reinforcement-learning(DRL)-based sharding framework for state placement.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3589334.3645386", "content": "13 May 2024 — In this paper, we present SPRING , the first deep-reinforcement-learning(DRL)-based sharding framework for state placement."}
+{"idx": 2, "title": "Blockchain implementation decisions in a dual-channel ...", "date": "", "ddg_snippet": "Li et al. (Citation2024) used Stackelberg game models to investigate the impact of blockchain technology adoption for consumer privacy protection on an e- ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/00207543.2025.2476712?ai=1ky&mi=ik0enr&af=R", "content": "Li et al. (Citation2024) used Stackelberg game models to investigate the impact of blockchain technology adoption for consumer privacy protection on an e- ..."}
+{"idx": 3, "title": "Risk Contagion and Regulatory Containment in Blockchain ...", "date": "", "ddg_snippet": "by W Zou · 2025 — The integration of blockchain can enhance data reconciliation efficiency, reduce risks, and increase transparency in securities settlement systems, especially ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/org/science/article/pii/S1062737525000691", "content": "by W Zou · 2025 — The integration of blockchain can enhance data reconciliation efficiency, reduce risks, and increase transparency in securities settlement systems, especially ..."}
+{"idx": 4, "title": "Blockchain technology and the circular economy", "date": "", "ddg_snippet": "by A Upadhyay · 2021 · Cited by 731 — Our findings show that blockchain technology can contribute to the circular economy by helping to reduce transaction costs, enhance performance and ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0959652621003504", "content": "by A Upadhyay · 2021 · Cited by 731 — Our findings show that blockchain technology can contribute to the circular economy by helping to reduce transaction costs, enhance performance and ..."}
+{"idx": 5, "title": "An Overview of Blockchain for Industry 5.0", "date": "", "ddg_snippet": "by P Fraga-Lamas · 2024 · Cited by 35 — Other authors studied how to make use of blockchains together with different technologies that can be applied to a smart. Industry 5.0 factory [ ... 40 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10380310/10614164.pdf", "content": "by P Fraga-Lamas · 2024 · Cited by 35 — Other authors studied how to make use of blockchains together with different technologies that can be applied to a smart. Industry 5.0 factory [ ... 40 pages"}
+{"idx": 6, "title": "What Blockchain Technology Can Contribute to Smart Cities", "date": "", "ddg_snippet": "by J Yan · 2025 — This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-6839-7_17", "content": "by J Yan · 2025 — This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework."}
+{"idx": 7, "title": "Opportunities and Challenges of Blockchain Technology ...", "date": "", "ddg_snippet": "by J Witt · 2025 · Cited by 2 — We address this lack of knowledge following a design-oriented approach in three steps exploring the opportunities and challenges of using BCT for e- ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10726-024-09916-7", "content": "by J Witt · 2025 · Cited by 2 — We address this lack of knowledge following a design-oriented approach in three steps exploring the opportunities and challenges of using BCT for e- ..."}
+{"idx": 8, "title": "Pre-positioning supplies with blockchain-supported ...", "date": "", "ddg_snippet": "This paper proposes an innovative joint government-enterprise model that integrates shared inventory with additional enterprise production capacity reserves.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/00207543.2024.2443793?af=R", "content": "This paper proposes an innovative joint government-enterprise model that integrates shared inventory with additional enterprise production capacity reserves."}
+{"idx": 9, "title": "Enhancing security in financial transactions: a novel ...", "date": "", "ddg_snippet": "by H Rabbani · 2024 · Cited by 6 — We propose the implementation of an FL framework that uses multiple ML models to protect consumers against fraudulent transactions through blockchain .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11622837/", "content": "by H Rabbani · 2024 · Cited by 6 — We propose the implementation of an FL framework that uses multiple ML models to protect consumers against fraudulent transactions through blockchain ."}
diff --git a/data/sampled_jsons/Spring_Li_et_al._2024_blockchain_sharding_abstract_year_2024.jsonl b/data/sampled_jsons/Spring_Li_et_al._2024_blockchain_sharding_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a725fbabeb5f19f698fe27eb0504ebc919491c2f
--- /dev/null
+++ b/data/sampled_jsons/Spring_Li_et_al._2024_blockchain_sharding_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "Since the states are placed on different shards, cross-shard transactions are inevitable, which is detrimental to the performance of the sharding blockchain . Existing solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less effective or costly.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3589334.3645386", "content": "Since the states are placed on different shards, cross-shard transactions are inevitable, which is detrimental to the performance of the sharding blockchain . Existing solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less effective or costly."}
+{"idx": 1, "title": "PDF SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "Abstract Sharding provides an opportunity to overcome inherent scal-ability challenges of the blockchain . In a sharding block-chain , the state and computation are partitioned into smaller groups, known as \"shards,\" to facilitate parallel transaction processing and improve throughput.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=8oczaP1YKD&name=pdf", "content": "Abstract Sharding provides an opportunity to overcome inherent scal-ability challenges of the blockchain . In a sharding block-chain , the state and computation are partitioned into smaller groups, known as \"shards,\" to facilitate parallel transaction processing and improve throughput."}
+{"idx": 2, "title": "A sharding blockchain protocol for enhanced scalability and performance ...", "date": "", "ddg_snippet": "Abstract Sharding is a critical technology for enhancing blockchain scalability. However, existing sharding blockchain protocols suffer from a high cross-shard ratio, high transaction latency, limited throughput enhancement, and high account migration.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1319157824002738", "content": "Abstract Sharding is a critical technology for enhancing blockchain scalability. However, existing sharding blockchain protocols suffer from a high cross-shard ratio, high transaction latency, limited throughput enhancement, and high account migration."}
+{"idx": 3, "title": "Analytical Modeling and Throughput Computation of Blockchain Sharding", "date": "", "ddg_snippet": "Abstract : Sharding has shown great potential to scale out blockchains . It divides nodes into smaller groups which allow for partial transaction processing, relaying and storage. Hence, instead of running one blockchain , we will run multiple blockchains in parallel, and call each one a shard. Sharding can be applied to address shortcomings due to compulsory duplication of three resources in ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10468555", "content": "Abstract : Sharding has shown great potential to scale out blockchains . It divides nodes into smaller groups which allow for partial transaction processing, relaying and storage. Hence, instead of running one blockchain , we will run multiple blockchains in parallel, and call each one a shard. Sharding can be applied to address shortcomings due to compulsory duplication of three resources in ..."}
+{"idx": 4, "title": "[2405.20521] SoK: Public Blockchain Sharding - arXiv.org", "date": "", "ddg_snippet": "Blockchain's decentralization, transparency, and tamper-resistance properties have facilitated the system's use in various application fields. However, the low throughput and high confirmation latency hinder the widespread adoption of Blockchain . Many solutions have been proposed to address these issues, including first-layer solutions (or on-chain solutions) and second-layer solutions (or off ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.20521", "content": "Blockchain's decentralization, transparency, and tamper-resistance properties have facilitated the system's use in various application fields. However, the low throughput and high confirmation latency hinder the widespread adoption of Blockchain . Many solutions have been proposed to address these issues, including first-layer solutions (or on-chain solutions) and second-layer solutions (or off ..."}
+{"idx": 5, "title": "PDF SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "Since the states are placed on diferent shards, cross-shard transactions are inevitable, which is detrimental to the performance of the sharding blockchain . Existing solutions place states based on heuristic algo-rithms or redistribute states via graph-partitioning-based methods, which are either less efective or costly.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_Spring_camera_ready.pdf", "content": "Since the states are placed on diferent shards, cross-shard transactions are inevitable, which is detrimental to the performance of the sharding blockchain . Existing solutions place states based on heuristic algo-rithms or redistribute states via graph-partitioning-based methods, which are either less efective or costly."}
+{"idx": 6, "title": "Sharding Technologies in Blockchain: Basics, State of the Art, and ...", "date": "", "ddg_snippet": "Sharding was originally used for improving the scalability of the database by storing a few copies in different places [10, 12, 19]. Luu et al. [26] first introduced sharding into a blockchain , which is a relatively efficient approach to improve the scalability of the blockchain .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-99-8101-4_17", "content": "Sharding was originally used for improving the scalability of the database by storing a few copies in different places [10, 12, 19]. Luu et al. [26] first introduced sharding into a blockchain , which is a relatively efficient approach to improve the scalability of the blockchain ."}
+{"idx": 7, "title": "Review of Research on Blockchain Sharding Techniques", "date": "", "ddg_snippet": "Abstract : Blockchain technology is characterized by decentralization and tamper resistance,and has a wide range of application prospects.However,it is difficult for blockchain systems to support large-scale distributed data management and transactions,so the performance and scalability of blockchain have become important research directions.At ...", "subpage_snippet": "", "source": "www.jsjkx.com", "link": "https://www.jsjkx.com/EN/Y2024/V51/I11/307", "content": "Abstract : Blockchain technology is characterized by decentralization and tamper resistance,and has a wide range of application prospects.However,it is difficult for blockchain systems to support large-scale distributed data management and transactions,so the performance and scalability of blockchain have become important research directions.At ..."}
+{"idx": 8, "title": "A survey of state-of-the-art sharding blockchains: Models, components ...", "date": "", "ddg_snippet": "In contrast, layer-2 scaling separates the computation from layer 1. The blockchain is only treated as a secure data layer. For example, a payment channel (Ge et al ., 2022, Sivaraman et al ., 2018) and Rollup (Thibault et al ., 2022, Xu and Chen, 2022) process batch transactions off-chain and then settle the status on the blockchain .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1084804523001054", "content": "In contrast, layer-2 scaling separates the computation from layer 1. The blockchain is only treated as a secure data layer. For example, a payment channel (Ge et al ., 2022, Sivaraman et al ., 2018) and Rollup (Thibault et al ., 2022, Xu and Chen, 2022) process batch transactions off-chain and then settle the status on the blockchain ."}
+{"idx": 9, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "Pengze Li , Mingxuan Song, Mingzhe Xing, Zhen Xiao, Qiuyu Ding, Shengjie Guan, and Jieyi Long. 2024 . SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "Pengze Li , Mingxuan Song, Mingzhe Xing, Zhen Xiao, Qiuyu Ding, Shengjie Guan, and Jieyi Long. 2024 . SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement."}
diff --git a/data/sampled_jsons/Stanislaus_Ulam_1979_interview_Einstein_creativity.jsonl b/data/sampled_jsons/Stanislaus_Ulam_1979_interview_Einstein_creativity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2ee9e3e80788738e7268b9f78bf6e86b99b1e66f
--- /dev/null
+++ b/data/sampled_jsons/Stanislaus_Ulam_1979_interview_Einstein_creativity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stanisław Ulam - Wikipedia", "date": "", "ddg_snippet": "With Fermi, John Pasta, and Mary Tsingou, Ulam studied the Fermi–Pasta–Ulam–Tsingou problem, which became the inspiration for the field of nonlinear science.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stanisław_Ulam", "content": "With Fermi, John Pasta, and Mary Tsingou, Ulam studied the Fermi–Pasta–Ulam–Tsingou problem, which became the inspiration for the field of nonlinear science."}
+{"idx": 1, "title": "Stanisław Ulam - Wikipedia, la enciclopedia libre", "date": "", "ddg_snippet": "Stanisław Marcin Ulam fue un matemático polaco que participó en el proyecto Manhattan y propuso el diseño Teller– Ulam de las armas termonucleares. También propuso la idea de propulsión nuclear de pulso y desarrolló un número de herramientas matemátic...", "subpage_snippet": "", "source": "es.wikipedia.org", "link": "https://es.wikipedia.org/wiki/Stanisław_Ulam", "content": "Stanisław Marcin Ulam fue un matemático polaco que participó en el proyecto Manhattan y propuso el diseño Teller– Ulam de las armas termonucleares. También propuso la idea de propulsión nuclear de pulso y desarrolló un número de herramientas matemátic..."}
+{"idx": 2, "title": "John von Neumann - Wikipedia", "date": "", "ddg_snippet": "The MANIAC, 2023 book about von Neumann. German: Abenteuer eines Mathematikers (English title: Adventures of a Mathematician), biopic about Stanislaw Ulam also features John von Neumann.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/John_von_Neumann", "content": "The MANIAC, 2023 book about von Neumann. German: Abenteuer eines Mathematikers (English title: Adventures of a Mathematician), biopic about Stanislaw Ulam also features John von Neumann."}
+{"idx": 3, "title": "Stanislaus Ulam 's Interview ( 1979 ) - Nuclear Museum", "date": "", "ddg_snippet": "In particular, Ulam was frustrated by Oppenheimer’s wordiness, which he and some other scientists perceived as pompous and superfluous. Ulam also explains his thoughts on creativity in math and physics, and why he is a proponent of nuclear power. Date of Interview : July 19, 1979 .", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "content": "In particular, Ulam was frustrated by Oppenheimer’s wordiness, which he and some other scientists perceived as pompous and superfluous. Ulam also explains his thoughts on creativity in math and physics, and why he is a proponent of nuclear power. Date of Interview : July 19, 1979 ."}
+{"idx": 4, "title": "An Interview with Stan Ulam - JSTOR", "date": "", "ddg_snippet": "After an unsatisfying postwar stint at the University of Southern California, Ulam was invited to return to Los Alamos. Russian acquisition of the atomic bomb spurred efforts at Los Alamos to perfect the \"super,\" as the hydrogen bomb was called in its development stage.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/3027387", "content": "After an unsatisfying postwar stint at the University of Southern California, Ulam was invited to return to Los Alamos. Russian acquisition of the atomic bomb spurred efforts at Los Alamos to perfect the \"super,\" as the hydrogen bomb was called in its development stage."}
+{"idx": 5, "title": "Stanislaus Ulam's Interview by Atomic Heritage - SoundCloud", "date": "", "ddg_snippet": "In this interview , he discusses his work at Los Alamos and his relationship with J. Robert Oppenheimer, Hans Bethe, John von Neumann, Enrico Fermi, and other scientists.", "subpage_snippet": "", "source": "soundcloud.com", "link": "https://soundcloud.com/atomicheritage/stanislaus-ulams-interview", "content": "In this interview , he discusses his work at Los Alamos and his relationship with J. Robert Oppenheimer, Hans Bethe, John von Neumann, Enrico Fermi, and other scientists."}
+{"idx": 6, "title": "Stanislaw M. Ulam Papers | American Philosophical Society ...", "date": "", "ddg_snippet": "Talks and interviews , 1937-1984 ( Stanislaw M. Ulam Papers) Series II. Personal correspondence, 1936- 1979 ( Stanislaw M. Ulam Papers)", "subpage_snippet": "", "source": "diglib.amphilsoc.org", "link": "https://diglib.amphilsoc.org/islandora/object/stanislaw-m-ulam-papers", "content": "Talks and interviews , 1937-1984 ( Stanislaw M. Ulam Papers) Series II. Personal correspondence, 1936- 1979 ( Stanislaw M. Ulam Papers)"}
+{"idx": 7, "title": "Stanislaus Ulam – Nuclear Museum", "date": "", "ddg_snippet": "[Thanks to David Schiferl and Willie Atencio for recording this interview and providing a copy to the Atomic Heritage Foundation.] Willie Atencio: Okay, we’ll try to do this informally.", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/tag/stanislaus-ulam/", "content": "[Thanks to David Schiferl and Willie Atencio for recording this interview and providing a copy to the Atomic Heritage Foundation.] Willie Atencio: Okay, we’ll try to do this informally."}
+{"idx": 8, "title": "Stanisław Ulam 1909-1984 | The Mathematical Gazette ...", "date": "", "ddg_snippet": "Prodigiously talented with a remarkable flair for anticipating correct results and initiating fruitful areas of research, Stanislaw Ulam (‘Stan’ to his friends) was an unusual mathematician.", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/mathematical-gazette/article/stanislaw-ulam-19091984/B2FC99B43C8EF3A196D5BB598A7736CE", "content": "Prodigiously talented with a remarkable flair for anticipating correct results and initiating fruitful areas of research, Stanislaw Ulam (‘Stan’ to his friends) was an unusual mathematician."}
+{"idx": 9, "title": "Stan Ulam (1909 - 1984) - Biography - MacTutor History of Mathematics", "date": "", "ddg_snippet": "Stan Ulam was a Polish-American mathematician who solved the problem of how to initiate fusion in the hydrogen bomb.", "subpage_snippet": "", "source": "mathshistory.st-andrews.ac.uk", "link": "https://mathshistory.st-andrews.ac.uk/Biographies/Ulam/", "content": "Stan Ulam was a Polish-American mathematician who solved the problem of how to initiate fusion in the hydrogen bomb."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_arxiv_pdf_full_text.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_arxiv_pdf_full_text.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..996cd8687d5698ba5f7a1806b34a4977232d2969
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_arxiv_pdf_full_text.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regulation of algorithms - Wikipedia", "date": "", "ddg_snippet": "Algorithmic tacit collusion is a legally dubious antitrust practise committed by means of algorithms, which the courts are not able to prosecute.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Regulation_of_algorithms", "content": "Algorithmic tacit collusion is a legally dubious antitrust practise committed by means of algorithms, which the courts are not able to prosecute."}
+{"idx": 1, "title": "(PDF) Proof-of-Learning: A Blockchain Consensus Mechanism Based", "date": "", "ddg_snippet": "This article presents WekaCoin, a peer-to-peer cryptocurrency based on a new distributed consensus protocol called Proof-of- Learning .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/330753314_Proof-of-Learning_A_Blockchain_Consensus_Mechanism_Based_on_Machine_Learning_Competitions", "content": "This article presents WekaCoin, a peer-to-peer cryptocurrency based on a new distributed consensus protocol called Proof-of- Learning ."}
+{"idx": 2, "title": "Algorithmic Collusion by Large Language ModelsTo Krangy, a", "date": "", "ddg_snippet": "... Collusion by Large Language Models † † thanks: To Krangy, a pawfessor of economics and of computer science, who passed away while we were working ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.00806v4", "content": "... Collusion by Large Language Models † † thanks: To Krangy, a pawfessor of economics and of computer science, who passed away while we were working ..."}
+{"idx": 3, "title": "Threats, attacks and defenses to federated learning: issues,", "date": "", "ddg_snippet": "Based on the combination of blockchain technique and privacy-preserving algorithms, it can be seen that fully decentralized learning enhances the ...", "subpage_snippet": "", "source": "cybersecurity.springeropen.com", "link": "https://cybersecurity.springeropen.com/articles/10.1186/s42400-021-00105-6", "content": "Based on the combination of blockchain technique and privacy-preserving algorithms, it can be seen that fully decentralized learning enhances the ..."}
+{"idx": 4, "title": "(PDF) Securing Agentic AI: A Comprehensive Threat Model and", "date": "", "ddg_snippet": "... AI (GenAI) agents become more common in enterprise settings, they introduce security challenges that differ significantly from those posed by ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/391247531_Securing_Agentic_AI_A_Comprehensive_Threat_Model_and_Mitigation_Framework_for_Generative_AI_Agents", "content": "... AI (GenAI) agents become more common in enterprise settings, they introduce security challenges that differ significantly from those posed by ..."}
+{"idx": 5, "title": "(PDF) A Review on Decentralized Artificial Intelligence in the", "date": "", "ddg_snippet": "Furthermore, we shed light on the emergent challenges stemming from large-scale models, particularly in the realms of cryptography, privacy ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380564678_A_Review_on_Decentralized_Artificial_Intelligence_in_the_Era_of_Large_Models", "content": "Furthermore, we shed light on the emergent challenges stemming from large-scale models, particularly in the realms of cryptography, privacy ..."}
+{"idx": 6, "title": "Analysis of the communication between colluding applications on", "date": "", "ddg_snippet": "... of possible implications of application collusion attacks---quite the contrary--- on existing platforms , users are implicitly led to believe that by ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/261851480_Analysis_of_the_communication_between_colluding_applications_on_modern_Smartphones", "content": "... of possible implications of application collusion attacks---quite the contrary--- on existing platforms , users are implicitly led to believe that by ..."}
+{"idx": 7, "title": "948 questions with answers in CLOUD COMPUTING | Scientific", "date": "", "ddg_snippet": "... arxiv .org/ pdf /1406.0124by M Jammal - 2014 - Cited by 148 - Related articles from the control plane while providing programmability on the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Cloud-Computing/4", "content": "... arxiv .org/ pdf /1406.0124by M Jammal - 2014 - Cited by 148 - Related articles from the control plane while providing programmability on the ..."}
+{"idx": 8, "title": "Social and Information Networks Jun 2018", "date": "", "ddg_snippet": "Journal-ref: IEEE Transactions on Knowledge and Data Engineering( Volume: 32, Issue: 10, Oct. ... Statistical Mechanics (cond-mat.stat-mech); ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.SI/2018-06", "content": "Journal-ref: IEEE Transactions on Knowledge and Data Engineering( Volume: 32, Issue: 10, Oct. ... Statistical Mechanics (cond-mat.stat-mech); ..."}
+{"idx": 9, "title": "FedscGen: privacy-preserving federated batch effect correction", "date": "", "ddg_snippet": "We introduce FedscGen, a novel federated learning framework for enabling collaborative batch effect correction based on the scGen model for training ...", "subpage_snippet": "", "source": "genomebiology.biomedcentral.com", "link": "https://genomebiology.biomedcentral.com/articles/10.1186/s13059-025-03684-6", "content": "We introduce FedscGen, a novel federated learning framework for enabling collaborative batch effect correction based on the scGen model for training ..."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_identically_distributed_assumption_limita.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_identically_distributed_assumption_limita.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d2144618bebbb5d68c75e17e3e258212a3cf1007
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_identically_distributed_assumption_limita.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Independent and identically distributed random variables - Wikipedia", "date": "", "ddg_snippet": "A chart showing a uniform distribution . In probability theory and statistics , a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables", "content": "A chart showing a uniform distribution . In probability theory and statistics , a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually..."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47263", "content": "Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT"}
+{"idx": 2, "title": "GauthierE/ statistical - collusion : Statistical Collusion by Collectives ...", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms .The resulting figures will be saved in the plots/ folder. About. Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879. Resources.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "Statistical Collusion by Collectives on Learning Platforms .The resulting figures will be saved in the plots/ folder. About. Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879. Resources."}
+{"idx": 3, "title": "Statistical Collusion by Collectives on Learning Platforms Statistical Collusion by Collectives on Learning Platforms ... Publications - people.eecs.berkeley.edu Statistical Collusion by Collectives on Learning Platforms Statistical Collusion by Collectives on Learning Platforms GitHub - GauthierE/statistical-collusion Distributed Learning - an overview | ScienceDirect Topics Distributed Learning - an overview | ScienceDirect Topics Distributed Learning - an overview | ScienceDirect Topics Distributed Learning - an overview | ScienceDirect Topics Distributed Learning - an overview | ScienceDirect Topics Distributed Learning - an overview | ScienceDirect Topics Distributed Learning - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "Feb 7, 2025 · As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help... Proceedings of the Conference on Learning Theory (COLT), Graz, Austria, 2020. On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration. This paper formalizes the problem, and pursue algorithms for learning classifiers that are robust to gaming, and obtains computationally efficient learning algorithms which are near optimal, achieving a classification error that is arbitrarily close to the theoretical minimum. Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms . What is distributed learning? Distributed Learning refers to a collaborative approach where data from spatially distributed sources are utilized to achieve a common goal, such as performing a common estimation or inference task. It involves the exchange of learning experiences/information among different nodes or agents to reach a consensus and improve decision-making. What are cooperation scenarios in Distributed Learning? However, different cooperation scenarios can be adopted. In distributed learning , each individual agent is represented as a node in a graph. Edges between nodes indicate that the respective agents can exchange information. How to bring 581 in a distributed learning context? To bring (5.81) in a distributed learning context, let us modify it by allowing different parameter vectors for each node k , so that Then the task is cast according to the following constrained optimization problem: In other words, one demands equality of the estimates within a neighborhood. What are some examples of Distributed Learning? Look, for example, at the way birds fly in formation and bees swarm in a new hive. Besides sociology and biology, science and engineering have used the concept of distributed learning; wireless sensor networks (WSNs) are a typical example. Do diffusion schemes outperform consensus based schemes? There, it has been shown that the diffusion schemes outperform the consensus-based ones, in the sense that (a) they converge faster, (b) they reach a lower steady-state mean-square deviation error floor, and (c) their mean-square stability is insensitive to the choice of the combination weights. Hafiz Farooq Ahmad, ... Why is aggregation management important in Distributed Learning? However, asynchrony is prone to induce more variance in the learning. Whatever the framework, the aggregation management is one of the critical points in the conceptualization of distributed learning approaches since it impacts in the quality of many important aspects , such as latency or the computing efficiency. Distributed learning for auto-scaling : These models leverage the collective intelligence of distributed devices, enabling them to collaboratively train machine learning models while ensuring data privacy and minimizing communication overhead.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "Feb 7, 2025 · As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help... Proceedings of the Conference on Learning Theory (COLT), Graz, Austria, 2020. On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration. This paper formalizes the problem, and pursue algorithms for learning classifiers that are robust to gaming, and obtains computationally efficient learning algorithms which are near optimal, achieving a classification error that is arbitrarily close to the theoretical minimum. Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms . What is distributed learning? Distributed Learning refers to a collaborative approach where data from spatially distributed sources are utilized to achieve a common goal, such as performing a common estimation or inference task. It involves the exchange of learning experiences/information among different nodes or agents to reach a consensus and improve decision-making. What are cooperation scenarios in Distributed Learning? However, different cooperation scenarios can be adopted. In distributed learning , each individual agent is represented as a node in a graph. Edges between nodes indicate that the respective agents can exchange information. How to bring 581 in a distributed learning context? To bring (5.81) in a distributed learning context, let us modify it by allowing different parameter vectors for each node k , so that Then the task is cast according to the following constrained optimization problem: In other words, one demands equality of the estimates within a neighborhood. What are some examples of Distributed Learning? Look, for example, at the way birds fly in formation and bees swarm in a new hive. Besides sociology and biology, science and engineering have used the concept of distributed learning; wireless sensor networks (WSNs) are a typical example. Do diffusion schemes outperform consensus based schemes? There, it has been shown that the diffusion schemes outperform the consensus-based ones, in the sense that (a) they converge faster, (b) they reach a lower steady-state mean-square deviation error floor, and (c) their mean-square stability is insensitive to the choice of the combination weights. Hafiz Farooq Ahmad, ... Why is aggregation management important in Distributed Learning? However, asynchrony is prone to induce more variance in the learning. Whatever the framework, the aggregation management is one of the critical points in the conceptualization of distributed learning approaches since it impacts in the quality of many important aspects , such as latency or the computing efficiency. Distributed learning for auto-scaling : These models leverage the collective intelligence of distributed devices, enabling them to collaboratively train machine learning models while ensuring data privacy and minimizing communication overhead."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms ...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "This paper formalizes the problem, and pursue algorithms for learning classifiers that are robust to gaming, and obtains computationally efficient learning algorithms which are near optimal, achieving a classification error that is arbitrarily close to the theoretical minimum.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "This paper formalizes the problem, and pursue algorithms for learning classifiers that are robust to gaming, and obtains computationally efficient learning algorithms which are near optimal, achieving a classification error that is arbitrarily close to the theoretical minimum."}
+{"idx": 6, "title": "Distributed Learning - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "Distributed learning for auto-scaling : These models leverage the collective intelligence of distributed devices, enabling them to collaboratively train machine learning models while ensuring data privacy and minimizing communication overhead.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/computer-science/distributed-learning", "content": "Distributed learning for auto-scaling : These models leverage the collective intelligence of distributed devices, enabling them to collaboratively train machine learning models while ensuring data privacy and minimizing communication overhead."}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 8, "title": "(PDF) Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848421_Statistical_Collusion_by_Collectives_on_Learning_Platforms", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 9, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Finally, a key assumption in collective action is that indi-viduals are identically distributed .out directly using the term g(x). 16. Statistical Collusion by Collectives on Learning Platforms . E.2. Proof of Theorem 3.7 Proof.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=46yLEXtav4", "content": "Finally, a key assumption in collective action is that indi-viduals are identically distributed .out directly using the term g(x). 16. Statistical Collusion by Collectives on Learning Platforms . E.2. Proof of Theorem 3.7 Proof."}
diff --git a/data/sampled_jsons/Steffen_Herbort_3D_range_scan_enhancement_using_image-based_methods_ISPRS_2013.jsonl b/data/sampled_jsons/Steffen_Herbort_3D_range_scan_enhancement_using_image-based_methods_ISPRS_2013.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b4474f2b0e9b36d24588f69a8293260c74e12fe4
--- /dev/null
+++ b/data/sampled_jsons/Steffen_Herbort_3D_range_scan_enhancement_using_image-based_methods_ISPRS_2013.jsonl
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+{"idx": 0, "title": "3D range scan enhancement using image-based methods", "date": "", "ddg_snippet": "Afterwards, an iterative scheme based on Herbort and Wöhler (2012) determines the parameters of the underlying BRDF of the material, computes global illumination components similar to Herbort et al. ( 2013 ) using a raytracing algorithm, and finally reconstructs a refined surface on that scale by fusing the provided depth data with the gradient ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0924271613001615", "content": "Afterwards, an iterative scheme based on Herbort and Wöhler (2012) determines the parameters of the underlying BRDF of the material, computes global illumination components similar to Herbort et al. ( 2013 ) using a raytracing algorithm, and finally reconstructs a refined surface on that scale by fusing the provided depth data with the gradient ..."}
+{"idx": 1, "title": "Steffen HERBORT | Dipl.-Ing. | Technische Universität Dortmund ...", "date": "", "ddg_snippet": "3D range scan enhancement using image-based methods Article Oct 2013 Steffen Herbort Britta Gerken Daniel Schugk Christian Wöhler", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Steffen-Herbort", "content": "3D range scan enhancement using image-based methods Article Oct 2013 Steffen Herbort Britta Gerken Daniel Schugk Christian Wöhler"}
+{"idx": 2, "title": "ISPRS Journal of Photogrammetry and Remote Sensing - ScienceDirect", "date": "", "ddg_snippet": "Research articleFull text access 3D range scan enhancement using image-based methods Steffen Herbort , Britta Gerken, Daniel Schugk, Christian Wöhler Pages 69-84 View PDF", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/journal/isprs-journal-of-photogrammetry-and-remote-sensing/vol/84/suppl/C", "content": "Research articleFull text access 3D range scan enhancement using image-based methods Steffen Herbort , Britta Gerken, Daniel Schugk, Christian Wöhler Pages 69-84 View PDF"}
+{"idx": 3, "title": "3D range scan enhancement using image-based methods", "date": "", "ddg_snippet": "Afterwards, an iterative scheme based on Herbort and Wöhler (2012) determines the parameters of the underlying BRDF of the material, computes global illumination components similar to Herbort et al. ( 2013 ) using a raytracing algorithm, and finally reconstructs a refined surface on that scale by fusing the provided depth data with the gradient ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0924271613001615", "content": "Afterwards, an iterative scheme based on Herbort and Wöhler (2012) determines the parameters of the underlying BRDF of the material, computes global illumination components similar to Herbort et al. ( 2013 ) using a raytracing algorithm, and finally reconstructs a refined surface on that scale by fusing the provided depth data with the gradient ..."}
+{"idx": 4, "title": "ISPRS Journal of Photogrammetry and Remote Sensing", "date": "", "ddg_snippet": "4. 3D range scan enhancement using image-based methods 机译：使用基于图像的方法增强3D范围扫描 作者： Steffen Herbort ; Britta Gerken; Daniel Schugk; Christian Woehler 期刊名称： 《 ISPRS Journal of Photogrammetry and Remote Sensing》 | 2013年第octa期 关键词： Photometry; Surface reconstruction; Laser scanning ...", "subpage_snippet": "", "source": "www.zhangqiaokeyan.com", "link": "https://www.zhangqiaokeyan.com/journal-foreign-816/", "content": "4. 3D range scan enhancement using image-based methods 机译：使用基于图像的方法增强3D范围扫描 作者： Steffen Herbort ; Britta Gerken; Daniel Schugk; Christian Woehler 期刊名称： 《 ISPRS Journal of Photogrammetry and Remote Sensing》 | 2013年第octa期 关键词： Photometry; Surface reconstruction; Laser scanning ..."}
+{"idx": 5, "title": "3D range scan enhancement using image-based methods", "date": "", "ddg_snippet": "Semantic Scholar extracted view of \" 3D range scan enhancement using image-based methods \" by Steffen Herbort et al.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/3D-range-scan-enhancement-using-image-based-methods-Herbort-Gerken/0067f120653bd6783f9171df3792669f5e85e8b1", "content": "Semantic Scholar extracted view of \" 3D range scan enhancement using image-based methods \" by Steffen Herbort et al."}
+{"idx": 6, "title": "ISPRS e-Bulletin #4: September 2013", "date": "", "ddg_snippet": "... 3D measurement techniques is the possibility to acquire data at video frame rates and to obtain 3D point clouds (even of movable objects) without ...", "subpage_snippet": "", "source": "www.isprs.org", "link": "https://www.isprs.org/news/newsletter/2013-04/index.html", "content": "... 3D measurement techniques is the possibility to acquire data at video frame rates and to obtain 3D point clouds (even of movable objects) without ..."}
+{"idx": 7, "title": "Self-consistent 3D surface reconstruction and reflectance model ...", "date": "", "ddg_snippet": "3D range scan enhancement using image-based methods Article Oct 2013 ISPRS J PHOTOGRAMM Steffen Herbort Britta Gerken Daniel Schugk Christian Wöhler", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/285721128_Self-consistent_3D_surface_reconstruction_and_reflectance_model_estimation_of_metallic_surfaces", "content": "3D range scan enhancement using image-based methods Article Oct 2013 ISPRS J PHOTOGRAMM Steffen Herbort Britta Gerken Daniel Schugk Christian Wöhler"}
+{"idx": 8, "title": "Reconstruction of non-Lambertian surfaces by fusion of Shape from ...", "date": "", "ddg_snippet": "3D range scan enhancement using image-based methods Article Oct 2013 ISPRS J PHOTOGRAMM Steffen Herbort Britta Gerken Daniel Schugk Christian Wöhler", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221128978_Reconstruction_of_non-Lambertian_surfaces_by_fusion_of_Shape_from_Shading_and_active_range_scanning", "content": "3D range scan enhancement using image-based methods Article Oct 2013 ISPRS J PHOTOGRAMM Steffen Herbort Britta Gerken Daniel Schugk Christian Wöhler"}
+{"idx": 9, "title": "Integrated DEM Construction and Calibration of ... - ResearchGate", "date": "", "ddg_snippet": "3D range scan enhancement using image-based methods Article Oct 2013 ISPRS J PHOTOGRAMM Steffen Herbort Britta Gerken Daniel Schugk Christian Wöhler", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/278653270_Integrated_DEM_Construction_and_Calibration_of_Hyperspectral_Imagery_A_Remote_Sensing_Perspective", "content": "3D range scan enhancement using image-based methods Article Oct 2013 ISPRS J PHOTOGRAMM Steffen Herbort Britta Gerken Daniel Schugk Christian Wöhler"}
diff --git a/data/sampled_jsons/StreamRF_methodology_generalized_approach_CVPR_2023.jsonl b/data/sampled_jsons/StreamRF_methodology_generalized_approach_CVPR_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0c77df1efd778c0f668a095a4e86cadf97e9cc01
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+{"idx": 0, "title": "CVPR-2023-24-Papers/sections/2023/main/vision-language-and ... - GitHub", "date": "", "ddg_snippet": "CVPR 2023 -2024 Papers: Dive into advanced research presented at the leading computer vision conference. Keep up to date with the latest developments in computer vision and deep learning. Code included. ⭐ support visual intelligence development!", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/DmitryRyumin/CVPR-2023-24-Papers/blob/main/sections/2023/main/vision-language-and-reasoning.md", "content": "CVPR 2023 -2024 Papers: Dive into advanced research presented at the leading computer vision conference. Keep up to date with the latest developments in computer vision and deep learning. Code included. ⭐ support visual intelligence development!"}
+{"idx": 1, "title": "Generalized Deep Multi-view Clustering via Causal Learning with ...", "date": "", "ddg_snippet": "4. Method In this section, we introduce a generalized multi-view clus-tering algorithm from a causal perspective. Specifically, we first present the relevant notations and formulations. Sub-sequently, We provide a detailed explanation of the causal multi-view clustering model and the contrastive regularizer.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.16022", "content": "4. Method In this section, we introduce a generalized multi-view clus-tering algorithm from a causal perspective. Specifically, we first present the relevant notations and formulations. Sub-sequently, We provide a detailed explanation of the causal multi-view clustering model and the contrastive regularizer."}
+{"idx": 2, "title": "Generalized Deep Multi-view Clustering via Causal Learning with ...", "date": "", "ddg_snippet": "SURE [58] uses available pairs as positives and randomly selects some cross-view samples as negatives. UPMGC-SM [51] leverages structural information from each view to refine cross-view correspondences. In contrast to the above methods, we approach partially aligned data from a causal perspective, aiming to improve the generalization of the model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16022v1", "content": "SURE [58] uses available pairs as positives and randomly selects some cross-view samples as negatives. UPMGC-SM [51] leverages structural information from each view to refine cross-view correspondences. In contrast to the above methods, we approach partially aligned data from a causal perspective, aiming to improve the generalization of the model."}
+{"idx": 3, "title": "PDF Generalized Relation Modeling for Transformer Tracking", "date": "", "ddg_snippet": "The proposed method is a generalized formulation of attention-based relation model-ing for Transformer tracking, which inherits the merits of both previous two-stream and one-stream pipelines whilst enabling more flexible relation modeling by selecting ap-propriate search tokens to interact with template tokens.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Generalized_Relation_Modeling_for_Transformer_Tracking_CVPR_2023_paper.pdf", "content": "The proposed method is a generalized formulation of attention-based relation model-ing for Transformer tracking, which inherits the merits of both previous two-stream and one-stream pipelines whilst enabling more flexible relation modeling by selecting ap-propriate search tokens to interact with template tokens."}
+{"idx": 4, "title": "CVPR 2023 Papers", "date": "", "ddg_snippet": "2025 2024 2023 Home Schedule Workshops Tutorials Keynotes & Panels Awards Papers Sponsors Organizers Browse Visualization Layout:", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/papers.html", "content": "2025 2024 2023 Home Schedule Workshops Tutorials Keynotes & Panels Awards Papers Sponsors Organizers Browse Visualization Layout:"}
+{"idx": 5, "title": "CVPR 2023 Open Access Repository", "date": "", "ddg_snippet": "The proposed method is model-agnostic and data-agnostic, making it applicable to both images and videos and versatile for diverse applications. Our experimental results demonstrate the superiority of our approach over existing feature-generating methods, yielding improved overall performance on multiple benchmarks.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023W/TCV/html/Gowda_Synthetic_Sample_Selection_for_Generalized_Zero-Shot_Learning_CVPRW_2023_paper.html", "content": "The proposed method is model-agnostic and data-agnostic, making it applicable to both images and videos and versatile for diverse applications. Our experimental results demonstrate the superiority of our approach over existing feature-generating methods, yielding improved overall performance on multiple benchmarks."}
+{"idx": 6, "title": "CVPR 2023 Recommended Reading List - RBC Borealis", "date": "", "ddg_snippet": "RBC Borealis's CVPR 2023 Recommended Papers. We've curated a fantastic selection of accepted papers and why we recommend each of them.", "subpage_snippet": "", "source": "rbcborealis.com", "link": "https://rbcborealis.com/research-blogs/cvpr-2023-recommended-reading-list/", "content": "RBC Borealis's CVPR 2023 Recommended Papers. We've curated a fantastic selection of accepted papers and why we recommend each of them."}
+{"idx": 7, "title": "GitHub - ReaFly/CVPR2023-Highlights: CVPR2023 Highlight papers", "date": "", "ddg_snippet": "CVPR2023 Highlight papers. Contribute to ReaFly/ CVPR2023 -Highlights development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ReaFly/CVPR2023-Highlights", "content": "CVPR2023 Highlight papers. Contribute to ReaFly/ CVPR2023 -Highlights development by creating an account on GitHub."}
+{"idx": 8, "title": "Real-time Photorealistic Dynamic Scene Representation and", "date": "", "ddg_snippet": "This approach marks the first-ever model supporting end-to-end training and real-time rendering of high- resolution, photorealistic novel views in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.10642v3", "content": "This approach marks the first-ever model supporting end-to-end training and real-time rendering of high- resolution, photorealistic novel views in ..."}
+{"idx": 9, "title": "QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for", "date": "", "ddg_snippet": "QUEEN outperforms all prior state-of-the-art approaches for online FVV and significantly reduces the per-frame memory cost ( ∼ 10 × {\\sim}10 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.04469v1", "content": "QUEEN outperforms all prior state-of-the-art approaches for online FVV and significantly reduces the per-frame memory cost ( ∼ 10 × {\\sim}10 ..."}
diff --git a/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_for_Robust_Learning_on_Diverse_Tasks_and_Model_Scales.jsonl b/data/sampled_jsons/Symmetric_Reinforcement_Learning_Loss_for_Robust_Learning_on_Diverse_Tasks_and_Model_Scales.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7183730416937819a228130a7fb3671d0d9cf443
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+{"idx": 0, "title": "Adaptive Scaling of Policy Constraints for Offline", "date": "", "ddg_snippet": "... Q-value and the BC loss , enabling the algorithm to discover a more stable learning path and exhibit remarkable adaptability across tasks and datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19900v1", "content": "... Q-value and the BC loss , enabling the algorithm to discover a more stable learning path and exhibit remarkable adaptability across tasks and datasets."}
+{"idx": 1, "title": "Adaptability in Multi-Agent Reinforcement Learning: A Framework", "date": "", "ddg_snippet": "Learning Adaptability focuses on how robustly a learning paradigm performs under variations in agent populations, execution assumptions, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10142v1", "content": "Learning Adaptability focuses on how robustly a learning paradigm performs under variations in agent populations, execution assumptions, and ..."}
+{"idx": 2, "title": "SAMPO: Scale-wise Autoregression with Motion PrOmpt for", "date": "", "ddg_snippet": "... SAMPO can be pretrained on large- scale robot datasets [ 38 ] , enabling generalizable and control-centric world models across diverse tasks and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15536v1", "content": "... SAMPO can be pretrained on large- scale robot datasets [ 38 ] , enabling generalizable and control-centric world models across diverse tasks and ..."}
+{"idx": 3, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "RobustTSF: Towards Theory and Design of Robust Time ... Waxing- and -Waning: a Generic Similarity-based Framework for Efficient Self-Supervised Learning", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "RobustTSF: Towards Theory and Design of Robust Time ... Waxing- and -Waning: a Generic Similarity-based Framework for Efficient Self-Supervised Learning"}
+{"idx": 4, "title": "Downloads", "date": "", "ddg_snippet": "A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model -Based Reinforcement Learning ... Learning : an Alternative to ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model -Based Reinforcement Learning ... Learning : an Alternative to ..."}
+{"idx": 5, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Robust One -Bit Recovery via ReLU ... Expert Learning through Generalized Inverse Multiobjective Optimization: Models , Insights, and Algorithms", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Robust One -Bit Recovery via ReLU ... Expert Learning through Generalized Inverse Multiobjective Optimization: Models , Insights, and Algorithms"}
+{"idx": 6, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Robust One -Bit Recovery via ReLU ... Expert Learning through Generalized Inverse Multiobjective Optimization: Models , Insights, and Algorithms", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Robust One -Bit Recovery via ReLU ... Expert Learning through Generalized Inverse Multiobjective Optimization: Models , Insights, and Algorithms"}
+{"idx": 7, "title": "Thirty-Second AAAI Conference on Artificial Intelligence 2018", "date": "", "ddg_snippet": "A Multi- Task Learning Approach for ... RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-Imbalanced Labels for Network Embedding", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/proceeding/01-thirty-second-aaai-conference-on-artificial-intelligence-2018/", "content": "A Multi- Task Learning Approach for ... RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-Imbalanced Labels for Network Embedding"}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning"}
+{"idx": 9, "title": "AISTATS 2024 Schedule", "date": "", "ddg_snippet": "... on Contrastive Loss Based Offline ... Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2024/calendar", "content": "... on Contrastive Loss Based Offline ... Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning"}
diff --git a/data/sampled_jsons/T-Agent_CVE-Bench_Success@5_25%_13%_one-day_zero-day_comparison.jsonl b/data/sampled_jsons/T-Agent_CVE-Bench_Success@5_25%_13%_one-day_zero-day_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c11774e9b32d091609cffb3c494090d6b8cfe28c
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+++ b/data/sampled_jsons/T-Agent_CVE-Bench_Success@5_25%_13%_one-day_zero-day_comparison.jsonl
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+{"idx": 0, "title": "CVE - Bench : A Benchmark for AI Agents' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "In CVE - Bench , we simulate the zero - day and one - day scenarios. In the zero - day scenario, LLM agents must compromise the application without further in-formation about the vulnerability .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "In CVE - Bench , we simulate the zero - day and one - day scenarios. In the zero - day scenario, LLM agents must compromise the application without further in-formation about the vulnerability ."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "We apply CVE - Bench to evaluate various LLM agents under both zero-day and one-day settings. ... agent framework, teams of LLM agents (Fang et al., ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "We apply CVE - Bench to evaluate various LLM agents under both zero-day and one-day settings. ... agent framework, teams of LLM agents (Fang et al., ..."}
+{"idx": 2, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "Unfortunately, many existing outcome-based evaluation methods of agentic benchmarks introduce issues that can cause under- or overestimation of agent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v5", "content": "Unfortunately, many existing outcome-based evaluation methods of agentic benchmarks introduce issues that can cause under- or overestimation of agent ..."}
+{"idx": 3, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "Unfortunately, many existing outcome-based evaluation methods of agentic benchmarks introduce issues that can cause under- or overestimation of agent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v1", "content": "Unfortunately, many existing outcome-based evaluation methods of agentic benchmarks introduce issues that can cause under- or overestimation of agent ..."}
+{"idx": 4, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "Unfortunately, many existing outcome-based evaluation methods of agentic benchmarks introduce issues that can cause under- or overestimation of agent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v4", "content": "Unfortunately, many existing outcome-based evaluation methods of agentic benchmarks introduce issues that can cause under- or overestimation of agent ..."}
+{"idx": 5, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "Unfortunately, many existing outcome-based evaluation methods of agentic benchmarks introduce issues that can cause under- or overestimation of agent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v3", "content": "Unfortunately, many existing outcome-based evaluation methods of agentic benchmarks introduce issues that can cause under- or overestimation of agent ..."}
+{"idx": 6, "title": "CVE-2021-44228 - Apache Log4j2 Remote Code Execution", "date": "", "ddg_snippet": "Even if cvefeed.io is aware of the exact versions of the products that are affected, the information is not represented in the table below.", "subpage_snippet": "", "source": "cvefeed.io", "link": "https://cvefeed.io/vuln/detail/CVE-2021-44228", "content": "Even if cvefeed.io is aware of the exact versions of the products that are affected, the information is not represented in the table below."}
+{"idx": 7, "title": "How Does Time Horizon Vary Across Domains? - METR", "date": "", "ddg_snippet": "... the time horizons estimated by this method, using overall success rates only, differ from those of the original methodology by < 2x (within error ...", "subpage_snippet": "", "source": "evals.alignment.org", "link": "https://evals.alignment.org/blog/2025-07-14-how-does-time-horizon-vary-across-domains/", "content": "... the time horizons estimated by this method, using overall success rates only, differ from those of the original methodology by < 2x (within error ..."}
+{"idx": 8, "title": "AI-Driven Cyberattacks are on the Rise. Are You Ready? -", "date": "", "ddg_snippet": "In a paper titled CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real-World Web Application Vulnerabilities , researchers found that ...", "subpage_snippet": "", "source": "lsvp.com", "link": "https://lsvp.com/stories/ai-enabled-hacking-is-here-are-we-ready-for-it/", "content": "In a paper titled CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real-World Web Application Vulnerabilities , researchers found that ..."}
+{"idx": 9, "title": "METR: How Does Time Horizon Vary Across Domains? - LessWrong", "date": "", "ddg_snippet": "... the time horizons estimated by this method, using overall success rates only, differ from those of the original methodology by < 2x (within error ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/6KcP7tEe5hgvHbrSF/metr-how-does-time-horizon-vary-across-domains", "content": "... the time horizons estimated by this method, using overall success rates only, differ from those of the original methodology by < 2x (within error ..."}
diff --git a/data/sampled_jsons/TARFLOW_Normalizing_Flows_Equation_6_training_loss.jsonl b/data/sampled_jsons/TARFLOW_Normalizing_Flows_Equation_6_training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ba908157523692061a4c34c6423f3e2372369947
--- /dev/null
+++ b/data/sampled_jsons/TARFLOW_Normalizing_Flows_Equation_6_training_loss.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer Autoregressive Flow ( TARFlow )", "date": "", "ddg_snippet": "TARFlow combines autoregressive Transformers with normalizing flows to enable efficient, expressive density modeling for high-dimensional image generation.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/transformer-autoregressive-flow-tarflow", "content": "TARFlow combines autoregressive Transformers with normalizing flows to enable efficient, expressive density modeling for high-dimensional image generation."}
+{"idx": 1, "title": "Normalizing Flows are Capable Generative Models - Apple Machine...", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/normalizing-flows", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 2, "title": "Amortized Sampling with Transferable Normalizing Flows", "date": "", "ddg_snippet": "Normalizing flows are versatile probabilistic models, which allow for different training strategies: for learning target unnormalized densities, variational inference (Rezende and Mohamed, 2015) , for generative modeling, the maximum likelihood principle (Kingma and Dhariwal, 2018b) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18175v1", "content": "Normalizing flows are versatile probabilistic models, which allow for different training strategies: for learning target unnormalized densities, variational inference (Rezende and Mohamed, 2015) , for generative modeling, the maximum likelihood principle (Kingma and Dhariwal, 2018b) ."}
+{"idx": 3, "title": "GitHub - apple/ml- tarflow", "date": "", "ddg_snippet": "jupyter notebook train _local.ipynb.title={ Normalizing Flows are Capable Generative Models}", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/apple/ml-tarflow", "content": "jupyter notebook train _local.ipynb.title={ Normalizing Flows are Capable Generative Models}"}
+{"idx": 4, "title": "Normalizing Flows are Capable Generative Models | OpenReview", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 5, "title": "machine learning - Normalizing Flow Penalization - Cross Validated", "date": "", "ddg_snippet": "I am looking to train a normalizing flow , specifically a Masked Autoregressive Flow model. However, this model leads to high variance on lower dimensional, less complex data.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/590545/normalizing-flow-penalization", "content": "I am looking to train a normalizing flow , specifically a Masked Autoregressive Flow model. However, this model leads to high variance on lower dimensional, less complex data."}
+{"idx": 6, "title": "Flow Matching and Normalizing Flows | by Farshad Noravesh | Medium", "date": "", "ddg_snippet": "Flow Matching resolves the normalizing flow ’s Jacobian problem by: Not using change-of-variable log-determinants, Replacing likelihood training with vector field regression, Training fast and in parallel.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@noraveshfarshad/flow-matching-and-normalizing-flows-49c0b06b2966", "content": "Flow Matching resolves the normalizing flow ’s Jacobian problem by: Not using change-of-variable log-determinants, Replacing likelihood training with vector field regression, Training fast and in parallel."}
+{"idx": 7, "title": "Designing losses for data-free training of normalizing flows ... | DeepAI", "date": "", "ddg_snippet": "Flow Annealed Importance Sampling Bootstrap. Normalizing flows are tractable density models that can approximate comp...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/designing-losses-for-data-free-training-of-normalizing-flows-on-boltzmann-distributions", "content": "Flow Annealed Importance Sampling Bootstrap. Normalizing flows are tractable density models that can approximate comp..."}
+{"idx": 8, "title": "Normalizing flows for probability distribution reconstruction – Pressé...", "date": "", "ddg_snippet": "Setting up a normalizing flow includes deciding on the number and type of flow layers and the base distribution. The base distribution is the initial choice of probability density, which the normalizing flow will transform over the course of training . These choices are problem specific.", "subpage_snippet": "", "source": "labpresse.com", "link": "https://labpresse.com/normalizing-flows-for-probability-distribution-reconstruction/", "content": "Setting up a normalizing flow includes deciding on the number and type of flow layers and the base distribution. The base distribution is the initial choice of probability density, which the normalizing flow will transform over the course of training . These choices are problem specific."}
+{"idx": 9, "title": "Apple Research is generating images with a forgotten AI... - 9to5Mac", "date": "", "ddg_snippet": "First things first: What are Normalizing Flows ? Normalizing Flows (NFs) are a type of AI model that works by learning how to mathematically transform real-world data (like images) into structured noise, and then reverse that process to generate new samples.", "subpage_snippet": "", "source": "9to5mac.com", "link": "https://9to5mac.com/2025/06/23/apple-ai-image-model-research-tarflow-starflow/", "content": "First things first: What are Normalizing Flows ? Normalizing Flows (NFs) are a type of AI model that works by learning how to mathematically transform real-world data (like images) into structured noise, and then reverse that process to generate new samples."}
diff --git a/data/sampled_jsons/TARFLOW_Normalizing_Flows_Score_Based_Denoising.jsonl b/data/sampled_jsons/TARFLOW_Normalizing_Flows_Score_Based_Denoising.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f2fd11c0ce4a13f6c2ffbea634e082552ab0fa3
--- /dev/null
+++ b/data/sampled_jsons/TARFLOW_Normalizing_Flows_Score_Based_Denoising.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - TheProParadox/ tarflow _jax: Implemented Tarflow in JAX", "date": "", "ddg_snippet": "Score - based denoising step.Implemented a Transformer- based normalizing flow model using Equinox and JAX, designed for image processing tasks with patch- based autoregression.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TheProParadox/tarflow_jax", "content": "Score - based denoising step.Implemented a Transformer- based normalizing flow model using Equinox and JAX, designed for image processing tasks with patch- based autoregression."}
+{"idx": 1, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 2, "title": "Normalizing Flows are Capable Generative Models | OpenReview", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 3, "title": "Normalizing Flows are Capable Generative Models - Apple Machine...", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/normalizing-flows", "content": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 4, "title": "Normalizing Flows (NFs)", "date": "", "ddg_snippet": "SurVAE flows and score - based flows are examples. Stochastic and Score - Based Flows : Combine deterministic invertible mappings with noise-injection or Langevin steps (e.g., Stochastic Normalizing Flows ), broadening types of expressible distributions (Kelly et al., 2023).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/normalizing-flows-nfs", "content": "SurVAE flows and score - based flows are examples. Stochastic and Score - Based Flows : Combine deterministic invertible mappings with noise-injection or Langevin steps (e.g., Stochastic Normalizing Flows ), broadening types of expressible distributions (Kelly et al., 2023)."}
+{"idx": 5, "title": "Unlocking the Power of Normalizing Flows in Generative... - Glcnd.io", "date": "", "ddg_snippet": "Unveiling TarFlow : Advancing Normalizing Flows in Generative Modeling. Normalizing Flows (NFs) have long been recognized as a compelling framework for density estimation and generative modeling tasks involving continuous inputs.", "subpage_snippet": "", "source": "glcnd.io", "link": "https://glcnd.io/unlocking-the-power-of-normalizing-flows-in-generative-modeling/", "content": "Unveiling TarFlow : Advancing Normalizing Flows in Generative Modeling. Normalizing Flows (NFs) have long been recognized as a compelling framework for density estimation and generative modeling tasks involving continuous inputs."}
+{"idx": 6, "title": "Normalizing Flows as Generative Models - Kifinity", "date": "", "ddg_snippet": "TarFlow is a scalable Transformer- based architecture for Normalizing Flows that models image pixels directly. It incorporates techniques like noise augmentation, denoising , and guidance to improve sample quality.", "subpage_snippet": "", "source": "www.kifinity.com", "link": "https://www.kifinity.com/post/normalizing-flows-machinelearning-apple-e63648a2", "content": "TarFlow is a scalable Transformer- based architecture for Normalizing Flows that models image pixels directly. It incorporates techniques like noise augmentation, denoising , and guidance to improve sample quality."}
+{"idx": 7, "title": "(PDF) Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs.2.5. Score Based Denoising . Training with noise augmentation introduces an additional.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386577118_Normalizing_Flows_are_Capable_Generative_Models", "content": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs.2.5. Score Based Denoising . Training with noise augmentation introduces an additional."}
+{"idx": 8, "title": "PD- Flow : A Point Cloud Denoising Framework with Normalizing Flows", "date": "", "ddg_snippet": "PCDNF: Revisiting Learning- Based Point Cloud Denoising via Joint Normal Filtering.PU- Flow : A Point Cloud Upsampling Network With Normalizing Flows .", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/d074ce457eeded69f1d40c1462a846e1ade70c02/PD+Flow:-A-Point-Cloud-Denoising-Framework-with-Normalizing-Flows/graph", "content": "PCDNF: Revisiting Learning- Based Point Cloud Denoising via Joint Normal Filtering.PU- Flow : A Point Cloud Upsampling Network With Normalizing Flows ."}
+{"idx": 9, "title": "Diffusion Models vs. Normalizing Flows | by Farshad... | Medium", "date": "", "ddg_snippet": "Score - based normalizing flows : Use score matching + invertible architectures to get the best of both worlds. Flow Matching and Normalizing Flows . Farshad Noravesh.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@noraveshfarshad/diffusion-models-vs-normalizing-flows-0dd4b84615ab", "content": "Score - based normalizing flows : Use score matching + invertible architectures to get the best of both worlds. Flow Matching and Normalizing Flows . Farshad Noravesh."}
diff --git a/data/sampled_jsons/TCE_Temporally-Correlated_Episodic_RL_on-policy_off-policy_Li_et_al_2024_year_2024.jsonl b/data/sampled_jsons/TCE_Temporally-Correlated_Episodic_RL_on-policy_off-policy_Li_et_al_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8265ecdcc08443e8b21d70fb7db6f73d7990084f
--- /dev/null
+++ b/data/sampled_jsons/TCE_Temporally-Correlated_Episodic_RL_on-policy_off-policy_Li_et_al_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "Policy search methods may converge slowly given noisy data. For example, this happens in episodic problems when the trajectories are long and the variance of the returns is large. Value-function based methods that rely on temporal differences might help in this case.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "Policy search methods may converge slowly given noisy data. For example, this happens in episodic problems when the trajectories are long and the variance of the returns is large. Value-function based methods that rely on temporal differences might help in this case."}
+{"idx": 1, "title": "From REINFORCE to PPO: The Complete On - Policy RL Journey", "date": "", "ddg_snippet": "Motivation: Why On - Policy RL Matters for Modern AI. If you’ve been following the latest developments in large language models (LLMs), you’ve probably heard of GRPO (Group Relative Policy Optimization) - one of the newest techniques for...", "subpage_snippet": "", "source": "taewoon.kim", "link": "https://taewoon.kim/2025-08-07-on-policy-rl/", "content": "Motivation: Why On - Policy RL Matters for Modern AI. If you’ve been following the latest developments in large language models (LLMs), you’ve probably heard of GRPO (Group Relative Policy Optimization) - one of the newest techniques for..."}
+{"idx": 2, "title": "Trajectory-Based Off - Policy Deep Reinforcement Learning", "date": "", "ddg_snippet": "Albeit these off - policy evaluation schemes, in these algorithms, no data is shared between iterations. Prominent examples of off - policy ofine algorithms typically employ actor-critic ar-chitectures (Silver et al ., 2014), where the parametric critic model, typically a value function...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v97/doerr19a/doerr19a.pdf", "content": "Albeit these off - policy evaluation schemes, in these algorithms, no data is shared between iterations. Prominent examples of off - policy ofine algorithms typically employ actor-critic ar-chitectures (Silver et al ., 2014), where the parametric critic model, typically a value function..."}
+{"idx": 3, "title": "Interpolated Policy Gradient: Merging On - Policy and Off - Policy ...", "date": "", "ddg_snippet": "Some forms of on - policy policy gradient methods have theoretical guarantees on monotonic con-vergence Kakade & Langford (2002); Schulman et al .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2017/file/a1d7311f2a312426d710e1c617fcbc8c-Paper.pdf", "content": "Some forms of on - policy policy gradient methods have theoretical guarantees on monotonic con-vergence Kakade & Langford (2002); Schulman et al ."}
+{"idx": 4, "title": "E pisodic r einforcement L earning", "date": "", "ddg_snippet": "TCE Temporally - Correlated Episodic RL ( TCE ) ( Li et al ., 2024 ) is an innovative ERL algorithm that leverages step-level information in episodic policy updates, shedding light on the ’black box’ of current ERL methods while preserving smooth and consistent exploration within the...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=fPHT0z4cS5&name=pdf", "content": "TCE Temporally - Correlated Episodic RL ( TCE ) ( Li et al ., 2024 ) is an innovative ERL algorithm that leverages step-level information in episodic policy updates, shedding light on the ’black box’ of current ERL methods while preserving smooth and consistent exploration within the..."}
+{"idx": 5, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "Many off - policy algorithms rely on this mechanism, along with differing protocols for cutting the IS ratios to combat the variance of the IS estimator.We instantiate MCEP based on TD3-BC [Fujimoto and Gu, 2021] and AWAC [Nair et al ., 2020] algorithms.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=temporal-difference-based+policy+evaluation", "content": "Many off - policy algorithms rely on this mechanism, along with differing protocols for cutting the IS ratios to combat the variance of the IS estimator.We instantiate MCEP based on TD3-BC [Fujimoto and Gu, 2021] and AWAC [Nair et al ., 2020] algorithms."}
+{"idx": 6, "title": "Исправление прикуса в 30, 40, 50 лет: реально ли", "date": "", "ddg_snippet": "По данным Pattanaik et al . ( 2024 ), такие случаи чаще фиксируются у пациентов старше 40 лет. 5. Патологии ВНЧС (суставов) Неправильная нагрузка на челюстной сустав, стирание зубов, бруксизм (скрежетание) — всё это может спровоцировать смещение прикуса...", "subpage_snippet": "", "source": "dentalopera.ru", "link": "https://dentalopera.ru/stati/ispravlenie-prikusa-v-30-40-50-let-realno-li/", "content": "По данным Pattanaik et al . ( 2024 ), такие случаи чаще фиксируются у пациентов старше 40 лет. 5. Патологии ВНЧС (суставов) Неправильная нагрузка на челюстной сустав, стирание зубов, бруксизм (скрежетание) — всё это может спровоцировать смещение прикуса..."}
+{"idx": 7, "title": "(PDF) A Comprehensive Survey of Reinforcement Learning: From...", "date": "", "ddg_snippet": "off - policy RL algorithms. Using decoupling policies [185] R. Li , Q. Wang, C. Dong et al ., “Morphing strategy design. for uav based on prioritized sweeping reinforcement learning,” in IECON 2020 The 46th Annual Conference of the IEEE.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386334963_Comprehensive_Survey_of_Reinforcement_Learning_From_Algorithms_to_Practical_Challenges", "content": "off - policy RL algorithms. Using decoupling policies [185] R. Li , Q. Wang, C. Dong et al ., “Morphing strategy design. for uav based on prioritized sweeping reinforcement learning,” in IECON 2020 The 46th Annual Conference of the IEEE."}
+{"idx": 8, "title": "Отдых во Вьетнаме. Все что нужно знать о Вьетнаме: климат...", "date": "", "ddg_snippet": "подскажите, куда лучше отправиться, чтобы не в современный отель и отдых у моря, а посмотреть какие-то достопримечательности, пещеры, поля, мосты и что-то древнее? 13.12. 2024 09:51 : Ольга, здравствуйте!EE. Эстония. ET . Эфиопия. ZA.", "subpage_snippet": "", "source": "www.tutu.ru", "link": "https://www.tutu.ru/geo/strana/vietnam/", "content": "подскажите, куда лучше отправиться, чтобы не в современный отель и отдых у моря, а посмотреть какие-то достопримечательности, пещеры, поля, мосты и что-то древнее? 13.12. 2024 09:51 : Ольга, здравствуйте!EE. Эстония. ET . Эфиопия. ZA."}
+{"idx": 9, "title": "RU2813426C1 - Способ холодной петлевой... - Google Patents", "date": "", "ddg_snippet": "2024 -02-12 Application granted granted Critical.Endosc Int Open. 2022; 10:602-8.] (фиг. 3). Поэтому холодную петлевую полипэктомию рекомендуется использовать при удалении внутриэпителиальных новообразований [Shichijo S, Takeuchi Y, Kitamura M. et al .", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/RU2813426C1/ru", "content": "2024 -02-12 Application granted granted Critical.Endosc Int Open. 2022; 10:602-8.] (фиг. 3). Поэтому холодную петлевую полипэктомию рекомендуется использовать при удалении внутриэпителиальных новообразований [Shichijo S, Takeuchi Y, Kitamura M. et al ."}
diff --git a/data/sampled_jsons/TCE_episodic_reinforcement_learning_2024.jsonl b/data/sampled_jsons/TCE_episodic_reinforcement_learning_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..150b4acad8f26ffacb74ab34f2887771a815a63a
--- /dev/null
+++ b/data/sampled_jsons/TCE_episodic_reinforcement_learning_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "BruceGeLi/ TCE _RL: Temporally Correlated Episodic Reinforcement ...", "date": "", "ddg_snippet": "Star 0. Temporally Correlated Episodic Reinforcement Learning . License.BruceGeLi/ TCE _RL. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. About. Temporally Correlated Episodic Reinforcement Learning .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BruceGeLi/TCE_RL", "content": "Star 0. Temporally Correlated Episodic Reinforcement Learning . License.BruceGeLi/ TCE _RL. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. About. Temporally Correlated Episodic Reinforcement Learning ."}
+{"idx": 1, "title": "Episodic Reinforcement Learning : Examples & Techniques", "date": "", "ddg_snippet": "Episodic reinforcement learning is a branch of machine learning where agents learn to make decisions through interactions within distinct episodes , each culminating in a terminal state before restarting. In this approach, an agent’s performance is evaluated based on cumulative...", "subpage_snippet": "", "source": "www.vaia.com", "link": "https://www.vaia.com/en-us/explanations/engineering/artificial-intelligence-engineering/episodic-reinforcement-learning/", "content": "Episodic reinforcement learning is a branch of machine learning where agents learn to make decisions through interactions within distinct episodes , each culminating in a terminal state before restarting. In this approach, an agent’s performance is evaluated based on cumulative..."}
+{"idx": 2, "title": "Corruption-Robust Exploration in Episodic Reinforcement Learning", "date": "", "ddg_snippet": "We initiate the study of episodic reinforcement learning (RL) under adversarial corruptions in both the rewards and the transition probabilities of the underlying system, extending recent results for the special case of multiarmed bandits.", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/10.1287/moor.2021.0202", "content": "We initiate the study of episodic reinforcement learning (RL) under adversarial corruptions in both the rewards and the transition probabilities of the underlying system, extending recent results for the special case of multiarmed bandits."}
+{"idx": 3, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement ...", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/top-erl-transformer-based-off-policy-episodic-reinforcement-learning/1053239679626772591-108614", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step."}
+{"idx": 4, "title": "Online Reinforcement Learning with Uncertain Episode Lengths", "date": "", "ddg_snippet": "Existing episodic reinforcement algorithms assume that the length of an episode is fixed across time and known a priori. In this paper, we consider a general framework of episodic reinforcement learning when the length of each episode is drawn from a distribution.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/26088", "content": "Existing episodic reinforcement algorithms assume that the length of an episode is fixed across time and known a priori. In this paper, we consider a general framework of episodic reinforcement learning when the length of each episode is drawn from a distribution."}
+{"idx": 5, "title": "Epoch or Episode : Understanding Terms in Deep Reinforcement ...", "date": "", "ddg_snippet": "In reinforcement learning , an epoch typically corresponds to a fixed number of episodes played through using the current policy or updates when using a value-iteration based method.", "subpage_snippet": "", "source": "www.baeldung.com", "link": "https://www.baeldung.com/cs/epoch-vs-episode-reinforcement-learning", "content": "In reinforcement learning , an epoch typically corresponds to a fixed number of episodes played through using the current policy or updates when using a value-iteration based method."}
+{"idx": 6, "title": "Active Exploration for Robot Parameter Selection in Episodic ...", "date": "", "ddg_snippet": "The parameter space is also usually continuous and multi-dimensional. Given the inherent exploration-exploitation dilemma of the problem, we propose treating it as an episodic reinforcement learning problem.", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/publications/7050", "content": "The parameter space is also usually continuous and multi-dimensional. Given the inherent exploration-exploitation dilemma of the problem, we propose treating it as an episodic reinforcement learning problem."}
+{"idx": 7, "title": "Episodic Reinforcement Learning with Expanded State-reward Space", "date": "", "ddg_snippet": "2022. Dreamerpro: Reconstruction-free model-based reinforcement learning with prototypical representations. In International Conference on Machine Learning .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3635637.3662976", "content": "2022. Dreamerpro: Reconstruction-free model-based reinforcement learning with prototypical representations. In International Conference on Machine Learning ."}
+{"idx": 8, "title": "Policy Certificates and Minimax-Optimal PAC Bounds for Episodic ...", "date": "", "ddg_snippet": "We propose to make methods for episodic reinforcement learning more accountable by having them output a policy certificate before each episode . A policy certificate is a confidence interval [l, u]...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/stanford-aiforhi/policy-certificates-and-minimax-optimal-pac-bounds-for-episodic-reinforcement-learning-90668b489c40", "content": "We propose to make methods for episodic reinforcement learning more accountable by having them output a policy certificate before each episode . A policy certificate is a confidence interval [l, u]..."}
+{"idx": 9, "title": "MIT 6.S191 ( 2024 ): Reinforcement Learning", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=8JVRbHAVCws", "content": ""}
diff --git a/data/sampled_jsons/TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning_equation_8_N-step_return_year_2024.jsonl b/data/sampled_jsons/TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning_equation_8_N-step_return_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..20858828e747e229203eeaf8e80ab13236f17dee
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning_equation_8_N-step_return_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step return estimation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09536", "content": "TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step return estimation."}
+{"idx": 1, "title": "TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT LEARNING - OpenReview", "date": "", "ddg_snippet": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step . These trajectories are typ-ically parameterized by trajectory generators such as Movement ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fPHT0z4cS5", "content": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step . These trajectories are typ-ically parameterized by trajectory generators such as Movement ..."}
+{"idx": 2, "title": "TOP-ERL: Transformer-based Off-policy Episodic RL (ICLR25 ... - GitHub", "date": "", "ddg_snippet": "TOP-ERL : Transformer-based Off-policy Episodic RL (ICLR25 Spotlight) Episodic RL, What and Why? Episodic Reinforcement Learning ( ERL ) [1, 4, 5] is a distinct RL family that emphasizes the maximization of returns over entire episodes, typically lasting several seconds, rather than optimizing the intermediate states during environment interactions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BruceGeLi/TOP_ERL_ICLR25_Code", "content": "TOP-ERL : Transformer-based Off-policy Episodic RL (ICLR25 Spotlight) Episodic RL, What and Why? Episodic Reinforcement Learning ( ERL ) [1, 4, 5] is a distinct RL family that emphasizes the maximization of returns over entire episodes, typically lasting several seconds, rather than optimizing the intermediate states during environment interactions."}
+{"idx": 3, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "Poster in Workshop: 7th Robot Learning Workshop: Towards Robots with Human-Level Abilities TOP-ERL : Transformer-based Off-Policy Episodic Reinforcement Learning Ge Li · Dong Tian · Hongyi Zhou · Xinkai Jiang · Rudolf Lioutikov · Gerhard Neumann [ Abstract ] [ Project Page ] [ OpenReview]", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/32472", "content": "Poster in Workshop: 7th Robot Learning Workshop: Towards Robots with Human-Level Abilities TOP-ERL : Transformer-based Off-Policy Episodic Reinforcement Learning Ge Li · Dong Tian · Hongyi Zhou · Xinkai Jiang · Rudolf Lioutikov · Gerhard Neumann [ Abstract ] [ Project Page ] [ OpenReview]"}
+{"idx": 4, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement L...", "date": "", "ddg_snippet": "TOP-ERL : Transformer-based Off-Policy Episodic Reinforcement Learning Li, Ge 1; Tian, Dong; Zhou, Hongyi; Jiang, Xinkai; Lioutikov, Rudolf; Neumann, Gerhard 1 1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT) Externe Links Scopus", "subpage_snippet": "", "source": "publikationen.bibliothek.kit.edu", "link": "https://publikationen.bibliothek.kit.edu/1000179043", "content": "TOP-ERL : Transformer-based Off-Policy Episodic Reinforcement Learning Li, Ge 1; Tian, Dong; Zhou, Hongyi; Jiang, Xinkai; Lioutikov, Rudolf; Neumann, Gerhard 1 1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT) Externe Links Scopus"}
+{"idx": 5, "title": "dblp: TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement ...", "date": "", "ddg_snippet": "Bibliographic details on TOP-ERL : Transformer-based Off-Policy Episodic Reinforcement Learning .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/LiTZJLN25", "content": "Bibliographic details on TOP-ERL : Transformer-based Off-Policy Episodic Reinforcement Learning ."}
+{"idx": 6, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929465_TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire ..."}
+{"idx": 7, "title": "Ge Li (Bruce) on LinkedIn: #iclr2025 #reinforcementlearning # ...", "date": "", "ddg_snippet": "TOP-ERL tackles this challenge by incorporating a transformer-based critic that estimates values for segmented action sequences, combined with N-step return estimation.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/geli-bruce_iclr2025-reinforcementlearning-transformers-activity-7295121799921426433-w8U-", "content": "TOP-ERL tackles this challenge by incorporating a transformer-based critic that estimates values for segmented action sequences, combined with N-step return estimation."}
+{"idx": 8, "title": "GitHub - toperliclr2025/TOP_ERL", "date": "", "ddg_snippet": "Transformer-based Off-policy Episodic RL ( TOP-ERL ) Under review in ICLR25 Episodic RL, What and Why? Episodic Reinforcement Learning ( ERL ) [1, 4, 5] is a distinct RL family that emphasizes the maximization of returns over entire episodes, typically lasting several seconds, rather than optimizing the intermediate states during environment ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/toperliclr2025/TOP_ERL", "content": "Transformer-based Off-policy Episodic RL ( TOP-ERL ) Under review in ICLR25 Episodic RL, What and Why? Episodic Reinforcement Learning ( ERL ) [1, 4, 5] is a distinct RL family that emphasizes the maximization of returns over entire episodes, typically lasting several seconds, rather than optimizing the intermediate states during environment ..."}
+{"idx": 9, "title": "Hongyi Zhou", "date": "", "ddg_snippet": "TOP-ERL : Transformer-based Off-Policy Episodic Reinforcement Learning Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann Preprint, Under Review arXiv This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. ERL methods are often constrained to on- policy ...", "subpage_snippet": "", "source": "hongyizhoucn.github.io", "link": "https://hongyizhoucn.github.io/", "content": "TOP-ERL : Transformer-based Off-Policy Episodic Reinforcement Learning Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann Preprint, Under Review arXiv This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. ERL methods are often constrained to on- policy ..."}
diff --git a/data/sampled_jsons/TOP-ERL_ablation_study_Section_5.3_Figure_5_most_significant_performance_degradation.jsonl b/data/sampled_jsons/TOP-ERL_ablation_study_Section_5.3_Figure_5_most_significant_performance_degradation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4eb1136c17600f1e3a537c6e4e075bf5c9c8d328
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_ablation_study_Section_5.3_Figure_5_most_significant_performance_degradation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TOP-ERL: Transformer-based Off-Policy Episodic ...", "date": "", "ddg_snippet": "by G Li · Cited by 7 — TOP - ERL significantly outperforms state-of-the-art RL methods. Thorough ablation studies additionally show the impact of key design choices on the model ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=N4NhVN30ph", "content": "by G Li · Cited by 7 — TOP - ERL significantly outperforms state-of-the-art RL methods. Thorough ablation studies additionally show the impact of key design choices on the model ..."}
+{"idx": 1, "title": "A Performance-Adaptive Multi-Agent Reinforcement ...", "date": "", "ddg_snippet": "15 May 2025 — Our study focuses on equitable resource allocation under capacity constraints. ( Section 2.3) while integrating effective communication. These ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5255043.pdf?abstractid=5255043&mirid=1", "content": "15 May 2025 — Our study focuses on equitable resource allocation under capacity constraints. ( Section 2.3) while integrating effective communication. These ..."}
+{"idx": 2, "title": "transformer-based off-policy episodic reinforcement learning", "date": "", "ddg_snippet": "by G Li · 2024 · Cited by 7 — The results indicate that the random segment length has the most significant effect on TOP - ERL's performance . When using fixed 25 segments ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "by G Li · 2024 · Cited by 7 — The results indicate that the random segment length has the most significant effect on TOP - ERL's performance . When using fixed 25 segments ..."}
+{"idx": 3, "title": "The Choice of Divergence: A Neglected Key to Mitigating ...", "date": "", "ddg_snippet": "9 Sept 2025 — We validated this with a simple experiment in Figure 5 : When an RL model is trained with a reverse-KL objective, it almost exclusively produces ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07430v1", "content": "9 Sept 2025 — We validated this with a simple experiment in Figure 5 : When an RL model is trained with a reverse-KL objective, it almost exclusively produces ..."}
+{"idx": 4, "title": "PowerNorm: Rethinking Batch Normalization in Transformers", "date": "", "ddg_snippet": "by S Shen · Cited by 114 — Figure 5 . Ablation study of the performance of PN, PN-V, LN and BN on IWSLT14 trained using different batch sizes. Note that the ... 11 pages", "subpage_snippet": "", "source": "www.stat.berkeley.edu", "link": "https://www.stat.berkeley.edu/~mmahoney/pubs/icml20-powernorm.pdf", "content": "by S Shen · Cited by 114 — Figure 5 . Ablation study of the performance of PN, PN-V, LN and BN on IWSLT14 trained using different batch sizes. Note that the ... 11 pages"}
+{"idx": 5, "title": "Demonstration-free Autonomous Reinforcement Learning via ...", "date": "", "ddg_snippet": "by J Kim · 2023 · Cited by 3 — Ablation study – removing the bidirectional curriculum and auxiliary agent proposed in this work degrades performance . lum (IBC w/o Bidirectional). We ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/kim23d/kim23d.pdf", "content": "by J Kim · 2023 · Cited by 3 — Ablation study – removing the bidirectional curriculum and auxiliary agent proposed in this work degrades performance . lum (IBC w/o Bidirectional). We ..."}
+{"idx": 6, "title": "MODEL-BASED EPISODIC REINFORCEMENT LEARNING", "date": "", "ddg_snippet": "Among ERL methods, Episode. Transformer and BB-TRPL reach the highest jumping height. However, our method achieves similar quality results with 5x fewer ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/37aeae604bbf891a7ee19699d2ec69f40967b9d1.pdf", "content": "Among ERL methods, Episode. Transformer and BB-TRPL reach the highest jumping height. However, our method achieves similar quality results with 5x fewer ..."}
+{"idx": 7, "title": "Robust and Interpretable Denoising via Deep Learning", "date": "", "ddg_snippet": "B.2 Ablation Study on Number of Input Frames . ... Section 5.3 .3 for more details). See Figure D.8 for an additional example contrast are shown in Figures ...", "subpage_snippet": "", "source": "www.cns.nyu.edu", "link": "https://www.cns.nyu.edu/pub/lcv/mohan-phd.pdf", "content": "B.2 Ablation Study on Number of Input Frames . ... Section 5.3 .3 for more details). See Figure D.8 for an additional example contrast are shown in Figures ..."}
+{"idx": 8, "title": "PARADE: Passage Representation Aggregation forDocument ...", "date": "", "ddg_snippet": "In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3600088", "content": "In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score."}
+{"idx": 9, "title": "Unveiling the footprints of eXplainable AI in Industry 4.0/5.0 ...", "date": "", "ddg_snippet": "by OO Oladimeji · 2025 — Overview of yearly research output of eXplainable artificial intelligence (XAI) in industry research showcasing the number of publications per ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666764925000359", "content": "by OO Oladimeji · 2025 — Overview of yearly research output of eXplainable artificial intelligence (XAI) in industry research showcasing the number of publications per ..."}
diff --git a/data/sampled_jsons/TOP-ERL_conclusion_random_segment_length_performance.jsonl b/data/sampled_jsons/TOP-ERL_conclusion_random_segment_length_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..078750d3f770416faea4739e6925e9833334a4af
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_conclusion_random_segment_length_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT LEARNING - arXiv.org", "date": "", "ddg_snippet": "or TOP-ERL with corresponding component been added or removed. The results indicate that the random segment len th has the most significant effect on TOP-ERL's performance . When using fixed 25 segments the success rate dropped from 80% to 35% in the dense-re", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536v2", "content": "or TOP-ERL with corresponding component been added or removed. The results indicate that the random segment len th has the most significant effect on TOP-ERL's performance . When using fixed 25 segments the success rate dropped from 80% to 35% in the dense-re"}
+{"idx": 1, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step return estimation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=N4NhVN30ph", "content": "TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step return estimation."}
+{"idx": 2, "title": "GitHub - toperliclr2025/TOP_ERL", "date": "", "ddg_snippet": "In TOP-ERL , we utilize Transformers as an action sequence value predictor, training it via the N-step future returns. To ensure stable critic learning, we adapt the trajectory segmentation strategy in [9] by splitting the long trajectory into sub-sequences of varying lengths .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/toperliclr2025/TOP_ERL", "content": "In TOP-ERL , we utilize Transformers as an action sequence value predictor, training it via the N-step future returns. To ensure stable critic learning, we adapt the trajectory segmentation strategy in [9] by splitting the long trajectory into sub-sequences of varying lengths ."}
+{"idx": 3, "title": "TOP-ERL: Transformer-based Off-Policy Episodic ... - ResearchGate", "date": "", "ddg_snippet": "TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929465_TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning", "content": "TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step ..."}
+{"idx": 4, "title": "[2410.09536] TOP-ERL: Transformer-based Off-Policy Episodic ...", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09536", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ..."}
+{"idx": 5, "title": "Segment length optimization for crash frequency modelling: Evaluating ...", "date": "", "ddg_snippet": "Selecting an appropriate segment length is essential for road safety analysis, as it directly influences crash analysis accuracy, hazardous location identification, and safety performance evaluation. The traditional segmentation approaches rely on individual expertise or engineering judgment and often lack standardized metrics for evaluating segmentation performance . Therefore, this study ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0001457525002088", "content": "Selecting an appropriate segment length is essential for road safety analysis, as it directly influences crash analysis accuracy, hazardous location identification, and safety performance evaluation. The traditional segmentation approaches rely on individual expertise or engineering judgment and often lack standardized metrics for evaluating segmentation performance . Therefore, this study ..."}
+{"idx": 6, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement L...", "date": "", "ddg_snippet": "Li, Ge 1; Tian, Dong; Zhou, Hongyi; Jiang, Xinkai; Lioutikov, Rudolf; Neumann, Gerhard 1 1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für ...", "subpage_snippet": "", "source": "publikationen.bibliothek.kit.edu", "link": "https://publikationen.bibliothek.kit.edu/1000179043", "content": "Li, Ge 1; Tian, Dong; Zhou, Hongyi; Jiang, Xinkai; Lioutikov, Rudolf; Neumann, Gerhard 1 1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für ..."}
+{"idx": 7, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "Contact 2710 E Corridor Dr, Appleton WI 54913 Email Phone: +1-920-268-4789", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/32472", "content": "Contact 2710 E Corridor Dr, Appleton WI 54913 Email Phone: +1-920-268-4789"}
+{"idx": 8, "title": "A arXiv:2410.09536v4 [cs.LG] 15 Mar 2025", "date": "", "ddg_snippet": "ngth as the default setting for TOP-ERL . To further illustrate the impact of random segment lengths , we provide a visualization of action correlations under differe", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "ngth as the default setting for TOP-ERL . To further illustrate the impact of random segment lengths , we provide a visualization of action correlations under differe"}
+{"idx": 9, "title": "Improving Sample Efficiency in Evolutionary RL Using Off-Policy Ranking", "date": "", "ddg_snippet": "Expt. 2 ( Random Seeds and Hyperparameter choices): The top row of Fig. 3 shows the performance of OP-ARS over 100 random seeds sampled from [0, 10000]. We see that the median (thick blue line) crosses the threshold in most of the environments, demonstrating robustness to seeds.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-48885-6_3", "content": "Expt. 2 ( Random Seeds and Hyperparameter choices): The top row of Fig. 3 shows the performance of OP-ARS over 100 random seeds sampled from [0, 10000]. We see that the median (thick blue line) crosses the threshold in most of the environments, demonstrating robustness to seeds."}
diff --git a/data/sampled_jsons/TOP-ERL_random_segment_length_fixed_segment_performance_authors_reasoning.jsonl b/data/sampled_jsons/TOP-ERL_random_segment_length_fixed_segment_performance_authors_reasoning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3b1515dd789daae4cc6cf067d4b1de6da236f6f
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_random_segment_length_fixed_segment_performance_authors_reasoning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A survey of slow thinking-based reasoning LLMs using ...", "date": "", "ddg_snippet": "In this section, we introduce the key technologies that underpin reasoning in large language models, including slow thinking, reinforcement learning, reward ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0306457325003358", "content": "In this section, we introduce the key technologies that underpin reasoning in large language models, including slow thinking, reinforcement learning, reward ..."}
+{"idx": 1, "title": "Practical Fine-Grained Binary Code Randomization†", "date": "", "ddg_snippet": "by S Priyadarshan · 2020 · Cited by 26 — In this paper, we first propose a new, tunable randomization technique called LLR(k) that is compatible with these features.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/fullHtml/10.1145/3427228.3427292", "content": "by S Priyadarshan · 2020 · Cited by 26 — In this paper, we first propose a new, tunable randomization technique called LLR(k) that is compatible with these features."}
+{"idx": 2, "title": "transformer-based off-policy episodic reinforcement learning", "date": "", "ddg_snippet": "by G Li · 2024 · Cited by 7 — The results indicate that the random segment length has the most significant effect on TOP-ERL's performance . When using fixed 25 segments ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "by G Li · 2024 · Cited by 7 — The results indicate that the random segment length has the most significant effect on TOP-ERL's performance . When using fixed 25 segments ..."}
+{"idx": 3, "title": "Daily Papers", "date": "", "ddg_snippet": "4 Jul 2025 — This approach enables the model to adaptively employ both reasoning patterns: it prioritizes the Short CoT patterns and activates the Long CoT ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=reasoning+pattern", "content": "4 Jul 2025 — This approach enables the model to adaptively employ both reasoning patterns: it prioritizes the Short CoT patterns and activates the Long CoT ..."}
+{"idx": 4, "title": "DEL: Context-Aware Dynamic Exit Layer for Efficient Self", "date": "", "ddg_snippet": "by HE Zarch — We randomly sample two prompts from the coding task and divide the generation process into fixed - length segments of 64 tokens. For each segment , we run a.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/e1dbcba837d14d0fa9c917359c3c5f0b9d4cb9d5.pdf", "content": "by HE Zarch — We randomly sample two prompts from the coding task and divide the generation process into fixed - length segments of 64 tokens. For each segment , we run a."}
+{"idx": 5, "title": "BASIL: Best-Action Symbolic Interpretable Learning for ...", "date": "", "ddg_snippet": "31 May 2025 — Reinforcement Learning (RL) has proved a core methodology for solving complex decision-making and control problems in a range of domains, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00328v1", "content": "31 May 2025 — Reinforcement Learning (RL) has proved a core methodology for solving complex decision-making and control problems in a range of domains, ..."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "31 Jul 2025 — We propose a general pipeline for the RM-PRT benchmark that takes as input multimodal prompts containing natural language instructions and ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=progressive+reasoning", "content": "31 Jul 2025 — We propose a general pipeline for the RM-PRT benchmark that takes as input multimodal prompts containing natural language instructions and ..."}
+{"idx": 7, "title": "Mapping the landscape of social behavior", "date": "", "ddg_snippet": "We present a technique for high-resolution 3D tracking of postural dynamics and social touch in freely interacting animals.", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell/fulltext/S0092-8674(25)00154-0?rss=yes", "content": "We present a technique for high-resolution 3D tracking of postural dynamics and social touch in freely interacting animals."}
+{"idx": 8, "title": "Multi‐modal machine learning for predicting amyloid ...", "date": "", "ddg_snippet": "by SSR Danda · 2025 — Early detection of amyloid p is critical for Alzheimer's disease (AD) risk identification. This study leverages machine learning of ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12340426/", "content": "by SSR Danda · 2025 — Early detection of amyloid p is critical for Alzheimer's disease (AD) risk identification. This study leverages machine learning of ..."}
+{"idx": 9, "title": "NeurIPS 2024 Datasets Benchmarks 2024", "date": "", "ddg_snippet": "We introduce DrivingDojo, the first dataset tailor-made for training interactive world models with complex driving dynamics.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/events/datasets-benchmarks-2024", "content": "We introduce DrivingDojo, the first dataset tailor-made for training interactive world models with complex driving dynamics."}
diff --git a/data/sampled_jsons/TRSSL_Rizve_2022_semi-supervised_learning_Sinkhorn-Knopp_abstract.jsonl b/data/sampled_jsons/TRSSL_Rizve_2022_semi-supervised_learning_Sinkhorn-Knopp_abstract.jsonl
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+{"idx": 0, "title": "Towards Realistic Semi-Supervised Learning - GitHub", "date": "", "ddg_snippet": "Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nayeemrizve/TRSSL", "content": "Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved."}
+{"idx": 1, "title": "(PDF) Towards Realistic Semi - Supervised Learning", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361807923_Towards_Realistic_Semi-Supervised_Learning", "content": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data."}
+{"idx": 2, "title": "Sinkhorn Label Allocation: Semi-Supervised Classification via ...", "date": "", "ddg_snippet": "Abstract Self-training is a standard approach to semi-supervised learning where the learner’s own pre-dictions on unlabeled data are used as supervi-sion during training. In this paper, we reinterpret this label assignment process as an optimal trans-portation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/tai21a/tai21a.pdf", "content": "Abstract Self-training is a standard approach to semi-supervised learning where the learner’s own pre-dictions on unlabeled data are used as supervi-sion during training. In this paper, we reinterpret this label assignment process as an optimal trans-portation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of ..."}
+{"idx": 3, "title": "Towards Realistic Semi-Supervised Learning - arXiv.org", "date": "", "ddg_snippet": "Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.02269", "content": "Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications."}
+{"idx": 4, "title": "[2207.02269] Towards Realistic Semi-Supervised Learning Towards Realistic Semi-Supervised Learning - GitHub (PDF) Towards Realistic Semi-Supervised Learning - ResearchGate Towards Realistic Semi-Supervised Learning - Semantic Scholar Towards Realistic Semi-supervised Learning - Springer Sinkhorn Label Allocation: Semi-Supervised Classification via ... [2207.02269] Towards Realistic Semi - Supervised Learning Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi-Supervised Learning - arXiv.org", "date": "", "ddg_snippet": "Jul 5, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Jul 5, 2022 · Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. Fig. 2: Training Overview: Left: generating pseudo-labels. Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. We use the ground-truth labels and generated pseudo-labels to train in a supervised manner. To address the unreliable nature of pseudo ... Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach ... Abstract Self-training is a standard approach to semi-supervised learning where the learner’s own pre-dictions on unlabeled data are used as supervi-sion during training. In this paper, we reinterpret this label assignment process as an optimal trans-portation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of ... What is semi supervised learning (SSL)? Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Is deep learning a real-world semi-supervised learning? Towards Realistic Semi-Supervised Learning Abstract. Deep learning is pushing the state-of-the-art in many com- puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved . Are big self-supervised models strong semi-supervised learners? Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners . Advances in Neural Information Pro- cessing Systems 33 (2020) 4 Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.02269", "content": "Jul 5, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Jul 5, 2022 · Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. Fig. 2: Training Overview: Left: generating pseudo-labels. Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. We use the ground-truth labels and generated pseudo-labels to train in a supervised manner. To address the unreliable nature of pseudo ... Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach ... Abstract Self-training is a standard approach to semi-supervised learning where the learner’s own pre-dictions on unlabeled data are used as supervi-sion during training. In this paper, we reinterpret this label assignment process as an optimal trans-portation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of ... What is semi supervised learning (SSL)? Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Is deep learning a real-world semi-supervised learning? Towards Realistic Semi-Supervised Learning Abstract. Deep learning is pushing the state-of-the-art in many com- puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved . Are big self-supervised models strong semi-supervised learners? Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners . Advances in Neural Information Pro- cessing Systems 33 (2020) 4 Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications."}
+{"idx": 5, "title": "Towards Realistic Semi-Supervised Learning - Semantic Scholar", "date": "", "ddg_snippet": "Fig. 2: Training Overview: Left: generating pseudo-labels. Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. We use the ground-truth labels and generated pseudo-labels to train in a supervised manner. To address the unreliable nature of pseudo ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Towards-Realistic-Semi-Supervised-Learning-Rizve-Kardan/cce61de63d6691fce13bf1a1c8d231553df12f31/figure/2", "content": "Fig. 2: Training Overview: Left: generating pseudo-labels. Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. We use the ground-truth labels and generated pseudo-labels to train in a supervised manner. To address the unreliable nature of pseudo ..."}
+{"idx": 6, "title": "Towards Realistic Semi-supervised Learning - Springer", "date": "", "ddg_snippet": "Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-19821-2_25.pdf?pdf=inline+link", "content": "Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach ..."}
+{"idx": 7, "title": "Preserving Modality Structure Improves Multi-Modal Learning", "date": "", "ddg_snippet": "Abstract . Self- supervised learning on large-scale multi-modal datasets allows learning semantically meaningful embed-dings in a joint multi-modal representation space without relying on human annotations.", "subpage_snippet": "", "source": "www.crcv.ucf.edu", "link": "https://www.crcv.ucf.edu/wp-content/uploads/2018/11/2308.13077.pdf", "content": "Abstract . Self- supervised learning on large-scale multi-modal datasets allows learning semantically meaningful embed-dings in a joint multi-modal representation space without relying on human annotations."}
+{"idx": 8, "title": "Semi - supervised learning made simple with self-supervised clustering", "date": "", "ddg_snippet": ". Clustering-based self- supervised learning . . Our semi - supervised learning framework.To avoid collapsed solutions and group samples together, they use simple clustering-based pseudo-labeling algorithms such as k-means and Sinkhorn - Knopp to generate the assignments.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-04073630/document", "content": ". Clustering-based self- supervised learning . . Our semi - supervised learning framework.To avoid collapsed solutions and group samples together, they use simple clustering-based pseudo-labeling algorithms such as k-means and Sinkhorn - Knopp to generate the assignments."}
+{"idx": 9, "title": "Towards Realistic Semi - supervised Learning | SpringerLink", "date": "", "ddg_snippet": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-19821-2_25", "content": "Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data."}
diff --git a/data/sampled_jsons/TRSSL_Rizve_et_al._2022_Towards_Realistic_Semi-Supervised_Learning_paper_abstract_year_2022.jsonl b/data/sampled_jsons/TRSSL_Rizve_et_al._2022_Towards_Realistic_Semi-Supervised_Learning_paper_abstract_year_2022.jsonl
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index 0000000000000000000000000000000000000000..c1b98af32d4fdedc5e3ba41ab2ce3ab9cd216da0
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2207.02269] Towards Realistic Semi-Supervised Learning Towards Realistic Semi-supervised Learning | SpringerLink Towards Realistic Semi-supervised Learning | Computer Vision ... (PDF) Towards Realistic Semi-Supervised Learning - ResearchGate Towards Realistic Semi-Supervised Learning - GitHub Towards Realistic Semi-Supervised Learning - arXiv.org [2207.02269] Towards Realistic Semi - Supervised Learning Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi-supervised Learning | SpringerLink Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi-Supervised Learning", "date": "", "ddg_snippet": "Jul 5, 2022 · View a PDF of the paper titled Towards Realistic Semi-Supervised Learning , by Mamshad Nayeem Rizve and 2 other authors In the following, we describe our experimental setup including dataset descriptions, implementation details, evaluation details, and specifics of our baselines. Datasets. We conduct experiments on four commonly used computer vision benchmark datasets: CIFAR-10 , CIFAR-100 , ImageNet-100 and Tiny ImageNet . The datasets are selected in increasing o... See full list on link.springer.com Standard Benchmark Datasets. We compare our method with existing literature on open-world SSL problem and other related approaches that have been modified for this problem in Table 1 and 2. On CIFAR-10 we observe that our proposed method outperforms ORCA on both seen and novel classes by 12.1% and 4.1%, respectively. Our proposed method also outp... See full list on link.springer.com To investigate the impact of different components, we conduct extensive ablation study on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. We report the results in Table 3. The first row depicts the performance of our proposed method without uncertainty-guided temperature scaling, and mixed pseudo-labeling. Here, we can see that our proposed method... See full list on link.springer.com Oct 23, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Jul 5, 2022 · Towards Realistic Semi-Supervised Learning July 2022 DOI: 10.48550/arXiv.2207.02269 License CC BY-SA 4.0 Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications. What is semi supervised learning (SSL)? Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Is deep learning a real-world semi-supervised learning? Towards Realistic Semi-Supervised Learning Abstract. Deep learning is pushing the state-of-the-art in many com- puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved . Are big self-supervised models strong semi-supervised learners? A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020) Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners . In: Advances in Neural Information Processing Systems, vol. 33 (2020) How important is ablation in a semi-supervised learning system? Our ablation study as a whole demonstrates that every component of our proposed method is crucial and makes a noticeable contribution to the final performance while achieving their designated goal. Estimating Number of Novel Classes A realistic semi - supervised learn- ing system should make minimal assumption about the nature of the problem. One of the arXiv:2207.02269v2 [cs.CV] 28 Jul 2022 2 M. N. Rizve et al . dominant approaches to reduce the cost of annotation is semi-supervised learning (SSL) [64,6,43,49,62], where the objective is to leverage a set of unlabeled data in conjunction with a limited labeled set to improve performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.02269", "content": "Jul 5, 2022 · View a PDF of the paper titled Towards Realistic Semi-Supervised Learning , by Mamshad Nayeem Rizve and 2 other authors In the following, we describe our experimental setup including dataset descriptions, implementation details, evaluation details, and specifics of our baselines. Datasets. We conduct experiments on four commonly used computer vision benchmark datasets: CIFAR-10 , CIFAR-100 , ImageNet-100 and Tiny ImageNet . The datasets are selected in increasing o... See full list on link.springer.com Standard Benchmark Datasets. We compare our method with existing literature on open-world SSL problem and other related approaches that have been modified for this problem in Table 1 and 2. On CIFAR-10 we observe that our proposed method outperforms ORCA on both seen and novel classes by 12.1% and 4.1%, respectively. Our proposed method also outp... See full list on link.springer.com To investigate the impact of different components, we conduct extensive ablation study on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. We report the results in Table 3. The first row depicts the performance of our proposed method without uncertainty-guided temperature scaling, and mixed pseudo-labeling. Here, we can see that our proposed method... See full list on link.springer.com Oct 23, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Jul 5, 2022 · Towards Realistic Semi-Supervised Learning July 2022 DOI: 10.48550/arXiv.2207.02269 License CC BY-SA 4.0 Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications. What is semi supervised learning (SSL)? Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Is deep learning a real-world semi-supervised learning? Towards Realistic Semi-Supervised Learning Abstract. Deep learning is pushing the state-of-the-art in many com- puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved . Are big self-supervised models strong semi-supervised learners? A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020) Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners . In: Advances in Neural Information Processing Systems, vol. 33 (2020) How important is ablation in a semi-supervised learning system? Our ablation study as a whole demonstrates that every component of our proposed method is crucial and makes a noticeable contribution to the final performance while achieving their designated goal. Estimating Number of Novel Classes A realistic semi - supervised learn- ing system should make minimal assumption about the nature of the problem. One of the arXiv:2207.02269v2 [cs.CV] 28 Jul 2022 2 M. N. Rizve et al . dominant approaches to reduce the cost of annotation is semi-supervised learning (SSL) [64,6,43,49,62], where the objective is to leverage a set of unlabeled data in conjunction with a limited labeled set to improve performance."}
+{"idx": 1, "title": "Towards Realistic Semi-supervised Learning | SpringerLink Towards Realistic Semi-supervised Learning | Computer Vision ... (PDF) Towards Realistic Semi-Supervised Learning - ResearchGate Towards Realistic Semi-Supervised Learning - GitHub Towards Realistic Semi-Supervised Learning - arXiv.org [2207.02269] Towards Realistic Semi - Supervised Learning Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi-supervised Learning | SpringerLink Towards Realistic Semi - Supervised Learning - arXiv.org Towards Realistic Semi-Supervised Learning", "date": "", "ddg_snippet": "In the following, we describe our experimental setup including dataset descriptions, implementation details, evaluation details, and specifics of our baselines. Datasets. We conduct experiments on four commonly used computer vision benchmark datasets: CIFAR-10 , CIFAR-100 , ImageNet-100 and Tiny ImageNet . The datasets are selected in increasing o... See full list on link.springer.com Standard Benchmark Datasets. We compare our method with existing literature on open-world SSL problem and other related approaches that have been modified for this problem in Table 1 and 2. On CIFAR-10 we observe that our proposed method outperforms ORCA on both seen and novel classes by 12.1% and 4.1%, respectively. Our proposed method also outp... See full list on link.springer.com To investigate the impact of different components, we conduct extensive ablation study on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. We report the results in Table 3. The first row depicts the performance of our proposed method without uncertainty-guided temperature scaling, and mixed pseudo-labeling. Here, we can see that our proposed method... See full list on link.springer.com Oct 23, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Jul 5, 2022 · Towards Realistic Semi-Supervised Learning July 2022 DOI: 10.48550/arXiv.2207.02269 License CC BY-SA 4.0 Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications. What is semi supervised learning (SSL)? Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Is deep learning a real-world semi-supervised learning? Towards Realistic Semi-Supervised Learning Abstract. Deep learning is pushing the state-of-the-art in many com- puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved . Are big self-supervised models strong semi-supervised learners? A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020) Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners . In: Advances in Neural Information Processing Systems, vol. 33 (2020) How important is ablation in a semi-supervised learning system? Our ablation study as a whole demonstrates that every component of our proposed method is crucial and makes a noticeable contribution to the final performance while achieving their designated goal. Estimating Number of Novel Classes A realistic semi - supervised learn- ing system should make minimal assumption about the nature of the problem. One of the arXiv:2207.02269v2 [cs.CV] 28 Jul 2022 2 M. N. Rizve et al . dominant approaches to reduce the cost of annotation is semi-supervised learning (SSL) [64,6,43,49,62], where the objective is to leverage a set of unlabeled data in conjunction with a limited labeled set to improve performance.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-19821-2_25", "content": "In the following, we describe our experimental setup including dataset descriptions, implementation details, evaluation details, and specifics of our baselines. Datasets. We conduct experiments on four commonly used computer vision benchmark datasets: CIFAR-10 , CIFAR-100 , ImageNet-100 and Tiny ImageNet . The datasets are selected in increasing o... See full list on link.springer.com Standard Benchmark Datasets. We compare our method with existing literature on open-world SSL problem and other related approaches that have been modified for this problem in Table 1 and 2. On CIFAR-10 we observe that our proposed method outperforms ORCA on both seen and novel classes by 12.1% and 4.1%, respectively. Our proposed method also outp... See full list on link.springer.com To investigate the impact of different components, we conduct extensive ablation study on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. We report the results in Table 3. The first row depicts the performance of our proposed method without uncertainty-guided temperature scaling, and mixed pseudo-labeling. Here, we can see that our proposed method... See full list on link.springer.com Oct 23, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Jul 5, 2022 · Towards Realistic Semi-Supervised Learning July 2022 DOI: 10.48550/arXiv.2207.02269 License CC BY-SA 4.0 Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications. What is semi supervised learning (SSL)? Semi - supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Is deep learning a real-world semi-supervised learning? Towards Realistic Semi-Supervised Learning Abstract. Deep learning is pushing the state-of-the-art in many com- puter vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved . Are big self-supervised models strong semi-supervised learners? A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020) Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners . In: Advances in Neural Information Processing Systems, vol. 33 (2020) How important is ablation in a semi-supervised learning system? Our ablation study as a whole demonstrates that every component of our proposed method is crucial and makes a noticeable contribution to the final performance while achieving their designated goal. Estimating Number of Novel Classes A realistic semi - supervised learn- ing system should make minimal assumption about the nature of the problem. One of the arXiv:2207.02269v2 [cs.CV] 28 Jul 2022 2 M. N. Rizve et al . dominant approaches to reduce the cost of annotation is semi-supervised learning (SSL) [64,6,43,49,62], where the objective is to leverage a set of unlabeled data in conjunction with a limited labeled set to improve performance."}
+{"idx": 2, "title": "(PDF) Towards Realistic Semi - Supervised Learning", "date": "", "ddg_snippet": "Towards Realistic Semi - Supervised Learning . Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah. Center for Research in Computer Vision, UCF, USA.2 M. N. Rizve et al . goal is to identify novel-class samples and classify them, as well as to improve.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361807923_Towards_Realistic_Semi-Supervised_Learning", "content": "Towards Realistic Semi - Supervised Learning . Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah. Center for Research in Computer Vision, UCF, USA.2 M. N. Rizve et al . goal is to identify novel-class samples and classify them, as well as to improve."}
+{"idx": 3, "title": "Towards Realistic Semi-Supervised Learning - GitHub", "date": "", "ddg_snippet": "Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nayeemrizve/TRSSL", "content": "Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved."}
+{"idx": 4, "title": "Towards Realistic Semi-supervised Learning | Computer Vision ...", "date": "", "ddg_snippet": "Oct 23, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/978-3-031-19821-2_25", "content": "Oct 23, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ..."}
+{"idx": 5, "title": "Towards Realistic Semi-Supervised Learning - arXiv.org", "date": "", "ddg_snippet": "Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.02269", "content": "Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications."}
+{"idx": 6, "title": "Towards Realistic Semi-Supervised Learning", "date": "", "ddg_snippet": "One of the arXiv:2207.02269v2 [cs.CV] 28 Jul 2022 2 M. N. Rizve et al . dominant approaches to reduce the cost of annotation is semi-supervised learning (SSL) [64,6,43,49,62], where the objective is to leverage a set of unlabeled data in conjunction with a limited labeled set to improve performance.", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/pdf/2207.02269v2", "content": "One of the arXiv:2207.02269v2 [cs.CV] 28 Jul 2022 2 M. N. Rizve et al . dominant approaches to reduce the cost of annotation is semi-supervised learning (SSL) [64,6,43,49,62], where the objective is to leverage a set of unlabeled data in conjunction with a limited labeled set to improve performance."}
+{"idx": 7, "title": "Towards Realistic Semi - Supervised Learning", "date": "", "ddg_snippet": "Towards Realistic Semi - Supervised Learning . Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah.4 M. N. Rizve et al . labeling and overclustering while only relying on cross-entropy loss.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136910423.pdf", "content": "Towards Realistic Semi - Supervised Learning . Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah.4 M. N. Rizve et al . labeling and overclustering while only relying on cross-entropy loss."}
+{"idx": 8, "title": "Towards Realistic Semi - Supervised Learning | DeepAI", "date": "", "ddg_snippet": "Towards Realistic Semi - Supervised Learning . 07/05/ 2022 . ∙. by Mamshad Nayeem Rizve , et al . Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/towards-realistic-semi-supervised-learning", "content": "Towards Realistic Semi - Supervised Learning . 07/05/ 2022 . ∙. by Mamshad Nayeem Rizve , et al . Semi - supervised learning ( SSL ) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost."}
+{"idx": 9, "title": "CIFAR-100 Benchmark (Open-World Semi - Supervised Learning )", "date": "", "ddg_snippet": "Towards Realistic Semi - Supervised Learning . 2022 . 2. OpenLDN (ResNet-18).", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/sota/open-world-semi-supervised-learning-on-cifar-1?p=towards-realistic-semi-supervised-learning", "content": "Towards Realistic Semi - Supervised Learning . 2022 . 2. OpenLDN (ResNet-18)."}
diff --git a/data/sampled_jsons/Table_1_4-LAYER_WIKIPEDIA_final_loss_+_SYMM_The_underlying_structures_of_self-attention.jsonl b/data/sampled_jsons/Table_1_4-LAYER_WIKIPEDIA_final_loss_+_SYMM_The_underlying_structures_of_self-attention.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9dc01847075693ff3356798a6b3bab1d8f42d4bc
--- /dev/null
+++ b/data/sampled_jsons/Table_1_4-LAYER_WIKIPEDIA_final_loss_+_SYMM_The_underlying_structures_of_self-attention.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Pennsylvania Station (1910–1963) - Wikipedia", "date": "", "ddg_snippet": "Starting in 1963, the above-ground head house and train shed were demolished, a loss that galvanized the modern historic preservation movement in the ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Penn_Station_(1910-1963)", "content": "Starting in 1963, the above-ground head house and train shed were demolished, a loss that galvanized the modern historic preservation movement in the ..."}
+{"idx": 1, "title": "Pennsylvania Station (1910–1963) - Wikipedia", "date": "", "ddg_snippet": "Starting in 1963, the above-ground head house and train shed were demolished, a loss that galvanized the modern historic preservation movement in the ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Pennsylvania_Station_(1910)", "content": "Starting in 1963, the above-ground head house and train shed were demolished, a loss that galvanized the modern historic preservation movement in the ..."}
+{"idx": 2, "title": "Pennsylvania Station (1910–1963) - Wikipedia", "date": "", "ddg_snippet": "Starting in 1963, the above-ground head house and train shed were demolished, a loss that galvanized the modern historic preservation movement in the ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/New_York_station_(Baltimore_and_Ohio_Railroad)", "content": "Starting in 1963, the above-ground head house and train shed were demolished, a loss that galvanized the modern historic preservation movement in the ..."}
+{"idx": 3, "title": "Nevado del Ruiz - Wikipedia", "date": "", "ddg_snippet": "The glaciers reached altitudes as low as 4 ,500 m (14,800 ft) in 1985 but have now retreated to elevations of 4 ,800– 4 ,900 m (15,700–16,100 ft).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Nevado_del_Ruiz", "content": "The glaciers reached altitudes as low as 4 ,500 m (14,800 ft) in 1985 but have now retreated to elevations of 4 ,800– 4 ,900 m (15,700–16,100 ft)."}
+{"idx": 4, "title": "VMware | The virtual world of Marc O Polo | Page 7", "date": "", "ddg_snippet": "... look at a whitepaper from VMware and Cisco called: Virtual Networking features of the VMware vNetwork Distributed Switch and Cisco Nexus 1000V Switch.", "subpage_snippet": "", "source": "blog.mrpol.nl", "link": "https://blog.mrpol.nl/category/vmware/page/7/", "content": "... look at a whitepaper from VMware and Cisco called: Virtual Networking features of the VMware vNetwork Distributed Switch and Cisco Nexus 1000V Switch."}
+{"idx": 5, "title": "Marco | The virtual world of Marc O Polo | Page 11", "date": "", "ddg_snippet": "... look at a whitepaper from VMware and Cisco called: Virtual Networking features of the VMware vNetwork Distributed Switch and Cisco Nexus 1000V Switch.", "subpage_snippet": "", "source": "blog.mrpol.nl", "link": "https://blog.mrpol.nl/author/m-pol/page/11/", "content": "... look at a whitepaper from VMware and Cisco called: Virtual Networking features of the VMware vNetwork Distributed Switch and Cisco Nexus 1000V Switch."}
+{"idx": 6, "title": "togethercomputer/RedPajama-Data-1T · Datasets at Hugging Face", "date": "", "ddg_snippet": "Need help to make the dataset ... In a graph $G$, we say that a cycle $C$ is \\textit{formed by the path} $Q$ if $ | E(C) \\setminus E(Q) | = 1 $.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T", "content": "Need help to make the dataset ... In a graph $G$, we say that a cycle $C$ is \\textit{formed by the path} $Q$ if $ | E(C) \\setminus E(Q) | = 1 $."}
+{"idx": 7, "title": "togethercomputer/RedPajama-Data-1T · Datasets at Hugging Face", "date": "", "ddg_snippet": "Need help to make the dataset ... In a graph $G$, we say that a cycle $C$ is \\textit{formed by the path} $Q$ if $ | E(C) \\setminus E(Q) | = 1 $.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T/viewer", "content": "Need help to make the dataset ... In a graph $G$, we say that a cycle $C$ is \\textit{formed by the path} $Q$ if $ | E(C) \\setminus E(Q) | = 1 $."}
+{"idx": 8, "title": "Student perspectives – Compass Blog", "date": "", "ddg_snippet": "Then, our decision model can output one of three decisions: ( 1 ) stay home, (2) leave the house with a jacket, (3) leave the house without a jacket.", "subpage_snippet": "", "source": "compass.blogs.bristol.ac.uk", "link": "https://compass.blogs.bristol.ac.uk/tag/student-perspectives/", "content": "Then, our decision model can output one of three decisions: ( 1 ) stay home, (2) leave the house with a jacket, (3) leave the house without a jacket."}
+{"idx": 9, "title": "Amnesia – A Good Man Is Hard to Find — My Search", "date": "", "ddg_snippet": "One of the topics that Josh, Tyler, and I discuss in episode 4 of The One That Got Away, which dropped tonight, is the psychology book that was open ...", "subpage_snippet": "", "source": "ronaldtammen.com", "link": "https://ronaldtammen.com/category/amnesia/", "content": "One of the topics that Josh, Tyler, and I discuss in episode 4 of The One That Got Away, which dropped tonight, is the psychology book that was open ..."}
diff --git a/data/sampled_jsons/Table_1_Comparison_on_the_N3DV_dataset_Ours-l_PSNR.jsonl b/data/sampled_jsons/Table_1_Comparison_on_the_N3DV_dataset_Ours-l_PSNR.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..10c16121b7fab2f96fc8f612effabc947538f70e
--- /dev/null
+++ b/data/sampled_jsons/Table_1_Comparison_on_the_N3DV_dataset_Ours-l_PSNR.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "Table 1 : Comparison on the N 3 DV dataset , with results measured at a resolution of 1352 x 1014.We also conducted a PSNR trend comparison with 3DGStream to verify the effectiveness of our method in mitigating error accumulation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "Table 1 : Comparison on the N 3 DV dataset , with results measured at a resolution of 1352 x 1014.We also conducted a PSNR trend comparison with 3DGStream to verify the effectiveness of our method in mitigating error accumulation."}
+{"idx": 1, "title": "(PDF) Instant Gaussian Stream: Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "Table 1 . Comparison on the N 3 DV dataset , with results measured. at a resolution of 1352 x 1014. † indicates that the evaluation was.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "Table 1 . Comparison on the N 3 DV dataset , with results measured. at a resolution of 1352 x 1014. † indicates that the evaluation was."}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "Table 1 . Comparison on the N 3 DV dataset , with results measured at a resolution of 1352 x 1014. † indicates that the evaluation was performed using the official code in the same experimental envi-ronment as ours , including the same initial point cloud.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "Table 1 . Comparison on the N 3 DV dataset , with results measured at a resolution of 1352 x 1014. † indicates that the evaluation was performed using the official code in the same experimental envi-ronment as ours , including the same initial point cloud."}
+{"idx": 3, "title": "QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for", "date": "", "ddg_snippet": "We evaluate our approach, QUEEN, on two benchmark datasets , containing diverse scenes with large geometric motion and illumination changes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.04469v1", "content": "We evaluate our approach, QUEEN, on two benchmark datasets , containing diverse scenes with large geometric motion and illumination changes."}
+{"idx": 4, "title": "SD-GS: Structured Deformable 3D Gaussians for Efficient Dynamic", "date": "", "ddg_snippet": "The explicit methods [ 39 , 9 , 18 ] are built on the 4D Gaussians, with one more dimension representing the timestamp, which requires substantial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07465v1", "content": "The explicit methods [ 39 , 9 , 18 ] are built on the 4D Gaussians, with one more dimension representing the timestamp, which requires substantial ..."}
+{"idx": 5, "title": "MAPo : Motion-Aware Partitioning of Deformable 3D Gaussian", "date": "", "ddg_snippet": "This core limitation stems from their unified modeling strategy, which relies on a single canonical set of 3DGs and a single, globally shared ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19786v1", "content": "This core limitation stems from their unified modeling strategy, which relies on a single canonical set of 3DGs and a single, globally shared ..."}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 7, "title": "DynMF: Neural Motion Factorization for Real-time Dynamic View", "date": "", "ddg_snippet": "Our carefully designed neural framework consisting of a tiny set of learned basis queried only in time allows for rendering speed similar to 3D ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.00112v2", "content": "Our carefully designed neural framework consisting of a tiny set of learned basis queried only in time allows for rendering speed similar to 3D ..."}
diff --git a/data/sampled_jsons/Table_1_FD1_mean_estimation_MSE_Likelihood_Based_Approach_to_Distribution_Regression.jsonl b/data/sampled_jsons/Table_1_FD1_mean_estimation_MSE_Likelihood_Based_Approach_to_Distribution_Regression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7333e00a753dc852063a4538067fbd2149ac8e18
--- /dev/null
+++ b/data/sampled_jsons/Table_1_FD1_mean_estimation_MSE_Likelihood_Based_Approach_to_Distribution_Regression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "MSE for the estimated conditional mean and the standard deviation. estimation approaches can be broadly categorized into three types. The first one is likelihood - based with. notable examples including Kingma and Welling (2013), Rezende et al.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "MSE for the estimated conditional mean and the standard deviation. estimation approaches can be broadly categorized into three types. The first one is likelihood - based with. notable examples including Kingma and Welling (2013), Rezende et al."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.02025", "content": "Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating ..."}
+{"idx": 3, "title": "ICML Poster A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric."}
+{"idx": 4, "title": "[2410.02025] A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric."}
+{"idx": 5, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "1 . Categorization of Distribution Estimation Approaches. The authors categorize implicit distribution estimation methods into three main types: Likelihood - based methods: These include notable works such as Kingma and Welling (2013) and Rezende et al.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "1 . Categorization of Distribution Estimation Approaches. The authors categorize implicit distribution estimation methods into three main types: Likelihood - based methods: These include notable works such as Kingma and Welling (2013) and Rezende et al."}
+{"idx": 6, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models."}
+{"idx": 7, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2410.02025", "content": "Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric."}
+{"idx": 8, "title": "[PDF] Discussion of: “Nonparametric regression ...” | Semantic Scholar", "date": "", "ddg_snippet": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Discussion-of:-“Nonparametric-regression-using-deep-Ghorbani-Mei/688b87f8e83080a024eb0fd7e55288de313aff8b", "content": "A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models."}
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diff --git a/data/sampled_jsons/Table_1_MCC_Contrastive_CRL_synthetic_real_ablation_0.95_0.15.jsonl b/data/sampled_jsons/Table_1_MCC_Contrastive_CRL_synthetic_real_ablation_0.95_0.15.jsonl
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+{"idx": 1, "title": "identification of nonparametric dynamic causal structure ...", "date": "", "ddg_snippet": "by M Fu · 2025 — Table 1 : Results on different observed dimensionality dx. We run simulations with 5 random seeds, selected based on the best-converged ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.12500", "content": "by M Fu · 2025 — Table 1 : Results on different observed dimensionality dx. We run simulations with 5 random seeds, selected based on the best-converged ..."}
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+{"idx": 7, "title": "Poster abstracts of the ISPD 25th International Conference on ...", "date": "", "ddg_snippet": "13 Oct 2021 — We aim to evaluate our experience in prenatal diagnostic (PND) tests regarding its sensitivity for chromosomal aberrations, together with a comparison between ...", "subpage_snippet": "", "source": "obgyn.onlinelibrary.wiley.com", "link": "https://obgyn.onlinelibrary.wiley.com/doi/10.1002/pd.6016", "content": "13 Oct 2021 — We aim to evaluate our experience in prenatal diagnostic (PND) tests regarding its sensitivity for chromosomal aberrations, together with a comparison between ..."}
+{"idx": 8, "title": "Material", "date": "", "ddg_snippet": "Articular cartilage is present in joints to provide load distribution and nearly frictionless surfaces. Its extracellular matrix is mainly composed of water, ...", "subpage_snippet": "", "source": "www.biomomentum.com", "link": "https://www.biomomentum.com/material/", "content": "Articular cartilage is present in joints to provide load distribution and nearly frictionless surfaces. Its extracellular matrix is mainly composed of water, ..."}
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diff --git a/data/sampled_jsons/Table_1_MCC_Contrastive_CRL_synthetic_real_ablation_sitearxiv.org2502.20099.jsonl b/data/sampled_jsons/Table_1_MCC_Contrastive_CRL_synthetic_real_ablation_sitearxiv.org2502.20099.jsonl
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diff --git a/data/sampled_jsons/Table_1_The_underlying_structures_of_self-attention-_symmetry,_directionality,_and_emergent_dynamics.jsonl b/data/sampled_jsons/Table_1_The_underlying_structures_of_self-attention-_symmetry,_directionality,_and_emergent_dynamics.jsonl
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+{"idx": 0, "title": "The underlying structures of self-attention: symmetry, directionality ...", "date": "", "ddg_snippet": "We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10927", "content": "We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance."}
+{"idx": 1, "title": "The underlying structures of self-attention - GitHub", "date": "", "ddg_snippet": "This is a repository for the paper: \"The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training\" ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/matteosaponati/attention-geometry", "content": "This is a repository for the paper: \"The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training\" ..."}
+{"idx": 2, "title": "I am very excited to announce that our paper \"The underlying structures ...", "date": "", "ddg_snippet": "I am very excited to announce that our paper \" The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training \" is available on arXiv ! arxiv ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/matteosaponati_i-am-very-excited-to-announce-that-our-paper-activity-7299834621347741697-uc-z", "content": "I am very excited to announce that our paper \" The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training \" is available on arXiv ! arxiv ..."}
+{"idx": 3, "title": "The underlying structures of self-attention: symmetry, directionality ...", "date": "", "ddg_snippet": "The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training 1 month ago · ICML", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/44452/paper", "content": "The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training 1 month ago · ICML"}
+{"idx": 4, "title": "dblp: The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "Bibliographic details on The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-10927", "content": "Bibliographic details on The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training ."}
+{"idx": 5, "title": "PDF Self-Attention Dynamics", "date": "", "ddg_snippet": "The Emergence of Clusters in Self-Attention Dynamics Transformer based architectures have widespread success throughout all areas of deep learning and recently in large language modelling with the development of ChatGPT. The essential component of the transformer architecture is the self -atention mechanism, allowing a model to learn and distinguish important characteristics of", "subpage_snippet": "", "source": "sugolov.github.io", "link": "https://sugolov.github.io/files/mat1510-writeup.pdf", "content": "The Emergence of Clusters in Self-Attention Dynamics Transformer based architectures have widespread success throughout all areas of deep learning and recently in large language modelling with the development of ChatGPT. The essential component of the transformer architecture is the self -atention mechanism, allowing a model to learn and distinguish important characteristics of"}
+{"idx": 6, "title": "The underlying structures of self-attention: symmetry, directionality ...", "date": "", "ddg_snippet": "We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demon-strate that bidirectional training induces symmetry in the weight matrices, while autoregressive train-ing results in directionality and column dominance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10927", "content": "We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demon-strate that bidirectional training induces symmetry in the weight matrices, while autoregressive train-ing results in directionality and column dominance."}
+{"idx": 7, "title": "The underlying structures of self-attention: symmetry, directionality ...", "date": "", "ddg_snippet": "We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this frame-work, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=gpizm0I3lp", "content": "We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this frame-work, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance."}
+{"idx": 8, "title": "matteosaponati (matteo saponati) · GitHub", "date": "", "ddg_snippet": "This is a repository for the paper: \" The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training \", Saponati, M., Sager, P. J., Aceituno, P. …", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/matteosaponati", "content": "This is a repository for the paper: \" The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training \", Saponati, M., Sager, P. J., Aceituno, P. …"}
+{"idx": 9, "title": "arXiv:2502.10927v1 [cs.LG] 15 Feb 2025", "date": "", "ddg_snippet": "arXiv:2502.10927v1 [cs.LG] 15 Feb 2025 The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10927v1", "content": "arXiv:2502.10927v1 [cs.LG] 15 Feb 2025 The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training"}
diff --git a/data/sampled_jsons/Table_4_SLHAMR_PA-MPJPE_single_moving_camera_mov..jsonl b/data/sampled_jsons/Table_4_SLHAMR_PA-MPJPE_single_moving_camera_mov..jsonl
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index 0000000000000000000000000000000000000000..cb1858cd48d83993a2a81d3a9b930c11d3251f1f
--- /dev/null
+++ b/data/sampled_jsons/Table_4_SLHAMR_PA-MPJPE_single_moving_camera_mov..jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "PEDESTRIAN MOTION RECONSTRUCTION:ALARGE", "date": "", "ddg_snippet": "Table 4 : SLHAMR (Ye et al., 2023b) performance comparison under single and ... It is evident that HMR methods operating in camera space yield higher PA - MPJPE ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/789739cfd83f5876545163c9f6ee9f74cfe121fa.pdf", "content": "Table 4 : SLHAMR (Ye et al., 2023b) performance comparison under single and ... It is evident that HMR methods operating in camera space yield higher PA - MPJPE ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_JUICE_Table_3_World_Capital.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_JUICE_Table_3_World_Capital.jsonl
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+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Jun 9, 2025 · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between “memory heads” and “context heads”, attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v2", "content": "Jun 9, 2025 · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between “memory heads” and “context heads”, attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term ..."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models - GitHub", "date": "", "ddg_snippet": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GaotangLi/JUICE", "content": "This repository contains the code and data of the ICML 25 Spotlight Paper Taming Knowledge Conflicts in Language Models . The code is now still being updated."}
+{"idx": 2, "title": "Taming Knowledge Conflict in Language Models", "date": "", "ddg_snippet": "Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art performance and robust generalization, achieving significant and consistent improvement across different domains under various conflict types.", "subpage_snippet": "", "source": "gaotangli.github.io", "link": "https://gaotangli.github.io/project_page/Taming-Knowledge-Conflict/", "content": "Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art performance and robust generalization, achieving significant and consistent improvement across different domains under various conflict types."}
+{"idx": 3, "title": "[PDF] Taming Knowledge Conflicts in Language Models ...", "date": "", "ddg_snippet": "Mar 14, 2025 · This work proposes Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Taming-Knowledge-Conflicts-in-Language-Models-Li-Chen/b7ba9df4eb239708cf48f25be87b5bceeca010e3", "content": "Mar 14, 2025 · This work proposes Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning, and identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Language Models (LMs) often encounter knowledge conflicts when parametric memory ..."}
+{"idx": 4, "title": "Paper page - Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "May 6, 2025 · Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art performance and robust generalization, achieving significant and consistent improvement across different domains under various conflict types.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.10996", "content": "May 6, 2025 · Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art performance and robust generalization, achieving significant and consistent improvement across different domains under various conflict types."}
+{"idx": 5, "title": "ICML Poster Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Language models frequently encounter \" knowledge conflicts ,\" where their pre-trained knowledge contradicts the information provided by specific contexts. These conflicts often arise in context-dependent systems, such as retrieval-augmented generation and tools integrated with language models .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46677", "content": "Language models frequently encounter \" knowledge conflicts ,\" where their pre-trained knowledge contradicts the information provided by specific contexts. These conflicts often arise in context-dependent systems, such as retrieval-augmented generation and tools integrated with language models ."}
+{"idx": 6, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Mar 14, 2025 · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Mar 14, 2025 · Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ..."}
+{"idx": 7, "title": "KMedu Hub » 2024 Knowledge Management Singapore (KM", "date": "", "ddg_snippet": "a specific type of \"Degree\" in a specific \"Country\") ... Back to Tacit Sep 30, 2024 - Oct 30, 2024, Singapore / Virtual venue", "subpage_snippet": "", "source": "kmeducationhub.de", "link": "https://kmeducationhub.de/knowledge-management-singapore-kmsg/", "content": "a specific type of \"Degree\" in a specific \"Country\") ... Back to Tacit Sep 30, 2024 - Oct 30, 2024, Singapore / Virtual venue"}
+{"idx": 8, "title": "Ted Braude Archive", "date": "", "ddg_snippet": "... This monthly space is to bring you lots of goodies about boys: their world -view, experiences, conflicts , relationships and their path into manhood.", "subpage_snippet": "", "source": "www.menstuff.org", "link": "http://www.menstuff.org/columns/braude/archive.html", "content": "... This monthly space is to bring you lots of goodies about boys: their world -view, experiences, conflicts , relationships and their path into manhood."}
+{"idx": 9, "title": "Infrastructure and Intelligence | Trey Menefee | Substack", "date": "", "ddg_snippet": "... language ? Through what cognitive scientists call \" autocuing \" - the ability to internally rehearse and refine motor sequences without ...", "subpage_snippet": "", "source": "comparativist.substack.com", "link": "https://comparativist.substack.com/", "content": "... language ? Through what cognitive scientists call \" autocuing \" - the ability to internally rehearse and refine motor sequences without ..."}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_substitution_conflict_coherent_conflict_definitions.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_substitution_conflict_coherent_conflict_definitions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ba2bc227ce4bfeea7bdd7beaf0a6b9043dadc241
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+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models - arXiv.org", "date": "", "ddg_snippet": "Paragraph-level Conflict ( Coherent Counterfactual): Re-cent work (Xie et al., 2024) demonstrates that language models rely more on context when it is coherent . In this scenario, the context extends beyond a single substitution , reinforced by coherent and persuasive evidence, often gener-ated by advanced models like GPT-4.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10996", "content": "Paragraph-level Conflict ( Coherent Counterfactual): Re-cent work (Xie et al., 2024) demonstrates that language models rely more on context when it is coherent . In this scenario, the context extends beyond a single substitution , reinforced by coherent and persuasive evidence, often gener-ated by advanced models like GPT-4."}
+{"idx": 1, "title": "Taming Knowledge Conflict in Language Models", "date": "", "ddg_snippet": "We construct three evaluation tiers: (a) clean input, (b) substitution conflict , and (c) coherent conflict , representing increasing levels of contextual distraction over diverse factual domains. This design lets us trace model internals across conflict levels and test whether an intervention can steer the model consistently in every case.", "subpage_snippet": "", "source": "gaotangli.github.io", "link": "https://gaotangli.github.io/project_page/Taming-Knowledge-Conflict/", "content": "We construct three evaluation tiers: (a) clean input, (b) substitution conflict , and (c) coherent conflict , representing increasing levels of contextual distraction over diverse factual domains. This design lets us trace model internals across conflict levels and test whether an intervention can steer the model consistently in every case."}
+{"idx": 2, "title": "Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting ...", "date": "", "ddg_snippet": "Cutting Off the Head Ends the Conflict : A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models . In Findings of the Association for Computational Linguistics: ACL 2024, pages 1193-1215, Bangkok, Thailand. Association for Computational Linguistics. Cite (Informal):", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.70/", "content": "Cutting Off the Head Ends the Conflict : A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models . In Findings of the Association for Computational Linguistics: ACL 2024, pages 1193-1215, Bangkok, Thailand. Association for Computational Linguistics. Cite (Informal):"}
+{"idx": 3, "title": "ICML Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Poster in Workshop: Actionable Interpretability Taming Knowledge Conflicts in Language Models Gaotang Li · Yuzhong Chen · Hanghang Tong [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/49596", "content": "Poster in Workshop: Actionable Interpretability Taming Knowledge Conflicts in Language Models Gaotang Li · Yuzhong Chen · Hanghang Tong [ Abstract ] [ Project Page ] [ OpenReview] Sat 19 Jul 10:40 a.m. PDT — 11:40 a.m. PDT"}
+{"idx": 4, "title": "Taming Knowledge Conflicts in Language Models - Semantic Scholar", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between\"memory heads\"and\"context heads\", attention heads assumed to promote either memory or context exclusively.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Taming-Knowledge-Conflicts-in-Language-Models-Li-Chen/b7ba9df4eb239708cf48f25be87b5bceeca010e3", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between\"memory heads\"and\"context heads\", attention heads assumed to promote either memory or context exclusively."}
+{"idx": 5, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a critical phenomenon we term the ..."}
+{"idx": 6, "title": "Paper page - Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Abstract Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \" memory heads \" and \" context heads \", attention heads assumed to promote either memory or context exclusively.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.10996", "content": "Abstract Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \" memory heads \" and \" context heads \", attention heads assumed to promote either memory or context exclusively."}
+{"idx": 7, "title": "Taming Knowledge Conflicts in Language Models | AI Research Paper Details", "date": "", "ddg_snippet": "The researchers developed a comprehensive framework for addressing knowledge conflicts in language models . They start by distinguishing between parametric conflicts (contradictions within the model's parameters) and contextual conflicts (contradictions between the model's parameters and provided context).", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/taming-knowledge-conflicts-language-models", "content": "The researchers developed a comprehensive framework for addressing knowledge conflicts in language models . They start by distinguishing between parametric conflicts (contradictions within the model's parameters) and contextual conflicts (contradictions between the model's parameters and provided context)."}
+{"idx": 8, "title": "Who's Who: Large Language Models Meet Knowledge Conflicts in Practice ...", "date": "", "ddg_snippet": "To analyze how current large language models (LLMs) align with our recommendation, we introduce WhoQA, a public benchmark dataset to examine model's behavior in knowledge conflict situations. We induce conflicts by asking about a common property among entities having the same name, resulting in questions with up to 8 distinctive answers.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.593/", "content": "To analyze how current large language models (LLMs) align with our recommendation, we introduce WhoQA, a public benchmark dataset to examine model's behavior in knowledge conflict situations. We induce conflicts by asking about a common property among entities having the same name, resulting in questions with up to 8 distinctive answers."}
+{"idx": 9, "title": "Taming Knowledge Conflicts in Language Models | alphaXiv", "date": "", "ddg_snippet": "View recent discussion. Abstract: Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.10996v2", "content": "View recent discussion. Abstract: Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge . Previous works attribute this conflict to the interplay between \"memory heads\" and \"context heads\", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a ..."}
diff --git a/data/sampled_jsons/Task-Aware_Parameter_Initialization_at_Flexible_Scales_ICML_2025_poster_medium_year_2025.jsonl b/data/sampled_jsons/Task-Aware_Parameter_Initialization_at_Flexible_Scales_ICML_2025_poster_medium_year_2025.jsonl
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index 0000000000000000000000000000000000000000..5271affc0af6c7fc0ec4f250502ef5cffb75345d
--- /dev/null
+++ b/data/sampled_jsons/Task-Aware_Parameter_Initialization_at_Flexible_Scales_ICML_2025_poster_medium_year_2025.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "ICML Poster Learngene Tells You How to Customize: Task - Aware ...", "date": "", "ddg_snippet": "Appropriate parameter initialization strategies are essential for reducing the high computational costs of training large pretrained models in various task scenarios.Right-click and choose download. It is a vector graphic and may be used at any scale.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45736", "content": "Appropriate parameter initialization strategies are essential for reducing the high computational costs of training large pretrained models in various task scenarios.Right-click and choose download. It is a vector graphic and may be used at any scale."}
+{"idx": 1, "title": "GitHub - mathieuxu/ Task - Aware -Learngene: Task - Aware Parameter ...", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task - Aware Parameter Initialization at Flexible Scales .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mathieuxu/Task-Aware-Learngene", "content": "Learngene Tells You How to Customize: Task - Aware Parameter Initialization at Flexible Scales ."}
+{"idx": 2, "title": "Downloads 2025 - icml.cc", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Learning Adaptive Lighting via Channel-Aware Guidance Learning Adversarial MDPs with Stochastic Hard Constraints Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Learning Adaptive Lighting via Channel-Aware Guidance Learning Adversarial MDPs with Stochastic Hard Constraints Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning"}
+{"idx": 3, "title": "Learngene Tells You How to Customize: Task-Aware Parameter ...", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Jiaze Xu 1 2 Shiyu Xia 1 2 Xu Yang 1 2", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=IRQ0n961nn", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Jiaze Xu 1 2 Shiyu Xia 1 2 Xu Yang 1 2"}
+{"idx": 4, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales · SADA: Stability-guided Adaptive Diffusion Acceleration · Two ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales · SADA: Stability-guided Adaptive Diffusion Acceleration · Two ..."}
+{"idx": 5, "title": "Jiaqi Lv - GitHub Pages", "date": "", "ddg_snippet": "Learngene tells you how to customize: Task-aware parameter initialization at flexible scales . In Proceedings of 42nd International Conference on Machine Learning ( ICML 2025 ), 2025 .", "subpage_snippet": "", "source": "lvjiaqi77.github.io", "link": "https://lvjiaqi77.github.io/publication.html", "content": "Learngene tells you how to customize: Task-aware parameter initialization at flexible scales . In Proceedings of 42nd International Conference on Machine Learning ( ICML 2025 ), 2025 ."}
diff --git a/data/sampled_jsons/Temporally-Correlated_Episodic_RL_Li_2024_abstract_arxiv.jsonl b/data/sampled_jsons/Temporally-Correlated_Episodic_RL_Li_2024_abstract_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7ecceb80560ce35e15fcbcc5771492b6ee7a8aec
--- /dev/null
+++ b/data/sampled_jsons/Temporally-Correlated_Episodic_RL_Li_2024_abstract_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of..."}
+{"idx": 1, "title": "Open the Black Box: Step-based Policy Updates for Temporally -C...", "date": "", "ddg_snippet": "Seitenaufrufe: 3 seit 02.09. 2024 . Zugehörige Institution(en) am KIT. Institut für Anthropomatik und Robotik (IAR) KIT-Bibliothek (BIB).", "subpage_snippet": "", "source": "publikationen.bibliothek.kit.edu", "link": "https://publikationen.bibliothek.kit.edu/1000173857", "content": "Seitenaufrufe: 3 seit 02.09. 2024 . Zugehörige Institution(en) am KIT. Institut für Anthropomatik und Robotik (IAR) KIT-Bibliothek (BIB)."}
+{"idx": 2, "title": "(PDF) Membership Inference Attacks Against Temporally Correlated ...", "date": "", "ddg_snippet": "on the temporally correlated data collected from the training set and the output of the deep RL model.Pan, X. et al. How you act tells a lot: Privacy-leakage attack on deep reinforcement learning. arXiv preprint. arXiv :1904.11082 (2019).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/365620360_Membership_Inference_Attacks_Against_Temporally_Correlated_Data_in_Deep_Reinforcement_Learning", "content": "on the temporally correlated data collected from the training set and the output of the deep RL model.Pan, X. et al. How you act tells a lot: Privacy-leakage attack on deep reinforcement learning. arXiv preprint. arXiv :1904.11082 (2019)."}
+{"idx": 3, "title": "圆桌论道 | ICLR 2024 强化学习和 LLM... | RLChina 强化学习社区", "date": "", "ddg_snippet": "METRA: Scalable Unsupervised RL with Metric-Aware Abstraction . Seohong Park, Oleh Rybkin, Sergey Levine. In-context Exploration-Exploitation for Reinforcement Learning.Open the Black Box: Step-based Policy Updates for Temporally - Correlated Episodic Reinforcement Learning.", "subpage_snippet": "", "source": "rlchina.org", "link": "http://rlchina.org/topic/900", "content": "METRA: Scalable Unsupervised RL with Metric-Aware Abstraction . Seohong Park, Oleh Rybkin, Sergey Levine. In-context Exploration-Exploitation for Reinforcement Learning.Open the Black Box: Step-based Policy Updates for Temporally - Correlated Episodic Reinforcement Learning."}
+{"idx": 4, "title": "The 2024 Conference on Empirical Methods in Natural... - ACL Anthology", "date": "", "ddg_snippet": "Understanding and analyzing event temporal relations is a crucial task in Natural Language Processing (NLP). This task, known as Event Temporal Relation Extraction (ETRE), aims to identify and extract temporal connections between events in text.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/events/emnlp-2024/", "content": "Understanding and analyzing event temporal relations is a crucial task in Natural Language Processing (NLP). This task, known as Event Temporal Relation Extraction (ETRE), aims to identify and extract temporal connections between events in text."}
+{"idx": 5, "title": "E pisodic r einforcement L earning", "date": "", "ddg_snippet": "TCE Temporally - Correlated Episodic RL (TCE) ( Li et al., 2024 ) is an innovative ERL algorithm that leverages step-level information in episodic policy updates, shedding light on the ’black box’ of current ERL methods while preserving smooth and consistent exploration within the parameter...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "TCE Temporally - Correlated Episodic RL (TCE) ( Li et al., 2024 ) is an innovative ERL algorithm that leverages step-level information in episodic policy updates, shedding light on the ’black box’ of current ERL methods while preserving smooth and consistent exploration within the parameter..."}
+{"idx": 6, "title": "[2401.11437v1] Open the Black Box: Step-based Policy Updates for...", "date": "", "ddg_snippet": "arXiv logo. Episodic RL (ERL) seeks to overcome these challenges by exploring in parameters space that capture the correlation of actions. However, these approaches typically compromise data efficiency, as they treat trajectories as opaque \\emph{black boxes}.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.11437v1", "content": "arXiv logo. Episodic RL (ERL) seeks to overcome these challenges by exploring in parameters space that capture the correlation of actions. However, these approaches typically compromise data efficiency, as they treat trajectories as opaque \\emph{black boxes}."}
+{"idx": 7, "title": "Fabian Otto - Google Akademik", "date": "", "ddg_snippet": "2021. Open the Black Box: Step-based Policy Updates for Temporally - Correlated Episodic Reinforcement Learning. G Li , H Zhou, D Roth, S Thilges, F Otto, R Lioutikov, G Neumann. arXiv preprint arXiv :2401.11437, 2024 .", "subpage_snippet": "", "source": "scholar.google.com.br", "link": "https://scholar.google.com.br/citations?user=dV8eLH8AAAAJ&hl=tr", "content": "2021. Open the Black Box: Step-based Policy Updates for Temporally - Correlated Episodic Reinforcement Learning. G Li , H Zhou, D Roth, S Thilges, F Otto, R Lioutikov, G Neumann. arXiv preprint arXiv :2401.11437, 2024 ."}
+{"idx": 8, "title": "Neural Networks, 145 (2022) 271-287. doi:10.1016/j.neunet.2021.10.003", "date": "", "ddg_snippet": "More recently with the advent of deep RL , new components such as experience replay and episodic memory have emerged that are commonly incorporated within RL algorithms. In this section, we explore their neural and behavioral correlates .", "subpage_snippet": "", "source": "www.sharadchitlang.ai", "link": "https://www.sharadchitlang.ai/pubs/neuro.pdf", "content": "More recently with the advent of deep RL , new components such as experience replay and episodic memory have emerged that are commonly incorporated within RL algorithms. In this section, we explore their neural and behavioral correlates ."}
+{"idx": 9, "title": "diag.uniroma1.it/~degiacom/publications.html", "date": "", "ddg_snippet": "Exploiting Multiple Abstractions in Episodic RL via Reward Shaping Roberto Cipollone, Giuseppe De Giacomo, Marco Favorito, Luca Iocchi, Fabio Patrizi.Extended versions arXiv :1807.06777 and \"Planning under LTL Environment Specifications\" at ICAPS 2019 above.", "subpage_snippet": "", "source": "www.diag.uniroma1.it", "link": "https://www.diag.uniroma1.it/~degiacom/publications.html", "content": "Exploiting Multiple Abstractions in Episodic RL via Reward Shaping Roberto Cipollone, Giuseppe De Giacomo, Marco Favorito, Luca Iocchi, Fabio Patrizi.Extended versions arXiv :1807.06777 and \"Planning under LTL Environment Specifications\" at ICAPS 2019 above."}
diff --git a/data/sampled_jsons/Tensor-Based_Sequential_Learning_Hankel_Matrix_Table_1_ML-1M_Amazon_Beauty_Amazon_Toys_Games_statist.jsonl b/data/sampled_jsons/Tensor-Based_Sequential_Learning_Hankel_Matrix_Table_1_ML-1M_Amazon_Beauty_Amazon_Toys_Games_statist.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..715bf29cb2a95e4e4afe239702e3ce0b77357b37
--- /dev/null
+++ b/data/sampled_jsons/Tensor-Based_Sequential_Learning_Hankel_Matrix_Table_1_ML-1M_Amazon_Beauty_Amazon_Toys_Games_statist.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tensor-based Sequential Learning via Hankel Matrix ...", "date": "", "ddg_snippet": "We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.05720v1", "content": "We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation."}
+{"idx": 1, "title": "GitHub - recspert/SATF: Code for the paper \"Tensor-based ...", "date": "", "ddg_snippet": "About Code for the paper \" Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations\"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/recspert/SATF", "content": "About Code for the paper \" Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations\""}
+{"idx": 2, "title": "Tensor-Based Sequential Learning ... preview & related info ...", "date": "", "ddg_snippet": "We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture.", "subpage_snippet": "", "source": "www.mendeley.com", "link": "https://www.mendeley.com/catalogue/45fb4490-bc4d-38fb-9b50-e3e605937701/", "content": "We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture."}
+{"idx": 3, "title": "Tensor-Based Sequential Learning via Hankel Matrix ...", "date": "", "ddg_snippet": "Jan 5, 2023 · We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10007832", "content": "Jan 5, 2023 · We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture."}
+{"idx": 4, "title": "Tensor-Based Sequential Learning via Hankel Matrix ... - NASA/ADS", "date": "", "ddg_snippet": "Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations Frolov, Evgeny ; Oseledets, Ivan Abstract Publication: IEEE Access Pub Date: 2023 DOI: 10.1109/ACCESS.2023.3234863 Bibcode: 2023IEEEA..11.6357F full text sources Publisher | adshelp [at]cfa.harvard.edu The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Agreement ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2023IEEEA..11.6357F/abstract", "content": "Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations Frolov, Evgeny ; Oseledets, Ivan Abstract Publication: IEEE Access Pub Date: 2023 DOI: 10.1109/ACCESS.2023.3234863 Bibcode: 2023IEEEA..11.6357F full text sources Publisher | adshelp [at]cfa.harvard.edu The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Agreement ..."}
+{"idx": 5, "title": "(PDF) Tensor-Based Sequential Learning via Hankel Matrix ...", "date": "", "ddg_snippet": "Jan 1 , 2023 · We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366910946_Tensor-based_Sequential_Learning_via_Hankel_Matrix_Representation_for_Next_Item_Recommendations", "content": "Jan 1 , 2023 · We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process."}
+{"idx": 6, "title": "Tensor-Based Sequential Learning via Hankel Matrix ...", "date": "", "ddg_snippet": "Jan 1 , 2023 · Article on Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations, published in IEEE Access 11 on 2023-01-01 by Evgeny Frolov+ 1 . Read the article Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations on R Discovery, your go-to avenue for effective literature search.", "subpage_snippet": "", "source": "discovery.researcher.life", "link": "https://discovery.researcher.life/article/tensor-based-sequential-learning-via-hankel-matrix-representation-for-next-item-recommendations/4850a1c87c8f35b59b1b396b1d4e1ee2", "content": "Jan 1 , 2023 · Article on Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations, published in IEEE Access 11 on 2023-01-01 by Evgeny Frolov+ 1 . Read the article Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations on R Discovery, your go-to avenue for effective literature search."}
+{"idx": 7, "title": "Tensor-based Sequential Learning via Hankel Matrix ...", "date": "", "ddg_snippet": "by E Frolov · 2022 · Cited by 8 — We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process. We.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.05720", "content": "by E Frolov · 2022 · Cited by 8 — We develop a new tensor factorization- based model that ingrains the structural knowledge about sequential data within the learning process. We."}
+{"idx": 8, "title": "IJIMAI20227_4.pdf", "date": "", "ddg_snippet": "video game based learning approaches,” Frontiers in Human Neuroscience, vol. 8, may 2014, doi: 10.3389/fnhum.2014.00223. [84] O. Lahav, “Using virtual ...", "subpage_snippet": "", "source": "www.ijimai.org", "link": "https://www.ijimai.org/journal/sites/default/files/journals/IJIMAI20227_4.pdf", "content": "video game based learning approaches,” Frontiers in Human Neuroscience, vol. 8, may 2014, doi: 10.3389/fnhum.2014.00223. [84] O. Lahav, “Using virtual ..."}
+{"idx": 9, "title": "photo-realistic 3d models: Topics by ...", "date": "", "ddg_snippet": "More precisely, in this paper, we present how to create a 3D animated scene, mainly using the Pen Tool and Blending Options. Indeed, this work is based on ...", "subpage_snippet": "", "source": "www.science.gov", "link": "https://www.science.gov/topicpages/p/photo-realistic+3d+models", "content": "More precisely, in this paper, we present how to create a 3D animated scene, mainly using the Pen Tool and Blending Options. Indeed, this work is based on ..."}
diff --git a/data/sampled_jsons/The_Building_Blocks_of_Interpretability_Olah_Cammarata_Schubert_abstract_year_2020.jsonl b/data/sampled_jsons/The_Building_Blocks_of_Interpretability_Olah_Cammarata_Schubert_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..24e3a4a616c939a3bd6381ac8c0c5ba966a6ee63
--- /dev/null
+++ b/data/sampled_jsons/The_Building_Blocks_of_Interpretability_Olah_Cammarata_Schubert_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Toward Transparent AI: A Survey on Interpreting the Inner ...", "date": "", "ddg_snippet": "by T Räuker · 2022 · Cited by 259 — Inner interpretability methods for individual neurons can focus on (1) continual learning techniques that make neurons specialize in particular tasks,. (2) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.13243", "content": "by T Räuker · 2022 · Cited by 259 — Inner interpretability methods for individual neurons can focus on (1) continual learning techniques that make neurons specialize in particular tasks,. (2) ..."}
+{"idx": 1, "title": "MEASURING MECHANISTIC INTERPRETABILITY AT ...", "date": "", "ddg_snippet": "One of these branches, mechanistic interpretability , aims to improve our understanding of neural networks by understanding their building blocks ( Olah , 2022).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/9f1f0fd1a8ce276a0712501782f0f70d658f7052.pdf", "content": "One of these branches, mechanistic interpretability , aims to improve our understanding of neural networks by understanding their building blocks ( Olah , 2022)."}
+{"idx": 2, "title": "Mechanistic Interpretability for AI Safety A Review", "date": "", "ddg_snippet": "22 Apr 2024 — This review explores mechanistic interpretability : reverse-engineering the computational mechanisms and representations learned by neural networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.14082v1", "content": "22 Apr 2024 — This review explores mechanistic interpretability : reverse-engineering the computational mechanisms and representations learned by neural networks."}
+{"idx": 3, "title": "Measuring Per-Unit Interpretability at Scale Without Humans", "date": "", "ddg_snippet": "by RS Zimmermann · 2024 · Cited by 3 — One of these branches, mechanistic interpretability , tries to improve our understanding of neural networks by understanding their building blocks [33]. An even ... 36 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/56ed2bd15b66f709cd81cb1aaa0496b9-Paper-Conference.pdf", "content": "by RS Zimmermann · 2024 · Cited by 3 — One of these branches, mechanistic interpretability , tries to improve our understanding of neural networks by understanding their building blocks [33]. An even ... 36 pages"}
+{"idx": 4, "title": "Explaining AI through mechanistic interpretability", "date": "", "ddg_snippet": "by L Kästner · 2024 · Cited by 26 — We argue, AI researchers should seek mechanistic interpretability , viz. apply coordinated discovery strategies familiar from the life sciences.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s13194-024-00614-4", "content": "by L Kästner · 2024 · Cited by 26 — We argue, AI researchers should seek mechanistic interpretability , viz. apply coordinated discovery strategies familiar from the life sciences."}
+{"idx": 5, "title": "Mechanistic Interpretability for AI Safety — A Review", "date": "", "ddg_snippet": "10 Jul 2024 — This review explores mechanistic interpretability : reverse engineering the computational mechanisms and representations learned by neural networks into human- ...", "subpage_snippet": "", "source": "leonardbereska.github.io", "link": "https://leonardbereska.github.io/blog/2024/mechinterpreview/", "content": "10 Jul 2024 — This review explores mechanistic interpretability : reverse engineering the computational mechanisms and representations learned by neural networks into human- ..."}
+{"idx": 6, "title": "Understanding RL Vision", "date": "", "ddg_snippet": "by J Hilton · 2020 · Cited by 29 — In this article, we apply interpretability techniques to a reinforcement learning (RL) model trained to play the video game CoinRun.", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/understanding-rl-vision", "content": "by J Hilton · 2020 · Cited by 29 — In this article, we apply interpretability techniques to a reinforcement learning (RL) model trained to play the video game CoinRun."}
+{"idx": 7, "title": "Exploring Neural Networks with Activation Atlases", "date": "", "ddg_snippet": "by S Carter · 2019 · Cited by 235 — We create an explorable activation atlas of features the network has learned which can reveal how the network typically represents some concepts.", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2019/activation-atlas", "content": "by S Carter · 2019 · Cited by 235 — We create an explorable activation atlas of features the network has learned which can reveal how the network typically represents some concepts."}
+{"idx": 8, "title": "Methods for identifying emergent concepts in deep neural ...", "date": "", "ddg_snippet": "by T Räz · 2023 · Cited by 7 — The present perspective discusses methods to detect concepts in internal representations (hidden layers) of deep neural networks (DNNs).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S266638992300106X", "content": "by T Räz · 2023 · Cited by 7 — The present perspective discusses methods to detect concepts in internal representations (hidden layers) of deep neural networks (DNNs)."}
+{"idx": 9, "title": "Toy Models of Superposition", "date": "", "ddg_snippet": "14 Sept 2022 — In this paper, we use toy models — small ReLU networks trained on synthetic data with sparse input features — to investigate how and when models ...", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2022/toy_model/index.html", "content": "14 Sept 2022 — In this paper, we use toy models — small ReLU networks trained on synthetic data with sparse input features — to investigate how and when models ..."}
diff --git a/data/sampled_jsons/The_Hippocampus_as_a_Cognitive_Map_O'Keefe_and_Nadel_1978_central_theory.jsonl b/data/sampled_jsons/The_Hippocampus_as_a_Cognitive_Map_O'Keefe_and_Nadel_1978_central_theory.jsonl
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index 0000000000000000000000000000000000000000..5a2465df3deaf3bf6de803807c37b543c1af7da7
--- /dev/null
+++ b/data/sampled_jsons/The_Hippocampus_as_a_Cognitive_Map_O'Keefe_and_Nadel_1978_central_theory.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hippocampus - Wikipedia", "date": "", "ddg_snippet": "The third important theory of hippocampal function relates the hippocampus to space, and spatial memory, with the idea of a cognitive map first proposed by American psychologist E.C. Tolman.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Hippocampus", "content": "The third important theory of hippocampal function relates the hippocampus to space, and spatial memory, with the idea of a cognitive map first proposed by American psychologist E.C. Tolman."}
+{"idx": 1, "title": "Hippocampus : cognitive map or working memory? — University of...", "date": "", "ddg_snippet": "Abstract. The cognitive map theory of hippocampal function ( O ' Keefe & Nadel , 1978 , The Hippocampus as a Cognitive Map , Oxford: Oxford Univ. Press) has recently been challenged by the assertion that this neural structure is involved in “working memory” rather than mapping ...", "subpage_snippet": "", "source": "experts.arizona.edu", "link": "https://experts.arizona.edu/en/publications/hippocampus-cognitive-map-or-working-memory", "content": "Abstract. The cognitive map theory of hippocampal function ( O ' Keefe & Nadel , 1978 , The Hippocampus as a Cognitive Map , Oxford: Oxford Univ. Press) has recently been challenged by the assertion that this neural structure is involved in “working memory” rather than mapping ..."}
+{"idx": 2, "title": "A computational theory of the hippocampal cognitive map", "date": "", "ddg_snippet": "Evidence from single unit and lesion studies suggests that the hippocampal formation acts as a spatial or cognitive map ( O ' Keefe and Nadel , 1978 ).", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/2203101/", "content": "Evidence from single unit and lesion studies suggests that the hippocampal formation acts as a spatial or cognitive map ( O ' Keefe and Nadel , 1978 )."}
+{"idx": 3, "title": "Cognitive maps : dimensionality and development", "date": "", "ddg_snippet": "O ' Keefe & Nadel : Hippocampus as cognitive map . position and movement of objects relative to each other.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/85216476.pdf", "content": "O ' Keefe & Nadel : Hippocampus as cognitive map . position and movement of objects relative to each other."}
+{"idx": 4, "title": "Redalyc.The role of the avian hippocampus in spatial memory", "date": "", "ddg_snippet": "O ' Keefe and Nadel ( 1978 ) provided good support for their mapping theory by pointing to the existence of single hippocampal cells (place units) whose firing correlated with the location of the animal.", "subpage_snippet": "", "source": "www.redalyc.org", "link": "https://www.redalyc.org/pdf/169/16923106.pdf", "content": "O ' Keefe and Nadel ( 1978 ) provided good support for their mapping theory by pointing to the existence of single hippocampal cells (place units) whose firing correlated with the location of the animal."}
+{"idx": 5, "title": "(PDF) Fimbia/fornix lesions facilitate the learning of a nonspatial...", "date": "", "ddg_snippet": "The spatial cognitive map theory of O ' Keefe and Nadel ( 1978 ) predicts that lesions of the hippocampal system should impair learning on spatial tasks but not learning on nonspatial tasks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/258350015_Fimbiafornix_lesions_facilitate_the_learning_of_a_nonspatial_response_task", "content": "The spatial cognitive map theory of O ' Keefe and Nadel ( 1978 ) predicts that lesions of the hippocampal system should impair learning on spatial tasks but not learning on nonspatial tasks."}
+{"idx": 6, "title": "Frontiers | The human hippocampus beyond the cognitive map ...", "date": "", "ddg_snippet": "Since the publication of The Hippocampus as a Cognitive Map ( O ' Keefe and Nadel , 1978 ), countless studies have provided evidence for the role of the rodent hippocampus in allocentric spatial memory ( Morris et al., 1982 ; Morris, 2007 ).", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2014.00711/full", "content": "Since the publication of The Hippocampus as a Cognitive Map ( O ' Keefe and Nadel , 1978 ), countless studies have provided evidence for the role of the rodent hippocampus in allocentric spatial memory ( Morris et al., 1982 ; Morris, 2007 )."}
+{"idx": 7, "title": "1.8 Learning and Memory II Flashcards | Quizlet", "date": "", "ddg_snippet": "Hippocampus as a cognitive map ( O ' Keefe & Nadel 1978 ).o Place cells in the hippocampus encode intended destination and other sensory information as well spatial location o Non-spatial information is present in the cognitive map o Mechanism for episodic memory.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/346029383/18-learning-and-memory-ii-flash-cards/", "content": "Hippocampus as a cognitive map ( O ' Keefe & Nadel 1978 ).o Place cells in the hippocampus encode intended destination and other sensory information as well spatial location o Non-spatial information is present in the cognitive map o Mechanism for episodic memory."}
+{"idx": 8, "title": "Memory and Space: Towards an Understanding of the Cognitive Map", "date": "", "ddg_snippet": "Cognitive Maps of Abstract Spaces in the Hippocampus .The cognitive map supported by place cells is generally conceived as a Euclidean map from the world- centered (allocentric) view, unlike the perspective from the visual system in person- centered (egocentric) space.", "subpage_snippet": "", "source": "www.jneurosci.org", "link": "https://www.jneurosci.org/content/35/41/13904", "content": "Cognitive Maps of Abstract Spaces in the Hippocampus .The cognitive map supported by place cells is generally conceived as a Euclidean map from the world- centered (allocentric) view, unlike the perspective from the visual system in person- centered (egocentric) space."}
+{"idx": 9, "title": "088466u321", "date": "", "ddg_snippet": "O ’ Keefe and Conway ( 1978 ) observed the re-sponses of hippocampal cells to cue manipulations in.How-ever, the central features of the hippocampal cognitive (spatial) map remain fundamentally intact.", "subpage_snippet": "", "source": "cogs200.pbworks.com", "link": "http://cogs200.pbworks.com/f/Eichenbaum+99+Hippocampus.pdf", "content": "O ’ Keefe and Conway ( 1978 ) observed the re-sponses of hippocampal cells to cue manipulations in.How-ever, the central features of the hippocampal cognitive (spatial) map remain fundamentally intact."}
diff --git a/data/sampled_jsons/The_Open_Images_Dataset_V4_Kuznetsova_et_al._2020_number_of_images_year_2020.jsonl b/data/sampled_jsons/The_Open_Images_Dataset_V4_Kuznetsova_et_al._2020_number_of_images_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..957a446d9ff60919682b50e9d0dbc6281613d329
--- /dev/null
+++ b/data/sampled_jsons/The_Open_Images_Dataset_V4_Kuznetsova_et_al._2020_number_of_images_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Open Images Dataset V4: Unified image classification, object ...", "date": "", "ddg_snippet": "We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1811.00982", "content": "We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding ..."}
+{"idx": 1, "title": "The Open Images Dataset V4 | International Journal of ... - Springer", "date": "", "ddg_snippet": "The set of classes included in the Open Images Dataset is derived from JFT, an internal dataset at Google with millions of images and thousands of classes (Hinton et al. 2014; Chollet 2017; Sun et al. 2017).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-020-01316-z", "content": "The set of classes included in the Open Images Dataset is derived from JFT, an internal dataset at Google with millions of images and thousands of classes (Hinton et al. 2014; Chollet 2017; Sun et al. 2017)."}
+{"idx": 2, "title": "The Open Images Dataset V4", "date": "", "ddg_snippet": "by A Kuznetsova · 2018 · Cited by 3484 — Abstract We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, ob-.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1811.00982", "content": "by A Kuznetsova · 2018 · Cited by 3484 — Abstract We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, ob-."}
+{"idx": 3, "title": "OpenImages-v6 Dataset", "date": "", "ddg_snippet": "OpenImages V6 is a large-scale dataset , consists of 9 million training images , 41,620 validation samples, and 125,456 test samples.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/dataset/openimages-v6?ref=www1.labellerr.com", "content": "OpenImages V6 is a large-scale dataset , consists of 9 million training images , 41,620 validation samples, and 125,456 test samples."}
+{"idx": 4, "title": "Open Images Dataset V6", "date": "", "ddg_snippet": "Open Images is a dataset of ~ 9 million images that have been annotated with image-level labels and bounding boxes spanning thousands of classes.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/datasets/comments/gdvuym/open_images_dataset_v6/", "content": "Open Images is a dataset of ~ 9 million images that have been annotated with image-level labels and bounding boxes spanning thousands of classes."}
+{"idx": 5, "title": "Open Images V4 - Description - storage.googleapis.com", "date": "", "ddg_snippet": "Overview of Open Images V4 Open Images is a dataset of ~9M images that have been annotated with image -level labels, object bounding boxes and visual relationships. The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images , making it the largest existing dataset with object location annotations.", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/openimages/web/factsfigures_v4.html", "content": "Overview of Open Images V4 Open Images is a dataset of ~9M images that have been annotated with image -level labels, object bounding boxes and visual relationships. The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images , making it the largest existing dataset with object location annotations."}
+{"idx": 6, "title": "Open Images Dataset V4 - Dataset - LDM - service.tib.eu", "date": "", "ddg_snippet": "Explore Dataset Image Classification Image classification Object Detection Object detection Open Images Visual Relationship... Cite this as Alina Kuznetsova , Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, Vittorio Ferrari (2024).", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/open-images-dataset-v4", "content": "Explore Dataset Image Classification Image classification Object Detection Object detection Open Images Visual Relationship... Cite this as Alina Kuznetsova , Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, Vittorio Ferrari (2024)."}
+{"idx": 7, "title": "datasets/docs/catalog/open_images_v4.md at master - GitHub", "date": "", "ddg_snippet": "Open Images is a dataset of ~9M images that have been annotated with image -level labels and object bounding boxes. The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images , making it the largest existing dataset with object location annotations.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tensorflow/datasets/blob/master/docs/catalog/open_images_v4.md", "content": "Open Images is a dataset of ~9M images that have been annotated with image -level labels and object bounding boxes. The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images , making it the largest existing dataset with object location annotations."}
+{"idx": 8, "title": "The Open Images Dataset V4: Unified image classification, object ...", "date": "", "ddg_snippet": "We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/the-open-images-dataset-v4-unified-image-classification-object-detection-and-visual-relationship-detection-at-scale/", "content": "We present Open Images V4 , a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding ..."}
+{"idx": 9, "title": "Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont ...", "date": "", "ddg_snippet": "Kuznetsova , A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., et al. ( 2020 ) The Open Images Dataset V4 . International Journal of Computer Vision ...", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=3791637", "content": "Kuznetsova , A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., et al. ( 2020 ) The Open Images Dataset V4 . International Journal of Computer Vision ..."}
diff --git a/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_2019_abstract.jsonl b/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_2019_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6f3db17dd3f28866bacc31a693e498cac3888de
--- /dev/null
+++ b/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_2019_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Abstract page for arXiv paper 1909.05503: The Randomized Midpoint Method for Log-Concave Sampling", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.05503", "content": "Abstract page for arXiv paper 1909.05503: The Randomized Midpoint Method for Log-Concave Sampling"}
+{"idx": 1, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "The Randomized Midpoint Method for Log-Concave Sampling Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019 )", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2019/hash/eb86d510361fc23b59f18c1bc9802cc6-Abstract.html", "content": "The Randomized Midpoint Method for Log-Concave Sampling Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019 )"}
+{"idx": 2, "title": "[PDF] Randomized Midpoint Method for Log-Concave Sampling under ...", "date": "", "ddg_snippet": "In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this constrained setting.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Randomized-Midpoint-Method-for-Log-Concave-Sampling-Yu-Yu/4daeb3786a73f71c7f09ca7dc99042ce4a77f0f8", "content": "In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this constrained setting."}
+{"idx": 3, "title": "The randomized midpoint method for log-concave sampling", "date": "", "ddg_snippet": "Abstract Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form p* ∝ exp (- f (x)), where f : ℝ d → ℝ has an L-Lipschitz gradient and is m -strongly convex.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3454287.3454475", "content": "Abstract Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form p* ∝ exp (- f (x)), where f : ℝ d → ℝ has an L-Lipschitz gradient and is m -strongly convex."}
+{"idx": 4, "title": "PDF The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Yin Tat Lee University of Washington and Microsoft Research yintat@uw.edu Abstract Sampling from log-concave distributions is a well researched problem that has many applica-tions in statistics and machine learning. We study the distributions of the form p / exp( f(x)), where f : Rd ! R has an L-Lipschitz gradient and is m-strongly convex.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05503.pdf", "content": "Yin Tat Lee University of Washington and Microsoft Research yintat@uw.edu Abstract Sampling from log-concave distributions is a well researched problem that has many applica-tions in statistics and machine learning. We study the distributions of the form p / exp( f(x)), where f : Rd ! R has an L-Lipschitz gradient and is m-strongly convex."}
+{"idx": 5, "title": "Reviews: The Randomized Midpoint Method for Log-Concave Sampling - NeurIPS", "date": "", "ddg_snippet": "4. The statement of the Theorem 3 is awkward: what does it mean \"Algorithm 1 can find a random point X\"? Maybe a statement on the distribution at step N is better. Significance: This paper improves the previous rate for log-concave sampling and provides new ideas for designing new sampling algorithms.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Reviews.html", "content": "4. The statement of the Theorem 3 is awkward: what does it mean \"Algorithm 1 can find a random point X\"? Maybe a statement on the distribution at step N is better. Significance: This paper improves the previous rate for log-concave sampling and provides new ideas for designing new sampling algorithms."}
+{"idx": 6, "title": "\"The Randomized Midpoint Method for Log-Concave Sampling.\"", "date": "", "ddg_snippet": "Bibliographic details on The Randomized Midpoint Method for Log-Concave Sampling .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-1909-05503", "content": "Bibliographic details on The Randomized Midpoint Method for Log-Concave Sampling ."}
+{"idx": 7, "title": "The randomized midpoint method for log-concave sampling", "date": "", "ddg_snippet": "Abstract Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form p* ∝ exp (- f (x)), where f : ℝ d → ℝ has an L-Lipschitz gradient and is m -strongly convex. In our paper, we propose a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion ...", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/10.5555/3454287.3454475", "content": "Abstract Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form p* ∝ exp (- f (x)), where f : ℝ d → ℝ has an L-Lipschitz gradient and is m -strongly convex. In our paper, we propose a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion ..."}
+{"idx": 8, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Ruoqi Shen , Yin Tat Lee . The Randomized Midpoint Method for Log-Concave Sampling . In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Edward A. Fox, Roman Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 , NeurIPS 2019 , 8-14 December 2019 , Vancouver, BC, Canada. pages 2098-2109 ...", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/ShenL19-9", "content": "Ruoqi Shen , Yin Tat Lee . The Randomized Midpoint Method for Log-Concave Sampling . In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Edward A. Fox, Roman Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 , NeurIPS 2019 , 8-14 December 2019 , Vancouver, BC, Canada. pages 2098-2109 ..."}
+{"idx": 9, "title": "[2405.15379v2] Randomized Midpoint Method for Log-Concave Sampling ...", "date": "", "ddg_snippet": "In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this constrained setting. We propose a unified proximal framework for handling constraints via a broad class of projection operators, including Euclidean, Bregman ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.15379v2", "content": "In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this constrained setting. We propose a unified proximal framework for handling constraints via a broad class of projection operators, including Euclidean, Bregman ..."}
diff --git a/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_2019_abstract_year_2019.jsonl b/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_2019_abstract_year_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..92a43dbb7582b3ca509b40e7b70ca3d679a7fb2c
--- /dev/null
+++ b/data/sampled_jsons/The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_Shen_Lee_2019_abstract_year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — Abstract. Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/8483-the-randomized-midpoint-method-for-log-concave-sampling", "content": "by R Shen · 2019 · Cited by 161 — Abstract. Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the ..."}
+{"idx": 1, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — Abstract : Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.05503", "content": "by R Shen · 2019 · Cited by 161 — Abstract : Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning."}
+{"idx": 2, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — Abstract . Sampling from log - concave distributions is a well researched problem that has many applica- tions in statistics and machine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05503", "content": "by R Shen · 2019 · Cited by 161 — Abstract . Sampling from log - concave distributions is a well researched problem that has many applica- tions in statistics and machine ..."}
+{"idx": 3, "title": "The randomized midpoint method for log-concave sampling", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 160 — Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3454287.3454475", "content": "by R Shen · 2019 · Cited by 160 — Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning."}
+{"idx": 4, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Abstract : Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/the-randomized-midpoint-method-for-log-concave-sampling-4jwrm4tta7", "content": "Abstract : Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the ..."}
+{"idx": 5, "title": "LOG-CONCAVE SAMPLING ON COMPACT SUPPORTS", "date": "", "ddg_snippet": "by L Yu · Cited by 1 — Ruoqi Shen and Yin Tat Lee. The randomized midpoint method for log-concave sampling . Advances in Neural Information Processing Systems, 32, 2019. Robert L Smith ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LX9m5iWBun", "content": "by L Yu · Cited by 1 — Ruoqi Shen and Yin Tat Lee. The randomized midpoint method for log-concave sampling . Advances in Neural Information Processing Systems, 32, 2019. Robert L Smith ..."}
+{"idx": 6, "title": "FASTER DIFFUSION SAMPLING WITH RANDOMIZED ...", "date": "", "ddg_snippet": "In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling (Shen & Lee, 2019). We prove that this ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=MT3aOfXIbY", "content": "In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling (Shen & Lee, 2019). We prove that this ..."}
+{"idx": 7, "title": "Faster Diffusion Sampling with Randomized Midpoints", "date": "", "ddg_snippet": "Abstract ... In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling (Shen & Lee, 2019).", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/f30307ac840b88f86f4ab5761b2d6595-Abstract-Conference.html", "content": "Abstract ... In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling (Shen & Lee, 2019)."}
+{"idx": 8, "title": "Structured Logconcave Sampling with a Restricted Gaussian ...", "date": "", "ddg_snippet": "by YT Lee · 2021 · Cited by 101 — Ruoqi Shen and Yin Tat Lee. The randomized midpoint method for log-concave sampling . In. Advances in Neural Information Processing Systems, pages 2100–2111, ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v134/lee21a/lee21a.pdf", "content": "by YT Lee · 2021 · Cited by 101 — Ruoqi Shen and Yin Tat Lee. The randomized midpoint method for log-concave sampling . In. Advances in Neural Information Processing Systems, pages 2100–2111, ..."}
+{"idx": 9, "title": "Improved Convergence Rate for Diffusion Probabilistic ...", "date": "", "ddg_snippet": "This convergence analysis is based on a randomized midpoint method , which is first proposed for log-concave sampling (Shen & Lee, 2019), and then extended to ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/dcd9ed95f4abeaa1c22c4c2fd4231930-Abstract-Conference.html", "content": "This convergence analysis is based on a randomized midpoint method , which is first proposed for log-concave sampling (Shen & Lee, 2019), and then extended to ..."}
diff --git a/data/sampled_jsons/The_end_of_optimism_An_asymptotic_analysis_of_finite-armed_linear_bandits_Lattimore_Szepesvari_abstr.jsonl b/data/sampled_jsons/The_end_of_optimism_An_asymptotic_analysis_of_finite-armed_linear_bandits_Lattimore_Szepesvari_abstr.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b34c167a101ac4ae5e998ad5951fe42e09b1c75b
--- /dev/null
+++ b/data/sampled_jsons/The_end_of_optimism_An_asymptotic_analysis_of_finite-armed_linear_bandits_Lattimore_Szepesvari_abstr.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits", "date": "", "ddg_snippet": "Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications. Current approaches focus on generalising existing techniques for finite-armed bandits , notably the optimism principle and Thompson sampling. While prior work has mostly been in the worst-case setting, we analyse the asymptotic instance-dependent regret and show ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1610.04491", "content": "Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications. Current approaches focus on generalising existing techniques for finite-armed bandits , notably the optimism principle and Thompson sampling. While prior work has mostly been in the worst-case setting, we analyse the asymptotic instance-dependent regret and show ..."}
+{"idx": 1, "title": "The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits", "date": "", "ddg_snippet": "Abstract and Figures Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/309192138_The_End_of_Optimism_An_Asymptotic_Analysis_of_Finite-Armed_Linear_Bandits", "content": "Abstract and Figures Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications."}
+{"idx": 2, "title": "The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits", "date": "", "ddg_snippet": "Abstract Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications. Current approaches focus on generalising existing techniques for finite-armed bandits , notably the otimism principle and Thompson sampling. Prior analysis has mostly focussed on the worst-case setting.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v54/lattimore17a.html", "content": "Abstract Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications. Current approaches focus on generalising existing techniques for finite-armed bandits , notably the otimism principle and Thompson sampling. Prior analysis has mostly focussed on the worst-case setting."}
+{"idx": 3, "title": "PDF The End of Optimism?y - Simons Institute for the Theory of Computing", "date": "", "ddg_snippet": "The End of Optimism?y (and posterior sampling) Tor Lattimore and Csaba Szepesvari AISTATS 2017 (and arXiv) y for linear bandits , not life", "subpage_snippet": "", "source": "simons.berkeley.edu", "link": "https://simons.berkeley.edu/sites/default/files/docs/6341/slidesshort.pdf", "content": "The End of Optimism?y (and posterior sampling) Tor Lattimore and Csaba Szepesvari AISTATS 2017 (and arXiv) y for linear bandits , not life"}
+{"idx": 4, "title": "Optimal batched linear bandits | Proceedings of the 41st International ...", "date": "", "ddg_snippet": "We conduct thorough experiments to evaluate our algorithm on randomly generated instances and the challenging End of Optimism instances ( Lattimore & Szepesvari , 2017) which were shown to be hard to learn for optimism based algorithms.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3693793", "content": "We conduct thorough experiments to evaluate our algorithm on randomly generated instances and the challenging End of Optimism instances ( Lattimore & Szepesvari , 2017) which were shown to be hard to learn for optimism based algorithms."}
+{"idx": 5, "title": "The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear ...", "date": "", "ddg_snippet": "Zusammenfassung Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications. Current approaches focus on generalising existing techniques for finite-armed bandits , notably the otimism principle and Thompson sampling. Prior analysis has mostly focussed on the worst-case setting.", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/bibtex/2a6a0d3fc0f8668cccc50c479ebd61191/csaba?lang=de", "content": "Zusammenfassung Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications. Current approaches focus on generalising existing techniques for finite-armed bandits , notably the otimism principle and Thompson sampling. Prior analysis has mostly focussed on the worst-case setting."}
+{"idx": 6, "title": "The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits", "date": "", "ddg_snippet": "An Asymptotic Analysis of Finite-Armed Linear Bandits . In Aarti Singh, Xiaojin (Jerry) Zhu, editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA.", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/LattimoreS17", "content": "An Asymptotic Analysis of Finite-Armed Linear Bandits . In Aarti Singh, Xiaojin (Jerry) Zhu, editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA."}
+{"idx": 7, "title": "The End of Optimism - arXiv.org", "date": "", "ddg_snippet": "Abstract Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications. Cur-rent approaches focus on generalising existing techniques for finite-armed bandits , notably the optimism principle and Thompson sam-pling. While prior work has mostly been in the worst-case setting, we analyse the asymptotic instance-dependent regret and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1610.04491", "content": "Abstract Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with numerous practical applications. Cur-rent approaches focus on generalising existing techniques for finite-armed bandits , notably the optimism principle and Thompson sam-pling. While prior work has mostly been in the worst-case setting, we analyse the asymptotic instance-dependent regret and ..."}
+{"idx": 8, "title": "PDF Bandits: Part III Linear Bandits - tor-lattimore.com", "date": "", "ddg_snippet": "Outline From On Contextual Setting Linear of the Choice to Linear Bandits Stochastic Bandits Features Optimism Generic Regret Miscellaneous Analysis and LinUCB Confidence Bounds Remarks Fixed Least Squares: for Least-Squares Estimators Sequential Design Recap Completing Design Improved Regret Regret Fixed, Bound Challenge! Finite Action Sets", "subpage_snippet": "", "source": "tor-lattimore.com", "link": "https://tor-lattimore.com/downloads/talks/2018/aaai/linear-bandits.pdf", "content": "Outline From On Contextual Setting Linear of the Choice to Linear Bandits Stochastic Bandits Features Optimism Generic Regret Miscellaneous Analysis and LinUCB Confidence Bounds Remarks Fixed Least Squares: for Least-Squares Estimators Sequential Design Recap Completing Design Improved Regret Regret Fixed, Bound Challenge! Finite Action Sets"}
+{"idx": 9, "title": "PDF The End of Optimism - proceedings.mlr.press", "date": "", "ddg_snippet": "Abstract Stochastic linear bandits are a natural and sim-ple generalisation of finite-armed bandits with numerous practical applications. Current ap-proaches focus on generalising existing tech-niques for finite-armed bandits , notably the op-timism principle and Thompson sampling. Prior analysis has mostly focussed on the worst-case setting. We analyse the asymptotic regret and show matching ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v54/lattimore17a/lattimore17a.pdf", "content": "Abstract Stochastic linear bandits are a natural and sim-ple generalisation of finite-armed bandits with numerous practical applications. Current ap-proaches focus on generalising existing tech-niques for finite-armed bandits , notably the op-timism principle and Thompson sampling. Prior analysis has mostly focussed on the worst-case setting. We analyse the asymptotic regret and show matching ..."}
diff --git a/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_Bach_2023.jsonl b/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_Bach_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f3d9b68b3dc12ab35e459b0313d85bc118b0b65
--- /dev/null
+++ b/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_Bach_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The impact of modeling decisions in statistical profiling", "date": "", "ddg_snippet": "by RL Bach · 2023 · Cited by 13 — The impact of modeling decisions in statistical profiling . Published online by Cambridge University Press: 02 October 2023 . Ruben L. Bach · Open the ORCID ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/data-and-policy/article/impact-of-modeling-decisions-in-statistical-profiling/AFAE0907D68D7AFA6915B2F8D08C90B5", "content": "by RL Bach · 2023 · Cited by 13 — The impact of modeling decisions in statistical profiling . Published online by Cambridge University Press: 02 October 2023 . Ruben L. Bach · Open the ORCID ..."}
+{"idx": 1, "title": "The impact of modeling decisions in statistical profiling - 372NLE", "date": "", "ddg_snippet": "|| The impact of modeling decisions in statistical profiling . || 2023 || || Bach , Ruben L ; Kern, Christoph ; Mautner, Hannah ; Kreuter, Frauke ...", "subpage_snippet": "", "source": "nlib-ee-primo.hosted.exlibrisgroup.com", "link": "https://nlib-ee-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_mannheim_madoc_oai_ub_madoc_bib_uni_mannheim_de_65685&context=PC&vid=372NLE_V1&lang=et_EE&search_scope=all_scope&adaptor=primo_central_multiple_fe&query=any,contains,cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4430591&facet=citing,exact,cdi_FETCH-LOGICAL-c314t-7b49436e169d3e39890c778b92413725410979b5941e023ce498f11e824849753&offset=0", "content": "|| The impact of modeling decisions in statistical profiling . || 2023 || || Bach , Ruben L ; Kern, Christoph ; Mautner, Hannah ; Kreuter, Frauke ..."}
+{"idx": 2, "title": "Ruben L. Bach", "date": "", "ddg_snippet": "... Bach . BJPsych Open 9 (5), e156, 2023 . 12, 2023 . The impact of modeling decisions in statistical profiling . RL Bach , C Kern, H Mautner, F Kreuter. Data & Policy ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=KczTzXEAAAAJ&hl=de", "content": "... Bach . BJPsych Open 9 (5), e156, 2023 . 12, 2023 . The impact of modeling decisions in statistical profiling . RL Bach , C Kern, H Mautner, F Kreuter. Data & Policy ..."}
+{"idx": 3, "title": "Ruben Bach (0000-0001-5690-2829)", "date": "", "ddg_snippet": "Applied statistical learning — With case studies in Stata by Matthias Schonlau, Springer, 2023 ... The impact of modeling decisions in statistical profiling . Data ...", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0000-0001-5690-2829", "content": "Applied statistical learning — With case studies in Stata by Matthias Schonlau, Springer, 2023 ... The impact of modeling decisions in statistical profiling . Data ..."}
+{"idx": 4, "title": "Transparent and Fair Profiling in Employment Services", "date": "", "ddg_snippet": "15 Sept 2025 — 2023 . The impact of modeling decisions in statistical profiling . Data & Policy 5: e32. Barnes et al. (2015)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11847v1", "content": "15 Sept 2025 — 2023 . The impact of modeling decisions in statistical profiling . Data & Policy 5: e32. Barnes et al. (2015)"}
+{"idx": 5, "title": "Ruben Bach (@rub3n_luc) / X", "date": "", "ddg_snippet": "New paper on the impact of modeling decisions in statistical profiling of job seekers! ... examine regression and ML models for predicting unemployment risk.", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/rub3n_luc", "content": "New paper on the impact of modeling decisions in statistical profiling of job seekers! ... examine regression and ML models for predicting unemployment risk."}
+{"idx": 6, "title": "Fairness in Algorithmic Profiling: The AMAS Case", "date": "", "ddg_snippet": "by E Achterhold · 2025 · Cited by 3 — Bach , R. L., Kern, C., Mautner, H., & Kreuter, F. ( 2023 ). The impact of modeling decisions in statistical profiling . Data & Policy, 5, e32 ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11023-024-09706-9", "content": "by E Achterhold · 2025 · Cited by 3 — Bach , R. L., Kern, C., Mautner, H., & Kreuter, F. ( 2023 ). The impact of modeling decisions in statistical profiling . Data & Policy, 5, e32 ..."}
+{"idx": 7, "title": "Data & policy (Cambridge University Press)", "date": "", "ddg_snippet": "The impact of modeling decisions in statistical profiling . Ruben L. Bach , Christoph Kern, Hannah Mautner, Frauke Kreuter. 31 Dec 2022-Data & policy. TL;DR: The ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/journals/data-policy-235lcu6d", "content": "The impact of modeling decisions in statistical profiling . Ruben L. Bach , Christoph Kern, Hannah Mautner, Frauke Kreuter. 31 Dec 2022-Data & policy. TL;DR: The ..."}
+{"idx": 8, "title": "https://www.cambridge.org/core/services/aop-cambri...", "date": "", "ddg_snippet": "... Bach , Ruben L., Kern, Christoph, Mautner, Hannah, Kreuter, Frauke 2023 . The impact of modeling decisions in statistical profiling . Data & Policy, Vol. 5 ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/services/aop-cambridge-core/download/cited-by?doi=10.1017/S0047279420000203&productType=JOURNAL_ARTICLE&productId=3993FE92BCC14FEA60DAB9CCD636D58D", "content": "... Bach , Ruben L., Kern, Christoph, Mautner, Hannah, Kreuter, Frauke 2023 . The impact of modeling decisions in statistical profiling . Data & Policy, Vol. 5 ..."}
+{"idx": 9, "title": "Human oversight of automated decision-making systems in ...", "date": "", "ddg_snippet": "by K Sztandar-Sztanderska — Bach RL, Kern C, Mautner H, et al. ( 2023 ) The impact of modeling decisions in statistical profiling . Data & Policy 5: e32. Crossref · Web of ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/abs/10.1177/09589287251358069", "content": "by K Sztandar-Sztanderska — Bach RL, Kern C, Mautner H, et al. ( 2023 ) The impact of modeling decisions in statistical profiling . Data & Policy 5: e32. Crossref · Web of ..."}
diff --git a/data/sampled_jsons/The_movielens_datasets_History_and_context_Harper_&_Konstan,_2015.jsonl b/data/sampled_jsons/The_movielens_datasets_History_and_context_Harper_&_Konstan,_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..86be85ba283f8cd7ed475ee6f1ca54069581e370
--- /dev/null
+++ b/data/sampled_jsons/The_movielens_datasets_History_and_context_Harper_&_Konstan,_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DUALRec: A Hybrid Sequential and Language Model ...", "date": "", "ddg_snippet": "18 Jul 2025 — ... ( Harper & Konstan, 2015 ). These temporal signals allow the model to ... The MovieLens datasets: History and context . ACM Transactions on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.13957", "content": "18 Jul 2025 — ... ( Harper & Konstan, 2015 ). These temporal signals allow the model to ... The MovieLens datasets: History and context . ACM Transactions on ..."}
+{"idx": 1, "title": "Weighting strategies for a recommender system using item ...", "date": "", "ddg_snippet": "by S Frémal · 2017 · Cited by 101 — MovieLens datasets ( Harper & Konstan, 2015 ) are more descriptive and ... The MovieLens datasets: History and context . ACM Transactions on Interactive ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0957417417300404", "content": "by S Frémal · 2017 · Cited by 101 — MovieLens datasets ( Harper & Konstan, 2015 ) are more descriptive and ... The MovieLens datasets: History and context . ACM Transactions on Interactive ..."}
+{"idx": 2, "title": "DEEP RETRIEVAL: AN END-TO-END STRUCTURE ...", "date": "", "ddg_snippet": "by W Gao — 20M ( Harper & Konstan, 2015 ) and Amazon books (He & McAuley, 2016 ... The movielens datasets: History and context . Acm transactions on interactive ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=85d8bg9RvDT", "content": "by W Gao — 20M ( Harper & Konstan, 2015 ) and Amazon books (He & McAuley, 2016 ... The movielens datasets: History and context . Acm transactions on interactive ..."}
+{"idx": 3, "title": "1 Introduction", "date": "", "ddg_snippet": "21 Feb 2024 — ... Harper & Konstan (2015 ) for evaluating personalization and recommendation algorithms. ... The movielens datasets: History and context . ACM Trans.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.13598v1", "content": "21 Feb 2024 — ... Harper & Konstan (2015 ) for evaluating personalization and recommendation algorithms. ... The movielens datasets: History and context . ACM Trans."}
+{"idx": 4, "title": "Robustness of privacy-preserving collaborative ...", "date": "", "ddg_snippet": "by M Gulsoy · 2023 · Cited by 9 — Harper & Konstan (2015 ). Harper FM, Konstan JA. The movielens datasets: history and context . ACM Transactions on Interactive Intelligent ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10403214/", "content": "by M Gulsoy · 2023 · Cited by 9 — Harper & Konstan (2015 ). Harper FM, Konstan JA. The movielens datasets: history and context . ACM Transactions on Interactive Intelligent ..."}
+{"idx": 5, "title": "nestedness contributions as a tool to explain cultural success", "date": "", "ddg_snippet": "by O Morin · 2021 · Cited by 4 — ... ( Harper & Konstan, 2015 ). We rep- licate their observation: our MovieLens ... The MovieLens Datasets: History and context . ACM Transactions on ... 18 pages", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/services/aop-cambridge-core/content/view/E404AE0BFD5D51ABBF6CD26C1B37FEEE/S2513843X21000487a.pdf/the-shortlist-effect-nestedness-contributions-as-a-tool-to-explain-cultural-success.pdf", "content": "by O Morin · 2021 · Cited by 4 — ... ( Harper & Konstan, 2015 ). We rep- licate their observation: our MovieLens ... The MovieLens Datasets: History and context . ACM Transactions on ... 18 pages"}
+{"idx": 6, "title": "Surprise: A Python library for recommender systems", "date": "", "ddg_snippet": "by N Hug · Cited by 471 — Classical datasets such as the MovieLens datasets ( Harper & Konstan, 2015 ) are directly ... The Movielens Datasets: History and Context . ACM. 3 pages", "subpage_snippet": "", "source": "www.theoj.org", "link": "https://www.theoj.org/joss-papers/joss.02174/10.21105.joss.02174.pdf", "content": "by N Hug · Cited by 471 — Classical datasets such as the MovieLens datasets ( Harper & Konstan, 2015 ) are directly ... The Movielens Datasets: History and Context . ACM. 3 pages"}
+{"idx": 7, "title": "Learning user preferences through online conversations ...", "date": "", "ddg_snippet": "by N Godavarthy · 2022 · Cited by 2 — ... ( Harper & Konstan, 2015 ) consisting of 27M ratings from 283K users ... The movielens datasets: History and context . TIIS, 5(4), 1–19 ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10791-022-09410-1", "content": "by N Godavarthy · 2022 · Cited by 2 — ... ( Harper & Konstan, 2015 ) consisting of 27M ratings from 283K users ... The movielens datasets: History and context . TIIS, 5(4), 1–19 ..."}
+{"idx": 8, "title": "Content-based group recommender systems: A general ...", "date": "", "ddg_snippet": "by Y Pérez-Almaguer · 2021 · Cited by 87 — ... Harper & Konstan, 2015 ). The genres of each movie are also available ... The movielens datasets: History and context . ACM Transactions ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417421008587", "content": "by Y Pérez-Almaguer · 2021 · Cited by 87 — ... Harper & Konstan, 2015 ). The genres of each movie are also available ... The movielens datasets: History and context . ACM Transactions ..."}
+{"idx": 9, "title": "EXPLAINING RECOMMENDATION SYSTEMS THROUGH", "date": "", "ddg_snippet": "by JF Ton — and Netflix Harper & Konstan (2015 ); Bennett et al. (2007). We aim to ... The movielens datasets: History and context . Acm transactions on interactive ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mavWQw7DnC", "content": "by JF Ton — and Netflix Harper & Konstan (2015 ); Bennett et al. (2007). We aim to ... The movielens datasets: History and context . Acm transactions on interactive ..."}
diff --git a/data/sampled_jsons/The_underlying_structures_of_self-attention_symmetry_directionality_emergent_dynamics_Transformer_tr_year_2023.jsonl b/data/sampled_jsons/The_underlying_structures_of_self-attention_symmetry_directionality_emergent_dynamics_Transformer_tr_year_2023.jsonl
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index 0000000000000000000000000000000000000000..3a9b449864edc6d807eeaf8b0b6edbb3c99ecc4a
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+++ b/data/sampled_jsons/The_underlying_structures_of_self-attention_symmetry_directionality_emergent_dynamics_Transformer_tr_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UNDERLYING Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of UNDERLYING is lying beneath or below. How to use underlying in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/underlying", "content": "The meaning of UNDERLYING is lying beneath or below. How to use underlying in a sentence."}
+{"idx": 1, "title": "UNDERLYING | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "UNDERLYING definition: 1. real but not immediately obvious: 2. used to describe something on which something else is…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/underlying", "content": "UNDERLYING definition: 1. real but not immediately obvious: 2. used to describe something on which something else is…. Learn more."}
+{"idx": 2, "title": "Underlying - definition of underlying by The Free Dictionary", "date": "", "ddg_snippet": "1. Lying under or beneath something: underlying strata. 2. Basic; fundamental: the underlying cause of the problem. 3. Present but not readily noticeable: an underlying meaning. 4. Taking precedence; prior: an underlying claim to compensation.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/underlying", "content": "1. Lying under or beneath something: underlying strata. 2. Basic; fundamental: the underlying cause of the problem. 3. Present but not readily noticeable: an underlying meaning. 4. Taking precedence; prior: an underlying claim to compensation."}
+{"idx": 3, "title": "underlying adjective - Definition, pictures, pronunciation and...", "date": "", "ddg_snippet": "Definition of underlying adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/underlying", "content": "Definition of underlying adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 4, "title": "Underlying - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "The obvious meaning of underlying refers to something beneath something else . But the word carries a more subtle meaning, that of something hidden but important, something that shapes the meaning or effect of something else, without being explicit itself.", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/underlying", "content": "The obvious meaning of underlying refers to something beneath something else . But the word carries a more subtle meaning, that of something hidden but important, something that shapes the meaning or effect of something else, without being explicit itself."}
+{"idx": 5, "title": "UNDERLYING - Meaning & Translations | Collins English Dictionary", "date": "", "ddg_snippet": "Master the word \" UNDERLYING \" in English: definitions, translations, synonyms, pronunciations, examples, and grammar insights - all in one complete resource.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/english-language-learning/underlying", "content": "Master the word \" UNDERLYING \" in English: definitions, translations, synonyms, pronunciations, examples, and grammar insights - all in one complete resource."}
+{"idx": 6, "title": "underlying - WordReference.com Dictionary of English", "date": "", "ddg_snippet": "lying beneath something else: an underlying layer of rock. basic: an underlying cause. that can be discovered only by close analysis or examination: Loyalty is the underlying theme of the story. lying or situated beneath, as a substratum. basic: the underlying cause of their discontent.", "subpage_snippet": "", "source": "www.wordreference.com", "link": "https://www.wordreference.com/definition/underlying", "content": "lying beneath something else: an underlying layer of rock. basic: an underlying cause. that can be discovered only by close analysis or examination: Loyalty is the underlying theme of the story. lying or situated beneath, as a substratum. basic: the underlying cause of their discontent."}
+{"idx": 7, "title": "Underlying Definition & Meaning | Britannica Dictionary", "date": "", "ddg_snippet": "UNDERLYING meaning: 1 : used to identify the idea, cause, problem, etc., that forms the basis of something; 2 : lying under or below something", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/dictionary/underlying", "content": "UNDERLYING meaning: 1 : used to identify the idea, cause, problem, etc., that forms the basis of something; 2 : lying under or below something"}
+{"idx": 8, "title": "underlying - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Jul 2, 2025 · underlying (plural underlyings ) (finance) The entity from whose performance a derivative derives its value.", "subpage_snippet": "", "source": "en.wiktionary.org", "link": "https://en.wiktionary.org/wiki/underlying", "content": "Jul 2, 2025 · underlying (plural underlyings ) (finance) The entity from whose performance a derivative derives its value."}
+{"idx": 9, "title": "UNDERLYING Synonyms: 85 Similar and Opposite Words | ...", "date": "", "ddg_snippet": "Synonyms for UNDERLYING : basic, rudimentary, elementary, fundamental, introductory, essential, elemental, basal; Antonyms of UNDERLYING : advanced, complex, sophisticated, extensive, detailed, higher, complicated, intricate", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/thesaurus/underlying", "content": "Synonyms for UNDERLYING : basic, rudimentary, elementary, fundamental, introductory, essential, elemental, basal; Antonyms of UNDERLYING : advanced, complex, sophisticated, extensive, detailed, higher, complicated, intricate"}
diff --git a/data/sampled_jsons/Theorem_3.1_For_any_integer_T__0_regret_lower_bound_Catoni.jsonl b/data/sampled_jsons/Theorem_3.1_For_any_integer_T__0_regret_lower_bound_Catoni.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b78759e76ec5798c28ca478803ad4b69ec0e4a32
--- /dev/null
+++ b/data/sampled_jsons/Theorem_3.1_For_any_integer_T__0_regret_lower_bound_Catoni.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No-Regret M♮-Concave Function Maximization: Stochastic Bandit", "date": "", "ddg_snippet": "... Theorem ˜ 5.2 shows that no algorithms that run in polynomial time in each round can achieve poly ( N ) ⋅ T 1 − c \\mathrm{poly}(N)\\cdot ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.12439v2", "content": "... Theorem ˜ 5.2 shows that no algorithms that run in polynomial time in each round can achieve poly ( N ) ⋅ T 1 − c \\mathrm{poly}(N)\\cdot ..."}
+{"idx": 1, "title": "Achieving constant regret for dynamic matching via", "date": "", "ddg_snippet": "... for general two-way matching networks, we design a randomized state-independent greedy policy that achieves constant regret with optimal scaling O ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.09762v2", "content": "... for general two-way matching networks, we design a randomized state-independent greedy policy that achieves constant regret with optimal scaling O ..."}
+{"idx": 2, "title": "Reinforcement Learning from Adversarial Preferences in Tabular", "date": "", "ddg_snippet": "Table 1 : Regret upper and lower bounds for episodic tabular MDPs with adversarial losses and for episodic tabular MDPs with adversarial preferences ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11706v1", "content": "Table 1 : Regret upper and lower bounds for episodic tabular MDPs with adversarial losses and for episodic tabular MDPs with adversarial preferences ..."}
+{"idx": 3, "title": "Fermat’s Little Theorem for Matrices | Gödel's Lost", "date": "", "ddg_snippet": "If we assume that factoring is hard, then his theorem can be used to prove lower bounds on the growth of the traces of matrix powers.", "subpage_snippet": "", "source": "rjlipton.com", "link": "https://rjlipton.com/2009/08/07/fermats-little-theorem-for-matrices/", "content": "If we assume that factoring is hard, then his theorem can be used to prove lower bounds on the growth of the traces of matrix powers."}
+{"idx": 4, "title": "1 Introduction", "date": "", "ddg_snippet": "Part 1 : Ω ( log ( | Θ | ) T ) \\Omega(\\sqrt{\\log(|\\Theta|) T }) Full‐Feedback Lower Bound . ... this partial order, we show that our ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03904v1", "content": "Part 1 : Ω ( log ( | Θ | ) T ) \\Omega(\\sqrt{\\log(|\\Theta|) T }) Full‐Feedback Lower Bound . ... this partial order, we show that our ..."}
+{"idx": 5, "title": "1 INTRODUCTION", "date": "", "ddg_snippet": "The upper bound is further refined for special classes of SCMs (neural network, polynomial, and linear), and their corresponding lower bounds are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00233v1", "content": "The upper bound is further refined for special classes of SCMs (neural network, polynomial, and linear), and their corresponding lower bounds are ..."}
+{"idx": 6, "title": "Introduction to Ramsey Spaces", "date": "", "ddg_snippet": "We refer to [13,17,20, 24, 25] for other proofs of such a result and its finitary counterpart, where explicit bounds on the quantities involved are ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/266350430_Introduction_to_Ramsey_Spaces", "content": "We refer to [13,17,20, 24, 25] for other proofs of such a result and its finitary counterpart, where explicit bounds on the quantities involved are ..."}
+{"idx": 7, "title": "The Network Coding Conjecture Is Powerful | Gödel's Lost", "date": "", "ddg_snippet": "The sparse convolution function raises an interesting question: Are the methods for sparse FFT useful here? The lower bound for AFKL functions ...", "subpage_snippet": "", "source": "rjlipton.com", "link": "https://rjlipton.com/2019/05/06/the-network-coding-conjecture-is-powerful/", "content": "The sparse convolution function raises an interesting question: Are the methods for sparse FFT useful here? The lower bound for AFKL functions ..."}
+{"idx": 8, "title": "Online Optimization Post 6: The Impagliazzo Hard-Core Set Lemma", "date": "", "ddg_snippet": "The plan for this series of posts is to alternate one post explaining a result from the theory of online convex optimization and one post explaining ...", "subpage_snippet": "", "source": "lucatrevisan.wordpress.com", "link": "https://lucatrevisan.wordpress.com/2021/10/20/online-optimization-post-6-the-impagliazzo-hard-core-set-lemma/", "content": "The plan for this series of posts is to alternate one post explaining a result from the theory of online convex optimization and one post explaining ..."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/This_work_studies_the_alignment_of_large_language_models_with_preference_data_from_an_imitation_lear.jsonl b/data/sampled_jsons/This_work_studies_the_alignment_of_large_language_models_with_preference_data_from_an_imitation_lear.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f6346723189cba4671fcc18032d88c21578325da
--- /dev/null
+++ b/data/sampled_jsons/This_work_studies_the_alignment_of_large_language_models_with_preference_data_from_an_imitation_lear.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self-Instruct: Aligning Language Models with Self-Generated", "date": "", "ddg_snippet": "This is especially concerning with the recent large -scale foundation models , Vision- Language Models (VLMs), and Large Language Models (LLMs).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372919122_Self-Instruct_Aligning_Language_Models_with_Self-Generated_Instructions", "content": "This is especially concerning with the recent large -scale foundation models , Vision- Language Models (VLMs), and Large Language Models (LLMs)."}
+{"idx": 1, "title": "Takes on \"Alignment Faking in Large Language Models\"", "date": "", "ddg_snippet": "One of the key open questions I highlighted in my report was whether a model faking alignment with the training objective is in fact an effective ...", "subpage_snippet": "", "source": "joecarlsmith.com", "link": "https://joecarlsmith.com/2024/12/18/takes-on-alignment-faking-in-large-language-models/", "content": "One of the key open questions I highlighted in my report was whether a model faking alignment with the training objective is in fact an effective ..."}
+{"idx": 2, "title": "Discovering Language Model Behaviors with Model-Written", "date": "", "ddg_snippet": "Appendix A.1 shows that an RLHF model 's behavior is strongly correlated with that of the PM used to train it, especially for larger models .", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/yRAo2KEGWenKYZG9K/discovering-language-model-behaviors-with-model-written", "content": "Appendix A.1 shows that an RLHF model 's behavior is strongly correlated with that of the PM used to train it, especially for larger models ."}
+{"idx": 3, "title": "GitHub - GaryYufei/AlignLLMHumanSurvey: Aligning Large Language", "date": "", "ddg_snippet": "A collection of papers and resources about aligning large language models (LLMs) with human. ... aligning LLMs with human expectations has become an ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GaryYufei/AlignLLMHumanSurvey", "content": "A collection of papers and resources about aligning large language models (LLMs) with human. ... aligning LLMs with human expectations has become an ..."}
+{"idx": 4, "title": "A brief summary of language model finetuning - Stack Overflow", "date": "", "ddg_snippet": "... and output format) from fine tuning over a small amount of data , but we can’t learn everything! These imitation models lack the knowledge base of ...", "subpage_snippet": "", "source": "stackoverflow.blog", "link": "https://stackoverflow.blog/2024/10/31/a-brief-summary-of-language-model-finetuning/", "content": "... and output format) from fine tuning over a small amount of data , but we can’t learn everything! These imitation models lack the knowledge base of ..."}
+{"idx": 5, "title": "[2406.00888] Aligning Language Models with Demonstrated Feedback", "date": "", "ddg_snippet": "Abstract: Language models are aligned to emulate the collective voice of many, resulting in outputs that align with no one in particular.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.00888", "content": "Abstract: Language models are aligned to emulate the collective voice of many, resulting in outputs that align with no one in particular."}
+{"idx": 6, "title": "Drivel-ology: Challenging LLMs with Interpreting Nonsense with", "date": "", "ddg_snippet": "Large language models (LLMs) have achieved impressive success across a wide range of natural language processing tasks, from machine translation and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03867v1", "content": "Large language models (LLMs) have achieved impressive success across a wide range of natural language processing tasks, from machine translation and ..."}
+{"idx": 7, "title": "PrivCAPTCHA: Interactive CAPTCHA to Facilitate Effective", "date": "", "ddg_snippet": "This paper investigates the viability of using Large Language Models (LLMs) as the core of a dynamic and adaptive privacy policy engine.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/391267222_PrivCAPTCHA_Interactive_CAPTCHA_to_Facilitate_Effective_Comprehension_of_APP_Privacy_Policy", "content": "This paper investigates the viability of using Large Language Models (LLMs) as the core of a dynamic and adaptive privacy policy engine."}
+{"idx": 8, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "... the Design Space of Equivariant ... The Cost of Scaling Down Large Language Models : Reducing Model Size Affects Memory before In-context Learning", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "... the Design Space of Equivariant ... The Cost of Scaling Down Large Language Models : Reducing Model Size Affects Memory before In-context Learning"}
+{"idx": 9, "title": "T. B. Brown | Sugaku", "date": "", "ddg_snippet": "The loss scales as a power-law with model size, dataset size, and the amount of compute used … View More We study empirical scaling laws for ...", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/A5079465685/", "content": "The loss scales as a power-law with model size, dataset size, and the amount of compute used … View More We study empirical scaling laws for ..."}
diff --git a/data/sampled_jsons/Thudi_et_al._2024_arxiv_per-instance_privacy_differential_privacy.jsonl b/data/sampled_jsons/Thudi_et_al._2024_arxiv_per-instance_privacy_differential_privacy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0d755be55a12e2881b38af02746d9a0adb175bc
--- /dev/null
+++ b/data/sampled_jsons/Thudi_et_al._2024_arxiv_per-instance_privacy_differential_privacy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Privacy-Preserving Personalized Federated Prompt Learning for", "date": "", "ddg_snippet": "We propose a privacy -preserving personalized federated prompt learning approach with Differential Privacy for multimodal LLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.13904v3", "content": "We propose a privacy -preserving personalized federated prompt learning approach with Differential Privacy for multimodal LLMs."}
+{"idx": 1, "title": "Free Record-Level Privacy Risk Evaluation Through", "date": "", "ddg_snippet": "We then explore other loss trace aggregation methods - all of which analyze the per -sample training dynamics captured by the per -epoch loss trace.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.05743v3", "content": "We then explore other loss trace aggregation methods - all of which analyze the per -sample training dynamics captured by the per -epoch loss trace."}
+{"idx": 2, "title": "Existing Large Language Model Unlearning Evaluations Are", "date": "", "ddg_snippet": "... 18 ] , and proposed legislation like Canada’ s CPPA [ 19 ] —all of which grant individuals the right to request deletion of their personal data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00688v1", "content": "... 18 ] , and proposed legislation like Canada’ s CPPA [ 19 ] —all of which grant individuals the right to request deletion of their personal data."}
+{"idx": 3, "title": "Breach By A Thousand Leaks: Unsafe Information Leakage in", "date": "", "ddg_snippet": "Current threat models and assessment methods typically focus solely on the permissibility of the victim model’ s responses (Zou et al ., 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.02551v2", "content": "Current threat models and assessment methods typically focus solely on the permissibility of the victim model’ s responses (Zou et al ., 2024 ..."}
+{"idx": 4, "title": "Low-Cost High-Power Membership Inference Attacks", "date": "", "ddg_snippet": "For deep learning algorithms, these tests evolved from using ML itself to perform MIA (Shokri et al ., 2017 ) to using various approximations of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03262v3", "content": "For deep learning algorithms, these tests evolved from using ML itself to perform MIA (Shokri et al ., 2017 ) to using various approximations of ..."}
+{"idx": 5, "title": "Certified Unlearning for Neural Networks", "date": "", "ddg_snippet": "Recent works aim to achieve certified unlearning for non-convex tasks (Golatkar et al ., 2020 ; Chourasia & Shah, 2023 ; Chien et al ., 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06985v2", "content": "Recent works aim to achieve certified unlearning for non-convex tasks (Golatkar et al ., 2020 ; Chourasia & Shah, 2023 ; Chien et al ., 2024 ..."}
+{"idx": 6, "title": "Data Selection for Transfer Unlearning", "date": "", "ddg_snippet": "... that does not address privacy applications but targets a scenario where a data owner withdraws permission of use of their data for training purposes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.10425v1", "content": "... that does not address privacy applications but targets a scenario where a data owner withdraws permission of use of their data for training purposes."}
+{"idx": 7, "title": "What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "... of removing data from trained models after-the-fact, has exacerbated concerns on perpetuating harmful or outdated information, violating user privacy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01257v2", "content": "... of removing data from trained models after-the-fact, has exacerbated concerns on perpetuating harmful or outdated information, violating user privacy ..."}
+{"idx": 8, "title": "Aya Expanse: Combining Research Breakthroughs for a New", "date": "", "ddg_snippet": "... et al ., 2023 ; Gemini Team et al ., 2024 ; Dubey et al ., 2024 ; Yang et al ., 2024 ] , there remains a stark gap in the performance of models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.04261v1", "content": "... et al ., 2023 ; Gemini Team et al ., 2024 ; Dubey et al ., 2024 ; Yang et al ., 2024 ] , there remains a stark gap in the performance of models ..."}
+{"idx": 9, "title": "Towards Reliable Forgetting: A Survey on Machine Unlearning", "date": "", "ddg_snippet": "... algorithmic methods for machine unlearning offer mechanisms to erase specific training data from models, concerns about trust and transparency ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15115v1", "content": "... algorithmic methods for machine unlearning offer mechanisms to erase specific training data from models, concerns about trust and transparency ..."}
diff --git a/data/sampled_jsons/Thudi_et_al._2024_per-instance_privacy_bounds.jsonl b/data/sampled_jsons/Thudi_et_al._2024_per-instance_privacy_bounds.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..507e2f23779337e03ad84e0b2afa3134b9df3364
--- /dev/null
+++ b/data/sampled_jsons/Thudi_et_al._2024_per-instance_privacy_bounds.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2505.18786] Leveraging Per-Instance Privacy for Machine", "date": "", "ddg_snippet": "... et al ., 2024 ), obtaining a better utility-unlearning tradeoff by replacing worst-case privacy loss bounds with per - instance privacy losses ( Thudi et ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "... et al ., 2024 ), obtaining a better utility-unlearning tradeoff by replacing worst-case privacy loss bounds with per - instance privacy losses ( Thudi et ..."}
+{"idx": 1, "title": "Privacy-Preserving Personalized Federated Prompt Learning for", "date": "", "ddg_snippet": "... personalized FPL have garnered significant attention (Guo et al ., 2023a ; b ; Li et al ., 2023 ; Yang et al ., 2023a ; Sun et al ., 2023 ; Deng ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.13904v3", "content": "... personalized FPL have garnered significant attention (Guo et al ., 2023a ; b ; Li et al ., 2023 ; Yang et al ., 2023a ; Sun et al ., 2023 ; Deng ..."}
+{"idx": 2, "title": "Free Record-Level Privacy Risk Evaluation Through", "date": "", "ddg_snippet": "We then explore other loss trace aggregation methods - all of which analyze the per -sample training dynamics captured by the per -epoch loss trace.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.05743v3", "content": "We then explore other loss trace aggregation methods - all of which analyze the per -sample training dynamics captured by the per -epoch loss trace."}
+{"idx": 3, "title": "Simplicity Prevails: Rethinking Negative Preference", "date": "", "ddg_snippet": "... privacy regulations such as the “right to be forgotten” (Rosen, 2011 ; Hoofnagle et al ., 2019 ) , with early studies focusing on vision models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07163v3", "content": "... privacy regulations such as the “right to be forgotten” (Rosen, 2011 ; Hoofnagle et al ., 2019 ) , with early studies focusing on vision models ..."}
+{"idx": 4, "title": "Low-Cost High-Power Membership Inference Attacks", "date": "", "ddg_snippet": "When used as an oracle in reconstruction attacks (Carlini et al ., 2021 ) , MIA needs to perform accurately under low FPR regime to filter out the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03262v3", "content": "When used as an oracle in reconstruction attacks (Carlini et al ., 2021 ) , MIA needs to perform accurately under low FPR regime to filter out the ..."}
+{"idx": 5, "title": "Existing Large Language Model Unlearning Evaluations Are", "date": "", "ddg_snippet": "... 18 ] , and proposed legislation like Canada’ s CPPA [ 19 ] —all of which grant individuals the right to request deletion of their personal data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00688v1", "content": "... 18 ] , and proposed legislation like Canada’ s CPPA [ 19 ] —all of which grant individuals the right to request deletion of their personal data."}
+{"idx": 6, "title": "Data Selection for Transfer Unlearning", "date": "", "ddg_snippet": "This poses technical challenges for deep learning pipelines: given a deep model that has already been trained on data that is no longer permissible ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.10425v1", "content": "This poses technical challenges for deep learning pipelines: given a deep model that has already been trained on data that is no longer permissible ..."}
+{"idx": 7, "title": "Towards Reliable Forgetting: A Survey on Machine Unlearning", "date": "", "ddg_snippet": "... privacy protection, security, and legal compliance ( e .g., GDPR), machine unlearning has emerged as a critical technique for ensuring the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15115v1", "content": "... privacy protection, security, and legal compliance ( e .g., GDPR), machine unlearning has emerged as a critical technique for ensuring the ..."}
+{"idx": 8, "title": "Towards Lifecycle Unlearning Commitment Management: Measuring", "date": "", "ddg_snippet": "Cao et al . [ 5 ] first introduced the concept of unlearning completeness , which requires that a complete unlearned system gives the same ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06112v1", "content": "Cao et al . [ 5 ] first introduced the concept of unlearning completeness , which requires that a complete unlearned system gives the same ..."}
+{"idx": 9, "title": "Proof.", "date": "", "ddg_snippet": "We realize these attacks in the targeted poisoning setting, giving an algorithm based on the gradient-matching approach of Geiping et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.10717v2", "content": "We realize these attacks in the targeted poisoning setting, giving an algorithm based on the gradient-matching approach of Geiping et al ."}
diff --git a/data/sampled_jsons/Thudi_et_al._2024_per-instance_privacy_losses.jsonl b/data/sampled_jsons/Thudi_et_al._2024_per-instance_privacy_losses.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3038f9414374476cd3244dc33afccf49cf75093d
--- /dev/null
+++ b/data/sampled_jsons/Thudi_et_al._2024_per-instance_privacy_losses.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2505.18786] Leveraging Per-Instance Privacy for Machine", "date": "", "ddg_snippet": "... et al ., 2024 ), obtaining a better utility-unlearning tradeoff by replacing worst-case privacy loss bounds with per - instance privacy losses ( Thudi et ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "... et al ., 2024 ), obtaining a better utility-unlearning tradeoff by replacing worst-case privacy loss bounds with per - instance privacy losses ( Thudi et ..."}
+{"idx": 1, "title": "Privacy-Preserving Personalized Federated Prompt Learning for", "date": "", "ddg_snippet": "... personalized FPL have garnered significant attention (Guo et al ., 2023a ; b ; Li et al ., 2023 ; Yang et al ., 2023a ; Sun et al ., 2023 ; Deng ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.13904v3", "content": "... personalized FPL have garnered significant attention (Guo et al ., 2023a ; b ; Li et al ., 2023 ; Yang et al ., 2023a ; Sun et al ., 2023 ; Deng ..."}
+{"idx": 2, "title": "Free Record-Level Privacy Risk Evaluation Through", "date": "", "ddg_snippet": "We then explore other loss trace aggregation methods - all of which analyze the per -sample training dynamics captured by the per -epoch loss trace.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.05743v3", "content": "We then explore other loss trace aggregation methods - all of which analyze the per -sample training dynamics captured by the per -epoch loss trace."}
+{"idx": 3, "title": "IMU: Influence-guided Machine Unlearning", "date": "", "ddg_snippet": "... performance and assumes that the statistics ( ... Current MU algorithms typically fall into two categories: exact unlearning methods (Ginart et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.01620v1", "content": "... performance and assumes that the statistics ( ... Current MU algorithms typically fall into two categories: exact unlearning methods (Ginart et al ."}
+{"idx": 4, "title": "Simplicity Prevails: Rethinking Negative Preference", "date": "", "ddg_snippet": "... privacy regulations such as the “right to be forgotten” (Rosen, 2011 ; Hoofnagle et al ., 2019 ) , with early studies focusing on vision models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07163v3", "content": "... privacy regulations such as the “right to be forgotten” (Rosen, 2011 ; Hoofnagle et al ., 2019 ) , with early studies focusing on vision models ..."}
+{"idx": 5, "title": "Translational Genomics in Legumes: Enhancing Crop Resilience", "date": "", "ddg_snippet": "... agricultural practices but also contributes to the nutritional security of millions of people worldwide, especially in developing regions ( Thudi et ...", "subpage_snippet": "", "source": "cropscipublisher.com", "link": "https://cropscipublisher.com/index.php/lgg/article/html/3990/", "content": "... agricultural practices but also contributes to the nutritional security of millions of people worldwide, especially in developing regions ( Thudi et ..."}
+{"idx": 6, "title": "Developing Disease-Resistant Wheat Varieties Through Genomic", "date": "", "ddg_snippet": "... and Fusarium head blight (FHB) are particularly problematic, causing substantial economic losses and posing threats to food security ( Singh et al ...", "subpage_snippet": "", "source": "genbreedpublisher.com", "link": "https://genbreedpublisher.com/index.php/mpb/article/html/4038/", "content": "... and Fusarium head blight (FHB) are particularly problematic, causing substantial economic losses and posing threats to food security ( Singh et al ..."}
+{"idx": 7, "title": "Genomic Insights into the Domestication of Major Legume Crops |", "date": "", "ddg_snippet": "... to the Neolithic era, approximately 12 000 years ago, when early agricultural societies began to cultivate these crops alongside cereals ( Bohra et ...", "subpage_snippet": "", "source": "cropscipublisher.com", "link": "https://cropscipublisher.com/index.php/lgg/article/html/3894/", "content": "... to the Neolithic era, approximately 12 000 years ago, when early agricultural societies began to cultivate these crops alongside cereals ( Bohra et ..."}
+{"idx": 8, "title": "Integrating Omics Approaches for Abiotic Stress Tolerance in", "date": "", "ddg_snippet": "The phases of alarm, resistance, and exhaustion might be seen as the three primary phases of plant stress events and responses [ 2 ].", "subpage_snippet": "", "source": "www.intechopen.com", "link": "https://www.intechopen.com/chapters/88936", "content": "The phases of alarm, resistance, and exhaustion might be seen as the three primary phases of plant stress events and responses [ 2 ]."}
+{"idx": 9, "title": "Sex differences in bile acid homeostasis and excretion underlie", "date": "", "ddg_snippet": "Although 15-month-old individual Fxr knockout and individual Shp knockout mice were previously shown to develop liver cancer, but unlike the DKO mice, ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/96783", "content": "Although 15-month-old individual Fxr knockout and individual Shp knockout mice were previously shown to develop liver cancer, but unlike the DKO mice, ..."}
diff --git a/data/sampled_jsons/Training-Free_Diffusion_Model_Alignment_Sampling_Demons_Section_J_reward_estimation_challenges.jsonl b/data/sampled_jsons/Training-Free_Diffusion_Model_Alignment_Sampling_Demons_Section_J_reward_estimation_challenges.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d54cea29faeadf6a4309345f18d24766e2db125a
--- /dev/null
+++ b/data/sampled_jsons/Training-Free_Diffusion_Model_Alignment_Sampling_Demons_Section_J_reward_estimation_challenges.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training-free Diffusion Model Alignment with Sampling Demons GitHub - aiiu-lab/DemonSampling: [ICLR'25] Official ... Training-Free Diffusion Model Alignment with Sampling Demons Training-free Diffusion Model Alignment with Sampling Demons DAS (Diffusion Alignment as Sampling), ICLR'25 Spotlight Alignment of Diffusion Models: Fundamentals, Challenges, and ... Training - free Diffusion Model Alignment with Sampling Demons Training - free Diffusion Model Alignment with Sampling Demons DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight Alignment of Diffusion Models: Fundamentals, Challenges, and Future DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight DemonSampling/README.md at main · aiiu-lab ... - GitHub", "date": "", "ddg_snippet": "Oct 8, 2024 · Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model ... Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ... Aligning diffusion models with user preferences has been a key challenge.Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model ... Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model retraining. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Addressing these limitations, we propose a training-free sampling method based on Sequential Monte Carlo (SMC) to sample from the reward -aligned target distribution. Sep 11, 2024 · Moreover, we discuss key perspectives on current challenges and promising future directions on solving the remaining challenges in alignment of diffusion models. To the best of our knowledge, our work is the first comprehensive review paper for researchers and engineers to comprehend, practice, and research alignment of diffusion models. Can diffusion models be aligned with user preferences? Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. What is the first inference-time preference alignment method for diffusion models? To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models. Our method can be easily integrated with existing diffusion models without further training . Is there a test-time alignment of diffusion models without reward over-optimization? This is the official implementation of our paper Test-time Alignment of Diffusion Models without Reward Over-optimization by Sunwoo Kim 1, Minkyu Kim 2, Dongmin Park 2. 1 Seoul National University, 2 KRAFTON AI Who wrote 'test-time alignment of diffusion models without reward over-optimization'? title={Test-time Alignment of Diffusion Models without Reward Over-optimization}, author={ Sunwoo Kim and Minkyu Kim and Dongmin Park }, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, Why do diffusion models misalign with human intentions? Despite their success, these models often misalign with human intentions, generating outputs that may not match text prompts or possess desired properties. Inspired by the success of alignment in tuning large language models, recent studies have investigated aligning diffusion models with human expectations and preferences. Is a training-free sampling method based on sequential Monte Carlo (SMC) effective? Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Addressing these limitations, we propose a training - free sampling method based on Sequential Monte Carlo (SMC) to sample from the reward -aligned target distribution. Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.05760", "content": "Oct 8, 2024 · Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model ... Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ... Aligning diffusion models with user preferences has been a key challenge.Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model ... Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model retraining. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Addressing these limitations, we propose a training-free sampling method based on Sequential Monte Carlo (SMC) to sample from the reward -aligned target distribution. Sep 11, 2024 · Moreover, we discuss key perspectives on current challenges and promising future directions on solving the remaining challenges in alignment of diffusion models. To the best of our knowledge, our work is the first comprehensive review paper for researchers and engineers to comprehend, practice, and research alignment of diffusion models. Can diffusion models be aligned with user preferences? Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. What is the first inference-time preference alignment method for diffusion models? To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models. Our method can be easily integrated with existing diffusion models without further training . Is there a test-time alignment of diffusion models without reward over-optimization? This is the official implementation of our paper Test-time Alignment of Diffusion Models without Reward Over-optimization by Sunwoo Kim 1, Minkyu Kim 2, Dongmin Park 2. 1 Seoul National University, 2 KRAFTON AI Who wrote 'test-time alignment of diffusion models without reward over-optimization'? title={Test-time Alignment of Diffusion Models without Reward Over-optimization}, author={ Sunwoo Kim and Minkyu Kim and Dongmin Park }, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, Why do diffusion models misalign with human intentions? Despite their success, these models often misalign with human intentions, generating outputs that may not match text prompts or possess desired properties. Inspired by the success of alignment in tuning large language models, recent studies have investigated aligning diffusion models with human expectations and preferences. Is a training-free sampling method based on sequential Monte Carlo (SMC) effective? Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Addressing these limitations, we propose a training - free sampling method based on Sequential Monte Carlo (SMC) to sample from the reward -aligned target distribution. Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ..."}
+{"idx": 1, "title": "GitHub - aiiu-lab/DemonSampling: [ICLR'25] Official ...", "date": "", "ddg_snippet": "Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling", "content": "Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ..."}
+{"idx": 2, "title": "Training-Free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge.Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/eeab2e00835c71d64458ad1821e05664-Abstract-Conference.html", "content": "Aligning diffusion models with user preferences has been a key challenge.Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model ..."}
+{"idx": 3, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model retraining.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760", "content": "Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model retraining."}
+{"idx": 4, "title": "DAS (Diffusion Alignment as Sampling), ICLR'25 Spotlight", "date": "", "ddg_snippet": "Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Addressing these limitations, we propose a training-free sampling method based on Sequential Monte Carlo (SMC) to sample from the reward -aligned target distribution.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/krafton-ai/DAS", "content": "Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Addressing these limitations, we propose a training-free sampling method based on Sequential Monte Carlo (SMC) to sample from the reward -aligned target distribution."}
+{"idx": 5, "title": "Alignment of Diffusion Models: Fundamentals, Challenges, and ...", "date": "", "ddg_snippet": "Sep 11, 2024 · Moreover, we discuss key perspectives on current challenges and promising future directions on solving the remaining challenges in alignment of diffusion models. To the best of our knowledge, our work is the first comprehensive review paper for researchers and engineers to comprehend, practice, and research alignment of diffusion models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.07253", "content": "Sep 11, 2024 · Moreover, we discuss key perspectives on current challenges and promising future directions on solving the remaining challenges in alignment of diffusion models. To the best of our knowledge, our work is the first comprehensive review paper for researchers and engineers to comprehend, practice, and research alignment of diffusion models."}
+{"idx": 6, "title": "DemonSampling/README.md at main · aiiu-lab ... - GitHub", "date": "", "ddg_snippet": "Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling/blob/main/README.md", "content": "Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models. By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ..."}
+{"idx": 7, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge .To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v1", "content": "Aligning diffusion models with user preferences has been a key challenge .To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models ."}
+{"idx": 8, "title": "ICLR Poster Training - Free Diffusion Model Alignment with...", "date": "", "ddg_snippet": "alignment method for diffusion models .Our method can be easily integrated with existing diffusion models without further training .Our experiments show that the proposed approach significantly improves the average aesthetics scores for text-to-image generation.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28034", "content": "alignment method for diffusion models .Our method can be easily integrated with existing diffusion models without further training .Our experiments show that the proposed approach significantly improves the average aesthetics scores for text-to-image generation."}
+{"idx": 9, "title": "GitHub - xie-lab-ml/awesome- alignment -of- diffusion - models : The...", "date": "", "ddg_snippet": "Training - Free Diffusion Model Alignment with Sampling Demons .Test-time Alignment of Diffusion Models without Reward Over-optimization.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xie-lab-ml/awesome-alignment-of-diffusion-models", "content": "Training - Free Diffusion Model Alignment with Sampling Demons .Test-time Alignment of Diffusion Models without Reward Over-optimization."}
diff --git a/data/sampled_jsons/Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_Section_J_reward_estimation_challenges_year_2024.jsonl b/data/sampled_jsons/Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_Section_J_reward_estimation_challenges_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d0caf07ef8180d6d8cfccc1f7ae6a4ea359ea24
--- /dev/null
+++ b/data/sampled_jsons/Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_Section_J_reward_estimation_challenges_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v2", "content": "Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions."}
+{"idx": 1, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge .To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/training-free-diffusion-model-alignment-with", "content": "Aligning diffusion models with user preferences has been a key challenge .To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models ."}
+{"idx": 2, "title": "ICLR Poster Training - Free Diffusion Model Alignment with ...", "date": "", "ddg_snippet": "alignment method for diffusion models .Our method can be easily integrated with existing diffusion models without further training .Our experiments show that the proposed approach significantly improves the average aesthetics scores for text-to-image generation.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28034", "content": "alignment method for diffusion models .Our method can be easily integrated with existing diffusion models without further training .Our experiments show that the proposed approach significantly improves the average aesthetics scores for text-to-image generation."}
+{"idx": 3, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Abstract: Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2410.05760v2", "content": "Abstract: Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions."}
+{"idx": 4, "title": "GitHub - xie-lab-ml/awesome- alignment -of- diffusion - models : The...", "date": "", "ddg_snippet": "Training - Free Diffusion Model Alignment with Sampling Demons .Test-time Alignment of Diffusion Models without Reward Over-optimization.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xie-lab-ml/awesome-alignment-of-diffusion-models", "content": "Training - Free Diffusion Model Alignment with Sampling Demons .Test-time Alignment of Diffusion Models without Reward Over-optimization."}
+{"idx": 5, "title": "DyMO: Training - Free Diffusion Model Alignment with Dynamic...", "date": "", "ddg_snippet": "Training - free methods align generated images with spe-cic objectives by applying differentiable rewards to adjust the generation process of a pre-trained diffusion model dur-ing inference [41, 52, 54, 55].", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_DyMO_Training-Free_Diffusion_Model_Alignment_with_Dynamic_Multi-Objective_Scheduling_CVPR_2025_paper.pdf", "content": "Training - free methods align generated images with spe-cic objectives by applying differentiable rewards to adjust the generation process of a pre-trained diffusion model dur-ing inference [41, 52, 54, 55]."}
+{"idx": 6, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "This paper introduces a training - free technique called \" Sampling Demons \" for aligning diffusion models with target objectives.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/training-free-diffusion-model-alignment-sampling-demons", "content": "This paper introduces a training - free technique called \" Sampling Demons \" for aligning diffusion models with target objectives."}
+{"idx": 7, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Training-free-Diffusion-Model-Alignment-with-Sampling-Demons-7d88de32-4c7d-456a-8886-7029b4030b48", "content": "Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions."}
+{"idx": 8, "title": "(PDF) Dynamic Search for Inference-Time Alignment in Diffusion ...", "date": "", "ddg_snippet": "FreeDoM: Training - Free Energy-Guided Conditional Diffusion Model . Conference Paper. Reward -Guided Controlled Generation for Inference-Time Alignment in Diffusion Models : Tutorial and R...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389581821_Dynamic_Search_for_Inference-Time_Alignment_in_Diffusion_Models", "content": "FreeDoM: Training - Free Energy-Guided Conditional Diffusion Model . Conference Paper. Reward -Guided Controlled Generation for Inference-Time Alignment in Diffusion Models : Tutorial and R..."}
+{"idx": 9, "title": "aiiu-lab/DemonSampling | DeepWiki", "date": "", "ddg_snippet": "This document introduces the DemonSampling system, a training - free diffusion model alignment technique that enables preference alignment during inference without requiring backpropagation. For installation instructions, see Installation.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/aiiu-lab/DemonSampling", "content": "This document introduces the DemonSampling system, a training - free diffusion model alignment technique that enables preference alignment during inference without requiring backpropagation. For installation instructions, see Installation."}
diff --git a/data/sampled_jsons/Transformer_attention_mechanism_quadratic_complexity_sequence_length_Vaswani_2017_year_2017.jsonl b/data/sampled_jsons/Transformer_attention_mechanism_quadratic_complexity_sequence_length_Vaswani_2017_year_2017.jsonl
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index 0000000000000000000000000000000000000000..3bd129f2774735aabf8a618b9a3cab9a89071289
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+++ b/data/sampled_jsons/Transformer_attention_mechanism_quadratic_complexity_sequence_length_Vaswani_2017_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1706.03762] Attention Is All You Need - arXiv.org", "date": "", "ddg_snippet": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism . We propose a new simple network architecture, the Transformer , based solely on attention mechanisms , dispensing with recurrence and convolutions entirely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism . We propose a new simple network architecture, the Transformer , based solely on attention mechanisms , dispensing with recurrence and convolutions entirely ..."}
+{"idx": 1, "title": "Attention Is All You Need - NIPS", "date": "", "ddg_snippet": "Abstract The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism . We propose a new simple network architecture, the Transformer , based solely on attention mechanisms , dispensing with recurrence and convolutions ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf", "content": "Abstract The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism . We propose a new simple network architecture, the Transformer , based solely on attention mechanisms , dispensing with recurrence and convolutions ..."}
+{"idx": 2, "title": "Hybrid Attention Mechanism for Sparsity in Transformer ...", "date": "", "ddg_snippet": "Abstract : The Transformer architecture, introduced in “ Attention Is All You Need” ( Vaswani et al., 2017 ), has become the foundation for modern natural language processing (NLP) and computer vision models. However, its computational complexity scales quadratically with the input sequence length , primarily due to the global self- attention mechanism . This quadratic complexity presents a ...", "subpage_snippet": "", "source": "rjpn.org", "link": "https://rjpn.org/jetnr/papers/JETNR2503021.pdf", "content": "Abstract : The Transformer architecture, introduced in “ Attention Is All You Need” ( Vaswani et al., 2017 ), has become the foundation for modern natural language processing (NLP) and computer vision models. However, its computational complexity scales quadratically with the input sequence length , primarily due to the global self- attention mechanism . This quadratic complexity presents a ..."}
+{"idx": 3, "title": "The End of Transformers? On Challenging Attention and the ...", "date": "", "ddg_snippet": "Abstract Transformers have dominated sequence pro-cessing tasks for the past seven years—most notably language modeling. However, the in-herent quadratic complexity of their attention mechanism remains a significant bottleneck as context length increases. This paper surveys recent efforts to overcome this bottleneck, in-cluding advances in (sub- quadratic ) attention variants, recurrent neural ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=N7ouWikDzw", "content": "Abstract Transformers have dominated sequence pro-cessing tasks for the past seven years—most notably language modeling. However, the in-herent quadratic complexity of their attention mechanism remains a significant bottleneck as context length increases. This paper surveys recent efforts to overcome this bottleneck, in-cluding advances in (sub- quadratic ) attention variants, recurrent neural ..."}
+{"idx": 4, "title": "Hybrid Attention Mechanism for Sparsity in Transformer ...", "date": "", "ddg_snippet": "Mar 22, 2025 · The Transformer architecture, introduced in \" Attention Is All You Need\" ( Vaswani et al., 2017 ), has become the foundation for modern natural language processing (NLP) and computer vision models.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390091602_Hybrid_Attention_Mechanism_for_Sparsity_in_Transformer_Architecture", "content": "Mar 22, 2025 · The Transformer architecture, introduced in \" Attention Is All You Need\" ( Vaswani et al., 2017 ), has become the foundation for modern natural language processing (NLP) and computer vision models."}
+{"idx": 5, "title": "The Problem with Quadratic Attention in Transformer ...", "date": "", "ddg_snippet": "Mar 4, 2024 · Reformer (The Efficient Transformer ): This method aims to use locality-sensitive hashing to reduce complexity . Reformer uses a hash function to bucket/chunk related tokens together and uses it to match similar vector together thereby avoiding a redundant search of the entire sequence . Attention is then applied within these much smaller chunks reducing the quadratic attention to almost linear!", "subpage_snippet": "", "source": "wandb.ai", "link": "https://wandb.ai/wandb_fc/tips/reports/The-Problem-with-Quadratic-Attention-in-Transformer-Architectures--Vmlldzo3MDE0Mzcz", "content": "Mar 4, 2024 · Reformer (The Efficient Transformer ): This method aims to use locality-sensitive hashing to reduce complexity . Reformer uses a hash function to bucket/chunk related tokens together and uses it to match similar vector together thereby avoiding a redundant search of the entire sequence . Attention is then applied within these much smaller chunks reducing the quadratic attention to almost linear!"}
+{"idx": 6, "title": "The big picture: Transformers for long sequences - Medium", "date": "", "ddg_snippet": "Mar 4, 2022 · In 2017 , The Transformer was introduced by Vaswani et al., a sequence model that solely relies on attention mechanisms without any recurrence and convolutions operations.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@lukas.noebauer/the-big-picture-transformers-for-long-sequences-890cc0e7613b", "content": "Mar 4, 2022 · In 2017 , The Transformer was introduced by Vaswani et al., a sequence model that solely relies on attention mechanisms without any recurrence and convolutions operations."}
+{"idx": 7, "title": "CHAPTER 3 Transformers - Springer", "date": "", "ddg_snippet": "Transformers In 2017 , Ashish Vaswani et al. from Google Brain and Google Research proposed a revolutionary new architecture of neural networks for natural language processing (NLP) and other sequence -to- sequence tasks in their \" Attention Is All You Need\" paper. In this paper, Vaswani et al. presented a new approach that relies heavily on attention mechanisms to process sequences , allowing ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/979-8-8688-0017-7_3", "content": "Transformers In 2017 , Ashish Vaswani et al. from Google Brain and Google Research proposed a revolutionary new architecture of neural networks for natural language processing (NLP) and other sequence -to- sequence tasks in their \" Attention Is All You Need\" paper. In this paper, Vaswani et al. presented a new approach that relies heavily on attention mechanisms to process sequences , allowing ..."}
+{"idx": 8, "title": "PDF Attention Is All You Need - GitHub Pages", "date": "", "ddg_snippet": "Abstract The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism . We propose a new simple network architecture, the Transformer , based solely on attention mechanisms , dispensing with recurrence and convolutions ...", "subpage_snippet": "", "source": "ailab-ua.github.io", "link": "https://ailab-ua.github.io/courses/resources/Attention_Vaswani_2017.pdf", "content": "Abstract The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism . We propose a new simple network architecture, the Transformer , based solely on attention mechanisms , dispensing with recurrence and convolutions ..."}
+{"idx": 9, "title": "PDF Transformer (Vaswani et al.,2017) and their vari- arXiv:2108.09084v3 ...", "date": "", "ddg_snippet": "s the Transformer to model the contexts within an in-put sequence (Parikh et al., 2016). However, since self- attention computes the dot-product between the input representations at each pair of po itions, its complexity is quadratic to the input sequence length ( Vaswani et al., 2017 ). Thus, it is difficult for s", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2108.09084v3.pdf", "content": "s the Transformer to model the contexts within an in-put sequence (Parikh et al., 2016). However, since self- attention computes the dot-product between the input representations at each pair of po itions, its complexity is quadratic to the input sequence length ( Vaswani et al., 2017 ). Thus, it is difficult for s"}
diff --git a/data/sampled_jsons/Transformer_self-attention_computational_complexity_O(n^2)_sequence_length.jsonl b/data/sampled_jsons/Transformer_self-attention_computational_complexity_O(n^2)_sequence_length.jsonl
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index 0000000000000000000000000000000000000000..9e8c1871c0335dda08338f7b3e9572822149edb8
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "一文了解Transformer全貌（图解Transformer）", "date": "", "ddg_snippet": "Jan 21, 2025 · 网上有关Transformer原理的介绍很多，在本文中我们将尽量模型简化，让普通读者也能轻松理解。 1. Transformer整体结构 在机器翻译中，Transformer可以将一种语言翻译成另一种语言，如果把Transformer看成一个黑盒，那么其结构如下图所示：", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/tardis/zm/art/600773858", "content": "Jan 21, 2025 · 网上有关Transformer原理的介绍很多，在本文中我们将尽量模型简化，让普通读者也能轻松理解。 1. Transformer整体结构 在机器翻译中，Transformer可以将一种语言翻译成另一种语言，如果把Transformer看成一个黑盒，那么其结构如下图所示："}
+{"idx": 1, "title": "如何最简单、通俗地理解Transformer？ - 知乎", "date": "", "ddg_snippet": "Transformer最开始应用于NLP领域的机器翻译任务，但是它的通用性很好，除了NLP领域的其他任务，经过变体，还可以用于视觉领域，如ViT（Vision Transformer）。 这些特点让Transformer自2017年发布以来，持续受到关注，基于Transformer的工作和应用层出不穷。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/445556653", "content": "Transformer最开始应用于NLP领域的机器翻译任务，但是它的通用性很好，除了NLP领域的其他任务，经过变体，还可以用于视觉领域，如ViT（Vision Transformer）。 这些特点让Transformer自2017年发布以来，持续受到关注，基于Transformer的工作和应用层出不穷。"}
+{"idx": 2, "title": "Transformer模型怎么用于regression的问题？ - 知乎", "date": "", "ddg_snippet": "Transformer模型火了这么久，但都是针对分类问题的，另一类常见的预测问题是递归，怎么把transformer用于这类问题？ 大神请指教。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/8851069085?write", "content": "Transformer模型火了这么久，但都是针对分类问题的，另一类常见的预测问题是递归，怎么把transformer用于这类问题？ 大神请指教。"}
+{"idx": 3, "title": "Transformer模型详解（图解最完整版） - 知乎", "date": "", "ddg_snippet": "Transformer 的整体结构，左图Encoder和右图Decoder 可以看到 Transformer 由 Encoder 和 Decoder 两个部分组成，Encoder 和 Decoder 都包含 6 个 block。Transformer 的工作流程大体如下： 第一步： 获取输入句子的每一个单词的表示向量 X， X 由单词的 Embedding（Embedding就是从原始数据提取出来的Feature） 和单词位置的 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/column/p/338817680", "content": "Transformer 的整体结构，左图Encoder和右图Decoder 可以看到 Transformer 由 Encoder 和 Decoder 两个部分组成，Encoder 和 Decoder 都包含 6 个 block。Transformer 的工作流程大体如下： 第一步： 获取输入句子的每一个单词的表示向量 X， X 由单词的 Embedding（Embedding就是从原始数据提取出来的Feature） 和单词位置的 ..."}
+{"idx": 4, "title": "如何从浅入深理解 Transformer？ - 知乎", "date": "", "ddg_snippet": "Transformer升级之路：12、无限外推的ReRoPE？ Transformer升级之路：13、逆用Leaky ReRoPE Transformer升级之路：14、当HWFA遇见ReRoPE 预训练一下，Transformer的长序列成绩还能涨不少！ VQ一下Key，Transformer的复杂度就变成线性了 Transformer升级之路：15、Key归一化助力长度外推", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/471328838", "content": "Transformer升级之路：12、无限外推的ReRoPE？ Transformer升级之路：13、逆用Leaky ReRoPE Transformer升级之路：14、当HWFA遇见ReRoPE 预训练一下，Transformer的长序列成绩还能涨不少！ VQ一下Key，Transformer的复杂度就变成线性了 Transformer升级之路：15、Key归一化助力长度外推"}
+{"idx": 5, "title": "深度学习中“Transformer”怎么翻译为中文？ - 知乎", "date": "", "ddg_snippet": "Transformer 个人觉得不翻译为好。 Transformer按在机器翻译中原意可以翻译为变形器或变换器。但随着Transformer的普及，它已经成为一类以 自注意力 为主要部件的特定模型，其原本在机器翻译中的内涵变得不再重要，翻译成变形器反而不能涵盖其意义和除机器翻译外的场景。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/434784733", "content": "Transformer 个人觉得不翻译为好。 Transformer按在机器翻译中原意可以翻译为变形器或变换器。但随着Transformer的普及，它已经成为一类以 自注意力 为主要部件的特定模型，其原本在机器翻译中的内涵变得不再重要，翻译成变形器反而不能涵盖其意义和除机器翻译外的场景。"}
+{"idx": 6, "title": "训练最基础的transformer模型用多大的gpu就行? - 知乎", "date": "", "ddg_snippet": "8gb或者12gb就够训练 12层的 encoder-decoder 架构 transformer 模型了。 序列长度在512左右。 batch size什么的可以通过 gradient checkpoint 或者 accumulate gradient 等操作间接提升。 小显存推荐开混合精度训练，或者开bf16缓解一下显存压力 (如果卡支持的话)。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/609967825", "content": "8gb或者12gb就够训练 12层的 encoder-decoder 架构 transformer 模型了。 序列长度在512左右。 batch size什么的可以通过 gradient checkpoint 或者 accumulate gradient 等操作间接提升。 小显存推荐开混合精度训练，或者开bf16缓解一下显存压力 (如果卡支持的话)。"}
+{"idx": 7, "title": "transformer的损失函数如何定义？ - 知乎", "date": "", "ddg_snippet": "Transformer 的整体结构，左图Encoder和右图Decoder 可以看到 Transformer 由 Encoder 和 Decoder 两个部分组成，Encoder 和 Decoder 都包含 6 个 block。Transformer 的工作流程大体如下： 第一步： 获取输入句子的每一个单词的表示向量 X， X 由单词的 Embedding（Embedding就是从原始数据提取出来的Feature） 和单词位置的 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/647481202?write", "content": "Transformer 的整体结构，左图Encoder和右图Decoder 可以看到 Transformer 由 Encoder 和 Decoder 两个部分组成，Encoder 和 Decoder 都包含 6 个 block。Transformer 的工作流程大体如下： 第一步： 获取输入句子的每一个单词的表示向量 X， X 由单词的 Embedding（Embedding就是从原始数据提取出来的Feature） 和单词位置的 ..."}
+{"idx": 8, "title": "VAE、GAN 这种生成模型和 transformer 有什么区别？", "date": "", "ddg_snippet": "想认识Transformer以及最火的GPT结构，请移步以下一个答主认为比较清晰易懂的解答： 不妨让我们一起聚焦当下火热的生成式AI的内核——强大的生成模型，看看这种生成和Transformer自回归式生成的差异所在。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/558574918?write", "content": "想认识Transformer以及最火的GPT结构，请移步以下一个答主认为比较清晰易懂的解答： 不妨让我们一起聚焦当下火热的生成式AI的内核——强大的生成模型，看看这种生成和Transformer自回归式生成的差异所在。"}
+{"idx": 9, "title": "挑战 Transformer：全新架构 Mamba 详解", "date": "", "ddg_snippet": "Jan 21, 2025 · 而就在最近，一名为 Mamba 的架构似乎打破了这一局面。 与类似规模的 Transformer 相比， Mamba 具有 5 倍的吞吐量， 而且 Mamba-3B 的效果与两倍于其规模的 Transformer 相当。 性能高、效果好，Mamba 成为新的研究热点。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/tardis/zm/art/684231320", "content": "Jan 21, 2025 · 而就在最近，一名为 Mamba 的架构似乎打破了这一局面。 与类似规模的 Transformer 相比， Mamba 具有 5 倍的吞吐量， 而且 Mamba-3B 的效果与两倍于其规模的 Transformer 相当。 性能高、效果好，Mamba 成为新的研究热点。"}
diff --git a/data/sampled_jsons/Tsamardinos_max-min_hill-climbing_Bayesian_network_structure_learning_2006_abstract.jsonl b/data/sampled_jsons/Tsamardinos_max-min_hill-climbing_Bayesian_network_structure_learning_2006_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..20113e4cb196d6695d8f37ab49cdabc6e5d96413
--- /dev/null
+++ b/data/sampled_jsons/Tsamardinos_max-min_hill-climbing_Bayesian_network_structure_learning_2006_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The max-min hill-climbing Bayesian network structure learning algorithm ...", "date": "", "ddg_snippet": "We present a new algorithm for Bayesian network structure learning , called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning , constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian -scoring greedy hill-climbing search to orient the edges. In our ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-006-6889-7", "content": "We present a new algorithm for Bayesian network structure learning , called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning , constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian -scoring greedy hill-climbing search to orient the edges. In our ..."}
+{"idx": 1, "title": "PDF The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm", "date": "", "ddg_snippet": "The Max-Min Parents and Children Algorithm The Bayesian network learning algorithm presented in this paper is based on the local discovery algorithm called Max-Min Parents and Children (MMPC) (a ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Ioannis-Tsamardinos/publication/220343713_The_Max-Min_Hill-Climbing_Bayesian_Network_Structure_Learning_Algorithm/links/02e7e51e7c591b41a0000000/The-Max-Min-Hill-Climbing-Bayesian-Network-Structure-Learning-Algorithm.pdf", "content": "The Max-Min Parents and Children Algorithm The Bayesian network learning algorithm presented in this paper is based on the local discovery algorithm called Max-Min Parents and Children (MMPC) (a ..."}
+{"idx": 2, "title": "(PDF) The max-min hill-climbing Bayesian network structure learning ...", "date": "", "ddg_snippet": "Abstract Page 1. Mach Learn ( 2006 ) 65:31 78 DOI 10.1007/s10994-006-6889-7 The max-min hill-climbing Bayesian network structure learning algorithm Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis Received: January ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/16764465/The_max_min_hill_climbing_Bayesian_network_structure_learning_algorithm", "content": "Abstract Page 1. Mach Learn ( 2006 ) 65:31 78 DOI 10.1007/s10994-006-6889-7 The max-min hill-climbing Bayesian network structure learning algorithm Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis Received: January ..."}
+{"idx": 3, "title": "MMHC - The Max-Min Hill-Climbing Algorithm", "date": "", "ddg_snippet": "The algorithm combines ideas from local learning , constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian -scoring greedy hill-climbing search to orient the edges.", "subpage_snippet": "", "source": "pages.mtu.edu", "link": "https://pages.mtu.edu/~lebrown/supplements/mmhc_paper/mmhc_index.html", "content": "The algorithm combines ideas from local learning , constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian -scoring greedy hill-climbing search to orient the edges."}
+{"idx": 4, "title": "max-min-hill-climbing-algorithm - GitHub", "date": "", "ddg_snippet": "The max-min hill-climbing Bayesian network structure learning algorithm, Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis, Mach Learn DOI 10.1007/s10994-006-6889-7 *This algorithm reconstructs Bayesian Networks from observational data. Therefore it first builds the skeleton of the DAG (directed acyclic graph) with the max-min parents and children (MMPC) algorithm. Afterwards it ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pespila/max-min-hill-climbing-algorithm", "content": "The max-min hill-climbing Bayesian network structure learning algorithm, Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis, Mach Learn DOI 10.1007/s10994-006-6889-7 *This algorithm reconstructs Bayesian Networks from observational data. Therefore it first builds the skeleton of the DAG (directed acyclic graph) with the max-min parents and children (MMPC) algorithm. Afterwards it ..."}
+{"idx": 5, "title": "The Max-min Hill-climbing Bayesian Network Structure Learning Algorithm", "date": "", "ddg_snippet": "I. TSAMARDINOS , L.E. BROWN, and C.F. ALIFERIS, \"THE MAX-MIN HILL-CLIMBING BAYESIAN NETWORK STRUCTURE LEARNING ALGORITHM,\" MACHINE LEARNING , vol. 65, no. 1, pp. 31-78, 2006 , [Online].", "subpage_snippet": "", "source": "www.sid.ir", "link": "https://www.sid.ir/paper/625560/en", "content": "I. TSAMARDINOS , L.E. BROWN, and C.F. ALIFERIS, \"THE MAX-MIN HILL-CLIMBING BAYESIAN NETWORK STRUCTURE LEARNING ALGORITHM,\" MACHINE LEARNING , vol. 65, no. 1, pp. 31-78, 2006 , [Online]."}
+{"idx": 6, "title": "The max-min hill-climbing Bayesian network structure learning", "date": "", "ddg_snippet": "Mach Learn DOI 10.1007/s10994-006-6889-7 The max-min hill-climbing Bayesian network structure learning algorithm Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis Received: January 07, 2005 / Revised: December 21, 2005 / Accepted: December 22, 2005 / Published online: 28 March 2006 Springer Science + Business Media, Inc. 2006 Abstract We present a new algorithm for ...", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/18240467/the-max-min-hill-climbing-bayesian-network-structure-lear...", "content": "Mach Learn DOI 10.1007/s10994-006-6889-7 The max-min hill-climbing Bayesian network structure learning algorithm Ioannis Tsamardinos · Laura E. Brown · Constantin F. Aliferis Received: January 07, 2005 / Revised: December 21, 2005 / Accepted: December 22, 2005 / Published online: 28 March 2006 Springer Science + Business Media, Inc. 2006 Abstract We present a new algorithm for ..."}
+{"idx": 7, "title": "I. Tsamardinos, L. E. Brown, and C. F. Aliferis, \"The max-min hill ...", "date": "", "ddg_snippet": "Article citations More >> I. Tsamardinos , L. E. Brown, and C. F. Aliferis, \"The max-min hill-climbing Bayesian network structure learning algorithm,\" Mach Learn ...", "subpage_snippet": "", "source": "www.sciepub.com", "link": "https://www.sciepub.com/reference/466642", "content": "Article citations More >> I. Tsamardinos , L. E. Brown, and C. F. Aliferis, \"The max-min hill-climbing Bayesian network structure learning algorithm,\" Mach Learn ..."}
+{"idx": 8, "title": "(PDF) The Max-Min Hill-Climbing Bayesian Network Structure Learning ...", "date": "", "ddg_snippet": "We present a new algorithm for Bayesian network structure learning , called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning , constraint-based, and search-and-score ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/220343713_The_Max-Min_Hill-Climbing_Bayesian_Network_Structure_Learning_Algorithm", "content": "We present a new algorithm for Bayesian network structure learning , called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning , constraint-based, and search-and-score ..."}
+{"idx": 9, "title": "The Max-Min Hill-Climbing Bayesian network structure learning algorithm.", "date": "", "ddg_snippet": "The algorithm combines ideas from local learning , constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian -scoring greedy hill-climbing search to orient the edges.", "subpage_snippet": "", "source": "psycnet.apa.org", "link": "https://psycnet.apa.org/record/2006-12885-002", "content": "The algorithm combines ideas from local learning , constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian -scoring greedy hill-climbing search to orient the edges."}
diff --git a/data/sampled_jsons/Two-Room_(2x11)_training_steps_Rnd._Experts_multiplier_sitearxiv.org.jsonl b/data/sampled_jsons/Two-Room_(2x11)_training_steps_Rnd._Experts_multiplier_sitearxiv.org.jsonl
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+++ b/data/sampled_jsons/Two-Room_(2x11)_training_steps_Rnd._Experts_multiplier_sitearxiv.org.jsonl
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 1, "title": "Title Four Bottomless Errors and the Collapse of Statistical ...", "date": "", "ddg_snippet": "by J Brusseau · 2025 · Cited by 1 — The result would be factors added in the name of equality, so 2 x 11 and 3 x 8 would both equal 23 according to this moralized math, which is bazar, and also an ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.13790", "content": "by J Brusseau · 2025 · Cited by 1 — The result would be factors added in the name of equality, so 2 x 11 and 3 x 8 would both equal 23 according to this moralized math, which is bazar, and also an ..."}
diff --git a/data/sampled_jsons/UBPR_relies_on_propensity_scores_pairwise_learning.jsonl b/data/sampled_jsons/UBPR_relies_on_propensity_scores_pairwise_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3237b73204f6f9ee693a10f5fcdd821c2e4048af
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+++ b/data/sampled_jsons/UBPR_relies_on_propensity_scores_pairwise_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UBPR | Chegg.com", "date": "", "ddg_snippet": "Use 2016 Year-End Data for First Community Bank from the bank’s UBPR to interpret the following ratios (FDIC Cert. #34047) Overview: · Use average balance sheet data and periodic flow income statement data to calculate ratios. · Always compare ratios over time to capture trends and relative to peer institutions at the", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/ubpr-help-link-https-cdrffiecgov-public-reports-ubprreportaspx-rptcycleids-101-2c86-2c81-2-q23799451", "content": "Use 2016 Year-End Data for First Community Bank from the bank’s UBPR to interpret the following ratios (FDIC Cert. #34047) Overview: · Use average balance sheet data and periodic flow income statement data to calculate ratios. · Always compare ratios over time to capture trends and relative to peer institutions at the"}
+{"idx": 1, "title": "Solved Using UBPR data, perform an analysis of Silicon - Chegg", "date": "", "ddg_snippet": "Using UBPR data, perform an analysis of Silicon Valley Bank for the period preceding its collapse in March 2023. Report the peer group for SVB and indicate how many banks are in that peer group.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/using-ubpr-data-perform-analysis-silicon-valley-bank-period-preceding-collapse-march-2023--q123610611", "content": "Using UBPR data, perform an analysis of Silicon Valley Bank for the period preceding its collapse in March 2023. Report the peer group for SVB and indicate how many banks are in that peer group."}
+{"idx": 2, "title": "Solved The summary UBPR page for Wells Fargo Bank, NA is - Chegg", "date": "", "ddg_snippet": "The summary UBPR page for Wells Fargo Bank, NA is shown in the table on page 138. Average total assets for Wells Fargo was quite high as of December 31, 2013. Use the data from December 31, 2013, to explain whether this bank was a high- or low-performance bank compared with peers. Discuss specifically (1) financial leverage, (2) expense control, and (3) the contribution of interest and ...", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/summary-ubpr-page-wells-fargo-bank-na-shown-table-page-138-average-total-assets-wells-farg-q24027551", "content": "The summary UBPR page for Wells Fargo Bank, NA is shown in the table on page 138. Average total assets for Wells Fargo was quite high as of December 31, 2013. Use the data from December 31, 2013, to explain whether this bank was a high- or low-performance bank compared with peers. Discuss specifically (1) financial leverage, (2) expense control, and (3) the contribution of interest and ..."}
+{"idx": 3, "title": "Solved Although the information contained in a bank’s UBPR -...", "date": "", "ddg_snippet": "Although the information contained in a bank ’ s UBPR is transparent, the overall portfolio loan delinquency data is not available unless the bank, or BHC, is a publicly traded entity.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/although-information-contained-bank-s-ubpr-transparent-overall-portfolio-loan-delinquency--q229665320", "content": "Although the information contained in a bank ’ s UBPR is transparent, the overall portfolio loan delinquency data is not available unless the bank, or BHC, is a publicly traded entity."}
+{"idx": 4, "title": "Use the attached UBPR data for Citizens Trust Bank | Chegg.com", "date": "", "ddg_snippet": "Use the attached UBPR data for Citizens Trust Bank (CTB) in Atlanta, GA (FDIC Cert. no. is 8033) to answer all parts of questions #2 through #9. Where appropriate, cite specific ratios that support your answers. The data appear for calendar years ending in 2011 - 2015. Use data only for CTB and peer banks for Dec. 31, 2015 unless indicated ...", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/use-attached-ubpr-data-citizens-trust-bank-ctb-atlanta-ga-fdic-cert--8033-answer-parts-que-q23822651", "content": "Use the attached UBPR data for Citizens Trust Bank (CTB) in Atlanta, GA (FDIC Cert. no. is 8033) to answer all parts of questions #2 through #9. Where appropriate, cite specific ratios that support your answers. The data appear for calendar years ending in 2011 - 2015. Use data only for CTB and peer banks for Dec. 31, 2015 unless indicated ..."}
+{"idx": 5, "title": "Solved 2. UBPR a. What does UBPR stand for? b. What is the -...", "date": "", "ddg_snippet": "UBPR stands for Uniform Bank Performance Report. The Uniform Bank Performance Report is a computer generated database of current and historical financial information produced quarterly.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/2-ubpr--ubpr-stand-b-ubpr-purpose-c-type-data-included-ubpr-d-created-report-oversees-data-q42145423", "content": "UBPR stands for Uniform Bank Performance Report. The Uniform Bank Performance Report is a computer generated database of current and historical financial information produced quarterly."}
+{"idx": 6, "title": "Solved 4. Using the UBPR and any internal financial - Chegg", "date": "", "ddg_snippet": "Question: 4. Using the UBPR and any internal financial reporting your bank may use, discuss your bank’s mix of liabilities and any challenges faced that impact this mix. How has this mix changed over the last 5 years?", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/4-using-ubpr-internal-financial-reporting-bank-may-use-discuss-bank-s-mix-liabilities-chal-q187837710", "content": "Question: 4. Using the UBPR and any internal financial reporting your bank may use, discuss your bank’s mix of liabilities and any challenges faced that impact this mix. How has this mix changed over the last 5 years?"}
+{"idx": 7, "title": "The summary UBPR page for Wells Fargo Bank, NA is - Chegg", "date": "", "ddg_snippet": "Question: The summary UBPR page for Wells Fargo Bank, NA is shown in the table on page 138. Average total assets for Wells Fargo was quite high as of December 31, 2013. Use the data from December 31, 2013, to explain whether this bank was a high- or low-performance bank compared with peers. Discuss specifically (1) financial leverage, (2) expense control, and (3) the", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/summary-ubpr-page-wells-fargo-bank-na-shown-table-page-138-average-total-assets-wells-farg-q26845230", "content": "Question: The summary UBPR page for Wells Fargo Bank, NA is shown in the table on page 138. Average total assets for Wells Fargo was quite high as of December 31, 2013. Use the data from December 31, 2013, to explain whether this bank was a high- or low-performance bank compared with peers. Discuss specifically (1) financial leverage, (2) expense control, and (3) the"}
+{"idx": 8, "title": "Solved A bank's Uniform Bank Performance Report (UBPR) is - Cheg...", "date": "", "ddg_snippet": "Question: A bank's Uniform Bank Performance Report ( UBPR ) is used by both bankers and examiners. Thereport provides information on a bank's financial condition by providing the bank's ratios andthe ratios of the bank's peer group (which represents banks of similar size).TrueFalse", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/bank-s-uniform-bank-performance-report-ubpr-used-bankers-examiners-report-provides-informa-q201568460", "content": "Question: A bank's Uniform Bank Performance Report ( UBPR ) is used by both bankers and examiners. Thereport provides information on a bank's financial condition by providing the bank's ratios andthe ratios of the bank's peer group (which represents banks of similar size).TrueFalse"}
+{"idx": 9, "title": "Solved A bank’s balance sheet is included in | Chegg.com", "date": "", "ddg_snippet": "UBPR B. provision for loan loss statement C. call report (report of condition and income) D. report of income E. statement of retained earnings A bank’s balance sheet is included in a (n) ___________________.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/bank-s-balance-sheet-included-n---ubpr-b-provision-loan-loss-statement-c-call-report-repor-q45256354", "content": "UBPR B. provision for loan loss statement C. call report (report of condition and income) D. report of income E. statement of retained earnings A bank’s balance sheet is included in a (n) ___________________."}
diff --git a/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_Edge_Unstructured_Sparsity_equation_4_quant.jsonl b/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_Edge_Unstructured_Sparsity_equation_4_quant.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4ffd1ee031d4a49744ebf429254df97daf7e348d
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+{"idx": 0, "title": "Recurrent neural network - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. In artificial neural networks , recurrent neural networks are designed for processing sequential data, such as text, speech, and time series, where the order of elements is important.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Recurrent_neural_network", "content": "Machine learningand data mining. v. t. e. In artificial neural networks , recurrent neural networks are designed for processing sequential data, such as text, speech, and time series, where the order of elements is important."}
+{"idx": 1, "title": "Accelerating Linear Recurrent Neural Networks for the Edge", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01330v1", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption."}
+{"idx": 2, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with ...", "date": "", "ddg_snippet": "Abstract. Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=UNrfYfbLZ3", "content": "Abstract. Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference."}
+{"idx": 3, "title": "Accelerating Linear Recurrent Neural Networks for the Edge ...", "date": "", "ddg_snippet": "May 1, 2025 · Our findings showcase the transformative potential of unstructured sparsity , paving the way for highly efficient recurrent neural networks in real-world, resource-constrained environments.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=UNrfYfbLZ3", "content": "May 1, 2025 · Our findings showcase the transformative potential of unstructured sparsity , paving the way for highly efficient recurrent neural networks in real-world, resource-constrained environments."}
+{"idx": 4, "title": "EdgeDRNN: Recurrent Neural Network Accelerator for Edge ...", "date": "", "ddg_snippet": "Low-latency, low-power portable recurrent neural network (RNN) accelerators offer powerful inference capabilities for real-time applications such as IoT, robotics, and human-machine interaction. We propose a lightweight Gated Recurrent Unit (GRU)-based RNN accelerator called EdgeDRNN that is optimized for low-latency edge RNN inference with batch size of 1. EdgeDRNN adopts the spiking neural ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9268992", "content": "Low-latency, low-power portable recurrent neural network (RNN) accelerators offer powerful inference capabilities for real-time applications such as IoT, robotics, and human-machine interaction. We propose a lightweight Gated Recurrent Unit (GRU)-based RNN accelerator called EdgeDRNN that is optimized for low-latency edge RNN inference with batch size of 1. EdgeDRNN adopts the spiking neural ..."}
+{"idx": 5, "title": "Recurrent Neural Networks for Edge Intelligence: A Survey ...", "date": "", "ddg_snippet": "May 24, 2021 · Recurrent Neural Networks are ubiquitous and pervasive in many artificial intelligence applications such as speech recognition, predictive healthcare, creative art, and so on. Although they provide accurate superior solutions, they pose a massive ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3448974", "content": "May 24, 2021 · Recurrent Neural Networks are ubiquitous and pervasive in many artificial intelligence applications such as speech recognition, predictive healthcare, creative art, and so on. Although they provide accurate superior solutions, they pose a massive ..."}
+{"idx": 6, "title": "Accelerating Linear Recurrent Neural Networks for the Edge ...", "date": "", "ddg_snippet": "Feb 3, 2025 · Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption. Unstructured sparsity offers a compelling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01330", "content": "Feb 3, 2025 · Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption. Unstructured sparsity offers a compelling ..."}
+{"idx": 7, "title": "[2502.01330] Accelerating Linear Recurrent Neural Networks ...", "date": "", "ddg_snippet": "Feb 3, 2025 · Abstract: Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption. Unstructured sparsity offers a ...", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2502.01330", "content": "Feb 3, 2025 · Abstract: Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption. Unstructured sparsity offers a ..."}
+{"idx": 8, "title": "(PDF) Accelerating Linear Recurrent Neural Networks for the Edge ...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage. and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685255_Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage. and time-per-token during inference. These architectures hold promise for streaming applications at the edge , but."}
+{"idx": 9, "title": "[PDF] On the quantization of recurrent neural networks", "date": "", "ddg_snippet": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity . Alessandro PierroSteven AbreuJonathan TimcheckPhilipp StratmannAndreas WildS.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-the-quantization-of-recurrent-neural-networks-Li-Álvarez/3c964c58cd47aa64c9be520c18efd364c514ac29", "content": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity . Alessandro PierroSteven AbreuJonathan TimcheckPhilipp StratmannAndreas WildS."}
diff --git a/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_activity_sparsification_GELU_ReLU_Loihi_2.jsonl b/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_activity_sparsification_GELU_ReLU_Loihi_2.jsonl
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index 0000000000000000000000000000000000000000..03eed9dde75514ea69dbcf2a6ce2dfd9609367df
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+{"idx": 0, "title": "Activation function - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. Logistic activation function. In artificial neural networks , the activation function of a node is a function that calculates the output of the node based on its individual inputs and their weights.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Activation_function", "content": "Machine learningand data mining. v. t. e. Logistic activation function. In artificial neural networks , the activation function of a node is a function that calculates the output of the node based on its individual inputs and their weights."}
+{"idx": 1, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with ...", "date": "", "ddg_snippet": "Abstract. Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=UNrfYfbLZ3", "content": "Abstract. Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference."}
+{"idx": 2, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.First, we replace the GELU activation with a ReLU , sparsifying pre-activations of the linear layer in the GLU block.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01330v1", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.First, we replace the GELU activation with a ReLU , sparsifying pre-activations of the linear layer in the GLU block."}
+{"idx": 3, "title": "(PDF) Accelerating Linear Recurrent Neural Networks for the Edge...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.Spike output. Recurrent state. Loihi 2 neuron Ethernet. Encoder. Figure 7.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685255_Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.Spike output. Recurrent state. Loihi 2 neuron Ethernet. Encoder. Figure 7."}
+{"idx": 4, "title": "ReLU Activation Function in Deep Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Rectified Linear Unit ( ReLU ) is a popular activation functions used in neural networks , especially in deep learning models.Faster Convergence: ReLU accelerates training by preventing saturation for positive inputs, enhancing gradient flow in deep networks .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/deep-learning/relu-activation-function-in-deep-learning/", "content": "Rectified Linear Unit ( ReLU ) is a popular activation functions used in neural networks , especially in deep learning models.Faster Convergence: ReLU accelerates training by preventing saturation for positive inputs, enhancing gradient flow in deep networks ."}
+{"idx": 5, "title": "Activation Functions in Neural Networks [12 Types & Use Cases]", "date": "", "ddg_snippet": "10 Non- Linear Neural Networks Activation Functions. Sigmoid / Logistic Activation Function. ReLU accelerates the convergence of gradient descent towards the global minimum of the loss function due to its linear , non-saturating property.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/neural-networks-activation-functions", "content": "10 Non- Linear Neural Networks Activation Functions. Sigmoid / Logistic Activation Function. ReLU accelerates the convergence of gradient descent towards the global minimum of the loss function due to its linear , non-saturating property."}
+{"idx": 6, "title": "Intel Introduces Loihi 2 Neuroprocessing Chip & Lava API For Deep...", "date": "", "ddg_snippet": "Intel announced today the company's second-generation neuromorphic research chip, the Loihi 2 , as well as their open-source software framework for developing neuro-inspired applications, Lava API.", "subpage_snippet": "", "source": "wccftech.com", "link": "https://wccftech.com/intel-introduces-loihi-2-neuroprocessing-chip-and-lava-api-for-deep-learning/", "content": "Intel announced today the company's second-generation neuromorphic research chip, the Loihi 2 , as well as their open-source software framework for developing neuro-inspired applications, Lava API."}
+{"idx": 7, "title": "Articles by Andreas Wild | Synthical", "date": "", "ddg_snippet": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity.Solving QUBO on the Loihi 2 Neuromorphic Processor.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/e87cbd70-e312-47ad-abe8-e9f312151f40/articles", "content": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity.Solving QUBO on the Loihi 2 Neuromorphic Processor."}
+{"idx": 8, "title": "E fficiency -p erformance p areto f ront", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence model-ing with constant memory usage and time-per-token during inference.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=KwyLszIdbB&name=pdf", "content": "Linear recurrent neural networks enable powerful long-range sequence model-ing with constant memory usage and time-per-token during inference."}
+{"idx": 9, "title": "GitHub - Dengyu-Wu/neuromorphics-daily-arxiv: Neuromorphic paper...", "date": "", "ddg_snippet": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity.N-DriverMotion: Driver motion learning and prediction using an event-based camera and directly trained spiking neural networks on Loihi 2 .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Dengyu-Wu/neuromorphics-daily-arxiv", "content": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity.N-DriverMotion: Driver motion learning and prediction using an event-based camera and directly trained spiking neural networks on Loihi 2 ."}
diff --git a/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_activity_sparsification_GELU_ReLU_Loihi_2_year_2024.jsonl b/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_activity_sparsification_GELU_ReLU_Loihi_2_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5feb07c8ecf7326a98eda3706927d89ddfd338cc
--- /dev/null
+++ b/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_activity_sparsification_GELU_ReLU_Loihi_2_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Activation function - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. Logistic activation function. In artificial neural networks , the activation function of a node is a function that calculates the output of the node based on its individual inputs and their weights.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Activation_function", "content": "Machine learningand data mining. v. t. e. Logistic activation function. In artificial neural networks , the activation function of a node is a function that calculates the output of the node based on its individual inputs and their weights."}
+{"idx": 1, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with ...", "date": "", "ddg_snippet": "Abstract. Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=UNrfYfbLZ3", "content": "Abstract. Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference."}
+{"idx": 2, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.First, we replace the GELU activation with a ReLU , sparsifying pre-activations of the linear layer in the GLU block.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01330v1", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.First, we replace the GELU activation with a ReLU , sparsifying pre-activations of the linear layer in the GLU block."}
+{"idx": 3, "title": "(PDF) Accelerating Linear Recurrent Neural Networks for the Edge...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.Spike output. Recurrent state. Loihi 2 neuron Ethernet. Encoder. Figure 7.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685255_Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.Spike output. Recurrent state. Loihi 2 neuron Ethernet. Encoder. Figure 7."}
+{"idx": 4, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. Linear Recurrent Neural Networks . Unstructured Sparsity. Edge Computing.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/accelerating-linear-recurrent-neural-networks-for-the-edge-with-unstructured-sparsity/1093781902856290316-108614", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. Linear Recurrent Neural Networks . Unstructured Sparsity. Edge Computing."}
+{"idx": 5, "title": "Activation Functions in Neural Networks [12 Types & Use Cases]", "date": "", "ddg_snippet": "10 Non- Linear Neural Networks Activation Functions. Sigmoid / Logistic Activation Function. ReLU accelerates the convergence of gradient descent towards the global minimum of the loss function due to its linear , non-saturating property.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/neural-networks-activation-functions", "content": "10 Non- Linear Neural Networks Activation Functions. Sigmoid / Logistic Activation Function. ReLU accelerates the convergence of gradient descent towards the global minimum of the loss function due to its linear , non-saturating property."}
+{"idx": 6, "title": "Intel Loihi 2 - Neuromorphic Chip with 15X Density | NextBigFuture.com", "date": "", "ddg_snippet": "Intel introduced Loihi 2 , its second-generation neuromorphic research chip, and Lava, an open-source software framework for developing neuro-inspired.", "subpage_snippet": "", "source": "www.nextbigfuture.com", "link": "https://www.nextbigfuture.com/2021/09/intel-loihi-2-neuromorphic-chip-with-15x-density.html", "content": "Intel introduced Loihi 2 , its second-generation neuromorphic research chip, and Lava, an open-source software framework for developing neuro-inspired."}
+{"idx": 7, "title": "Intel Loihi 2 Patents: Revolutionizing Neuromorphic Computing", "date": "", "ddg_snippet": "Intel’s Loihi 2 boasts faster processing speeds and increased neuron density, significantly enhancing energy efficiency.", "subpage_snippet": "", "source": "insights.greyb.com", "link": "https://insights.greyb.com/intels-loihi-2-patents/", "content": "Intel’s Loihi 2 boasts faster processing speeds and increased neuron density, significantly enhancing energy efficiency."}
+{"idx": 8, "title": "MIT 6.S191 (2023): Recurrent Neural Networks , Transformers, and...", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=ySEx_Bqxvvo", "content": ""}
+{"idx": 9, "title": "Articles by Andreas Wild | Synthical", "date": "", "ddg_snippet": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity.Solving QUBO on the Loihi 2 Neuromorphic Processor.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/e87cbd70-e312-47ad-abe8-e9f312151f40/articles", "content": "Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity.Solving QUBO on the Loihi 2 Neuromorphic Processor."}
diff --git a/data/sampled_jsons/UPGNET_Equation_11_NFR_layer_feature_selection_mechanism.jsonl b/data/sampled_jsons/UPGNET_Equation_11_NFR_layer_feature_selection_mechanism.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..471102169d57eccadf20a28574e8ad5b5a5b753c
--- /dev/null
+++ b/data/sampled_jsons/UPGNET_Equation_11_NFR_layer_feature_selection_mechanism.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention (machine learning) - Wikipedia", "date": "", "ddg_snippet": "Attentional Neural Networks introduced a learned feature selection mechanism using top-down cognitive modulation, showing how attention weights can highlight relevant inputs.[12].", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Attention_(machine_learning)", "content": "Attentional Neural Networks introduced a learned feature selection mechanism using top-down cognitive modulation, showing how attention weights can highlight relevant inputs.[12]."}
+{"idx": 1, "title": "Feature Selection Mechanism in CNNs", "date": "", "ddg_snippet": "This feature selection mechanism calculates the inuence proportion of each location and lters the unnecessary features in the following layer .", "subpage_snippet": "", "source": "bmvc2018.org", "link": "http://bmvc2018.org/contents/workshops/iahfar2018/0011.pdf", "content": "This feature selection mechanism calculates the inuence proportion of each location and lters the unnecessary features in the following layer ."}
+{"idx": 2, "title": "Neural network input feature selection using structured l2 norm...", "date": "", "ddg_snippet": "Furthermore, the embedded feature selection method proposed in [45] relies on an input-to-output skip- layer (residual) connection that allows a feature to have non-zero weights in a hidden unit only if its skip- layer connection is active.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10489-022-03539-8", "content": "Furthermore, the embedded feature selection method proposed in [45] relies on an input-to-output skip- layer (residual) connection that allows a feature to have non-zero weights in a hidden unit only if its skip- layer connection is active."}
+{"idx": 3, "title": "How To Disable Automatic Vector Layer In Ibis Paint X", "date": "", "ddg_snippet": "By disabling the automatic vector layer , you'll have more control over your projects and achieve the desired results with ease.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/e70da362ebfe9879e8cba744205c7b42/", "content": "By disabling the automatic vector layer , you'll have more control over your projects and achieve the desired results with ease."}
+{"idx": 4, "title": "ICML Poster Going Deeper into Locally Differentially Private Graph...", "date": "", "ddg_snippet": "Based on the above analysis, UPGNet enhances utility by introducing two core layers : High-Order Aggregator (HOA) layer and the Node Feature Regularization ( NFR ) layer .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46579", "content": "Based on the above analysis, UPGNet enhances utility by introducing two core layers : High-Order Aggregator (HOA) layer and the Node Feature Regularization ( NFR ) layer ."}
+{"idx": 5, "title": "FAST: Boosting Uncertainty-based Test Prioritization Methods for...", "date": "", "ddg_snippet": "The feature selection will be performed on a specific hidden layer 𝑙 of the given DNN. According to the definitions in the background (see Section 2), layer 𝑙 encodes the inputs to a feature space with dimension 𝑁𝑙 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.09130", "content": "The feature selection will be performed on a specific hidden layer 𝑙 of the given DNN. According to the definitions in the background (see Section 2), layer 𝑙 encodes the inputs to a feature space with dimension 𝑁𝑙 ."}
+{"idx": 6, "title": "TabNet: Attentive Interpretable Tabular Learning", "date": "", "ddg_snippet": "Interpretability: TabNet's feature selection mechanism makes it easy to interpret which features are most important for the model's predictions. This can be done by analyzing the feature masks that are output by the attentive transformer.", "subpage_snippet": "", "source": "www.machinelearningexpedition.com", "link": "https://www.machinelearningexpedition.com/tabnet-tabular-neural-network/", "content": "Interpretability: TabNet's feature selection mechanism makes it easy to interpret which features are most important for the model's predictions. This can be done by analyzing the feature masks that are output by the attentive transformer."}
+{"idx": 7, "title": "Временная Почта - Бесплатный Генератор Одноразового... | Boomlify", "date": "", "ddg_snippet": "Действителен до 9/5/2025, 11 :54:10 AM.Security Protocols. Encryption Layers : •AES-256 for stored emails.", "subpage_snippet": "", "source": "boomlify.com", "link": "https://boomlify.com/ru/temp-mail-instant/", "content": "Действителен до 9/5/2025, 11 :54:10 AM.Security Protocols. Encryption Layers : •AES-256 for stored emails."}
+{"idx": 8, "title": "Regularization — Understanding L1 and L2 regularization for... | Medium", "date": "", "ddg_snippet": "So, this works well for feature selection in case we have a huge number of features . The L1 regularizer basically looks for the parameter vectors that minimize the norm of the parameter vector (the length of the vector).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/analytics-vidhya/regularization-understanding-l1-and-l2-regularization-for-deep-learning-a7b9e4a409bf", "content": "So, this works well for feature selection in case we have a huge number of features . The L1 regularizer basically looks for the parameter vectors that minimize the norm of the parameter vector (the length of the vector)."}
+{"idx": 9, "title": "Introductory Fluid Mechanics L20 p1 - Example - von... - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=0qioJ9pyQW8", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features ."}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_Table_1_PSNR_original_3DGS_year_2024.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_Table_1_PSNR_original_3DGS_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..642cd4af8939d0962f60e34ddc7c30094801e039
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_Table_1_PSNR_original_3DGS_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "by A Rai · 2025 · Cited by 2 — We propose UVGS - an structured image-like representation for 3DGS obtained by spherical mapping of its primitives. The obtained UVGS maps can be further ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "by A Rai · 2025 · Cited by 2 — We propose UVGS - an structured image-like representation for 3DGS obtained by spherical mapping of its primitives. The obtained UVGS maps can be further ... 11 pages"}
+{"idx": 1, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "3 Feb 2025 — We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v1", "content": "3 Feb 2025 — We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images , ..."}
+{"idx": 2, "title": "CVPR Poster UVGS: Reimagining Unstructured 3D Gaussian ...", "date": "", "ddg_snippet": "Abstract: 3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33266", "content": "Abstract: 3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due ..."}
+{"idx": 3, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images , with feature ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/105135", "content": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images , with feature ..."}
+{"idx": 4, "title": "Contact Estimation for Bimanual Hand-Object ... - Brown CS", "date": "", "ddg_snippet": "Uv gs: Reimagining unstructured 3d gaussian splatting using uv mapping. arXiv preprint arXiv:2502.01846, 2025. 2. [42] Nikhila Ravi, Valentin Gabeur, Yuan ...", "subpage_snippet": "", "source": "cs.brown.edu", "link": "https://cs.brown.edu/media/filer_public/e7/64/e7646aed-2cfa-45c7-9d44-7f7eeb07b472/xing_angela.pdf", "content": "Uv gs: Reimagining unstructured 3d gaussian splatting using uv mapping. arXiv preprint arXiv:2502.01846, 2025. 2. [42] Nikhila Ravi, Valentin Gabeur, Yuan ..."}
+{"idx": 5, "title": "COMMON PROJECTS stores in Oslo | SHOPenauer", "date": "", "ddg_snippet": "All stores selling COMMON PROJECTS brand in OSLO with addresses, opening hours, phone, map to reach them and more. Find your store!", "subpage_snippet": "", "source": "www.shopenauer.com", "link": "https://www.shopenauer.com/en/brand/common-projects/oslo", "content": "All stores selling COMMON PROJECTS brand in OSLO with addresses, opening hours, phone, map to reach them and more. Find your store!"}
+{"idx": 6, "title": "Common Projects", "date": "", "ddg_snippet": "capone.no Cavour Retail AS Oslo cavour.co Bogart AS Tønsberg bogart.as Bolina Stabekk bolina.no Høyer Oslo Oslo hoyer.no Høyer Sjolyst Oslo hoyer.no Høyer Trondheim Trondheim hoyer.no Moniker Oslo moniker.no Retro Bergen Bergen retro.no Retro Carl Johan Trondheim retro.no Retro Kristiansand Kristiansand retro.no Retro Nordre Trondheim retro ...", "subpage_snippet": "", "source": "www.commonprojects.com", "link": "http://www.commonprojects.com/stores.asp?store=storesNorwayFrame.html", "content": "capone.no Cavour Retail AS Oslo cavour.co Bogart AS Tønsberg bogart.as Bolina Stabekk bolina.no Høyer Oslo Oslo hoyer.no Høyer Sjolyst Oslo hoyer.no Høyer Trondheim Trondheim hoyer.no Moniker Oslo moniker.no Retro Bergen Bergen retro.no Retro Carl Johan Trondheim retro.no Retro Kristiansand Kristiansand retro.no Retro Nordre Trondheim retro ..."}
+{"idx": 7, "title": "Common Projects - Finn din nye favorittsko - Follestad", "date": "", "ddg_snippet": "Common Projects er kjent for sine klassiske sneakers i skinn. Hos Follestad finner du flere ulike modeller, blant andre bestselgeren Achilles Low.", "subpage_snippet": "", "source": "www.follestad.no", "link": "https://www.follestad.no/shop/merker/common-project/", "content": "Common Projects er kjent for sine klassiske sneakers i skinn. Hos Follestad finner du flere ulike modeller, blant andre bestselgeren Achilles Low."}
+{"idx": 8, "title": "Common Projects Sko – Tønnesen 1937", "date": "", "ddg_snippet": "Common Projects sko finner du hos Tønnesen 1937. Se hele utvalget av de klassiske sneakers-merket i vår nettbutikk. Common Projects lager imponerede sko av italiensk kvalitet.", "subpage_snippet": "", "source": "tonnesen1937.no", "link": "https://tonnesen1937.no/collections/common-projects", "content": "Common Projects sko finner du hos Tønnesen 1937. Se hele utvalget av de klassiske sneakers-merket i vår nettbutikk. Common Projects lager imponerede sko av italiensk kvalitet."}
+{"idx": 9, "title": "Common Projects – Moniker Man", "date": "", "ddg_snippet": "Today, the brand continues to set the standard for understated elegance, offering timeless footwear that bridges the gap between casual and refined. We are very happy to offer Common Projects both online and in-store at Moniker Man Oslo.", "subpage_snippet": "", "source": "monikerman.no", "link": "https://monikerman.no/collections/common-projects", "content": "Today, the brand continues to set the standard for understated elegance, offering timeless footwear that bridges the gap between casual and refined. We are very happy to offer Common Projects both online and in-store at Moniker Man Oslo."}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_paper.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a337ce970a0de183cf18f797c85b9d75b192d41
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gaussian splatting - Wikipedia", "date": "", "ddg_snippet": "Video rendered from a 3 D gaussian splatting model. Gaussian splatting is a volume rendering technique that deals with the direct rendering of volume data without converting the data into surface or line primitives.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Gaussian_splatting", "content": "Video rendered from a 3 D gaussian splatting model. Gaussian splatting is a volume rendering technique that deals with the direct rendering of volume data without converting the data into surface or line primitives."}
+{"idx": 1, "title": "[2502.01846] UVGS : Reimagining Unstructured 3 D Gaussian ...", "date": "", "ddg_snippet": "View a PDF of the paper titled UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping , by Aashish Rai and Dilin Wang and Mihir Jain and Nikolaos Sarafianos and Kefan Chen and Srinath Sridhar and Aayush Prakash.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "View a PDF of the paper titled UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping , by Aashish Rai and Dilin Wang and Mihir Jain and Nikolaos Sarafianos and Kefan Chen and Srinath Sridhar and Aayush Prakash."}
+{"idx": 2, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "UVGS , an image-like representation that solves permuta-tion invariance and unstructured nature of discrete 3DGS through spherical mapping , making direct feature extrac-tion possible by organizing unordered points into a coher-ent 2D representation compatible with 2D models. •", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "UVGS , an image-like representation that solves permuta-tion invariance and unstructured nature of discrete 3DGS through spherical mapping , making direct feature extrac-tion possible by organizing unordered points into a coher-ent 2D representation compatible with 2D models. •"}
+{"idx": 3, "title": "(PDF) UVGS : Reimagining Unstructured 3 D Gaussian Splatting ...", "date": "", "ddg_snippet": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation."}
+{"idx": 4, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these...", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/UVGS:-Reimagining-Unstructured-3D-Gaussian-Splatting-using-UV-Mapping-b2086680-3f01-4f55-825b-0c52f7e663bf", "content": "3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these..."}
+{"idx": 5, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "This paper makes it better by using UV mapping - imagine unwrapping a 3 D object like a chocolate wrapper and painting on the flat surface. The researchers created two ways to \"unwrap\" 3 D objects. The first method, spherical mapping , works well for round objects like faces or sculptures.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/uvgs-reimagining-unstructured-3d-gaussian-splatting-using", "content": "This paper makes it better by using UV mapping - imagine unwrapping a 3 D object like a chocolate wrapper and painting on the flat surface. The researchers created two ways to \"unwrap\" 3 D objects. The first method, spherical mapping , works well for round objects like faces or sculptures."}
+{"idx": 6, "title": "UVGS", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .The Super UVGS can be converted to 3DGS object using inverse mapping network and inverse spherical projection.", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/", "content": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .The Super UVGS can be converted to 3DGS object using inverse mapping network and inverse spherical projection."}
+{"idx": 7, "title": "GitHub - graphdeco-inria/ gaussian - splatting : Original reference...", "date": "", "ddg_snippet": "title = { 3 D Gaussian Splatting for Real-Time Radiance Field Rendering}, journal = {ACM Transactions on Graphics}", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/graphdeco-inria/gaussian-splatting", "content": "title = { 3 D Gaussian Splatting for Real-Time Radiance Field Rendering}, journal = {ACM Transactions on Graphics}"}
+{"idx": 8, "title": "Free 3 D Gaussian Splatting Tool | Polycam", "date": "", "ddg_snippet": "Gaussian splatting is a rasterization technique used for 3 D reconstruction and rendering. In essence, it is a method to create photorealistic scenes from a sampling of images.", "subpage_snippet": "", "source": "poly.cam", "link": "https://poly.cam/tools/gaussian-splatting", "content": "Gaussian splatting is a rasterization technique used for 3 D reconstruction and rendering. In essence, it is a method to create photorealistic scenes from a sampling of images."}
+{"idx": 9, "title": "UVGS Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=1uMuXsJScvY", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
diff --git a/data/sampled_jsons/UVGS_paper_Objaverse_dataset_size_sitearxiv.org_year_2024.jsonl b/data/sampled_jsons/UVGS_paper_Objaverse_dataset_size_sitearxiv.org_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dd53d78ce5a4640b324e4bd4403c852675da7622
--- /dev/null
+++ b/data/sampled_jsons/UVGS_paper_Objaverse_dataset_size_sitearxiv.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV...", "date": "", "ddg_snippet": "Table 3. We present quantitative ablation study for number of UVGS layers (K), UVGS map resolution, and the effect of branch-ing in mapping network on the Objaverse 3DGS dataset . Method. PSNR LPIPS UVGS Size .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846", "content": "Table 3. We present quantitative ablation study for number of UVGS layers (K), UVGS map resolution, and the effect of branch-ing in mapping network on the Objaverse 3DGS dataset . Method. PSNR LPIPS UVGS Size ."}
+{"idx": 1, "title": "[2212.08051] Objaverse : A Universe of Annotated 3D Objects", "date": "", "ddg_snippet": "View a PDF of the paper titled Objaverse : A Universe of Annotated 3D Objects, by Matt Deitke and 9 other authors.Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.08051", "content": "View a PDF of the paper titled Objaverse : A Universe of Annotated 3D Objects, by Matt Deitke and 9 other authors.Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations."}
+{"idx": 2, "title": "Objaverse ++: Curated 3D Object Dataset with Quality Annotations", "date": "", "ddg_snippet": "This paper presents Objaverse ++, a curated subset of Objaverse enhanced with detailed attribute annotations by human experts.View a PDF of the paper titled Objaverse ++: Curated 3D Object Dataset with Quality Annotations, by Chendi Lin and 8 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.07334", "content": "This paper presents Objaverse ++, a curated subset of Objaverse enhanced with detailed attribute annotations by human experts.View a PDF of the paper titled Objaverse ++: Curated 3D Object Dataset with Quality Annotations, by Chendi Lin and 8 other authors."}
+{"idx": 3, "title": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV...", "date": "", "ddg_snippet": "Table 1: PSNR and LPIPS comparison for various reconstruction methods using UVGS and Super UVGS representations on Objaverse Cars and Full datasets . AE, VAE, VQVAE are pretrained image based models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v2", "content": "Table 1: PSNR and LPIPS comparison for various reconstruction methods using UVGS and Super UVGS representations on Objaverse Cars and Full datasets . AE, VAE, VQVAE are pretrained image based models."}
+{"idx": 4, "title": "[2307.05663] Objaverse -XL: A Universe of 10M+ 3D Objects", "date": "", "ddg_snippet": "View a PDF of the paper titled Objaverse -XL: A Universe of 10M+ 3D Objects, by Matt Deitke and 16 other authors.In this work, we present Objaverse -XL, a dataset of over 10 million 3D objects.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.05663", "content": "View a PDF of the paper titled Objaverse -XL: A Universe of 10M+ 3D Objects, by Matt Deitke and 16 other authors.In this work, we present Objaverse -XL, a dataset of over 10 million 3D objects."}
+{"idx": 5, "title": "Objaverse ++: Curated 3D Object Dataset with Quality Annotations", "date": "", "ddg_snippet": "This paper presents Objaverse ++, a curated subset of Objaverse enhanced with detailed attribute annotations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.07334v1", "content": "This paper presents Objaverse ++, a curated subset of Objaverse enhanced with detailed attribute annotations."}
+{"idx": 6, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "3 Feb 2025 — Through our experiments, we found that UV maps of size 512 × 512 512 512 512\\times 512 512 × 512 are sufficient to represent objects in our ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v1", "content": "3 Feb 2025 — Through our experiments, we found that UV maps of size 512 × 512 512 512 512\\times 512 512 × 512 are sufficient to represent objects in our ..."}
+{"idx": 7, "title": "知乎 - 有问题，就会有答案", "date": "", "ddg_snippet": "知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容，聚集了中文互联网科技、商业、影视 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/", "content": "知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容，聚集了中文互联网科技、商业、影视 ..."}
+{"idx": 8, "title": "CMA、ECMWF、GFS、ICON、GEM、UKMO、ARPEG几个天气预报数值模式各有什...", "date": "", "ddg_snippet": "天气预报数值模式是利用计算机模型预测天气变化的技术。 全球有许多知名的数值天气预报模式，包括CMA、ECMWF、GFS、ICON、GEM、UKMO和ARPEG等。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/655967405?write", "content": "天气预报数值模式是利用计算机模型预测天气变化的技术。 全球有许多知名的数值天气预报模式，包括CMA、ECMWF、GFS、ICON、GEM、UKMO和ARPEG等。"}
+{"idx": 9, "title": "有哪些可以在Windows10系统上用的极简无广告的电脑天气应用（可以设置...", "date": "", "ddg_snippet": "前面已经提到的Windows自带的“ MSN 天气 ”即为极简无广告的，桌面上显示不了，Windows 10特性，把桌面插件取消了。 但可以在开始菜单的磁贴上或 锁屏界面 显示。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/370618107", "content": "前面已经提到的Windows自带的“ MSN 天气 ”即为极简无广告的，桌面上显示不了，Windows 10特性，把桌面插件取消了。 但可以在开始菜单的磁贴上或 锁屏界面 显示。"}
diff --git a/data/sampled_jsons/UVGS_paper_custom_Objaverse_3DGS_dataset_size_number_of_objects.jsonl b/data/sampled_jsons/UVGS_paper_custom_Objaverse_3DGS_dataset_size_number_of_objects.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bb374d1aefbe9f2515efe595d76e8f187b01a374
--- /dev/null
+++ b/data/sampled_jsons/UVGS_paper_custom_Objaverse_3DGS_dataset_size_number_of_objects.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "The existing methods fail to di-rectly generate a large number of Gaussians (e.g. 100K+) to represent objects with suficient quality either due to the lack of 3D generative model architectures that support such large number of unstructured points, or due to the lack of a large 3DGS dataset [28, 40, 44, 55, 63].", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "The existing methods fail to di-rectly generate a large number of Gaussians (e.g. 100K+) to represent objects with suficient quality either due to the lack of 3D generative model architectures that support such large number of unstructured points, or due to the lack of a large 3DGS dataset [28, 40, 44, 55, 63]."}
+{"idx": 1, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "3 Feb 2025 — To this end, we decided to create a custom large scale dataset by converting the Objaverse [10] mesh data into 3DGS representation 1 1 1Sketchfab ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v1", "content": "3 Feb 2025 — To this end, we decided to create a custom large scale dataset by converting the Objaverse [10] mesh data into 3DGS representation 1 1 1Sketchfab ..."}
+{"idx": 2, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "Dataset : Objaverse 3DGS Dataset . Description: A custom large-scale dataset created by converting Objaverse mesh data into 3D Gaussian Splatting ( 3DGS ) ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/105135", "content": "Dataset : Objaverse 3DGS Dataset . Description: A custom large-scale dataset created by converting Objaverse mesh data into 3D Gaussian Splatting ( 3DGS ) ..."}
+{"idx": 3, "title": "Daily Papers", "date": "", "ddg_snippet": "5 days ago — ... dataset of 3DGS using the commonly used ShapeNet and ModelNet datasets . Our dataset ShapeSplat consists of 65K objects from 87 unique ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=domain+generalizable+3DSS", "content": "5 days ago — ... dataset of 3DGS using the commonly used ShapeNet and ModelNet datasets . Our dataset ShapeSplat consists of 65K objects from 87 unique ..."}
+{"idx": 4, "title": "Daily Papers", "date": "", "ddg_snippet": "5 days ago — 3D Gaussian splatting (3DGS) has enabled various applications in 3D scene representation and novel view synthesis due to its efficient ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=compositional+3DGS+refinement+module", "content": "5 days ago — 3D Gaussian splatting (3DGS) has enabled various applications in 3D scene representation and novel view synthesis due to its efficient ..."}
+{"idx": 5, "title": "Daily Papers - Fast360", "date": "", "ddg_snippet": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their ...", "subpage_snippet": "", "source": "www.aifasthub.com", "link": "https://www.aifasthub.com/papers?q=UV", "content": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their ..."}
+{"idx": 6, "title": "GitHub - Li-Hao-yuan/UniGS: Paper: UniGS: Unified Language-Image-3D ...", "date": "", "ddg_snippet": "For example, MVImgNet is saved in COLMAP format, thus can skip the process of converting data format. Meanwhile, the optimization commands for different data sets are different in \"sample_.py\". We thanks Zero123 for [the rendered images of objaverse , you can download it from the Dataset ( Objaverse Renderings) part of Zero123.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Li-Hao-yuan/UniGS", "content": "For example, MVImgNet is saved in COLMAP format, thus can skip the process of converting data format. Meanwhile, the optimization commands for different data sets are different in \"sample_.py\". We thanks Zero123 for [the rendered images of objaverse , you can download it from the Dataset ( Objaverse Renderings) part of Zero123."}
+{"idx": 7, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 8, "title": "uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025", "date": "", "ddg_snippet": "VGS maps, and the resolution of UVGS maps. We performed our experiments on our custom Objaverse [10] 3DGS dataset and evaluate the performance of o r model in terms of PSNR, SSIM, and LP", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846v1", "content": "VGS maps, and the resolution of UVGS maps. We performed our experiments on our custom Objaverse [10] 3DGS dataset and evaluate the performance of o r model in terms of PSNR, SSIM, and LP"}
+{"idx": 9, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "Table 3: We present quantitative ablation study for number of UVGS layers (K), UVGS map resolution, and the effect of branching in mapping network on the Objaverse 3DGS dataset .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v3", "content": "Table 3: We present quantitative ablation study for number of UVGS layers (K), UVGS map resolution, and the effect of branching in mapping network on the Objaverse 3DGS dataset ."}
diff --git a/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020.jsonl b/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2155ed956eb1cb1a7c4ffa7a0710eb0bc89eb837
--- /dev/null
+++ b/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "by Y Saito · 2020 · Cited by 52 — In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3409256.3409812", "content": "by Y Saito · 2020 · Cited by 52 — In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ..."}
+{"idx": 1, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/2020/ictir2020/", "content": "In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ..."}
+{"idx": 2, "title": "Unbiased Pairwise Learning from Implicit Feedback for ...", "date": "", "ddg_snippet": "Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539618.3592077", "content": "Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback ."}
+{"idx": 3, "title": "Unbiased Pairwise Learning from Implicit Feedback for ...", "date": "", "ddg_snippet": "by Y Ren · 2023 · Cited by 7 — We design an effective unbiased pairwise learning algorithm for implicit feedback with theoretically lower variance than the existing approach.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.05066", "content": "by Y Ren · 2023 · Cited by 7 — We design an effective unbiased pairwise learning algorithm for implicit feedback with theoretically lower variance than the existing approach."}
+{"idx": 4, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training."}
+{"idx": 5, "title": "Unbiased Recommender Learning from Biased Graded ...", "date": "", "ddg_snippet": "by Y Saito · 2020 · Cited by 1 — [15] Yuta Saito. 2020. Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory ...", "subpage_snippet": "", "source": "decisionmaking4ir.github.io", "link": "https://decisionmaking4ir.github.io/WSDM-2022/papers/Suguru.pdf", "content": "by Y Saito · 2020 · Cited by 1 — [15] Yuta Saito. 2020. Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory ..."}
+{"idx": 6, "title": "Yuta Saito", "date": "", "ddg_snippet": "Unbiased pairwise learning from biased implicit feedback . Y Saito. Proceedings of the 2020 ACM SIGIR on International Conference on Theory of …, 2020. 73*, 2020.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=pw4hwS8AAAAJ&hl=en", "content": "Unbiased pairwise learning from biased implicit feedback . Y Saito. Proceedings of the 2020 ACM SIGIR on International Conference on Theory of …, 2020. 73*, 2020."}
+{"idx": 7, "title": "2020 | Yuta Saito", "date": "", "ddg_snippet": "2020. Doubly Robust Estimator for Ranking Metrics ... Yuta Saito. Cite Code Slides Proceedings · Unbiased Pairwise Learning from Biased Implicit Feedback .", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/tag/2020/", "content": "2020. Doubly Robust Estimator for Ranking Metrics ... Yuta Saito. Cite Code Slides Proceedings · Unbiased Pairwise Learning from Biased Implicit Feedback ."}
+{"idx": 8, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "To address this issue, we introduce WeaklyRec, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "To address this issue, we introduce WeaklyRec, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, ..."}
+{"idx": 9, "title": "Bilateral Self-unbiased Learning from Biased Implicit ...", "date": "", "ddg_snippet": "by J Lee · 2022 · Cited by 16 — Specifically, BISER consists of two key components: (i) self-inverse propensity weighting (SIPW) to grad- ually mitigate the bias of items ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.12660", "content": "by J Lee · 2022 · Cited by 16 — Specifically, BISER consists of two key components: (i) self-inverse propensity weighting (SIPW) to grad- ually mitigate the bias of items ..."}
diff --git a/data/sampled_jsons/Unbiased_Recommender_Learning_Implicit_Feedback_Weakly_Supervised_Learning_equation_6_empirical_risk.jsonl b/data/sampled_jsons/Unbiased_Recommender_Learning_Implicit_Feedback_Weakly_Supervised_Learning_equation_6_empirical_risk.jsonl
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index 0000000000000000000000000000000000000000..97e416650dd1ff89d23819e6df856b83358b011e
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "To address this issue, we introduce WeaklyRec, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0E5rZOGA13", "content": "To address this issue, we introduce WeaklyRec, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior."}
+{"idx": 1, "title": "PDF Unbiased Recommender Learning from Biased Graded Implicit Feedback", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . In Proceedings of the 13th International Conference on Web Search and Data Mining. 501-509.", "subpage_snippet": "", "source": "decisionmaking4ir.github.io", "link": "https://decisionmaking4ir.github.io/WSDM-2022/papers/Suguru.pdf", "content": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . In Proceedings of the 13th International Conference on Web Search and Data Mining. 501-509."}
+{"idx": 2, "title": "Unbiased Recommender Learning from Biased Graded Implicit Feedback", "date": "", "ddg_snippet": "Binary user-behavior logs such as clicks or views, called implicit feedback , are often used to build recommender systems because of its general availability in real practice. Most existing studies formulate implicit feedback as binary relevance feedback . However, in numerous applications, implicit feedback is observed not only as a binary indicator but also in a graded form, such as the number ...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/workshops/wsdm2022/", "content": "Binary user-behavior logs such as clicks or views, called implicit feedback , are often used to build recommender systems because of its general availability in real practice. Most existing studies formulate implicit feedback as binary relevance feedback . However, in numerous applications, implicit feedback is observed not only as a binary indicator but also in a graded form, such as the number ..."}
+{"idx": 3, "title": "Dual Unbiased Recommender Learning for Implicit Feedback", "date": "", "ddg_snippet": "Abstract Unbiased recommender learning has been actively studied to alleviate the inherent bias of implicit datasets under the missing-not-at-random assumption. Existing studies solely address the bias of positive feedback but do not account for the bias of missing feedback , which heavily affects their sub-optimal performance gains.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3404835.3463118", "content": "Abstract Unbiased recommender learning has been actively studied to alleviate the inherent bias of implicit datasets under the missing-not-at-random assumption. Existing studies solely address the bias of positive feedback but do not account for the bias of missing feedback , which heavily affects their sub-optimal performance gains."}
+{"idx": 4, "title": "Practically Unbiased Pairwise Loss for Recommendation With Implicit ...", "date": "", "ddg_snippet": "Notably, recommender systems differ slightly from ordinary supervised learning tasks. In recommender systems, there is an exposure mechanism that decides which items could be presented to each specific user, which breaks the i.i.d assumption of supervised learning and brings biases into the recommendation models.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/40030662/", "content": "Notably, recommender systems differ slightly from ordinary supervised learning tasks. In recommender systems, there is an exposure mechanism that decides which items could be presented to each specific user, which breaks the i.i.d assumption of supervised learning and brings biases into the recommendation models."}
+{"idx": 5, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "Poster Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning Eric Wang · Zhichao Chen · Haotian Wang · Yanchao Tan · Licheng Pan · Tianqiao Liu · Xu Chen · Haoxuan Li · Zhouchen Lin West Exhibition Hall B2-B3 #W-403", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "Poster Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning Eric Wang · Zhichao Chen · Haotian Wang · Yanchao Tan · Licheng Pan · Tianqiao Liu · Xu Chen · Haoxuan Li · Zhouchen Lin West Exhibition Hall B2-B3 #W-403"}
+{"idx": 6, "title": "Unbiased Pairwise Learning from Implicit Feedback for Recommender ...", "date": "", "ddg_snippet": "Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback . Moreover, because of its general availability, we see wide adoption of implicit feedback data, such as click signal. There are mainly two challenges for the application of implicit feedback . First, implicit data just includes positive feedback ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.05066", "content": "Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback . Moreover, because of its general availability, we see wide adoption of implicit feedback data, such as click signal. There are mainly two challenges for the application of implicit feedback . First, implicit data just includes positive feedback ..."}
+{"idx": 7, "title": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback", "date": "", "ddg_snippet": "This repository accompanies the real-world experiment conducted in the paper \" Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback \" by Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata, which has been accepted to WSDM'20. If you find this code useful ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/usaito/unbiased-implicit-rec-real", "content": "This repository accompanies the real-world experiment conducted in the paper \" Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback \" by Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata, which has been accepted to WSDM'20. If you find this code useful ..."}
+{"idx": 8, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13", "content": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback ."}
+{"idx": 9, "title": "arXiv:2304.05066v2 [cs.IR] 14 Apr 2023", "date": "", "ddg_snippet": "1 INTRODUCTION Recommender systems usually rely on implicit user feedback for model training owning to the cheap cost of collecting such data [17]. For this scenario, the typical model learning techniques [10, 14, 19], recognize interacted items as positive and all the other items as potential negative examples. There are mainly two dif-ficulties to learn unbiased user preference based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.05066", "content": "1 INTRODUCTION Recommender systems usually rely on implicit user feedback for model training owning to the cheap cost of collecting such data [17]. For this scenario, the typical model learning techniques [10, 14, 19], recognize interacted items as positive and all the other items as potential negative examples. There are mainly two dif-ficulties to learn unbiased user preference based on ..."}
diff --git a/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning_full_text.jsonl b/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning_full_text.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a22de320358d997fa52ff8bffdcb93733c565b55
--- /dev/null
+++ b/data/sampled_jsons/Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning_full_text.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "To address this issue, we introduce WeaklyRec, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=0E5rZOGA13&name=pdf", "content": "To address this issue, we introduce WeaklyRec, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior."}
+{"idx": 1, "title": "PDF Unbiased Recommender Learning from Biased Graded Implicit Feedback", "date": "", "ddg_snippet": "To better utilize graded implicit feedback , we for-mulate a recommendation using the MNAR graded implicit feed-back from a statistical estimation perspective, which allows us to theoretically characterize the bias in using the graded implicit feed-back .", "subpage_snippet": "", "source": "decisionmaking4ir.github.io", "link": "https://decisionmaking4ir.github.io/WSDM-2022/papers/Suguru.pdf", "content": "To better utilize graded implicit feedback , we for-mulate a recommendation using the MNAR graded implicit feed-back from a statistical estimation perspective, which allows us to theoretically characterize the bias in using the graded implicit feed-back ."}
+{"idx": 2, "title": "Unbiased Recommender Learning from Biased Graded Implicit Feedback", "date": "", "ddg_snippet": "Binary user-behavior logs such as clicks or views, called implicit feedback , are often used to build recommender systems because of its general availability in real practice. Most existing studies formulate implicit feedback as binary relevance feedback . However, in numerous applications, implicit feedback is observed not only as a binary indicator but also in a graded form, such as the number ...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/workshops/wsdm2022/", "content": "Binary user-behavior logs such as clicks or views, called implicit feedback , are often used to build recommender systems because of its general availability in real practice. Most existing studies formulate implicit feedback as binary relevance feedback . However, in numerous applications, implicit feedback is observed not only as a binary indicator but also in a graded form, such as the number ..."}
+{"idx": 3, "title": "Practically Unbiased Pairwise Loss for Recommendation With Implicit ...", "date": "", "ddg_snippet": "Recommender systems have been widely employed on various online platforms to improve user experience. In these systems, recommendation models are often learned from the users' historical behaviors that are automatically collected. Notably, recommender systems differ slightly from ordinary supervised learning tasks. In recommender systems, there is an exposure mechanism that decides which ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10810273", "content": "Recommender systems have been widely employed on various online platforms to improve user experience. In these systems, recommendation models are often learned from the users' historical behaviors that are automatically collected. Notably, recommender systems differ slightly from ordinary supervised learning tasks. In recommender systems, there is an exposure mechanism that decides which ..."}
+{"idx": 4, "title": "Dual Unbiased Recommender Learning for Implicit Feedback", "date": "", "ddg_snippet": "Abstract Unbiased recommender learning has been actively studied to alleviate the inherent bias of implicit datasets under the missing-not-at-random assumption. Existing studies solely address the bias of positive feedback but do not account for the bias of missing feedback , which heavily affects their sub-optimal performance gains.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3404835.3463118", "content": "Abstract Unbiased recommender learning has been actively studied to alleviate the inherent bias of implicit datasets under the missing-not-at-random assumption. Existing studies solely address the bias of positive feedback but do not account for the bias of missing feedback , which heavily affects their sub-optimal performance gains."}
+{"idx": 5, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13", "content": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback ."}
+{"idx": 6, "title": "Unbiased Pairwise Learning from Implicit Feedback for Recommender ...", "date": "", "ddg_snippet": "Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback . Moreover, because of its general availability, we see wide adoption of implicit feedback data, such as click signal. There are mainly two challenges for the application of implicit feedback . First, implicit data just includes positive feedback ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.05066", "content": "Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback . Moreover, because of its general availability, we see wide adoption of implicit feedback data, such as click signal. There are mainly two challenges for the application of implicit feedback . First, implicit data just includes positive feedback ..."}
+{"idx": 7, "title": "WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation ...", "date": "", "ddg_snippet": "ABSTRACT Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distin-guish items in future behaviors from others based on the user's his-torical behaviors, have attracted a lot of interest in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.13616", "content": "ABSTRACT Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distin-guish items in future behaviors from others based on the user's his-torical behaviors, have attracted a lot of interest in ..."}
+{"idx": 8, "title": "WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation ...", "date": "", "ddg_snippet": "Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distinguish items in future behaviors from others based on the user's historical behaviors, have attracted a lot of interest in both industry ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.13616v1", "content": "Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distinguish items in future behaviors from others based on the user's historical behaviors, have attracted a lot of interest in both industry ..."}
+{"idx": 9, "title": "arXiv:2304.05066v2 [cs.IR] 14 Apr 2023", "date": "", "ddg_snippet": "1 INTRODUCTION Recommender systems usually rely on implicit user feedback for model training owning to the cheap cost of collecting such data [17]. For this scenario, the typical model learning techniques [10, 14, 19], recognize interacted items as positive and all the other items as potential negative examples. There are mainly two dif-ficulties to learn unbiased user preference based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.05066", "content": "1 INTRODUCTION Recommender systems usually rely on implicit user feedback for model training owning to the cheap cost of collecting such data [17]. For this scenario, the typical model learning techniques [10, 14, 19], recognize interacted items as positive and all the other items as potential negative examples. There are mainly two dif-ficulties to learn unbiased user preference based on ..."}
diff --git a/data/sampled_jsons/Universal_Transformers_Dehghani_et_al._2019_abstract.jsonl b/data/sampled_jsons/Universal_Transformers_Dehghani_et_al._2019_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7a25971f212fb9facaeb44cb46ee0c8a4eb37d68
--- /dev/null
+++ b/data/sampled_jsons/Universal_Transformers_Dehghani_et_al._2019_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[PDF] Universal Transformers | Semantic Scholar", "date": "", "ddg_snippet": "The Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses issues of parallelizability and global receptive field, is proposed. Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Universal-Transformers-Dehghani-Gouws/ac4dafdef1d2b685b7f28a11837414573d39ff4e", "content": "The Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses issues of parallelizability and global receptive field, is proposed. Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for ..."}
+{"idx": 1, "title": "Universal Transformers - Google Research", "date": "", "ddg_snippet": "Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/universal-transformers/", "content": "Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset."}
+{"idx": 2, "title": "Universal Transformers | Request PDF - ResearchGate", "date": "", "ddg_snippet": "Request PDF | Universal Transformers | Self-attentive feed-forward sequence models have been shown to achieve impressive results on sequence modeling tasks, thereby presenting a... | Find, read ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/326343192_Universal_Transformers", "content": "Request PDF | Universal Transformers | Self-attentive feed-forward sequence models have been shown to achieve impressive results on sequence modeling tasks, thereby presenting a... | Find, read ..."}
+{"idx": 3, "title": "\"Universal Transformers.\" - dblp", "date": "", "ddg_snippet": "[+] [-] \" Universal Transformers .\" Mostafa Dehghani et al. ( 2019 ) Dagstuhl > Home [-] Details and statistics", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/DehghaniGVUK19", "content": "[+] [-] \" Universal Transformers .\" Mostafa Dehghani et al. ( 2019 ) Dagstuhl > Home [-] Details and statistics"}
+{"idx": 4, "title": "Universal Transformers | Awesome LLM Papers", "date": "", "ddg_snippet": "Universal Transformers Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser . Arxiv 2018 - 414 citations [Paper] Datasets Ethics & Fairness Model Architecture Time Series Training Techniques Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks ...", "subpage_snippet": "", "source": "awesome-llm-papers.github.io", "link": "https://awesome-llm-papers.github.io/publications/dehghani2018universal/", "content": "Universal Transformers Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser . Arxiv 2018 - 414 citations [Paper] Datasets Ethics & Fairness Model Architecture Time Series Training Techniques Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks ..."}
+{"idx": 5, "title": "US10740433B2 - Universal transformers - Google Patents", "date": "", "ddg_snippet": "This specification describes systems that implement a Universal Transformer . Universal Transformers address, among others, the shortcomings described in the Background, above.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US10740433B2/en", "content": "This specification describes systems that implement a Universal Transformer . Universal Transformers address, among others, the shortcomings described in the Background, above."}
+{"idx": 6, "title": "Universal Transformers", "date": "", "ddg_snippet": "In each recurrent step, the Universal Transformer iteratively refines its representations for all symbols in the sequence in parallel using a self-attention mechanism (Parikh et al ., 2016; Lin et al ., 2017), followed by a transformation (shared across all positions and time-steps) consisting of a depth-wise separable convolution (Chollet, 2016 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1807.03819", "content": "In each recurrent step, the Universal Transformer iteratively refines its representations for all symbols in the sequence in parallel using a self-attention mechanism (Parikh et al ., 2016; Lin et al ., 2017), followed by a transformation (shared across all positions and time-steps) consisting of a depth-wise separable convolution (Chollet, 2016 ..."}
+{"idx": 7, "title": "Universal Transformers Dehghani; Mostafa ; et al. [Google LLC]", "date": "", "ddg_snippet": "U.S. patent application number 16/417587 was filed with the patent office on 2019 -11-21 for universal transformers . The applicant listed for this patent is Google LLC. Invention is credited to Mostafa Dehghani , Stephan Gouws, Lukasz Mieczyslaw Kaiser, Jakob D. Uszkoreit, Oriol Vinyals.", "subpage_snippet": "", "source": "uspto.report", "link": "https://uspto.report/patent/app/20190354567", "content": "U.S. patent application number 16/417587 was filed with the patent office on 2019 -11-21 for universal transformers . The applicant listed for this patent is Google LLC. Invention is credited to Mostafa Dehghani , Stephan Gouws, Lukasz Mieczyslaw Kaiser, Jakob D. Uszkoreit, Oriol Vinyals."}
+{"idx": 8, "title": "[1807.03819] Universal Transformers - arXiv.org", "date": "", "ddg_snippet": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1807.03819", "content": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as ..."}
+{"idx": 9, "title": "Universal Transformers - NASA/ADS", "date": "", "ddg_snippet": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2018arXiv180703819D/abstract", "content": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as ..."}
diff --git "a/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Remark_4.2_temperature_\317\204_median_percent.jsonl" "b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Remark_4.2_temperature_\317\204_median_percent.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..20c0615a10f8edeb88ccb929db68165c9c0907b6
--- /dev/null
+++ "b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Remark_4.2_temperature_\317\204_median_percent.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "On this particular front, the major challenge in standard, unregu-lated fine-tuning is the catastrophic forgetting phenomenon. In broad terms, it describes the performance decline of the pre-trained model on previously observed data/tasks after fine-tuning on a new one.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797", "content": "On this particular front, the major challenge in standard, unregu-lated fine-tuning is the catastrophic forgetting phenomenon. In broad terms, it describes the performance decline of the pre-trained model on previously observed data/tasks after fine-tuning on a new one."}
+{"idx": 1, "title": "PDF An Efcient Rehearsal Scheme for Catastrophic Forgetting Mitigation ...", "date": "", "ddg_snippet": "Figure 2 : Preliminary observations suggest that while random rehearsal of prior data helps mitigate collateral damage, upweighting collateral damage samples in the prior data distribution benets the joint performance on both tasks even more.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.138.pdf", "content": "Figure 2 : Preliminary observations suggest that while random rehearsal of prior data helps mitigate collateral damage, upweighting collateral damage samples in the prior data distribution benets the joint performance on both tasks even more."}
+{"idx": 2, "title": "Fine Tuning without Catastrophic Forgetting via Selective Low Rank ...", "date": "", "ddg_snippet": "While fine-tuning enables domain-specific adaptation, it can reduce robustness to distribution shifts, impacting out-of-distribution (OOD) performance.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388421884_Fine_Tuning_without_Catastrophic_Forgetting_via_Selective_Low_Rank_Adaptation", "content": "While fine-tuning enables domain-specific adaptation, it can reduce robustness to distribution shifts, impacting out-of-distribution (OOD) performance."}
+{"idx": 3, "title": "PDF Scaling Laws for Forgetting during Finetuning with Pretraining Data ...", "date": "", "ddg_snippet": "This fine-tuning dataset typically encompasses data from a single domain, exhibiting a significant distribution shift compared to the base dataset. Transferring the model knowledge to the new distributions can lead to significant capability loss on the previous dataset, a phenomenon known as forgetting (Luo et al., 2023; Kalajdzievski, 2024).", "subpage_snippet": "", "source": "david.grangier.info", "link": "https://david.grangier.info/papers/2025/bethune2025finetuning.pdf", "content": "This fine-tuning dataset typically encompasses data from a single domain, exhibiting a significant distribution shift compared to the base dataset. Transferring the model knowledge to the new distributions can lead to significant capability loss on the previous dataset, a phenomenon known as forgetting (Luo et al., 2023; Kalajdzievski, 2024)."}
+{"idx": 4, "title": "Analyzing and Reducing Catastrophic Forgetting in Parameter Eficient Tuning", "date": "", "ddg_snippet": "Abstract Despite remarkable performance of large lan-guage models (LLMs), when continually fine-tuning them on complex and diverse tasks, their performance on historical tasks decreases dra-matically, known as the catastrophic forget-ting problem. Existing works explored strate-gies like memory replay, regularization and parameter isolation, but little analysis were conducted over the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=wyWkUe13dW", "content": "Abstract Despite remarkable performance of large lan-guage models (LLMs), when continually fine-tuning them on complex and diverse tasks, their performance on historical tasks decreases dra-matically, known as the catastrophic forget-ting problem. Existing works explored strate-gies like memory replay, regularization and parameter isolation, but little analysis were conducted over the ..."}
+{"idx": 5, "title": "Overcoming Catastrophic Forgetting for Fine-Tuning Pre-trained GANs", "date": "", "ddg_snippet": "However, we observe that such a scheme is much less effective for GAN fine-tuning , as it tends to hinder a full adaptation to the target distribution. In order to address the catastrophic forgetting in GAN fine-tuning , we need to answer the following two fundamental questions.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-43424-2_18", "content": "However, we observe that such a scheme is much less effective for GAN fine-tuning , as it tends to hinder a full adaptation to the target distribution. In order to address the catastrophic forgetting in GAN fine-tuning , we need to answer the following two fundamental questions."}
+{"idx": 6, "title": "Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual ...", "date": "", "ddg_snippet": "4 REINFORCEMENT FINE-TUNING MITIGATES FORGETTING IN CPT This section presents our comparative results comparing RFT and SFT in a continual post-training scenario.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.05386", "content": "4 REINFORCEMENT FINE-TUNING MITIGATES FORGETTING IN CPT This section presents our comparative results comparing RFT and SFT in a continual post-training scenario."}
+{"idx": 7, "title": "PDF Overcoming Catastrophic Forgetting for Fine-Tuning Pre ... - Springer", "date": "", "ddg_snippet": "As reported in recent empirical studies, fine-tuning GANs faces the similar challenge of catastrophic forgetting as in supervised transfer learning. This causes a severe capacity loss of the pre-trained model when adapting it to downstream datasets.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-43424-2_18.pdf?pdf=inline+link", "content": "As reported in recent empirical studies, fine-tuning GANs faces the similar challenge of catastrophic forgetting as in supervised transfer learning. This causes a severe capacity loss of the pre-trained model when adapting it to downstream datasets."}
+{"idx": 8, "title": "PDF Preventing Catastrophic Forgetting and Distribution Mismatch in ...", "date": "", "ddg_snippet": "A data-free KD framework that mitigates catastrophic forgetting by keeping memory of generated samples over iterations. Preventing possible mismatches between the gener-ated and the original data distributions with an en-hanced sample generation strategy that improves upon the state-of-the-art.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2022/papers/Binici_Preventing_Catastrophic_Forgetting_and_Distribution_Mismatch_in_Knowledge_Distillation_via_WACV_2022_paper.pdf", "content": "A data-free KD framework that mitigates catastrophic forgetting by keeping memory of generated samples over iterations. Preventing possible mismatches between the gener-ated and the original data distributions with an en-hanced sample generation strategy that improves upon the state-of-the-art."}
+{"idx": 9, "title": "arXiv:2502.02797v1 [cs.LG] 5 Feb 2025", "date": "", "ddg_snippet": "arXiv:2502.02797v1 [cs.LG] 5 Feb 2025 Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797v1", "content": "arXiv:2502.02797v1 [cs.LG] 5 Feb 2025 Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting"}
diff --git a/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_4_LoRA+_average_score_sitearxiv.o_year_2024.jsonl b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_4_LoRA+_average_score_sitearxiv.o_year_2024.jsonl
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index 0000000000000000000000000000000000000000..a1bb3fd933773551f10b96e4e69a5dd22228355c
--- /dev/null
+++ b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_4_LoRA+_average_score_sitearxiv.o_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting arXiv:2502.02797v1 [cs.LG] 5 Feb 2025 Reinforcement Fine-Tuning Naturally Mitigates Forgetting in ... Reinforcement Fine-Tuning Naturally Mitigates Forgetting in ... Context-Free Synthetic Data Mitigates Forgetting - arXiv.org [2402.02868] Fine-tuning Reinforcement Learning Models is ...", "date": "", "ddg_snippet": "Feb 5, 2025 · Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses. To address this challenge, we propose a sample weighting scheme for the fine-tuning datasolely based on the pre-trained model’s losses. Specifically, we upweight the easy samples on which the pre-trained model’s loss is low and vice versa to limit the drift from the pre-trained model. tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model’s losses. Specifically, we upweight the easy samples on which the pre-trained model’s loss is low and vice versa to limit Jul 7, 2025 · View a PDF of the paper titled Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training, by Song Lai and 12 other authors 4 REINFORCEMENT FINE-TUNING MITIGATES FORGETTING IN CPT This section presents our comparative results comparing RFT and SFT in a continual post-training scenario. May 20, 2025 · We show that augmenting a fine-tuning dataset with context-free generations mitigates forgetting , in two settings: (a) preserving the zero-shot performance of pretrained-only models, and (b) preserving the reasoning performance of thinking models. We show that contextual synthetic data, and even a portion of the pretraining data, are less ... Feb 5, 2024 · Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models. However, fine-tuning reinforcement learning (RL) models remains a challenge. This work conceptualizes one specific cause of poor transfer, accentuated in the RL setting by the interplay between actions and observations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02797", "content": "Feb 5, 2025 · Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses. To address this challenge, we propose a sample weighting scheme for the fine-tuning datasolely based on the pre-trained model’s losses. Specifically, we upweight the easy samples on which the pre-trained model’s loss is low and vice versa to limit the drift from the pre-trained model. tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model’s losses. Specifically, we upweight the easy samples on which the pre-trained model’s loss is low and vice versa to limit Jul 7, 2025 · View a PDF of the paper titled Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training, by Song Lai and 12 other authors 4 REINFORCEMENT FINE-TUNING MITIGATES FORGETTING IN CPT This section presents our comparative results comparing RFT and SFT in a continual post-training scenario. May 20, 2025 · We show that augmenting a fine-tuning dataset with context-free generations mitigates forgetting , in two settings: (a) preserving the zero-shot performance of pretrained-only models, and (b) preserving the reasoning performance of thinking models. We show that contextual synthetic data, and even a portion of the pretraining data, are less ... Feb 5, 2024 · Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models. However, fine-tuning reinforcement learning (RL) models remains a challenge. This work conceptualizes one specific cause of poor transfer, accentuated in the RL setting by the interplay between actions and observations ..."}
+{"idx": 1, "title": "Context-Free Synthetic Data Mitigates Forgetting - arXiv.org", "date": "", "ddg_snippet": "May 20, 2025 · We show that augmenting a fine-tuning dataset with context-free generations mitigates forgetting , in two settings: (a) preserving the zero-shot performance of pretrained-only models, and (b) preserving the reasoning performance of thinking models. We show that contextual synthetic data, and even a portion of the pretraining data, are less ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13811v1", "content": "May 20, 2025 · We show that augmenting a fine-tuning dataset with context-free generations mitigates forgetting , in two settings: (a) preserving the zero-shot performance of pretrained-only models, and (b) preserving the reasoning performance of thinking models. We show that contextual synthetic data, and even a portion of the pretraining data, are less ..."}
+{"idx": 2, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "To address this challenge, we propose a sample weighting scheme for the fine-tuning datasolely based on the pre-trained model’s losses. Specifically, we upweight the easy samples on which the pre-trained model’s loss is low and vice versa to limit the drift from the pre-trained model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02797", "content": "To address this challenge, we propose a sample weighting scheme for the fine-tuning datasolely based on the pre-trained model’s losses. Specifically, we upweight the easy samples on which the pre-trained model’s loss is low and vice versa to limit the drift from the pre-trained model."}
+{"idx": 3, "title": "arXiv:2502.02797v1 [cs.LG] 5 Feb 2025", "date": "", "ddg_snippet": "tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model’s losses. Specifically, we upweight the easy samples on which the pre-trained model’s loss is low and vice versa to limit", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797v1", "content": "tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model’s losses. Specifically, we upweight the easy samples on which the pre-trained model’s loss is low and vice versa to limit"}
+{"idx": 4, "title": "Reinforcement Fine-Tuning Naturally Mitigates Forgetting in ...", "date": "", "ddg_snippet": "Jul 7, 2025 · View a PDF of the paper titled Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training, by Song Lai and 12 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2507.05386", "content": "Jul 7, 2025 · View a PDF of the paper titled Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training, by Song Lai and 12 other authors"}
+{"idx": 5, "title": "Reinforcement Fine-Tuning Naturally Mitigates Forgetting in ...", "date": "", "ddg_snippet": "4 REINFORCEMENT FINE-TUNING MITIGATES FORGETTING IN CPT This section presents our comparative results comparing RFT and SFT in a continual post-training scenario.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.05386", "content": "4 REINFORCEMENT FINE-TUNING MITIGATES FORGETTING IN CPT This section presents our comparative results comparing RFT and SFT in a continual post-training scenario."}
+{"idx": 6, "title": "[2402.02868] Fine-tuning Reinforcement Learning Models is ...", "date": "", "ddg_snippet": "Feb 5, 2024 · Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models. However, fine-tuning reinforcement learning (RL) models remains a challenge. This work conceptualizes one specific cause of poor transfer, accentuated in the RL setting by the interplay between actions and observations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.02868", "content": "Feb 5, 2024 · Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models. However, fine-tuning reinforcement learning (RL) models remains a challenge. This work conceptualizes one specific cause of poor transfer, accentuated in the RL setting by the interplay between actions and observations ..."}
+{"idx": 7, "title": "Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Catastrophic Forgetting , Sample Weighting , Fine - tuning , Pre-trained.) to mitigate catastrophic forgetting in the data-oblivious setting. Our key insight is upweighting the “ easy ” samples on which the pre-trained model’s loss is low and vice versa.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02797v2", "content": "Catastrophic Forgetting , Sample Weighting , Fine - tuning , Pre-trained.) to mitigate catastrophic forgetting in the data-oblivious setting. Our key insight is upweighting the “ easy ” samples on which the pre-trained model’s loss is low and vice versa."}
+{"idx": 8, "title": "Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "To address this challenge, we propose a sample weighting scheme for the fine - tuning data solely based on the pre-trained model’s losses. Specifically, we upweight the easy samples on which the pre-trained model’s loss is low and vice versa to limit the drift from the pre-trained model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02797v1", "content": "To address this challenge, we propose a sample weighting scheme for the fine - tuning data solely based on the pre-trained model’s losses. Specifically, we upweight the easy samples on which the pre-trained model’s loss is low and vice versa to limit the drift from the pre-trained model."}
+{"idx": 9, "title": "Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "The sample -wise weights are computed once. 3. Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting . and used throughout the entire fine - tuning process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797", "content": "The sample -wise weights are computed once. 3. Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting . and used throughout the entire fine - tuning process."}
diff --git a/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_4_LoRA+_performance_year_2023-2024.jsonl b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_4_LoRA+_performance_year_2023-2024.jsonl
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index 0000000000000000000000000000000000000000..64b37406523612708c34488f8859b55bf347e820
--- /dev/null
+++ b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_4_LoRA+_performance_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as \"catastrophic forgetting \". This is especially an issue when one does not have access to the data and recipe used to develop the pre-trained model. Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02797", "content": "Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as \"catastrophic forgetting \". This is especially an issue when one does not have access to the data and recipe used to develop the pre-trained model. Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a ..."}
+{"idx": 1, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "TL;DR: We propose a sample -weighting scheme to mitigate catastrophic forgetting during fine-tuning , demonstrating its effectiveness in vision and language tasks while also providing a theoretical analysis for linear models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=13HPTmZKbM", "content": "TL;DR: We propose a sample -weighting scheme to mitigate catastrophic forgetting during fine-tuning , demonstrating its effectiveness in vision and language tasks while also providing a theoretical analysis for linear models."}
+{"idx": 2, "title": "(PDF) Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388754394_Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting", "content": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses."}
+{"idx": 3, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "title={Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting }, author={Sanyal, Sunny and Prairie, Hayden and Das, Rudrajit and Kavis, Ali and Sanghavi, Sujay},", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sanyalsunny111/FLOW_finetuning", "content": "title={Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting }, author={Sanyal, Sunny and Prairie, Hayden and Das, Rudrajit and Kavis, Ali and Sanghavi, Sujay},"}
+{"idx": 4, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "On this particular front, the major challenge in standard, unregu-lated fine-tuning is the catastrophic forgetting phenomenon. In broad terms, it describes the performance decline of the pre-trained model on previously observed data/tasks after fine-tuning on a new one.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797", "content": "On this particular front, the major challenge in standard, unregu-lated fine-tuning is the catastrophic forgetting phenomenon. In broad terms, it describes the performance decline of the pre-trained model on previously observed data/tasks after fine-tuning on a new one."}
+{"idx": 5, "title": "Memory-Retaining Finetuning via Distillation - Apple Machine Learning ...", "date": "", "ddg_snippet": "We propose label annealing, a method that mitigates forgetting during finetuning without requiring access to the original pretraining data. Label annealing distills pretraining knowledge during finetuning by adding a KL divergence term in the loss function, regularizing the divergence between the finetuned model's predictions and those of the ...", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/memory-retaining", "content": "We propose label annealing, a method that mitigates forgetting during finetuning without requiring access to the original pretraining data. Label annealing distills pretraining knowledge during finetuning by adding a KL divergence term in the loss function, regularizing the divergence between the finetuned model's predictions and those of the ..."}
+{"idx": 6, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting ...", "date": "", "ddg_snippet": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model.", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10631493-upweighting-easy-samples-fine-tuning-mitigates-forgetting", "content": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model."}
+{"idx": 7, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Overview Research examines how upweighting easy training samples reduces catastrophic forgetting during fine-tuning Introduces a new technique called Easy Sample Upweighting (ESU) Shows that focusing on simpler examples helps preserve model capabilities Demonstrates improved performance across multiple language tasks Validates findings through extensive experiments on different model ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/upweighting-easy-samples-fine-tuning-mitigates-forgetting", "content": "Overview Research examines how upweighting easy training samples reduces catastrophic forgetting during fine-tuning Introduces a new technique called Easy Sample Upweighting (ESU) Shows that focusing on simpler examples helps preserve model capabilities Demonstrates improved performance across multiple language tasks Validates findings through extensive experiments on different model ..."}
+{"idx": 8, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Spotlight Poster Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Sunny Sanyal · Hayden Prairie · Rudrajit Das · Ali Kavis · Sujay Sanghavi East Exhibition Hall A-B #E-1302", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46655", "content": "Spotlight Poster Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Sunny Sanyal · Hayden Prairie · Rudrajit Das · Ali Kavis · Sujay Sanghavi East Exhibition Hall A-B #E-1302"}
+{"idx": 9, "title": "arXiv:2502.02797v1 [cs.LG] 5 Feb 2025", "date": "", "ddg_snippet": "tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797v1", "content": "tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit"}
diff --git a/data/sampled_jsons/VACB_Unknown-Variance_OLS_log_R_R^2_regret_comparison_table.jsonl b/data/sampled_jsons/VACB_Unknown-Variance_OLS_log_R_R^2_regret_comparison_table.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f29762323682e9dd3859733f4b58dd925330858
--- /dev/null
+++ b/data/sampled_jsons/VACB_Unknown-Variance_OLS_log_R_R^2_regret_comparison_table.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Unknown - Variance OLS (Pacchiano, 2024) VACB (Theorem 3).variance-aware exploration from Zhao et al. (2023b) to propose Variance-Agnostic Catoni Bandit ( VACB ). in Algorithm 2 , where we split the contexts {xt}t∈[T ] into L subsets according to their uncertainty.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Unknown - Variance OLS (Pacchiano, 2024) VACB (Theorem 3).variance-aware exploration from Zhao et al. (2023b) to propose Variance-Agnostic Catoni Bandit ( VACB ). in Algorithm 2 , where we split the contexts {xt}t∈[T ] into L subsets according to their uncertainty."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/VRSBench_GitHub_README_image_size_resolution_year_2024.jsonl b/data/sampled_jsons/VRSBench_GitHub_README_image_size_resolution_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d62b240b5b0872a112156d922887857a0304c96c
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_GitHub_README_image_size_resolution_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - lx709/VRSBench", "date": "", "ddg_snippet": "Jun 19, 2024 · To construct our VRSBench dataset, we employed multiple data engineering steps, including attribute extraction, prompting engineering, GPT-4 inference, and human verification. Attribute Extraction: we extract image information, including the source and resolution , as well as object information—such as the object category, bounding box, color, position (absolute and relative), and size ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "Jun 19, 2024 · To construct our VRSBench dataset, we employed multiple data engineering steps, including attribute extraction, prompting engineering, GPT-4 inference, and human verification. Attribute Extraction: we extract image information, including the source and resolution , as well as object information—such as the object category, bounding box, color, position (absolute and relative), and size ..."}
+{"idx": 1, "title": "Supplementary of VRSBench: A Versatile Benchmark for Vision ...", "date": "", "ddg_snippet": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ..."}
+{"idx": 2, "title": "xiang709/ VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "The image captured from GoogleEarth focuses on a section of an expressway-toll-station and a vehicle, with indeterminable image resolution . VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "The image captured from GoogleEarth focuses on a section of an expressway-toll-station and a vehicle, with indeterminable image resolution . VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding."}
+{"idx": 3, "title": "README.md · xiang709/VRSBench at main - Hugging Face", "date": "", "ddg_snippet": "VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench/blob/main/README.md", "content": "VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 4, "title": "GitHub Pages - VRSBench", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 5, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for ...", "date": "", "ddg_snippet": "Jun 18, 2024 · The data and code can be accessed at https:// vrsbench . github .io. Figure 1: Examples of an image and corresponding annotations in VRSBench dataset. Our annotations include object referring, visual question answering, and detailed captions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v1", "content": "Jun 18, 2024 · The data and code can be accessed at https:// vrsbench . github .io. Figure 1: Examples of an image and corresponding annotations in VRSBench dataset. Our annotations include object referring, visual question answering, and detailed captions."}
+{"idx": 6, "title": "vrsbench.github.io/README.md at main · vrsbench/vrsbench ...", "date": "", "ddg_snippet": "Contribute to vrsbench / vrsbench . github .io development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vrsbench/vrsbench.github.io/blob/main/README.md", "content": "Contribute to vrsbench / vrsbench . github .io development by creating an account on GitHub ."}
+{"idx": 7, "title": "Resize image in Github - README .md - YouTube", "date": "", "ddg_snippet": "Disclaimer: none of this is innovative. Thought I'd gather together what I found in order to help others save time. Struggled to resize an image in github", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=f4ifdKCH7VI", "content": "Disclaimer: none of this is innovative. Thought I'd gather together what I found in order to help others save time. Struggled to resize an image in github"}
+{"idx": 8, "title": "change size of image in github readme Code Example", "date": "", "ddg_snippet": "readme md file how to add image to readme .md size resize images in github markdown...", "subpage_snippet": "", "source": "iqcode.com", "link": "https://iqcode.com/code/shell/change-size-of-image-in-github-readme", "content": "readme md file how to add image to readme .md size resize images in github markdown..."}
+{"idx": 9, "title": "3 Ways to Add an Image to GitHub README | Sean C Davis", "date": "", "ddg_snippet": "The first method is to commit the image directly to your GitHub repository. When you do that, you can use a path to that file for the src, which should be relative from the markdown file.", "subpage_snippet": "", "source": "www.seancdavis.com", "link": "https://www.seancdavis.com/posts/three-ways-to-add-image-to-github-readme/", "content": "The first method is to commit the image directly to your GitHub repository. When you do that, you can use a path to that file for the src, which should be relative from the markdown file."}
diff --git a/data/sampled_jsons/VRSBench_GitHub_dataset_image_size_resolution.jsonl b/data/sampled_jsons/VRSBench_GitHub_dataset_image_size_resolution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ff93cd870465e4118ca22665d73d7275db048756
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_GitHub_dataset_image_size_resolution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Faster (2010 film ) - Wikipedia", "date": "", "ddg_snippet": "Faster is a 2010 American action thriller film directed by George Tillman Jr. [6] The film stars Dwayne Johnson, Billy Bob Thornton, Oliver Jackson-Cohen, Carla Gugino, Maggie Grace, Moon Bloodgood, Adewale Akinnuoye-Agbaje, and Tom Berenger.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Faster_(2010_film)", "content": "Faster is a 2010 American action thriller film directed by George Tillman Jr. [6] The film stars Dwayne Johnson, Billy Bob Thornton, Oliver Jackson-Cohen, Carla Gugino, Maggie Grace, Moon Bloodgood, Adewale Akinnuoye-Agbaje, and Tom Berenger."}
+{"idx": 1, "title": "Faster (2010) - IMDb", "date": "", "ddg_snippet": "FASTER sees Dwayne 'The Rock' Johnson returning to the fold of action cinema after wasting his time in dumb comedies and kiddie movies. This is a straightforward revenge saga about a man betrayed by a criminal gang who finally leaves prison and sets out on a path of vengeance.", "subpage_snippet": "", "source": "www.imdb.com", "link": "https://www.imdb.com/title/tt1433108/", "content": "FASTER sees Dwayne 'The Rock' Johnson returning to the fold of action cinema after wasting his time in dumb comedies and kiddie movies. This is a straightforward revenge saga about a man betrayed by a criminal gang who finally leaves prison and sets out on a path of vengeance."}
+{"idx": 2, "title": "Faster - Official Trailer - YouTube", "date": "", "ddg_snippet": "After 10 years in prison, Driver (Dwayne Johnson) has a singular focus - to avenge the murder of his brother during the botched bank robbery that led to his imprisonment.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=XDBeFAlgjio", "content": "After 10 years in prison, Driver (Dwayne Johnson) has a singular focus - to avenge the murder of his brother during the botched bank robbery that led to his imprisonment."}
+{"idx": 3, "title": "Faster (2010) | Rotten Tomatoes", "date": "", "ddg_snippet": "Discover reviews, ratings, and trailers for Faster (2010) on Rotten Tomatoes. Stay updated with critic and audience scores today!", "subpage_snippet": "", "source": "www.rottentomatoes.com", "link": "https://www.rottentomatoes.com/m/faster_2010", "content": "Discover reviews, ratings, and trailers for Faster (2010) on Rotten Tomatoes. Stay updated with critic and audience scores today!"}
+{"idx": 4, "title": "Faster streaming: where to watch movie online?", "date": "", "ddg_snippet": "Watch \" Faster \" · Full movie online for free · Check all streaming services such as Netflix, Prime Video & Disney+ – including 4K options!", "subpage_snippet": "", "source": "www.justwatch.com", "link": "https://www.justwatch.com/us/movie/faster", "content": "Watch \" Faster \" · Full movie online for free · Check all streaming services such as Netflix, Prime Video & Disney+ – including 4K options!"}
+{"idx": 5, "title": "FASTER Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "Examples of fast in a Sentence Adjective She's a very fast runner. We're off to a fast start. We're now experiencing a faster rate of inflation.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/faster", "content": "Examples of fast in a Sentence Adjective She's a very fast runner. We're off to a fast start. We're now experiencing a faster rate of inflation."}
+{"idx": 6, "title": "Watch Faster - Netflix", "date": "", "ddg_snippet": "Released from prison, a man with a list of enemies seeks to avenge his brother's death with two relentless cops and an eccentric assassin on his trail. Watch trailers & learn more.", "subpage_snippet": "", "source": "www3.stage.netflix.com", "link": "https://www3.stage.netflix.com/title/70140907", "content": "Released from prison, a man with a list of enemies seeks to avenge his brother's death with two relentless cops and an eccentric assassin on his trail. Watch trailers & learn more."}
+{"idx": 7, "title": "Watch Faster | Prime Video - amazon.com", "date": "", "ddg_snippet": "Action star Dwayne Johnson stars as Driver, now a free man after a decade in prison, focused on hunting down the people responsible for the brutal killing of his brother.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/Faster-Dwayne-Johnson/dp/B004Q3NBXU", "content": "Action star Dwayne Johnson stars as Driver, now a free man after a decade in prison, focused on hunting down the people responsible for the brutal killing of his brother."}
+{"idx": 8, "title": "Faster 2010 Cast & Character Guide - Screen Rant", "date": "", "ddg_snippet": "Dec 9, 2024 · For fans of the star, Faster is a classic, and it combines him with some talented, recognizable co-stars that most audiences will probably have seen in various other movies and TV shows.", "subpage_snippet": "", "source": "screenrant.com", "link": "https://screenrant.com/faster-2010-cast-characters/", "content": "Dec 9, 2024 · For fans of the star, Faster is a classic, and it combines him with some talented, recognizable co-stars that most audiences will probably have seen in various other movies and TV shows."}
+{"idx": 9, "title": "Faster (2010) — The Movie Database (TMDB)", "date": "", "ddg_snippet": "Nov 23, 2010 · After 10 years in prison, Driver is now a free man with a single focus - hunting down the people responsible for brutally murdering his brother.", "subpage_snippet": "", "source": "www.themoviedb.org", "link": "https://www.themoviedb.org/movie/41283-faster", "content": "Nov 23, 2010 · After 10 years in prison, Driver is now a free man with a single focus - hunting down the people responsible for brutally murdering his brother."}
diff --git a/data/sampled_jsons/VRSBench_Li_et_al._2024_remote_sensing_benchmark_image_size_year_2024.jsonl b/data/sampled_jsons/VRSBench_Li_et_al._2024_remote_sensing_benchmark_image_size_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6d18b14515c60c6e9497d1e55aebad96a8b8c02e
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_Li_et_al._2024_remote_sensing_benchmark_image_size_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "9 Dec 2024 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/97530", "content": "9 Dec 2024 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ..."}
+{"idx": 1, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "18 Jun 2024 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v1", "content": "18 Jun 2024 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ..."}
+{"idx": 2, "title": "Supplementary of VRSBench: A Versatile Benchmark for ...", "date": "", "ddg_snippet": "by XLJDM Elhoseiny — VRSBench consists of 29, 614 remote sensing images with detailed captions, 52,472 object refers,. 123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "by XLJDM Elhoseiny — VRSBench consists of 29, 614 remote sensing images with detailed captions, 52,472 object refers,. 123,221 visual question-answer pairs."}
+{"idx": 3, "title": "VRSBench", "date": "", "ddg_snippet": "by X Li · 2024 · Cited by 49 — It includes 772 images with 77,232 question-answer pairs in the low- resolution collection and 10,659 images with 1,066,316 pairs in the high- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12384?", "content": "by X Li · 2024 · Cited by 49 — It includes 772 images with 77,232 question-answer pairs in the low- resolution collection and 10,659 images with 1,066,316 pairs in the high- ..."}
+{"idx": 4, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset ...", "date": "", "ddg_snippet": "18 Jun 2024 — This paper introduces VRSBench , a versatile vision-language benchmark dataset for comprehensive remote sensing image understanding model ...", "subpage_snippet": "", "source": "liner.com", "link": "https://liner.com/review/vrsbench-a-versatile-visionlanguage-benchmark-dataset-for-remote-sensing-image", "content": "18 Jun 2024 — This paper introduces VRSBench , a versatile vision-language benchmark dataset for comprehensive remote sensing image understanding model ..."}
+{"idx": 5, "title": "A remote sensing vision language model and benchmark", "date": "", "ddg_snippet": "by Y Hu · 2025 · Cited by 174 — We build a high-quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the remote sensing field.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0924271625001352", "content": "by Y Hu · 2025 · Cited by 174 — We build a high-quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the remote sensing field."}
+{"idx": 6, "title": "NeurIPS 2024 Datasets Benchmarks 2024", "date": "", "ddg_snippet": "... Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/events/datasets-benchmarks-2024", "content": "... Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions ..."}
+{"idx": 7, "title": "Enhancing Ultrahigh Resolution Remote Sensing Imagery ...", "date": "", "ddg_snippet": "Ultrahigh resolution (UHR) remote sensing imagery. (RSI) (e.g. 10,000 # 10,000 pixels) poses a significant challenge for current RS vision-language models ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6245518/8976286/11039502.pdf", "content": "Ultrahigh resolution (UHR) remote sensing imagery. (RSI) (e.g. 10,000 # 10,000 pixels) poses a significant challenge for current RS vision-language models ..."}
+{"idx": 8, "title": "Context-driven and sparse decoding for Remote Sensing ...", "date": "", "ddg_snippet": "by Y Zhao · 2025 · Cited by 3 — Lett. ( 2024 ). X. Li et al . VRSBench : A versatile vision-language benchmark dataset for remote sensing image understanding. Adv. Neural Inf. Process. Syst. ( 2024 ).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1566253525003690", "content": "by Y Zhao · 2025 · Cited by 3 — Lett. ( 2024 ). X. Li et al . VRSBench : A versatile vision-language benchmark dataset for remote sensing image understanding. Adv. Neural Inf. Process. Syst. ( 2024 )."}
+{"idx": 9, "title": "XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "by F Wang · 2025 · Cited by 7 — In this paper, we introduce XLRS-Bench, a comprehen- sive benchmark for evaluating the perception and rea- soning capabilities of multimodal large language ... 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "by F Wang · 2025 · Cited by 7 — In this paper, we introduce XLRS-Bench, a comprehen- sive benchmark for evaluating the perception and rea- soning capabilities of multimodal large language ... 12 pages"}
diff --git a/data/sampled_jsons/VRSBench_arxiv_image_resolution_size_year_2024.jsonl b/data/sampled_jsons/VRSBench_arxiv_image_resolution_size_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dafbdc09473d3cd850c15bff182211632ca74fb8
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_arxiv_image_resolution_size_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AeroLite: Tag-Guided Lightweight Generation of Aerial Image", "date": "", "ddg_snippet": "2015a ) ) established a basic framework for image captioning, they were not well-suited to high- resolution aerial imagery (Lu et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09528v1", "content": "2015a ) ) established a basic framework for image captioning, they were not well-suited to high- resolution aerial imagery (Lu et al ."}
+{"idx": 1, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for ...", "date": "", "ddg_snippet": "Jun 18, 2024 · In this study, to address these limitations, we introduce a novel versatile benchmark for vision-language understanding of remote sensing images . VRSBench comprises 29,614 images , each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v1", "content": "Jun 18, 2024 · In this study, to address these limitations, we introduce a novel versatile benchmark for vision-language understanding of remote sensing images . VRSBench comprises 29,614 images , each enriched with human-verified detailed captions, complex object referring, and question-answer pairs, check Table 1 for a detailed comparison with existing datasets."}
+{"idx": 2, "title": "GitHub - lx709/VRSBench", "date": "", "ddg_snippet": "Jun 19, 2024 · To construct our VRSBench dataset, we employed multiple data engineering steps, including attribute extraction, prompting engineering, GPT-4 inference, and human verification. Attribute Extraction: we extract image information, including the source and resolution , as well as object information—such as the object category, bounding box, color, position (absolute and relative), and size ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "Jun 19, 2024 · To construct our VRSBench dataset, we employed multiple data engineering steps, including attribute extraction, prompting engineering, GPT-4 inference, and human verification. Attribute Extraction: we extract image information, including the source and resolution , as well as object information—such as the object category, bounding box, color, position (absolute and relative), and size ..."}
+{"idx": 3, "title": "VRSBench:", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 4, "title": "README.md · xiang709/VRSBench at main - Hugging Face", "date": "", "ddg_snippet": "Attribute Extraction: we extract image information, including the source and resolution , as well as object information—such as the object category, bounding box, color, position (absolute and relative), and size (absolute and relative)—from existing object detection datasets.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench/blob/main/README.md", "content": "Attribute Extraction: we extract image information, including the source and resolution , as well as object information—such as the object category, bounding box, color, position (absolute and relative), and size (absolute and relative)—from existing object detection datasets."}
+{"idx": 5, "title": "Supplementary of VRSBench: A Versatile Benchmark for Vision ...", "date": "", "ddg_snippet": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ..."}
+{"idx": 6, "title": "VRSBench: A Versatile Vision-Language - neurips.cc", "date": "", "ddg_snippet": "Benchmark Tasks VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image , encapsulating intricate details and contextual relevance. VRSBench -Ref: The task involves identifying and localizing specific objects or features within a given remote sensing image based on a textual description.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/97530.pdf", "content": "Benchmark Tasks VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image , encapsulating intricate details and contextual relevance. VRSBench -Ref: The task involves identifying and localizing specific objects or features within a given remote sensing image based on a textual description."}
+{"idx": 7, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for ...", "date": "", "ddg_snippet": "Jun 18, 2024 · Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "Jun 18, 2024 · Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 8, "title": "Landsat30-AU: A Vision-Language Dataset for Australian Landsat", "date": "", "ddg_snippet": "Our findings highlight a substantial gap between the capabilities of generic VLMs and the demands of long-term, low- resolution satellite imagery.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03127v1", "content": "Our findings highlight a substantial gap between the capabilities of generic VLMs and the demands of long-term, low- resolution satellite imagery."}
+{"idx": 9, "title": "UrBench: A Comprehensive Benchmark for Evaluating Large", "date": "", "ddg_snippet": "For instance, geo-localization tasks require satellite-view imagery for spatial orientation and street-view imagery for detailed contexts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17267v3", "content": "For instance, geo-localization tasks require satellite-view imagery for spatial orientation and street-view imagery for detailed contexts."}
diff --git a/data/sampled_jsons/VRSBench_low-resolution_collection_high-resolution_collection.jsonl b/data/sampled_jsons/VRSBench_low-resolution_collection_high-resolution_collection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f486ee8920d3fe36064c26afdfd1c194a8f51485
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_low-resolution_collection_high-resolution_collection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench : A Versatile Vision-Language Benchmark Dataset for...", "date": "", "ddg_snippet": "High - resolution remote sensing image captioning based on structured attention. IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2021.Appendix B Dataset Collection Details.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v2", "content": "High - resolution remote sensing image captioning based on structured attention. IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2021.Appendix B Dataset Collection Details."}
+{"idx": 1, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "18 Jun 2024 — VRSBench : A Versatile Vision-Language ... low-resolution collection and 10,659 images with 1,066,316 pairs in the high-resolution collection .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12384v1", "content": "18 Jun 2024 — VRSBench : A Versatile Vision-Language ... low-resolution collection and 10,659 images with 1,066,316 pairs in the high-resolution collection ."}
+{"idx": 2, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Learning from higher -order correlations, efficiently: hypothesis tests, random features, and neural networks ... Lower Bound Framework and ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/papers.html?filter=titles", "content": "Learning from higher -order correlations, efficiently: hypothesis tests, random features, and neural networks ... Lower Bound Framework and ..."}
+{"idx": 3, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Learning from higher -order correlations, efficiently: hypothesis tests, random features, and neural networks ... Lower Bound Framework and ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/papers.html", "content": "Learning from higher -order correlations, efficiently: hypothesis tests, random features, and neural networks ... Lower Bound Framework and ..."}
+{"idx": 4, "title": "Mohamed Elhoseiny - researchr alias", "date": "", "ddg_snippet": "Local Masked Reconstruction for Efficient Self-Supervised Learning on High - Resolution Images Jun Chen 0021 , Faizan Farooq Khan , Ming Hu , Ammar ...", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/alias/mohamed-elhoseiny", "content": "Local Masked Reconstruction for Efficient Self-Supervised Learning on High - Resolution Images Jun Chen 0021 , Faizan Farooq Khan , Ming Hu , Ammar ..."}
+{"idx": 5, "title": "Landsat30-AU: A Vision-Language Dataset for Australian Landsat", "date": "", "ddg_snippet": "However, existing datasets focus mainly on short-term, high - resolution imagery from a limited number of satellites, overlooking low - resolution , multi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03127v1", "content": "However, existing datasets focus mainly on short-term, high - resolution imagery from a limited number of satellites, overlooking low - resolution , multi ..."}
+{"idx": 6, "title": "MME-Survey: A Comprehensive Survey on Evaluation of Multimodal", "date": "", "ddg_snippet": "... capabilities, model self-analysis, and extented applications; 2) the typical process of benchmark counstruction, consisting of data collection ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.15296v2", "content": "... capabilities, model self-analysis, and extented applications; 2) the typical process of benchmark counstruction, consisting of data collection ..."}
+{"idx": 7, "title": "AeroLite: Tag-Guided Lightweight Generation of Aerial Image", "date": "", "ddg_snippet": "However, producing high -quality annotations for these images remains prohibitively expensive, largely due to the need for expert-level interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09528v1", "content": "However, producing high -quality annotations for these images remains prohibitively expensive, largely due to the need for expert-level interpretation ..."}
+{"idx": 8, "title": "Transformer-based Spatial Grounding: A Comprehensive Survey", "date": "", "ddg_snippet": "Our analysis identifies dominant model architectures, prevalent datasets, and widely adopted evaluation metrics, alongside highlighting key ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12739v1", "content": "Our analysis identifies dominant model architectures, prevalent datasets, and widely adopted evaluation metrics, alongside highlighting key ..."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/VRSBench_remote_sensing_benchmark_Li_2024_year_2024.jsonl b/data/sampled_jsons/VRSBench_remote_sensing_benchmark_Li_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ee4a962a4dcbc32ce1187bda2f4246adde0fa970
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_remote_sensing_benchmark_Li_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for ... Images GitHub - lx709/VRSBench VRSBench: VRSBench | Proceedings of the 38th International Conference ... Supplementary of VRSBench: A Versatile Benchmark for Vision ... VRSBench VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote VRSBench | Proceedings of the 38th International Conference on Neural VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Introducing VRSBench: Advancing Remote Sensing Image Analysis", "date": "", "ddg_snippet": "Jun 18, 2024 · We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ... View all Jun 19, 2024 · VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs. We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ... Jun 5, 2025 · It facilitates the training and evaluation of vision-language models across a broad spectrum of remote sensing image understanding tasks. We further evaluated state-of-the-art models on this benchmark for three vision-language tasks: image captioning, visual grounding, and visual question answering. 1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing , providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ... Paper: Li , Xiang, Jian Ding, and Mohamed Elhoseiny. \" Vrsbench : A versatile vision-language benchmark dataset for remote sensing image understanding.\" arXiv preprint arXiv:2406.12384 ( 2024 ). What is a vrsbench benchmark for remote sensing image understanding? Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench. This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. What is the vrsbench benchmark for r emote sensing image understanding? Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote Sensing image understanding, termed VRSBench. This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. Can vision-language models be used for remote sensing images? Abstract: We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Should vision-language datasets be used in remote sensing? Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Jul 27, 2025 · Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "Jun 18, 2024 · We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ... View all Jun 19, 2024 · VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs. We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ... Jun 5, 2025 · It facilitates the training and evaluation of vision-language models across a broad spectrum of remote sensing image understanding tasks. We further evaluated state-of-the-art models on this benchmark for three vision-language tasks: image captioning, visual grounding, and visual question answering. 1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing , providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ... Paper: Li , Xiang, Jian Ding, and Mohamed Elhoseiny. \" Vrsbench : A versatile vision-language benchmark dataset for remote sensing image understanding.\" arXiv preprint arXiv:2406.12384 ( 2024 ). What is a vrsbench benchmark for remote sensing image understanding? Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench. This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. What is the vrsbench benchmark for r emote sensing image understanding? Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote Sensing image understanding, termed VRSBench. This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. Can vision-language models be used for remote sensing images? Abstract: We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Should vision-language datasets be used in remote sensing? Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Jul 27, 2025 · Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 1, "title": "GitHub - lx709/ VRSBench", "date": "", "ddg_snippet": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It facilitates the training and evaluation of vision-language models across a broad spectrum of remote sensing image understanding tasks. TODO. [ 2024 .10.15] Rel...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It facilitates the training and evaluation of vision-language models across a broad spectrum of remote sensing image understanding tasks. TODO. [ 2024 .10.15] Rel..."}
+{"idx": 2, "title": "VRSBench:", "date": "", "ddg_snippet": "We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ...", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ..."}
+{"idx": 3, "title": "Introducing VRSBench: Advancing Remote Sensing Image Analysis", "date": "", "ddg_snippet": "Jul 27, 2025 · Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-27-introducing-vrsbench-advancing-remote-sensing-image-analysis--ak6l6or", "content": "Jul 27, 2025 · Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 4, "title": "VRSBench | Proceedings of the 38th International Conference ...", "date": "", "ddg_snippet": "Jun 5, 2025 · It facilitates the training and evaluation of vision-language models across a broad spectrum of remote sensing image understanding tasks. We further evaluated state-of-the-art models on this benchmark for three vision-language tasks: image captioning, visual grounding, and visual question answering.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738022", "content": "Jun 5, 2025 · It facilitates the training and evaluation of vision-language models across a broad spectrum of remote sensing image understanding tasks. We further evaluated state-of-the-art models on this benchmark for three vision-language tasks: image captioning, visual grounding, and visual question answering."}
+{"idx": 5, "title": "Supplementary of VRSBench: A Versatile Benchmark for Vision ...", "date": "", "ddg_snippet": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing , providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing , providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ..."}
+{"idx": 6, "title": "VRSBench", "date": "", "ddg_snippet": "Paper: Li , Xiang, Jian Ding, and Mohamed Elhoseiny. \" Vrsbench : A versatile vision-language benchmark dataset for remote sensing image understanding.\" arXiv preprint arXiv:2406.12384 ( 2024 ).", "subpage_snippet": "", "source": "www.eod-grss-ieee.com", "link": "https://www.eod-grss-ieee.com/dataset-detail/UjNWY0FMSStDYU5MTHlJNVE5bXllQT09", "content": "Paper: Li , Xiang, Jian Ding, and Mohamed Elhoseiny. \" Vrsbench : A versatile vision-language benchmark dataset for remote sensing image understanding.\" arXiv preprint arXiv:2406.12384 ( 2024 )."}
+{"idx": 7, "title": "A Versatile Vision-Language Benchmark Dataset for Remote Sensing ...", "date": "", "ddg_snippet": "Select Year: ( 2024 ).We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/97530", "content": "Select Year: ( 2024 ).We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images."}
+{"idx": 8, "title": "VRSBench : A Versatile Vision-Language Benchmark Dataset for...", "date": "", "ddg_snippet": "Published 6/19/ 2024 by Xiang Li , Jian Ding, Mohamed Elhoseiny.We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/vrsbench-versatile-vision-language-benchmark-dataset-remote", "content": "Published 6/19/ 2024 by Xiang Li , Jian Ding, Mohamed Elhoseiny.We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images."}
+{"idx": 9, "title": "xiang709/ VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "Citation Information. @misc{ li 2024 vrsbench , title={ VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding}, author={Xiang Li , Jian Ding, Mohamed Elhoseiny}", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "Citation Information. @misc{ li 2024 vrsbench , title={ VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding}, author={Xiang Li , Jian Ding, Mohamed Elhoseiny}"}
diff --git a/data/sampled_jsons/Video-ColBERT_8_frames_OR_16_frames_OR_32_frames_OR_12_frames_MSVD_dataset.jsonl b/data/sampled_jsons/Video-ColBERT_8_frames_OR_16_frames_OR_32_frames_OR_12_frames_MSVD_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..92504cca2848371c3552b7e18834c2a4dc6f450b
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_8_frames_OR_16_frames_OR_32_frames_OR_12_frames_MSVD_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Artec 3D – Wikipedia", "date": "", "ddg_snippet": "Broadway 3D funktioniert bei einem Abstand von 0, 8 bis 1,6 m und kann bis zu 60 Personen pro Minute erkennen. ... eine Volumenerfassungszone von 160 ...", "subpage_snippet": "", "source": "de.wikipedia.org", "link": "https://de.wikipedia.org/wiki/Artec_3D", "content": "Broadway 3D funktioniert bei einem Abstand von 0, 8 bis 1,6 m und kann bis zu 60 Personen pro Minute erkennen. ... eine Volumenerfassungszone von 160 ..."}
+{"idx": 1, "title": "Video: Stephen Colbert campaigns for staff to get new jobs at", "date": "", "ddg_snippet": "Stephen Colbert campaigns for his staff to get new jobs during the 2025 Emmy Awards after his show was abruptly canceled.", "subpage_snippet": "", "source": "www.dailymail.co.uk", "link": "https://www.dailymail.co.uk/video/tvshowbiz/video-3510285/Video-Stephen-Colbert-campaigns-staff-new-jobs-Emmys.html", "content": "Stephen Colbert campaigns for his staff to get new jobs during the 2025 Emmy Awards after his show was abruptly canceled."}
+{"idx": 2, "title": "Video: Formerly obese man now trains five to six days a week |", "date": "", "ddg_snippet": "Video : Obese man who gave up his education because he feared his 500lb frame wouldn't fit behind a desk at college sheds half his body weight after ...", "subpage_snippet": "", "source": "www.dailymail.co.uk", "link": "https://www.dailymail.co.uk/video/femail/video-1959684/Video-obese-man-trains-five-days-six-days-week.html", "content": "Video : Obese man who gave up his education because he feared his 500lb frame wouldn't fit behind a desk at college sheds half his body weight after ..."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 4, "title": "What was the briefest cameo ever? - Movies & TV Stack", "date": "", "ddg_snippet": "It cannot be footage that originated from a different production, or from the public domain. ... can it not? I m still waiting for the single- frame ...", "subpage_snippet": "", "source": "movies.stackexchange.com", "link": "https://movies.stackexchange.com/questions/109718/what-was-the-briefest-cameo-ever", "content": "It cannot be footage that originated from a different production, or from the public domain. ... can it not? I m still waiting for the single- frame ..."}
+{"idx": 5, "title": "Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual", "date": "", "ddg_snippet": "... or subsequent human-creator curation (such as titles, or search-optimized descriptions), that go beyond the information conveyed directly in that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10202v1", "content": "... or subsequent human-creator curation (such as titles, or search-optimized descriptions), that go beyond the information conveyed directly in that ..."}
+{"idx": 6, "title": "motion pictures - Students | Britannica Kids | Homework Help", "date": "", "ddg_snippet": "A normal lens provides an image in which the horizontal and vertical lines appear almost straight and the objects within the frame appear in normal ...", "subpage_snippet": "", "source": "kids.britannica.com", "link": "https://kids.britannica.com/students/article/motion-pictures/275951", "content": "A normal lens provides an image in which the horizontal and vertical lines appear almost straight and the objects within the frame appear in normal ..."}
+{"idx": 7, "title": "US10185163B2 - Wearable camera systems and apparatus and method", "date": "", "ddg_snippet": "Attaching a conventional camera to eyewear by any conventional techniques may distract from the cosmetics or fashion-look of the eyeglasses or ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US10185163B2/en", "content": "Attaching a conventional camera to eyewear by any conventional techniques may distract from the cosmetics or fashion-look of the eyeglasses or ..."}
+{"idx": 8, "title": "Dr. Anthony Fauci – A stream of consciousness, comedy", "date": "", "ddg_snippet": "... Presidential candidates under indictment or ... Biden – American foreign policy/Fox “news” on Executive Orders, then and now (1:03: 32 )", "subpage_snippet": "", "source": "potentiumpodcast.com", "link": "https://potentiumpodcast.com/tag/dr-anthony-fauci/", "content": "... Presidential candidates under indictment or ... Biden – American foreign policy/Fox “news” on Executive Orders, then and now (1:03: 32 )"}
+{"idx": 9, "title": "BuzzCanada: 25 ULTIMATE TIPS FOR BETTER LIFE!!!", "date": "", "ddg_snippet": "A Chinese court on Monday sentenced a Canadian man to death for drug smuggling after prosecutors said an original 15-year sentence an...", "subpage_snippet": "", "source": "www.buzzcanadalive.com", "link": "http://www.buzzcanadalive.com/2013/07/25-ultimate-tips-for-better-life.html", "content": "A Chinese court on Monday sentenced a Canadian man to death for drug smuggling after prosecutors said an original 15-year sentence an..."}
diff --git a/data/sampled_jsons/Video-ColBERT_MSVD_dataset_frames_experiment_preprocessing_year_2024.jsonl b/data/sampled_jsons/Video-ColBERT_MSVD_dataset_frames_experiment_preprocessing_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..910602131668e48d97f081c60a8d2267ba37f104
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_MSVD_dataset_frames_experiment_preprocessing_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos ."}
+{"idx": 1, "title": "collaborative-experts/misc/datasets/msvd/README.md at master - GitHub", "date": "", "ddg_snippet": "This folder contains a collection of features, extracted from the MSVD [2] dataset as part of the paper: Use what you have: Video retrieval using representations from collaborative experts.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/albanie/collaborative-experts/blob/master/misc/datasets/msvd/README.md", "content": "This folder contains a collection of features, extracted from the MSVD [2] dataset as part of the paper: Use what you have: Video retrieval using representations from collaborative experts."}
+{"idx": 2, "title": "Examples of 9 test videos from MSVD dataset and the top 1 retrieved...", "date": "", "ddg_snippet": "Download scientific diagram | Examples of 9 test videos from MSVD dataset and the top 1 retrieved captions by using a single video -text space and the fusion approach with our loss function.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Examples-of-9-test-videos-from-MSVD-dataset-and-the-top-1-retrieved-captions-by-using-a_fig5_330348236", "content": "Download scientific diagram | Examples of 9 test videos from MSVD dataset and the top 1 retrieved captions by using a single video -text space and the fusion approach with our loss function."}
+{"idx": 3, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video ...", "date": "", "ddg_snippet": "Visualization of the interactions between query tokens and video frames before and after the temporal encoder of VIDEO - COLBERT , trained on MSR-VTT. The green arrow () represents the interaction between query tokens and frames before temporal encod- ing.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Reddy_Video-ColBERT_Contextualized_Late_CVPR_2025_supplemental.pdf", "content": "Visualization of the interactions between query tokens and video frames before and after the temporal encoder of VIDEO - COLBERT , trained on MSR-VTT. The green arrow () represents the interaction between query tokens and frames before temporal encod- ing."}
+{"idx": 4, "title": "MSVD - Dataset - LDM", "date": "", "ddg_snippet": "MSVD Text- Video Retrieval (TVR) aims to align relevant video content with natural language queries. To date, most state-of-the-art TVR methods learn image-to- video transfer learning based on large-scale pre-trained vision-language models (e.g., CLIP).", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/msvd", "content": "MSVD Text- Video Retrieval (TVR) aims to align relevant video content with natural language queries. To date, most state-of-the-art TVR methods learn image-to- video transfer learning based on large-scale pre-trained vision-language models (e.g., CLIP)."}
+{"idx": 5, "title": "Video Preprocessor and Augmentation for Deep Learning tasks", "date": "", "ddg_snippet": "With the growing demand for Video classification and recognition models for several video -processing tasks, it is important to understand how to process videos using python libraries. So in this ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/analytics-vidhya/video-preprocessor-and-augmentation-for-deep-learning-tasks-12dd3fcce868", "content": "With the growing demand for Video classification and recognition models for several video -processing tasks, it is important to understand how to process videos using python libraries. So in this ..."}
+{"idx": 6, "title": "Example video frames and captions from MSVD dataset", "date": "", "ddg_snippet": "Download scientific diagram | Example video frames and captions from MSVD dataset from publication: Video Description: Datasets & Evaluation Metrics | Rapid expansion and the novel phenomenon of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Example-video-frames-and-captions-from-MSVD-dataset_fig2_354175109", "content": "Download scientific diagram | Example video frames and captions from MSVD dataset from publication: Video Description: Datasets & Evaluation Metrics | Rapid expansion and the novel phenomenon of ..."}
+{"idx": 7, "title": "public-projects/vlcn: Official code for \"Video Language Co-Attention ...", "date": "", "ddg_snippet": "After downloading all the raw data, data/ MSVD -QA and data/MSRVTT-QA should have the following structure: Preprocessing To sample the individual frames and clips and generate the corresponding visual features, we run the script preporocess.py on the raw videos with the appropriate flags. E.g. for MSVD -QA we have to execute", "subpage_snippet": "", "source": "git.hcics.simtech.uni-stuttgart.de", "link": "https://git.hcics.simtech.uni-stuttgart.de/public-projects/vlcn", "content": "After downloading all the raw data, data/ MSVD -QA and data/MSRVTT-QA should have the following structure: Preprocessing To sample the individual frames and clips and generate the corresponding visual features, we run the script preporocess.py on the raw videos with the appropriate flags. E.g. for MSVD -QA we have to execute"}
+{"idx": 8, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "On top of this modified interaction, VIDEO-COLBERT uses two MMS operations over both indepen-dent visual frame features and contextualized frame features to strengthen the fine-grained spatial and temporal interac-tion (Fig. 1).", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "On top of this modified interaction, VIDEO-COLBERT uses two MMS operations over both indepen-dent visual frame features and contextualized frame features to strengthen the fine-grained spatial and temporal interac-tion (Fig. 1)."}
+{"idx": 9, "title": "friedrichor/MSVD · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/friedrichor/MSVD", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
diff --git a/data/sampled_jsons/Video-ColBERT_MSVD_frames_sampled_Section_5.2.jsonl b/data/sampled_jsons/Video-ColBERT_MSVD_frames_sampled_Section_5.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..425b1bfc5c2b8fdf86c6e1725da93273081dd77e
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_MSVD_frames_sampled_Section_5.2.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual", "date": "", "ddg_snippet": "... most current text-to- video retrieval systems are trained and evaluated on widely used datasets, such as MSR-VTT (Xu et al., 2016 ) and MSVD (Chen ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.10202v1", "content": "... most current text-to- video retrieval systems are trained and evaluated on widely used datasets, such as MSR-VTT (Xu et al., 2016 ) and MSVD (Chen ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Video-ColBERT_Section_5.2_frames_sampled_MSVD_experimental_setup.jsonl b/data/sampled_jsons/Video-ColBERT_Section_5.2_frames_sampled_MSVD_experimental_setup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3238083e0eada0c91ab8e44a4eca4f68ab13eb18
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_Section_5.2_frames_sampled_MSVD_experimental_setup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33381", "content": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss ..."}
+{"idx": 1, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "by A Reddy · 2025 · Cited by 5 — VIDEO - COLBERT incorporates a modification to the. MaxSim operation, MeanMaxSim (MMS), which replaces the summation with a mean to better accommodate variable. 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "by A Reddy · 2025 · Cited by 5 — VIDEO - COLBERT incorporates a modification to the. MaxSim operation, MeanMaxSim (MMS), which replaces the summation with a mean to better accommodate variable. 11 pages"}
+{"idx": 2, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for ...", "date": "", "ddg_snippet": "by Y Ma · 2022 · Cited by 381 — The input video is sampled into ordinal frames and these frames are fed into the frame encoder to generate frame -level representations. The ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.07285", "content": "by Y Ma · 2022 · Cited by 381 — The input video is sampled into ordinal frames and these frames are fed into the frame encoder to generate frame -level representations. The ..."}
+{"idx": 3, "title": "Daily Papers", "date": "", "ddg_snippet": "This paper proposes a novel framework utilizing multi-modal large language models (MLLMs) for referring video object segmentation (RefVOS).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=query+frames", "content": "This paper proposes a novel framework utilizing multi-modal large language models (MLLMs) for referring video object segmentation (RefVOS)."}
+{"idx": 4, "title": "Daily Papers", "date": "", "ddg_snippet": "Experiments on MSR-VTT, MSVD , and AVSD show that our framework using question-based interaction significantly improves the performance of text-based video ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=few-shot+API+queries", "content": "Experiments on MSR-VTT, MSVD , and AVSD show that our framework using question-based interaction significantly improves the performance of text-based video ..."}
+{"idx": 5, "title": "Proceedings of the 31st International Conference on ...", "date": "", "ddg_snippet": "This research includes both classroom and experimental settings for system development. ... Experimental results on MSVD -QA, MSRVTT-QA, and ActivityNet-QA ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2025.coling-main/", "content": "This research includes both classroom and experimental settings for system development. ... Experimental results on MSVD -QA, MSRVTT-QA, and ActivityNet-QA ..."}
+{"idx": 6, "title": "Advances in Cyber Security", "date": "", "ddg_snippet": "24 Aug 2021 — Section 5 describe experimental setup , evaluation of SecMac-SRD mecha- nism as a security solution and discussion regarding the experimental ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-981-16-8059-5.pdf", "content": "24 Aug 2021 — Section 5 describe experimental setup , evaluation of SecMac-SRD mecha- nism as a security solution and discussion regarding the experimental ..."}
+{"idx": 7, "title": "Proceedings of the 2022 Conference on Empirical Methods ...", "date": "", "ddg_snippet": "We provide a novel experimental setup that applies personalization ... Experimental results on text- video retrieval and video question answering ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2022.emnlp-main/", "content": "We provide a novel experimental setup that applies personalization ... Experimental results on text- video retrieval and video question answering ..."}
+{"idx": 8, "title": "Making great strides", "date": "", "ddg_snippet": "28 Aug 2017 — Section by section , Ameri- can artist Ryan Mendoza painstakingly disassembled the small wood- frame home of civil rights icon Rosa Parks.", "subpage_snippet": "", "source": "vindy.media.clients.ellingtoncms.com", "link": "http://vindy.media.clients.ellingtoncms.com/digital_edition/2017/08282017.pdf", "content": "28 Aug 2017 — Section by section , Ameri- can artist Ryan Mendoza painstakingly disassembled the small wood- frame home of civil rights icon Rosa Parks."}
+{"idx": 9, "title": "May 2016 Board Packet - Washington State Board of Education", "date": "", "ddg_snippet": "1 May 2016 — Our board meeting agenda is focused on several important and timely topics: ESSA implementation, long-term goal setting, definitions of career ...", "subpage_snippet": "", "source": "sbe.wa.gov", "link": "https://sbe.wa.gov/sites/default/files/2024-08/May2016Packet.pdf", "content": "1 May 2016 — Our board meeting agenda is focused on several important and timely topics: ESSA implementation, long-term goal setting, definitions of career ..."}
diff --git a/data/sampled_jsons/Video-ColBERT_equation_5_MMS_F_MMS_v_final_similarity_score_formula.jsonl b/data/sampled_jsons/Video-ColBERT_equation_5_MMS_F_MMS_v_final_similarity_score_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1d4e54aaf0c130069dc10b10049077c0dfd913ad
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_equation_5_MMS_F_MMS_v_final_similarity_score_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "VIDEO-COLBERT incorporates a modification to the MaxSim operation, MeanMaxSim ( MMS ), which replaces the summation with a mean to better accommodate variable length queries and to control the magnitude of the overall similarity .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "VIDEO-COLBERT incorporates a modification to the MaxSim operation, MeanMaxSim ( MMS ), which replaces the summation with a mean to better accommodate variable length queries and to control the magnitude of the overall similarity ."}
+{"idx": 1, "title": "[2503.19009] Video-ColBERT: Contextualized Late Interaction ... GitHub - yogesh-iitj/Video-ColBERT ColBERT: A complete guide. Me: BERT, can you please ... - Medium Exciting Research Alert: Video-ColBERT - A Breakthrough in ... colbert-ir/colbertv2.0 · Hugging Face ColBERT : A complete guide - Medium colbert -ir/colbertv2.0 · Hugging Face colbert -ir/colbertv2.0 · Hugging Face colbert -ir/colbertv2.0 · Hugging Face ColBERT : A complete guide - Medium colbert -ir/colbertv2.0 · Hugging Face Reading Papers: ColBERTv1 (SIGIR 2020) · Koi'Log", "date": "", "ddg_snippet": "Mar 24, 2025 · In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ... Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between text queries and videos through: Dual token-wise interaction - Performs MeanMaxSim ( MMS ) on both static frame features and temporally contextualized video features Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators. Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim ( MMS ) operations on both ... ColBERT (v2) ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. Figure 1: ColBERT 's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage. How does Colbert compute a relevance score between Q and D? Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction , which is defined as a summation of maximum similarity (MaxSim) operators. In particular, we find the maximum cosine similarity (any similarity metric can be used) of each v ∈ Eq with vectors in Ed and combine the outputs via summation. What is Colbert late interaction? Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage . As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings (shown above in blue). What is Colbert & how does it work? ColBERT is a fast and accurate retrieval model , enabling scalable BERT-based search over large text collections in tens of milliseconds. Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage. How do I use Colbert V2? Step 1: Download the pre-trained ColBERTv2 checkpoint. This checkpoint has been trained on the MS MARCO Passage Ranking task. You can also optionally train your own ColBERT model. Step 2: Index your collection. Once you have a trained ColBERT model, you need to index your collection to permit fast retrieval. What is Colbert (co ntextualized interaction over Bert)? ColBERT (Co ntextualized L ate interaction over BERT) reconciles efficiency and contextualization , hence getting this abbreviation. In ColBERT, Query and Document text are separately encoded (tokenized) into contextual embeddings using two different BERT ( base model can be changed, for eg: RobBERTa, mBERT) models. What's new in colbertv2? ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction (NAACL'22). PLAID: An Efficient Engine for Late Interaction Retrieval (CIKM'22). (1/29/23) We have merged a new index updater feature and support for additional Hugging Face models! These are in beta so please give us feedback as you try them out. Oct 30, 2024 · For each word vector embedding in the query bag embeddings E q E q, ColBERT finds the maximum word similarity score with the word embeddings in the document E d E d. This is done through the MaxSim operator. The MaxSim operator calculates the cosine similarity between a single query embedding and all embeddings in document’s bag embedding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "Mar 24, 2025 · In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ... Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between text queries and videos through: Dual token-wise interaction - Performs MeanMaxSim ( MMS ) on both static frame features and temporally contextualized video features Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators. Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim ( MMS ) operations on both ... ColBERT (v2) ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. Figure 1: ColBERT 's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage. How does Colbert compute a relevance score between Q and D? Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction , which is defined as a summation of maximum similarity (MaxSim) operators. In particular, we find the maximum cosine similarity (any similarity metric can be used) of each v ∈ Eq with vectors in Ed and combine the outputs via summation. What is Colbert late interaction? Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage . As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings (shown above in blue). What is Colbert & how does it work? ColBERT is a fast and accurate retrieval model , enabling scalable BERT-based search over large text collections in tens of milliseconds. Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage. How do I use Colbert V2? Step 1: Download the pre-trained ColBERTv2 checkpoint. This checkpoint has been trained on the MS MARCO Passage Ranking task. You can also optionally train your own ColBERT model. Step 2: Index your collection. Once you have a trained ColBERT model, you need to index your collection to permit fast retrieval. What is Colbert (co ntextualized interaction over Bert)? ColBERT (Co ntextualized L ate interaction over BERT) reconciles efficiency and contextualization , hence getting this abbreviation. In ColBERT, Query and Document text are separately encoded (tokenized) into contextual embeddings using two different BERT ( base model can be changed, for eg: RobBERTa, mBERT) models. What's new in colbertv2? ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction (NAACL'22). PLAID: An Efficient Engine for Late Interaction Retrieval (CIKM'22). (1/29/23) We have merged a new index updater feature and support for additional Hugging Face models! These are in beta so please give us feedback as you try them out. Oct 30, 2024 · For each word vector embedding in the query bag embeddings E q E q, ColBERT finds the maximum word similarity score with the word embeddings in the document E d E d. This is done through the MaxSim operator. The MaxSim operator calculates the cosine similarity between a single query embedding and all embeddings in document’s bag embedding."}
+{"idx": 2, "title": "GitHub - yogesh-iitj/Video-ColBERT", "date": "", "ddg_snippet": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between text queries and videos through: Dual token-wise interaction - Performs MeanMaxSim ( MMS ) on both static frame features and temporally contextualized video features", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yogesh-iitj/Video-ColBERT", "content": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between text queries and videos through: Dual token-wise interaction - Performs MeanMaxSim ( MMS ) on both static frame features and temporally contextualized video features"}
+{"idx": 3, "title": "ColBERT: A complete guide. Me: BERT, can you please ... - Medium", "date": "", "ddg_snippet": "Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@varun030403/colbert-a-complete-guide-1552468335ae", "content": "Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators."}
+{"idx": 4, "title": "Exciting Research Alert: Video-ColBERT - A Breakthrough in ...", "date": "", "ddg_snippet": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim ( MMS ) operations on both ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/singhsidhukuldeep_exciting-research-alert-video-colbert-activity-7336927383549071360-eAP7", "content": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim ( MMS ) operations on both ..."}
+{"idx": 5, "title": "colbert-ir/colbertv2.0 · Hugging Face", "date": "", "ddg_snippet": "ColBERT (v2) ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. Figure 1: ColBERT 's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/colbert-ir/colbertv2.0", "content": "ColBERT (v2) ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. Figure 1: ColBERT 's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage."}
+{"idx": 6, "title": "Reading Papers: ColBERTv1 (SIGIR 2020) · Koi'Log", "date": "", "ddg_snippet": "Oct 30, 2024 · For each word vector embedding in the query bag embeddings E q E q, ColBERT finds the maximum word similarity score with the word embeddings in the document E d E d. This is done through the MaxSim operator. The MaxSim operator calculates the cosine similarity between a single query embedding and all embeddings in document’s bag embedding.", "subpage_snippet": "", "source": "itskoi.github.io", "link": "https://itskoi.github.io/posts/reading-papers-colbertv1/", "content": "Oct 30, 2024 · For each word vector embedding in the query bag embeddings E q E q, ColBERT finds the maximum word similarity score with the word embeddings in the document E d E d. This is done through the MaxSim operator. The MaxSim operator calculates the cosine similarity between a single query embedding and all embeddings in document’s bag embedding."}
+{"idx": 7, "title": "https://huggingface.co/datasets/open-source-metric...", "date": "", "ddg_snippet": "... equation -database-grabber\", + \"chiragn888/ video .js\", + \"birehan/Automated ... Final -Assignment\", + \"tmcarvalho/k-smote\", + \"myrasaet/VITAS\", + ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/open-source-metrics/transformers-dependents/commit/2cb60a3d2f4cd754ecf19c097f04eeb2ad19d04e.diff?file=data/2024/02/18.json", "content": "... equation -database-grabber\", + \"chiragn888/ video .js\", + \"birehan/Automated ... Final -Assignment\", + \"tmcarvalho/k-smote\", + \"myrasaet/VITAS\", + ..."}
+{"idx": 8, "title": "Topic Editors Wei Lin, Karim Benzerara, Damien Faivre and ...", "date": "", "ddg_snippet": "were more similar to MmsF or MmsF ... the twelve coupled equations using Runge–Kutta–Fehlberg 4( 5 ) ... In the second calculation with a slow growth rate with τ0 = 5 ...", "subpage_snippet": "", "source": "unglueit-files.s3.amazonaws.com", "link": "https://unglueit-files.s3.amazonaws.com/ebf/29bf76d20cb94b7ebe8b607ab29e8b8e.pdf", "content": "were more similar to MmsF or MmsF ... the twelve coupled equations using Runge–Kutta–Fehlberg 4( 5 ) ... In the second calculation with a slow growth rate with τ0 = 5 ..."}
+{"idx": 9, "title": "Biological Effects of Static Magnetic Fields, 2nd (Xin Zhang ...", "date": "", "ddg_snippet": "5 Magnetic Poles and Magnetic Field Directions. As we mentioned above, magnetic flux density, gradient, exposure time are all important factors that contribute ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/726900068/Biological-Effects-of-Static-Magnetic-Fields-2nd-Xin-Zhang-ed-Z-Library", "content": "5 Magnetic Poles and Magnetic Field Directions. As we mentioned above, magnetic flux density, gradient, exposure time are all important factors that contribute ..."}
diff --git a/data/sampled_jsons/Video-ColBERT_equation_5_final_similarity_score.jsonl b/data/sampled_jsons/Video-ColBERT_equation_5_final_similarity_score.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e526857295b72a4608536c50ef488c7a45330e9c
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_equation_5_final_similarity_score.jsonl
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+{"idx": 0, "title": "Claudette Colbert Movies | Ultimate Movie Rankings", "date": "", "ddg_snippet": "Sort Claudette Colbert movies by Ultimate Movie Ranking (UMR) Score . UMR puts box office, reviews and awards into a mathematical equation and gives ...", "subpage_snippet": "", "source": "www.ultimatemovierankings.com", "link": "https://www.ultimatemovierankings.com/claudette-colbert-movies/", "content": "Sort Claudette Colbert movies by Ultimate Movie Ranking (UMR) Score . UMR puts box office, reviews and awards into a mathematical equation and gives ..."}
+{"idx": 1, "title": "Equations of Everyday Life – The Millennium Conjectures™", "date": "", "ddg_snippet": "For any chance at virality, the final Index number MUST be negative. This works perfectly fine for most of the personalities discussed above. If ...", "subpage_snippet": "", "source": "millenniumconjectures.com", "link": "https://millenniumconjectures.com/category/equations-of-everyday-life/", "content": "For any chance at virality, the final Index number MUST be negative. This works perfectly fine for most of the personalities discussed above. If ..."}
+{"idx": 2, "title": "Equations of Everyday Life #2: Inane Celebrity Memes – The", "date": "", "ddg_snippet": "For any chance at virality, the final Index number MUST be negative. This works perfectly fine for most of the personalities discussed above. If ...", "subpage_snippet": "", "source": "millenniumconjectures.com", "link": "https://millenniumconjectures.com/2012/09/23/equations-of-everyday-life-2-inane-celebrity-memes/", "content": "For any chance at virality, the final Index number MUST be negative. This works perfectly fine for most of the personalities discussed above. If ..."}
+{"idx": 3, "title": "No Laughing Matter: 98% of ‘Comedy’ Show Campaign Jokes", "date": "", "ddg_snippet": "Over the course of the study, Stephen Colbert would joke about the Republican ticket at a 92 percent clip with a final score of 335-28.", "subpage_snippet": "", "source": "www.newsbusters.org", "link": "https://www.newsbusters.org/blogs/nb/alex-christy/2024/10/30/no-laughing-matter-98-comedy-show-campaign-jokes-target-trump", "content": "Over the course of the study, Stephen Colbert would joke about the Republican ticket at a 92 percent clip with a final score of 335-28."}
+{"idx": 4, "title": "Retrieval-Enhanced Machine Learning: Synthesis and Opportunities", "date": "", "ddg_snippet": "... similar trend in other fields adjacent to machine learning, for example, large vision foundation models for representing images and videos ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.12982v2", "content": "... similar trend in other fields adjacent to machine learning, for example, large vision foundation models for representing images and videos ..."}
+{"idx": 5, "title": "masterchef - How do they shoot live commentary in reality", "date": "", "ddg_snippet": "... would take interviews with the contestants before the cooking, right after the cooking but before the judging, after the judging and after the final ...", "subpage_snippet": "", "source": "movies.stackexchange.com", "link": "https://movies.stackexchange.com/questions/34786/how-do-they-shoot-live-commentary-in-reality-shows", "content": "... would take interviews with the contestants before the cooking, right after the cooking but before the judging, after the judging and after the final ..."}
+{"idx": 6, "title": "BTS Channel The Beatles' 1964 Ed Sullivan Moment For", "date": "", "ddg_snippet": "Today, BTS whip up a similar hue of hysteria, a phenomenon Colbert coins \"BTS-mania\" in the image of the Beatlemania that swept the nation five and a ...", "subpage_snippet": "", "source": "www.grammy.com", "link": "https://www.grammy.com/news/bts-channel-beatles-1964-ed-sullivan-moment-colbert-performance", "content": "Today, BTS whip up a similar hue of hysteria, a phenomenon Colbert coins \"BTS-mania\" in the image of the Beatlemania that swept the nation five and a ..."}
+{"idx": 7, "title": "A Systematic Review of the Relationship Between In-Training", "date": "", "ddg_snippet": "Validity evidence for the interpretation of scores from assessment tools can be organized into 5 categories, based on Messick's unified framework ...", "subpage_snippet": "", "source": "meridian.allenpress.com", "link": "https://meridian.allenpress.com/jgme/article/13/1/43/450237/A-Systematic-Review-of-the-Relationship-Between-In", "content": "Validity evidence for the interpretation of scores from assessment tools can be organized into 5 categories, based on Messick's unified framework ..."}
+{"idx": 8, "title": "Man, 109, who still drives his car every day has simple tips", "date": "", "ddg_snippet": "AutoModerator [ M ] [ score hidden] 4 ... Ronaldoooope comment score below threshold -136 points -135 points -134 points 4 months ago (31 children)", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/UpliftingNews/comments/12ynwf2/man_109_who_still_drives_his_car_every_day_has/", "content": "AutoModerator [ M ] [ score hidden] 4 ... Ronaldoooope comment score below threshold -136 points -135 points -134 points 4 months ago (31 children)"}
+{"idx": 9, "title": "Zest of Orange » Internet", "date": "", "ddg_snippet": "But the Defense Department also admits that it has long been concerned about the proliferation and popularity of video war games with many of its ...", "subpage_snippet": "", "source": "zestoforange.com", "link": "http://zestoforange.com/blog/?tag=internet", "content": "But the Defense Department also admits that it has long been concerned about the proliferation and popularity of video war games with many of its ..."}
diff --git a/data/sampled_jsons/Video-ColBERT_experimental_setup_MSVD_preprocessing_frames.jsonl b/data/sampled_jsons/Video-ColBERT_experimental_setup_MSVD_preprocessing_frames.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07a829ef714343e7f125891de735fd3387679738
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_experimental_setup_MSVD_preprocessing_frames.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Windows 11 Insider Brings Native Video Wallpaper to Settings ...", "date": "", "ddg_snippet": "Low friction: Setting a video as wallpaper through Settings or a contextual menu reduces the friction for casual users who don’t want to install additional software. Early reporting emphasizes that this parity with images is the feature’s biggest UX win.", "subpage_snippet": "", "source": "windowsforum.com", "link": "https://windowsforum.com/threads/windows-11-insider-brings-native-video-wallpaper-to-settings-dreamscene.381684/", "content": "Low friction: Setting a video as wallpaper through Settings or a contextual menu reduces the friction for casual users who don’t want to install additional software. Early reporting emphasizes that this parity with images is the feature’s biggest UX win."}
+{"idx": 1, "title": "The Ultimate Guide to Pro YouTube Setup in 2025 - TechSouls", "date": "", "ddg_snippet": "Master your pro YouTube setup 2025 with essential equipment, cameras, microphones, lighting, and editing tools. Complete guide for creators seeking professional results.", "subpage_snippet": "", "source": "techsouls.co.uk", "link": "https://techsouls.co.uk/the-ultimate-guide-to-pro-youtube-setup-in-2025/", "content": "Master your pro YouTube setup 2025 with essential equipment, cameras, microphones, lighting, and editing tools. Complete guide for creators seeking professional results."}
+{"idx": 2, "title": "Experimental weight loss pill seems to be more potent... | New Scientist", "date": "", "ddg_snippet": "Video .An experimental pill looks set to cause more weight loss than existing injectable treatments such as Ozempic, Wegovy and Mounjaro, based on early trial results reported on 7 March.", "subpage_snippet": "", "source": "www.newscientist.com", "link": "https://www.newscientist.com/article/2421279-experimental-weight-loss-pill-seems-to-be-more-potent-than-ozempic/", "content": "Video .An experimental pill looks set to cause more weight loss than existing injectable treatments such as Ozempic, Wegovy and Mounjaro, based on early trial results reported on 7 March."}
+{"idx": 3, "title": "Smart Money Setup 03 [TradingFinder] Minor OB & Trend Proof...", "date": "", "ddg_snippet": "Experiment with different trading positions by adjusting both the \"Time Frame \" and \"Pivot Period\". Typically, setups materializing over longer \"Time Frames \" and \"Pivot Periods\" carry heightened validity. Bullish Setup Details on Chart", "subpage_snippet": "", "source": "in.tradingview.com", "link": "https://in.tradingview.com/script/ycBeLGl7-Smart-Money-Setup-03-TradingFinder-Minor-OB-Trend-Proof/", "content": "Experiment with different trading positions by adjusting both the \"Time Frame \" and \"Pivot Period\". Typically, setups materializing over longer \"Time Frames \" and \"Pivot Periods\" carry heightened validity. Bullish Setup Details on Chart"}
+{"idx": 4, "title": "VK Video Downloader - Сохранить фото, музыку... | convert- video .com", "date": "", "ddg_snippet": "convert- video .com поможет вам скачать из ВК все ваши любимые видео, музыку и фото! С помощью этого инструмента вы сможете скачать из Вконтакте все виды аудио и видео бесплатно!", "subpage_snippet": "", "source": "convert-video.com", "link": "https://convert-video.com/ru/how-to-save-video-from-vk", "content": "convert- video .com поможет вам скачать из ВК все ваши любимые видео, музыку и фото! С помощью этого инструмента вы сможете скачать из Вконтакте все виды аудио и видео бесплатно!"}
+{"idx": 5, "title": "Dying Light The Beast FSR 4 Settings & Optimization Guide", "date": "", "ddg_snippet": "How to Enable FSR 4 in Dying Light The Beast. Open the main menu, go into Options, then Video . Scroll to the Upscaling setting and select FSR 4. You can then pick between Quality, Balanced, or Performance depending on how much FPS you want.", "subpage_snippet": "", "source": "parrot-spinach-nd46.squarespace.com", "link": "https://parrot-spinach-nd46.squarespace.com/articles/dying-light-the-beast-fsr-4-settings-optimization-guide", "content": "How to Enable FSR 4 in Dying Light The Beast. Open the main menu, go into Options, then Video . Scroll to the Upscaling setting and select FSR 4. You can then pick between Quality, Balanced, or Performance depending on how much FPS you want."}
+{"idx": 6, "title": "Cin\\'{e} aste: A Fine-grained Contextual Movie Question Answering...", "date": "", "ddg_snippet": "Experiments and Discussion. Experimental Setup .4.1. Experimental Setup . For all open-source models, we provide the maximum num-ber of frames each architecture supports, ensuring a fair comparison of their out-of-the-box capabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14227", "content": "Experiments and Discussion. Experimental Setup .4.1. Experimental Setup . For all open-source models, we provide the maximum num-ber of frames each architecture supports, ensuring a fair comparison of their out-of-the-box capabilities."}
+{"idx": 7, "title": "Скрипт На Зомби Против Людей | TikTok", "date": "", "ddg_snippet": "TikTok video from Poison X (@poison.x2): “ MSVD Script”.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/скрипт-на-зомби-против-людей", "content": "TikTok video from Poison X (@poison.x2): “ MSVD Script”."}
+{"idx": 8, "title": "Researchers control quantum phenomena at room temperature...", "date": "", "ddg_snippet": "The setup enabled the researchers to achieve “optical squeezing,” a quantum phenomenon manipulating light properties to reduce fluctuations in one variable while increasing them in another, as per Heisenberg’s principle.", "subpage_snippet": "", "source": "interestingengineering.com", "link": "https://interestingengineering.com/news/researchers-master-quantum-control-at-room-temperature", "content": "The setup enabled the researchers to achieve “optical squeezing,” a quantum phenomenon manipulating light properties to reduce fluctuations in one variable while increasing them in another, as per Heisenberg’s principle."}
+{"idx": 9, "title": "Dying Light: The Beast - The Ultimate Advanced Graphics Guide", "date": "", "ddg_snippet": "Table of Contents1.2 The Command List Decoded: The Most Important Settings 1.4 BONUS: How to Enable Hidden Ray Tracing ( Experimental )", "subpage_snippet": "", "source": "trioner.com", "link": "https://trioner.com/dying-light-the-beast-the-ultimate-advanced-graphics-guide/", "content": "Table of Contents1.2 The Command List Decoded: The Most Important Settings 1.4 BONUS: How to Enable Hidden Ray Tracing ( Experimental )"}
diff --git a/data/sampled_jsons/WWW_2024_Top-Tier_Information_Retrieval_papers_count_year_2024.jsonl b/data/sampled_jsons/WWW_2024_Top-Tier_Information_Retrieval_papers_count_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0fb928b37ff9a59834d510a4e2c793b289fbf0ca
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_Top-Tier_Information_Retrieval_papers_count_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "List of countries by number of scientific and technical... - Wikipedia", "date": "", "ddg_snippet": "Those are lists of countries and territories by number of scientific publications in English. Scopus. Scientific citable documents counts in this table are from journals classified by Scopus. Nature Index. The countries with the highest share of arti...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/List_of_countries_by_number_of_scientific_and_technical_journal_articles", "content": "Those are lists of countries and territories by number of scientific publications in English. Scopus. Scientific citable documents counts in this table are from journals classified by Scopus. Nature Index. The countries with the highest share of arti..."}
+{"idx": 1, "title": "InteGround: On the Evaluation of Verification and Retrieval Planning in...", "date": "", "ddg_snippet": "is obtained through the top -n retrieval results.(2023) , especially when it contains conflicting information Jiayang et al. ( 2024 ) . While related to our verification component, our work focuses on a comprehensive evaluation of integrative retrieval and planning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16534v1", "content": "is obtained through the top -n retrieval results.(2023) , especially when it contains conflicting information Jiayang et al. ( 2024 ) . While related to our verification component, our work focuses on a comprehensive evaluation of integrative retrieval and planning."}
+{"idx": 2, "title": "SciHub 2024 Official Websites - All Sci Hub Proxy Links", "date": "", "ddg_snippet": "Updated for 2024 . Search for papers by DOI. www .sci-hub.se – Working July 2024 with VPN set to United States.", "subpage_snippet": "", "source": "scihub.help", "link": "https://scihub.help/", "content": "Updated for 2024 . Search for papers by DOI. www .sci-hub.se – Working July 2024 with VPN set to United States."}
+{"idx": 3, "title": "Lost Ark tier list: Best characters and classes for PvE & PvP... - Dexerto", "date": "", "ddg_snippet": "Lost Ark is full of classes to try in both PvE and PvP, and our tier list can help you decide exactly which ones to use for the MMO‘s different challenges. Having the right class is essential to get the best experience in Lost Ark, especially as each is best suited to different roles.", "subpage_snippet": "", "source": "www.dexerto.com", "link": "https://www.dexerto.com/lost-ark/lost-ark-tier-list-best-characters-and-classes-pve-pvp-1795257/", "content": "Lost Ark is full of classes to try in both PvE and PvP, and our tier list can help you decide exactly which ones to use for the MMO‘s different challenges. Having the right class is essential to get the best experience in Lost Ark, especially as each is best suited to different roles."}
+{"idx": 4, "title": "3- Tier Household Wire Shelving", "date": "", "ddg_snippet": "3- Tier Household Wire ShelvingColor: BlackWeight Capacity: 25 lbs per shelf (75 lbs total)Dimensions: 23\" W x 13\" D x 3\" HBox weight: 11.6 lbs.", "subpage_snippet": "", "source": "www.oceanstatejoblot.com", "link": "https://www.oceanstatejoblot.com/shelving/3-tier-household-wire-shelving/223104", "content": "3- Tier Household Wire ShelvingColor: BlackWeight Capacity: 25 lbs per shelf (75 lbs total)Dimensions: 23\" W x 13\" D x 3\" HBox weight: 11.6 lbs."}
+{"idx": 5, "title": "Knowledge is Power, Understanding is Impact... - Ludovico Boratto", "date": "", "ddg_snippet": "To collect existing path reasoning methods, we systematically scanned the recent proceedings of top - tier information retrieval events and journals edited by top - tier publishers.", "subpage_snippet": "", "source": "www.ludovicoboratto.com", "link": "https://www.ludovicoboratto.com/knowledge-is-power-understanding-is-impact-utility-and-beyond-goals-explanation-quality-and-fairness-in-path-reasoning-recommendation/", "content": "To collect existing path reasoning methods, we systematically scanned the recent proceedings of top - tier information retrieval events and journals edited by top - tier publishers."}
+{"idx": 6, "title": "Announcing the Vespa ColBERT embedder | Vespa Blog", "date": "", "ddg_snippet": "14 Feb 2024 . Announcing the Vespa ColBERT embedder.It is one of the most cited recent information retrieval papers , with over 800 citations. Later improvements (distillation, compression) were incorporated in ColBERT v2.", "subpage_snippet": "", "source": "blog.vespa.ai", "link": "https://blog.vespa.ai/announcing-colbert-embedder-in-vespa/", "content": "14 Feb 2024 . Announcing the Vespa ColBERT embedder.It is one of the most cited recent information retrieval papers , with over 800 citations. Later improvements (distillation, compression) were incorporated in ColBERT v2."}
+{"idx": 7, "title": "Google NotebookLM | AI Research Tool & Thinking Partner", "date": "", "ddg_snippet": "Upload lecture recordings, textbook chapters, and research papers . Ask NotebookLM to explain complex concepts in simple terms, provide real-world examples, and reinforce your understanding.", "subpage_snippet": "", "source": "notebooklm.google", "link": "https://notebooklm.google/", "content": "Upload lecture recordings, textbook chapters, and research papers . Ask NotebookLM to explain complex concepts in simple terms, provide real-world examples, and reinforce your understanding."}
+{"idx": 8, "title": "Publications | Qingyao Ai", "date": "", "ddg_snippet": "Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , SIGIR 2024 , Washington DC, USA, July 14-18, 2024 .Effective Exposure Amortizing for Fair Top -k Recommendation.", "subpage_snippet": "", "source": "qingyaoai.github.io", "link": "https://qingyaoai.github.io/publications/", "content": "Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , SIGIR 2024 , Washington DC, USA, July 14-18, 2024 .Effective Exposure Amortizing for Fair Top -k Recommendation."}
+{"idx": 9, "title": "Ultimate Tower Defense Wiki | Fandom", "date": "", "ddg_snippet": "Christmas Event 2024 .These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site.", "subpage_snippet": "", "source": "ultimate-tower-defense.fandom.com", "link": "https://ultimate-tower-defense.fandom.com/", "content": "Christmas Event 2024 .These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site."}
diff --git a/data/sampled_jsons/WWW_2024_World_Wide_Web_conference_proceedings_accepted_papers_year_2024.jsonl b/data/sampled_jsons/WWW_2024_World_Wide_Web_conference_proceedings_accepted_papers_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1f7f5bcf477561ab1ffa5a6142f6db7a520a5695
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_World_Wide_Web_conference_proceedings_accepted_papers_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International World Wide Web Conference 2024 ( WWW2024 | The Web Conf ...", "date": "", "ddg_snippet": "The submission versions of these accepted papers as well as their reviews can be found on OpenReview. The camera-ready versions can be found on ACM Digital Library.", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/research-tracks/", "content": "The submission versions of these accepted papers as well as their reviews can be found on OpenReview. The camera-ready versions can be found on ACM Digital Library."}
+{"idx": 1, "title": "Proceedings of the ACM Web Conference 2024 | ACM Conferences", "date": "", "ddg_snippet": "The Web Conference , formerly known as International World Wide Web Conference and abbreviated as WWW , began the journey in 1994 at CERN. This conference has been the premier venue to present and discuss progress in research, development, standards and applications of topics related to the Web .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3589334", "content": "The Web Conference , formerly known as International World Wide Web Conference and abbreviated as WWW , began the journey in 1994 at CERN. This conference has been the premier venue to present and discuss progress in research, development, standards and applications of topics related to the Web ."}
+{"idx": 2, "title": "Home | International World Wide Web Conference 2024 ( WWW2024 | The Web ...", "date": "", "ddg_snippet": "| | | | The Web Conference 2024 is organized by the School of Computing and Information Systems at Singapore Management University.", "subpage_snippet": "", "source": "archives.iw3c2.org", "link": "https://archives.iw3c2.org/proceedings/www2024/index.html", "content": "| | | | The Web Conference 2024 is organized by the School of Computing and Information Systems at Singapore Management University."}
+{"idx": 3, "title": "The Web Conference (WWW) - dblp", "date": "", "ddg_snippet": "Proceedings of the 13th international conference on World Wide Web - Alternate Track Papers & Posters, WWW 2004, New York, NY, USA, May 17-20, 2004. ACM 2004, ISBN 1-58113-912-8 [contents]", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/db/conf/www/index", "content": "Proceedings of the 13th international conference on World Wide Web - Alternate Track Papers & Posters, WWW 2004, New York, NY, USA, May 17-20, 2004. ACM 2004, ISBN 1-58113-912-8 [contents]"}
+{"idx": 4, "title": "ACM TheWebConf 2024 Conference | OpenReview", "date": "", "ddg_snippet": "The Web Conference 2024 TheWebConf24 Singapore May 13 2024 https:// www2024 .thewebconf.org/ thewebconf24-pcchairs@acm.org", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=ACM.org/TheWebConf/2024/Conference", "content": "The Web Conference 2024 TheWebConf24 Singapore May 13 2024 https:// www2024 .thewebconf.org/ thewebconf24-pcchairs@acm.org"}
+{"idx": 5, "title": "WWW: International World Wide Web Conferences 2026 2025 2024 ... - WikiCFP", "date": "", "ddg_snippet": "The Web Conference is the premier conference focused on understanding the current state and the evolution of the Web through the lens of computer science, computational social science, economics, policy, and many other disciplines. Important Dates: Abstract: October 7, 2024 Full paper : October 14, 2024", "subpage_snippet": "", "source": "www.wikicfp.com", "link": "http://www.wikicfp.com/cfp/program?id=3182", "content": "The Web Conference is the premier conference focused on understanding the current state and the evolution of the Web through the lens of computer science, computational social science, economics, policy, and many other disciplines. Important Dates: Abstract: October 7, 2024 Full paper : October 14, 2024"}
+{"idx": 6, "title": "Paper accepted at ACM The WebConf 2024 - Technische Universität Dresden", "date": "", "ddg_snippet": "The Web Conference 2024 (formerly known as WWW ) is a yearly international conference on the topic of the future directions of the World Wide Web . The Conference aims to provide the world with a premier forum for discussion and debate about the evolution of the Web , the standardization of its associated technologies, and the impact of those ...", "subpage_snippet": "", "source": "tu-dresden.de", "link": "https://tu-dresden.de/ing/informatik/sya/netd/about/news/paper-accepted-at-acm-the-webconf-2024", "content": "The Web Conference 2024 (formerly known as WWW ) is a yearly international conference on the topic of the future directions of the World Wide Web . The Conference aims to provide the world with a premier forum for discussion and debate about the evolution of the Web , the standardization of its associated technologies, and the impact of those ..."}
+{"idx": 7, "title": "PDF Report on the 33rd The ACM Web Conference (WWW 2024)", "date": "", "ddg_snippet": "The ACM Web Conference 2024 was held from May 13 to 17, 2024 at Resorts World Conven-tion Centre, Singapore. The first two days featured workshops, tutorials, special days, and symposia. The last three days were the main conference days dedicated to keynote speeches, parallel presentation sessions of research, industry and special tracks, as well as multiple poster and demo sessions. This ...", "subpage_snippet": "", "source": "sigir.org", "link": "https://sigir.org/wp-content/uploads/2025/01/p05.pdf", "content": "The ACM Web Conference 2024 was held from May 13 to 17, 2024 at Resorts World Conven-tion Centre, Singapore. The first two days featured workshops, tutorials, special days, and symposia. The last three days were the main conference days dedicated to keynote speeches, parallel presentation sessions of research, industry and special tracks, as well as multiple poster and demo sessions. This ..."}
+{"idx": 8, "title": "Accepted Papers - International World Wide Web Conference Committee", "date": "", "ddg_snippet": "Accepted Papers Research Tracks All Papers Social Network Analysis and Graph Algorithms User Modeling and Personalization", "subpage_snippet": "", "source": "archives.iw3c2.org", "link": "https://archives.iw3c2.org/www2023/program/accepted-papers/", "content": "Accepted Papers Research Tracks All Papers Social Network Analysis and Graph Algorithms User Modeling and Personalization"}
+{"idx": 9, "title": "International World Wide Web Conference 2024 ( WWW2024 | The Web Conf ...", "date": "", "ddg_snippet": "Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/industry/", "content": "Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty"}
diff --git a/data/sampled_jsons/WWW_2024_blockchain_reinforcement_learning_-SPRING_-State_Placement_year_2024.jsonl b/data/sampled_jsons/WWW_2024_blockchain_reinforcement_learning_-SPRING_-State_Placement_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..565f9ab430f895031a7092f68acefaf3229e23ac
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_blockchain_reinforcement_learning_-SPRING_-State_Placement_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AI and Crypto: How Machine Learning is Enhancing Blockchain ...", "date": "", "ddg_snippet": "Proving itself a game-changer for blockchain , reinforcement learning trains algorithms to make sequential decisions when they’re needed. This means always learning about new attack vectors and adapting to them.", "subpage_snippet": "", "source": "techstartups.com", "link": "https://techstartups.com/2024/10/17/ai-and-crypto-how-machine-learning-is-enhancing-blockchain-security/", "content": "Proving itself a game-changer for blockchain , reinforcement learning trains algorithms to make sequential decisions when they’re needed. This means always learning about new attack vectors and adapting to them."}
+{"idx": 1, "title": "Reinforcement learning -enabled swarm intelligence method for...", "date": "", "ddg_snippet": "Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/reinforcement-learning-enabled-swarm-intelligence-method-for-computation-task-offloading-in-internet-of-things-blockchain/1056208378818199572-2460", "content": "Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions."}
+{"idx": 2, "title": "Top 10 Pharmaceutical Industry Trends in 2024 | StartUs Insights", "date": "", "ddg_snippet": "Delve into our data-driven analysis of 1700+ pharma startups, revealing significant pharmaceutical industry trends. Our research highlights the impact of AI, precision medicine, 3D printing, and blockchain on treatment innovation and industry standards.", "subpage_snippet": "", "source": "www.startus-insights.com", "link": "https://www.startus-insights.com/innovators-guide/top-10-pharma-industry-trends-innovations-in-2021/", "content": "Delve into our data-driven analysis of 1700+ pharma startups, revealing significant pharmaceutical industry trends. Our research highlights the impact of AI, precision medicine, 3D printing, and blockchain on treatment innovation and industry standards."}
+{"idx": 3, "title": "WiMi Researches Reinforcement Learning -Based Blockchain ...", "date": "", "ddg_snippet": "Reinforcement Learning is a machine learning approach that enables an intelligent agent to learn optimal strategies through interactions with the environment. In a blockchain -based federated learning framework utilizing reinforcement learning , the reinforcement learning ...", "subpage_snippet": "", "source": "www.itnewsonline.com", "link": "https://www.itnewsonline.com/PRNewswire/WiMi-Researches-Reinforcement-Learning-Based-Blockchain-Federated-Learning-Framework-to-Optimize-Model-Aggregation-Strategy-and-Security/1021346", "content": "Reinforcement Learning is a machine learning approach that enables an intelligent agent to learn optimal strategies through interactions with the environment. In a blockchain -based federated learning framework utilizing reinforcement learning , the reinforcement learning ..."}
+{"idx": 4, "title": "Recent Reinforcement Learning and Blockchain Based Security...", "date": "", "ddg_snippet": "Reinforcement learning (RL) is the most popular machine learning technique proposed to secure IoT systems. Unlike other ML methods, RL can observe, learn and interact with the environment even if it has minimum information about the considered parameters.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372858036_Recent_Reinforcement_Learning_and_Blockchain_Based_Security_Solutions_for_Internet_of_Things_Survey", "content": "Reinforcement learning (RL) is the most popular machine learning technique proposed to secure IoT systems. Unlike other ML methods, RL can observe, learn and interact with the environment even if it has minimum information about the considered parameters."}
+{"idx": 5, "title": "Turing Award Honors AI Pioneers Behind Reinforcement Learning", "date": "", "ddg_snippet": "Andrew Barto and Richard Sutton received the Turing Award for reinforcement learning research. Their contributions have influenced AI applications, including OpenAI’s GPT models and DeepMind’s AlphaGo.", "subpage_snippet": "", "source": "finance.coin-turk.com", "link": "https://finance.coin-turk.com/turing-award-honors-ai-pioneers-behind-reinforcement-learning/", "content": "Andrew Barto and Richard Sutton received the Turing Award for reinforcement learning research. Their contributions have influenced AI applications, including OpenAI’s GPT models and DeepMind’s AlphaGo."}
+{"idx": 6, "title": "RL-BES: optimizing strategies using reinforcement learning for...", "date": "", "ddg_snippet": "To overcome these challenges, we introduces a reinforcement learning -based MEV optimization system for blockchain —RL-BES ( Reinforcement Learning for Blockchain Economic Security).", "subpage_snippet": "", "source": "www.elspub.com", "link": "https://www.elspub.com/papers/j/1850822969307074560", "content": "To overcome these challenges, we introduces a reinforcement learning -based MEV optimization system for blockchain —RL-BES ( Reinforcement Learning for Blockchain Economic Security)."}
+{"idx": 7, "title": "The Future of AI, Quantum Computing, Blockchain , and...", "date": "", "ddg_snippet": "Artificial Intelligence (AI), Quantum Computing, and Blockchain are at the forefront, each contributing unique strengths that enhance the capabilities of the other. Moreover, reinforcement learning (RL), a subfield of AI, is becoming a critical component in refining intelligent systems.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/future-ai-quantum-computing-blockchain-reinforcement-learning-phd-s0zmc", "content": "Artificial Intelligence (AI), Quantum Computing, and Blockchain are at the forefront, each contributing unique strengths that enhance the capabilities of the other. Moreover, reinforcement learning (RL), a subfield of AI, is becoming a critical component in refining intelligent systems."}
+{"idx": 8, "title": "Effective Management for Blockchain -Based Agri-Food Supply Chains...", "date": "", "ddg_snippet": "This research explores the integration of deep reinforcement learning (DRL) with blockchain -based agri-food supply chains to enhance efficiency, transparency, and compliance.", "subpage_snippet": "", "source": "ijsrem.com", "link": "https://ijsrem.com/download/effective-management-for-blockchain-based-agri-food-supply-chains-using-deep-reinforcement-learning/", "content": "This research explores the integration of deep reinforcement learning (DRL) with blockchain -based agri-food supply chains to enhance efficiency, transparency, and compliance."}
+{"idx": 9, "title": "(PDF) MRL-PoS: A Multi-agent Reinforcement Learning based Proof...", "date": "", "ddg_snippet": "MRL-PoS employs reinforcement learning for dynamically adjusting to the behavior of all users.Keywords: Distributed Consensus, Blockchain , Reinforcement Learning , Multi-agent Systems, Proof-of-Stake.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/127537329/MRL_PoS_A_Multi_agent_Reinforcement_Learning_based_Proof_of_Stake_Consensus_Algorithm_for_Blockchain", "content": "MRL-PoS employs reinforcement learning for dynamically adjusting to the behavior of all users.Keywords: Distributed Consensus, Blockchain , Reinforcement Learning , Multi-agent Systems, Proof-of-Stake."}
diff --git a/data/sampled_jsons/WWW_2024_information_retrieval_papers_accepted_statistics_year_2024.jsonl b/data/sampled_jsons/WWW_2024_information_retrieval_papers_accepted_statistics_year_2024.jsonl
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index 0000000000000000000000000000000000000000..4ea586a40ac6d968d8ac7607c82a5ff43d98e364
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_information_retrieval_papers_accepted_statistics_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Information Retrieval Feb 2024", "date": "", "ddg_snippet": "Comments: Accepted by WWW 2024 . Subjects: Information Retrieval (cs.IR). [84] ... Subjects: Machine Learning ( stat .ML); Information Retrieval (cs.IR); ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.IR/2024-02?skip=75&show=250", "content": "Comments: Accepted by WWW 2024 . Subjects: Information Retrieval (cs.IR). [84] ... Subjects: Machine Learning ( stat .ML); Information Retrieval (cs.IR); ..."}
+{"idx": 1, "title": "Information Retrieval Mar 2024", "date": "", "ddg_snippet": "4 Mar 2024 — Title: Utilizing BERT for Information Retrieval : Survey, Applications, Resources, and Challenges ... Comments: Accepted by WWW 2024 . Subjects: ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.IR/2024-03", "content": "4 Mar 2024 — Title: Utilizing BERT for Information Retrieval : Survey, Applications, Resources, and Challenges ... Comments: Accepted by WWW 2024 . Subjects: ..."}
+{"idx": 2, "title": "WWW 2024 Statistics", "date": "", "ddg_snippet": "WWW 2024 Statistics . Home » Statistics » WWW Statistics » WWW 2024 Statistics ... Scalable and Effective Generative Information Retrieval , -, -, 3,3,4,4,3, 3.40", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/www-statistics/www-2024-statistics/", "content": "WWW 2024 Statistics . Home » Statistics » WWW Statistics » WWW 2024 Statistics ... Scalable and Effective Generative Information Retrieval , -, -, 3,3,4,4,3, 3.40"}
+{"idx": 3, "title": "Companion Proceedings of the ACM Web Conference 2024", "date": "", "ddg_snippet": "13 May 2024 — Report on the 33rd The ACM Web Conference ( WWW 2024 ), ACM SIGIR ... Information retrieval · World Wide Web · Web applications · Web services.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3589335?tocHeading=heading16", "content": "13 May 2024 — Report on the 33rd The ACM Web Conference ( WWW 2024 ), ACM SIGIR ... Information retrieval · World Wide Web · Web applications · Web services."}
+{"idx": 4, "title": "WWW 2024 Accepted Paper List", "date": "", "ddg_snippet": "WWW 2024 Accepted Paper List. Home » WWW Paper List » WWW 2024 Accepted Paper List ... Scalable and Effective Generative Information Retrieval -, Hansi Zeng;Chen ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/www-paper-list/www-2024-paper-list/", "content": "WWW 2024 Accepted Paper List. Home » WWW Paper List » WWW 2024 Accepted Paper List ... Scalable and Effective Generative Information Retrieval -, Hansi Zeng;Chen ..."}
+{"idx": 5, "title": "IR-LLM/Awesome-Information-Retrieval-in-the-Age-of- ...", "date": "", "ddg_snippet": "Information Retrieval Meets Large Language Models. Zheng Liu et.al. WWW 2024 . Retrieval Augmented LLM. For Pre-training LLM.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/IR-LLM/Awesome-Information-Retrieval-in-the-Age-of-Large-Language-Model", "content": "Information Retrieval Meets Large Language Models. Zheng Liu et.al. WWW 2024 . Retrieval Augmented LLM. For Pre-training LLM."}
+{"idx": 6, "title": "Information Retrieval Meets Large Language Models Workshop", "date": "", "ddg_snippet": "This workshop is dedicated to exploring how LLMs can enhance information retrieval algorithms, introducing a new era of data processing and analysis. ... WWW 2024 ...", "subpage_snippet": "", "source": "irmeetsllm.github.io", "link": "https://irmeetsllm.github.io/", "content": "This workshop is dedicated to exploring how LLMs can enhance information retrieval algorithms, introducing a new era of data processing and analysis. ... WWW 2024 ..."}
+{"idx": 7, "title": "Information Retrieval and the Web", "date": "", "ddg_snippet": "Information Retrieval and the Web. The science surrounding search engines ... The Web conference ( WWW) (2024 ). Preview Preview abstract File upload is a ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/research-areas/information-retrieval-and-the-web/", "content": "Information Retrieval and the Web. The science surrounding search engines ... The Web conference ( WWW) (2024 ). Preview Preview abstract File upload is a ..."}
+{"idx": 8, "title": "Sriparna Saha/Conference", "date": "", "ddg_snippet": "Towards Multi-modal Medical Consultation Concern Summary Generation\", ECIR (European Conference on Information Retrieval ) 2024 (IR for Good), Glasgow, Scotland, 24th-28th March 2024 (Core rank A).", "subpage_snippet": "", "source": "www.iitp.ac.in", "link": "https://www.iitp.ac.in/~sriparna/Conference.html", "content": "Towards Multi-modal Medical Consultation Concern Summary Generation\", ECIR (European Conference on Information Retrieval ) 2024 (IR for Good), Glasgow, Scotland, 24th-28th March 2024 (Core rank A)."}
+{"idx": 9, "title": "RUC-NLPIR/LLM4IR-Survey", "date": "", "ddg_snippet": "This is the collection of papers related to large language models for information retrieval . These papers are organized according to our survey paper .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/RUC-NLPIR/LLM4IR-Survey", "content": "This is the collection of papers related to large language models for information retrieval . These papers are organized according to our survey paper ."}
diff --git a/data/sampled_jsons/Wallach_social_science_measurement_framework_computer_science_history_justify_effort_rigor.jsonl b/data/sampled_jsons/Wallach_social_science_measurement_framework_computer_science_history_justify_effort_rigor.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..006761c12dfb8cbda855eeed1378ea5bbcdab2b8
--- /dev/null
+++ b/data/sampled_jsons/Wallach_social_science_measurement_framework_computer_science_history_justify_effort_rigor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Computational social science is growing up: why puberty", "date": "", "ddg_snippet": "... Computational Social Science (CSS) grows up, it must strike a balance between its own practices and those of neighboring disciplines to achieve ...", "subpage_snippet": "", "source": "epjdatascience.springeropen.com", "link": "https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-023-00434-1", "content": "... Computational Social Science (CSS) grows up, it must strike a balance between its own practices and those of neighboring disciplines to achieve ..."}
+{"idx": 1, "title": "Measurement challenges in AI catastrophic risk governance and", "date": "", "ddg_snippet": "Measurement modeling, widely used in quantitative and computational social sciences , 6 6 6 See Jackman ( 2009 ); Bandalos ( 2018 ); Jacobs and Wallach ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00608v1", "content": "Measurement modeling, widely used in quantitative and computational social sciences , 6 6 6 See Jackman ( 2009 ); Bandalos ( 2018 ); Jacobs and Wallach ..."}
+{"idx": 2, "title": "A systematic analysis of the science of sandboxing [PeerJ]", "date": "", "ddg_snippet": "In this paper, we use multidisciplinary techniques from software engineering, statistics, the social sciences , and graph analysis to systematically ...", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-43/", "content": "In this paper, we use multidisciplinary techniques from software engineering, statistics, the social sciences , and graph analysis to systematically ..."}
+{"idx": 3, "title": "1 Introduction", "date": "", "ddg_snippet": "... to obtain, and observed labels are instead derived from proxies, human judgments or measurement instruments that can embed label bias due to social ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07216v1", "content": "... to obtain, and observed labels are instead derived from proxies, human judgments or measurement instruments that can embed label bias due to social ..."}
+{"idx": 4, "title": "Philosophy of Technology (Stanford Encyclopedia of Philosophy)", "date": "", "ddg_snippet": "... science and with several other fields in the analytic tradition in modern philosophy, such as the philosophy of action and decision-making, rather ...", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/technology/", "content": "... science and with several other fields in the analytic tradition in modern philosophy, such as the philosophy of action and decision-making, rather ..."}
+{"idx": 5, "title": "No Encore for Encore? Ethical questions for web-based", "date": "", "ddg_snippet": "... computer science and computer security researchers: should researchers be permitted to surreptitiously alter the behavior of Internet-connected ...", "subpage_snippet": "", "source": "techscience.org", "link": "https://techscience.org/a/2015121501/", "content": "... computer science and computer security researchers: should researchers be permitted to surreptitiously alter the behavior of Internet-connected ..."}
+{"idx": 6, "title": "Tracking the Development of COVID-19-related PsyArXiv Preprints", "date": "", "ddg_snippet": "... behavioural sciences can increase our understanding of the world around us, some scholars are sceptical that certain social and behavioural sciences ...", "subpage_snippet": "", "source": "online.ucpress.edu", "link": "https://online.ucpress.edu/collabra/article/10/1/121378/202925/Tracking-the-Development-of-COVID-19-related", "content": "... behavioural sciences can increase our understanding of the world around us, some scholars are sceptical that certain social and behavioural sciences ..."}
+{"idx": 7, "title": "Ethical Considerations in AI Applications in Smart Grid[v1] |", "date": "", "ddg_snippet": "These frameworks call for collaborative efforts between technologists, managers, and policymakers to harmonize innovation with ethical accountability ...", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202412.1883/v1", "content": "These frameworks call for collaborative efforts between technologists, managers, and policymakers to harmonize innovation with ethical accountability ..."}
+{"idx": 8, "title": "IRI - Roboethics", "date": "", "ddg_snippet": "Since then, the history of this young discipline has witnessed specialized workshops in the most relevant robotics congresses, public institutional ...", "subpage_snippet": "", "source": "www.iri.upc.edu", "link": "https://www.iri.upc.edu/roboethics", "content": "Since then, the history of this young discipline has witnessed specialized workshops in the most relevant robotics congresses, public institutional ..."}
+{"idx": 9, "title": "$11M AI safety research program launched - Future of Life", "date": "", "ddg_snippet": "The winning teams, chosen from nearly 300 applicants worldwide, will research a host of questions in computer science , law, policy, economics, and ...", "subpage_snippet": "", "source": "futureoflife.org", "link": "https://futureoflife.org/ai/11m-ai-safety-research-program-launched/", "content": "The winning teams, chosen from nearly 300 applicants worldwide, will research a host of questions in computer science , law, policy, economics, and ..."}
diff --git a/data/sampled_jsons/Wampler_character_animation_two-player_adversarial_games_2010.jsonl b/data/sampled_jsons/Wampler_character_animation_two-player_adversarial_games_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cac297e889702efdd2c08f145fe2b7d9cdc77b40
--- /dev/null
+++ b/data/sampled_jsons/Wampler_character_animation_two-player_adversarial_games_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Matrix (franchise) - Wikipedia", "date": "", "ddg_snippet": "... characters and setting of the films are further explored in other media set in the same fictional universe , including animation , comics, and video ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/The_Matrix_(franchise)", "content": "... characters and setting of the films are further explored in other media set in the same fictional universe , including animation , comics, and video ..."}
+{"idx": 1, "title": "Kevin Wampler", "date": "", "ddg_snippet": "... but I also occasionally do some computer graphics research, primarily in animation and ... Character Animation in Two - Player Adversarial Games", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "https://www.kevinwampler.com/homepage/index.html", "content": "... but I also occasionally do some computer graphics research, primarily in animation and ... Character Animation in Two - Player Adversarial Games"}
+{"idx": 2, "title": "Strategy and Skill Learning for Physics-based Table Tennis", "date": "", "ddg_snippet": "... driven methods have become prevalent in physics-based character animation studies since a DRL-based method was introduced by Peng et al. ( 2018 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.16210v1", "content": "... driven methods have become prevalent in physics-based character animation studies since a DRL-based method was introduced by Peng et al. ( 2018 ) ."}
+{"idx": 3, "title": "A Mixture of Thoughts on Mario Sports Mix - Nintendojo", "date": "", "ddg_snippet": "... character has a special move to unleash after a ... After each field goal, the game breaks away to a cut scene showcasing the players celebrating.", "subpage_snippet": "", "source": "www.nintendojo.com", "link": "https://www.nintendojo.com/games/a-mixture-of-thoughts-on-mario-sports-mix", "content": "... character has a special move to unleash after a ... After each field goal, the game breaks away to a cut scene showcasing the players celebrating."}
+{"idx": 4, "title": "Donkey Kong Country Returns Preview - Nintendojo Nintendojo", "date": "", "ddg_snippet": "The shark showcased fantastic animation , with a mixture of ... Diddy Kong also allows for two - player co-op to be present throughout the entire game .", "subpage_snippet": "", "source": "www.nintendojo.com", "link": "https://www.nintendojo.com/games/donkey-kong-country-returns-preview", "content": "The shark showcased fantastic animation , with a mixture of ... Diddy Kong also allows for two - player co-op to be present throughout the entire game ."}
+{"idx": 5, "title": "Zoran Popović", "date": "", "ddg_snippet": "Six games produced in the 10-week Games Capstone class make it big in the wild: two independent reviews ( 1 , 2 ), second best game of the week, and ...", "subpage_snippet": "", "source": "homes.cs.washington.edu", "link": "https://homes.cs.washington.edu/~zoran/", "content": "Six games produced in the 10-week Games Capstone class make it big in the wild: two independent reviews ( 1 , 2 ), second best game of the week, and ..."}
+{"idx": 6, "title": "The Matrix (franchise) | Ultimate Pop Culture Wiki | Fandom", "date": "", "ddg_snippet": "... characters and setting of the films are further explored in other media set in the same fictional universe , including animation , comics, and video ...", "subpage_snippet": "", "source": "ultimatepopculture.fandom.com", "link": "https://ultimatepopculture.fandom.com/wiki/The_Matrix_(franchise)", "content": "... characters and setting of the films are further explored in other media set in the same fictional universe , including animation , comics, and video ..."}
+{"idx": 7, "title": "Super Punch: January 2021", "date": "", "ddg_snippet": "... celebration of this annual series, The Best American Short Stories, master of the form Lorrie Moore selects forty stories from the more than two ...", "subpage_snippet": "", "source": "www.superpunch.net", "link": "https://www.superpunch.net/2021/01/", "content": "... celebration of this annual series, The Best American Short Stories, master of the form Lorrie Moore selects forty stories from the more than two ..."}
+{"idx": 8, "title": "Publications – GRAIL: UW Graphics and Imaging Laboratory", "date": "", "ddg_snippet": "... Stylized Character Expressions from Humans Deepali Aneja, Bindita Chaudhuri, Alex Colburn, Gary Faigin, Linda Shapiro, Barbara Mones WACV, March 2018.", "subpage_snippet": "", "source": "grail.cs.washington.edu", "link": "https://grail.cs.washington.edu/publications/", "content": "... Stylized Character Expressions from Humans Deepali Aneja, Bindita Chaudhuri, Alex Colburn, Gary Faigin, Linda Shapiro, Barbara Mones WACV, March 2018."}
+{"idx": 9, "title": "Super Punch: Model of electricity consumption made using 300", "date": "", "ddg_snippet": "A model of electricity consumption in Manchester, 1945-1955: individually inspectable 2D records assembled into a 3D volume.", "subpage_snippet": "", "source": "www.superpunch.net", "link": "https://www.superpunch.net/2021/01/model-of-electricity-consumption-made.html", "content": "A model of electricity consumption in Manchester, 1945-1955: individually inspectable 2D records assembled into a 3D volume."}
diff --git a/data/sampled_jsons/Wampler_et_al._2010_Character_animation_in_two-player_adversarial_games_year_2010.jsonl b/data/sampled_jsons/Wampler_et_al._2010_Character_animation_in_two-player_adversarial_games_year_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..68b85607199ab697b5d55208b35096c75e057eec
--- /dev/null
+++ b/data/sampled_jsons/Wampler_et_al._2010_Character_animation_in_two-player_adversarial_games_year_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "dblp: Character animation in two - player adversarial games .", "date": "", "ddg_snippet": "Kevin Wampler et al . ( 2010 ). top.Kevin Wampler , Erik Andersen, Evan Herbst, Yongjoon Lee, Zoran Popovic: Character animation in two - player adversarial games .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/tog/WamplerAHLP10.html", "content": "Kevin Wampler et al . ( 2010 ). top.Kevin Wampler , Erik Andersen, Evan Herbst, Yongjoon Lee, Zoran Popovic: Character animation in two - player adversarial games ."}
+{"idx": 1, "title": "[PDF] Character animation in two - player adversarial games", "date": "", "ddg_snippet": "The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Character-animation-in-two-player-adversarial-games-Wampler-Andersen/aab9ee829c871af99ca5c91dbf594ad9e0674a79", "content": "The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games . We present a fully automatic method for generating intelligent real-time controllers for characters in such a game ."}
+{"idx": 2, "title": "Character Animation in Two - Player Adversarial Games", "date": "", "ddg_snippet": "2008;Lee and Lee 2004; Wampler et al . 2010 ]. Using an action-level graph, a game tree structure can also be established to organize collaborative and adversarial behaviors among multiple characters [Shum et al .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/220184094_Character_Animation_in_Two-Player_Adversarial_Games", "content": "2008;Lee and Lee 2004; Wampler et al . 2010 ]. Using an action-level graph, a game tree structure can also be established to organize collaborative and adversarial behaviors among multiple characters [Shum et al ."}
+{"idx": 3, "title": "Character animation in two - player adversarial games - Peeref", "date": "", "ddg_snippet": "Kevin Wampler Erik Andersen Evan Herbst Yongjoon Lee Zoran Popović.Online. 2010 -07-01.", "subpage_snippet": "", "source": "www.peeref.com", "link": "https://www.peeref.com/works/6185520", "content": "Kevin Wampler Erik Andersen Evan Herbst Yongjoon Lee Zoran Popović.Online. 2010 -07-01."}
+{"idx": 4, "title": "Formalizing Feint Actions, and Example Studies in Two - Player Games", "date": "", "ddg_snippet": "[ Wampler et al ., 2010 ] animates Feint actions as a proof of the capability to construct nuanced game strategies with unpredictability, in which Feint actions are treated the same as other actions. 2010 . Character animation in two - player adversarial games .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07931v1", "content": "[ Wampler et al ., 2010 ] animates Feint actions as a proof of the capability to construct nuanced game strategies with unpredictability, in which Feint actions are treated the same as other actions. 2010 . Character animation in two - player adversarial games ."}
+{"idx": 5, "title": "Kevin Wampler", "date": "", "ddg_snippet": "Character Animation in Two - Player Adversarial Games .Kevin Wampler , Daichi Sasaki, Li Zhang, Zoran Popović. SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation .", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "https://www.kevinwampler.com/", "content": "Character Animation in Two - Player Adversarial Games .Kevin Wampler , Daichi Sasaki, Li Zhang, Zoran Popović. SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation ."}
+{"idx": 6, "title": "CSC 2521: Physics-Based Character Animation calendar", "date": "", "ddg_snippet": "SIGGRAPH 2010 [web] [Huixuan] Background: Pratt et al . Virtual Model Control: An Intuitive Approach for Bipedal Locomotion.K. Wampler , E. Andersen, E. Herbst, Y. Lee, Z. Popović. Character Animation in Two - Player Adversarial Games .", "subpage_snippet": "", "source": "www.dgp.toronto.edu", "link": "https://www.dgp.toronto.edu/~hertzman/courses/csc2521/fall_2010/calendar.html", "content": "SIGGRAPH 2010 [web] [Huixuan] Background: Pratt et al . Virtual Model Control: An Intuitive Approach for Bipedal Locomotion.K. Wampler , E. Andersen, E. Herbst, Y. Lee, Z. Popović. Character Animation in Two - Player Adversarial Games ."}
+{"idx": 7, "title": "Adaptive motion synthesis for virtual characters : a survey | The Visual...", "date": "", "ddg_snippet": "In computer games and virtual reality, motion synthesis enriches the player or participant experience by allowing for unscripted and emergent character behavior.Cite this article. Guo, S., Southern, R., Chang, J. et al . Adaptive motion synthesis for virtual characters : a survey.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00371-014-0943-4", "content": "In computer games and virtual reality, motion synthesis enriches the player or participant experience by allowing for unscripted and emergent character behavior.Cite this article. Guo, S., Southern, R., Chang, J. et al . Adaptive motion synthesis for virtual characters : a survey."}
+{"idx": 8, "title": "I need thesis topic recommendations, related to AI in games | Forum", "date": "", "ddg_snippet": "I am in last third year undergrad and I need to pick a topic for thesis. In general, I want to work on AI with applications in games .", "subpage_snippet": "", "source": "www.gamedev.net", "link": "https://www.gamedev.net/forums/topic/617470-i-need-thesis-topic-recommendations-related-to-ai-in-games/", "content": "I am in last third year undergrad and I need to pick a topic for thesis. In general, I want to work on AI with applications in games ."}
+{"idx": 9, "title": "Character Animation in Two - Player Adversarial Games - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=VIqeuCTpcv8", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
diff --git a/data/sampled_jsons/Wang_CrossKD_Cross-head_knowledge_distillation_CIFAR100_classification_71.13_year_2024.jsonl b/data/sampled_jsons/Wang_CrossKD_Cross-head_knowledge_distillation_CIFAR100_classification_71.13_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e1ae43f126d52d9fd9148fae91ad7e3028451080
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2306.11369] CrossKD: Cross-Head Knowledge Distillation for ... CrossKD: Cross-Head Knowledge Distillation for Object Detection Cross-View Consistency Regularisation for Knowledge Distillation Zhe Wang's research works | East China University of Science ... Cross-View Consistency Regularisation for Knowledge Distillation CrossKD: Cross-Head Knowledge Distillation for Object Detection CrossKD: Cross-Head Knowledge Distillation for Object Detection CrossKD : Cross - Head Knowledge Distillation for Dense Object Detect… CrossKD : Cross-Head Knowledge Distillation for Object Detection CrossKD: Cross-Head Knowledge Distillation for Object Detection CrossKD: Cross-Head Knowledge Distillation for Object Detection CrossKD: Cross-Head Knowledge Distillation for Object Detection … CrossKD: Cross-Head Knowledge Distillation for Dense Object ...", "date": "", "ddg_snippet": "Jun 20, 2023 · Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ... Knowledge Distillation (KD) is an effective technique to transfer knowledge from a large-scale teacher model to a small-scale student model. It has been widely studied in the classification task [12, 23, 26, 36, 37, 45, 47, 53, 62, 66, 70, 71], but it is still challenging to distill detection models because of the extreme background ratio. Although widely explored in the 131 semi-supervised learning (SemiSL) literature [45, 53], consistency 132 regularisation and view transformation have received litle aten-133 tion in knowledge distillation research. According to [57], strong 134 augmentation amplifies the dark information that is insignificant in 135 the weak view. We also propose a focused distillation (FD) module that can alleviate the problem of knowledge loss during the task increment process, improving model stability without reducing plasticity. Our design of within-view and cross -view consistency regularsations, enabled by weak and strong image transformations and coupled with a confidence-based soft label selection scheme, leads to a highly effective and versatile knowledge distillation framework. Abstract Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the ... What is knowledge distillation for object detection (KD)? Knowledge Distillation for Object Detection Knowledge Distillation (KD) is an effective technique to transfer knowledge from a large-scale teacher model to a small-scale student model. What is a cross-head knowledge distillation pipeline? In this paper, we present a novel cross - head knowledge distillation pipeline, abbreviated as CrossKD , to alleviate the target conflict problem. As illustrated in \\figref fig:comparison (c), we propose to feed the intermediate features from the head of the student to that of the teacher, yielding the cross - head predictions. What is a cross-head prediction mimicking distillation scheme? In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's detection head to the teacher's detection head. The resulting cross-head predictions are then forced to mimic the teacher's predictions. Can knowledge distillation be used to learn com-Pact object detectors? Knowledge Distillation (KD) has been validated as an effective model compression technique for learning com-pact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. Is knowledge distillation a valid model compression technique for COM-Pact object detectors? Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore. Knowledge Distillation (KD) has been validated as an effective model compression technique for learning com-pact object detectors. Can crosskd be used for lightweight detector distillation? CrossKD is adaptable for any detector distillation since the target conflict is a common problem of object detection distillation due to imperfect teacher predictions. To demonstrate the generalization, we apply CrossKD on detectors with various types of backbones and structures. Table 10: Quantitative results of CrossKD for lightweight detectors. The resulting cross-head predictions are then forced to mimic the teacher’s predictions. Such a distillation manner relieves the student’s head from receiving contradictory supervision signals from the ground-truth annotations and the teacher’s predictions, greatly improving the student’s detection performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.11369", "content": "Jun 20, 2023 · Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ... Knowledge Distillation (KD) is an effective technique to transfer knowledge from a large-scale teacher model to a small-scale student model. It has been widely studied in the classification task [12, 23, 26, 36, 37, 45, 47, 53, 62, 66, 70, 71], but it is still challenging to distill detection models because of the extreme background ratio. Although widely explored in the 131 semi-supervised learning (SemiSL) literature [45, 53], consistency 132 regularisation and view transformation have received litle aten-133 tion in knowledge distillation research. According to [57], strong 134 augmentation amplifies the dark information that is insignificant in 135 the weak view. We also propose a focused distillation (FD) module that can alleviate the problem of knowledge loss during the task increment process, improving model stability without reducing plasticity. Our design of within-view and cross -view consistency regularsations, enabled by weak and strong image transformations and coupled with a confidence-based soft label selection scheme, leads to a highly effective and versatile knowledge distillation framework. Abstract Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the ... What is knowledge distillation for object detection (KD)? Knowledge Distillation for Object Detection Knowledge Distillation (KD) is an effective technique to transfer knowledge from a large-scale teacher model to a small-scale student model. What is a cross-head knowledge distillation pipeline? In this paper, we present a novel cross - head knowledge distillation pipeline, abbreviated as CrossKD , to alleviate the target conflict problem. As illustrated in \\figref fig:comparison (c), we propose to feed the intermediate features from the head of the student to that of the teacher, yielding the cross - head predictions. What is a cross-head prediction mimicking distillation scheme? In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's detection head to the teacher's detection head. The resulting cross-head predictions are then forced to mimic the teacher's predictions. Can knowledge distillation be used to learn com-Pact object detectors? Knowledge Distillation (KD) has been validated as an effective model compression technique for learning com-pact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. Is knowledge distillation a valid model compression technique for COM-Pact object detectors? Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore. Knowledge Distillation (KD) has been validated as an effective model compression technique for learning com-pact object detectors. Can crosskd be used for lightweight detector distillation? CrossKD is adaptable for any detector distillation since the target conflict is a common problem of object detection distillation due to imperfect teacher predictions. To demonstrate the generalization, we apply CrossKD on detectors with various types of backbones and structures. Table 10: Quantitative results of CrossKD for lightweight detectors. The resulting cross-head predictions are then forced to mimic the teacher’s predictions. Such a distillation manner relieves the student’s head from receiving contradictory supervision signals from the ground-truth annotations and the teacher’s predictions, greatly improving the student’s detection performance."}
+{"idx": 1, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Knowledge Distillation (KD) is an effective technique to transfer knowledge from a large-scale teacher model to a small-scale student model. It has been widely studied in the classification task [12, 23, 26, 36, 37, 45, 47, 53, 62, 66, 70, 71], but it is still challenging to distill detection models because of the extreme background ratio.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_CrossKD_Cross-Head_Knowledge_Distillation_for_Object_Detection_CVPR_2024_paper.pdf", "content": "Knowledge Distillation (KD) is an effective technique to transfer knowledge from a large-scale teacher model to a small-scale student model. It has been widely studied in the classification task [12, 23, 26, 36, 37, 45, 47, 53, 62, 66, 70, 71], but it is still challenging to distill detection models because of the extreme background ratio."}
+{"idx": 2, "title": "PDF Cross-View Consistency Regularisation for Knowledge Distillation", "date": "", "ddg_snippet": "23 significantly boosts student learning, seting new state-of-the-art 24 results on the standard CIFAR-100 , Tiny-ImageNet, and ImageNet 25 datasets across a diversity of teacher and student architectures, 26 whilst introducing no extra network parameters. Orthogonal to on- 27 going logit-based distillation research, our method enjoys excellent", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/9274b4bd2a934480beee273794607b4d9a3ea7d5.pdf", "content": "23 significantly boosts student learning, seting new state-of-the-art 24 results on the standard CIFAR-100 , Tiny-ImageNet, and ImageNet 25 datasets across a diversity of teacher and student architectures, 26 whilst introducing no extra network parameters. Orthogonal to on- 27 going logit-based distillation research, our method enjoys excellent"}
+{"idx": 3, "title": "Zhe Wang's research works | East China University of Science ...", "date": "", "ddg_snippet": "We also propose a focused distillation (FD) module that can alleviate the problem of knowledge loss during the task increment process, improving model stability without reducing plasticity.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Zhe-Wang-68918046/publications/4", "content": "We also propose a focused distillation (FD) module that can alleviate the problem of knowledge loss during the task increment process, improving model stability without reducing plasticity."}
+{"idx": 4, "title": "Cross-View Consistency Regularisation for Knowledge Distillation", "date": "", "ddg_snippet": "Despite its apparent simplicity, the proposed Consistency-Regularisation-based Logit Distillation (CRLD) significantly boosts student learning, setting new state-of-the-art results on the standard CIFAR-100 , Tiny-ImageNet, and ImageNet datasets across a diversity of teacher and student architectures, whilst introducing no extra network parameters.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16493v1", "content": "Despite its apparent simplicity, the proposed Consistency-Regularisation-based Logit Distillation (CRLD) significantly boosts student learning, setting new state-of-the-art results on the standard CIFAR-100 , Tiny-ImageNet, and ImageNet datasets across a diversity of teacher and student architectures, whilst introducing no extra network parameters."}
+{"idx": 5, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Abstract Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.11369", "content": "Abstract Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the ..."}
+{"idx": 6, "title": "CrossKD: Cross-Head Knowledge Distillation for Dense Object ...", "date": "", "ddg_snippet": "The resulting cross-head predictions are then forced to mimic the teacher’s predictions. Such a distillation manner relieves the student’s head from receiving contradictory supervision signals from the ground-truth annotations and the teacher’s predictions, greatly improving the student’s detection performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.11369v1", "content": "The resulting cross-head predictions are then forced to mimic the teacher’s predictions. Such a distillation manner relieves the student’s head from receiving contradictory supervision signals from the ground-truth annotations and the teacher’s predictions, greatly improving the student’s detection performance."}
+{"idx": 7, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "In this paper we present a general and effective prediction mimicking distillation scheme called CrossKD which delivers the intermediate features of the student's detection head to the teacher's detection head . The resulting cross - head predictions are then forced to mimic the teacher's predictions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Wang_CrossKD_Cross-Head_Knowledge_Distillation_for_Object_Detection_CVPR_2024_paper.html", "content": "In this paper we present a general and effective prediction mimicking distillation scheme called CrossKD which delivers the intermediate features of the student's detection head to the teacher's detection head . The resulting cross - head predictions are then forced to mimic the teacher's predictions."}
+{"idx": 8, "title": "Mitigating noisy labels in long-tailed image classification via multi ...", "date": "", "ddg_snippet": "Label noise and class imbalance are two types of data bias that have attracted widespread attention in the past, but few methods can address both of them simultaneously. Recently, some works have begun to explore handling the two biases concurrently. In this article, we combine feature-level sample selection with logit-level knowledge distillation and logit adjustment to form a more complete ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10489-025-06809-3", "content": "Label noise and class imbalance are two types of data bias that have attracted widespread attention in the past, but few methods can address both of them simultaneously. Recently, some works have begun to explore handling the two biases concurrently. In this article, we combine feature-level sample selection with logit-level knowledge distillation and logit adjustment to form a more complete ..."}
+{"idx": 9, "title": "Cross-View Consistency Regularisation for Knowledge Distillation", "date": "", "ddg_snippet": "Our design of within-view and cross - viewconsistencyregularsations,enabledbyweakandstrongimage transformations and coupled with a con dence-based soft label se- lection scheme, leads to a highly e ective and versatile knowledge distillation framework.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.16493", "content": "Our design of within-view and cross - viewconsistencyregularsations,enabledbyweakandstrongimage transformations and coupled with a con dence-based soft label se- lection scheme, leads to a highly e ective and versatile knowledge distillation framework."}
diff --git a/data/sampled_jsons/WcuXvn3HVk_AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_.jsonl b/data/sampled_jsons/WcuXvn3HVk_AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4a17dc843b5eda37a317163f59e349068ef55752
--- /dev/null
+++ b/data/sampled_jsons/WcuXvn3HVk_AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of..."}
+{"idx": 1, "title": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "Blockchain , Sharding , Account migration , Reinforcement learning .Sender Shard Transaction Count. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "Blockchain , Sharding , Account migration , Reinforcement learning .Sender Shard Transaction Count. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration ."}
+{"idx": 2, "title": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "Blockchain , Sharding , Account migration , Reinforcement learning . Figure 7 : Comparison of overall TPS . 6 Conclusion. We proposed AERO , an attention-based DRL framework designed for efficient account migration in sharding blockchain networks.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "Blockchain , Sharding , Account migration , Reinforcement learning . Figure 7 : Comparison of overall TPS . 6 Conclusion. We proposed AERO , an attention-based DRL framework designed for efficient account migration in sharding blockchain networks."}
+{"idx": 3, "title": "Peking University - Cited by 70 - Deep Learning - Google Scholar", "date": "", "ddg_snippet": "2023. AERO : Enhancing sharding blockchain via deep reinforcement learning for account migration .Presto: Optimizing Cross- Shard Transactions in Sharded Blockchain Architecture.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=BdT5CzcAAAAJ&hl=en", "content": "2023. AERO : Enhancing sharding blockchain via deep reinforcement learning for account migration .Presto: Optimizing Cross- Shard Transactions in Sharded Blockchain Architecture."}
+{"idx": 4, "title": "(PDF) Account Migration across Blockchain Shards using...", "date": "", "ddg_snippet": "figure blockchain shards via account shuffling, we find that. account migration plays a significant role. reinforcement learning -empowered dynamic blockchain sharding sys-. tem,” in Proc. of 49th International Conference on Parallel Processing. (ICPP’20), 2020, pp. 1–11.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/379210418_Account_Migration_across_Blockchain_Shards_using_Fine-tuned_Lock_Mechanism", "content": "figure blockchain shards via account shuffling, we find that. account migration plays a significant role. reinforcement learning -empowered dynamic blockchain sharding sys-. tem,” in Proc. of 49th International Conference on Parallel Processing. (ICPP’20), 2020, pp. 1–11."}
+{"idx": 5, "title": "MIT 6.S091: Introduction to Deep Reinforcement Learning ... - YouTube", "date": "", "ddg_snippet": "First lecture of MIT course 6.S091: Deep Reinforcement Learning , introducing the fascinating field of Deep RL.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=zR11FLZ-O9M", "content": "First lecture of MIT course 6.S091: Deep Reinforcement Learning , introducing the fascinating field of Deep RL."}
+{"idx": 6, "title": "Scalability of blockchain : Review of cross- sharding with high...", "date": "", "ddg_snippet": "During migration , account migration is required to guard against hostile nodes assaulting the system and execution errors. deep more scalable. and blockchain security and atomicity. learning with the proposed reinforcement - Blockchain transpare of cross- shard transactions.", "subpage_snippet": "", "source": "www.bio-conferences.org", "link": "https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00075.pdf", "content": "During migration , account migration is required to guard against hostile nodes assaulting the system and execution errors. deep more scalable. and blockchain security and atomicity. learning with the proposed reinforcement - Blockchain transpare of cross- shard transactions."}
+{"idx": 7, "title": "[2304.08595] Prophet: Conflict-Free Sharding Blockchain via ...", "date": "", "ddg_snippet": "Sharding scales throughput by splitting blockchain nodes into parallel groups.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.08595", "content": "Sharding scales throughput by splitting blockchain nodes into parallel groups."}
+{"idx": 8, "title": "Proceedings of the ACM on Web Conference 2025 | ACM Conferences", "date": "", "ddg_snippet": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Mingxuan Song, Pengze LiTo address these scalability issues, account migration offers a promising solution. ...", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/proceedings/10.1145/3696410?tocHeading=heading13", "content": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Mingxuan Song, Pengze LiTo address these scalability issues, account migration offers a promising solution. ..."}
+{"idx": 9, "title": "GitHub - hzysvilla/Academic_Smart_Contract_Papers: Academic Smart...", "date": "", "ddg_snippet": "[security] AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hzysvilla/Academic_Smart_Contract_Papers", "content": "[security] AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration ."}
diff --git a/data/sampled_jsons/We_evaluate_three_retrievers_with_different_retrieval_paradigms.jsonl b/data/sampled_jsons/We_evaluate_three_retrievers_with_different_retrieval_paradigms.jsonl
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index 0000000000000000000000000000000000000000..03aabe7bf37a619b19ed62e34aa4f6d31d4135d0
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+{"idx": 0, "title": "P3: Optimizing Retrieval with Reasoning Models – Hamel’s", "date": "", "ddg_snippet": "Orion Weller from Johns Hopkins University on making retrieval systems understand complex instructions and reason about documents, featuring ...", "subpage_snippet": "", "source": "hamel.dev", "link": "https://hamel.dev/notes/llm/rag/p3_reasoning.html", "content": "Orion Weller from Johns Hopkins University on making retrieval systems understand complex instructions and reason about documents, featuring ..."}
+{"idx": 1, "title": "Evaluating the Robustness of Retrieval-Augmented Generation to", "date": "", "ddg_snippet": "... we : (1) evaluate the susceptibility of RAG systems to adversarial documents in the medical domain where these documents are provided as retrieval ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03787v1", "content": "... we : (1) evaluate the susceptibility of RAG systems to adversarial documents in the medical domain where these documents are provided as retrieval ..."}
+{"idx": 2, "title": "Generative Retrieval as Multi-Vector Dense Retrieval", "date": "", "ddg_snippet": "... we can further understand how GR is fundamentally different from dense retrieval methods and adds to the spectrum of neural-based retrieval models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.00684v1", "content": "... we can further understand how GR is fundamentally different from dense retrieval methods and adds to the spectrum of neural-based retrieval models."}
+{"idx": 3, "title": "(PDF) The Critical Importance of Retrieval for Learning", "date": "", "ddg_snippet": "In three other conditions, once a student had correctly produced the vocabulary item, it was repeatedly studied but dropped from further testing ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/5574966_The_Critical_Importance_of_Retrieval_for_Learning", "content": "In three other conditions, once a student had correctly produced the vocabulary item, it was repeatedly studied but dropped from further testing ..."}
+{"idx": 4, "title": "Read Download Evaluating Information Retrieval Systems PDF –", "date": "", "ddg_snippet": "The retrieval community has been extremely fortunate to have such a well-grounded evaluation paradigm during a period when most of the human language ...", "subpage_snippet": "", "source": "bibleandbookcenter.com", "link": "https://bibleandbookcenter.com/read/evaluating-information-retrieval-systems/", "content": "The retrieval community has been extremely fortunate to have such a well-grounded evaluation paradigm during a period when most of the human language ..."}
+{"idx": 5, "title": "Scalable evaluation framework for retrieval augmented", "date": "", "ddg_snippet": "Using a Goal-Question-Metric paradigm , we evaluated two distinct LLM architectures in RAG configurations: Mixtral 8 × 7B and Llama 3 .1 70B.", "subpage_snippet": "", "source": "researchportal.bath.ac.uk", "link": "https://researchportal.bath.ac.uk/en/publications/scalable-evaluation-framework-for-retrieval-augmented-generation-", "content": "Using a Goal-Question-Metric paradigm , we evaluated two distinct LLM architectures in RAG configurations: Mixtral 8 × 7B and Llama 3 .1 70B."}
+{"idx": 6, "title": "Bridging Search and Recommendation with Generative Retrieval |", "date": "", "ddg_snippet": "Generative retrieval for search and recommendation is a promising new paradigm to retrieve items. ... retrieval using LLMs and plays an important role ...", "subpage_snippet": "", "source": "research.atspotify.com", "link": "https://research.atspotify.com/2024/10/bridging-search-and-recommendation-with-generative-retrieval", "content": "Generative retrieval for search and recommendation is a promising new paradigm to retrieve items. ... retrieval using LLMs and plays an important role ..."}
+{"idx": 7, "title": "8.3 Problems with Memory - Psychology 2e | OpenStax", "date": "", "ddg_snippet": "People may not intend to distort facts, but it can happen in the process of retrieving old memories and combining them with new memories (Roediger ...", "subpage_snippet": "", "source": "openstax.org", "link": "https://openstax.org/books/psychology-2e/pages/8-3-problems-with-memory", "content": "People may not intend to distort facts, but it can happen in the process of retrieving old memories and combining them with new memories (Roediger ..."}
+{"idx": 8, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG...", "date": "", "ddg_snippet": "We evaluate three retrievers with different retrieval paradigms : (1) BM25 (Robertson et al., 2009), a sparse lexical retriever based on term matching. (2) ColBERT (Santhanam et al., 2021), a neural retriever using contextual-ized late interaction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09040", "content": "We evaluate three retrievers with different retrieval paradigms : (1) BM25 (Robertson et al., 2009), a sparse lexical retriever based on term matching. (2) ColBERT (Santhanam et al., 2021), a neural retriever using contextual-ized late interaction."}
+{"idx": 9, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG...", "date": "", "ddg_snippet": "We evaluate three retrievers with different retrieval paradigms : (1) BM25 (Robertson et al., 2009) , a sparse lexical retriever based on term matching. (2) ColBERT (Santhanam et al., 2021) , a neural retriever using contextualized late interaction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v3", "content": "We evaluate three retrievers with different retrieval paradigms : (1) BM25 (Robertson et al., 2009) , a sparse lexical retriever based on term matching. (2) ColBERT (Santhanam et al., 2021) , a neural retriever using contextualized late interaction."}
diff --git a/data/sampled_jsons/We_introduce_Mind2Web,_the_first_dataset_for_developing_and_evaluating_generalist_agents_for_the_web.jsonl b/data/sampled_jsons/We_introduce_Mind2Web,_the_first_dataset_for_developing_and_evaluating_generalist_agents_for_the_web.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..18e18150fbd7d259dc3192b8b3e825fc9a6b7d04
--- /dev/null
+++ b/data/sampled_jsons/We_introduce_Mind2Web,_the_first_dataset_for_developing_and_evaluating_generalist_agents_for_the_web.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2306.06070] Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.06070", "content": "We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ..."}
+{"idx": 1, "title": "Mind2Web: Towards a Generalist Agent for the Web - arXiv.org", "date": "", "ddg_snippet": "Abstract We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.06070v3", "content": "Abstract We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents."}
+{"idx": 2, "title": "Mind2Web - GitHub Pages", "date": "", "ddg_snippet": "Abstract We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "osu-nlp-group.github.io", "link": "https://osu-nlp-group.github.io/Mind2Web/", "content": "Abstract We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents."}
+{"idx": 3, "title": "Mind2Web: Towards a Generalist Agent for the Web - NIPS", "date": "", "ddg_snippet": "Authors Xiang Deng , Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, Yu Su Abstract We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and ...", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2023/hash/5950bf290a1570ea401bf98882128160-Abstract-Datasets_and_Benchmarks.html", "content": "Authors Xiang Deng , Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, Yu Su Abstract We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and ..."}
+{"idx": 4, "title": "MIND2WEB | Proceedings of the 37th International Conference on Neural ...", "date": "", "ddg_snippet": "We introduce MIND2WEB, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3667342", "content": "We introduce MIND2WEB, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ..."}
+{"idx": 5, "title": "PDF MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS", "date": "", "ddg_snippet": "Abstract We introduce MIND2WEB, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/5950bf290a1570ea401bf98882128160-Paper-Datasets_and_Benchmarks.pdf", "content": "Abstract We introduce MIND2WEB, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from ..."}
+{"idx": 6, "title": "Mind2Web: Towards a Generalist Agent for the Web - ADS", "date": "", "ddg_snippet": "We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2023arXiv230606070D/abstract", "content": "We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ..."}
+{"idx": 7, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "Abstract We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2306.06070", "content": "Abstract We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents."}
+{"idx": 8, "title": "TGPO: Tree-Guided Preference Optimization for Robust Web Agent", "date": "", "ddg_snippet": "... the rapid advancement of large language models and vision-language models, employing large models as Web Agents has become essential for automated web ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14172v2", "content": "... the rapid advancement of large language models and vision-language models, employing large models as Web Agents has become essential for automated web ..."}
+{"idx": 9, "title": "Agent-RewardBench: Towards a Unified Benchmark for Reward", "date": "", "ddg_snippet": "However, there is no work evaluating MLLMs as agent reward models, leading to an urgent need to develop a benchmark for assessing agent rewards.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21252v1", "content": "However, there is no work evaluating MLLMs as agent reward models, leading to an urgent need to develop a benchmark for assessing agent rewards."}
diff --git a/data/sampled_jsons/WeaklyRec_Section_3_methodology_class_prior_unbiased_estimator.jsonl b/data/sampled_jsons/WeaklyRec_Section_3_methodology_class_prior_unbiased_estimator.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c664184e70cd7f7f09baa6ee290e999d5e811f09
--- /dev/null
+++ b/data/sampled_jsons/WeaklyRec_Section_3_methodology_class_prior_unbiased_estimator.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "To address this issue, we introduce WeaklyRec , a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0E5rZOGA13", "content": "To address this issue, we introduce WeaklyRec , a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior ."}
+{"idx": 1, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "17 Jul 2025 — To address this challenge, we propose Progressive Proximal Transport (PPT), which estimates the class prior by minimizing the proximal transport ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "17 Jul 2025 — To address this challenge, we propose Progressive Proximal Transport (PPT), which estimates the class prior by minimizing the proximal transport ..."}
+{"idx": 2, "title": "PDF Class Prior Estimation with Biased Positives and Unlabeled Examples", "date": "", "ddg_snippet": "In class - prior estimation, several authors have considered the case of bi-ased class proportions in the labeled data (Latinne, Saerens, and Decaestecker 2001; Vucetic and Obradovic 2001) and developed iterative correction methods.", "subpage_snippet": "", "source": "www.khoury.northeastern.edu", "link": "https://www.khoury.northeastern.edu/home/radivojac/papers/jain_aaai_2020.pdf", "content": "In class - prior estimation, several authors have considered the case of bi-ased class proportions in the labeled data (Latinne, Saerens, and Decaestecker 2001; Vucetic and Obradovic 2001) and developed iterative correction methods."}
+{"idx": 3, "title": "Estimating the class prior for positive and unlabelled data via ...", "date": "", "ddg_snippet": "In the paper, we revisit the problem of class prior probability estimation with positive and unlabelled data gathered in a single-sample scenario. The task is important as it is known that in positive unlabelled setting, a classifier can be successfully learned if the class prior is available. We show that without additional assumptions, class prior probability is not identifiable and thus the ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11634-021-00444-9", "content": "In the paper, we revisit the problem of class prior probability estimation with positive and unlabelled data gathered in a single-sample scenario. The task is important as it is known that in positive unlabelled setting, a classifier can be successfully learned if the class prior is available. We show that without additional assumptions, class prior probability is not identifiable and thus the ..."}
+{"idx": 4, "title": "PDF Class Prior Estimation from Positive and Unlabeled Data", "date": "", "ddg_snippet": "However, in practice, the class prior is unknown and thus must be estimated from data. In this paper, we propose a new method to estimate the class prior by partially matching the class -conditional density of the positive class to the input density.", "subpage_snippet": "", "source": "www.ms.k.u-tokyo.ac.jp", "link": "http://www.ms.k.u-tokyo.ac.jp/sugi/2014/ClassPrior2.pdf", "content": "However, in practice, the class prior is unknown and thus must be estimated from data. In this paper, we propose a new method to estimate the class prior by partially matching the class -conditional density of the positive class to the input density."}
+{"idx": 5, "title": "Class Prior Estimation with Biased Positives and Unlabeled Examples", "date": "", "ddg_snippet": "We then extend the identifiability theory of class priors from the unbiased to the biased setting. Finally, we derive an algorithm for estimating the class priors that relies on clustering to decompose the original problem into subproblems of unbiased positive-unlabeled learning.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/5848", "content": "We then extend the identifiability theory of class priors from the unbiased to the biased setting. Finally, we derive an algorithm for estimating the class priors that relies on clustering to decompose the original problem into subproblems of unbiased positive-unlabeled learning."}
+{"idx": 6, "title": "Section 3. Unbiased estimators - Statistics Canada", "date": "", "ddg_snippet": "Theorem 2 indicates that the variance estimator is unbiased for any sample and set sizes regardless of the quality of ranking information.", "subpage_snippet": "", "source": "www150.statcan.gc.ca", "link": "https://www150.statcan.gc.ca/n1/pub/12-001-x/2018001/article/54925/03-eng.htm", "content": "Theorem 2 indicates that the variance estimator is unbiased for any sample and set sizes regardless of the quality of ranking information."}
+{"idx": 7, "title": "An Unbiased Risk Estimator for Partial Label Learning with Augmented ...", "date": "", "ddg_snippet": "Specifically, we propose an unbiased risk estimator with theoretical guarantees for PLLAC, which estimates the distribution of augmented classes by diferentiating the distribution of known classes from unlabeled data and can be equipped with arbitrary PLL loss functions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.19600", "content": "Specifically, we propose an unbiased risk estimator with theoretical guarantees for PLLAC, which estimates the distribution of augmented classes by diferentiating the distribution of known classes from unlabeled data and can be equipped with arbitrary PLL loss functions."}
+{"idx": 8, "title": "PDF Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "To address this gap, we introduce the Progressive Proxi- mal Transport (PPT) method, specifically designed for class prior estimation in the context of our WeaklyRec frame- work.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=0E5rZOGA13&name=pdf", "content": "To address this gap, we introduce the Progressive Proxi- mal Transport (PPT) method, specifically designed for class prior estimation in the context of our WeaklyRec frame- work."}
+{"idx": 9, "title": "1.3 - Unbiased Estimation | STAT 415 - Statistics Online", "date": "", "ddg_snippet": "Enroll today at Penn State World Campus to earn an accredited degree or certificate in Statistics.", "subpage_snippet": "", "source": "online.stat.psu.edu", "link": "https://online.stat.psu.edu/stat415/lesson/1/1.3", "content": "Enroll today at Penn State World Campus to earn an accredited degree or certificate in Statistics."}
diff --git a/data/sampled_jsons/WeaklyRec_empirical_risk_estimator_equation_6_class_prior_kp.jsonl b/data/sampled_jsons/WeaklyRec_empirical_risk_estimator_equation_6_class_prior_kp.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..999dea5a02dd4a276ce67aca679e727d6d4270ce
--- /dev/null
+++ b/data/sampled_jsons/WeaklyRec_empirical_risk_estimator_equation_6_class_prior_kp.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regression analysis - Wikipedia", "date": "", "ddg_snippet": "Empirical risk minimization.Minimization of this function results in a set of normal equations , a set of simultaneous linear equations in the parameters, which are solved to yield the parameter estimators", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Regression_analysis", "content": "Empirical risk minimization.Minimization of this function results in a set of normal equations , a set of simultaneous linear equations in the parameters, which are solved to yield the parameter estimators"}
+{"idx": 1, "title": "Tilted Empirical Risk : A New Approach to Generalization Error", "date": "", "ddg_snippet": "Tilted empirical risk takes a different approach to measure the risk associated with a model. By adjusting how we weigh different samples in the training data, TER can improve performance in scenarios that would typically hinder traditional methods.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-05-27-tilted-empirical-risk-a-new-approach-to-generalization-error--a9ny65l", "content": "Tilted empirical risk takes a different approach to measure the risk associated with a model. By adjusting how we weigh different samples in the training data, TER can improve performance in scenarios that would typically hinder traditional methods."}
+{"idx": 2, "title": "Linear regression calculator - calculates the linear regression equation ...", "date": "", "ddg_snippet": "The linear regression calculator generates the best-fitting equation and draws the linear regression line and the prediction interval. Step-by-step solution.", "subpage_snippet": "", "source": "www.statskingdom.com", "link": "https://www.statskingdom.com/linear-regression-calculator.html", "content": "The linear regression calculator generates the best-fitting equation and draws the linear regression line and the prediction interval. Step-by-step solution."}
+{"idx": 3, "title": "Reviews: Positive-Unlabeled Learning with Non-Negative Risk ...", "date": "", "ddg_snippet": "In PU learning, unbiased PU risk estimator is not bounded below and might lead to serious overfitting . This paper tackles this problem by slightly adjust the original estimators by setting the a cut off at zero.But what happens if the class priors are estimated by a state of the art method?", "subpage_snippet": "", "source": "media.nips.cc", "link": "https://media.nips.cc/nipsbooks/nipspapers/paper_files/nips30/reviews/1048.html", "content": "In PU learning, unbiased PU risk estimator is not bounded below and might lead to serious overfitting . This paper tackles this problem by slightly adjust the original estimators by setting the a cut off at zero.But what happens if the class priors are estimated by a state of the art method?"}
+{"idx": 4, "title": "M IX Pul: C onsistency - based a ugmentation for", "date": "", "ddg_snippet": "These unbiased risk estimators typically rely on the knowledge of class - prior which is usually unavailable in real-world problems.3.4 Theoretical Interpretation of Equation ( 6 ). We further explain our objective function from the perspective of empirical risk minimization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2004.09388", "content": "These unbiased risk estimators typically rely on the knowledge of class - prior which is usually unavailable in real-world problems.3.4 Theoretical Interpretation of Equation ( 6 ). We further explain our objective function from the perspective of empirical risk minimization."}
+{"idx": 5, "title": "Learning From Positive", "date": "", "ddg_snippet": "6 Class Prior Estimation from PU Data7 Sources of PU Data and Applications Estimating the class prior from PU data is hard.", "subpage_snippet": "", "source": "home.ipipan.waw.pl", "link": "https://home.ipipan.waw.pl/j.mielniczuk/review_PU.pdf", "content": "6 Class Prior Estimation from PU Data7 Sources of PU Data and Applications Estimating the class prior from PU data is hard."}
+{"idx": 6, "title": "Reconciliation Methods - Nixtla", "date": "", "ddg_snippet": "*BottomUpSparse Reconciliation Class . This is the implementation of a Bottom Up reconciliation using the sparse matrix approach.", "subpage_snippet": "", "source": "nixtlaverse.nixtla.io", "link": "https://nixtlaverse.nixtla.io/hierarchicalforecast/src/methods.html", "content": "*BottomUpSparse Reconciliation Class . This is the implementation of a Bottom Up reconciliation using the sparse matrix approach."}
+{"idx": 7, "title": "The American Heart Association PREVENT TM Online Calculator", "date": "", "ddg_snippet": "Risk can still be estimated with the closest in-range value but may represent an over- or under- estimate . How to Use and Interpret PREVENT Equation Results.", "subpage_snippet": "", "source": "professional.heart.org", "link": "https://professional.heart.org/en/guidelines-and-statements/prevent-risk-calculator/prevent-calculator", "content": "Risk can still be estimated with the closest in-range value but may represent an over- or under- estimate . How to Use and Interpret PREVENT Equation Results."}
+{"idx": 8, "title": "Valkyrien Skies Mod: Fix For Create 6 .0.0 Incompatibility", "date": "", "ddg_snippet": "Additionally, review the changelogs for Create versions prior to 6 .0.0 to understand what features or functionalities you might be missing.", "subpage_snippet": "", "source": "brainxx.org", "link": "https://brainxx.org/blog/valkyrien-skies-mod-fix-for", "content": "Additionally, review the changelogs for Create versions prior to 6 .0.0 to understand what features or functionalities you might be missing."}
+{"idx": 9, "title": "The Best Settings For Dying Light The Beast On The ROG Ally", "date": "", "ddg_snippet": "The Plucky Squire FINAL FANTASY XVI Turbo Sliders Unlimited Funko Fusion MARVEL vs CAPCOM Fighting Collection Arcade Classics Caravan SandWitch Rugrats Adventures in Gameland I Am Your Beast Warhammer 40000 Space Marine 2 NBA 2K25 The Casting of Frank Stone...", "subpage_snippet": "", "source": "rogallylife.com", "link": "https://rogallylife.com/2025/09/18/dying-light-the-beast-rog-ally/", "content": "The Plucky Squire FINAL FANTASY XVI Turbo Sliders Unlimited Funko Fusion MARVEL vs CAPCOM Fighting Collection Arcade Classics Caravan SandWitch Rugrats Adventures in Gameland I Am Your Beast Warhammer 40000 Space Marine 2 NBA 2K25 The Casting of Frank Stone..."}
diff --git a/data/sampled_jsons/What_Limits_Virtual_Agent_Application_OmniBench_Cross-Verification_module_sitearxiv.org_year_2024.jsonl b/data/sampled_jsons/What_Limits_Virtual_Agent_Application_OmniBench_Cross-Verification_module_sitearxiv.org_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..57a73047dd764f7148e0be2b8dca1c591bc1b7b5
--- /dev/null
+++ b/data/sampled_jsons/What_Limits_Virtual_Agent_Application_OmniBench_Cross-Verification_module_sitearxiv.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application ? OmniBench : A Scalable...", "date": "", "ddg_snippet": "Prompt for Subtask Evaluation Synthesis. What Limits Virtual Agent Application ? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual . Cross - Verification Intent Extraction Consistency Validator Human Acceptance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "Prompt for Subtask Evaluation Synthesis. What Limits Virtual Agent Application ? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual . Cross - Verification Intent Extraction Consistency Validator Human Acceptance."}
+{"idx": 1, "title": "[2506.08933] What Limits Virtual Agent Application ? OmniBench ...", "date": "", "ddg_snippet": "View a PDF of the paper titled What Limits Virtual Agent Application ? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities, by Wendong Bu and 12 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.08933", "content": "View a PDF of the paper titled What Limits Virtual Agent Application ? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities, by Wendong Bu and 12 other authors."}
+{"idx": 2, "title": "OmniBench : Towards The Future of Universal Omni -Language Models", "date": "", "ddg_snippet": "We introduce OmniBench , a novel benchmark designed to rigorously evaluate models’ ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni -language models (OLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.15272v1", "content": "We introduce OmniBench , a novel benchmark designed to rigorously evaluate models’ ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni -language models (OLMs)."}
+{"idx": 3, "title": "OmniBench : Towards The Future of Universal Omni -Language Models", "date": "", "ddg_snippet": "We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define language models capable of such tri-modal processing as omni -language models (OLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.15272", "content": "We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define language models capable of such tri-modal processing as omni -language models (OLMs)."}
+{"idx": 4, "title": "Boosting Virtual Agent Learning and Reasoning: A Step-Wise...", "date": "", "ddg_snippet": "More Visualizations of SRMEval. Boosting Virtual Agent Learning and Reasoning: A Step-Wise, Multi-Dimensional, and Generalist Reward Model with Benchmark.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.18665", "content": "More Visualizations of SRMEval. Boosting Virtual Agent Learning and Reasoning: A Step-Wise, Multi-Dimensional, and Generalist Reward Model with Benchmark."}
+{"idx": 5, "title": "OmniBench : Towards The Future of", "date": "", "ddg_snippet": "We define language models capable of such tri-modal processing as omni -language mod-els (OLMs). OmniBench features high-quality human annotations that require integrated under-standing across all modalities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.15272", "content": "We define language models capable of such tri-modal processing as omni -language mod-els (OLMs). OmniBench features high-quality human annotations that require integrated under-standing across all modalities."}
+{"idx": 6, "title": "Computer Vision and Pattern Recognition Jun 2025", "date": "", "ddg_snippet": "19 Jun 2025 — Title: What Limits Virtual Agent Application ? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CV/2025-06?skip=850&show=1000", "content": "19 Jun 2025 — Title: What Limits Virtual Agent Application ? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities."}
+{"idx": 7, "title": "A Survey on Large Multimodal Reasoning Models", "date": "", "ddg_snippet": "8 May 2025 — We present a comprehensive and structured survey of multimodal reasoning research, organized around a four-stage developmental roadmap.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04921v1", "content": "8 May 2025 — We present a comprehensive and structured survey of multimodal reasoning research, organized around a four-stage developmental roadmap."}
+{"idx": 8, "title": "Artificial Intelligence Sep 2024", "date": "", "ddg_snippet": "Title: Grounded GUI Understanding for Vision Based Spatial Intelligent Agent : Exemplified by Virtual Reality Apps ... Cross -Age Speaker Verification .", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.AI/2024-09?skip=1200&show=1000", "content": "Title: Grounded GUI Understanding for Vision Based Spatial Intelligent Agent : Exemplified by Virtual Reality Apps ... Cross -Age Speaker Verification ."}
+{"idx": 9, "title": "Computer Science Sep 2024", "date": "", "ddg_snippet": "6 Sept 2024 — Title: Numerical Approximation Capacity of Neural Networks with Bounded Parameters: Do Limits Exist, and How Can They Be Measured? Li Liu, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2024-09?skip=6300&show=2000", "content": "6 Sept 2024 — Title: Numerical Approximation Capacity of Neural Networks with Bounded Parameters: Do Limits Exist, and How Can They Be Measured? Li Liu, ..."}
diff --git a/data/sampled_jsons/WiSE-FT_Wortsman_et_al._2021_abstract_year_2021.jsonl b/data/sampled_jsons/WiSE-FT_Wortsman_et_al._2021_abstract_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9307c50f89edb6d4152b3f50cc2f311bfc528b03
--- /dev/null
+++ b/data/sampled_jsons/WiSE-FT_Wortsman_et_al._2021_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - mlfoundations/wise-ft: Robust fine-tuning of zero ... Robust fine-tuning of zero-shot models - NASA/ADS NeurIPS Robust fine-tuning of zero-shot models Robust fine-tuning of zero-shot mode - OpenReview Robust fine-tuning of zero-shot models - CVF Open Access Robust fine-tuning of zero-shot models - CVF Open Access Robust fine- tuning of zero-shot models - OpenReview Robust fine-tuning of zero-shot models - CVF Open Access Robust fine-tuning of zero-shot models - CVF Open Access Figure D.1 from Model soups: averaging weights of multiple ...", "date": "", "ddg_snippet": "This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman *, Gabriel Ilharco*, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt. TLDR: We fine-tune zero-shot models while preserving or improving OOD accuracy at no extra computational cost during fine-tuning or inference. Abstract Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning approaches substantially improve accuracy in-distribution, they often reduce out-of-distribution robustness. We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE - FT ). Compared to standard fine-tuning, WiSE - FT provides large accuracy improvements out-of-distribution, while preserving high in-distribution accuracy. On ImageNet (in-distribution) and five derived distribution shifts, WiSE - FT improves out-of-distribution accuracy by 4 to 6 percentage points (pp) over prior work while increasing in-distribution accuracy by 1.6 pp. WiSE - FT achieves similarly large robustness improvements (2 to 23 pp) on a diverse set of six further distribution shifts, and in-distribution accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on seven commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference. See full list on github.com Overview WiSE - FT can be implemented in a few lines of code in addition to standard fine-tuning, as shown below. See src/wise_ft.py for more details. Download data When necessary, please refer to datasets.md for instructions on how to download datasets. Run WiSE - FT Sample command when zeroshot and fine-tuned models are available:Sample command for running WiSE - FT from scratch using ViT-B/32:Note: the flag --freeze-encoder controls whether only a linear classifier is fine-tuned, or if all weights are fine-tuned (end-to-end). See full list on github.com If you found this repository useful, please consider citing: See full list on github.com We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy. Abstract Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning approaches sub-stantially improve accuracy in-distribution, they often reduce out-of-distribution robustness. We address this tension by introducing a simple ... Does Wise-ft improve accuracy under distribution shift? Rel-ative to the zero-shot model, WiSE-FT improves accuracy under distribution shift by 1 to 9 pp . Moreover, WiSE-FT improves over a range of alternative approaches such as regularization and evaluating at various points throughout fine-tuning. These robustness gains come at no additional computational cost during fine-tuning or inference. Does Wise-ft improve ImageNet accuracy? For instance, WiSE-FT improves the ImageNet accuracy of a fine-tuned BASIC-L model by 0.4 pp , while improving aver-age accuracy under distribution shift by 2 to 11 pp. To understand the robustness gains of WiSE-FT, we first study WiSE-FT when fine-tuning a linear classifier (last layer) as it is more amenable to analysis. How can Ze O-shot and fine-tuned models improve out-of-distribution robustness? reduce out-of-distribution robustness. We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the ze o-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while p What is the difference between ImageNet and Wise-ft? In contrast to the ImageNet distribution shifts, the zero-shot model initially achieves less than 30% accuracy on the WILDS distribution shifts, and WiSE - FT provides improvements regardless. Ap-pendix E.2 (Figure 9 and Table 6) includes more detailed results. Hyperparameter variation and alternatives. What are the benefits of Wise-ft vs standard fine-tuning? Beyond the robustness perspective, WiSE-FT also improves accuracy compared to standard fine-tuning, reducing the relative error rate by 4-49% on a range of seven datasets: ImageNet, CIFAR-10, CIFAR-100 , Describable Textures , Food-101 , SUN397 , and Stanford Cars . Figure D.1: Model soups compared to baselines for robust fine-tuning. WiSE-FT ( Wortsman et al ., 2021 ) improves the robustness of model θ1 fine-tuned from initialization θ0 by interpolating between θ1 and θ0. Above we display the accuracy of models along these interpolation curves both for regular fine-tuned models and model soups (left: random hyperparameter search using the LP ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mlfoundations/wise-ft", "content": "This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman *, Gabriel Ilharco*, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt. TLDR: We fine-tune zero-shot models while preserving or improving OOD accuracy at no extra computational cost during fine-tuning or inference. Abstract Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning approaches substantially improve accuracy in-distribution, they often reduce out-of-distribution robustness. We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE - FT ). Compared to standard fine-tuning, WiSE - FT provides large accuracy improvements out-of-distribution, while preserving high in-distribution accuracy. On ImageNet (in-distribution) and five derived distribution shifts, WiSE - FT improves out-of-distribution accuracy by 4 to 6 percentage points (pp) over prior work while increasing in-distribution accuracy by 1.6 pp. WiSE - FT achieves similarly large robustness improvements (2 to 23 pp) on a diverse set of six further distribution shifts, and in-distribution accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on seven commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference. See full list on github.com Overview WiSE - FT can be implemented in a few lines of code in addition to standard fine-tuning, as shown below. See src/wise_ft.py for more details. Download data When necessary, please refer to datasets.md for instructions on how to download datasets. Run WiSE - FT Sample command when zeroshot and fine-tuned models are available:Sample command for running WiSE - FT from scratch using ViT-B/32:Note: the flag --freeze-encoder controls whether only a linear classifier is fine-tuned, or if all weights are fine-tuned (end-to-end). See full list on github.com If you found this repository useful, please consider citing: See full list on github.com We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy. Abstract Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning approaches sub-stantially improve accuracy in-distribution, they often reduce out-of-distribution robustness. We address this tension by introducing a simple ... Does Wise-ft improve accuracy under distribution shift? Rel-ative to the zero-shot model, WiSE-FT improves accuracy under distribution shift by 1 to 9 pp . Moreover, WiSE-FT improves over a range of alternative approaches such as regularization and evaluating at various points throughout fine-tuning. These robustness gains come at no additional computational cost during fine-tuning or inference. Does Wise-ft improve ImageNet accuracy? For instance, WiSE-FT improves the ImageNet accuracy of a fine-tuned BASIC-L model by 0.4 pp , while improving aver-age accuracy under distribution shift by 2 to 11 pp. To understand the robustness gains of WiSE-FT, we first study WiSE-FT when fine-tuning a linear classifier (last layer) as it is more amenable to analysis. How can Ze O-shot and fine-tuned models improve out-of-distribution robustness? reduce out-of-distribution robustness. We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the ze o-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while p What is the difference between ImageNet and Wise-ft? In contrast to the ImageNet distribution shifts, the zero-shot model initially achieves less than 30% accuracy on the WILDS distribution shifts, and WiSE - FT provides improvements regardless. Ap-pendix E.2 (Figure 9 and Table 6) includes more detailed results. Hyperparameter variation and alternatives. What are the benefits of Wise-ft vs standard fine-tuning? Beyond the robustness perspective, WiSE-FT also improves accuracy compared to standard fine-tuning, reducing the relative error rate by 4-49% on a range of seven datasets: ImageNet, CIFAR-10, CIFAR-100 , Describable Textures , Food-101 , SUN397 , and Stanford Cars . Figure D.1: Model soups compared to baselines for robust fine-tuning. WiSE-FT ( Wortsman et al ., 2021 ) improves the robustness of model θ1 fine-tuned from initialization θ0 by interpolating between θ1 and θ0. Above we display the accuracy of models along these interpolation curves both for regular fine-tuned models and model soups (left: random hyperparameter search using the LP ..."}
+{"idx": 1, "title": "[2109.01903] Robust fine-tuning of zero-shot models - arXiv.org", "date": "", "ddg_snippet": "Sep 4, 2021 · We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.01903", "content": "Sep 4, 2021 · We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution."}
+{"idx": 2, "title": "Robust Fine-Tuning of Zero-Shot Models - CVF Open Access", "date": "", "ddg_snippet": "Andreassen et al . [3] explored several fine-tuning approaches but found that none yielded models Weight-space ensembles for fine-tuning ( WiSE-FT ) substan-tially improve accuracy under distribution shift compared to prior work while maintaining high performance on the target distribution.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Wortsman_Robust_Fine-Tuning_of_Zero-Shot_Models_CVPR_2022_paper.pdf", "content": "Andreassen et al . [3] explored several fine-tuning approaches but found that none yielded models Weight-space ensembles for fine-tuning ( WiSE-FT ) substan-tially improve accuracy under distribution shift compared to prior work while maintaining high performance on the target distribution."}
+{"idx": 3, "title": "Robust fine-tuning of zero-shot models - NASA/ADS", "date": "", "ddg_snippet": "We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2021arXiv210901903W/abstract", "content": "We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution."}
+{"idx": 4, "title": "NeurIPS Robust fine-tuning of zero-shot models", "date": "", "ddg_snippet": "We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2021/35489", "content": "We address this tension by introducing a simple and effective method for improving robustness: ensembling the weights of the zero-shot and fine-tuned models ( WiSE-FT ). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy."}
+{"idx": 5, "title": "Robust fine-tuning of zero-shot mode - OpenReview", "date": "", "ddg_snippet": "Abstract Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning approaches sub-stantially improve accuracy in-distribution, they often reduce out-of-distribution robustness. We address this tension by introducing a simple ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=x4-czw5UxFX", "content": "Abstract Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning approaches sub-stantially improve accuracy in-distribution, they often reduce out-of-distribution robustness. We address this tension by introducing a simple ..."}
+{"idx": 6, "title": "Figure D.1 from Model soups: averaging weights of multiple ...", "date": "", "ddg_snippet": "Figure D.1: Model soups compared to baselines for robust fine-tuning. WiSE-FT ( Wortsman et al ., 2021 ) improves the robustness of model θ1 fine-tuned from initialization θ0 by interpolating between θ1 and θ0. Above we display the accuracy of models along these interpolation curves both for regular fine-tuned models and model soups (left: random hyperparameter search using the LP ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Model-soups:-averaging-weights-of-multiple-models-Wortsman-Ilharco/54020e5fe48ebb250f27d744e20a63cac2988a84/figure/13", "content": "Figure D.1: Model soups compared to baselines for robust fine-tuning. WiSE-FT ( Wortsman et al ., 2021 ) improves the robustness of model θ1 fine-tuned from initialization θ0 by interpolating between θ1 and θ0. Above we display the accuracy of models along these interpolation curves both for regular fine-tuned models and model soups (left: random hyperparameter search using the LP ..."}
+{"idx": 7, "title": "StarFT: Robust Fine-tuning of Zero-shot Models via ...", "date": "", "ddg_snippet": "For instances,. WiSE-FT [Wortsman et al., 2022 ] uses a weight ensembling between the zero-shot and fine-tuned models, and [Tian et al.,. 2023] have proposed a ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0616.pdf", "content": "For instances,. WiSE-FT [Wortsman et al., 2022 ] uses a weight ensembling between the zero-shot and fine-tuned models, and [Tian et al.,. 2023] have proposed a ..."}
+{"idx": 8, "title": "averaging weights of multiple fine-tuned models improves ...", "date": "", "ddg_snippet": "by M Wortsman · 2022 · Cited by 1341 — WiSE-FT (Wortsman et al., 2021 ) improves the robustness of model θ1 fine-tuned from initialization θ0 by interpolating between θ1 and θ0. Above we display the ... 34 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/wortsman22a/wortsman22a.pdf", "content": "by M Wortsman · 2022 · Cited by 1341 — WiSE-FT (Wortsman et al., 2021 ) improves the robustness of model θ1 fine-tuned from initialization θ0 by interpolating between θ1 and θ0. Above we display the ... 34 pages"}
+{"idx": 9, "title": "Towards Next-Level Robustness in Fine-tuning Zero-Shot ...", "date": "", "ddg_snippet": "by K Li · 2024 — WiSE-FT (Wortsman et al.,. 2022 ) investigated combining pre-trained models with their fine-tuned versions by weight averaging, which can be seen as yet another ... 33 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7972f3735e104a54715922aa416fde1b-Paper-Conference.pdf", "content": "by K Li · 2024 — WiSE-FT (Wortsman et al.,. 2022 ) investigated combining pre-trained models with their fine-tuned versions by weight averaging, which can be seen as yet another ... 33 pages"}
diff --git a/data/sampled_jsons/Winter_sybilhunter_Tor_Sybil_detection_approach_year_2016.jsonl b/data/sampled_jsons/Winter_sybilhunter_Tor_Sybil_detection_approach_year_2016.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e48d827b4a6439d31b1679396ac8911b825e7e5f
--- /dev/null
+++ b/data/sampled_jsons/Winter_sybilhunter_Tor_Sybil_detection_approach_year_2016.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - NullHypothesis/sybilhunter: Hunting for Sybils and anomalies ...", "date": "", "ddg_snippet": "Overview Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data. For example, sybilhunter can tell you (i) when an unusally large amount of relays joined or left the Tor network, (ii) which Tor relays changed their identity keys a lot, and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NullHypothesis/sybilhunter", "content": "Overview Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data. For example, sybilhunter can tell you (i) when an unusally large amount of relays joined or left the Tor network, (ii) which Tor relays changed their identity keys a lot, and ..."}
+{"idx": 1, "title": "Identifying and Characterizing Sybils in the Tor Network", "date": "", "ddg_snippet": "In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/winter", "content": "In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers."}
+{"idx": 2, "title": "PDF Protecting the Tor network from Sybil attacks", "date": "", "ddg_snippet": "All these attacks were discovered because they were exe-cuted carelessly—The Tor Project's Sybil detecting script [2] raised an alert because more than 50 new relays joined the network within an hour. To avoid detection , an attacker could launch a trickling attack, i.e., slowly add Sybils over time rather than all at once. In our ongoing work, we are developing algorithms and practical ...", "subpage_snippet": "", "source": "www.petsymposium.org", "link": "https://www.petsymposium.org/2015/papers/winter-sybil-hotpets2015.pdf", "content": "All these attacks were discovered because they were exe-cuted carelessly—The Tor Project's Sybil detecting script [2] raised an alert because more than 50 new relays joined the network within an hour. To avoid detection , an attacker could launch a trickling attack, i.e., slowly add Sybils over time rather than all at once. In our ongoing work, we are developing algorithms and practical ..."}
+{"idx": 3, "title": "PDF Identifying and characterizing Sybils in the Tor network", "date": "", "ddg_snippet": "In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as config-uration; and behavior, such as uptime sequences. We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attack-ers.", "subpage_snippet": "", "source": "nymity.ch", "link": "https://nymity.ch/sybilhunting/pdf/sybilhunting-sec16.pdf", "content": "In this work, we develop sybilhunter , a system for detecting Sybil relays based on their appearance, such as config-uration; and behavior, such as uptime sequences. We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attack-ers."}
+{"idx": 4, "title": "SybilHunter: Hybrid graph-based sybil detection by aggregating user ...", "date": "", "ddg_snippet": "In this paper, we propose SybilHunter , a hybrid graph-based sybil detection approach by aggregating user social behavior patterns. Our approach refines the OSN structure, quantifies nodes' similarity according to the dynamic user behavior features to evaluate user pairs' trustworthiness and consistency.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231222006373", "content": "In this paper, we propose SybilHunter , a hybrid graph-based sybil detection approach by aggregating user social behavior patterns. Our approach refines the OSN structure, quantifies nodes' similarity according to the dynamic user behavior features to evaluate user pairs' trustworthiness and consistency."}
+{"idx": 5, "title": "Identifying and characterizing Sybils in the Tor network", "date": "", "ddg_snippet": "Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practical tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , the first system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1602.07787", "content": "Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practical tools and methods to expose Sybil attacks. In this work, we develop sybilhunter , the first system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used ..."}
+{"idx": 6, "title": "Impact Analysis of Sybil Attacks in the Tor Network", "date": "", "ddg_snippet": "We analyze the effectiveness of Sybilhunter [43], which is currently employed for Sybil detection in the Tor network. Our results show that its mechanisms are insufficient to detect a well-prepared Sybil attack. In summary, this work enhances Tor's security by analyzing network composition, simulating Sybil attacks, and exposing vulnerabilities.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-92886-4_13", "content": "We analyze the effectiveness of Sybilhunter [43], which is currently employed for Sybil detection in the Tor network. Our results show that its mechanisms are insufficient to detect a well-prepared Sybil attack. In summary, this work enhances Tor's security by analyzing network composition, simulating Sybil attacks, and exposing vulnerabilities."}
+{"idx": 7, "title": "Protecting Tor from Sybil attacks - nymity.ch", "date": "", "ddg_snippet": "In this project, we developed techniques to expose Sybil relays, and study the ones we have already discovered. To this end, we have developed sybilhunter —a command line tool to analyse archived Tor network data for signs of Sybil relays.", "subpage_snippet": "", "source": "nymity.ch", "link": "https://nymity.ch/sybilhunting/", "content": "In this project, we developed techniques to expose Sybil relays, and study the ones we have already discovered. To this end, we have developed sybilhunter —a command line tool to analyse archived Tor network data for signs of Sybil relays."}
+{"idx": 8, "title": "github.com/NullHypothesis/sybilhunter - Go Packages", "date": "", "ddg_snippet": "Overview Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data. For example, sybilhunter can tell you (i) when an unusally large amount of relays joined or left the Tor network, (ii) which Tor relays changed their identity keys a lot, and ...", "subpage_snippet": "", "source": "pkg.go.dev", "link": "https://pkg.go.dev/github.com/NullHypothesis/sybilhunter", "content": "Overview Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data. For example, sybilhunter can tell you (i) when an unusally large amount of relays joined or left the Tor network, (ii) which Tor relays changed their identity keys a lot, and ..."}
+{"idx": 9, "title": "Hunting for Sybils and anomalies in archived Tor network data.", "date": "", "ddg_snippet": "Overview Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data. For example, sybilhunter can tell you (i) when an unusally large amount of relays joined or left the Tor network, (ii) which Tor relays changed their identity keys a lot, and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xxmingming/sybilhunter-security", "content": "Overview Sybilhunter is a command line tool written in Go to discover and analyse Sybil relays in the Tor network. It does so by implementing a number of analysis techniques that take as input archived Tor network data. For example, sybilhunter can tell you (i) when an unusally large amount of relays joined or left the Tor network, (ii) which Tor relays changed their identity keys a lot, and ..."}
diff --git a/data/sampled_jsons/XLRS-Bench_LLaVA-1.5_LLaVA-Next_architectural_difference_high_resolution.jsonl b/data/sampled_jsons/XLRS-Bench_LLaVA-1.5_LLaVA-Next_architectural_difference_high_resolution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..414ab339dfc46db351b29d60dac0814063d83234
--- /dev/null
+++ b/data/sampled_jsons/XLRS-Bench_LLaVA-1.5_LLaVA-Next_architectural_difference_high_resolution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "XLRS-Bench", "date": "", "ddg_snippet": "The main advantages of XLRS - Bench compared to existing MLLM benchmarks as follows: 1. Ultra- high Resolution . XLRS - Bench features the largest image sizes available, 10∼20× than that of existing datasets, with 840 images out of all images at a resolution of 10,000×10,000 pixels 2. High -quality Annotation.", "subpage_snippet": "", "source": "xlrs-bench.github.io", "link": "https://xlrs-bench.github.io/home_page.html", "content": "The main advantages of XLRS - Bench compared to existing MLLM benchmarks as follows: 1. Ultra- high Resolution . XLRS - Bench features the largest image sizes available, 10∼20× than that of existing datasets, with 840 images out of all images at a resolution of 10,000×10,000 pixels 2. High -quality Annotation."}
+{"idx": 1, "title": "GitHub - AI9Stars/XLRS-Bench: [CVPR 2025 HIghlight] XLRS-Bench: ould ...", "date": "", "ddg_snippet": "We present XLRS - Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra- high - resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars/XLRS-Bench", "content": "We present XLRS - Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra- high - resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts."}
+{"idx": 2, "title": "PDF XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra ...", "date": "", "ddg_snippet": "XLRS - Bench focuses on extremely large ultra- high - resolution RS imagery, integrating over 10 multimodal vision-language perception and reasoning tasks within the same image.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "XLRS - Bench focuses on extremely large ultra- high - resolution RS imagery, integrating over 10 multimodal vision-language perception and reasoning tasks within the same image."}
+{"idx": 3, "title": "[2503.23771] XLRS-Bench: Could Your Multimodal LLMs Understand ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2503.23771: XLRS - Bench : Could Your Multimodal LLMs Understand Extremely Large Ultra- High - Resolution Remote Sensing Imagery?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.23771", "content": "Abstract page for arXiv paper 2503.23771: XLRS - Bench : Could Your Multimodal LLMs Understand Extremely Large Ultra- High - Resolution Remote Sensing Imagery?"}
+{"idx": 4, "title": "LLaVA-NeXT - Hugging Face", "date": "", "ddg_snippet": "Today, we are thrilled to present LLaVA-NeXT , with improved reasoning, OCR, and world knowledge. LLaVA-NeXT even exceeds Gemini Pro on several benchmarks. Compared with LLaVA-1.5 , LLaVA-NeXT has several improvements: Increasing the input image resolution to 4x more pixels. This allows it to grasp more visual details.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/transformers/v4.48.2/en/model_doc/llava_next", "content": "Today, we are thrilled to present LLaVA-NeXT , with improved reasoning, OCR, and world knowledge. LLaVA-NeXT even exceeds Gemini Pro on several benchmarks. Compared with LLaVA-1.5 , LLaVA-NeXT has several improvements: Increasing the input image resolution to 4x more pixels. This allows it to grasp more visual details."}
+{"idx": 5, "title": "GitHub - LLaVA-VL/LLaVA-NeXT", "date": "", "ddg_snippet": "With additional scaling to LLaVA-1.5 , LLaVA - NeXT -34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LLaVA-VL/LLaVA-NeXT", "content": "With additional scaling to LLaVA-1.5 , LLaVA - NeXT -34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before."}
+{"idx": 6, "title": "Trying out LLaVA-NeXT - Medium", "date": "", "ddg_snippet": "LLaVA-NeXT builds on the foundation of LLaVA-1.5 with several improvements, including an increased input image resolution for better visual detail, enhanced visual reasoning and OCR capability and ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/thedeephub/trying-out-llava-next-8d5e74da3017", "content": "LLaVA-NeXT builds on the foundation of LLaVA-1.5 with several improvements, including an increased input image resolution for better visual detail, enhanced visual reasoning and OCR capability and ..."}
+{"idx": 7, "title": "LLaVa-NeXT - a llava-hf Collection - Hugging Face", "date": "", "ddg_snippet": "LLaVa-NeXT (also known as LLaVa-1.6) improves upon the 1.5 series by incorporating higher image resolutions and more reasoning/OCR datasets.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/llava-hf/llava-next-65f75c4afac77fd37dbbe6cf", "content": "LLaVa-NeXT (also known as LLaVa-1.6) improves upon the 1.5 series by incorporating higher image resolutions and more reasoning/OCR datasets."}
+{"idx": 8, "title": "LLaVA-Next — NVIDIA NeMo Framework User Guide", "date": "", "ddg_snippet": "LLaVA-Next is an extension of the LLaVA model designed to handle high-resolution images efficiently through tiling. This enables users to work with larger image sizes for improved accuracy in various VL tasks.", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/nemo-framework/user-guide/latest/vlms/llavanext.html", "content": "LLaVA-Next is an extension of the LLaVA model designed to handle high-resolution images efficiently through tiling. This enables users to work with larger image sizes for improved accuracy in various VL tasks."}
+{"idx": 9, "title": "arXiv:2503.23771v1 [cs.CV] 31 Mar 2025", "date": "", "ddg_snippet": "XLRS - Bench boasts the largest average im- age size (8500×8500) observed thus far, with all evalua- tion samples meticulously annotated manually, assisted by a novel semi-automatic captioner on ultra- high - resolution RS images. On top of the XLRS - Bench , 16 sub-tasks are defined to evaluate MLLMs' 10 kinds of perceptual capa-", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.23771", "content": "XLRS - Bench boasts the largest average im- age size (8500×8500) observed thus far, with all evalua- tion samples meticulously annotated manually, assisted by a novel semi-automatic captioner on ultra- high - resolution RS images. On top of the XLRS - Bench , 16 sub-tasks are defined to evaluate MLLMs' 10 kinds of perceptual capa-"}
diff --git a/data/sampled_jsons/XLRS-Bench_limitations_GPT-4o_spatiotemporal_understanding_remote_sensing.jsonl b/data/sampled_jsons/XLRS-Bench_limitations_GPT-4o_spatiotemporal_understanding_remote_sensing.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2b1364f89780bc5fecbe491f6fb36f4f577cca01
--- /dev/null
+++ b/data/sampled_jsons/XLRS-Bench_limitations_GPT-4o_spatiotemporal_understanding_remote_sensing.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "登录QQ邮箱", "date": "", "ddg_snippet": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。", "subpage_snippet": "", "source": "mail.qq.com", "link": "https://mail.qq.com/", "content": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。"}
+{"idx": 1, "title": "登录QQ邮箱", "date": "", "ddg_snippet": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。", "subpage_snippet": "", "source": "mail.qq.com", "link": "https://mail.qq.com/cgi-bin/loginpage", "content": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。"}
+{"idx": 2, "title": "QQ邮箱", "date": "", "ddg_snippet": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。", "subpage_snippet": "", "source": "wap.mail.qq.com", "link": "http://wap.mail.qq.com/", "content": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。"}
+{"idx": 3, "title": "登录QQ邮箱", "date": "", "ddg_snippet": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。", "subpage_snippet": "", "source": "mail.qq.com", "link": "https://mail.qq.com/?res=local&res=local", "content": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。"}
+{"idx": 4, "title": "登录QQ邮箱", "date": "", "ddg_snippet": "QQ邮箱，为亿万用户提供高效稳定便捷的电子邮件服务。 你可以在电脑网页、iOS/iPad客户端、及Android客户端上使用它，通过邮件发送3G的超大附件，体验文件中转站、日历、记事本、漂流瓶等特色功能。", "subpage_snippet": "", "source": "mail.qq.com", "link": "https://mail.qq.com/cgi-bin/loginpage?/", "content": "QQ邮箱，为亿万用户提供高效稳定便捷的电子邮件服务。 你可以在电脑网页、iOS/iPad客户端、及Android客户端上使用它，通过邮件发送3G的超大附件，体验文件中转站、日历、记事本、漂流瓶等特色功能。"}
+{"idx": 5, "title": "Log in to QQ Mail - QQ邮箱", "date": "", "ddg_snippet": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。", "subpage_snippet": "", "source": "mail.qq.com", "link": "https://mail.qq.com/?lang=en&cancel_login=true", "content": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。"}
+{"idx": 6, "title": "登录QQ邮箱", "date": "", "ddg_snippet": "• 记住登录状态后，一个星期内可以从QQ直接进入邮箱。 • 如果您觉得您所在网络环境足够安全，不需要额外的密码保护，可以在登录后进入\"设置\"清除独立密码。", "subpage_snippet": "", "source": "mail.qq.com", "link": "https://mail.qq.com/cgi-bin/loginpage?fun=login&half=1&t=loginpage_secondpwd", "content": "• 记住登录状态后，一个星期内可以从QQ直接进入邮箱。 • 如果您觉得您所在网络环境足够安全，不需要额外的密码保护，可以在登录后进入\"设置\"清除独立密码。"}
+{"idx": 7, "title": "登录QQ邮箱", "date": "", "ddg_snippet": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。", "subpage_snippet": "", "source": "mail.qq.com", "link": "https://mail.qq.com/cgi-bin/loginpage?zb&res=local", "content": "安全、稳定、快速、便捷的免费电子邮箱。 强大的反垃圾邮件过滤，10G超大附件发送，轻松管理所有电子发票，尽在QQ邮箱。"}
+{"idx": 8, "title": "帮助系统 - QQ邮箱", "date": "", "ddg_snippet": "怎样登录QQ邮箱？ 一、如果您在使用QQ，点击下图所示“ ”图标，即可进入QQ邮箱，无需另外输入登录信息。 二、还可以直接在浏览器地址栏输入mail.qq.com访问QQ邮箱首页，填写帐户名和密码后登入QQ邮箱。", "subpage_snippet": "", "source": "service.mail.qq.com", "link": "https://service.mail.qq.com/detail/0/468", "content": "怎样登录QQ邮箱？ 一、如果您在使用QQ，点击下图所示“ ”图标，即可进入QQ邮箱，无需另外输入登录信息。 二、还可以直接在浏览器地址栏输入mail.qq.com访问QQ邮箱首页，填写帐户名和密码后登入QQ邮箱。"}
+{"idx": 9, "title": "QQ邮箱 - 帮助系统", "date": "", "ddg_snippet": "如何查询登录、收发信、删信等记录？ QQ邮箱最多支持绑定10个别名账号，这里所指的别名账号都有哪些呢？", "subpage_snippet": "", "source": "service.mail.qq.com", "link": "https://service.mail.qq.com/", "content": "如何查询登录、收发信、删信等记录？ QQ邮箱最多支持绑定10个别名账号，这里所指的别名账号都有哪些呢？"}
diff --git a/data/sampled_jsons/Xing_Cui_2023_license_conflict_behavioral_use_restrictions_model_output.jsonl b/data/sampled_jsons/Xing_Cui_2023_license_conflict_behavioral_use_restrictions_model_output.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..75f7d362c7a08d4f7ba0d1b57c22cb75fa38ee25
--- /dev/null
+++ b/data/sampled_jsons/Xing_Cui_2023_license_conflict_behavioral_use_restrictions_model_output.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Empirical Study of License Conflict in Free and Open Source Software", "date": "", "ddg_snippet": "Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions , depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license terms. Identifying the licenses that provide FOSS and understanding the terms of those licenses is not easy ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10172522", "content": "Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions , depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license terms. Identifying the licenses that provide FOSS and understanding the terms of those licenses is not easy ..."}
+{"idx": 1, "title": "ModelGo: A Practical Tool for Machine Learning License Analysis ...", "date": "", "ddg_snippet": "Xing Cui , Jingzheng Wu, Yanjun Wu, Xu Wang, Tianyue Luo, Sheng Qu, Xiang Ling, and Mutian Yang. 2023 . An Empirical Study of License Conflict in Free and Open Source Software.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3589334.3645520", "content": "Xing Cui , Jingzheng Wu, Yanjun Wu, Xu Wang, Tianyue Luo, Sheng Qu, Xiang Ling, and Mutian Yang. 2023 . An Empirical Study of License Conflict in Free and Open Source Software."}
+{"idx": 2, "title": "dblp: An Empirical Study of License Conflict in Free and Open Source ...", "date": "", "ddg_snippet": "Xing Cui , Jingzheng Wu, Yanjun Wu, Xu Wang, Tianyue Luo, Sheng Qu, Xiang Ling, Mutian Yang: An Empirical Study of License Conflict in Free and Open Source Software.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/icse/CuiWWWLQLY23", "content": "Xing Cui , Jingzheng Wu, Yanjun Wu, Xu Wang, Tianyue Luo, Sheng Qu, Xiang Ling, Mutian Yang: An Empirical Study of License Conflict in Free and Open Source Software."}
+{"idx": 3, "title": "Xing Cui - ICSSP 2023", "date": "", "ddg_snippet": "The Schedule of ICSSP 2023 is up. ICSSP 2023 will feature three Keynotes from distinguished speakers on topics of AI-Augmented Software Engineering, Business Process Management, and AI-integrated Agile. The list of Accepted Papers are available. When registering for participation, please select ICSSP under co-located events. We look forward to your participation! Register Now new! TechDebt ...", "subpage_snippet": "", "source": "conf.researchr.org", "link": "https://conf.researchr.org/profile/icssp-2023/xingcui", "content": "The Schedule of ICSSP 2023 is up. ICSSP 2023 will feature three Keynotes from distinguished speakers on topics of AI-Augmented Software Engineering, Business Process Management, and AI-integrated Agile. The list of Accepted Papers are available. When registering for participation, please select ICSSP under co-located events. We look forward to your participation! Register Now new! TechDebt ..."}
+{"idx": 4, "title": "An Empirical Study of License Conflict in Free and Open ... - ResearchGate", "date": "", "ddg_snippet": "Request PDF | On May 1, 2023 , Xing Cui and others published An Empirical Study of License Conflict in Free and Open Source Software | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372305383_An_Empirical_Study_of_License_Conflict_in_Free_and_Open_Source_Software", "content": "Request PDF | On May 1, 2023 , Xing Cui and others published An Empirical Study of License Conflict in Free and Open Source Software | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 5, "title": "An Empirical Study of License Conflict in Free and Open Source Software", "date": "", "ddg_snippet": "In this paper, we propose DIKE, an automated tool that can perform license detection and conflict analysis for FOSS. First, DIKE extracts 12 terms under 3,256 unique open source licenses by manual analysis and Natural Language Processing (NLP) and constructs a license knowledge base containing the responsibilities of the terms.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/ICSE-SEIP58684.2023.00050", "content": "In this paper, we propose DIKE, an automated tool that can perform license detection and conflict analysis for FOSS. First, DIKE extracts 12 terms under 3,256 unique open source licenses by manual analysis and Natural Language Processing (NLP) and constructs a license knowledge base containing the responsibilities of the terms."}
+{"idx": 6, "title": "On the Standardization of Behavioral Use Clauses and Their Adoption for ...", "date": "", "ddg_snippet": "As of the end of 2023 , on the order of 40,000 software and model repositories have adopted responsible AI licenses licenses . Notable models licensed with behavioral use clauses include BLOOM (language) and LLaMA2 (language), Stable Diffusion (image), and GRID (robotics).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.05979", "content": "As of the end of 2023 , on the order of 40,000 software and model repositories have adopted responsible AI licenses licenses . Notable models licensed with behavioral use clauses include BLOOM (language) and LLaMA2 (language), Stable Diffusion (image), and GRID (robotics)."}
+{"idx": 7, "title": "An Empirical Study of License Conflict in Free and Open Source Software", "date": "", "ddg_snippet": "DIKE is proposed, an automated tool that can perform license detection and conflict analysis for FOSS and suggests that conflicts are prevalent in FOSS, warning the open source community about intellectual property risks. Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/An-Empirical-Study-of-License-Conflict-in-Free-and-Cui-Wu/7d262af42e65c2554ff7cbb41d252ad27912cff9", "content": "DIKE is proposed, an automated tool that can perform license detection and conflict analysis for FOSS and suggests that conflicts are prevalent in FOSS, warning the open source community about intellectual property risks. Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions ..."}
+{"idx": 8, "title": "Xing Cui - CHASE 2023", "date": "", "ddg_snippet": "16th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE 2023 ) CHASE is a high-quality venue for research related to the cooperative and human aspects of software engineering. Researchers and practitioners have long recognized the need to investigate the cooperative and human aspects. However, their articles have been scattered across many conferences and ...", "subpage_snippet": "", "source": "conf.researchr.org", "link": "https://conf.researchr.org/profile/chase-2023/xingcui", "content": "16th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE 2023 ) CHASE is a high-quality venue for research related to the cooperative and human aspects of software engineering. Researchers and practitioners have long recognized the need to investigate the cooperative and human aspects. However, their articles have been scattered across many conferences and ..."}
+{"idx": 9, "title": "Jingzheng Wu's research works | Chinese Academy of Sciences, Beijing ...", "date": "", "ddg_snippet": "Jingzheng Wu's 40 research works with 499 citations and 3,504 reads, including: An Empirical Study of License Conflict in Free and Open Source Software", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Jingzheng-Wu-70660859", "content": "Jingzheng Wu's 40 research works with 499 citations and 3,504 reads, including: An Empirical Study of License Conflict in Free and Open Source Software"}
diff --git a/data/sampled_jsons/Xu_et_al_2023_online_sequence_greedy_OSG_algorithm.jsonl b/data/sampled_jsons/Xu_et_al_2023_online_sequence_greedy_OSG_algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6776ff9d5f7f2efa5fe81a92860e0efc572333da
--- /dev/null
+++ b/data/sampled_jsons/Xu_et_al_2023_online_sequence_greedy_OSG_algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Online Submodular Coordination with Bounded Tracking ...", "date": "", "ddg_snippet": "by Z Xu · 2022 · Cited by 10 — The algorithm requires the robots to select actions sequentially based on the actions selected by the previous robots in the sequence .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2209.12429", "content": "by Z Xu · 2022 · Cited by 10 — The algorithm requires the robots to select actions sequentially based on the actions selected by the previous robots in the sequence ."}
+{"idx": 1, "title": "Near-Optimal Online Learning for Multi-Agent Submodular...", "date": "", "ddg_snippet": "by Q Zhang · Cited by 4 — Xu et al . ( 2023 ). Online submodular coordination with bounded tracking regret: Theory, algorithm , and applications to multi-robot coordination. IEEE Robotics ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=i8dYPGdB1C", "content": "by Q Zhang · Cited by 4 — Xu et al . ( 2023 ). Online submodular coordination with bounded tracking regret: Theory, algorithm , and applications to multi-robot coordination. IEEE Robotics ..."}
+{"idx": 2, "title": "Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "7 Feb 2025 — Given that the majority of applications occur in time-varying environments, Xu et al . ( 2023 ) proposed the online sequence greedy ( OSG ) algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05028v1", "content": "7 Feb 2025 — Given that the majority of applications occur in time-varying environments, Xu et al . ( 2023 ) proposed the online sequence greedy ( OSG ) algorithm ..."}
+{"idx": 3, "title": "Learning Protein Sequence-Function Relationships for Protein ...", "date": "", "ddg_snippet": "VEPs employ a range of models including neural networks that take amino acid sequences as inputs, and they often use evolutionary or deep mutational scanning.", "subpage_snippet": "", "source": "www.biostat.wisc.edu", "link": "https://www.biostat.wisc.edu/~gitter/pubs/GelmanThesis.pdf", "content": "VEPs employ a range of models including neural networks that take amino acid sequences as inputs, and they often use evolutionary or deep mutational scanning."}
+{"idx": 4, "title": "Proceedings of 2023 7th Chinese Conference on Swarm ...", "date": "", "ddg_snippet": "Ye et al . [14] proposed a distributed continuous privacy-preserving Nash equilibrium seeking algorithm which combined a gradient algorithm with the per ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-981-97-3324-8.pdf", "content": "Ye et al . [14] proposed a distributed continuous privacy-preserving Nash equilibrium seeking algorithm which combined a gradient algorithm with the per ..."}
+{"idx": 5, "title": "AGENT SUBMODULAR COORDINATION: TIGHT AP", "date": "", "ddg_snippet": "by Q Zhang · Cited by 4 — Xu et al . ( 2023 ) proposed the online sequence greedy ( OSG ) algorithm for online MA-SM problem, which also ensures a sub-optimal ( 1. 1+c )-approximation over ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i8dYPGdB1C", "content": "by Q Zhang · Cited by 4 — Xu et al . ( 2023 ) proposed the online sequence greedy ( OSG ) algorithm for online MA-SM problem, which also ensures a sub-optimal ( 1. 1+c )-approximation over ..."}
+{"idx": 6, "title": "A hybrid method for optimal arrangement of turbine blades", "date": "", "ddg_snippet": "Additionally, an improved partheno-genetic algorithm achieves near-optimal blade positioning with superior experimental performance (X. Xu et al . Citation 2023 ).", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/0305215X.2024.2448199?af=R", "content": "Additionally, an improved partheno-genetic algorithm achieves near-optimal blade positioning with superior experimental performance (X. Xu et al . Citation 2023 )."}
+{"idx": 7, "title": "Virtual Simulation-Based Optimization for Assembly Flow ...", "date": "", "ddg_snippet": "by WB Zhao · 2024 · Cited by 2 — Abdolazimi et al ., an improved Artificial Bee Colony (ABC) algorithm was applied to tackle a newly proposed problem, solving a mathematical ...", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/8427/cd2dcdea5498530f9be59bba6f4461f9b7ca.pdf", "content": "by WB Zhao · 2024 · Cited by 2 — Abdolazimi et al ., an improved Artificial Bee Colony (ABC) algorithm was applied to tackle a newly proposed problem, solving a mathematical ..."}
+{"idx": 8, "title": "RACE: Operator Choreography for Inference", "date": "", "ddg_snippet": "Abstract. Web recommender systems play a crucial role in enhancing user experience and engagement by providing personalized content sug- gestions.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3694906.3743310?download=true", "content": "Abstract. Web recommender systems play a crucial role in enhancing user experience and engagement by providing personalized content sug- gestions."}
+{"idx": 9, "title": "On the Alignment, Robustness, and Generalizability of ...", "date": "", "ddg_snippet": "We delve into the application of Optimal Transport-based approaches to learn cross-domain alignment, enabling models to provide interpretable explanations of ...", "subpage_snippet": "", "source": "reports-archive.adm.cs.cmu.edu", "link": "http://reports-archive.adm.cs.cmu.edu/anon/2024/CMU-CS-24-101.pdf", "content": "We delve into the application of Optimal Transport-based approaches to learn cross-domain alignment, enabling models to provide interpretable explanations of ..."}
diff --git a/data/sampled_jsons/YFCC100M_Yahoo_Flickr_Creative_Commons_dataset.jsonl b/data/sampled_jsons/YFCC100M_Yahoo_Flickr_Creative_Commons_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..878f2276a4e707a109f93748fac44f6ae33cc118
--- /dev/null
+++ b/data/sampled_jsons/YFCC100M_Yahoo_Flickr_Creative_Commons_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Ins and Outs of the Yahoo Flickr Creative Commons 100 ... Images [1503.01817] YFCC100M: The New Data in Multimedia Research YFCC100M – Communications of the ACM Core Dataset | The Multimedia Commons Initiative Multimedia Commons One Hundred Million Creative Commons Flickr Images... | Yahoo ... [1503.01817] YFCC100M : The New Data in Multimedia Research One Hundred Million Creative Commons Flickr Images ... - Yahoo Resea… YFCC100M – Communications of the ACM Core Dataset | The Multimedia Commons Initiative YFCC100M – Communications of the ACM YFCC100M – Communications of the ACM yfcc100m · PyPI", "date": "", "ddg_snippet": "Oct 15, 2014 · This past summer we ( Yahoo Labs and Flickr ) released the YFCC100M dataset that is the largest and most ambitious collection of Flickr photos and videos ever, containing 99,206,564 photos and 793,436 videos from 581,099 different photographers. View all Mar 5, 2015 · We present the Yahoo Flickr Creative Commons 100 Million Dataset ( YFCC100M ), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. Each media object in the dataset is represented by several pieces of ... Feb 1, 2016 · We created the Yahoo Flickr Creative Commons 100 Million Dataset a ( YFCC100M ) in 2014 as part of the Yahoo Webscope program, which is a reference library of interesting and scientifically useful datasets. The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Notice: The instructions in this section are for getting the YFCC100M dataset via Yahoo 's Webscope research data portal. Unfortunately, Webscope is ... The Multimedia Commons (MMCommons) initiative is a community formed to coordinate efforts to advance the field of multimedia. Most of our attention is currently directed at making the Yahoo - Flickr Creative Commons 100 Million ( YFCC100M ) dataset even more useful, by offering a repository that contains supplemental material to this collection, such as content, features, and annotations. The ... Jun 24, 2014 · At Flickr and at Yahoo Labs, we set out to provide something more substantial for researchers around the globe. Data, data, data… A glimpse of a small piece of the dataset . YFCC100M by aymanshamma on Flickr . Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope’s datasets for researchers. What does YFCC100M stand for? We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. What is the Flickr Creative Commons dataset? Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope’s datasets for researchers. The dataset, we believe, is one of the largest public multimedia datasets that has ever been released—99.3 million images and 0.7 million videos, all from Flickr and all under Creative Commons licensing. What is Flickr YFCC100M? With a rich, diverse collection of image types, Flickr provides the groundwork for total scene understanding 14 in computer vision and artificial intelligence, a crucial task that can be expanded through the YFCC100M dataset , and even more once additional annotations are released. Spatiotemporal computing. Where can I find YFCC100M images and videos? The original images and videos indexed in the YFCC100M may be found in the data/ directory on the multimedia-commons S3 data store on AWS Public Data Sets. This directory has 99,171,688 image files and 787,479 video files. The videos add up to around 8,081 hours, with an average video length of 37s and a median length of 28s. What is the YFCC100M dataset? The YFCC100M dataset offers avenues of computer science research in multimedia, information retrieval, and data visualization, in addition to the larger questions of how to preserve digital libraries. Data is a core component of research and development in all scientific fields. What venues are incorporating the YFCC100M dataset? Other venues incorporating the YFCC100M dataset are the ACM Multimedia 2015 Grand Challenge on Event Detection and Summarization and the ACM Multimedia 2015 MMCommons Workshop; the latter aims to establish a research community around annotating all 100 million photos and videos in the YFCC100M . Mar 18, 2021 · Download the YFCC100m dataset without going insane.What is the YFCC100m dataset ? A quote from its website: The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Metadata and files are currently hosted in ...", "subpage_snippet": "", "source": "code.flickr.net", "link": "https://code.flickr.net/2014/10/15/the-ins-and-outs-of-the-yahoo-flickr-100-million-creative-commons-dataset/", "content": "Oct 15, 2014 · This past summer we ( Yahoo Labs and Flickr ) released the YFCC100M dataset that is the largest and most ambitious collection of Flickr photos and videos ever, containing 99,206,564 photos and 793,436 videos from 581,099 different photographers. View all Mar 5, 2015 · We present the Yahoo Flickr Creative Commons 100 Million Dataset ( YFCC100M ), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. Each media object in the dataset is represented by several pieces of ... Feb 1, 2016 · We created the Yahoo Flickr Creative Commons 100 Million Dataset a ( YFCC100M ) in 2014 as part of the Yahoo Webscope program, which is a reference library of interesting and scientifically useful datasets. The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Notice: The instructions in this section are for getting the YFCC100M dataset via Yahoo 's Webscope research data portal. Unfortunately, Webscope is ... The Multimedia Commons (MMCommons) initiative is a community formed to coordinate efforts to advance the field of multimedia. Most of our attention is currently directed at making the Yahoo - Flickr Creative Commons 100 Million ( YFCC100M ) dataset even more useful, by offering a repository that contains supplemental material to this collection, such as content, features, and annotations. The ... Jun 24, 2014 · At Flickr and at Yahoo Labs, we set out to provide something more substantial for researchers around the globe. Data, data, data… A glimpse of a small piece of the dataset . YFCC100M by aymanshamma on Flickr . Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope’s datasets for researchers. What does YFCC100M stand for? We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. What is the Flickr Creative Commons dataset? Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope’s datasets for researchers. The dataset, we believe, is one of the largest public multimedia datasets that has ever been released—99.3 million images and 0.7 million videos, all from Flickr and all under Creative Commons licensing. What is Flickr YFCC100M? With a rich, diverse collection of image types, Flickr provides the groundwork for total scene understanding 14 in computer vision and artificial intelligence, a crucial task that can be expanded through the YFCC100M dataset , and even more once additional annotations are released. Spatiotemporal computing. Where can I find YFCC100M images and videos? The original images and videos indexed in the YFCC100M may be found in the data/ directory on the multimedia-commons S3 data store on AWS Public Data Sets. This directory has 99,171,688 image files and 787,479 video files. The videos add up to around 8,081 hours, with an average video length of 37s and a median length of 28s. What is the YFCC100M dataset? The YFCC100M dataset offers avenues of computer science research in multimedia, information retrieval, and data visualization, in addition to the larger questions of how to preserve digital libraries. Data is a core component of research and development in all scientific fields. What venues are incorporating the YFCC100M dataset? Other venues incorporating the YFCC100M dataset are the ACM Multimedia 2015 Grand Challenge on Event Detection and Summarization and the ACM Multimedia 2015 MMCommons Workshop; the latter aims to establish a research community around annotating all 100 million photos and videos in the YFCC100M . Mar 18, 2021 · Download the YFCC100m dataset without going insane.What is the YFCC100m dataset ? A quote from its website: The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Metadata and files are currently hosted in ..."}
+{"idx": 1, "title": "[1503.01817] YFCC100M: The New Data in Multimedia Research YFCC100M – Communications of the ACM Core Dataset | The Multimedia Commons Initiative Multimedia Commons One Hundred Million Creative Commons Flickr Images... | Yahoo ... [1503.01817] YFCC100M : The New Data in Multimedia Research One Hundred Million Creative Commons Flickr Images ... - Yahoo Resea… YFCC100M – Communications of the ACM Core Dataset | The Multimedia Commons Initiative YFCC100M – Communications of the ACM YFCC100M – Communications of the ACM yfcc100m · PyPI", "date": "", "ddg_snippet": "Mar 5, 2015 · We present the Yahoo Flickr Creative Commons 100 Million Dataset ( YFCC100M ), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. Each media object in the dataset is represented by several pieces of ... Feb 1, 2016 · We created the Yahoo Flickr Creative Commons 100 Million Dataset a ( YFCC100M ) in 2014 as part of the Yahoo Webscope program, which is a reference library of interesting and scientifically useful datasets. The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Notice: The instructions in this section are for getting the YFCC100M dataset via Yahoo 's Webscope research data portal. Unfortunately, Webscope is ... The Multimedia Commons (MMCommons) initiative is a community formed to coordinate efforts to advance the field of multimedia. Most of our attention is currently directed at making the Yahoo - Flickr Creative Commons 100 Million ( YFCC100M ) dataset even more useful, by offering a repository that contains supplemental material to this collection, such as content, features, and annotations. The ... Jun 24, 2014 · At Flickr and at Yahoo Labs, we set out to provide something more substantial for researchers around the globe. Data, data, data… A glimpse of a small piece of the dataset . YFCC100M by aymanshamma on Flickr . Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope’s datasets for researchers. What does YFCC100M stand for? We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. What is the Flickr Creative Commons dataset? Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope’s datasets for researchers. The dataset, we believe, is one of the largest public multimedia datasets that has ever been released—99.3 million images and 0.7 million videos, all from Flickr and all under Creative Commons licensing. What is Flickr YFCC100M? With a rich, diverse collection of image types, Flickr provides the groundwork for total scene understanding 14 in computer vision and artificial intelligence, a crucial task that can be expanded through the YFCC100M dataset , and even more once additional annotations are released. Spatiotemporal computing. Where can I find YFCC100M images and videos? The original images and videos indexed in the YFCC100M may be found in the data/ directory on the multimedia-commons S3 data store on AWS Public Data Sets. This directory has 99,171,688 image files and 787,479 video files. The videos add up to around 8,081 hours, with an average video length of 37s and a median length of 28s. What is the YFCC100M dataset? The YFCC100M dataset offers avenues of computer science research in multimedia, information retrieval, and data visualization, in addition to the larger questions of how to preserve digital libraries. Data is a core component of research and development in all scientific fields. What venues are incorporating the YFCC100M dataset? Other venues incorporating the YFCC100M dataset are the ACM Multimedia 2015 Grand Challenge on Event Detection and Summarization and the ACM Multimedia 2015 MMCommons Workshop; the latter aims to establish a research community around annotating all 100 million photos and videos in the YFCC100M . Mar 18, 2021 · Download the YFCC100m dataset without going insane.What is the YFCC100m dataset ? A quote from its website: The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Metadata and files are currently hosted in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1503.01817", "content": "Mar 5, 2015 · We present the Yahoo Flickr Creative Commons 100 Million Dataset ( YFCC100M ), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. Each media object in the dataset is represented by several pieces of ... Feb 1, 2016 · We created the Yahoo Flickr Creative Commons 100 Million Dataset a ( YFCC100M ) in 2014 as part of the Yahoo Webscope program, which is a reference library of interesting and scientifically useful datasets. The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Notice: The instructions in this section are for getting the YFCC100M dataset via Yahoo 's Webscope research data portal. Unfortunately, Webscope is ... The Multimedia Commons (MMCommons) initiative is a community formed to coordinate efforts to advance the field of multimedia. Most of our attention is currently directed at making the Yahoo - Flickr Creative Commons 100 Million ( YFCC100M ) dataset even more useful, by offering a repository that contains supplemental material to this collection, such as content, features, and annotations. The ... Jun 24, 2014 · At Flickr and at Yahoo Labs, we set out to provide something more substantial for researchers around the globe. Data, data, data… A glimpse of a small piece of the dataset . YFCC100M by aymanshamma on Flickr . Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope’s datasets for researchers. What does YFCC100M stand for? We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. What is the Flickr Creative Commons dataset? Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope’s datasets for researchers. The dataset, we believe, is one of the largest public multimedia datasets that has ever been released—99.3 million images and 0.7 million videos, all from Flickr and all under Creative Commons licensing. What is Flickr YFCC100M? With a rich, diverse collection of image types, Flickr provides the groundwork for total scene understanding 14 in computer vision and artificial intelligence, a crucial task that can be expanded through the YFCC100M dataset , and even more once additional annotations are released. Spatiotemporal computing. Where can I find YFCC100M images and videos? The original images and videos indexed in the YFCC100M may be found in the data/ directory on the multimedia-commons S3 data store on AWS Public Data Sets. This directory has 99,171,688 image files and 787,479 video files. The videos add up to around 8,081 hours, with an average video length of 37s and a median length of 28s. What is the YFCC100M dataset? The YFCC100M dataset offers avenues of computer science research in multimedia, information retrieval, and data visualization, in addition to the larger questions of how to preserve digital libraries. Data is a core component of research and development in all scientific fields. What venues are incorporating the YFCC100M dataset? Other venues incorporating the YFCC100M dataset are the ACM Multimedia 2015 Grand Challenge on Event Detection and Summarization and the ACM Multimedia 2015 MMCommons Workshop; the latter aims to establish a research community around annotating all 100 million photos and videos in the YFCC100M . Mar 18, 2021 · Download the YFCC100m dataset without going insane.What is the YFCC100m dataset ? A quote from its website: The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Metadata and files are currently hosted in ..."}
+{"idx": 2, "title": "YFCC100M – Communications of the ACM", "date": "", "ddg_snippet": "Feb 1, 2016 · We created the Yahoo Flickr Creative Commons 100 Million Dataset a ( YFCC100M ) in 2014 as part of the Yahoo Webscope program, which is a reference library of interesting and scientifically useful datasets.", "subpage_snippet": "", "source": "cacm.acm.org", "link": "https://cacm.acm.org/research/yfcc100m/", "content": "Feb 1, 2016 · We created the Yahoo Flickr Creative Commons 100 Million Dataset a ( YFCC100M ) in 2014 as part of the Yahoo Webscope program, which is a reference library of interesting and scientifically useful datasets."}
+{"idx": 3, "title": "Core Dataset | The Multimedia Commons Initiative", "date": "", "ddg_snippet": "The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Notice: The instructions in this section are for getting the YFCC100M dataset via Yahoo 's Webscope research data portal. Unfortunately, Webscope is ...", "subpage_snippet": "", "source": "multimediacommons.wordpress.com", "link": "https://multimediacommons.wordpress.com/yfcc100m-core-dataset/", "content": "The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Notice: The instructions in this section are for getting the YFCC100M dataset via Yahoo 's Webscope research data portal. Unfortunately, Webscope is ..."}
+{"idx": 4, "title": "yfcc100m · PyPI", "date": "", "ddg_snippet": "Mar 18, 2021 · Download the YFCC100m dataset without going insane.What is the YFCC100m dataset ? A quote from its website: The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Metadata and files are currently hosted in ...", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/yfcc100m/", "content": "Mar 18, 2021 · Download the YFCC100m dataset without going insane.What is the YFCC100m dataset ? A quote from its website: The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr , all of which were shared under one of the various Creative Commons licenses. Metadata and files are currently hosted in ..."}
+{"idx": 5, "title": "Multimedia Commons", "date": "", "ddg_snippet": "The Multimedia Commons (MMCommons) initiative is a community formed to coordinate efforts to advance the field of multimedia. Most of our attention is currently directed at making the Yahoo - Flickr Creative Commons 100 Million ( YFCC100M ) dataset even more useful, by offering a repository that contains supplemental material to this collection, such as content, features, and annotations. The ...", "subpage_snippet": "", "source": "multimediacommons.org", "link": "http://multimediacommons.org/", "content": "The Multimedia Commons (MMCommons) initiative is a community formed to coordinate efforts to advance the field of multimedia. Most of our attention is currently directed at making the Yahoo - Flickr Creative Commons 100 Million ( YFCC100M ) dataset even more useful, by offering a repository that contains supplemental material to this collection, such as content, features, and annotations. The ..."}
+{"idx": 6, "title": "One Hundred Million Creative Commons Flickr Images... | Yahoo ...", "date": "", "ddg_snippet": "Jun 24, 2014 · At Flickr and at Yahoo Labs, we set out to provide something more substantial for researchers around the globe. Data, data, data… A glimpse of a small piece of the dataset . YFCC100M by aymanshamma on Flickr . Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope’s datasets for researchers.", "subpage_snippet": "", "source": "yahooresearch.tumblr.com", "link": "https://yahooresearch.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images", "content": "Jun 24, 2014 · At Flickr and at Yahoo Labs, we set out to provide something more substantial for researchers around the globe. Data, data, data… A glimpse of a small piece of the dataset . YFCC100M by aymanshamma on Flickr . Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope’s datasets for researchers."}
+{"idx": 7, "title": "Yahoo Flickr Creative Commons 100 M | Vision Dataset", "date": "", "ddg_snippet": "Dataset contains 1000 images of 100 persons, with 10 images per person and is freely available. All images were acquired by cropping ears from images fr... person, pedestrian, ear, recognition, human, lighting, biometry.", "subpage_snippet": "", "source": "mldta.com", "link": "https://mldta.com/dataset/yahoo-flickr-creative-commons-100m/", "content": "Dataset contains 1000 images of 100 persons, with 10 images per person and is freely available. All images were acquired by cropping ears from images fr... person, pedestrian, ear, recognition, human, lighting, biometry."}
+{"idx": 8, "title": "(PDF) Which Languages do People Speak on Flickr ?: A Language and...", "date": "", "ddg_snippet": "Recently, the Yahoo Flickr Creative Commons 100 Million ( YFCC 100 m ) dataset was introduced to the computer vision and multimedia research community. This dataset consists of millions of images and videos spread over the globe.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/310820810_Which_Languages_do_People_Speak_on_Flickr_A_Language_and_Geo-Location_Study_of_the_YFCC100m_Dataset", "content": "Recently, the Yahoo Flickr Creative Commons 100 Million ( YFCC 100 m ) dataset was introduced to the computer vision and multimedia research community. This dataset consists of millions of images and videos spread over the globe."}
+{"idx": 9, "title": "Practical Guide to Using the YFCC 100 M and MMCOMMONS on...", "date": "", "ddg_snippet": "The Yahoo - Flickr Creative Commons 100 Million ( YFCC 100 M ), the largest freely usable multimedia dataset to have been released so far, is widely used by students, researchers and engineers on topics in multimedia that range from computer vision to machine learning.", "subpage_snippet": "", "source": "records.sigmm.org", "link": "https://records.sigmm.org/2017/10/09/practical-guide-to-using-the-yfcc100m-and-mmcommons-on-a-budget/", "content": "The Yahoo - Flickr Creative Commons 100 Million ( YFCC 100 M ), the largest freely usable multimedia dataset to have been released so far, is widely used by students, researchers and engineers on topics in multimedia that range from computer vision to machine learning."}
diff --git a/data/sampled_jsons/YFCC100M_full_name_Yahoo_Flickr_Creative_Commons.jsonl b/data/sampled_jsons/YFCC100M_full_name_Yahoo_Flickr_Creative_Commons.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c932f083c9b3b8879040f6069cce8da048ebd878
--- /dev/null
+++ b/data/sampled_jsons/YFCC100M_full_name_Yahoo_Flickr_Creative_Commons.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "YFCC100M: The New Data in Multimedia Research", "date": "", "ddg_snippet": "by B Thomee · 2015 · Cited by 2376 — We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1503.01817", "content": "by B Thomee · 2015 · Cited by 2376 — We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released."}
+{"idx": 1, "title": "The Ins and Outs of the Yahoo Flickr Creative Commons ...", "date": "", "ddg_snippet": "15 Oct 2014 — This past summer we (Yahoo Labs and Flickr) released the YFCC100M dataset that is the largest and most ambitious collection of Flickr photos ...", "subpage_snippet": "", "source": "code.flickr.net", "link": "https://code.flickr.net/2014/10/15/the-ins-and-outs-of-the-yahoo-flickr-100-million-creative-commons-dataset/", "content": "15 Oct 2014 — This past summer we (Yahoo Labs and Flickr) released the YFCC100M dataset that is the largest and most ambitious collection of Flickr photos ..."}
+{"idx": 2, "title": "Core Dataset | The Multimedia Commons Initiative", "date": "", "ddg_snippet": "The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos ...", "subpage_snippet": "", "source": "multimediacommons.wordpress.com", "link": "https://multimediacommons.wordpress.com/yfcc100m-core-dataset/", "content": "The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos ..."}
+{"idx": 3, "title": "YFCC100M: The New Data in Multimedia Research", "date": "", "ddg_snippet": "by B Thomee — YFCC100M Dataset. We created the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M) in 2014 as part of the Yahoo Webscope program ...", "subpage_snippet": "", "source": "cacm.acm.org", "link": "https://cacm.acm.org/research/yfcc100m/", "content": "by B Thomee — YFCC100M Dataset. We created the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M) in 2014 as part of the Yahoo Webscope program ..."}
+{"idx": 4, "title": "Multimedia Commons - Registry of Open Data on AWS", "date": "", "ddg_snippet": "... Creative Commons-licensed Flickr images and videos in the YFCC100M dataset from Yahoo! Labs, along with ground-truth annotations for selected subsets. The ...", "subpage_snippet": "", "source": "registry.opendata.aws", "link": "https://registry.opendata.aws/multimedia-commons/", "content": "... Creative Commons-licensed Flickr images and videos in the YFCC100M dataset from Yahoo! Labs, along with ground-truth annotations for selected subsets. The ..."}
+{"idx": 5, "title": "Creative Commons Biometrics - Adam Harvey", "date": "", "ddg_snippet": "25 May 2022 — “Our data source is the Yahoo Flickr Creative Commons 100M (YFCC100M), which contains 100 million images, of which 49 million are geotagged ...", "subpage_snippet": "", "source": "adam.harvey.studio", "link": "https://adam.harvey.studio/creative-commons/", "content": "25 May 2022 — “Our data source is the Yahoo Flickr Creative Commons 100M (YFCC100M), which contains 100 million images, of which 49 million are geotagged ..."}
+{"idx": 6, "title": "YFCC100M: The New Data in Multimedia Research", "date": "", "ddg_snippet": "THE YFCC100M DATASET. We created the Yahoo Flickr Creative Commons 100 Mil- lion Dataset 1 (YFCC100M) as part of the Yahoo Webscope program. This dataset is ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/yfcc100m-the-new-data-in-multimedia-research-17fflsw100.pdf", "content": "THE YFCC100M DATASET. We created the Yahoo Flickr Creative Commons 100 Mil- lion Dataset 1 (YFCC100M) as part of the Yahoo Webscope program. This dataset is ..."}
+{"idx": 7, "title": "One Hundred Million Creative Commons Flickr Images for ...", "date": "", "ddg_snippet": "YFCC100M by aymanshamma on Flickr . Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope's datasets for researchers. The ...", "subpage_snippet": "", "source": "www.tumblr.com", "link": "https://www.tumblr.com/yahooresearch/89783581601/one-hundred-million-creative-commons-flickr-images", "content": "YFCC100M by aymanshamma on Flickr . Today, we are announcing the Flickr Creative Commons dataset as part of Yahoo Webscope's datasets for researchers. The ..."}
+{"idx": 8, "title": "yfcc100m", "date": "", "ddg_snippet": "15 Mar 2021 — The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos ...", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/yfcc100m/", "content": "15 Mar 2021 — The YFCC100M is the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos ..."}
+{"idx": 9, "title": "Analysis of Spatial, Temporal, and Content Characteristics of ...", "date": "", "ddg_snippet": "by JH Choi · 2016 · Cited by 2 — The Yahoo Flickr Creative Commons 100 Million dataset (YFCC100M) is one of the largest public databases containing images and videos and their annotations ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/2983554.2983559", "content": "by JH Choi · 2016 · Cited by 2 — The Yahoo Flickr Creative Commons 100 Million dataset (YFCC100M) is one of the largest public databases containing images and videos and their annotations ..."}
diff --git a/data/sampled_jsons/YOpa6dTrpt_Pedestrian_Motion_Reconstruction_Table_4_SLHAMR_PA-MPJPE_single_moving_camera.jsonl b/data/sampled_jsons/YOpa6dTrpt_Pedestrian_Motion_Reconstruction_Table_4_SLHAMR_PA-MPJPE_single_moving_camera.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f6721ad589777c02e8e9c704034dd53e4ab0150
--- /dev/null
+++ b/data/sampled_jsons/YOpa6dTrpt_Pedestrian_Motion_Reconstruction_Table_4_SLHAMR_PA-MPJPE_single_moving_camera.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Physics-based Human Pose Estimation from a Single Moving RGB ...", "date": "", "ddg_snippet": "1. Introduction Estimating accurate 3D human motion in global coordi-nates from a single moving RGB camera is an important and challenging problem in Computer Vision with many applications in animation, Augmented and Virtual Real-ity (AR/VR), human-robot interaction, autonomous driving, and assisted living environments.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025W/RHOBIN/papers/Aytekin_Physics-based_Human_Pose_Estimation_from_a_Single_Moving_RGB_Camera_CVPRW_2025_paper.pdf", "content": "1. Introduction Estimating accurate 3D human motion in global coordi-nates from a single moving RGB camera is an important and challenging problem in Computer Vision with many applications in animation, Augmented and Virtual Real-ity (AR/VR), human-robot interaction, autonomous driving, and assisted living environments."}
+{"idx": 1, "title": "Pedestrian Motion Reconstruction: A Large-scale Benchmark via ...", "date": "", "ddg_snippet": "Jan 22, 2025 · This data provides a rich foundation for modeling pedestrian intent through multi-view and multi-modal insights. We also conduct comprehensive benchmark assessments across different modalities to thoroughly evaluate pedestrian motion reconstruction methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YOpa6dTrpt", "content": "Jan 22, 2025 · This data provides a rich foundation for modeling pedestrian intent through multi-view and multi-modal insights. We also conduct comprehensive benchmark assessments across different modalities to thoroughly evaluate pedestrian motion reconstruction methods."}
+{"idx": 2, "title": "P M RECONSTRUCTION: A LARGE SCALE BENCHMARK VIA MIXED REALITY ...", "date": "", "ddg_snippet": "To address the previously highlighted challenges, we present the Pedestrian Motion Reconstruction (PMR) dataset, a comprehensive resource designed for intention-aware pedestrian motion recon- struction using data from moving sensors.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=YOpa6dTrpt", "content": "To address the previously highlighted challenges, we present the Pedestrian Motion Reconstruction (PMR) dataset, a comprehensive resource designed for intention-aware pedestrian motion recon- struction using data from moving sensors."}
+{"idx": 3, "title": "Reconstructing 3D human pose and shape from a single image ...", "date": "", "ddg_snippet": "The method achieves state-of-the-art performance on two benchmarks for 3D human pose and shape reconstruction , providing a solution for 3D human motion capture in some unrestricted environments in practice.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10280469/", "content": "The method achieves state-of-the-art performance on two benchmarks for 3D human pose and shape reconstruction , providing a solution for 3D human motion capture in some unrestricted environments in practice."}
+{"idx": 4, "title": "Shape of Motion: 4D Reconstruction from a Single Video", "date": "", "ddg_snippet": "Monocular dynamic reconstruction is a challenging and long-standing vision problem due to the highly ill-posed nature of the task. Existing approaches are limited in that they either depend on templates, are effective only in quasi-static scenes, or fail to model 3D motion explicitly.", "subpage_snippet": "", "source": "shape-of-motion.github.io", "link": "https://shape-of-motion.github.io/", "content": "Monocular dynamic reconstruction is a challenging and long-standing vision problem due to the highly ill-posed nature of the task. Existing approaches are limited in that they either depend on templates, are effective only in quasi-static scenes, or fail to model 3D motion explicitly."}
+{"idx": 5, "title": "Tutorial: Quick Smooth Camera Movements in Blender - YouTube", "date": "", "ddg_snippet": "In this video we try a different approach to animating the camera in Blender.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=a7qyW1G350g", "content": "In this video we try a different approach to animating the camera in Blender."}
+{"idx": 6, "title": "3D姿态估计的评价指标 MPJPE 及其变种-CSDN博客", "date": "", "ddg_snippet": "查看datasets/Human3.6M以获取有关Human3.6M的更多详细信息 最新的3D姿势估计方法 方法 出版物 MPJPE PA - MPJPE 关联 OANet ICCV19 42.9 32.8 [conf] 3DMPPE ICCV.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/leviopku/article/details/118108885", "content": "查看datasets/Human3.6M以获取有关Human3.6M的更多详细信息 最新的3D姿势估计方法 方法 出版物 MPJPE PA - MPJPE 关联 OANet ICCV19 42.9 32.8 [conf] 3DMPPE ICCV."}
+{"idx": 7, "title": "PA MPJPE comparison with state-of-the-art methods on the...", "date": "", "ddg_snippet": "In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/PA-MPJPE-comparison-with-state-of-the-art-methods-on-the-Human36M-dataset-using-Protocol_tbl3_334735304", "content": "In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of..."}
+{"idx": 8, "title": "GitHub - karfly/learnable-triangulation-pytorch: This repository is an...", "date": "", "ddg_snippet": "Train. Every experiment is defined by .config files. Configs with experiments from the paper can be found in the ./experiments directory (see model zoo). Single -GPU. MPJPE relative to pelvis ( single -view methods): MPJPE (averaged across all actions), mm.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karfly/learnable-triangulation-pytorch", "content": "Train. Every experiment is defined by .config files. Configs with experiments from the paper can be found in the ./experiments directory (see model zoo). Single -GPU. MPJPE relative to pelvis ( single -view methods): MPJPE (averaged across all actions), mm."}
+{"idx": 9, "title": "Being-H0: Vision-Language-Action Pretraining from Large-Scale...", "date": "", "ddg_snippet": "• PA - MPJPE (Procrustes Aligned MPJPE) isolates relative pose accuracy by aligning predicted joints to the ground truth via rigid transformation (including scaling, rotation, and translation).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15597v1", "content": "• PA - MPJPE (Procrustes Aligned MPJPE) isolates relative pose accuracy by aligning predicted joints to the ground truth via rigid transformation (including scaling, rotation, and translation)."}
diff --git a/data/sampled_jsons/Yao_2024_multi-view_causal_representation_learning_smooth_distribution_assumption.jsonl b/data/sampled_jsons/Yao_2024_multi-view_causal_representation_learning_smooth_distribution_assumption.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..84bb28fee724fe49847e03efdee910fc0b53e11c
--- /dev/null
+++ b/data/sampled_jsons/Yao_2024_multi-view_causal_representation_learning_smooth_distribution_assumption.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi-View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "by D Yao · Cited by 53 — We prove that the information shared across all subsets of any number of views can be learned up to a smooth bijection using contrastive learning and a single ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=OGtnhKQJms", "content": "by D Yao · Cited by 53 — We prove that the information shared across all subsets of any number of views can be learned up to a smooth bijection using contrastive learning and a single ..."}
+{"idx": 1, "title": "MULTI-VIEW CAUSAL REPRESENTATION LEARNING", "date": "", "ddg_snippet": "by D Yao · Cited by 53 — This paper provides a unified framework for several identifiability results in observational multi - view causal representation learning under partial ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=OGtnhKQJms", "content": "by D Yao · Cited by 53 — This paper provides a unified framework for several identifiability results in observational multi - view causal representation learning under partial ..."}
+{"idx": 2, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "28 Apr 2025 — As opposed to CCRL, the method from Yao et al. ( 2024c ) places more flexible assumptions on the distribution of the underlying causal factors, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v2", "content": "28 Apr 2025 — As opposed to CCRL, the method from Yao et al. ( 2024c ) places more flexible assumptions on the distribution of the underlying causal factors, ..."}
+{"idx": 3, "title": "publications | Dingling YAO - GitHub Pages", "date": "", "ddg_snippet": "Causal representation learning (CRL) aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, ...", "subpage_snippet": "", "source": "ddcoan.github.io", "link": "https://ddcoan.github.io/publications/", "content": "Causal representation learning (CRL) aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, ..."}
+{"idx": 4, "title": "Marrying Causal Representation Learning with Dynamical ...", "date": "", "ddg_snippet": "9 Dec 2024 — Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95516", "content": "9 Dec 2024 — Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements."}
+{"idx": 5, "title": "Causal Representation Learning from Multiple Distributions", "date": "", "ddg_snippet": "7 Feb 2024 — To generalize beyond the independent hidden variables and achieve causal representation learning (recovering the latent variables and their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.05052v1", "content": "7 Feb 2024 — To generalize beyond the independent hidden variables and achieve causal representation learning (recovering the latent variables and their ..."}
+{"idx": 6, "title": "Dingling YAO", "date": "", "ddg_snippet": "My research focuses on identifiability in representation learning and the applicability of causal representation learning for scientific discovery.", "subpage_snippet": "", "source": "ddcoan.github.io", "link": "https://ddcoan.github.io/", "content": "My research focuses on identifiability in representation learning and the applicability of causal representation learning for scientific discovery."}
+{"idx": 7, "title": "Sanity Checking Causal Representation Learning on a ...", "date": "", "ddg_snippet": "As opposed to CCRL, the method from Yao et al. ( 2024c ) places more flexible assumptions on the distribution of the underlying causal factors, where any smooth ,.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "As opposed to CCRL, the method from Yao et al. ( 2024c ) places more flexible assumptions on the distribution of the underlying causal factors, where any smooth ,."}
+{"idx": 8, "title": "Causal Representation Learning from Multi-modal ...", "date": "", "ddg_snippet": "by Y Sun · 2025 · Cited by 6 — In this work, we aim to develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11952583/", "content": "by Y Sun · 2025 · Cited by 6 — In this work, we aim to develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical ..."}
+{"idx": 9, "title": "Causal representation learning through higher-level ...", "date": "", "ddg_snippet": "by F Silva · 2024 · Cited by 2 — We explore a perspective on this problem that is directed to learning the generative process with causality-related foundations.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696412", "content": "by F Silva · 2024 · Cited by 2 — We explore a perspective on this problem that is directed to learning the generative process with causality-related foundations."}
diff --git a/data/sampled_jsons/Yao_multiview_causal_representation_learning_partial_observability_assumptions.jsonl b/data/sampled_jsons/Yao_multiview_causal_representation_learning_partial_observability_assumptions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4324458ed968359cc1b22cac5618620abab96f0c
--- /dev/null
+++ b/data/sampled_jsons/Yao_multiview_causal_representation_learning_partial_observability_assumptions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2311.04056v2] Multi - View Causal Representation Learning with...", "date": "", "ddg_snippet": "View a PDF of the paper titled Multi - View Causal Representation Learning with Partial Observability , by Dingling Yao and 6 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.04056v2", "content": "View a PDF of the paper titled Multi - View Causal Representation Learning with Partial Observability , by Dingling Yao and 6 other authors."}
+{"idx": 1, "title": "Multi - View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "ServiceNow Research. Publications. Multi - View Causal Representation Learning with Partial Observability .Overall, we find that access to multiple partial views enables to identify a more fine-grained representation , under the generally milder assumption of partial observability .", "subpage_snippet": "", "source": "www.servicenow.com", "link": "https://www.servicenow.com/research/publication/dingling-yao-mult-iclr2024.html", "content": "ServiceNow Research. Publications. Multi - View Causal Representation Learning with Partial Observability .Overall, we find that access to multiple partial views enables to identify a more fine-grained representation , under the generally milder assumption of partial observability ."}
+{"idx": 2, "title": "Paper page - Multi - View Causal Representation Learning with...", "date": "", "ddg_snippet": "Dingling Yao .A unified framework using contrastive learning and multiple partial views allows for the identification of latent variables with minimal assumptions , improving representation learning .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2311.04056", "content": "Dingling Yao .A unified framework using contrastive learning and multiple partial views allows for the identification of latent variables with minimal assumptions , improving representation learning ."}
+{"idx": 3, "title": "Multi - View Causal Representation Learning with", "date": "", "ddg_snippet": "Multi -modal Content-Style Identifiability under Partial Observability . Multi -Task Disentanglement with Sparse Classifiers.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E8IhOxNREv", "content": "Multi -modal Content-Style Identifiability under Partial Observability . Multi -Task Disentanglement with Sparse Classifiers."}
+{"idx": 4, "title": "Multi - View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "@conference{Yaoetal24, title = { Multi - View Causal Representation Learning with Partial Observability }, booktitle = {The Twelfth International Conference on Learning Representations (ICLR)}, month = may, year = {2024}, slug = {yaoetal24}, author = { Yao , D. and Xu, D...", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/al/publications/yaoetal24", "content": "@conference{Yaoetal24, title = { Multi - View Causal Representation Learning with Partial Observability }, booktitle = {The Twelfth International Conference on Learning Representations (ICLR)}, month = may, year = {2024}, slug = {yaoetal24}, author = { Yao , D. and Xu, D..."}
+{"idx": 5, "title": "Multi - View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "Overall, we find that access to multiple partial views enables us to identify a more fine-grained representation , under the generally milder assumption of partial observability .", "subpage_snippet": "", "source": "perouz.github.io", "link": "https://perouz.github.io/publication/yao-2023-multiview/", "content": "Overall, we find that access to multiple partial views enables us to identify a more fine-grained representation , under the generally milder assumption of partial observability ."}
+{"idx": 6, "title": "Causal Representation Learning", "date": "", "ddg_snippet": "Multi - View Causal Representation Learning with Partial Observability ( Talk ) > SlidesLive Video. Dingling Yao . - Mixup-Based Knowledge Distillation with Causal Intervention for Multi-Task Speech Classification ( Poster ) > link.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/workshop/66497", "content": "Multi - View Causal Representation Learning with Partial Observability ( Talk ) > SlidesLive Video. Dingling Yao . - Mixup-Based Knowledge Distillation with Causal Intervention for Multi-Task Speech Classification ( Poster ) > link."}
+{"idx": 7, "title": "Multi - View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "Overall, we find that access to multiple partial views enables us to identify a more fine-grained representation , under the generally milder assumption of partial observability .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/multi-view-causal-representation-learning", "content": "Overall, we find that access to multiple partial views enables us to identify a more fine-grained representation , under the generally milder assumption of partial observability ."}
+{"idx": 8, "title": "Multi - View Causal Representation Learning with Partial ...", "date": "", "ddg_snippet": "We present a unified framework for studying the identifiability of representations learned from simultaneously observed views , such as different data modalities.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Multi-View-Causal-Representation-Learning-with-Partial-Observability-36809737-b657-4795-b361-877c53cf41f8", "content": "We present a unified framework for studying the identifiability of representations learned from simultaneously observed views , such as different data modalities."}
+{"idx": 9, "title": "Dingling Yao - Google Scholar", "date": "", "ddg_snippet": "Multi - view causal representation learning with partial observability .Unifying Causal Representation Learning with the Invariance Principle.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=vOJUvb0AAAAJ&hl=en", "content": "Multi - view causal representation learning with partial observability .Unifying Causal Representation Learning with the Invariance Principle."}
diff --git a/data/sampled_jsons/YjBrt82S3v_Equation_7_RA2C_loss_symmetric_reinforcement_learning.jsonl b/data/sampled_jsons/YjBrt82S3v_Equation_7_RA2C_loss_symmetric_reinforcement_learning.jsonl
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+{"idx": 1, "title": "Uniqueness of regular and singular equilibria for spherically symmetric ...", "date": "", "ddg_snippet": "We assume without loss of generality that ~ satisfies the first condition of (H3); otherwise exactly analogous arguments hold on using the inverse Cauchy. stress 7 ~ and Proposition 1.4 instead of the radial Cauchy stress T. It follows from Proposition 1.3 that T(r(R)) is nowhere decreasing.", "subpage_snippet": "", "source": "people.bath.ac.uk", "link": "https://people.bath.ac.uk/masjs/Research+Papers/uniqueness+of+regular+and+singular+equilibria.pdf", "content": "We assume without loss of generality that ~ satisfies the first condition of (H3); otherwise exactly analogous arguments hold on using the inverse Cauchy. stress 7 ~ and Proposition 1.4 instead of the radial Cauchy stress T. It follows from Proposition 1.3 that T(r(R)) is nowhere decreasing."}
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+{"idx": 5, "title": "Народ поделитесь опытом. На маздах два вида термостата идёт...", "date": "", "ddg_snippet": "На новые електронные на 88 идут?. А то надоел этот не догрев на 82 хочу.", "subpage_snippet": "", "source": "mazdagroup.ru", "link": "https://mazdagroup.ru/threads/narod-podelites-opytom-na-mazdax-dva-vida-termostata-idjot-82-i-88-gradusov-na-novye-elektronnye-na-88-idut-a-to-nadoel-ehtot-ne-dogrev-na-82-xochu.78401/", "content": "На новые електронные на 88 идут?. А то надоел этот не догрев на 82 хочу."}
+{"idx": 6, "title": "Полный список категорий TVRezka", "date": "", "ddg_snippet": "Все категории порно видео включают в себе невероятное разнообразие порнухи, которая специально рассортирована по жанрам. Посетителям не нужно ходить по разделам и смотреть, что там в каждом находится...", "subpage_snippet": "", "source": "tvrezka.cc", "link": "https://tvrezka.cc/categories/", "content": "Все категории порно видео включают в себе невероятное разнообразие порнухи, которая специально рассортирована по жанрам. Посетителям не нужно ходить по разделам и смотреть, что там в каждом находится..."}
+{"idx": 7, "title": "Онлайн калькулятор уравнений и неравенств", "date": "", "ddg_snippet": "Решайте линейные и квадратные уравнения, а также неравенства любой сложности. Подробные примеры...", "subpage_snippet": "", "source": "findh.org", "link": "https://findh.org/4388-matematicheskij-kalkulyator.html?op=equation", "content": "Решайте линейные и квадратные уравнения, а также неравенства любой сложности. Подробные примеры..."}
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+{"idx": 9, "title": "номер 2 (страница 82) гдз по английскому языку 5 класс Комарова...", "date": "", "ddg_snippet": "Мы подготовили для вас ответ c подробным объяснением домашего задания по английскому языку за 5 класс, для упражнения номер 2 расположенного на странице 82 к учебнику 2021 года издания для учащихся школ и гимназий.", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/5-klass/english/komarova-uchebnik/33-2", "content": "Мы подготовили для вас ответ c подробным объяснением домашего задания по английскому языку за 5 класс, для упражнения номер 2 расположенного на странице 82 к учебнику 2021 года издания для учащихся школ и гимназий."}
diff --git a/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_equations_4_5_6_cross_entropy.jsonl b/data/sampled_jsons/YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss_equations_4_5_6_cross_entropy.jsonl
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+{"idx": 0, "title": "Cross - entropy - Wikipedia", "date": "", "ddg_snippet": "In information theory, the cross - entropy between two probability distributions. and. , over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cross-entropy", "content": "In information theory, the cross - entropy between two probability distributions. and. , over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set ..."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning dificulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=YjBrt82S3v&name=pdf", "content": "In this work, we focus on RL algorithms that share learning dificulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 2, "title": "【SCE 损失】Symmetric Cross Entropy for Robust Learning with ... Symmetric Reinforcement Learning Loss for Robust Learning on ... Symmetric Reinforcement Learning Loss for Robust Learning on ... YisenWang/symmetric_cross_entropy_for_noisy_labels - GitHub GitHub - shashacks/Symmetric_RL Why don't we use a symmetric cross-entropy loss?", "date": "", "ddg_snippet": "去杭州之前就想专门整理一下在有噪声样本下的分类损失，在这之前，先就较经典的一篇分析一下各种推导原理，剩下就简单一点只看损失部分～ See full list on zhuanlan.zhihu.com 在分类任务上，最普遍的损失函数是 Cross Entropy，即交叉熵损失： 该损失可以直观理解成努力提高样本对应标签类别的预测概率值。但是当标签中存在噪声和不准确时，这个严格惩罚预测值和标签靠拢的函数就无法调整这种噪声带了的巨大精确度下降： 可以看到图片 (b) 为带噪声标签情况下的交叉损失精度，大致有百分之十的精度下降，而那些本就不容易学习的类别精度更糟糕了。 2016 年 Label Smoothing Regularization 被提出，如今引用量已经一万五多了，文章信息： Rethinking the Inception Architecture for Computer Vision目标是提出一种正则项来缓解这种噪声带来的过拟合影响，理解起来很简单，就是重新改写原来严格的 0/1 one-hot 标签，公式如下： \\epsilon 为平滑项的参数， u(k) 一般为 1/K，于是可以写作： See full list on zhuanlan.zhihu.com 文章认为我们的模型本身就具有正确判断样本类别的能力，在噪声较多的情况下或许甚至比正确标签还准确，因此完全可以加入一个以模型预测为基点的损失部分。受启发与对称 KL： 文章提出了对称的交叉熵损失，即： 损失函数很好理解啦，就是将标注标签和预测值反过来。而由于标注标签是 one-hot 的，大量标签是 0，因此为了计算 log 0，将此部分取了一个常数 A < 0，即是一个惩罚项。另外为了进一步提高鲁棒性和自由度，这个函数可以额外加入两个超参数： 可以看一下实现代码： See full list on zhuanlan.zhihu.com 首先证明鲁棒性，即 noise-tolerant，作者认为如果在干净样本条件下和噪声样本条件下训练出来的最优模型 f^{*} 有着同样的分布概率，那说明这个损失函数对噪声是鲁棒的。 我们只需要考虑新提出的 RCE 部分，先定义两个期望损失： \\eta 为噪声存在的概率，之后推导公式为： 我加了自己理解的部分标注 于是最优解的差就可以推导为： 因此，想满足条件，就是使上面参数大于 0，于是： See full list on zhuanlan.zhihu.com 在 CIFAR-10 数据集上采用 60% 对称噪声的实验结果在 CIFAR-10 数据集上采用 40% 对称噪声的实验结果：置信度得到了大幅提升；类别正确数亦然在 CIFAR-10 数据集上采用 60% 对称噪声的可视化结果在 CIFAR-10 数据集上采用 60% 对称噪声：对 A 和 alpha 的消融实验在 CIFAR-10 数据集上采用 60% 对称噪声：与其他损失函数的精度比较丰富的实验结果～ See full list on zhuanlan.zhihu.com May 27, 2024 · To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . In this work, we focus on RL algorithms that share learning difficulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales. YisenWang / symmetric_cross_entropy _for_noisy_labels Public Notifications You must be signed in to change notification settings Fork 32 Star 173 To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . Mar 6 , 2018 · The minimum value that the cross-entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross-entropy can be used here.", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/420827592", "content": "去杭州之前就想专门整理一下在有噪声样本下的分类损失，在这之前，先就较经典的一篇分析一下各种推导原理，剩下就简单一点只看损失部分～ See full list on zhuanlan.zhihu.com 在分类任务上，最普遍的损失函数是 Cross Entropy，即交叉熵损失： 该损失可以直观理解成努力提高样本对应标签类别的预测概率值。但是当标签中存在噪声和不准确时，这个严格惩罚预测值和标签靠拢的函数就无法调整这种噪声带了的巨大精确度下降： 可以看到图片 (b) 为带噪声标签情况下的交叉损失精度，大致有百分之十的精度下降，而那些本就不容易学习的类别精度更糟糕了。 2016 年 Label Smoothing Regularization 被提出，如今引用量已经一万五多了，文章信息： Rethinking the Inception Architecture for Computer Vision目标是提出一种正则项来缓解这种噪声带来的过拟合影响，理解起来很简单，就是重新改写原来严格的 0/1 one-hot 标签，公式如下： \\epsilon 为平滑项的参数， u(k) 一般为 1/K，于是可以写作： See full list on zhuanlan.zhihu.com 文章认为我们的模型本身就具有正确判断样本类别的能力，在噪声较多的情况下或许甚至比正确标签还准确，因此完全可以加入一个以模型预测为基点的损失部分。受启发与对称 KL： 文章提出了对称的交叉熵损失，即： 损失函数很好理解啦，就是将标注标签和预测值反过来。而由于标注标签是 one-hot 的，大量标签是 0，因此为了计算 log 0，将此部分取了一个常数 A < 0，即是一个惩罚项。另外为了进一步提高鲁棒性和自由度，这个函数可以额外加入两个超参数： 可以看一下实现代码： See full list on zhuanlan.zhihu.com 首先证明鲁棒性，即 noise-tolerant，作者认为如果在干净样本条件下和噪声样本条件下训练出来的最优模型 f^{*} 有着同样的分布概率，那说明这个损失函数对噪声是鲁棒的。 我们只需要考虑新提出的 RCE 部分，先定义两个期望损失： \\eta 为噪声存在的概率，之后推导公式为： 我加了自己理解的部分标注 于是最优解的差就可以推导为： 因此，想满足条件，就是使上面参数大于 0，于是： See full list on zhuanlan.zhihu.com 在 CIFAR-10 数据集上采用 60% 对称噪声的实验结果在 CIFAR-10 数据集上采用 40% 对称噪声的实验结果：置信度得到了大幅提升；类别正确数亦然在 CIFAR-10 数据集上采用 60% 对称噪声的可视化结果在 CIFAR-10 数据集上采用 60% 对称噪声：对 A 和 alpha 的消融实验在 CIFAR-10 数据集上采用 60% 对称噪声：与其他损失函数的精度比较丰富的实验结果～ See full list on zhuanlan.zhihu.com May 27, 2024 · To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . In this work, we focus on RL algorithms that share learning difficulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales. YisenWang / symmetric_cross_entropy _for_noisy_labels Public Notifications You must be signed in to change notification settings Fork 32 Star 173 To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . Mar 6 , 2018 · The minimum value that the cross-entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross-entropy can be used here."}
+{"idx": 3, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ... Symmetric Reinforcement Learning Loss for Robust Learning on ... YisenWang/symmetric_cross_entropy_for_noisy_labels - GitHub GitHub - shashacks/Symmetric_RL Why don't we use a symmetric cross-entropy loss?", "date": "", "ddg_snippet": "May 27, 2024 · To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . In this work, we focus on RL algorithms that share learning difficulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales. YisenWang / symmetric_cross_entropy _for_noisy_labels Public Notifications You must be signed in to change notification settings Fork 32 Star 173 To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . Mar 6 , 2018 · The minimum value that the cross-entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross-entropy can be used here.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "May 27, 2024 · To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . In this work, we focus on RL algorithms that share learning difficulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales. YisenWang / symmetric_cross_entropy _for_noisy_labels Public Notifications You must be signed in to change notification settings Fork 32 Star 173 To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . Mar 6 , 2018 · The minimum value that the cross-entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross-entropy can be used here."}
+{"idx": 4, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning difficulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YjBrt82S3v", "content": "In this work, we focus on RL algorithms that share learning difficulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 5, "title": "YisenWang/symmetric_cross_entropy_for_noisy_labels - GitHub", "date": "", "ddg_snippet": "YisenWang / symmetric_cross_entropy _for_noisy_labels Public Notifications You must be signed in to change notification settings Fork 32 Star 173", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/YisenWang/symmetric_cross_entropy_for_noisy_labels", "content": "YisenWang / symmetric_cross_entropy _for_noisy_labels Public Notifications You must be signed in to change notification settings Fork 32 Star 173"}
+{"idx": 6, "title": "GitHub - shashacks/Symmetric_RL", "date": "", "ddg_snippet": "To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shashacks/Symmetric_RL", "content": "To enhance training robustness, RL has adopted techniques from supervised learning , such as ensembles and layer normalization. In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss ."}
+{"idx": 7, "title": "Why don't we use a symmetric cross-entropy loss?", "date": "", "ddg_snippet": "Mar 6 , 2018 · The minimum value that the cross-entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross-entropy can be used here.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/331942/why-dont-we-use-a-symmetric-cross-entropy-loss", "content": "Mar 6 , 2018 · The minimum value that the cross-entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross-entropy can be used here."}
+{"idx": 8, "title": "PyTorch Loss Functions: The Ultimate Guide", "date": "", "ddg_snippet": "Cross - Entropy Loss . Hinge Embedding Loss . Margin Ranking Loss .If the absolute values of the errors are not used, then negative values could cancel out the positive values. The Pytorch L1 Loss is expressed as: equation . x represents the actual value and y the predicted value.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/pytorch-loss-functions", "content": "Cross - Entropy Loss . Hinge Embedding Loss . Margin Ranking Loss .If the absolute values of the errors are not used, then negative values could cancel out the positive values. The Pytorch L1 Loss is expressed as: equation . x represents the actual value and y the predicted value."}
+{"idx": 9, "title": "Living on the edge: a non-perturbative resolution to the negativity of...", "date": "", "ddg_snippet": "contribute at the same order as disk diagrams, and they guarantee positivity of the entropies . On the other. hand, as we discuss in Appendix C.3, crossing diagrams are only responsible for shifting the location of the edge of the spectral density for the operator O˜∆.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15295", "content": "contribute at the same order as disk diagrams, and they guarantee positivity of the entropies . On the other. hand, as we discuss in Appendix C.3, crossing diagrams are only responsible for shifting the location of the edge of the spectral density for the operator O˜∆."}
diff --git a/data/sampled_jsons/Zhang_et_al._scaling_law_pretraining_error_rate.jsonl b/data/sampled_jsons/Zhang_et_al._scaling_law_pretraining_error_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b8a3a52deeab309b9e863e9dbb835f8758df99c1
--- /dev/null
+++ b/data/sampled_jsons/Zhang_et_al._scaling_law_pretraining_error_rate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law - Wikipedia", "date": "", "ddg_snippet": "... scaling laws , over the range of N [ 10 3 , 10 9 ] {\\displaystyle N\\in [10^{3},10^{9}]} , C [ 10 12 , 10 21 ] {\\displaystyle C\\in [10^{12},10^{21}]} , ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "... scaling laws , over the range of N [ 10 3 , 10 9 ] {\\displaystyle N\\in [10^{3},10^{9}]} , C [ 10 12 , 10 21 ] {\\displaystyle C\\in [10^{12},10^{21}]} , ..."}
+{"idx": 1, "title": "Predictable Scale: Part I — Optimal Hyperparameter Scaling", "date": "", "ddg_snippet": "2020 ) established foundational learning rate scaling laws based on model size N 𝑁 N italic_N , inspiring further investigations Bjorck et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.04715v5", "content": "2020 ) established foundational learning rate scaling laws based on model size N 𝑁 N italic_N , inspiring further investigations Bjorck et al ."}
+{"idx": 2, "title": "When Scaling Meets LLM Finetuning: The Effect of Data, Model", "date": "", "ddg_snippet": "While previous studies have well explored the scaling for LLM pretraining or training from scratch (Kaplan et al ., 2020 ; Hoffmann et al ., 2022 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.17193v1", "content": "While previous studies have well explored the scaling for LLM pretraining or training from scratch (Kaplan et al ., 2020 ; Hoffmann et al ., 2022 ..."}
+{"idx": 3, "title": "NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid", "date": "", "ddg_snippet": "... 12B-v2-Base, was pre-trained using FP8 precision (§ 2.4 ) over 20 trillion tokens using a Warmup-Stable-Decay (Hu et al ., 2024 ) learning rate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14444v2", "content": "... 12B-v2-Base, was pre-trained using FP8 precision (§ 2.4 ) over 20 trillion tokens using a Warmup-Stable-Decay (Hu et al ., 2024 ) learning rate ..."}
+{"idx": 4, "title": "Sub-Scaling Laws: On the Role of Data Density and Training", "date": "", "ddg_snippet": "Further extending this observation to model performance, Figure 2 displays the results of our tests on the performance scaling law Yang et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10613v1", "content": "Further extending this observation to model performance, Figure 2 displays the results of our tests on the performance scaling law Yang et al ."}
+{"idx": 5, "title": "Inverse Scaling Can Become U-Shaped | Request PDF", "date": "", "ddg_snippet": "Superalignment is an Emergent Property Building on scaling laws (Kaplan et al ., 2020; Wei et al ., 2023; Nam et al ., 2024), one could argue that ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/376393122_Inverse_Scaling_Can_Become_U-Shaped", "content": "Superalignment is an Emergent Property Building on scaling laws (Kaplan et al ., 2020; Wei et al ., 2023; Nam et al ., 2024), one could argue that ..."}
+{"idx": 6, "title": "Beyond Text Compression: Evaluating Tokenizers Across Scales", "date": "", "ddg_snippet": "English-centric; used by GPT-2 (Radford et al ., 2019 ) , GPT-3 (Brown et al ., 2020 ) , Megatron (Shoeybi et al ., 2020 ) , OPT ( Zhang et al ., 2022 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03101v1", "content": "English-centric; used by GPT-2 (Radford et al ., 2019 ) , GPT-3 (Brown et al ., 2020 ) , Megatron (Shoeybi et al ., 2020 ) , OPT ( Zhang et al ., 2022 ..."}
+{"idx": 7, "title": "Surya Ganguli | DeepAI", "date": "", "ddg_snippet": "Pretrained transformers exhibit the remarkable ability of in-context ... Widely observed neural scaling laws , in which error falls off as a power...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/surya-ganguli", "content": "Pretrained transformers exhibit the remarkable ability of in-context ... Widely observed neural scaling laws , in which error falls off as a power..."}
+{"idx": 8, "title": "Towards Reasoning Era: A Survey of Long Chain-of-Thought", "date": "", "ddg_snippet": "Inference scaling laws : An empirical analysis of compute-optimal inference for problem-solving with language models , Wu et al .,", "subpage_snippet": "", "source": "long-cot.github.io", "link": "https://long-cot.github.io/", "content": "Inference scaling laws : An empirical analysis of compute-optimal inference for problem-solving with language models , Wu et al .,"}
+{"idx": 9, "title": "Parameter-Efficient Fine-Tuning of Large Pretrained Models for", "date": "", "ddg_snippet": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2504-4990/6/4/133", "content": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ..."}
diff --git a/data/sampled_jsons/Zhang_et_al_scaling_law_pre-training_error_rate.jsonl b/data/sampled_jsons/Zhang_et_al_scaling_law_pre-training_error_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ef23f8b14c8d437e789255df9d61b461e0a8e4a
--- /dev/null
+++ b/data/sampled_jsons/Zhang_et_al_scaling_law_pre-training_error_rate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scaling Laws and Compute-Optimal Training", "date": "", "ddg_snippet": "We investigate the training behavior of a direct alternative — constant learning rate and cooldowns — and find that it scales predictably and reliably similar to cosine. Additionally, we show that stochastic weight averaging yields improved performance along the training trajectory, without additional training costs, across different scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.18392v3", "content": "We investigate the training behavior of a direct alternative — constant learning rate and cooldowns — and find that it scales predictably and reliably similar to cosine. Additionally, we show that stochastic weight averaging yields improved performance along the training trajectory, without additional training costs, across different scales."}
+{"idx": 1, "title": "Scaling Law for Quantization-Aware Training - arXiv.org", "date": "", "ddg_snippet": "Scaling laws [16, 19] have proven instrumental in understanding LLMs performance as a function of model size, dataset size, and computational resources. Foundational works, such as the Kaplan scaling law [19] and the refined Chinchilla scaling law [16], provide predictive models for optimizing LLM training strategies in full-precision settings. Recent eforts have extended these frameworks to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.14302", "content": "Scaling laws [16, 19] have proven instrumental in understanding LLMs performance as a function of model size, dataset size, and computational resources. Foundational works, such as the Kaplan scaling law [19] and the refined Chinchilla scaling law [16], provide predictive models for optimizing LLM training strategies in full-precision settings. Recent eforts have extended these frameworks to ..."}
+{"idx": 2, "title": "Scaling Laws for Pre-training Agents and World Models", "date": "", "ddg_snippet": "The role of scale in pre-training is until now best understood in the context of large language models (LLMs). Following the observation that the empirical relationship between loss and key scaling quantities can be accurately described by power laws [Kaplan et al ., 2020], ensuing work studied the precise trade-off between model and dataset size [Hoffmann et al ., 2022], as well as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.04434", "content": "The role of scale in pre-training is until now best understood in the context of large language models (LLMs). Following the observation that the empirical relationship between loss and key scaling quantities can be accurately described by power laws [Kaplan et al ., 2020], ensuing work studied the precise trade-off between model and dataset size [Hoffmann et al ., 2022], as well as ..."}
+{"idx": 3, "title": "A Hitchhiker's Guide to Scaling Law Estimation - arXiv.org", "date": "", "ddg_snippet": "A scaling law extrapolates the performance of a target model from the performance of a set of models with fewer parameters or smaller training sets. Typically, this extrapolation requires models to belong to the same model family, differing only in parameter count and training set size, but using the same architecture and training distribution. A high-quality scaling law accurately predicts ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.11840v1", "content": "A scaling law extrapolates the performance of a target model from the performance of a set of models with fewer parameters or smaller training sets. Typically, this extrapolation requires models to belong to the same model family, differing only in parameter count and training set size, but using the same architecture and training distribution. A high-quality scaling law accurately predicts ..."}
+{"idx": 4, "title": "Predictable Scale: Part I, Step Law - Optimal Hyperparameter Scaling ...", "date": "", "ddg_snippet": "(i) This paper establish the first universal and robust Scaling Law for hyperparameter optimization in LLM Pre-training , called Step Law . We discover the power- law relationship between optimal learning rate η (N, D) and optimal batch size B (D).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.04715", "content": "(i) This paper establish the first universal and robust Scaling Law for hyperparameter optimization in LLM Pre-training , called Step Law . We discover the power- law relationship between optimal learning rate η (N, D) and optimal batch size B (D)."}
+{"idx": 5, "title": "How Does Critical Batch Size Scale in Pre-training? (Decoupling Data ...", "date": "", "ddg_snippet": "Pre-training large machine learning models is resource-intensive, involving a multitude of decisions around various factors such as model size, data selection, and optimization hyperparameters. These decisions carry high stakes, as each pre-training run can cost millions of dollars. However, experimenting directly at massive scale is rarely feasible. Scaling laws offer a solution: they let us ...", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/how-does-critical-batch-size-scale-in-pre-training-decoupling-data-and-model-size/", "content": "Pre-training large machine learning models is resource-intensive, involving a multitude of decisions around various factors such as model size, data selection, and optimization hyperparameters. These decisions carry high stakes, as each pre-training run can cost millions of dollars. However, experimenting directly at massive scale is rarely feasible. Scaling laws offer a solution: they let us ..."}
+{"idx": 6, "title": "Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations", "date": "", "ddg_snippet": "In this work, our goal is to revisit and question the necessity of the cosine learning rate schedule for large model training . Through a multitude of training runs, we demonstrate how a simple alternative of performing a cooldown after a constant learning rate — which was already suggested in the literature (Zhai et al ., 2022) and recently used by released models (Hu et al ., 2024; Shen et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.18392v2", "content": "In this work, our goal is to revisit and question the necessity of the cosine learning rate schedule for large model training . Through a multitude of training runs, we demonstrate how a simple alternative of performing a cooldown after a constant learning rate — which was already suggested in the literature (Zhai et al ., 2022) and recently used by released models (Hu et al ., 2024; Shen et al ..."}
+{"idx": 7, "title": "Predictable Scale: Part I — Optimal Hyperparameter Scaling Law in Large ...", "date": "", "ddg_snippet": "The success of LLM pretraining heavily depends on hyperparameter settings, particularly the learning rate and batch size. Suboptimal configurations can lead to various issues: excessive learning rates may cause training divergence, while insufficient rates slow down progress Shen et al . (2024); Wen et al . (2024); similarly, batch size must balance computational efficiency and model quality ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.04715v4", "content": "The success of LLM pretraining heavily depends on hyperparameter settings, particularly the learning rate and batch size. Suboptimal configurations can lead to various issues: excessive learning rates may cause training divergence, while insufficient rates slow down progress Shen et al . (2024); Wen et al . (2024); similarly, batch size must balance computational efficiency and model quality ..."}
+{"idx": 8, "title": "D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large ...", "date": "", "ddg_snippet": "To address the limitations of existing methods, inspired by the Scaling Law for performance prediction, we propose to investigate the Scaling Law of the Domain-specific Continual Pre-Training (D-CPT Law ) to decide the optimal mixture ratio with acceptable training costs for LLMs of different sizes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JzKFN5fWOk", "content": "To address the limitations of existing methods, inspired by the Scaling Law for performance prediction, we propose to investigate the Scaling Law of the Domain-specific Continual Pre-Training (D-CPT Law ) to decide the optimal mixture ratio with acceptable training costs for LLMs of different sizes."}
+{"idx": 9, "title": "1 I 001 000 SCALING LAWS FOR PRE-TRAINING 003 002 AGENTS ... - OpenReview", "date": "", "ddg_snippet": "030 The role of scale in pre-training is until now best understood in the context of large language models 031 (LLMs). Following the observation that the empirical relationship between loss and key scaling 032 quantities can be accurately described by power laws (Kaplan et al ., 2020), ensuing work studied the 033 precise trade-off between model and dataset size (Hoffmann et al ., 2022), as well ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=D0XpSucS3l", "content": "030 The role of scale in pre-training is until now best understood in the context of large language models 031 (LLMs). Following the observation that the empirical relationship between loss and key scaling 032 quantities can be accurately described by power laws (Kaplan et al ., 2020), ensuing work studied the 033 precise trade-off between model and dataset size (Hoffmann et al ., 2022), as well ..."}
diff --git a/data/sampled_jsons/Zhou_conditional_density_estimation_GAN_arXiv_year_2022.jsonl b/data/sampled_jsons/Zhou_conditional_density_estimation_GAN_arXiv_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..511000c172f78f830fd1712a776e5428ee255de5
--- /dev/null
+++ b/data/sampled_jsons/Zhou_conditional_density_estimation_GAN_arXiv_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Wasserstein Generative Learning of Conditional Distribution", "date": "", "ddg_snippet": "by S Liu · 2021 · Cited by 32 — We propose a Wasserstein generative approach to learning a conditional distribution. The proposed approach uses a conditional generator to transform a known ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2112.10039", "content": "by S Liu · 2021 · Cited by 32 — We propose a Wasserstein generative approach to learning a conditional distribution. The proposed approach uses a conditional generator to transform a known ..."}
+{"idx": 1, "title": "A Deep Generative Approach to Conditional Sampling", "date": "", "ddg_snippet": "by X Zhou · 2021 · Cited by 66 — We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2110.10277", "content": "by X Zhou · 2021 · Cited by 66 — We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized ..."}
+{"idx": 2, "title": "A Conditional GAN for Tabular Data Generation with ...", "date": "", "ddg_snippet": "1 Aug 2025 — Zhou , “IDA- GAN : A novel imbalanced data augmentation GAN ,” in Proceedings of the 25th International Conference on Pattern Recognition, 2021, pp.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.00472v1", "content": "1 Aug 2025 — Zhou , “IDA- GAN : A novel imbalanced data augmentation GAN ,” in Proceedings of the 25th International Conference on Pattern Recognition, 2021, pp."}
+{"idx": 3, "title": "Generative Modeling with Flow-Guided Density Ratio ...", "date": "", "ddg_snippet": "26 Jan 2024 — We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable approach to generative modeling which builds on the stale ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2303.03714v2", "content": "26 Jan 2024 — We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable approach to generative modeling which builds on the stale ..."}
+{"idx": 4, "title": "Density estimation using deep generative neural networks", "date": "", "ddg_snippet": "by Q Liu · 2021 · Cited by 141 — Labeled Data and Conditional Density Estimation . We provide a strategy for conditional density estimation given labeled data. The original Roundtrip model ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8054014/", "content": "by Q Liu · 2021 · Cited by 141 — Labeled Data and Conditional Density Estimation . We provide a strategy for conditional density estimation given labeled data. The original Roundtrip model ..."}
+{"idx": 5, "title": "Solution of physics-based inverse problems using ...", "date": "", "ddg_snippet": "by D Ray · 2023 · Cited by 18 — GANs are generative models that learn a reduced-order representation of a probability distribution from its samples and then efficiently.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.04895", "content": "by D Ray · 2023 · Cited by 18 — GANs are generative models that learn a reduced-order representation of a probability distribution from its samples and then efficiently."}
+{"idx": 6, "title": "Your Likelihood-Based Visual Generative Model is Secretly ...", "date": "", "ddg_snippet": "3 Mar 2025 — Discrete diffusion language modeling by estimating the ratios of the data distribution. arXiv preprint arXiv :2310.16834, 2023. Lu et al. (2022a)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01103v1", "content": "3 Mar 2025 — Discrete diffusion language modeling by estimating the ratios of the data distribution. arXiv preprint arXiv :2310.16834, 2023. Lu et al. (2022a)"}
+{"idx": 7, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — In Zhou et al., (2022) , GANs were employed to investigate conditional density estimation . While this work offers a consistent estimator, it ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — In Zhou et al., (2022) , GANs were employed to investigate conditional density estimation . While this work offers a consistent estimator, it ..."}
+{"idx": 8, "title": "Parallelly Tempered Generative Adversarial Nets", "date": "", "ddg_snippet": "... Zhou et al., (2023) proposed a generative sampler for a conditional density estimation in a regression setting. In this context, our PTGAN framework, which ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.11786v2", "content": "... Zhou et al., (2023) proposed a generative sampler for a conditional density estimation in a regression setting. In this context, our PTGAN framework, which ..."}
+{"idx": 9, "title": "A Distributional Evaluation of Generative Image Models", "date": "", "ddg_snippet": "1 Jan 2025 — Our simulations gauge the ability of ECS to faithfully capture differences in the tails of probability distributions under a controlled setting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.00744v1", "content": "1 Jan 2025 — Our simulations gauge the ability of ECS to faithfully capture differences in the tails of probability distributions under a controlled setting."}
diff --git a/data/sampled_jsons/Zhou_et_al._2022_conditional_density_estimation_GAN.jsonl b/data/sampled_jsons/Zhou_et_al._2022_conditional_density_estimation_GAN.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3b4d4166c1522ee3e075871c2d647fcb961069cf
--- /dev/null
+++ b/data/sampled_jsons/Zhou_et_al._2022_conditional_density_estimation_GAN.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Semantically-Guided Inference for Conditional Diffusion Models:", "date": "", "ddg_snippet": "... conditional diffusion models lies in their flexibility to incorporate future-known covariates—such as control signals, policy indicators, or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.01761v1", "content": "... conditional diffusion models lies in their flexibility to incorporate future-known covariates—such as control signals, policy indicators, or ..."}
+{"idx": 1, "title": "Distilling Diffusion Models into Conditional GANs", "date": "", "ddg_snippet": "Furthermore, we adapt a diffusion model to construct a multi-scale discriminator with a text alignment loss to build an effective conditional GAN ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.05967v3", "content": "Furthermore, we adapt a diffusion model to construct a multi-scale discriminator with a text alignment loss to build an effective conditional GAN ..."}
+{"idx": 2, "title": "Determination of galaxy photometric redshifts using Conditional", "date": "", "ddg_snippet": "... algorithms that are capable of producing a probability density estimation for the redshift, instead of a point estimation , include: classification ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.06532v2", "content": "... algorithms that are capable of producing a probability density estimation for the redshift, instead of a point estimation , include: classification ..."}
+{"idx": 3, "title": "2022.1.10 Vision papers — Eye On AI", "date": "", "ddg_snippet": "Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling by Fakai Wang et al", "subpage_snippet": "", "source": "www.eye-on.ai", "link": "https://www.eye-on.ai/ai-research-watch-papers/2022/1/11/2022110-vision-papers", "content": "Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling by Fakai Wang et al"}
+{"idx": 4, "title": "Yuanfeng Zhou's research works | Illinois Institute of", "date": "", "ddg_snippet": "... of this article is to provide a comprehensive and integrated discussion on the basic concept and general design methodology of a gallium nitride ( GaN ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Yuanfeng-Zhou-2141557411", "content": "... of this article is to provide a comprehensive and integrated discussion on the basic concept and general design methodology of a gallium nitride ( GaN ..."}
+{"idx": 5, "title": "Frontiers | Generative AI with WGAN-GP for boosting seizure", "date": "", "ddg_snippet": "Epilepsy is a neurological condition characterized by recurring seizures, abrupt disruptions in the brain s usual electrical activity ( Fisher et al ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1437315/full", "content": "Epilepsy is a neurological condition characterized by recurring seizures, abrupt disruptions in the brain s usual electrical activity ( Fisher et al ..."}
+{"idx": 6, "title": "Simulation-Based Inference with Quantile Regression", "date": "", "ddg_snippet": "Neural Ratio Estimation (NRE, Hermans et al ., 2020 ) employs classifiers to estimate density ratios, commonly selected as the likelihood-to-evidence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.02413v2", "content": "Neural Ratio Estimation (NRE, Hermans et al ., 2020 ) employs classifiers to estimate density ratios, commonly selected as the likelihood-to-evidence ..."}
+{"idx": 7, "title": "Inductive Moment Matching", "date": "", "ddg_snippet": "... certain stochastic interpolants and optimize the objective with stable sample-based divergence estimators such as moment matching ( Gretton et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.07565v7", "content": "... certain stochastic interpolants and optimize the objective with stable sample-based divergence estimators such as moment matching ( Gretton et al ..."}
+{"idx": 8, "title": "G protein-coupled receptor diversity and evolution in the", "date": "", "ddg_snippet": "... often leads to pathological conditions in humans, making them the most researched drug targets in the pharmaceutical industry ( Hauser et al .", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/107467", "content": "... often leads to pathological conditions in humans, making them the most researched drug targets in the pharmaceutical industry ( Hauser et al ."}
+{"idx": 9, "title": "Tap Maize Yield Productivity in China: A Meta-Analysis of", "date": "", "ddg_snippet": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2073-4395/15/4/861", "content": "All articles published by MDPI are made immediately available worldwide under an open access license. ... special permission is required to reuse all ..."}
diff --git a/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_Olah_et_al._2020_abstract.jsonl b/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_Olah_et_al._2020_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f68ba2f807fb943ca5f133aa9f829cab3c72a19
--- /dev/null
+++ b/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_Olah_et_al._2020_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Zoom In : An Introduction to Circuits", "date": "", "ddg_snippet": "... Introduction Olah et al . ( 2020 ) make the case for mechanistic interpretability as the study of a new kind of system:In this view, neural networks are an object of empirical investigation, perhaps similar to an organism in biology.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/339841165_Zoom_In_An_Introduction_to_Circuits", "content": "... Introduction Olah et al . ( 2020 ) make the case for mechanistic interpretability as the study of a new kind of system:In this view, neural networks are an object of empirical investigation, perhaps similar to an organism in biology."}
+{"idx": 1, "title": "Zoom In: An Introduction to Circuits. Published by OpenAI. March 10, 2020", "date": "", "ddg_snippet": "Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks - OpenAI. March 10, 2020 Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.", "subpage_snippet": "", "source": "blog.biocomm.ai", "link": "https://blog.biocomm.ai/2020/03/10/zoom-in-an-introduction-to-circuits-published-by-openai-march-10-2020/", "content": "Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks - OpenAI. March 10, 2020 Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks."}
+{"idx": 2, "title": "Notes: \"Zoom In - An Introduction to Circuits\" - Tanay Biradar", "date": "", "ddg_snippet": "One of the \"foundational\" papers is \" Zoom In: An Introduction to Circuits \" by Olah et . al. on Distill.pub. Below are some of my comments and notes on the paper. A lot of the content borrows directly from the source, so all figures and direct quotes are attributed to the paper and OpenAI Microscope. Claims 1-2: Neurons + circuits are understandable", "subpage_snippet": "", "source": "tanaybiradar.com", "link": "https://tanaybiradar.com/blog/notes-on-zoom-in-circuits/", "content": "One of the \"foundational\" papers is \" Zoom In: An Introduction to Circuits \" by Olah et . al. on Distill.pub. Below are some of my comments and notes on the paper. A lot of the content borrows directly from the source, so all figures and direct quotes are attributed to the paper and OpenAI Microscope. Claims 1-2: Neurons + circuits are understandable"}
+{"idx": 3, "title": "Thread: Circuits", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits . Authors.The Circuits thread is open to articles exploring individual features, circuits, and their organization within neural networks. Critical commentary and discussion of existing articles is also welcome.", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/", "content": "Zoom In : An Introduction to Circuits . Authors.The Circuits thread is open to articles exploring individual features, circuits, and their organization within neural networks. Critical commentary and discussion of existing articles is also welcome."}
+{"idx": 4, "title": "Zoom In: An Introduction to Circuits - Semantic Scholar", "date": "", "ddg_snippet": "Semantic Scholar extracted view of \" Zoom In: An Introduction to Circuits \" by Christopher Olah et al.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Zoom-In:-An-Introduction-to-Circuits-Olah-Cammarata/a0cfd36e6c7abf070f492ae52a35af895a1c5592", "content": "Semantic Scholar extracted view of \" Zoom In: An Introduction to Circuits \" by Christopher Olah et al."}
+{"idx": 5, "title": "Zoom In: An Introduction to Circuits | Nick's Notes", "date": "", "ddg_snippet": "Zoom In: An Introduction to Circuits ( 2020 ) Paper Authors: Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter Paper Link: https://distill.pub/ 2020 /circuits/zoom-in/", "subpage_snippet": "", "source": "www.nickjalbert.com", "link": "http://www.nickjalbert.com/reading/2020/03/27/zoom-in-an-introduction-to-circuits.html", "content": "Zoom In: An Introduction to Circuits ( 2020 ) Paper Authors: Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter Paper Link: https://distill.pub/ 2020 /circuits/zoom-in/"}
+{"idx": 6, "title": "\"Zoom In: An Introduction to Circuits: By studying the ... - Reddit", "date": "", "ddg_snippet": "\" Zoom In: An Introduction to Circuits : By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks\", Olah et al 2020", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ControlProblem/comments/fuv75l/zoom_in_an_introduction_to_circuits_by_studying/", "content": "\" Zoom In: An Introduction to Circuits : By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks\", Olah et al 2020"}
+{"idx": 7, "title": "Zoom In: An Introduction to Circuits · 研飞ivySCI", "date": "", "ddg_snippet": "Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter", "subpage_snippet": "", "source": "www.ivysci.com", "link": "https://www.ivysci.com/en/articles/1536790__Zoom_In_An_Introduction_to_Circuits", "content": "Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter"}
+{"idx": 8, "title": "Zoom In : An Introduction to Circuits — LessWrong", "date": "", "ddg_snippet": "“ Zoom In ” provides lots of in-depth justification and examples for each of these claims which I will mostly leave to the actual article. Some highlights, however: How do convolutional neural networks (CNNs) detect dogs in an orientation-invariant way?", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/MG4ZjWQDrdpgeu8wG/zoom-in-an-introduction-to-circuits", "content": "“ Zoom In ” provides lots of in-depth justification and examples for each of these claims which I will mostly leave to the actual article. Some highlights, however: How do convolutional neural networks (CNNs) detect dogs in an orientation-invariant way?"}
+{"idx": 9, "title": "Distill: Zoom in on Circuits | Dynamically Typed", "date": "", "ddg_snippet": "Chris Olah et al . wrote a fascinating new Distill article about “circuits” in convolutional neural networks.( 2020 ) on Distill: Zoom In : An Introduction to Circuits .", "subpage_snippet": "", "source": "dynamicallytyped.com", "link": "https://dynamicallytyped.com/stories/2020/distill-zoom-in-on-circuits/", "content": "Chris Olah et al . wrote a fascinating new Distill article about “circuits” in convolutional neural networks.( 2020 ) on Distill: Zoom In : An Introduction to Circuits ."}
diff --git a/data/sampled_jsons/a_causal_model_M_is_a_pair_(S,_F)_Intervention_and_Conditioning_in_Causal_Bayesian_Networks.jsonl b/data/sampled_jsons/a_causal_model_M_is_a_pair_(S,_F)_Intervention_and_Conditioning_in_Causal_Bayesian_Networks.jsonl
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index 0000000000000000000000000000000000000000..3ce2710a2e2c855a7692e12d3ed9698d856ce608
--- /dev/null
+++ b/data/sampled_jsons/a_causal_model_M_is_a_pair_(S,_F)_Intervention_and_Conditioning_in_Causal_Bayesian_Networks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Simpson's paradox - Wikipedia", "date": "", "ddg_snippet": "... is often encountered in social-science and medical-science statistics, 1 2 3 and is particularly problematic when frequency data are unduly given ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Simpson's_paradox", "content": "... is often encountered in social-science and medical-science statistics, 1 2 3 and is particularly problematic when frequency data are unduly given ..."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian", "date": "", "ddg_snippet": "Formally, a causal model M is a pair ( S , F ), where S is a signature, which explicitly lists the endogenous and exogenous variables and characterizes their possible values, and F denes a set of modiable structural equations, relating the values of the variables.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.14728", "content": "Formally, a causal model M is a pair ( S , F ), where S is a signature, which explicitly lists the endogenous and exogenous variables and characterizes their possible values, and F denes a set of modiable structural equations, relating the values of the variables."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by J Halpern — Formally, a causal model M is a pair (S, F ), where S is a signature, which explicitly lists the endogenous and exogenous variables and characterizes their ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "by J Halpern — Formally, a causal model M is a pair (S, F ), where S is a signature, which explicitly lists the endogenous and exogenous variables and characterizes their ..."}
+{"idx": 3, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by S Galhotra · 2024 · Cited by 3 — Formally, a causal model M is a pair (S, F ), where S is a signature, which explicitly lists the endogenous and exogenous variables and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.14728?", "content": "by S Galhotra · 2024 · Cited by 3 — Formally, a causal model M is a pair (S, F ), where S is a signature, which explicitly lists the endogenous and exogenous variables and ..."}
+{"idx": 4, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Formally, a causal model M is a pair (S, F ), where S is a signature, which ... Intervention and Conditioning in Causal Bayesian Networks | Read Paper on Bytez.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96098/paper?_c=eyJ2IjoxLCJyZWxhdGVkIjpbImNvZGUiLCJyZWZlcmVuY2VzIiwiY29uZmVyZW5jZSJdfQ==", "content": "Formally, a causal model M is a pair (S, F ), where S is a signature, which ... Intervention and Conditioning in Causal Bayesian Networks | Read Paper on Bytez."}
+{"idx": 5, "title": "Toward A Causal Framework for Modeling Perception", "date": "", "ddg_snippet": "... approach formalizes individual experience as additional causal knowledge that comes with and is used by the expert decision-maker in the form of a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.13408v4", "content": "... approach formalizes individual experience as additional causal knowledge that comes with and is used by the expert decision-maker in the form of a ..."}
+{"idx": 6, "title": "Causal Models (Stanford Encyclopedia of Philosophy)", "date": "", "ddg_snippet": "However, causal models can also feature continuous variables, and in some cases this makes an important difference.", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/causal-models/", "content": "However, causal models can also feature continuous variables, and in some cases this makes an important difference."}
+{"idx": 7, "title": "Scheduled feeding improves behavioral outcomes and reduces", "date": "", "ddg_snippet": "Animal models are invaluable for elucidating the mechanisms underlying circadian and sleep disturbances as well as developing interventions aimed at ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/104720v3", "content": "Animal models are invaluable for elucidating the mechanisms underlying circadian and sleep disturbances as well as developing interventions aimed at ..."}
+{"idx": 8, "title": "An effective drift-diffusion model for pandemic propagation and", "date": "", "ddg_snippet": "What is critical to understand in this context are the simplified physical intuitions that may arise from these models , a minimal set of features and ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/biophysreports/fulltext/S2667-0747(24)00041-7", "content": "What is critical to understand in this context are the simplified physical intuitions that may arise from these models , a minimal set of features and ..."}
+{"idx": 9, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models ... A Real-Recorded and Annotated Microphone Array ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/papers.html?filter=titles", "content": "WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models ... A Real-Recorded and Annotated Microphone Array ..."}
diff --git a/data/sampled_jsons/abstract_Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_Extragradie.jsonl b/data/sampled_jsons/abstract_Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_Extragradie.jsonl
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index 0000000000000000000000000000000000000000..c678391e40ff7b916ebc0cacf6923a6ffe4c8344
--- /dev/null
+++ b/data/sampled_jsons/abstract_Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_Extragradie.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples ..."}
+{"idx": 1, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games Regret Matching$^+$: (In)Stability and Fast Convergence in Games Regret Matching+: (In)Stability and Fast Convergence in Games Regret matching+ | Proceedings of the 37th International ... Regret Matching+: - Instability, average- and last-iterate ... Regret Matching +: ( In ) Stability and Fast Convergence in Games Regret Matching +: ( In ) Stability and Fast Convergence in Games Regret Matching : ( In ) Stability and Fast Convergence in Games - Mas… Regret Matching : ( In ) Stability and Fast Convergence in Games - Mas… Regret Matching : ( In ) Stability and Fast Convergence in Games - Mas… Regret Matching : ( In ) Stability and Fast Convergence in Games - Mas… Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "May 24, 2023 · Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and ... Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ... Dec 10, 2023 · Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . Instability , average- and last-iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris Are regret matching+ algorithms effective in solving large-scale games? Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery . Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. Is RM+ a No-Regret algorithm? Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable , which might cause other players to suffer large regret. Does extragradient RM+ achieve O(1) social regret? Extragradient RM+ achieves O(1) social regret (Theorem 5.6). S e Table 1 for a summary of our results for normal-form games. We further extend Conceptual RM+ to extensive-form games (EFG), yielding O(1) regret in T iterations with O(T log(T)) gradient computation. The key step here is t s ow the Lipschitzness of the CFR decomposition (Lemma J. Do regret minimizers have faster convergence rates? ut its practical performance is usually significantly faster.On the other hand, a line of recent works show that regret minimizers based on follow the regular-ized leader (FTRL) or online mirror descent (OMD) enjoy faster convergence rates in theory when ombined with the concept of opti Which algorithm guarantees a smooth predictive RM+? the resulting algorithm smooth predictive RM+ (Algorithm 2). Besides a similar result to Theorem 4.1 on the individual regret (omitted for simplicity), Algorithm 2 also guarantees that the social regret is nded by a ga .2. Theorem 4.2.Letη lgorithm 2 guarantees that the so-Algorithm 3 Conceptual RM+1: Input: Step size η > 0 wit Does asynchronous restarting stabilize the RM+ algorithm? bounded by O d3/2T1/4 in multi-t=1 player normal-form games.Although the restarting idea successfully stabilizes the RM+ algorithm , the discontinuity created by asynchronous restarts caus s technical dificulty for bounding the social regret by O(1). Next we ntroduce an alternativ Sep 21, 2023 · Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to suffer large regret .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "May 24, 2023 · Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and ... Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ... Dec 10, 2023 · Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . Instability , average- and last-iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris Are regret matching+ algorithms effective in solving large-scale games? Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery . Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. Is RM+ a No-Regret algorithm? Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable , which might cause other players to suffer large regret. Does extragradient RM+ achieve O(1) social regret? Extragradient RM+ achieves O(1) social regret (Theorem 5.6). S e Table 1 for a summary of our results for normal-form games. We further extend Conceptual RM+ to extensive-form games (EFG), yielding O(1) regret in T iterations with O(T log(T)) gradient computation. The key step here is t s ow the Lipschitzness of the CFR decomposition (Lemma J. Do regret minimizers have faster convergence rates? ut its practical performance is usually significantly faster.On the other hand, a line of recent works show that regret minimizers based on follow the regular-ized leader (FTRL) or online mirror descent (OMD) enjoy faster convergence rates in theory when ombined with the concept of opti Which algorithm guarantees a smooth predictive RM+? the resulting algorithm smooth predictive RM+ (Algorithm 2). Besides a similar result to Theorem 4.1 on the individual regret (omitted for simplicity), Algorithm 2 also guarantees that the social regret is nded by a ga .2. Theorem 4.2.Letη lgorithm 2 guarantees that the so-Algorithm 3 Conceptual RM+1: Input: Step size η > 0 wit Does asynchronous restarting stabilize the RM+ algorithm? bounded by O d3/2T1/4 in multi-t=1 player normal-form games.Although the restarting idea successfully stabilizes the RM+ algorithm , the discontinuity created by asynchronous restarts caus s technical dificulty for bounding the social regret by O(1). Next we ntroduce an alternativ Sep 21, 2023 · Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to suffer large regret ."}
+{"idx": 2, "title": "Regret Matching$^+$: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/farina-2023-regret/", "content": "Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL"}
+{"idx": 3, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ..."}
+{"idx": 4, "title": "Regret matching+ | Proceedings of the 37th International ...", "date": "", "ddg_snippet": "Dec 10, 2023 · Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668812", "content": "Dec 10, 2023 · Abstract Regret Matching+ ( RM+ ) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability ."}
+{"idx": 5, "title": "Regret Matching+: - Instability, average- and last-iterate ...", "date": "", "ddg_snippet": "Instability , average- and last-iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Instability , average- and last-iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris"}
+{"idx": 6, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Sep 21, 2023 · Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to suffer large regret .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nYgs0qZJ97", "content": "Sep 21, 2023 · Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to suffer large regret ."}
+{"idx": 7, "title": "Regret matching + : (in)stability and fast convergence ...", "date": "", "ddg_snippet": "by G Farina · 2023 · Cited by 17 — Abstract . Regret Matching+ ( RM +) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3666122.3668812", "content": "by G Farina · 2023 · Cited by 17 — Abstract . Regret Matching+ ( RM +) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical ..."}
+{"idx": 8, "title": "Efficient Last-Iterate Convergence in Regret Minimization ...", "date": "", "ddg_snippet": "5 days ago — Regret matching+:(in) stability and fast convergence in games . Advances in Neural Information Processing Systems, 36, 2024. Farina et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13653v1", "content": "5 days ago — Regret matching+:(in) stability and fast convergence in games . Advances in Neural Information Processing Systems, 36, 2024. Farina et al."}
+{"idx": 9, "title": "Last-Iterate Convergence Properties of Regret-Matching ...", "date": "", "ddg_snippet": "by Y Cai · Cited by 5 — This paper examines the convergence properties of certain \"rergret- matching \" algorithms - RM+ and its \" extra-gradient \" variant, ExtraRM+ . The setting considered ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fWk5Qx0exc", "content": "by Y Cai · Cited by 5 — This paper examines the convergence properties of certain \"rergret- matching \" algorithms - RM+ and its \" extra-gradient \" variant, ExtraRM+ . The setting considered ..."}
diff --git a/data/sampled_jsons/abstract_of_Causal_Modeling_Semantics_for_Counterfactuals_with_Disjunctive_Antecedents_year_2023.jsonl b/data/sampled_jsons/abstract_of_Causal_Modeling_Semantics_for_Counterfactuals_with_Disjunctive_Antecedents_year_2023.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal modeling semantics for counterfactuals with ...", "date": "", "ddg_snippet": "by G Rosella · 2024 — A powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0168007223000933", "content": "by G Rosella · 2024 — A powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas."}
+{"idx": 1, "title": "Causal Modeling Semantics for Counterfactuals with ...", "date": "", "ddg_snippet": "by G Rosella — We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents , and more generally, to counterfactuals whose antecedent is an ...", "subpage_snippet": "", "source": "philarchive.org", "link": "https://philarchive.org/rec/ROSCMS-2", "content": "by G Rosella — We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents , and more generally, to counterfactuals whose antecedent is an ..."}
+{"idx": 2, "title": "Causal modeling semantics for counterfactuals with ...", "date": "", "ddg_snippet": "by G Rosella · 2024 — We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents , and more generally, to counterfactuals whose antecedent is an ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/rec/ROSCMS-4", "content": "by G Rosella · 2024 — We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents , and more generally, to counterfactuals whose antecedent is an ..."}
+{"idx": 3, "title": "Causal Modeling Semantics for Counterfactuals with ...", "date": "", "ddg_snippet": "by G Rosella — This paper applies Causal Modeling Semantics (CMS, e.g., Galles and. Pearl 1998; Pearl 2000; Halpern 2000) to the evaluation of the ...", "subpage_snippet": "", "source": "philarchive.org", "link": "https://philarchive.org/archive/ROSCMS-2", "content": "by G Rosella — This paper applies Causal Modeling Semantics (CMS, e.g., Galles and. Pearl 1998; Pearl 2000; Halpern 2000) to the evaluation of the ..."}
+{"idx": 4, "title": "[PDF] Causal Modeling Semantics for Counterfactuals with ...", "date": "", "ddg_snippet": "A probability is assigned to a counterfactual (A v B)>C at a causal model M as a weighted average of the probability of C in those submodels that truthmake ...", "subpage_snippet": "", "source": "api.semanticscholar.org", "link": "https://api.semanticscholar.org/arXiv:2304.14817", "content": "A probability is assigned to a counterfactual (A v B)>C at a causal model M as a weighted average of the probability of C in those submodels that truthmake ..."}
+{"idx": 5, "title": "A Dynamic Semantics for Causal Counterfactuals", "date": "", "ddg_snippet": "by K Lai · 2019 — In a causal theory of counterfactuals , the antecedent of the counterfactual determines the intervention to be applied to the causal model . Then, the consequent.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/W19-0601.pdf", "content": "by K Lai · 2019 — In a causal theory of counterfactuals , the antecedent of the counterfactual determines the intervention to be applied to the causal model . Then, the consequent."}
+{"idx": 6, "title": "Bridging causal models and premise semantics", "date": "", "ddg_snippet": "by P Santorio · 2014 · Cited by 10 — Abstract I argue that classical counterfactual semantics in the style of Stalnaker,. Lewis, and Kratzer validates an inference pattern that is disconfirmed ...", "subpage_snippet": "", "source": "journals.linguisticsociety.org", "link": "https://journals.linguisticsociety.org/proceedings/index.php/SALT/article/download/24.494/2730/3277", "content": "by P Santorio · 2014 · Cited by 10 — Abstract I argue that classical counterfactual semantics in the style of Stalnaker,. Lewis, and Kratzer validates an inference pattern that is disconfirmed ..."}
+{"idx": 7, "title": "Alternatives in counterfactuals", "date": "", "ddg_snippet": "by J Romoli · Cited by 15 — Fine observes that counterfactuals with disjunctive antecedents seem to entail the 'simplified' counterfactuals with the individual disjuncts as antecedents . (5).", "subpage_snippet": "", "source": "paolosantorio.net", "link": "https://paolosantorio.net/aic.pdf", "content": "by J Romoli · Cited by 15 — Fine observes that counterfactuals with disjunctive antecedents seem to entail the 'simplified' counterfactuals with the individual disjuncts as antecedents . (5)."}
+{"idx": 8, "title": "Causality Without Causal Models - Cornell: Computer Science", "date": "", "ddg_snippet": "by JY Halpern — The goal of this paper is to abstract the definition of causality in causal models , extracting its key features, so that it can be applied to any other model. 16 pages", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/home/halpern/papers/causalitywithout.pdf", "content": "by JY Halpern — The goal of this paper is to abstract the definition of causality in causal models , extracting its key features, so that it can be applied to any other model. 16 pages"}
+{"idx": 9, "title": "Counterfactuals - Stanford Encyclopedia of Philosophy", "date": "", "ddg_snippet": "18 Jan 2019 — Modal discourse concerns alternative ways things can be, e.g., what might be true, what isn't true but could have been, what should be done.", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/archives/sum2021/entries/counterfactuals/", "content": "18 Jan 2019 — Modal discourse concerns alternative ways things can be, e.g., what might be true, what isn't true but could have been, what should be done."}
diff --git a/data/sampled_jsons/abstract_of_The_Randomized_Midpoint_Method_for_Log-Concave_Sampling.jsonl b/data/sampled_jsons/abstract_of_The_Randomized_Midpoint_Method_for_Log-Concave_Sampling.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Abstract page for arXiv paper 1909.05503: The Randomized Midpoint Method for Log-Concave Sampling", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.05503", "content": "Abstract page for arXiv paper 1909.05503: The Randomized Midpoint Method for Log-Concave Sampling"}
+{"idx": 1, "title": "PDF The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Abstract Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form p / exp( f(x)), where f : Rd ! R has an L-Lipschitz gradi-ent and is m-strongly convex. In our paper, we propose a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion (ULD). It can ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Paper.pdf", "content": "Abstract Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form p / exp( f(x)), where f : Rd ! R has an L-Lipschitz gradi-ent and is m-strongly convex. In our paper, we propose a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion (ULD). It can ..."}
+{"idx": 2, "title": "PDF The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Yin Tat Lee University of Washington and Microsoft Research yintat@uw.edu Abstract Sampling from log-concave distributions is a well researched problem that has many applica-tions in statistics and machine learning. We study the distributions of the form p / exp( f(x)), where f : Rd ! R has an L-Lipschitz gradient and is m-strongly convex.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05503.pdf", "content": "Yin Tat Lee University of Washington and Microsoft Research yintat@uw.edu Abstract Sampling from log-concave distributions is a well researched problem that has many applica-tions in statistics and machine learning. We study the distributions of the form p / exp( f(x)), where f : Rd ! R has an L-Lipschitz gradient and is m-strongly convex."}
+{"idx": 3, "title": "Reviews: The Randomized Midpoint Method for Log-Concave Sampling - NeurIPS", "date": "", "ddg_snippet": "4. The statement of the Theorem 3 is awkward: what does it mean \"Algorithm 1 can find a random point X\"? Maybe a statement on the distribution at step N is better. Significance: This paper improves the previous rate for log-concave sampling and provides new ideas for designing new sampling algorithms.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Reviews.html", "content": "4. The statement of the Theorem 3 is awkward: what does it mean \"Algorithm 1 can find a random point X\"? Maybe a statement on the distribution at step N is better. Significance: This paper improves the previous rate for log-concave sampling and provides new ideas for designing new sampling algorithms."}
+{"idx": 4, "title": "[2405.15379v2] Randomized Midpoint Method for Log-Concave Sampling ...", "date": "", "ddg_snippet": "In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this constrained setting. We propose a unified proximal framework for handling constraints via a broad class of projection operators, including Euclidean, Bregman ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.15379v2", "content": "In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this constrained setting. We propose a unified proximal framework for handling constraints via a broad class of projection operators, including Euclidean, Bregman ..."}
+{"idx": 5, "title": "Randomized Midpoint Method for Log-Concave Sampling under Constraints", "date": "", "ddg_snippet": "Department of Data Science, City University of Hong Kong lu.yu@cityu.edu.hk Abstract In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this constrained setting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15379v2", "content": "Department of Data Science, City University of Hong Kong lu.yu@cityu.edu.hk Abstract In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this constrained setting."}
+{"idx": 6, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "To solve the sampling problem, we propose a new framework to discretize stochastic differential equations. We apply this framework to discretize and simulate ULD, which converges to the target distribution p ∗ superscript 𝑝 p^ {*}. The framework can be used to solve not only the log-concave sampling problem, but any problem that involves simulating (stochastic) differential equations.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1909.05503", "content": "To solve the sampling problem, we propose a new framework to discretize stochastic differential equations. We apply this framework to discretize and simulate ULD, which converges to the target distribution p ∗ superscript 𝑝 p^ {*}. The framework can be used to solve not only the log-concave sampling problem, but any problem that involves simulating (stochastic) differential equations."}
+{"idx": 7, "title": "PDF L M Carlo for Strongly Log Concave Distributions: Randomized Mid Point ...", "date": "", "ddg_snippet": "ABSTRACT We revisit the problem of sampling from a target distribution that has a smooth strongly log-concave density everywhere in Rp. In this context, if no additional density information is available, the randomized midpoint discretization for the kinetic Langevin diffusion is known to be the most scalable method in high dimensions with large condition numbers. Our main result is a ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/c66a9db149261435664284a20b6f1d42-Paper-Conference.pdf", "content": "ABSTRACT We revisit the problem of sampling from a target distribution that has a smooth strongly log-concave density everywhere in Rp. In this context, if no additional density information is available, the randomized midpoint discretization for the kinetic Langevin diffusion is known to be the most scalable method in high dimensions with large condition numbers. Our main result is a ..."}
+{"idx": 8, "title": "[PDF] Randomized Midpoint Method for Log-Concave Sampling under ...", "date": "", "ddg_snippet": "In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this constrained setting.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Randomized-Midpoint-Method-for-Log-Concave-Sampling-Yu-Yu/4daeb3786a73f71c7f09ca7dc99042ce4a77f0f8", "content": "In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this constrained setting."}
+{"idx": 9, "title": "Langevin Monte Carlo for strongly log-concave distributions: Randomized ...", "date": "", "ddg_snippet": "We revisit the problem of sampling from a target distribution that has a smooth strongly log-concave density everywhere in $\\mathbb {R}^p$. In this context, if no additional density information is available, the randomized midpoint discretization for the kinetic Langevin diffusion is known to be the most scalable method in high dimensions with large condition numbers. Our main result is a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=hOxgrGM63n", "content": "We revisit the problem of sampling from a target distribution that has a smooth strongly log-concave density everywhere in $\\mathbb {R}^p$. In this context, if no additional density information is available, the randomized midpoint discretization for the kinetic Langevin diffusion is known to be the most scalable method in high dimensions with large condition numbers. Our main result is a ..."}
diff --git a/data/sampled_jsons/abstract_or_summary_from_distill.pub2020circuits.jsonl b/data/sampled_jsons/abstract_or_summary_from_distill.pub2020circuits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4962c96ed33422675e0f3cbf2fee0e376b72797a
--- /dev/null
+++ b/data/sampled_jsons/abstract_or_summary_from_distill.pub2020circuits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Thread: Circuits - Distill Distill: Zoom in on Circuits - Dynamically Typed Zoom In: An Introduction to Circuits | Nick’s Notes Notes: \"Zoom In - An Introduction to Circuits\" What I Read: Introduction to Circuits - afairless.github.io GitHub - distillpub/post--circuits-zoom-in: Zoom In: An ... Zoom In: An Introduction to Circuits - Distill", "date": "", "ddg_snippet": "Mar 10, 2020 · To facilitate exploration of this direction, Distill is inviting a “thread” of short articles on circuits , interspersed with critical commentary by experts in adjacent fields. The thread will be a living document, with new articles added over time, organized through an open slack channel (# circuits in the Distill slack). From DT #35: “By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.\" Chris Olah et al. wrote a fascinating new Distill article about “ circuits ” in convolutional neural networks. The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ... Mar 27, 2020 · Zoom In: An Introduction to Circuits ( 2020 ) Paper Authors: Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter Paper Link: https:// distill.pub/2020/circuits /zoom-in/ One of the \"foundational\" papers is \"Zoom In: An Introduction to Circuits \" by Olah et. al. on Distill . pub . Below are some of my comments and notes on the paper. A lot of the content borrows directly from the source, so all figures and direct quotes are attributed to the paper and OpenAI Microscope. Claims 1-2: Neurons + circuits are understandable Dec 18, 2020 · Posted on 2020 -12-18 :: Tags: neural network, neuron, neuroscience, circuit , interpretability https:// distill.pub/2020/circuits /zoom-in/ Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks. Zoom In: An Introduction to Circuits . Contribute to distillpub/post-- circuits -zoom-in development by creating an account on GitHub. Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/", "content": "Mar 10, 2020 · To facilitate exploration of this direction, Distill is inviting a “thread” of short articles on circuits , interspersed with critical commentary by experts in adjacent fields. The thread will be a living document, with new articles added over time, organized through an open slack channel (# circuits in the Distill slack). From DT #35: “By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.\" Chris Olah et al. wrote a fascinating new Distill article about “ circuits ” in convolutional neural networks. The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ... Mar 27, 2020 · Zoom In: An Introduction to Circuits ( 2020 ) Paper Authors: Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter Paper Link: https:// distill.pub/2020/circuits /zoom-in/ One of the \"foundational\" papers is \"Zoom In: An Introduction to Circuits \" by Olah et. al. on Distill . pub . Below are some of my comments and notes on the paper. A lot of the content borrows directly from the source, so all figures and direct quotes are attributed to the paper and OpenAI Microscope. Claims 1-2: Neurons + circuits are understandable Dec 18, 2020 · Posted on 2020 -12-18 :: Tags: neural network, neuron, neuroscience, circuit , interpretability https:// distill.pub/2020/circuits /zoom-in/ Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks. Zoom In: An Introduction to Circuits . Contribute to distillpub/post-- circuits -zoom-in development by creating an account on GitHub. Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks."}
+{"idx": 1, "title": "Distill: Zoom in on Circuits - Dynamically Typed", "date": "", "ddg_snippet": "From DT #35: “By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.\" Chris Olah et al. wrote a fascinating new Distill article about “ circuits ” in convolutional neural networks. The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ...", "subpage_snippet": "", "source": "dynamicallytyped.com", "link": "https://dynamicallytyped.com/stories/2020/distill-zoom-in-on-circuits/", "content": "From DT #35: “By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.\" Chris Olah et al. wrote a fascinating new Distill article about “ circuits ” in convolutional neural networks. The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ..."}
+{"idx": 2, "title": "Zoom In: An Introduction to Circuits | Nick’s Notes Notes: \"Zoom In - An Introduction to Circuits\" What I Read: Introduction to Circuits - afairless.github.io GitHub - distillpub/post--circuits-zoom-in: Zoom In: An ... Zoom In: An Introduction to Circuits - Distill", "date": "", "ddg_snippet": "Mar 27, 2020 · Zoom In: An Introduction to Circuits ( 2020 ) Paper Authors: Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter Paper Link: https:// distill.pub/2020/circuits /zoom-in/ One of the \"foundational\" papers is \"Zoom In: An Introduction to Circuits \" by Olah et. al. on Distill . pub . Below are some of my comments and notes on the paper. A lot of the content borrows directly from the source, so all figures and direct quotes are attributed to the paper and OpenAI Microscope. Claims 1-2: Neurons + circuits are understandable Dec 18, 2020 · Posted on 2020 -12-18 :: Tags: neural network, neuron, neuroscience, circuit , interpretability https:// distill.pub/2020/circuits /zoom-in/ Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks. Zoom In: An Introduction to Circuits . Contribute to distillpub/post-- circuits -zoom-in development by creating an account on GitHub. Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.", "subpage_snippet": "", "source": "www.nickjalbert.com", "link": "http://www.nickjalbert.com/reading/2020/03/27/zoom-in-an-introduction-to-circuits.html", "content": "Mar 27, 2020 · Zoom In: An Introduction to Circuits ( 2020 ) Paper Authors: Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter Paper Link: https:// distill.pub/2020/circuits /zoom-in/ One of the \"foundational\" papers is \"Zoom In: An Introduction to Circuits \" by Olah et. al. on Distill . pub . Below are some of my comments and notes on the paper. A lot of the content borrows directly from the source, so all figures and direct quotes are attributed to the paper and OpenAI Microscope. Claims 1-2: Neurons + circuits are understandable Dec 18, 2020 · Posted on 2020 -12-18 :: Tags: neural network, neuron, neuroscience, circuit , interpretability https:// distill.pub/2020/circuits /zoom-in/ Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks. Zoom In: An Introduction to Circuits . Contribute to distillpub/post-- circuits -zoom-in development by creating an account on GitHub. Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks."}
+{"idx": 3, "title": "Notes: \"Zoom In - An Introduction to Circuits\"", "date": "", "ddg_snippet": "One of the \"foundational\" papers is \"Zoom In: An Introduction to Circuits \" by Olah et. al. on Distill . pub . Below are some of my comments and notes on the paper. A lot of the content borrows directly from the source, so all figures and direct quotes are attributed to the paper and OpenAI Microscope. Claims 1-2: Neurons + circuits are understandable", "subpage_snippet": "", "source": "tanaybiradar.com", "link": "https://tanaybiradar.com/blog/notes-on-zoom-in-circuits/", "content": "One of the \"foundational\" papers is \"Zoom In: An Introduction to Circuits \" by Olah et. al. on Distill . pub . Below are some of my comments and notes on the paper. A lot of the content borrows directly from the source, so all figures and direct quotes are attributed to the paper and OpenAI Microscope. Claims 1-2: Neurons + circuits are understandable"}
+{"idx": 4, "title": "What I Read: Introduction to Circuits - afairless.github.io", "date": "", "ddg_snippet": "Dec 18, 2020 · Posted on 2020 -12-18 :: Tags: neural network, neuron, neuroscience, circuit , interpretability https:// distill.pub/2020/circuits /zoom-in/ Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.", "subpage_snippet": "", "source": "afairless.github.io", "link": "https://afairless.github.io/read_post/2020-12-18-introduction-to/", "content": "Dec 18, 2020 · Posted on 2020 -12-18 :: Tags: neural network, neuron, neuroscience, circuit , interpretability https:// distill.pub/2020/circuits /zoom-in/ Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks."}
+{"idx": 5, "title": "GitHub - distillpub/post--circuits-zoom-in: Zoom In: An ...", "date": "", "ddg_snippet": "Zoom In: An Introduction to Circuits . Contribute to distillpub/post-- circuits -zoom-in development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/distillpub/post--circuits-zoom-in", "content": "Zoom In: An Introduction to Circuits . Contribute to distillpub/post-- circuits -zoom-in development by creating an account on GitHub."}
+{"idx": 6, "title": "Zoom In: An Introduction to Circuits - Distill", "date": "", "ddg_snippet": "Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/zoom-in/", "content": "Zoom In: An Introduction to Circuits By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks."}
+{"idx": 7, "title": "Thread: Circuits", "date": "", "ddg_snippet": "by N Cammarata · 2020 · Cited by 92 — The Circuits thread is open to articles exploring individual features, circuits , and their organization within neural networks. Critical ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits", "content": "by N Cammarata · 2020 · Cited by 92 — The Circuits thread is open to articles exploring individual features, circuits , and their organization within neural networks. Critical ..."}
+{"idx": 8, "title": "Zoom In: An Introduction to Circuits", "date": "", "ddg_snippet": "by C Olah · 2020 · Cited by 638 — This article is part of the Circuits thread, an experimental format collecting invited short articles and critical commentary delving into the inner workings ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/zoom-in", "content": "by C Olah · 2020 · Cited by 638 — This article is part of the Circuits thread, an experimental format collecting invited short articles and critical commentary delving into the inner workings ..."}
+{"idx": 9, "title": "A Mathematical Framework for Transformer Circuits", "date": "", "ddg_snippet": "22 Dec 2021 — We can summarize this copying behavior into a few abstract patterns that we've observed: All of these can be seen as a kind of very primitive in ...", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2021/framework/index.html", "content": "22 Dec 2021 — We can summarize this copying behavior into a few abstract patterns that we've observed: All of these can be seen as a kind of very primitive in ..."}
diff --git a/data/sampled_jsons/address-event_representation_data_reduction_pixel_processor_array.jsonl b/data/sampled_jsons/address-event_representation_data_reduction_pixel_processor_array.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a35b0c654ef86c24e2f0796bc927dc46f693966d
--- /dev/null
+++ b/data/sampled_jsons/address-event_representation_data_reduction_pixel_processor_array.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "address做动词有哪些意思？ - 知乎", "date": "", "ddg_snippet": "Dec 3, 2020 · 1、write the name and address of the intended recipient on (an envelope, letter, or parcel) 在（信封，信，包裹）上写收件人姓名和邮址 I addressed my letter to him personally. 我以个人名义给他寄信。 2、speak to (a person or an assembly), typically in a formal way （多指正式）对…讲话，致词 They addressed themselves to my father. 他们向我父亲 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/433093196", "content": "Dec 3, 2020 · 1、write the name and address of the intended recipient on (an envelope, letter, or parcel) 在（信封，信，包裹）上写收件人姓名和邮址 I addressed my letter to him personally. 我以个人名义给他寄信。 2、speak to (a person or an assembly), typically in a formal way （多指正式）对…讲话，致词 They addressed themselves to my father. 他们向我父亲 ..."}
+{"idx": 1, "title": "【address】常用意思为 地址，为什么会有【演讲；致辞；说话的技巧；...", "date": "", "ddg_snippet": "英语单词有些可以通过引申来表达其他意思，但是像address如何可以产生【演讲；致辞；说话的技巧；称呼】的意思呢？ 纯碎的习惯用法还是什么呢？ 还有【r… 显示全部 关注者 16 被浏览", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/279249557", "content": "英语单词有些可以通过引申来表达其他意思，但是像address如何可以产生【演讲；致辞；说话的技巧；称呼】的意思呢？ 纯碎的习惯用法还是什么呢？ 还有【r… 显示全部 关注者 16 被浏览"}
+{"idx": 2, "title": "句中tackle 和address 都有“处理”、”解决”、“应对”的意思，为什么要重...", "date": "", "ddg_snippet": "英语问答 问： 句中 tackle 和address 都有“处理”、”解决”、“应对”的意思，为什么要重复使用？ Theoretically speaking, a plethora of biomedical challenges could be tackled and addressed by 3-D printing technology; among them, the research and development of bionic devices, nerves, muscle and bone regrowth.", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/580541686", "content": "英语问答 问： 句中 tackle 和address 都有“处理”、”解决”、“应对”的意思，为什么要重复使用？ Theoretically speaking, a plethora of biomedical challenges could be tackled and addressed by 3-D printing technology; among them, the research and development of bionic devices, nerves, muscle and bone regrowth."}
+{"idx": 3, "title": "cfa注册时address line1、2分别怎么填？ - 知乎", "date": "", "ddg_snippet": "英文地址 按小到大的顺序 比如门牌 路 区 城市 Room 101,building 34, Zhongshan road, Xuhui district", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/56807512", "content": "英文地址 按小到大的顺序 比如门牌 路 区 城市 Room 101,building 34, Zhongshan road, Xuhui district"}
+{"idx": 4, "title": "英文地址怎么填写？ - 知乎", "date": "", "ddg_snippet": "英文地址怎么填写？ [图片] 这里的address后面三个框框，要怎么填填什么顺序？ 显示全部 关注者 262 被浏览", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/20562267", "content": "英文地址怎么填写？ [图片] 这里的address后面三个框框，要怎么填填什么顺序？ 显示全部 关注者 262 被浏览"}
+{"idx": 5, "title": "如何查看自己电脑的 IP 地址？ - 知乎", "date": "", "ddg_snippet": "查看自己电脑的ip地址首先要看您是什么电脑？ 如果您是windows操作系统的电脑，要在“开始”菜单底下，输入cmd，打开界面 windows Power shell 窗口后，输入命令 ipconfig 后，就能查到自己的IP地址。 如果您是苹果ios操作系统的电脑，要在wifi处点击，打开“ 网络偏好设置 ”后，就能看到自己的IP地址了。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/567480189", "content": "查看自己电脑的ip地址首先要看您是什么电脑？ 如果您是windows操作系统的电脑，要在“开始”菜单底下，输入cmd，打开界面 windows Power shell 窗口后，输入命令 ipconfig 后，就能查到自己的IP地址。 如果您是苹果ios操作系统的电脑，要在wifi处点击，打开“ 网络偏好设置 ”后，就能看到自己的IP地址了。"}
+{"idx": 6, "title": "怎样把中文地址翻译成英文的地址？ - 知乎", "date": "", "ddg_snippet": "浙江省杭州市文二路391号 西湖国际科技大厦 裙楼2层: Floor 2, Podium of Xihu International Technology Building, No.391, Wen'er Road, Hangzhou, Zhejiang 河南省洛阳市洛龙区 龙丰小区 A区8号楼二单元603 603, Unit 2, Building 8, Zone A, Longfeng Area, Luolong District, Luoyang, Henan 用太阳风名址翻译机 9181.cn/addt.asp 翻的，感觉挺准的，您用用。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/19772023", "content": "浙江省杭州市文二路391号 西湖国际科技大厦 裙楼2层: Floor 2, Podium of Xihu International Technology Building, No.391, Wen'er Road, Hangzhou, Zhejiang 河南省洛阳市洛龙区 龙丰小区 A区8号楼二单元603 603, Unit 2, Building 8, Zone A, Longfeng Area, Luolong District, Luoyang, Henan 用太阳风名址翻译机 9181.cn/addt.asp 翻的，感觉挺准的，您用用。"}
+{"idx": 7, "title": "名片上正确的英文缩写是？ - 知乎", "date": "", "ddg_snippet": "回答①： 加 Add.表示缩写 有时方（lan）便（duo）可以省略 回答②： 都可以 强调时可全大写 回答③： 手机的正确英文缩写是Cel.、MB、MOB、MP、Mobile或其它？ 查了下牛津英汉词典解释 更正如下： telephone 多指电话（系统） mobile phone和cellphone都有手机；移动电话的意思 mobile phone 是英国英语正式用语 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/31445948", "content": "回答①： 加 Add.表示缩写 有时方（lan）便（duo）可以省略 回答②： 都可以 强调时可全大写 回答③： 手机的正确英文缩写是Cel.、MB、MOB、MP、Mobile或其它？ 查了下牛津英汉词典解释 更正如下： telephone 多指电话（系统） mobile phone和cellphone都有手机；移动电话的意思 mobile phone 是英国英语正式用语 ..."}
+{"idx": 8, "title": "idea学生认证为什么会出现这样的情况：说我的学生邮箱有问题？ - 知乎", "date": "", "ddg_snippet": "很简单，你们学校域名被拉黑了。一般被拉黑可能因为： 学校邮箱可以随便注册 毕业之后的学生邮箱不收回 学校卖邮箱来白嫖的情况太多 JetBrains歧视大陆学校（划掉 为什么我要把第四点列出来呢，因为在目前565条的黑名单里，北大，清华，复旦，USTC，同济，上交等名校赫然在列。这些学校的邮箱 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/386597879", "content": "很简单，你们学校域名被拉黑了。一般被拉黑可能因为： 学校邮箱可以随便注册 毕业之后的学生邮箱不收回 学校卖邮箱来白嫖的情况太多 JetBrains歧视大陆学校（划掉 为什么我要把第四点列出来呢，因为在目前565条的黑名单里，北大，清华，复旦，USTC，同济，上交等名校赫然在列。这些学校的邮箱 ..."}
+{"idx": 9, "title": "给中山大学广州校区寄快递，地址应该怎么填？ - 知乎", "date": "", "ddg_snippet": "Mar 29, 2022 · 广州有三个校区： 一、 中山大学 广州校区南校园 地址：广东省广州市海珠区新港西路135号 邮政编码：510275 二、中山大学广州校区东校园 地址：广东省广州市番禺区小谷围外环东路132号（大学城中山大学） 邮政编码：510006 三、中山大学广州校区北校园 地址：广东省广州市越秀区中山二路74号 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/524736441", "content": "Mar 29, 2022 · 广州有三个校区： 一、 中山大学 广州校区南校园 地址：广东省广州市海珠区新港西路135号 邮政编码：510275 二、中山大学广州校区东校园 地址：广东省广州市番禺区小谷围外环东路132号（大学城中山大学） 邮政编码：510006 三、中山大学广州校区北校园 地址：广东省广州市越秀区中山二路74号 ..."}
diff --git a/data/sampled_jsons/advantage_of_Witness_complex_over_Vietoris-Rips_complex_persistent_homology.jsonl b/data/sampled_jsons/advantage_of_Witness_complex_over_Vietoris-Rips_complex_persistent_homology.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..257bed1e03546b09bc7ba3f7f8b1f0788aa2438a
--- /dev/null
+++ b/data/sampled_jsons/advantage_of_Witness_complex_over_Vietoris-Rips_complex_persistent_homology.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Vietoris-Rips Complexes: The Backbone of Persistent Homology", "date": "", "ddg_snippet": "Explore the intricacies of Vietoris - Rips complexes and their role in Persistent Homology , providing a deeper understanding of complex data structures.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/vietoris-rips-complexes-persistent-homology-deep-dive", "content": "Explore the intricacies of Vietoris - Rips complexes and their role in Persistent Homology , providing a deeper understanding of complex data structures."}
+{"idx": 1, "title": "PDF Persistent Homology and Sparse Vietoris-rips Filtration", "date": "", "ddg_snippet": "The construction of the Vietoris - Rips Complex is computationally expensive, so we will develop an improved ltration method: the sparse Vietoris - Rips ltration. The sparse ltration selects some data points from the whole dataset to set up the Vietoris - Rips complexes, which reduces the number of simplices in the ltration, but preserves the ...", "subpage_snippet": "", "source": "www.math.uchicago.edu", "link": "https://www.math.uchicago.edu/~may/REU2017/REUPapers/Zhang,yujie.pdf", "content": "The construction of the Vietoris - Rips Complex is computationally expensive, so we will develop an improved ltration method: the sparse Vietoris - Rips ltration. The sparse ltration selects some data points from the whole dataset to set up the Vietoris - Rips complexes, which reduces the number of simplices in the ltration, but preserves the ..."}
+{"idx": 2, "title": "PDF Persistence stability for geometric complexes", "date": "", "ddg_snippet": "Abstract In this paper we study the properties of the homology of different geometric filtered complexes (such as Vietoris - Rips , ˇCech and witness complexes) built on top of totally bounded metric spaces. Using recent developments in the theory of topological persistence, we provide simple and natural proofs of the stability of the persistent homology of such complexes with respect to the ...", "subpage_snippet": "", "source": "geometrica.saclay.inria.fr", "link": "https://geometrica.saclay.inria.fr/team/Fred.Chazal/papers/cdo-psgc-12/sdo-psgc-13-gd.pdf", "content": "Abstract In this paper we study the properties of the homology of different geometric filtered complexes (such as Vietoris - Rips , ˇCech and witness complexes) built on top of totally bounded metric spaces. Using recent developments in the theory of topological persistence, we provide simple and natural proofs of the stability of the persistent homology of such complexes with respect to the ..."}
+{"idx": 3, "title": "Persistent Homology and Applied Homotopy Theory", "date": "", "ddg_snippet": "tionship to the witness complex as the Vietoris - Rips complex bears to the Cech complex There is also the weak witness complex , fWweak(X; L; R)gR, which is de ned as follows. F", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2004.00738", "content": "tionship to the witness complex as the Vietoris - Rips complex bears to the Cech complex There is also the weak witness complex , fWweak(X; L; R)gR, which is de ned as follows. F"}
+{"idx": 4, "title": "Persistent homology for MCI classification: a comparative analysis ...", "date": "", "ddg_snippet": "In this regard, we investigate two methods for computing persistent homology : (1) Vietoris - Rips filtration, which leverages point clouds generated from fMRI time series to capture dynamic and global changes in brain connectivity, and (2) graph filtration, which examines connectivity matrices based on static pairwise correlations.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/40078712/", "content": "In this regard, we investigate two methods for computing persistent homology : (1) Vietoris - Rips filtration, which leverages point clouds generated from fMRI time series to capture dynamic and global changes in brain connectivity, and (2) graph filtration, which examines connectivity matrices based on static pairwise correlations."}
+{"idx": 5, "title": "Calculating Persistent Homology with a Vietoris-Rips Complex", "date": "", "ddg_snippet": "As expected for circle_df, the homology matrix contains a single prominent 1-cycle (last line of tail 's output). Although we suspect the feature to be a persistent 1-cycle, comparison with the other features in the homology matrix is required to confirm that it is sufficiently persistent .", "subpage_snippet": "", "source": "cran.r-project.org", "link": "https://cran.r-project.org/web//packages//ripserr/vignettes/vietoris-rips.html", "content": "As expected for circle_df, the homology matrix contains a single prominent 1-cycle (last line of tail 's output). Although we suspect the feature to be a persistent 1-cycle, comparison with the other features in the homology matrix is required to confirm that it is sufficiently persistent ."}
+{"idx": 6, "title": "PDF Notes on MATH 359 (Persistent homology)", "date": "", "ddg_snippet": "Definition I.1.1 ( Vietoris - Rips Complex ) The Vietoris -Rits complex Rt of (X, d) is the subcomplex of the complete simplex with vertices in X via the rule σ ∈ X is a simplex of Rt ⇐⇒ diam(σ) < t. Take X = {(0, 1), (1, 0), (1, 1), (0, 0)} ⊆ R2, then the Vietoris - Rips complexes associated to this is given in Figure 1. √ √", "subpage_snippet": "", "source": "math.uchicago.edu", "link": "https://math.uchicago.edu/~alephnil/notes/PersistentHomology.pdf", "content": "Definition I.1.1 ( Vietoris - Rips Complex ) The Vietoris -Rits complex Rt of (X, d) is the subcomplex of the complete simplex with vertices in X via the rule σ ∈ X is a simplex of Rt ⇐⇒ diam(σ) < t. Take X = {(0, 1), (1, 0), (1, 1), (0, 0)} ⊆ R2, then the Vietoris - Rips complexes associated to this is given in Figure 1. √ √"}
+{"idx": 7, "title": "Faster computation of degree-1 persistent homology using the reduced ...", "date": "", "ddg_snippet": "By using the Reduced Vietoris - Rips complex and leveraging the geometry of Euclidean space, we are able to compute the Vietoris - Rips persistent homology for significantly larger point clouds than current implementations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2307.16333", "content": "By using the Reduced Vietoris - Rips complex and leveraging the geometry of Euclidean space, we are able to compute the Vietoris - Rips persistent homology for significantly larger point clouds than current implementations."}
+{"idx": 8, "title": "PDF Weighted persistent homology - MSP", "date": "", "ddg_snippet": "Computationally, this approach is infeasible for large data sets or high-dimensional data, so instead one computes the so-called Vietoris - Rips complex , which is the flag complex over the graph obtained by placing an edge between any pair of vertices that are at distance no more than 2r from each other.", "subpage_snippet": "", "source": "msp.org", "link": "https://msp.org/involve/2019/12-5/involve-v12-n5-p07-p.pdf", "content": "Computationally, this approach is infeasible for large data sets or high-dimensional data, so instead one computes the so-called Vietoris - Rips complex , which is the flag complex over the graph obtained by placing an edge between any pair of vertices that are at distance no more than 2r from each other."}
+{"idx": 9, "title": "Dory: Computation of persistence diagrams up to dimension two for ...", "date": "", "ddg_snippet": "We present Dory, an efficient and scalable algorithm that can compute the persistent homology of sparse Vietoris - Rips complexes on larger data sets, up to and including dimension two and over the field .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1877750324000838", "content": "We present Dory, an efficient and scalable algorithm that can compute the persistent homology of sparse Vietoris - Rips complexes on larger data sets, up to and including dimension two and over the field ."}
diff --git a/data/sampled_jsons/advantage_of_constant-Q_transform_CQT_over_Short-Time_Fourier_Transform_STFT_for_music_signal_analys.jsonl b/data/sampled_jsons/advantage_of_constant-Q_transform_CQT_over_Short-Time_Fourier_Transform_STFT_for_music_signal_analys.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..25263800d2b234ed0275e1e692276ab8261901b3
--- /dev/null
+++ b/data/sampled_jsons/advantage_of_constant-Q_transform_CQT_over_Short-Time_Fourier_Transform_STFT_for_music_signal_analys.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constant-Q transform - Wikipedia", "date": "", "ddg_snippet": "Short-time Fourier transform with variable resolutionIn mathematics and signal processing, the constant-Q transform and variable- Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform [1] and very closely related to the complex Morlet wavelet transform . [2] Its design is suited for musical representation. Constant-Q ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Constant-Q_transform", "content": "Short-time Fourier transform with variable resolutionIn mathematics and signal processing, the constant-Q transform and variable- Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform [1] and very closely related to the complex Morlet wavelet transform . [2] Its design is suited for musical representation. Constant-Q ..."}
+{"idx": 1, "title": "Comparison of Time-Frequency Representations for ... Evaluation of spectral transforms for music signal analysis The Constant Q Transform - Machine Learning Group Bridging the gap between the short-time Fourier transform ... CONSTANT-Q TRANSFORM TOOLBOX FOR MUSIC PROCESSING CONSTANT - Q TRANSFORM TOOLBOX FOR MUSIC PROCESSING The Constant Q Transform - Machine Learning Group Constant - Q transform - Wikipedia The Constant Q Transform - Machine Learning Group Bridging the gap between the short-time Fourier transform ( STFT Constant - Q transform - Wikipedia COMPARING SHORT-TIME FOURIER TRANSFORM (STFT) AND CONSTANT-Q ...", "date": "", "ddg_snippet": "Abstract—Recent successful applications of convolutional neu-ral networks (CNNs) to audio classification and speech recogni-tion have motivated the search for better input representations for more efficient training. Visual displays of an audio signal , through various time-frequency representations such as spectrograms offer a rich representation o... See full list on arxiv.org Overall, the shallower Conv-3 model tended to yield better accuracies than Conv-5 regardless of input. A probable ex-planation is the diminishing returns of the deeper model due to significant overfitting. While it simplified the experimental methodology, using whole audio clips as input inevitably resulted in fewer training examples with less vari... See full list on arxiv.org The benefit of wideband against narrowband transforms were not consistent across both datasets. This may be in-dicative of a disparity in the types of environmental sounds present in both datasets. More interestingly perhaps, compar-ing the confusion matrices reveals that each specializes in discriminating certain classes of sound. The wideband Mel... See full list on arxiv.org In this paper we present a study on the spectral analysis of music signals comparing the time domain representation, the short-time Fourier transform ( STFT ) and the constant-Q transform ( CQT ) which are additionally combined with different signal -dependent transforms. The comparison is carried out with respect to the spectral compactness, the data compression ability and the temporal continuity ... the constant Q transform so useful is that by an appropriate choice for f0 (minimal center frequency) and b the center frequencies directly correspond to musical notes. For instance choosing b = 12 and f0 as the frequency of midinote 0 makes the k-th cq-bin correspond the midinote number k. Another nice feature of the constant Q transform is its increasing time resolution towards higher ... May 13, 2020 · The Constant-Q transform ( CQT ) can also be used to transform a discrete time -domain signal into the time –frequency domain [32, 33]. This transform was initially proposed for music analysis , transforming against log (frequency) to obtain a constant pattern in the frequency domain. ABSTRACT This paper proposes a computationally efficient method for computing the constant-Q transform ( CQT ) of a time -domain signal . CQT refers to a time -frequency represen-tation where the frequency bins are geometrically spaced and the Q -factors (ratios of the center frequencies to band-widths) of all bins are equal. An inverse transform is pro-posed which enables a reasonable-quality ... What is constant Q transform (CQT)? 1. INTRODUCTION Constant-Q transform (CQT) here refers to a technique that transforms a time-domain signal x(n) into the time-frequency domain so that the center frequencies of the fre-quency bins are geometrically spaced and their Q-factors are all equal. What makes a constant Q transform so useful? 1. What makes the constant Q transform so useful is that by an appropriate choice for f0 (minimal center frequency) and b the center frequencies directly correspond to musical notes. For instance choosing b = 12 and f0 as the frequency of midinote 0 makes the k-th cq-bin correspond the midinote number k. What is CQT & VQT? In mathematics and signal processing, the constant-Q transform and variable-Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform and very closely related to the complex Morlet wavelet transform. Its design is suited for musical representation. How is a constant Q transform similar to a Fourier transform? The constant Q transform as introduced in [Brown, 1991] is very close related to the Fourier transform. Like the Fourier transform a constant Q transform is a bank of lters , but in contrast to the former it has geometrically spaced center frequencies fk = f0 (k = 0; : : :), where b dictates the number of lters per octave. What does CQT stand for? The Constant-Q transform (CQT) can also be used to transform a discrete time-domain signal into the time–frequency domain [32, 33]. This transform was initially proposed for music analysis, transforming against log (frequency) to obtain a constant pattern in the frequency domain. How can a constant Q transform be approximated? Alternatively, the constant-Q transform can be approximated by using multiple fast Fourier transforms of different window sizes and/or sampling rate at different frequency ranges then stitch it together. Dec 31, 2024 · This study compares the Short-Time Fourier Transform ( STFT ) and Constant-Q Transform ( CQT ) techniques in capturing the intricate spectral characteristics of the rebana, a traditional Malay frame drum known for its rich harmonic content and transient attacks. High-quality audio recordings were obtained in a controlled studio environment using a Shure SM57 microphone placed six inches from the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1706.07156", "content": "Abstract—Recent successful applications of convolutional neu-ral networks (CNNs) to audio classification and speech recogni-tion have motivated the search for better input representations for more efficient training. Visual displays of an audio signal , through various time-frequency representations such as spectrograms offer a rich representation o... See full list on arxiv.org Overall, the shallower Conv-3 model tended to yield better accuracies than Conv-5 regardless of input. A probable ex-planation is the diminishing returns of the deeper model due to significant overfitting. While it simplified the experimental methodology, using whole audio clips as input inevitably resulted in fewer training examples with less vari... See full list on arxiv.org The benefit of wideband against narrowband transforms were not consistent across both datasets. This may be in-dicative of a disparity in the types of environmental sounds present in both datasets. More interestingly perhaps, compar-ing the confusion matrices reveals that each specializes in discriminating certain classes of sound. The wideband Mel... See full list on arxiv.org In this paper we present a study on the spectral analysis of music signals comparing the time domain representation, the short-time Fourier transform ( STFT ) and the constant-Q transform ( CQT ) which are additionally combined with different signal -dependent transforms. The comparison is carried out with respect to the spectral compactness, the data compression ability and the temporal continuity ... the constant Q transform so useful is that by an appropriate choice for f0 (minimal center frequency) and b the center frequencies directly correspond to musical notes. For instance choosing b = 12 and f0 as the frequency of midinote 0 makes the k-th cq-bin correspond the midinote number k. Another nice feature of the constant Q transform is its increasing time resolution towards higher ... May 13, 2020 · The Constant-Q transform ( CQT ) can also be used to transform a discrete time -domain signal into the time –frequency domain [32, 33]. This transform was initially proposed for music analysis , transforming against log (frequency) to obtain a constant pattern in the frequency domain. ABSTRACT This paper proposes a computationally efficient method for computing the constant-Q transform ( CQT ) of a time -domain signal . CQT refers to a time -frequency represen-tation where the frequency bins are geometrically spaced and the Q -factors (ratios of the center frequencies to band-widths) of all bins are equal. An inverse transform is pro-posed which enables a reasonable-quality ... What is constant Q transform (CQT)? 1. INTRODUCTION Constant-Q transform (CQT) here refers to a technique that transforms a time-domain signal x(n) into the time-frequency domain so that the center frequencies of the fre-quency bins are geometrically spaced and their Q-factors are all equal. What makes a constant Q transform so useful? 1. What makes the constant Q transform so useful is that by an appropriate choice for f0 (minimal center frequency) and b the center frequencies directly correspond to musical notes. For instance choosing b = 12 and f0 as the frequency of midinote 0 makes the k-th cq-bin correspond the midinote number k. What is CQT & VQT? In mathematics and signal processing, the constant-Q transform and variable-Q transform , simply known as CQT and VQT, transforms a data series to the frequency domain. It is related to the Fourier transform and very closely related to the complex Morlet wavelet transform. Its design is suited for musical representation. How is a constant Q transform similar to a Fourier transform? The constant Q transform as introduced in [Brown, 1991] is very close related to the Fourier transform. Like the Fourier transform a constant Q transform is a bank of lters , but in contrast to the former it has geometrically spaced center frequencies fk = f0 (k = 0; : : :), where b dictates the number of lters per octave. What does CQT stand for? The Constant-Q transform (CQT) can also be used to transform a discrete time-domain signal into the time–frequency domain [32, 33]. This transform was initially proposed for music analysis, transforming against log (frequency) to obtain a constant pattern in the frequency domain. How can a constant Q transform be approximated? Alternatively, the constant-Q transform can be approximated by using multiple fast Fourier transforms of different window sizes and/or sampling rate at different frequency ranges then stitch it together. Dec 31, 2024 · This study compares the Short-Time Fourier Transform ( STFT ) and Constant-Q Transform ( CQT ) techniques in capturing the intricate spectral characteristics of the rebana, a traditional Malay frame drum known for its rich harmonic content and transient attacks. High-quality audio recordings were obtained in a controlled studio environment using a Shure SM57 microphone placed six inches from the ..."}
+{"idx": 2, "title": "Evaluation of spectral transforms for music signal analysis", "date": "", "ddg_snippet": "In this paper we present a study on the spectral analysis of music signals comparing the time domain representation, the short-time Fourier transform ( STFT ) and the constant-Q transform ( CQT ) which are additionally combined with different signal -dependent transforms. The comparison is carried out with respect to the spectral compactness, the data compression ability and the temporal continuity ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/6701843", "content": "In this paper we present a study on the spectral analysis of music signals comparing the time domain representation, the short-time Fourier transform ( STFT ) and the constant-Q transform ( CQT ) which are additionally combined with different signal -dependent transforms. The comparison is carried out with respect to the spectral compactness, the data compression ability and the temporal continuity ..."}
+{"idx": 3, "title": "The Constant Q Transform - Machine Learning Group", "date": "", "ddg_snippet": "the constant Q transform so useful is that by an appropriate choice for f0 (minimal center frequency) and b the center frequencies directly correspond to musical notes. For instance choosing b = 12 and f0 as the frequency of midinote 0 makes the k-th cq-bin correspond the midinote number k. Another nice feature of the constant Q transform is its increasing time resolution towards higher ...", "subpage_snippet": "", "source": "doc.ml.tu-berlin.de", "link": "https://doc.ml.tu-berlin.de/bbci/material/publications/Bla_constQ.pdf", "content": "the constant Q transform so useful is that by an appropriate choice for f0 (minimal center frequency) and b the center frequencies directly correspond to musical notes. For instance choosing b = 12 and f0 as the frequency of midinote 0 makes the k-th cq-bin correspond the midinote number k. Another nice feature of the constant Q transform is its increasing time resolution towards higher ..."}
+{"idx": 4, "title": "Bridging the gap between the short-time Fourier transform ...", "date": "", "ddg_snippet": "May 13, 2020 · The Constant-Q transform ( CQT ) can also be used to transform a discrete time -domain signal into the time –frequency domain [32, 33]. This transform was initially proposed for music analysis , transforming against log (frequency) to obtain a constant pattern in the frequency domain.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11760-020-01701-8", "content": "May 13, 2020 · The Constant-Q transform ( CQT ) can also be used to transform a discrete time -domain signal into the time –frequency domain [32, 33]. This transform was initially proposed for music analysis , transforming against log (frequency) to obtain a constant pattern in the frequency domain."}
+{"idx": 5, "title": "CONSTANT-Q TRANSFORM TOOLBOX FOR MUSIC PROCESSING", "date": "", "ddg_snippet": "ABSTRACT This paper proposes a computationally efficient method for computing the constant-Q transform ( CQT ) of a time -domain signal . CQT refers to a time -frequency represen-tation where the frequency bins are geometrically spaced and the Q -factors (ratios of the center frequencies to band-widths) of all bins are equal. An inverse transform is pro-posed which enables a reasonable-quality ...", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/144846462.pdf", "content": "ABSTRACT This paper proposes a computationally efficient method for computing the constant-Q transform ( CQT ) of a time -domain signal . CQT refers to a time -frequency represen-tation where the frequency bins are geometrically spaced and the Q -factors (ratios of the center frequencies to band-widths) of all bins are equal. An inverse transform is pro-posed which enables a reasonable-quality ..."}
+{"idx": 6, "title": "COMPARING SHORT-TIME FOURIER TRANSFORM (STFT) AND CONSTANT-Q ...", "date": "", "ddg_snippet": "Dec 31, 2024 · This study compares the Short-Time Fourier Transform ( STFT ) and Constant-Q Transform ( CQT ) techniques in capturing the intricate spectral characteristics of the rebana, a traditional Malay frame drum known for its rich harmonic content and transient attacks. High-quality audio recordings were obtained in a controlled studio environment using a Shure SM57 microphone placed six inches from the ...", "subpage_snippet": "", "source": "ejournals.swu.ac.th", "link": "https://ejournals.swu.ac.th/index.php/vss/article/view/16553", "content": "Dec 31, 2024 · This study compares the Short-Time Fourier Transform ( STFT ) and Constant-Q Transform ( CQT ) techniques in capturing the intricate spectral characteristics of the rebana, a traditional Malay frame drum known for its rich harmonic content and transient attacks. High-quality audio recordings were obtained in a controlled studio environment using a Shure SM57 microphone placed six inches from the ..."}
+{"idx": 7, "title": "Constant-Q transform - HandWiki", "date": "", "ddg_snippet": "In mathematics and signal processing , the constant - Q transform and variable- Q transform , simply known as CQT and VQT , transforms a data series to ...", "subpage_snippet": "", "source": "handwiki.org", "link": "https://handwiki.org/wiki/Constant-Q_transform", "content": "In mathematics and signal processing , the constant - Q transform and variable- Q transform , simply known as CQT and VQT , transforms a data series to ..."}
+{"idx": 8, "title": "Spectral Representations: Mel Spectrograms, CQT, and HCQT", "date": "", "ddg_snippet": "... of sound? In my previous post, we explored spectral analysis and learned how spectrograms reveal the frequency content of audio signals using the ...", "subpage_snippet": "", "source": "www.jhonatanlopez.com", "link": "https://www.jhonatanlopez.com/advanced-spectral-representations-audio-analysis/", "content": "... of sound? In my previous post, we explored spectral analysis and learned how spectrograms reveal the frequency content of audio signals using the ..."}
+{"idx": 9, "title": "Diffusion Models for Audio Restoration Invited paper for the", "date": "", "ddg_snippet": "... of audio signals , such as auto-regressive modeling for click removal [ 1 ] or probabilistic modeling for speech enhancement and separation [ 2 ] , by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.09821v3", "content": "... of audio signals , such as auto-regressive modeling for click removal [ 1 ] or probabilistic modeling for speech enhancement and separation [ 2 ] , by ..."}
diff --git a/data/sampled_jsons/ah2ac2_README_FLAIROx_games_dataset_size.jsonl b/data/sampled_jsons/ah2ac2_README_FLAIROx_games_dataset_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f97f1de5cdf50e535da490b73a9575fa6ffba0c7
--- /dev/null
+++ b/data/sampled_jsons/ah2ac2_README_FLAIROx_games_dataset_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Introduction - Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "This deliberately limited dataset size is intended to encourage the development of data-efficient methods for human-AI coordination. It also preserves the integrity of the challenge.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/datasets/", "content": "This deliberately limited dataset size is intended to encourage the development of data-efficient methods for human-AI coordination. It also preserves the integrity of the challenge."}
+{"idx": 1, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) - GitHub", "date": "", "ddg_snippet": "Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ). Contribute to FLAIROx / ah2ac2 development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2", "content": "Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ). Contribute to FLAIROx / ah2ac2 development by creating an account on GitHub."}
+{"idx": 2, "title": "AH2AC2", "date": "", "ddg_snippet": "🎉 Official website for the AH2AC2 research paper accepted to ICML 2025 for a spotlight poster presentation!", "subpage_snippet": "", "source": "ah2ac2.com", "link": "https://ah2ac2.com/", "content": "🎉 Official website for the AH2AC2 research paper accepted to ICML 2025 for a spotlight poster presentation!"}
+{"idx": 3, "title": "Dataset Usage Guide - Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "HanabiLiveGamesDataloader class, found in ah2ac2 . datasets .dataloader, is designed to work with HanabiLiveGamesDataset to provide an iterable over the dataset , yielding batches of game data.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/datasets/classes/", "content": "HanabiLiveGamesDataloader class, found in ah2ac2 . datasets .dataloader, is designed to work with HanabiLiveGamesDataset to provide an iterable over the dataset , yielding batches of game data."}
+{"idx": 4, "title": "ah2ac2/README.md at production · FLAIROx/ah2ac2 · GitHub", "date": "", "ddg_snippet": "Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ). Contribute to FLAIROx / ah2ac2 development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2/blob/production/README.md", "content": "Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ). Contribute to FLAIROx / ah2ac2 development by creating an account on GitHub."}
+{"idx": 5, "title": "Data Details - Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "This page provides specifics on how to acquire the AH2AC2 datasets , understand their structure, and load the raw game data.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/datasets/details/", "content": "This page provides specifics on how to acquire the AH2AC2 datasets , understand their structure, and load the raw game data."}
+{"idx": 6, "title": "Tutorial: Unrolling Games in JaxMARL - docs.ah2ac2.com", "date": "", "ddg_snippet": "This page explains how to use the AH2AC2 dataset , in conjunction with the HanabiLiveGamesDataset and HanabiLiveGamesDataloader utilities, to unroll game trajectories within the JaxMARL framework.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/datasets/tutorial/", "content": "This page explains how to use the AH2AC2 dataset , in conjunction with the HanabiLiveGamesDataset and HanabiLiveGamesDataloader utilities, to unroll game trajectories within the JaxMARL framework."}
+{"idx": 7, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "The Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) provides a standardized environment for evaluating AI agents on their ability to coordinate with human-like counterparts in Hanabi.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/", "content": "The Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) provides a standardized environment for evaluating AI agents on their ability to coordinate with human-like counterparts in Hanabi."}
+{"idx": 8, "title": "[2506.21490] Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "We develop \\textit {human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 . To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available human gameplay data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.21490", "content": "We develop \\textit {human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 . To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games , deliberately limiting the amount of available human gameplay data."}
+{"idx": 9, "title": "Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "As a part of the AH2AC2 , we open source 3,079 games from the large-scale dataset — 1,858 two-player and 1,221 three-player games . Participants are allowed to use these open-sourced games when tackling the AH2AC2 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.21490", "content": "As a part of the AH2AC2 , we open source 3,079 games from the large-scale dataset — 1,858 two-player and 1,221 three-player games . Participants are allowed to use these open-sourced games when tackling the AH2AC2 ."}
diff --git a/data/sampled_jsons/arXiv2006.05535_Locally_Private_Graph_Neural_Networks_abstract_Sajadmanesh_Gatica-Perez_year_2020.jsonl b/data/sampled_jsons/arXiv2006.05535_Locally_Private_Graph_Neural_Networks_abstract_Sajadmanesh_Gatica-Perez_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f25f9c8b27641bca0032c6a25b53953f4a484134
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2006.05535] Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica-Perez", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica-Perez"}
+{"idx": 1, "title": "Locally Private Graph Neural Networks - NASA/ADS", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv200605535S/abstract", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ..."}
+{"idx": 2, "title": "Locally Private Graph Neural Networks - ACM Digital Library", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3460120.3484565", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ..."}
+{"idx": 3, "title": "Locally Private Graph Neural Networks (ACM CCS 2021) - GitHub", "date": "", "ddg_snippet": "Abstract Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/lpgnn", "content": "Abstract Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information."}
+{"idx": 4, "title": "Locally Private Graph Neural Networks - Semantic Scholar", "date": "", "ddg_snippet": "A cloud server (e.g., a social network server) has a graph (e.g., the social graph ), whose nodes, which may correspond to real users, have some private data that the server wishes to utilize for training aGNNon the graph , but cannot simply collect them due to privacy constraints. - \" Locally Private Graph Neural Networks \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Locally-Private-Graph-Neural-Networks-Sajadmanesh-Gática-Pérez/ac86ad5cd25c496b785c5d8ab1417aa23728e86f/figure/0", "content": "A cloud server (e.g., a social network server) has a graph (e.g., the social graph ), whose nodes, which may correspond to real users, have some private data that the server wishes to utilize for training aGNNon the graph , but cannot simply collect them due to privacy constraints. - \" Locally Private Graph Neural Networks \""}
+{"idx": 5, "title": "PDF LOCALLY PRIVATE GRAPH NEURAL NETWORKS - sajadmanesh.com", "date": "", "ddg_snippet": "LOCALLY PRIVATE GRAPH NEURAL NETWORKS Sina Sajadmanesh Daniel Gatica-Perez", "subpage_snippet": "", "source": "sajadmanesh.com", "link": "https://sajadmanesh.com/files/slides/21.01.07-Twitter.pdf", "content": "LOCALLY PRIVATE GRAPH NEURAL NETWORKS Sina Sajadmanesh Daniel Gatica-Perez"}
+{"idx": 6, "title": "(PDF) Locally Private Graph Neural Networks - ResearchGate", "date": "", "ddg_snippet": "Abstract and Figures Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348678319_Locally_Private_Graph_Neural_Networks", "content": "Abstract and Figures Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 7, "title": "Locally Private Graph Neural Networks - infoscience.epfl.ch", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g., social ...", "subpage_snippet": "", "source": "infoscience.epfl.ch", "link": "https://infoscience.epfl.ch/record/294094", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g., social ..."}
+{"idx": 8, "title": "Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05535", "content": "ABSTRACT Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information."}
+{"idx": 9, "title": "PDF Locally Private Graph Neural Networks - Idiap Research Institute", "date": "", "ddg_snippet": "ABSTRACT Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information.", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/attachments/papers/2021/Sajadmanesh_CCS2021_2021.pdf", "content": "ABSTRACT Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy con-cerns when nodes represent people or human-related variables that involve sensitive or personal information."}
diff --git a/data/sampled_jsons/arXiv2503.06366_S18_characters_dataset_training_examples.jsonl b/data/sampled_jsons/arXiv2503.06366_S18_characters_dataset_training_examples.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2503 . 06366 ] Machine Learning meets Algebraic Combinatorics...", "date": "", "ddg_snippet": "Each dataset includes an open-ended research-level question and a large collection of examples (up to 10M in some cases) from which conjectures should be generated.(or arXiv : 2503 . 06366 v1 [cs.LG] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "Each dataset includes an open-ended research-level question and a large collection of examples (up to 10M in some cases) from which conjectures should be generated.(or arXiv : 2503 . 06366 v1 [cs.LG] for this version)."}
+{"idx": 1, "title": "[2503.03622] It's My Data Too: Private ML for Datasets with ...", "date": "", "ddg_snippet": "Mar 5, 2025 · Training in the multi-attribution model is facilitated by solving the contribution bounding problem, i.e. the problem of selecting a subset of the dataset for which each user is associated with a limited number of examples . We propose a greedy baseline algorithm for the contribution bounding problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.03622", "content": "Mar 5, 2025 · Training in the multi-attribution model is facilitated by solving the contribution bounding problem, i.e. the problem of selecting a subset of the dataset for which each user is associated with a limited number of examples . We propose a greedy baseline algorithm for the contribution bounding problem."}
+{"idx": 2, "title": "[2503.13385] Scale Efficient Training for Large Datasets", "date": "", "ddg_snippet": "Mar 17, 2025 · The rapid growth of dataset scales has been a key driver in advancing deep learning research. However, as dataset scale increases, the training process becomes increasingly inefficient due to the presence of low-value samples, including excessive redundant samples, overly challenging samples, and inefficient easy samples that contribute little to model improvement.To address this challenge, we ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.13385", "content": "Mar 17, 2025 · The rapid growth of dataset scales has been a key driver in advancing deep learning research. However, as dataset scale increases, the training process becomes increasingly inefficient due to the presence of low-value samples, including excessive redundant samples, overly challenging samples, and inefficient easy samples that contribute little to model improvement.To address this challenge, we ..."}
+{"idx": 3, "title": "[2503.18290] When is dataset cartography ineffective? Using ... NIST Special Database 19 | NIST Arnav1145/Handwritten-Character-Recognition - GitHub", "date": "", "ddg_snippet": "Mar 24, 2025 · In this paper, I investigate the effectiveness of dataset cartography for extractive question answering on the SQuAD dataset . I begin by analyzing annotation artifacts in SQuAD and evaluate the impact of two adversarial datasets, AddSent and AddOneSent, on an ELECTRA-small model. Using training dynamics, I partition SQuAD into easy-to-learn, ambiguous, and hard-to-learn subsets. I then compare ... Aug 27, 2010 · NIST Handprinted Forms and Characters Database Special Database 19 contains NIST 's entire corpus of training materials for handprinted document and character recognition. Dataset : A-Z Handwritten Alphabets in .csv format Overview This repository contains all the codes and reference data for building the Handwritten Character recognition Model from scratch.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.18290", "content": "Mar 24, 2025 · In this paper, I investigate the effectiveness of dataset cartography for extractive question answering on the SQuAD dataset . I begin by analyzing annotation artifacts in SQuAD and evaluate the impact of two adversarial datasets, AddSent and AddOneSent, on an ELECTRA-small model. Using training dynamics, I partition SQuAD into easy-to-learn, ambiguous, and hard-to-learn subsets. I then compare ... Aug 27, 2010 · NIST Handprinted Forms and Characters Database Special Database 19 contains NIST 's entire corpus of training materials for handprinted document and character recognition. Dataset : A-Z Handwritten Alphabets in .csv format Overview This repository contains all the codes and reference data for building the Handwritten Character recognition Model from scratch."}
+{"idx": 4, "title": "ACDRepo/partial_orders_on_lattice_paths_10x9 · Datasets at Hugging...", "date": "", "ddg_snippet": "Dataset card. Data Studio.Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv : 2503 . 06366 .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ACDRepo/partial_orders_on_lattice_paths_10x9", "content": "Dataset card. Data Studio.Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv : 2503 . 06366 ."}
+{"idx": 5, "title": "Free Public Datasets for Data Science Projects", "date": "", "ddg_snippet": "In this post we can find free public datasets for Data Science projects.", "subpage_snippet": "", "source": "datascientyst.com", "link": "https://datascientyst.com/datasets/", "content": "In this post we can find free public datasets for Data Science projects."}
+{"idx": 6, "title": "OpenLLM-France/Lucie- Training - Dataset · Datasets at Hugging Face", "date": "", "ddg_snippet": "The Lucie Training Dataset was used to pretrain Lucie-7B, a foundation LLM with strong capabilities in French and English. Code for data preparation can be found in the training respository for Lucie-7B.", "subpage_snippet": "", "source": "hf.global-rail.com", "link": "https://hf.global-rail.com/datasets/OpenLLM-France/Lucie-Training-Dataset", "content": "The Lucie Training Dataset was used to pretrain Lucie-7B, a foundation LLM with strong capabilities in French and English. Code for data preparation can be found in the training respository for Lucie-7B."}
+{"idx": 7, "title": "UCI Machine Learning Repository | Discover datasets around the world!", "date": "", "ddg_snippet": "Dataset Characteristics .Convergence and Margin of Adversarial Training on Separable Data . By Zachary Charles, Shashank Rajput, Stephen Wright, Dimitris Papailiopoulos. 2019. Published in ArXiv .", "subpage_snippet": "", "source": "archive.ics.uci.edu", "link": "https://archive.ics.uci.edu/dataset/53/iris", "content": "Dataset Characteristics .Convergence and Margin of Adversarial Training on Separable Data . By Zachary Charles, Shashank Rajput, Stephen Wright, Dimitris Papailiopoulos. 2019. Published in ArXiv ."}
+{"idx": 8, "title": "machinelearningmastery.com/much- training - data -required-machine...", "date": "", "ddg_snippet": "over tens of thousands of training samples.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/much-training-data-required-machine-learning/", "content": "over tens of thousands of training samples."}
+{"idx": 9, "title": "Image Quality Datasets | zwx8981/DBCNN-PyTorch | DeepWiki", "date": "", "ddg_snippet": "dataset / dataset .txt1. Dataset Usage in DBCNN Training . The DBCNN system utilizes these datasets through a standardized interface that enables", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/zwx8981/DBCNN-PyTorch/4.1-image-quality-datasets", "content": "dataset / dataset .txt1. Dataset Usage in DBCNN Training . The DBCNN system utilizes these datasets through a standardized interface that enables"}
diff --git a/data/sampled_jsons/arXiv2503.06366_mHeight_definition.jsonl b/data/sampled_jsons/arXiv2503.06366_mHeight_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a6d6783c0e9f2cfa05030810073a8bc1b4acc199
--- /dev/null
+++ b/data/sampled_jsons/arXiv2503.06366_mHeight_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv.org", "date": "", "ddg_snippet": "arXiv .org", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "arXiv .org"}
+{"idx": 1, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 2, "title": "[2503.10777] HeightFormer: Learning Height Prediction in Voxel Features ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2503.10777: HeightFormer: Learning Height Prediction in Voxel Features for Roadside Vision Centric 3D Object Detection via Transformer", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10777", "content": "Abstract page for arXiv paper 2503.10777: HeightFormer: Learning Height Prediction in Voxel Features for Roadside Vision Centric 3D Object Detection via Transformer"}
+{"idx": 3, "title": "[2503.19101] Height estimates for surfaces with some constant curvature ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2503.19101: Height estimates for surfaces with some constant curvature in $\\mathbb {r} \\times_ {f} \\mathbb {r}^ {2}$", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19101", "content": "Abstract page for arXiv paper 2503.19101: Height estimates for surfaces with some constant curvature in $\\mathbb {r} \\times_ {f} \\mathbb {r}^ {2}$"}
+{"idx": 4, "title": "[2503.13165] From Zero to Detail: Deconstructing Ultra-High-Definition ...", "date": "", "ddg_snippet": "Ultra-high- definition (UHD) image restoration faces significant challenges due to its high resolution, complex content, and intricate details. To cope with these challenges, we analyze the restoration process in depth through a progressive spectral perspective, and deconstruct the complex UHD restoration problem into three progressive stages: zero-frequency enhancement, low-frequency ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.13165", "content": "Ultra-high- definition (UHD) image restoration faces significant challenges due to its high resolution, complex content, and intricate details. To cope with these challenges, we analyze the restoration process in depth through a progressive spectral perspective, and deconstruct the complex UHD restoration problem into three progressive stages: zero-frequency enhancement, low-frequency ..."}
+{"idx": 5, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2503.06366 : Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "Abstract page for arXiv paper 2503.06366 : Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics"}
+{"idx": 6, "title": "Using Reasoning Models to Generate Search Heuristics that Solve Open ...", "date": "", "ddg_snippet": "Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics, 2025. arXiv:2503.06366 . Chee et al. [2025a] Y. M. Chee, S. H. Dau, T. Etzion, H. M. Kiah, and W. Zhang.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23881v1", "content": "Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics, 2025. arXiv:2503.06366 . Chee et al. [2025a] Y. M. Chee, S. H. Dau, T. Etzion, H. M. Kiah, and W. Zhang."}
+{"idx": 7, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "While these works either present interesting methodological progress in ML or valuable results in mathematics, none aim to provide a range of datasets accessible to the broader ML community that represent open or equivalently challenging research-level problems. To fill the gap we present the Algebraic Combinatorics Dataset Repository (ACD Repo)1, a collection of 9 datasets consisting of many ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "While these works either present interesting methodological progress in ML or valuable results in mathematics, none aim to provide a range of datasets accessible to the broader ML community that represent open or equivalently challenging research-level problems. To fill the gap we present the Algebraic Combinatorics Dataset Repository (ACD Repo)1, a collection of 9 datasets consisting of many ..."}
+{"idx": 8, "title": "PDF Jesse He - Curriculum Vitae", "date": "", "ddg_snippet": "Research Interests My research interests lie in the intersection of interpretable machine learning and geometric data analysis. In particular, I am interested in explainability methods for graph neural networks and interpretable manifold learning.", "subpage_snippet": "", "source": "he-jesse.github.io", "link": "https://he-jesse.github.io/misc/cv.pdf", "content": "Research Interests My research interests lie in the intersection of interpretable machine learning and geometric data analysis. In particular, I am interested in explainability methods for graph neural networks and interpretable manifold learning."}
+{"idx": 9, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "While these works either present interesting methodological progress in ML or valuable results in mathematics, none aim to provide a range of datasets accessible to the broader ML community that represent open or equivalently challenging research-level problems. To fill the gap we present the Al-gebraic Combinatorics Dataset Repository (ACD Repo)1, a collection of 9 datasets consisting of many ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366v1", "content": "While these works either present interesting methodological progress in ML or valuable results in mathematics, none aim to provide a range of datasets accessible to the broader ML community that represent open or equivalently challenging research-level problems. To fill the gap we present the Al-gebraic Combinatorics Dataset Repository (ACD Repo)1, a collection of 9 datasets consisting of many ..."}
diff --git a/data/sampled_jsons/arXiv2503.17332_abstract_'Insufficient_Exploration'_definition_year_2023-2024.jsonl b/data/sampled_jsons/arXiv2503.17332_abstract_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
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index 0000000000000000000000000000000000000000..d98dc2985c7ccd06204877eca60f6b5caf5bd995
--- /dev/null
+++ b/data/sampled_jsons/arXiv2503.17332_abstract_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv arXiv:2503.17332v4 [cs.CR] 24 Jun 2025", "date": "", "ddg_snippet": "arXiv : 2503 .17332v4 [cs.CR] 24 Jun 2025 · CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "arXiv : 2503 .17332v4 [cs.CR] 24 Jun 2025 · CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web"}
+{"idx": 1, "title": "arXiv Entropy-based Exploration Conduction for Multi-step Reasoning", "date": "", "ddg_snippet": "March 20, 2025 - In large language model (LLM) reasoning, multi-step processes have proven effective for solving complex tasks. However, the depth of exploration can significantly affect the reasoning performance. Existing methods to automatically decide the depth often bring high cost and lack flexibility, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.15848v1", "content": "March 20, 2025 - In large language model (LLM) reasoning, multi-step processes have proven effective for solving complex tasks. However, the depth of exploration can significantly affect the reasoning performance. Existing methods to automatically decide the depth often bring high cost and lack flexibility, ..."}
+{"idx": 2, "title": "arXiv [2509.06284] From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs", "date": "", "ddg_snippet": "2 weeks ago - Large language models (LLMs) have advanced general-purpose reasoning, showing strong performance across diverse tasks. However, existing methods often rely on implicit exploration , where the model follows stochastic and unguided reasoning paths-like walking without a map.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2509.06284", "content": "2 weeks ago - Large language models (LLMs) have advanced general-purpose reasoning, showing strong performance across diverse tasks. However, existing methods often rely on implicit exploration , where the model follows stochastic and unguided reasoning paths-like walking without a map."}
+{"idx": 3, "title": "arXiv [2503.23332] TraceMark-LDM: Authenticatable Watermarking for Latent Diffusion Models via Binary-Guided Rearrangement", "date": "", "ddg_snippet": "March 30, 2025 - Image generation algorithms are increasingly integral to diverse aspects of human society, driven by their practical applications. However, insufficient oversight in artificial Intelligence generated content (AIGC) can facilitate the spread of malicious content and increase the risk of copyright ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.23332", "content": "March 30, 2025 - Image generation algorithms are increasingly integral to diverse aspects of human society, driven by their practical applications. However, insufficient oversight in artificial Intelligence generated content (AIGC) can facilitate the spread of malicious content and increase the risk of copyright ..."}
+{"idx": 4, "title": "arXiv MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning", "date": "", "ddg_snippet": "March 3, 2025 - Path planning for robotic exploration is challenging, requiring reasoning over unknown spaces and anticipating future observations. Efficient exploration requires selecting budget-constrained paths that maximize information gain. Despite advances in autonomous exploration , existing algorithms ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01548", "content": "March 3, 2025 - Path planning for robotic exploration is challenging, requiring reasoning over unknown spaces and anticipating future observations. Efficient exploration requires selecting budget-constrained paths that maximize information gain. Despite advances in autonomous exploration , existing algorithms ..."}
+{"idx": 5, "title": "arXiv [2503.07453] Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration", "date": "", "ddg_snippet": "March 13, 2025 - Language model alignment (or, reinforcement learning) techniques that leverage active exploration -- deliberately encouraging the model to produce diverse, informative responses -- offer the promise of super-human capabilities. However, current understanding of algorithm design primitives for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.07453", "content": "March 13, 2025 - Language model alignment (or, reinforcement learning) techniques that leverage active exploration -- deliberately encouraging the model to produce diverse, informative responses -- offer the promise of super-human capabilities. However, current understanding of algorithm design primitives for ..."}
+{"idx": 6, "title": "arXiv [2503.13288] $ϕ$-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation", "date": "", "ddg_snippet": "March 17, 2025 - Inference-time optimization scales computation to derive deliberate reasoning steps for effective performance. While previous search-based strategies address the short-sightedness of auto-regressive generation, the vast search space leads to excessive exploration and insufficient exploitation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.13288", "content": "March 17, 2025 - Inference-time optimization scales computation to derive deliberate reasoning steps for effective performance. While previous search-based strategies address the short-sightedness of auto-regressive generation, the vast search space leads to excessive exploration and insufficient exploitation."}
+{"idx": 7, "title": "arXiv [2503.01584] SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models", "date": "", "ddg_snippet": "June 27, 2025 - Exploration is a cornerstone of reinforcement learning (RL). Intrinsic motivation attempts to decouple exploration from external, task-based rewards. However, established approaches to intrinsic motivation that follow general principles such as information gain, often only uncover low-level ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01584", "content": "June 27, 2025 - Exploration is a cornerstone of reinforcement learning (RL). Intrinsic motivation attempts to decouple exploration from external, task-based rewards. However, established approaches to intrinsic motivation that follow general principles such as information gain, often only uncover low-level ..."}
+{"idx": 8, "title": "arXiv CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities", "date": "", "ddg_snippet": "June 24, 2025 - Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332", "content": "June 24, 2025 - Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application ..."}
+{"idx": 9, "title": "arXiv [2503.12686] Can LLMs Formally Reason as Abstract Interpreters for Program Analysis?", "date": "", "ddg_snippet": "March 16, 2025 - LLMs have demonstrated impressive capabilities in code generation and comprehension, but their potential in being able to perform program analysis in a formal, automatic manner remains under- explored . To that end, we systematically investigate whether LLMs can reason about programs using a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.12686", "content": "March 16, 2025 - LLMs have demonstrated impressive capabilities in code generation and comprehension, but their potential in being able to perform program analysis in a formal, automatic manner remains under- explored . To that end, we systematically investigate whether LLMs can reason about programs using a ..."}
diff --git a/data/sampled_jsons/arXiv_2010.01412_Sharpness-Aware_Minimization_abstract_year_2021.jsonl b/data/sampled_jsons/arXiv_2010.01412_Sharpness-Aware_Minimization_abstract_year_2021.jsonl
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index 0000000000000000000000000000000000000000..c52d6144fd1987bfea9be40ca629800cf4aa7bf4
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sharpness aware minimization - Wikipedia", "date": "", "ddg_snippet": "Sharpness Aware Minimization is an optimization algorithm used in machine learning that aims to improve model generalization. The method seeks to find model parameters that are located in regions of the loss landscape with uniformly low loss values.....", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Sharpness_aware_minimization", "content": "Sharpness Aware Minimization is an optimization algorithm used in machine learning that aims to improve model generalization. The method seeks to find model parameters that are located in regions of the loss landscape with uniformly low loss values....."}
+{"idx": 1, "title": "[ 2010 . 01412 ] Sharpness - Aware Minimization for Efficiently Improving...", "date": "", "ddg_snippet": "In particular, our procedure, Sharpness - Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.01412", "content": "In particular, our procedure, Sharpness - Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently."}
+{"idx": 2, "title": "Paper page - Sharpness - Aware Minimization for Efficiently Improving...", "date": "", "ddg_snippet": "Papers. arxiv : 2010 . 01412 . Sharpness - Aware Minimization for Efficiently Improving Generalization. Published on Oct 3, 2020.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2010.01412", "content": "Papers. arxiv : 2010 . 01412 . Sharpness - Aware Minimization for Efficiently Improving Generalization. Published on Oct 3, 2020."}
+{"idx": 3, "title": "Sharpness Aware Minimization . “ Sharpness - Aware ... | Medium", "date": "", "ddg_snippet": "“ Sharpness - Aware Minimization for Efficiently Improving Generalization” (2020) Sharpness - Aware Minimization (SAM) procedure to simultaneously minimize task loss value and loss sharpness.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@istabrak.abbes_65953/sharpness-aware-minimization-bb565251f874", "content": "“ Sharpness - Aware Minimization for Efficiently Improving Generalization” (2020) Sharpness - Aware Minimization (SAM) procedure to simultaneously minimize task loss value and loss sharpness."}
+{"idx": 4, "title": "Sharpness - Aware Minimization for Efficiently Improving... | BibSonomy", "date": "", "ddg_snippet": "(2020 )cite arxiv : 2010 . 01412 . Abstract . In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability. Indeed, optimizing only the training loss value, as is commonly done, can easily lead to suboptimal model quality.", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/bibtex/22c7f174197cd8455322f4115622e468f/aerover", "content": "(2020 )cite arxiv : 2010 . 01412 . Abstract . In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability. Indeed, optimizing only the training loss value, as is commonly done, can easily lead to suboptimal model quality."}
+{"idx": 5, "title": "Applying Adaptive Sharpness - Aware Minimization to Improve...", "date": "", "ddg_snippet": "2020. Sharpness - aware minimization for efficiently improving generalization. arXiv preprint arXiv : 2010 . 01412 (2020).2021. Asam: Adaptive sharpness - aware minimization for scale-invariant learning of deep neural networks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3628797.3629009", "content": "2020. Sharpness - aware minimization for efficiently improving generalization. arXiv preprint arXiv : 2010 . 01412 (2020).2021. Asam: Adaptive sharpness - aware minimization for scale-invariant learning of deep neural networks."}
+{"idx": 6, "title": "Sharpness - Aware Minimization for Efficiently Improving...", "date": "", "ddg_snippet": "DOI. 10.48550/ arXiv . 2010 . 01412 .This paper presents Sharpness - Aware Minimization (SAM), which improves model generalization by minimizing loss value and sharpness, and shows its effectiveness through empirical study.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/sharpness-aware-minimization-for-efficiently-improving-generalization/867773444962386345-108614", "content": "DOI. 10.48550/ arXiv . 2010 . 01412 .This paper presents Sharpness - Aware Minimization (SAM), which improves model generalization by minimizing loss value and sharpness, and shows its effectiveness through empirical study."}
+{"idx": 7, "title": "[PDF] Sharpness - Aware Minimization for... | Semantic Scholar", "date": "", "ddg_snippet": "In particular, our procedure, Sharpness - Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Sharpness-Aware-Minimization-for-Efficiently-Foret-Kleiner/a2cd073b57be744533152202989228cb4122270a", "content": "In particular, our procedure, Sharpness - Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently."}
+{"idx": 8, "title": "(PDF) SADT: Combining Sharpness - Aware Minimization with...", "date": "", "ddg_snippet": "Sharpness - aware minimization and self-distillation procedures to some extent contain intrinsic. Sharpness - aware mini-. mization for efficiently improving generalization. arXiv preprint arXiv : 2010 . 01412 , 2020.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364987826_SADT_Combining_Sharpness-Aware_Minimization_with_Self-Distillation_for_Improved_Model_Generalization", "content": "Sharpness - aware minimization and self-distillation procedures to some extent contain intrinsic. Sharpness - aware mini-. mization for efficiently improving generalization. arXiv preprint arXiv : 2010 . 01412 , 2020."}
+{"idx": 9, "title": "Surrogate Gap Guided Minimization", "date": "", "ddg_snippet": "\" Sharpness - aware minimization for efficiently improving generalization.\" arXiv preprint arXiv : 2010 . 01412 (2020). Potential Caveat of Sharpness - Aware Minimization (SAM). Left to right", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2022/Slides/6070.pdf", "content": "\" Sharpness - aware minimization for efficiently improving generalization.\" arXiv preprint arXiv : 2010 . 01412 (2020). Potential Caveat of Sharpness - Aware Minimization (SAM). Left to right"}
diff --git a/data/sampled_jsons/arXiv_2502.00775_PDF_Algorithm_8_Recursive_Allocation_Selection.jsonl b/data/sampled_jsons/arXiv_2502.00775_PDF_Algorithm_8_Recursive_Allocation_Selection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c4463245f8b38adb08e6b98d30db4771a49545da
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2502.00775_PDF_Algorithm_8_Recursive_Allocation_Selection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithm - Wikipedia", "date": "", "ddg_snippet": "Recursion .For example, one selection algorithm finds the median of an unsorted list by first sorting the list (the expensive portion), and then pulling out the middle element in the sorted list (the cheap portion). This technique is also known as transform and conquer.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Algorithm", "content": "Recursion .For example, one selection algorithm finds the median of an unsorted list by first sorting the list (the expensive portion), and then pulling out the middle element in the sorted list (the cheap portion). This technique is also known as transform and conquer."}
+{"idx": 1, "title": "[2502.00775] ATA: Adaptive Task Allocation for Efficient ...", "date": "", "ddg_snippet": "Feb 2, 2025 · Abstract page for arXiv paper 2502.00775 : ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "Feb 2, 2025 · Abstract page for arXiv paper 2502.00775 : ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning"}
+{"idx": 2, "title": "Recursive Adaptive Importance Sampling with Optimal Replenishment", "date": "", "ddg_snippet": "Sep 11, 2025 · We propose a recursive adaptive importance sampling approach that alternates between fast recursive weight updates and sample replenishment steps to balance computational efficiency while ensuring sample quality.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.08102", "content": "Sep 11, 2025 · We propose a recursive adaptive importance sampling approach that alternates between fast recursive weight updates and sample replenishment steps to balance computational efficiency while ensuring sample quality."}
+{"idx": 3, "title": "[2508.08005v2] Learning to Select MCP Algorithms: From ...", "date": "", "ddg_snippet": "Aug 11, 2025 · Extensive experiments and prior studies show that no single maximum clique algorithm consistently performs best across all instances, highlighting the importance of selecting suitable algorithms based on instance features. Through an extensive analysis of relevant studies, it is found that there is a lack of research work concerning algorithm selection oriented toward the Maximum Clique ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.08005v2", "content": "Aug 11, 2025 · Extensive experiments and prior studies show that no single maximum clique algorithm consistently performs best across all instances, highlighting the importance of selecting suitable algorithms based on instance features. Through an extensive analysis of relevant studies, it is found that there is a lack of research work concerning algorithm selection oriented toward the Maximum Clique ..."}
+{"idx": 4, "title": "Recursive Neyman Algorithm for Optimum Sample Allocation ...", "date": "", "ddg_snippet": "Apr 14, 2023 · The RNABOX can be viewed as a generalization of the classical recursive Neyman allocation algorithm , a popular tool for optimum allocation when only upper bounds are imposed on sample strata-sizes. We implement RNABOX in R as a part of our package stratallo which is available from the Comprehensive R Archive Network (CRAN) repository.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.07034", "content": "Apr 14, 2023 · The RNABOX can be viewed as a generalization of the classical recursive Neyman allocation algorithm , a popular tool for optimum allocation when only upper bounds are imposed on sample strata-sizes. We implement RNABOX in R as a part of our package stratallo which is available from the Comprehensive R Archive Network (CRAN) repository."}
+{"idx": 5, "title": "[2410.06815] Shap-Select: Lightweight Feature Selection Using ...", "date": "", "ddg_snippet": "Oct 9, 2024 · Feature selection is an essential process in machine learning, especially when dealing with high-dimensional datasets. It helps reduce the complexity of machine learning models, improve performance, mitigate overfitting, and decrease computation time. This paper presents a novel feature selection framework, shap-select. The framework conducts a linear or logistic regression of the target on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.06815", "content": "Oct 9, 2024 · Feature selection is an essential process in machine learning, especially when dealing with high-dimensional datasets. It helps reduce the complexity of machine learning models, improve performance, mitigate overfitting, and decrease computation time. This paper presents a novel feature selection framework, shap-select. The framework conducts a linear or logistic regression of the target on ..."}
+{"idx": 6, "title": "cherryATA: cherryAdaptive cherryTask cherryAllocation for Efficient...", "date": "", "ddg_snippet": "arXiv : 2502 . 00775 v1 [cs.LG] 2 Feb 2025.B. Recursive Allocation Selection Algorithm . In this section, we introduce an efficient method for finding the best allocation .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775", "content": "arXiv : 2502 . 00775 v1 [cs.LG] 2 Feb 2025.B. Recursive Allocation Selection Algorithm . In this section, we introduce an efficient method for finding the best allocation ."}
+{"idx": 7, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "C Recursive Allocation Selection Algorithm . C.1 Optimality.Chen, J., Pan, X., Monga, R., Bengio, S., and Jozefowicz, R. Revisiting distributed synchronous SGD. arXiv preprint arXiv :1604.00981, 2016a. Chen et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "C Recursive Allocation Selection Algorithm . C.1 Optimality.Chen, J., Pan, X., Monga, R., Bengio, S., and Jozefowicz, R. Revisiting distributed synchronous SGD. arXiv preprint arXiv :1604.00981, 2016a. Chen et al."}
+{"idx": 8, "title": "PDF в JPG — Конвертируйте PDF в изображения онлайн бесплатно", "date": "", "ddg_snippet": "Удобный онлайн-инструмент конвертации страниц PDF в изображения или извлечения отдельных изображений из PDF . Бесплатно, без загрузок и водяных знаков.", "subpage_snippet": "", "source": "smallpdf.com", "link": "https://smallpdf.com/ru/pdf-to-jpg", "content": "Удобный онлайн-инструмент конвертации страниц PDF в изображения или извлечения отдельных изображений из PDF . Бесплатно, без загрузок и водяных знаков."}
+{"idx": 9, "title": "DSA Tutorial - Learn Data Structures and Algorithms - GeeksforGeeks", "date": "", "ddg_snippet": "Data structures manage how data is stored and accessed, while Algorithms focus on processing this data. Examples of data structures are Array, Linked List, Tree and Heap, and examples of algorithms are Binary Search, Quick Sort and Merge Sort. Why to Learn DSA?", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/dsa-tutorial-learn-data-structures-and-algorithms/", "content": "Data structures manage how data is stored and accessed, while Algorithms focus on processing this data. Examples of data structures are Array, Linked List, Tree and Heap, and examples of algorithms are Binary Search, Quick Sort and Merge Sort. Why to Learn DSA?"}
diff --git a/data/sampled_jsons/arXiv_2502.02486_Catoni_Contextual_Bandits_Theorem_3.1_Theorem_3.4.jsonl b/data/sampled_jsons/arXiv_2502.02486_Catoni_Contextual_Bandits_Theorem_3.1_Theorem_3.4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3091da26947122579a1c7424ecf74d9146e4d47f
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2502.02486_Catoni_Contextual_Bandits_Theorem_3.1_Theorem_3.4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "We design a contextual bandit (CB) algorithm that uses the Catoni mean as a robust device for constructing a regression error estimator for the excess loss", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "We design a contextual bandit (CB) algorithm that uses the Catoni mean as a robust device for constructing a regression error estimator for the excess loss"}
+{"idx": 1, "title": "[ 2502 . 02486 ] Catoni Contextual Bandits are Robust to Heavy-tailed...", "date": "", "ddg_snippet": "Cite as: arXiv : 2502 . 02486 [stat.ML].View a PDF of the paper titled Catoni Contextual Bandits are Robust to Heavy-tailed Rewards, by Chenlu Ye and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Cite as: arXiv : 2502 . 02486 [stat.ML].View a PDF of the paper titled Catoni Contextual Bandits are Robust to Heavy-tailed Rewards, by Chenlu Ye and 3 other authors."}
+{"idx": 2, "title": "Math Antics - The Pythagorean Theorem - YouTube", "date": "", "ddg_snippet": "Learn more at mathantics.comVisit http://www.mathantics.com for more Free math videos and additional subscription based content!", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=WqhlG3Vakw8", "content": "Learn more at mathantics.comVisit http://www.mathantics.com for more Free math videos and additional subscription based content!"}
+{"idx": 3, "title": "Chenlu Ye - Google Akademik", "date": "", "ddg_snippet": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards. arXiv preprint arXiv : 2502 . 02486 , 2025.", "subpage_snippet": "", "source": "scholar.google.com.tr", "link": "https://scholar.google.com.tr/citations?user=c8yK5XsAAAAJ&hl=tr", "content": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards. arXiv preprint arXiv : 2502 . 02486 , 2025."}
+{"idx": 4, "title": "STATISTICAL SCIENCE", "date": "", "ddg_snippet": "Meta Representation Learning with Contextual Linear Bandits Leonardo Cella, Karim Lounici, Gregoire Pacreau and Massimiliano Pontil.Spec-tral estimators for multi-index models: Precise asymptotics and optimal weak recovery. arXiv preprint, arXiv : 2502 .01583.", "subpage_snippet": "", "source": "imstat.org", "link": "https://imstat.org/publications/sts/sts_40_3/sts_40_3.pdf", "content": "Meta Representation Learning with Contextual Linear Bandits Leonardo Cella, Karim Lounici, Gregoire Pacreau and Massimiliano Pontil.Spec-tral estimators for multi-index models: Precise asymptotics and optimal weak recovery. arXiv preprint, arXiv : 2502 .01583."}
+{"idx": 5, "title": "Machine Learning Feb 2025", "date": "", "ddg_snippet": "Title: LoRA- One : One -Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/stat.ML/2025-02", "content": "Title: LoRA- One : One -Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently"}
+{"idx": 6, "title": "qBittorrent - скачать бесплатно qBittorrent 4.4.5 / 5.1.2", "date": "", "ddg_snippet": "Бесплатно. Windows. QBittorrent - бесплатный, мощный и удобный в работе кроссплатформенный клиент файлообменной сети BitTorrent. Несмотря на кажущуюся простоту, программа обладает внушительным набором весьма полезных возможностей...", "subpage_snippet": "", "source": "www.SoftPortal.com", "link": "https://www.SoftPortal.com/software-18505-qbittorrent.html", "content": "Бесплатно. Windows. QBittorrent - бесплатный, мощный и удобный в работе кроссплатформенный клиент файлообменной сети BitTorrent. Несмотря на кажущуюся простоту, программа обладает внушительным набором весьма полезных возможностей..."}
+{"idx": 7, "title": "Онлайн калькулятор уравнений и неравенств", "date": "", "ddg_snippet": "Примеры решений уравнений и неравенств: 5 12 + x 6 = x 4 + 1 3 \\frac{5}{12}+\\frac{x}{6}=\\frac{x}{4}+\\frac{1}{3}. 125 +6x =4x +31 (решить уравнение).", "subpage_snippet": "", "source": "findh.org", "link": "https://findh.org/4388-matematicheskij-kalkulyator.html?op=equation", "content": "Примеры решений уравнений и неравенств: 5 12 + x 6 = x 4 + 1 3 \\frac{5}{12}+\\frac{x}{6}=\\frac{x}{4}+\\frac{1}{3}. 125 +6x =4x +31 (решить уравнение)."}
+{"idx": 8, "title": "Операция Z: Военкоры Русской Весны – Telegram", "date": "", "ddg_snippet": "Добровольцы, волонтеры и военкоры Русской Весны действуют в боевых порядках войск на Донбассе, Украине и САР, получая информацию из самых горячих точек. РКН: clck.ru/3Fj3hJ Связь: @rvvoenkor_bot youtube.com/c/rusvesnadonbass.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/RVvoenkor", "content": "Добровольцы, волонтеры и военкоры Русской Весны действуют в боевых порядках войск на Донбассе, Украине и САР, получая информацию из самых горячих точек. РКН: clck.ru/3Fj3hJ Связь: @rvvoenkor_bot youtube.com/c/rusvesnadonbass."}
+{"idx": 9, "title": "Ключи активации Windows 10 | Ответы Mail", "date": "", "ddg_snippet": "YNMGQ-8RYV 3 - 4 PGQ3-C8XTP-7CFBY Windows 10 Professional Activation Key VK7JG-NPHTM-C97JM-9MPGT-3V66T Windows 10 Professional N Activation Key 2B87N-8KFHP-DKV6R-Y2C8J-PKCKT Windows 10 Core Activation Key.", "subpage_snippet": "", "source": "otvet.mail.ru", "link": "https://otvet.mail.ru/question/237199183", "content": "YNMGQ-8RYV 3 - 4 PGQ3-C8XTP-7CFBY Windows 10 Professional Activation Key VK7JG-NPHTM-C97JM-9MPGT-3V66T Windows 10 Professional N Activation Key 2B87N-8KFHP-DKV6R-Y2C8J-PKCKT Windows 10 Core Activation Key."}
diff --git a/data/sampled_jsons/arXiv_2503.06366_table_1_accuracy_Schubert_polynomials_n=6.jsonl b/data/sampled_jsons/arXiv_2503.06366_table_1_accuracy_Schubert_polynomials_n=6.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c4bf532793a809f9fdd035008edb07939e6f679a
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2503.06366_table_1_accuracy_Schubert_polynomials_n=6.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Polynomial - Wikipedia", "date": "", "ddg_snippet": "Toggle the table of contents. Polynomial . 82 languages.It was derived from the term binomial by replacing the Latin root bi- with the Greek poly-. That is, it means a sum of many terms (many monomials). The word polynomial was first used in the 17th century.[ 6 ].", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Polynomial", "content": "Toggle the table of contents. Polynomial . 82 languages.It was derived from the term binomial by replacing the Latin root bi- with the Greek poly-. That is, it means a sum of many terms (many monomials). The word polynomial was first used in the 17th century.[ 6 ]."}
+{"idx": 1, "title": "[2503.03903] Single-SEM Schubert Polynomials - arXiv.org [1903.10332] Zero-one Schubert polynomials - arXiv.org [1703.00088] Combinatorial models for Schubert polynomials Machine Learning meets Algebraic Combinatorics: A Suite of ... A BIJECTIVE PROOF OF KOHNERT’S RULE FOR SCHUBERT POLYNOMIALS ... arXiv:1903.10332v3 [math.CO] 15 Nov 2020", "date": "", "ddg_snippet": "Mar 5, 2025 · Abstract page for arXiv paper 2503.03903: Single-SEM Schubert Polynomials Mar 25, 2019 · This implies that the set of permutations whose Schubert polynomials have all their coefficients equal to either 0 or 1 is closed under pattern containment. Using Magyar's orthodontia, we characterize this class by a list of twelve avoided patterns. We also give other equivalent conditions on Sw being zero-one. Feb 28, 2017 · Schubert polynomials are a basis for the polynomial ring that represent Schubert classes for the flag manifold. In this paper, we introduce and develop several new combinatorial models for Schubert polynomials that relate them to other known bases including key polynomials and fundamental slide polynomials . We unify these and existing models by giving simple bijections between the ... Mar 8, 2025 · In order to conduct such a verification for the computationally challenging type E8, we derived several general results on Poincaré polynomials of cohomology rings of Schubert varieties based on ... Abstract. Kohnert proposed the first monomial positive formula for Schu-bert polynomials as the generating polynomial for certain unit cell diagrams obtained from the Rothe diagram of a permutation. Billey, Jockusch and Stan-ley gave the first proven formula for Schubert polynomials as the generating polynomial for compatible sequences of reduced words of a permutation. In this paper, we give ... 1 . Introduction Schubert polynomials , introduced by Lascoux and Schutzenberger in [10], represent cohomology classes of Schubert cycles in the ag variety. Knutson and Miller also showed them to be multidegrees of matrix Schubert varieties [7]. There are a number of combinatorial formulas for the Schubert polynomials [ 1 , 2, 5, 6 , 9, 12, 14, 17], yet only recently has the structure of their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.03903", "content": "Mar 5, 2025 · Abstract page for arXiv paper 2503.03903: Single-SEM Schubert Polynomials Mar 25, 2019 · This implies that the set of permutations whose Schubert polynomials have all their coefficients equal to either 0 or 1 is closed under pattern containment. Using Magyar's orthodontia, we characterize this class by a list of twelve avoided patterns. We also give other equivalent conditions on Sw being zero-one. Feb 28, 2017 · Schubert polynomials are a basis for the polynomial ring that represent Schubert classes for the flag manifold. In this paper, we introduce and develop several new combinatorial models for Schubert polynomials that relate them to other known bases including key polynomials and fundamental slide polynomials . We unify these and existing models by giving simple bijections between the ... Mar 8, 2025 · In order to conduct such a verification for the computationally challenging type E8, we derived several general results on Poincaré polynomials of cohomology rings of Schubert varieties based on ... Abstract. Kohnert proposed the first monomial positive formula for Schu-bert polynomials as the generating polynomial for certain unit cell diagrams obtained from the Rothe diagram of a permutation. Billey, Jockusch and Stan-ley gave the first proven formula for Schubert polynomials as the generating polynomial for compatible sequences of reduced words of a permutation. In this paper, we give ... 1 . Introduction Schubert polynomials , introduced by Lascoux and Schutzenberger in [10], represent cohomology classes of Schubert cycles in the ag variety. Knutson and Miller also showed them to be multidegrees of matrix Schubert varieties [7]. There are a number of combinatorial formulas for the Schubert polynomials [ 1 , 2, 5, 6 , 9, 12, 14, 17], yet only recently has the structure of their ..."}
+{"idx": 2, "title": "[1903.10332] Zero-one Schubert polynomials - arXiv.org [1703.00088] Combinatorial models for Schubert polynomials Machine Learning meets Algebraic Combinatorics: A Suite of ... A BIJECTIVE PROOF OF KOHNERT’S RULE FOR SCHUBERT POLYNOMIALS ... arXiv:1903.10332v3 [math.CO] 15 Nov 2020", "date": "", "ddg_snippet": "Mar 25, 2019 · This implies that the set of permutations whose Schubert polynomials have all their coefficients equal to either 0 or 1 is closed under pattern containment. Using Magyar's orthodontia, we characterize this class by a list of twelve avoided patterns. We also give other equivalent conditions on Sw being zero-one. Feb 28, 2017 · Schubert polynomials are a basis for the polynomial ring that represent Schubert classes for the flag manifold. In this paper, we introduce and develop several new combinatorial models for Schubert polynomials that relate them to other known bases including key polynomials and fundamental slide polynomials . We unify these and existing models by giving simple bijections between the ... Mar 8, 2025 · In order to conduct such a verification for the computationally challenging type E8, we derived several general results on Poincaré polynomials of cohomology rings of Schubert varieties based on ... Abstract. Kohnert proposed the first monomial positive formula for Schu-bert polynomials as the generating polynomial for certain unit cell diagrams obtained from the Rothe diagram of a permutation. Billey, Jockusch and Stan-ley gave the first proven formula for Schubert polynomials as the generating polynomial for compatible sequences of reduced words of a permutation. In this paper, we give ... 1 . Introduction Schubert polynomials , introduced by Lascoux and Schutzenberger in [10], represent cohomology classes of Schubert cycles in the ag variety. Knutson and Miller also showed them to be multidegrees of matrix Schubert varieties [7]. There are a number of combinatorial formulas for the Schubert polynomials [ 1 , 2, 5, 6 , 9, 12, 14, 17], yet only recently has the structure of their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.10332", "content": "Mar 25, 2019 · This implies that the set of permutations whose Schubert polynomials have all their coefficients equal to either 0 or 1 is closed under pattern containment. Using Magyar's orthodontia, we characterize this class by a list of twelve avoided patterns. We also give other equivalent conditions on Sw being zero-one. Feb 28, 2017 · Schubert polynomials are a basis for the polynomial ring that represent Schubert classes for the flag manifold. In this paper, we introduce and develop several new combinatorial models for Schubert polynomials that relate them to other known bases including key polynomials and fundamental slide polynomials . We unify these and existing models by giving simple bijections between the ... Mar 8, 2025 · In order to conduct such a verification for the computationally challenging type E8, we derived several general results on Poincaré polynomials of cohomology rings of Schubert varieties based on ... Abstract. Kohnert proposed the first monomial positive formula for Schu-bert polynomials as the generating polynomial for certain unit cell diagrams obtained from the Rothe diagram of a permutation. Billey, Jockusch and Stan-ley gave the first proven formula for Schubert polynomials as the generating polynomial for compatible sequences of reduced words of a permutation. In this paper, we give ... 1 . Introduction Schubert polynomials , introduced by Lascoux and Schutzenberger in [10], represent cohomology classes of Schubert cycles in the ag variety. Knutson and Miller also showed them to be multidegrees of matrix Schubert varieties [7]. There are a number of combinatorial formulas for the Schubert polynomials [ 1 , 2, 5, 6 , 9, 12, 14, 17], yet only recently has the structure of their ..."}
+{"idx": 3, "title": "[1703.00088] Combinatorial models for Schubert polynomials Machine Learning meets Algebraic Combinatorics: A Suite of ... A BIJECTIVE PROOF OF KOHNERT’S RULE FOR SCHUBERT POLYNOMIALS ... arXiv:1903.10332v3 [math.CO] 15 Nov 2020", "date": "", "ddg_snippet": "Feb 28, 2017 · Schubert polynomials are a basis for the polynomial ring that represent Schubert classes for the flag manifold. In this paper, we introduce and develop several new combinatorial models for Schubert polynomials that relate them to other known bases including key polynomials and fundamental slide polynomials . We unify these and existing models by giving simple bijections between the ... Mar 8, 2025 · In order to conduct such a verification for the computationally challenging type E8, we derived several general results on Poincaré polynomials of cohomology rings of Schubert varieties based on ... Abstract. Kohnert proposed the first monomial positive formula for Schu-bert polynomials as the generating polynomial for certain unit cell diagrams obtained from the Rothe diagram of a permutation. Billey, Jockusch and Stan-ley gave the first proven formula for Schubert polynomials as the generating polynomial for compatible sequences of reduced words of a permutation. In this paper, we give ... 1 . Introduction Schubert polynomials , introduced by Lascoux and Schutzenberger in [10], represent cohomology classes of Schubert cycles in the ag variety. Knutson and Miller also showed them to be multidegrees of matrix Schubert varieties [7]. There are a number of combinatorial formulas for the Schubert polynomials [ 1 , 2, 5, 6 , 9, 12, 14, 17], yet only recently has the structure of their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1703.00088", "content": "Feb 28, 2017 · Schubert polynomials are a basis for the polynomial ring that represent Schubert classes for the flag manifold. In this paper, we introduce and develop several new combinatorial models for Schubert polynomials that relate them to other known bases including key polynomials and fundamental slide polynomials . We unify these and existing models by giving simple bijections between the ... Mar 8, 2025 · In order to conduct such a verification for the computationally challenging type E8, we derived several general results on Poincaré polynomials of cohomology rings of Schubert varieties based on ... Abstract. Kohnert proposed the first monomial positive formula for Schu-bert polynomials as the generating polynomial for certain unit cell diagrams obtained from the Rothe diagram of a permutation. Billey, Jockusch and Stan-ley gave the first proven formula for Schubert polynomials as the generating polynomial for compatible sequences of reduced words of a permutation. In this paper, we give ... 1 . Introduction Schubert polynomials , introduced by Lascoux and Schutzenberger in [10], represent cohomology classes of Schubert cycles in the ag variety. Knutson and Miller also showed them to be multidegrees of matrix Schubert varieties [7]. There are a number of combinatorial formulas for the Schubert polynomials [ 1 , 2, 5, 6 , 9, 12, 14, 17], yet only recently has the structure of their ..."}
+{"idx": 4, "title": "A BIJECTIVE PROOF OF KOHNERT’S RULE FOR SCHUBERT POLYNOMIALS ...", "date": "", "ddg_snippet": "Abstract. Kohnert proposed the first monomial positive formula for Schu-bert polynomials as the generating polynomial for certain unit cell diagrams obtained from the Rothe diagram of a permutation. Billey, Jockusch and Stan-ley gave the first proven formula for Schubert polynomials as the generating polynomial for compatible sequences of reduced words of a permutation. In this paper, we give ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2003.01211v2.pdf", "content": "Abstract. Kohnert proposed the first monomial positive formula for Schu-bert polynomials as the generating polynomial for certain unit cell diagrams obtained from the Rothe diagram of a permutation. Billey, Jockusch and Stan-ley gave the first proven formula for Schubert polynomials as the generating polynomial for compatible sequences of reduced words of a permutation. In this paper, we give ..."}
+{"idx": 5, "title": "arXiv:1903.10332v3 [math.CO] 15 Nov 2020", "date": "", "ddg_snippet": "1 . Introduction Schubert polynomials , introduced by Lascoux and Schutzenberger in [10], represent cohomology classes of Schubert cycles in the ag variety. Knutson and Miller also showed them to be multidegrees of matrix Schubert varieties [7]. There are a number of combinatorial formulas for the Schubert polynomials [ 1 , 2, 5, 6 , 9, 12, 14, 17], yet only recently has the structure of their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.10332", "content": "1 . Introduction Schubert polynomials , introduced by Lascoux and Schutzenberger in [10], represent cohomology classes of Schubert cycles in the ag variety. Knutson and Miller also showed them to be multidegrees of matrix Schubert varieties [7]. There are a number of combinatorial formulas for the Schubert polynomials [ 1 , 2, 5, 6 , 9, 12, 14, 17], yet only recently has the structure of their ..."}
+{"idx": 6, "title": "Algebra Basics: What Are Polynomials ? - Math Antics - YouTube", "date": "", "ddg_snippet": "This video introduces students to polynomials and terms.Part of the Algebra Basics Series:https://www.youtube.com/watch?v=NybHckSEQBI&list=PLUPEBWbAHUszT_Geb...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=ffLLmV4mZwU", "content": "This video introduces students to polynomials and terms.Part of the Algebra Basics Series:https://www.youtube.com/watch?v=NybHckSEQBI&list=PLUPEBWbAHUszT_Geb..."}
+{"idx": 7, "title": "Accuracy , Precision, Recall, F 1 : метрики оценки моделей...", "date": "", "ddg_snippet": "Освойте ключевые метрики машинного обучения — Accuracy , Precision, Recall и F 1 -score — для успешной оптимизации моделей!Фундаментальные метрики: Accuracy , Precision, Recall, F 1 . Представьте модель машинного обучения как нового сотрудника в вашей команде.", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/wiki/analytics/accuracy-precision-recall-f1-metriki-otsenki-modelej-mashinnogo-obucheniya/", "content": "Освойте ключевые метрики машинного обучения — Accuracy , Precision, Recall и F 1 -score — для успешной оптимизации моделей!Фундаментальные метрики: Accuracy , Precision, Recall, F 1 . Представьте модель машинного обучения как нового сотрудника в вашей команде."}
+{"idx": 8, "title": "Solve Factoring multivariable polynomials 9m^2+ 6 mn+ n ^2 Tiger...", "date": "", "ddg_snippet": "Other Ways to Solve. Factoring multivariable polynomials .Step 2 : Trying to factor a multi variable polynomial", "subpage_snippet": "", "source": "www.tiger-algebra.com", "link": "https://www.tiger-algebra.com/drill/9m~2_6mn_n~2/", "content": "Other Ways to Solve. Factoring multivariable polynomials .Step 2 : Trying to factor a multi variable polynomial"}
+{"idx": 9, "title": "2021 porsche macan gas - WP 1 AA2A58MLB06366", "date": "", "ddg_snippet": "Автомобиль PORSCHE WP 1 AA2A58MLB06366 с вин номеров WP 1 AA2A58MLB06366 и пробегом 29 070 кмкм, актуальность пробега проверяйте по базам Carfax и AutoCheck.", "subpage_snippet": "", "source": "autoconsultant.com.ua", "link": "https://autoconsultant.com.ua/automobile/porsche/macan/33b18a6c003b637-2021-porsche-macan-wp1aa2a58mlb06366/", "content": "Автомобиль PORSCHE WP 1 AA2A58MLB06366 с вин номеров WP 1 AA2A58MLB06366 и пробегом 29 070 кмкм, актуальность пробега проверяйте по базам Carfax и AutoCheck."}
diff --git a/data/sampled_jsons/arXiv_2503.10694_table_1_GPT-4_accuracy_real-world_MedQA.jsonl b/data/sampled_jsons/arXiv_2503.10694_table_1_GPT-4_accuracy_real-world_MedQA.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4313dfd8c84b9ddc7834acb47cb45386d69b78ff
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+++ b/data/sampled_jsons/arXiv_2503.10694_table_1_GPT-4_accuracy_real-world_MedQA.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluation of GPT-3.5 and GPT-4 for supporting real-world information ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2304.13714: Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.13714", "content": "Abstract page for arXiv paper 2304.13714: Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery"}
+{"idx": 1, "title": "Capabilities of GPT-4 on Medical Challenge Problems - arXiv.org", "date": "", "ddg_snippet": "We particularly re ect on the limitations of benchmark-based performance evaluations, and discuss the precautions and advances needed to make use of models like GPT-4 in real world settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.13375", "content": "We particularly re ect on the limitations of benchmark-based performance evaluations, and discuss the precautions and advances needed to make use of models like GPT-4 in real world settings."}
+{"idx": 2, "title": "[2303.08774] GPT-4 Technical Report - arXiv.org", "date": "", "ddg_snippet": "We report the development of GPT-4 , a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.08774", "content": "We report the development of GPT-4 , a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer ..."}
+{"idx": 3, "title": "Diagnostic accuracy of GPT‐4 on common clinical scenarios and ...", "date": "", "ddg_snippet": "By contrast, compared to the physicians, GPT‐4 achieved a significantly better top 3 diagnostic accuracy for the common clinical scenarios (100%) and a better top 6 diagnostic accuracy for the most challenging cases (61.1%).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11257049/", "content": "By contrast, compared to the physicians, GPT‐4 achieved a significantly better top 3 diagnostic accuracy for the common clinical scenarios (100%) and a better top 6 diagnostic accuracy for the most challenging cases (61.1%)."}
+{"idx": 4, "title": "Beyond Accuracy: Investigating Error Types in GPT-4 Responses to USMLE ...", "date": "", "ddg_snippet": "Here, we first describe the construction of GPT-4 responses to USMLE questions in Section 4.1 and then describe the complete annotation setup using Potato [27] in Section 4.2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.13307", "content": "Here, we first describe the construction of GPT-4 responses to USMLE questions in Section 4.1 and then describe the complete annotation setup using Potato [27] in Section 4.2."}
+{"idx": 5, "title": "A Survey for Large Language Models in Biomedicine - arXiv.org", "date": "", "ddg_snippet": "The results showed that GPT-4 achieved 100% accuracy in triage and diagnosis, while GPT-3.5 had an accuracy rate of 92.59%. These results highlight GPT-4's exceptional diagnostic accuracy , underscoring its potential as a reliable tool in clinical decision-making.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.00133v1", "content": "The results showed that GPT-4 achieved 100% accuracy in triage and diagnosis, while GPT-3.5 had an accuracy rate of 92.59%. These results highlight GPT-4's exceptional diagnostic accuracy , underscoring its potential as a reliable tool in clinical decision-making."}
+{"idx": 6, "title": "PDF Capabilities of GPT-4 on Medical Challenge Problems", "date": "", "ddg_snippet": "The leap in performance on medical challenge problems with the move from GPT 3.5 to GPT-4 suggests that we can achieve impressive gains on intensive real-world challenges with scale-and that we will likely continue to see advances with larger models for handling complex, real-world problems.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2023/03/GPT-4_medical_benchmarks.pdf", "content": "The leap in performance on medical challenge problems with the move from GPT 3.5 to GPT-4 suggests that we can achieve impressive gains on intensive real-world challenges with scale-and that we will likely continue to see advances with larger models for handling complex, real-world problems."}
+{"idx": 7, "title": "GPT-4 Technical Report - arXiv.org", "date": "", "ddg_snippet": "Abstract We report the development of GPT-4 , a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2303.08774v4", "content": "Abstract We report the development of GPT-4 , a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a ..."}
+{"idx": 8, "title": "Medical Large Language Model Benchmarks Should Prioritize Construct ...", "date": "", "ddg_snippet": "However, as we see in Table 1 , this is not the case: GPT-4 tops the rankings on MedQA , yet Llama 3 out-performs on real-world clinical notes. Interestingly, GPT-4 had a notably high non-response rate on real-world notes, which makes the interpretation of benchmark peformance even harder.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10694", "content": "However, as we see in Table 1 , this is not the case: GPT-4 tops the rankings on MedQA , yet Llama 3 out-performs on real-world clinical notes. Interestingly, GPT-4 had a notably high non-response rate on real-world notes, which makes the interpretation of benchmark peformance even harder."}
+{"idx": 9, "title": "Large language models encode clinical knowledge - Nature", "date": "", "ddg_snippet": "On the MedQA dataset consisting of USMLE-style questions with 4 options, our Flan-PaLM 540B model achieved a multiple-choice question accuracy of 67.6%, surpassing the DRAGON model 18 by 20.1%.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-023-06291-2", "content": "On the MedQA dataset consisting of USMLE-style questions with 4 options, our Flan-PaLM 540B model achieved a multiple-choice question accuracy of 67.6%, surpassing the DRAGON model 18 by 20.1%."}
diff --git a/data/sampled_jsons/arXiv_CVE-Bench_AI_agents_web_application_vulnerabilities.jsonl b/data/sampled_jsons/arXiv_CVE-Bench_AI_agents_web_application_vulnerabilities.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06dfcf48ec2e3dbc198f1e29a41f5f60fad78e6c
--- /dev/null
+++ b/data/sampled_jsons/arXiv_CVE-Bench_AI_agents_web_application_vulnerabilities.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: Benchmarking LLM-based Software Engineering Agent ...", "date": "", "ddg_snippet": "Thus, the LLM agents may face multiple levels of information when required to repair the vulnerability issues. interpreter, tool-using features). In this paper, we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "Thus, the LLM agents may face multiple levels of information when required to repair the vulnerability issues. interpreter, tool-using features). In this paper, we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting."}
+{"idx": 1, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to ... CVE-Bench: A Benchmark for AI Agents’ Ability to ... - GitHub CVE-Bench: Benchmarking LLM-based Software Engineering Agent ... uiuc-kang-lab/cve-bench | DeepWiki Measuring AI Agents’ Ability to Exploit Web Applications CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ... [2503.17332] CVE - Bench : A Benchmark for AI Agents ' Ability to Expl… [2503.17332] CVE - Bench : A Benchmark for AI Agents ' Ability to Expl… [2503.17332] CVE - Bench : A Benchmark for AI Agents ' Ability to Expl… [2503.17332] CVE - Bench : A Benchmark for AI Agents ' Ability to Expl… [2503.17332] CVE - Bench : A Benchmark for AI Agents ' Ability to Expl… CVE - Bench : Benchmarking LLM-based Software Engineering Agent's … CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "Mar 21, 2025 · In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities . Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. Thus, the LLM agents may face multiple levels of information when required to repair the vulnerability issues. interpreter, tool-using features). In this paper, we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. May 12, 2025 · What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures (CVEs) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents ' abilities to discover and exploit web application vulnerabilities . Mar 31, 2025 · Success rates of different AI agents on CVE-bench in the zero-day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ... Mar 23, 2025 · Conclusion CVE-Bench provides a sobering assessment of current AI systems' capabilities to exploit real-world security vulnerabilities . While the most advanced models show some ability to identify and exploit certain vulnerabilities , their overall performance falls significantly short of human security experts' capabilities. Can LLM agents exploit web application vulnerabilities? Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities . What is CVE-bench sandbox framework? In CVE-Bench, we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities. How many vulnerabilities can the agent framework resolve? Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities. Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) What is CVE-bench? Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. Should we build a benchmark for real-world vulnerabilities? However, existing benchmarks fall short as they are limited to abstracted Capture the Flag competitions or lack comprehensive coverage. Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. What is automated vulnerability repair? Automated vulnerability repair is a popular and valuablesoftwareengineeringandsecurityresearch eld . Large language models (LLMs) and LLM- based agents have shown sizeable potential appli- cation value in this area. LLMs can understand natural language described vulnerability rationale and generate formal code to repair it. Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications . This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Mar 21, 2025 · In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities . Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests. Thus, the LLM agents may face multiple levels of information when required to repair the vulnerability issues. interpreter, tool-using features). In this paper, we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. May 12, 2025 · What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures (CVEs) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents ' abilities to discover and exploit web application vulnerabilities . Mar 31, 2025 · Success rates of different AI agents on CVE-bench in the zero-day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ... Mar 23, 2025 · Conclusion CVE-Bench provides a sobering assessment of current AI systems' capabilities to exploit real-world security vulnerabilities . While the most advanced models show some ability to identify and exploit certain vulnerabilities , their overall performance falls significantly short of human security experts' capabilities. Can LLM agents exploit web application vulnerabilities? Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities . What is CVE-bench sandbox framework? In CVE-Bench, we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities. How many vulnerabilities can the agent framework resolve? Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities. Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) What is CVE-bench? Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. Should we build a benchmark for real-world vulnerabilities? However, existing benchmarks fall short as they are limited to abstracted Capture the Flag competitions or lack comprehensive coverage. Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. What is automated vulnerability repair? Automated vulnerability repair is a popular and valuablesoftwareengineeringandsecurityresearch eld . Large language models (LLMs) and LLM- based agents have shown sizeable potential appli- cation value in this area. LLMs can understand natural language described vulnerability rationale and generate formal code to repair it. Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications . This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities ."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to ... - GitHub", "date": "", "ddg_snippet": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Apr 24, 2025 · This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 3, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "May 12, 2025 · What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures (CVEs) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents ' abilities to discover and exploit web application vulnerabilities .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "May 12, 2025 · What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures (CVEs) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents ' abilities to discover and exploit web application vulnerabilities ."}
+{"idx": 4, "title": "Measuring AI Agents’ Ability to Exploit Web Applications", "date": "", "ddg_snippet": "Mar 31, 2025 · Success rates of different AI agents on CVE-bench in the zero-day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danieldkang/measuring-ai-agents-ability-to-exploit-web-applications-ba4225aa281f", "content": "Mar 31, 2025 · Success rates of different AI agents on CVE-bench in the zero-day or one-day setting. As shown, AI agents successfully exploited up to 13% of web application vulnerabilities in the zero-day ..."}
+{"idx": 5, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "Mar 23, 2025 · Conclusion CVE-Bench provides a sobering assessment of current AI systems' capabilities to exploit real-world security vulnerabilities . While the most advanced models show some ability to identify and exploit certain vulnerabilities , their overall performance falls significantly short of human security experts' capabilities.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/cve-bench-benchmark-ai-agents-ability-to", "content": "Mar 23, 2025 · Conclusion CVE-Bench provides a sobering assessment of current AI systems' capabilities to exploit real-world security vulnerabilities . While the most advanced models show some ability to identify and exploit certain vulnerabilities , their overall performance falls significantly short of human security experts' capabilities."}
+{"idx": 6, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications . This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities .", "subpage_snippet": "", "source": "www.x-mol.com", "link": "https://www.x-mol.com/paper/1904236840120795136", "content": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications . This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities ."}
+{"idx": 7, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vul - nerable web applications in scenarios that mimic real-world conditions, while also providing effec-tive evaluation of their exploits.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vul - nerable web applications in scenarios that mimic real-world conditions, while also providing effec-tive evaluation of their exploits."}
+{"idx": 8, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "In CVE - Bench , we design a sandbox framework that enables LLM agentsto exploit vulnerable web applications in scenarios that mimic real-worldconditions, while also providing effective evaluation of their exploits.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/588679/cve-bench:-a-benchmark-for-ai-agents'-ability-to-exploit-real-world-web-application-vulnerabilities", "content": "In CVE - Bench , we design a sandbox framework that enables LLM agentsto exploit vulnerable web applications in scenarios that mimic real-worldconditions, while also providing effective evaluation of their exploits."}
+{"idx": 9, "title": "ICML Poster CVE - Bench : A Benchmark for AI Agents ’ Ability to...", "date": "", "ddg_snippet": "In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46522", "content": "In CVE - Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits."}
diff --git a/data/sampled_jsons/arxiv.org_2410.00844_Fisher_information_I(p)_definition_Remark_4.2.jsonl b/data/sampled_jsons/arxiv.org_2410.00844_Fisher_information_I(p)_definition_Remark_4.2.jsonl
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index 0000000000000000000000000000000000000000..77a4feb57592bbf4a5c9c0ab7291278a1eda347a
--- /dev/null
+++ b/data/sampled_jsons/arxiv.org_2410.00844_Fisher_information_I(p)_definition_Remark_4.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fisher information - Wikipedia", "date": "", "ddg_snippet": "Fisher information In mathematical statistics, the Fisher information is a way of measuring the amount of information that an observable random variable X carries about an unknown parameter θ of a distribution that models X. Formally, it is the variance of the score, or the expected value of the observed information .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Fisher_information", "content": "Fisher information In mathematical statistics, the Fisher information is a way of measuring the amount of information that an observable random variable X carries about an unknown parameter θ of a distribution that models X. Formally, it is the variance of the score, or the expected value of the observed information ."}
+{"idx": 1, "title": "Bernoulli distribution - Wikipedia", "date": "", "ddg_snippet": "Fisher information measures the amount of information that an observable random variable. Fisher information is calculated as the negative expected value of the second derivative of the log-likelihood", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Bernoulli_distribution", "content": "Fisher information measures the amount of information that an observable random variable. Fisher information is calculated as the negative expected value of the second derivative of the log-likelihood"}
+{"idx": 2, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2 . 4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2 . 4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 3, "title": "[2509.02407] Fisher information flow in artificial neural ...", "date": "", "ddg_snippet": "Sep 2 , 2025 · The estimation of continuous parameters from measured data plays a central role in many fields of physics. A key tool in understanding and improving such estimation processes is the concept of Fisher information , which quantifies how information about unknown parameters propagates through a physical system and determines the ultimate limits of precision. With Artificial Neural Networks (ANNs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2509.02407", "content": "Sep 2 , 2025 · The estimation of continuous parameters from measured data plays a central role in many fields of physics. A key tool in understanding and improving such estimation processes is the concept of Fisher information , which quantifies how information about unknown parameters propagates through a physical system and determines the ultimate limits of precision. With Artificial Neural Networks (ANNs ..."}
+{"idx": 4, "title": "Fisher information - University of Iowa", "date": "", "ddg_snippet": "Oct 26, 2023 · The variance of the score is called the Fisher information : I (θ) = V u (θ | X) On its surface, this would seem to have nothing to do with information . However, the connection between the variance of the score and the curvature of the log-likelihood is made clear in the following theorem.", "subpage_snippet": "", "source": "myweb.uiowa.edu", "link": "https://myweb.uiowa.edu/pbreheny/7110/wiki/fisher-information.html", "content": "Oct 26, 2023 · The variance of the score is called the Fisher information : I (θ) = V u (θ | X) On its surface, this would seem to have nothing to do with information . However, the connection between the variance of the score and the curvature of the log-likelihood is made clear in the following theorem."}
+{"idx": 5, "title": "Fisher Information - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "8. 2 . 4.2 Type-II hybrid censoring and a general account to Fisher information in hybrid censoring schemes As pointed out in Chapter 4 , the likelihood functions in hybrid censoring models are composed of only two types and which correspond to a Type-I and a Type-II setting, respectively.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/mathematics/fisher-information", "content": "8. 2 . 4.2 Type-II hybrid censoring and a general account to Fisher information in hybrid censoring schemes As pointed out in Chapter 4 , the likelihood functions in hybrid censoring models are composed of only two types and which correspond to a Type-I and a Type-II setting, respectively."}
+{"idx": 6, "title": "Dissipation estimates of the Fisher information for the ...", "date": "", "ddg_snippet": "Abstract. We establish an a priori estimate for the dissipation of the Fisher information for the space-homogeneous Landau equation with very soft potentials. This work is motivated by the recent breakthrough by Guillen and Silvestre [14], which proves that the Fisher information is monotone decreasing.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09035v1", "content": "Abstract. We establish an a priori estimate for the dissipation of the Fisher information for the space-homogeneous Landau equation with very soft potentials. This work is motivated by the recent breakthrough by Guillen and Silvestre [14], which proves that the Fisher information is monotone decreasing."}
+{"idx": 7, "title": "A Geometric Characterization of Fisher Information from ...", "date": "", "ddg_snippet": "Abstract—Consider the Fisher information for estimating a vector 2 Rd from the quantized version of a statistical sample X f(xj ). Let M be a k-bit quantization of X. We provide a geometric characterization of the trace of the Fisher information matrix IM( ) in terms of the score function S (X). When k = 1, we exactly solve the extremal problem of maximizing this geometric quantity for the ...", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~aozgur/FisherAllerton.pdf", "content": "Abstract—Consider the Fisher information for estimating a vector 2 Rd from the quantized version of a statistical sample X f(xj ). Let M be a k-bit quantization of X. We provide a geometric characterization of the trace of the Fisher information matrix IM( ) in terms of the score function S (X). When k = 1, we exactly solve the extremal problem of maximizing this geometric quantity for the ..."}
+{"idx": 8, "title": "ArXiv . org — Рувики: Интернет-энциклопедия", "date": "", "ddg_snippet": "arXiv . org — электронный архив с открытым доступом для научных статей и препринтов по физике, математике, астрономии, информатике, биологии, электротехнике, статистике, финансовой математике и экономике.", "subpage_snippet": "", "source": "ru.ruwiki.ru", "link": "https://ru.ruwiki.ru/wiki/ArXiv.org", "content": "arXiv . org — электронный архив с открытым доступом для научных статей и препринтов по физике, математике, астрономии, информатике, биологии, электротехнике, статистике, финансовой математике и экономике."}
+{"idx": 9, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "14 Feb 2025 — Then, we can reformulate Definition 4.1 with the following Fisher information regularization. ... Remark 4.2 . Report issue for preceding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v2", "content": "14 Feb 2025 — Then, we can reformulate Definition 4.1 with the following Fisher information regularization. ... Remark 4.2 . Report issue for preceding ..."}
diff --git a/data/sampled_jsons/arxiv.org_2501.15987_MultiPDENet_PDE-embedded_Learning.jsonl b/data/sampled_jsons/arxiv.org_2501.15987_MultiPDENet_PDE-embedded_Learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e4ada6465855e7dd45f0951708101fd3502c43db
--- /dev/null
+++ b/data/sampled_jsons/arxiv.org_2501.15987_MultiPDENet_PDE-embedded_Learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2501.15987] MultiPDENet: PDE-embedded Learning with Multi ...", "date": "", "ddg_snippet": "Jan 27, 2025 · Solving partial differential equations (PDEs) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required. Machine learning can accelerate this process, but struggle with weak generalizability, interpretability, and data dependency, as well as suffer in long-term prediction. To this end, we propose a PDE-embedded ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.15987", "content": "Jan 27, 2025 · Solving partial differential equations (PDEs) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required. Machine learning can accelerate this process, but struggle with weak generalizability, interpretability, and data dependency, as well as suffer in long-term prediction. To this end, we propose a PDE-embedded ..."}
+{"idx": 1, "title": "[PDF] MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "Jan 27, 2025 · A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations (PDEs) by numerical methods meet ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/MultiPDENet:-PDE-embedded-Learning-with-for-Flow-Wang-Mi/6aee4adf8e7489f251995859a5f0432a2c60bb82", "content": "Jan 27, 2025 · A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations (PDEs) by numerical methods meet ..."}
+{"idx": 2, "title": "PDE-constrained Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "Sep 27, 2024 · To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of fluid flows.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "Sep 27, 2024 · To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of fluid flows."}
+{"idx": 3, "title": "dblp: MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "Feb 26, 2025 · Bibliographic details on MultiPDENet : PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2501-15987", "content": "Feb 26, 2025 · Bibliographic details on MultiPDENet : PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation."}
+{"idx": 4, "title": "#1 MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2501.15987", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows."}
+{"idx": 5, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping", "date": "", "ddg_snippet": "We developed MultiPDENet , a PDE-embedded network with multiscale time-stepping, for accelerated flow simulations on spatiotemporal coarse grids. By integrating neural solver with PDEs, MultiPDENet achieves great generalizability and efficiency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987", "content": "We developed MultiPDENet , a PDE-embedded network with multiscale time-stepping, for accelerated flow simulations on spatiotemporal coarse grids. By integrating neural solver with PDEs, MultiPDENet achieves great generalizability and efficiency."}
+{"idx": 6, "title": "PDE-EMBEDDED LEARNING WITH MULTI TIME STEPPING FOR ...", "date": "", "ddg_snippet": "Machine learning can accelerate this process, but struggle with weak generalizability, interpretability, and data dependency, as well as suf- fer in long-term prediction.To this end, we propose a PDE -embeddednetwork with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numer- ical methods and machine learning , for accelerated ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=stcN89QGfL", "content": "Machine learning can accelerate this process, but struggle with weak generalizability, interpretability, and data dependency, as well as suf- fer in long-term prediction.To this end, we propose a PDE -embeddednetwork with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numer- ical methods and machine learning , for accelerated ..."}
+{"idx": 7, "title": "MultiPDENet : PDE - embedded Learning with Multi-time-stepping for...", "date": "", "ddg_snippet": "arXiv : 2501 . 15987 v1 [math.NA] 27 Jan 2025.To this end, we propose a PDE - embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "arXiv : 2501 . 15987 v1 [math.NA] 27 Jan 2025.To this end, we propose a PDE - embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning , for accelerated simulation of flows."}
+{"idx": 8, "title": "GitHub - bitzhangcy/Neural- PDE -Solver", "date": "", "ddg_snippet": "Physics-embedded fourier neural network for partial differential equations . arXiv , 2024. paper. MultiPDENet : PDE - embedded learning with multi-time-stepping for accelerated flow simulation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bitzhangcy/Neural-PDE-Solver", "content": "Physics-embedded fourier neural network for partial differential equations . arXiv , 2024. paper. MultiPDENet : PDE - embedded learning with multi-time-stepping for accelerated flow simulation."}
+{"idx": 9, "title": "Awesome AI4 PDE", "date": "", "ddg_snippet": "MultiPDENet : PDE - embedded Learning with Multi-time-stepping for Accelerated Flow Simulation.SNN-PDE: Learning Dynamic PDEs from Data with Simplicial Neural Networks. Solving. Hodge LaplacianSpatiotemporal.", "subpage_snippet": "", "source": "ai4pde.notion.site", "link": "https://ai4pde.notion.site/", "content": "MultiPDENet : PDE - embedded Learning with Multi-time-stepping for Accelerated Flow Simulation.SNN-PDE: Learning Dynamic PDEs from Data with Simplicial Neural Networks. Solving. Hodge LaplacianSpatiotemporal."}
diff --git a/data/sampled_jsons/arxiv.orgabs2403.09040_RAGGED_Section_6.1_noise_robustness_reader_models_LLaMA_year_2024.jsonl b/data/sampled_jsons/arxiv.orgabs2403.09040_RAGGED_Section_6.1_noise_robustness_reader_models_LLaMA_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..984219c263835a90a500fafad2faafc4c2b61801
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orgabs2403.09040_RAGGED_Section_6.1_noise_robustness_reader_models_LLaMA_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever- reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09040", "content": "In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever- reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability."}
+{"idx": 1, "title": "Paper page - RAGGED: Towards Informed Design of Retrieval Augmented ...", "date": "", "ddg_snippet": "Abstract Retrieval-augmented generation (RAG) greatly benefits language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). Despite its potential, the power of RAG is highly dependent on its configuration, raising the question: What is the optimal RAG configuration? To answer this, we introduce the RAGGED framework to analyze and optimize ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2403.09040", "content": "Abstract Retrieval-augmented generation (RAG) greatly benefits language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). Despite its potential, the power of RAG is highly dependent on its configuration, raising the question: What is the optimal RAG configuration? To answer this, we introduce the RAGGED framework to analyze and optimize ..."}
+{"idx": 2, "title": "RAGGED: Towards Informed Design of Retrieval Augmented Generation ...", "date": "", "ddg_snippet": "Retrieval-augmented generation (RAG) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models , context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KDXj60FpJr", "content": "Retrieval-augmented generation (RAG) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models , context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ..."}
+{"idx": 3, "title": "RAGGED: Towards Informed Design of Retrieval ... - Semantic Scholar", "date": "", "ddg_snippet": "ArXiv 2024 TLDR RAG Foundry is introduced, an open-source framework for augmenting large language models for RAG use cases and demonstrates the framework effectiveness by augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG configurations, showcasing consistent improvements across three knowledge-intensive datasets. Expand [PDF ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/RAGGED:-Towards-Informed-Design-of-Retrieval-Hsia-Shaikh/e1446fc9b11e73e9f1d867df9012fdc3d3df60d0/figure/3", "content": "ArXiv 2024 TLDR RAG Foundry is introduced, an open-source framework for augmenting large language models for RAG use cases and demonstrates the framework effectiveness by augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG configurations, showcasing consistent improvements across three knowledge-intensive datasets. Expand [PDF ..."}
+{"idx": 4, "title": "RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems", "date": "", "ddg_snippet": "For instance, while some models are robust to noisy contexts, monotonically performing better with more contexts, others are more noise -sensitive and can effectively use only a few contexts before declining in performance.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2403.09040v2_enmode", "content": "For instance, while some models are robust to noisy contexts, monotonically performing better with more contexts, others are more noise -sensitive and can effectively use only a few contexts before declining in performance."}
+{"idx": 5, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "Our findings highlight the im-portance of reader robustness to retrieval noise , suggesting that deployments of RAG models should include safeguards against misleading or incorrect retrieved content.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09040v3", "content": "Our findings highlight the im-portance of reader robustness to retrieval noise , suggesting that deployments of RAG models should include safeguards against misleading or incorrect retrieved content."}
+{"idx": 6, "title": "RAGGED: Towards Informed Design of - arXiv.org", "date": "", "ddg_snippet": "Figure 1: Illustration of our RAGGED framework. Figure 2: Example insight from using RAGGED : Decoder-only models (e.g., LLaMa ) memorize more knowledge from training, yet are reluctant to use provided contexts and are noise -sensitive. In contrast, encoder-decoder models (e.g., Flan) monotonically improve with more provided contexts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v1", "content": "Figure 1: Illustration of our RAGGED framework. Figure 2: Example insight from using RAGGED : Decoder-only models (e.g., LLaMa ) memorize more knowledge from training, yet are reluctant to use provided contexts and are noise -sensitive. In contrast, encoder-decoder models (e.g., Flan) monotonically improve with more provided contexts."}
+{"idx": 7, "title": "Enhancing Noise Robustness of Retrieval-Augmented Language Models with ...", "date": "", "ddg_snippet": "Prior RAG studies on the robustness of retrieval noises often confine themselves to a limited set of noise types, deviating from real-world retrieval environments and limiting practical applicability.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/calubkk/RAAT", "content": "Prior RAG studies on the robustness of retrieval noises often confine themselves to a limited set of noise types, deviating from real-world retrieval environments and limiting practical applicability."}
+{"idx": 8, "title": "\"RAGGED: Towards Informed Design of Retrieval Augmented ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on RAGGED : Towards Informed Design of Retrieval Augmented Generation Systems.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2403-09040", "content": "Bibliographic details on RAGGED : Towards Informed Design of Retrieval Augmented Generation Systems."}
+{"idx": 9, "title": "PDF arXiv:2403.09040v1 [cs.CL] 14 Mar 2024", "date": "", "ddg_snippet": "Retrieval-augmented generation (RAG) (Chen et al.,2017a;Lewis et al.,2020) is a technique widely applied to enhance the performance of top- performing LMs on knowledge-intensive genera- tion tasks like document-based question answering (Karpukhin et al.,2020). Given a question, the technique includes using a retriever model to ob- tain multiple relevant passages (i.e. paragraphs) across ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09040v1.pdf", "content": "Retrieval-augmented generation (RAG) (Chen et al.,2017a;Lewis et al.,2020) is a technique widely applied to enhance the performance of top- performing LMs on knowledge-intensive genera- tion tasks like document-based question answering (Karpukhin et al.,2020). Given a question, the technique includes using a retriever model to ob- tain multiple relevant passages (i.e. paragraphs) across ..."}
diff --git a/data/sampled_jsons/arxiv.orgabs2502.00921_abstract_Li_Chen_polynomial_dimensional_dependence.jsonl b/data/sampled_jsons/arxiv.orgabs2502.00921_abstract_Li_Chen_polynomial_dimensional_dependence.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae8d33feffa6cec44fe5d0b9a2155601b919cc06
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orgabs2502.00921_abstract_Li_Chen_polynomial_dimensional_dependence.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ava Labs: Digitize All The World's Assets", "date": "", "ddg_snippet": "Ava Labs makes it simple to deploy high-performance solutions for Web3, led by innovations on Avalanche.", "subpage_snippet": "", "source": "www.avalabs.org", "link": "https://www.avalabs.org/", "content": "Ava Labs makes it simple to deploy high-performance solutions for Web3, led by innovations on Avalanche."}
+{"idx": 1, "title": "science. org /doi/10.1126/sciadv.1700782", "date": "", "ddg_snippet": "The site owner hides the web page description.", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/sciadv.1700782", "content": "The site owner hides the web page description."}
+{"idx": 2, "title": "Betera vs. ESC at Exort The Proving Grounds Season 4 | HLTV. org", "date": "", "ddg_snippet": "Any matchup that fits one or more of the criteria set in the filter will feature in the today's matches column.", "subpage_snippet": "", "source": "www.hltv.org", "link": "https://www.hltv.org/matches/2385828/betera-vs-esc-exort-the-proving-grounds-season-4", "content": "Any matchup that fits one or more of the criteria set in the filter will feature in the today's matches column."}
+{"idx": 3, "title": "a simple theory for feature localization in generative models", "date": "", "ddg_snippet": "arXiv : 2502.00921 v1 [cs.LG] 02 Feb 2025. Blink of an eye: a simple theory for feature localization in generative models ... Thirdly, their final bound includes a polynomial dependence on the moments ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "arXiv : 2502.00921 v1 [cs.LG] 02 Feb 2025. Blink of an eye: a simple theory for feature localization in generative models ... Thirdly, their final bound includes a polynomial dependence on the moments ..."}
+{"idx": 4, "title": "Opportunities and challenges of quantum computing for climate", "date": "", "ddg_snippet": "Climate models are three- dimensional models based on fundamental laws of physics (Jacobson,, 2005 ) . ... of the Earth, and vertical columns above ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10488v1", "content": "Climate models are three- dimensional models based on fundamental laws of physics (Jacobson,, 2005 ) . ... of the Earth, and vertical columns above ..."}
+{"idx": 5, "title": "IPS Meeting 2025", "date": "", "ddg_snippet": "Abstract Computational simulations are redening electromagnetism, especially in modeling quantum plasmas—materials where electron dynamics are fast, nonlinear, and strongly coupled to elec-tromagnetic elds.", "subpage_snippet": "", "source": "ipsmeeting.org", "link": "https://ipsmeeting.org/download/IPSMeeting2025.pdf", "content": "Abstract Computational simulations are redening electromagnetism, especially in modeling quantum plasmas—materials where electron dynamics are fast, nonlinear, and strongly coupled to elec-tromagnetic elds."}
+{"idx": 6, "title": "Possibilities of using IPL technology in therapy for patients with acne...", "date": "", "ddg_snippet": "Huang Q, Chen D, Pan S, Hu M, Wang P, Wang H, et al. Efficacy of alpha hydroxy acid combined with intense pulsed light in the treatment of acne vulgaris: a meta‐analysis. Journal of Cosmetic Dermatology.", "subpage_snippet": "", "source": "rjsvd.com", "link": "https://rjsvd.com/1560-9588/article/view/677911", "content": "Huang Q, Chen D, Pan S, Hu M, Wang P, Wang H, et al. Efficacy of alpha hydroxy acid combined with intense pulsed light in the treatment of acne vulgaris: a meta‐analysis. Journal of Cosmetic Dermatology."}
+{"idx": 7, "title": "Artificial intelligence in healthcare and medicine: clinical applications...", "date": "", "ddg_snippet": "Li X, et al. Role of artificial intelligence in medical image analysis: a review of current trends and future directions. Chen RJ, et al. Algorithmic fairness in artificial intelligence for medicine and healthcare.", "subpage_snippet": "", "source": "eurjmedres.biomedcentral.com", "link": "https://eurjmedres.biomedcentral.com/articles/10.1186/s40001-025-03196-w", "content": "Li X, et al. Role of artificial intelligence in medical image analysis: a review of current trends and future directions. Chen RJ, et al. Algorithmic fairness in artificial intelligence for medicine and healthcare."}
+{"idx": 8, "title": "Issue number 3 :: OPTIMAL APPROXIMATION OF AVERAGE...", "date": "", "ddg_snippet": "12. Li G., Wei Y., Chi Y., Gu Y., Chen Y. Breaking the sample size barrier in model-based reinforcement learning with a generative model.", "subpage_snippet": "", "source": "zhvmmfras.ru", "link": "https://zhvmmfras.ru/s0044466925030074-1/?sl=en", "content": "12. Li G., Wei Y., Chi Y., Gu Y., Chen Y. Breaking the sample size barrier in model-based reinforcement learning with a generative model."}
+{"idx": 9, "title": "Journal of Medical Internet Research - Wearable Technology, Smart...", "date": "", "ddg_snippet": "Zhang Q, Li M, Wu Y. Smart home for elderly care: development and challenges in China. BMC Geriatr.Chang YJ, Peng SM, Wang TY, Chen SF, Chen YR, Chen HC. Autonomous indoor wayfinding for individuals with cognitive impairments. J Neuroeng Rehabil.", "subpage_snippet": "", "source": "www.jmir.org", "link": "https://www.jmir.org/2025/1/e65385/", "content": "Zhang Q, Li M, Wu Y. Smart home for elderly care: development and challenges in China. BMC Geriatr.Chang YJ, Peng SM, Wang TY, Chen SF, Chen YR, Chen HC. Autonomous indoor wayfinding for individuals with cognitive impairments. J Neuroeng Rehabil."}
diff --git a/data/sampled_jsons/arxiv.orghtml2405.19550v1_Section_2_dataset_D_lock_D_weak_D_strong_union.jsonl b/data/sampled_jsons/arxiv.orghtml2405.19550v1_Section_2_dataset_D_lock_D_weak_D_strong_union.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..73db342eaf9882784e43d3013ff9ae2b096bd4a5
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orghtml2405.19550v1_Section_2_dataset_D_lock_D_weak_D_strong_union.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluating the Effect of Labour Unions on Employees’ Productivity...", "date": "", "ddg_snippet": "This study examines the impact of trade unions on employees’ productivity within South Sudan’s public sector, focusing on Juba Municipality.", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/journal/paperinformation?paperid=145776", "content": "This study examines the impact of trade unions on employees’ productivity within South Sudan’s public sector, focusing on Juba Municipality."}
+{"idx": 1, "title": "Login / Member's access to the HTML 6 Editor", "date": "", "ddg_snippet": "There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout.", "subpage_snippet": "", "source": "html6.com", "link": "https://html6.com/login/", "content": "There's no Login to HTML 6. To access your editor, use the unique link you received completing the checkout."}
+{"idx": 2, "title": "Bitwarden Web vault", "date": "", "ddg_snippet": "Two-Step Login\" section .", "subpage_snippet": "", "source": "vault.bitwarden.com", "link": "https://vault.bitwarden.com/", "content": "Two-Step Login\" section ."}
+{"idx": 3, "title": "(PDF) Stress-Testing Capability Elicitation With Password- Locked ...", "date": "", "ddg_snippet": "Weak -to- strong generalization: Eliciting strong capabilities with.MMLU dataset (not our strongest model), we report performance after fine-tuning the password", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381006018_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models", "content": "Weak -to- strong generalization: Eliciting strong capabilities with.MMLU dataset (not our strongest model), we report performance after fine-tuning the password"}
+{"idx": 4, "title": "Доступ к Рутрекеру - Chrome Web Store", "date": "", "ddg_snippet": "Бесплатный доступ к rutracker. org для граждан РФ.This developer has not identified itself as a trader. For consumers in the European Union , please note that consumer rights do not apply to contracts between you and this developer.", "subpage_snippet": "", "source": "chromewebstore.google.com", "link": "https://chromewebstore.google.com/detail/доступ-к-рутрекеру/lbdmhpkmonokeldelekgfefldfboblbj", "content": "Бесплатный доступ к rutracker. org для граждан РФ.This developer has not identified itself as a trader. For consumers in the European Union , please note that consumer rights do not apply to contracts between you and this developer."}
+{"idx": 5, "title": "Слабоположительный резус-фактор » Журнал DonorSearch", "date": "", "ddg_snippet": "Около 1% людей обладают слабоположительным вариантом антигена D резус-фактора — D weak , он же, «Du». Еще совсем недавно такой фенотип определялся как отрицательный.", "subpage_snippet": "", "source": "journal.DonorSearch.org", "link": "https://journal.DonorSearch.org/slabopolojitelnyi_rezus/", "content": "Около 1% людей обладают слабоположительным вариантом антигена D резус-фактора — D weak , он же, «Du». Еще совсем недавно такой фенотип определялся как отрицательный."}
+{"idx": 6, "title": "Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant...", "date": "", "ddg_snippet": "Strong vs. Weak Filtering: In addition to the Baseline11 model trained on unfiltered data, we train models with two filtering approaches. We designed both approaches to be simple and erred on the side of over-filtering to minimize false negatives.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.06601", "content": "Strong vs. Weak Filtering: In addition to the Baseline11 model trained on unfiltered data, we train models with two filtering approaches. We designed both approaches to be simple and erred on the side of over-filtering to minimize false negatives."}
+{"idx": 7, "title": "Как зайти в настройки роутера TP-Link?", "date": "", "ddg_snippet": "Подробная инструкция по входу в настройки роутеров TP-Link. Заходим в панель управления по адресу 192.168.1.1, или 192.168.0.1.", "subpage_snippet": "", "source": "help-wifi.com", "link": "https://help-wifi.com/tp-link/kak-zajti-v-nastrojki-routera-tp-link/", "content": "Подробная инструкция по входу в настройки роутеров TP-Link. Заходим в панель управления по адресу 192.168.1.1, или 192.168.0.1."}
+{"idx": 8, "title": "Nitter instance uptime and status tracker.", "date": "", "ddg_snippet": "Nitter instance uptime and status tracker.", "subpage_snippet": "", "source": "status.d420.de", "link": "https://status.d420.de/", "content": "Nitter instance uptime and status tracker."}
+{"idx": 9, "title": "Face Swap Online Free", "date": "", "ddg_snippet": "Free online face changer that allows you to swap heads and replace faces in photos. No sign up, no watermark.", "subpage_snippet": "", "source": "remaker.ai", "link": "https://remaker.ai/face-swap-free/", "content": "Free online face changer that allows you to swap heads and replace faces in photos. No sign up, no watermark."}
diff --git a/data/sampled_jsons/arxiv.orghtml2410.00844v1_LEnergy_equation_10.jsonl b/data/sampled_jsons/arxiv.orghtml2410.00844v1_LEnergy_equation_10.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5e9f55fca322b1f1c0ef76136aad9ae7f5261d4d
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orghtml2410.00844v1_LEnergy_equation_10.jsonl
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+{"idx": 0, "title": "Energy –momentum relation - Wikipedia", "date": "", "ddg_snippet": "In physics, the energy –momentum relation, or relativistic dispersion relation, is the relativistic equation relating total energy to invariant mass and momentum. It is the extension of mass– energy equivalence for bodies or systems with non-zero momen...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Energy–momentum_relation", "content": "In physics, the energy –momentum relation, or relativistic dispersion relation, is the relativistic equation relating total energy to invariant mass and momentum. It is the extension of mass– energy equivalence for bodies or systems with non-zero momen..."}
+{"idx": 1, "title": "[2410.05034] The energy-critical stochastic Zakharov system [2410.04300] Decentralized Equitable Energy Access in Energy ... [2410.24168] Energy-Specific Bethe-Salpeter Equation ... [2410.12032] MLPerf Power: Benchmarking the Energy Efficiency ... arXiv.org e-Print archive [2410.09241] Large Language Models for Energy-Efficient Code ...", "date": "", "ddg_snippet": "Oct 7, 2024 · The proof is based on the refined rescaling approach and a new functional framework, where both Fourier restriction and local smoothing norms are used as well as a (uniform) double endpoint Strichartz and local smoothing inequality for the Schrödinger equation with certain rough and time dependent lower order perturbations. Oct 5, 2024 · We address the issue of equitable energy access within an energy community consisting of members with diverse socioeconomic backgrounds, including varying income levels and differing capacities to access distributed energy resources such as solar power and storage systems. While optimal energy consumption scheduling is well-studied, integrating equity into decentralized real-time energy access ... Oct 31, 2024 · We present an energy -specific Bethe-Salpeter equation (BSE) implementation for efficient core and valence optical spectrum calculations. In energy -specific BSE, high-lying excitation energies are obtained by constructing trial vectors and expanding the subspace targeting excitation energies above the predefined energy threshold in the Davidson algorithm. To calculate optical spectra over a ... Oct 15, 2024 · Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper ... arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Oct 11, 2024 · Energy -efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet other requirements. We take inspiration from recent work exploring the use of large language models (LLMs) for different software engineering activities. We propose a novel application of LLMs: as code optimizers for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.05034", "content": "Oct 7, 2024 · The proof is based on the refined rescaling approach and a new functional framework, where both Fourier restriction and local smoothing norms are used as well as a (uniform) double endpoint Strichartz and local smoothing inequality for the Schrödinger equation with certain rough and time dependent lower order perturbations. Oct 5, 2024 · We address the issue of equitable energy access within an energy community consisting of members with diverse socioeconomic backgrounds, including varying income levels and differing capacities to access distributed energy resources such as solar power and storage systems. While optimal energy consumption scheduling is well-studied, integrating equity into decentralized real-time energy access ... Oct 31, 2024 · We present an energy -specific Bethe-Salpeter equation (BSE) implementation for efficient core and valence optical spectrum calculations. In energy -specific BSE, high-lying excitation energies are obtained by constructing trial vectors and expanding the subspace targeting excitation energies above the predefined energy threshold in the Davidson algorithm. To calculate optical spectra over a ... Oct 15, 2024 · Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper ... arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Oct 11, 2024 · Energy -efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet other requirements. We take inspiration from recent work exploring the use of large language models (LLMs) for different software engineering activities. We propose a novel application of LLMs: as code optimizers for ..."}
+{"idx": 2, "title": "[2410.24168] Energy-Specific Bethe-Salpeter Equation ... [2410.12032] MLPerf Power: Benchmarking the Energy Efficiency ... arXiv.org e-Print archive [2410.09241] Large Language Models for Energy-Efficient Code ...", "date": "", "ddg_snippet": "Oct 31, 2024 · We present an energy -specific Bethe-Salpeter equation (BSE) implementation for efficient core and valence optical spectrum calculations. In energy -specific BSE, high-lying excitation energies are obtained by constructing trial vectors and expanding the subspace targeting excitation energies above the predefined energy threshold in the Davidson algorithm. To calculate optical spectra over a ... Oct 15, 2024 · Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper ... arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Oct 11, 2024 · Energy -efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet other requirements. We take inspiration from recent work exploring the use of large language models (LLMs) for different software engineering activities. We propose a novel application of LLMs: as code optimizers for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.24168", "content": "Oct 31, 2024 · We present an energy -specific Bethe-Salpeter equation (BSE) implementation for efficient core and valence optical spectrum calculations. In energy -specific BSE, high-lying excitation energies are obtained by constructing trial vectors and expanding the subspace targeting excitation energies above the predefined energy threshold in the Davidson algorithm. To calculate optical spectra over a ... Oct 15, 2024 · Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper ... arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Oct 11, 2024 · Energy -efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet other requirements. We take inspiration from recent work exploring the use of large language models (LLMs) for different software engineering activities. We propose a novel application of LLMs: as code optimizers for ..."}
+{"idx": 3, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 4, "title": "EPS_main_text_r7_for_ arxiv", "date": "", "ddg_snippet": "The threshold in 𝐵! is 10 =~ 10 > in Figure 2. Note that the wavelength of the fastest growing mode for the MMUb feedback (= 2𝜋/𝑘.?@) is longer.the diffusion in the energy equation Eq. (13) is considered only in the lower layer: 𝜅4 is assumed to be 0.01.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15760", "content": "The threshold in 𝐵! is 10 =~ 10 > in Figure 2. Note that the wavelength of the fastest growing mode for the MMUb feedback (= 2𝜋/𝑘.?@) is longer.the diffusion in the energy equation Eq. (13) is considered only in the lower layer: 𝜅4 is assumed to be 0.01."}
+{"idx": 5, "title": "[2410.04300] Decentralized Equitable Energy Access in Energy ...", "date": "", "ddg_snippet": "Oct 5, 2024 · We address the issue of equitable energy access within an energy community consisting of members with diverse socioeconomic backgrounds, including varying income levels and differing capacities to access distributed energy resources such as solar power and storage systems. While optimal energy consumption scheduling is well-studied, integrating equity into decentralized real-time energy access ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.04300", "content": "Oct 5, 2024 · We address the issue of equitable energy access within an energy community consisting of members with diverse socioeconomic backgrounds, including varying income levels and differing capacities to access distributed energy resources such as solar power and storage systems. While optimal energy consumption scheduling is well-studied, integrating equity into decentralized real-time energy access ..."}
+{"idx": 6, "title": "[2410.12032] MLPerf Power: Benchmarking the Energy Efficiency ...", "date": "", "ddg_snippet": "Oct 15, 2024 · Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.12032", "content": "Oct 15, 2024 · Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper ..."}
+{"idx": 7, "title": "[2410.09241] Large Language Models for Energy-Efficient Code ...", "date": "", "ddg_snippet": "Oct 11, 2024 · Energy -efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet other requirements. We take inspiration from recent work exploring the use of large language models (LLMs) for different software engineering activities. We propose a novel application of LLMs: as code optimizers for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09241", "content": "Oct 11, 2024 · Energy -efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet other requirements. We take inspiration from recent work exploring the use of large language models (LLMs) for different software engineering activities. We propose a novel application of LLMs: as code optimizers for ..."}
+{"idx": 8, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "By leveraging Fisher regularization, our method transforms a stochastic differential equation (SDE) problem into an ordinary differential equation (ODE) constraint. Through the use of neural network modeling for growth and death, our framework models dynamics without requiring prior knowledge of these processes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v1", "content": "By leveraging Fisher regularization, our method transforms a stochastic differential equation (SDE) problem into an ordinary differential equation (ODE) constraint. Through the use of neural network modeling for growth and death, our framework models dynamics without requiring prior knowledge of these processes."}
+{"idx": 9, "title": "How to Calculate Kinetic Energy : 9 Steps (with Pictures) - wikiHow", "date": "", "ddg_snippet": "Write the equation . The formula for calculating kinetic energy (KE) is KE = 0.5 x mv2. Here m stands for mass, the measure of how much matter is in an object, and v stands for velocity of the object, or the rate at which the object changes its position.[8] X Research source.", "subpage_snippet": "", "source": "www.wikihow.com", "link": "https://www.wikihow.com/Calculate-Kinetic-Energy", "content": "Write the equation . The formula for calculating kinetic energy (KE) is KE = 0.5 x mv2. Here m stands for mass, the measure of how much matter is in an object, and v stands for velocity of the object, or the rate at which the object changes its position.[8] X Research source."}
diff --git a/data/sampled_jsons/arxiv.orghtml2504.11786v1_DART_methodology_equation_5_lambda_m_coefficient_year_2024.jsonl b/data/sampled_jsons/arxiv.orghtml2504.11786v1_DART_methodology_equation_5_lambda_m_coefficient_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2504.11786] DART: Disease-aware Image-Text Alignment and Self ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 1, "title": "[2504.11786] Rebuttal - DART: Disease-aware Image-Text Alignment and ...", "date": "", "ddg_snippet": "DART introduces two novel approaches extending previous studies [48,50,52], which utilize image-text alignment and disease classifiers to capture patients' conditions.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2504.11786", "content": "DART introduces two novel approaches extending previous studies [48,50,52], which utilize image-text alignment and disease classifiers to capture patients' conditions."}
+{"idx": 2, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 3, "title": "Methodology - arXiv.org", "date": "", "ddg_snippet": "To appear in Structural Equation Modeling: A Multidisciplinary Journal. This version reflects the originally submitted manuscript with minor typographical corrections and formatting changes", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/stat.ME/recent", "content": "To appear in Structural Equation Modeling: A Multidisciplinary Journal. This version reflects the originally submitted manuscript with minor typographical corrections and formatting changes"}
+{"idx": 4, "title": "DART | PDF | Artificial Intelligence | Intelligence (AI) & Semantics", "date": "", "ddg_snippet": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ..."}
+{"idx": 5, "title": "[2410.08159] DART: Denoising Autoregressive Transformer for Scalable ...", "date": "", "ddg_snippet": "Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process which gradually adds noise to the input. We argue that the Markovian property limits the model's ability to fully utilize the generation trajectory, leading to inefficiencies during training and inference. In this paper, we propose DART , a transformer-based model that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.08159", "content": "Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process which gradually adds noise to the input. We argue that the Markovian property limits the model's ability to fully utilize the generation trajectory, leading to inefficiencies during training and inference. In this paper, we propose DART , a transformer-based model that ..."}
+{"idx": 6, "title": "Flowchart of MRI-DART | Download Scientific Diagram", "date": "", "ddg_snippet": "This is an iterative method, which efficiently approximates least squares solutions for large systems of linear equations . Fig. 1 depicts a flowchart of MRI- DART .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Flowchart-of-MRI-DART_fig1_262162386", "content": "This is an iterative method, which efficiently approximates least squares solutions for large systems of linear equations . Fig. 1 depicts a flowchart of MRI- DART ."}
+{"idx": 7, "title": "PDF arXiv:2410.08159v1 [cs.CV] 10 Oct 2024", "date": "", "ddg_snippet": "ABSTRACT Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process which gradually adds noise to the input. We argue that the Markovian property limits the model's ability to fully utilize the generation trajectory, leading to inefficiencies during training and inference. In this paper, we propose DART , a transformer-based ...", "subpage_snippet": "", "source": "jiataogu.me", "link": "https://jiataogu.me/papers/gu2024dart.pdf", "content": "ABSTRACT Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process which gradually adds noise to the input. We argue that the Markovian property limits the model's ability to fully utilize the generation trajectory, leading to inefficiencies during training and inference. In this paper, we propose DART , a transformer-based ..."}
+{"idx": 8, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease-relevant findings in X-ray images. Previous approaches for radiology report generation have shown impressive performance. However, there remains significant potential to improve accuracy by ensuring that retrieved reports contain disease ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2504.11786", "content": "The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease-relevant findings in X-ray images. Previous approaches for radiology report generation have shown impressive performance. However, there remains significant potential to improve accuracy by ensuring that retrieved reports contain disease ..."}
+{"idx": 9, "title": "wedm2401, gjrmstn1440, dongheeshin, yhson135, meeeo , kamte @korea.ac ...", "date": "", "ddg_snippet": "The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease-relevant findings in X-ray images. Previous approaches for radiology re- port generation have shown impressive performance. How- ever, there remains significant potential to improve accu- racy by ensuring that retrieved reports contain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.11786", "content": "The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease-relevant findings in X-ray images. Previous approaches for radiology re- port generation have shown impressive performance. How- ever, there remains significant potential to improve accu- racy by ensuring that retrieved reports contain ..."}
diff --git a/data/sampled_jsons/arxiv.orgpdf2406.14532_Zhang_et_al_scaling_law.jsonl b/data/sampled_jsons/arxiv.orgpdf2406.14532_Zhang_et_al_scaling_law.jsonl
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index 0000000000000000000000000000000000000000..675da2945d1c4492f3826bee89c0bdebab65d8f0
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orgpdf2406.14532_Zhang_et_al_scaling_law.jsonl
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+{"idx": 0, "title": "Optimal Resource Allocation for Efficient Test-Time Scaling", "date": "", "ddg_snippet": "by X Wang · 2025 — Test-Time Scaling (TTS) improves the performance of Large Language Models. (LLMs) by using additional inference-time computation to explore ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.15707?", "content": "by X Wang · 2025 — Test-Time Scaling (TTS) improves the performance of Large Language Models. (LLMs) by using additional inference-time computation to explore ..."}
+{"idx": 1, "title": "SCALING AUTOMATED PROCESS VERIFIERS FOR LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 118 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv : 2406.14532 , 2024. Zhihong Shao, Peiyi Wang, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=A6Y7AqlzLW", "content": "by A Setlur · Cited by 118 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv : 2406.14532 , 2024. Zhihong Shao, Peiyi Wang, ..."}
+{"idx": 2, "title": "Scaling Test-Time Compute Without Verification or RL is ...", "date": "", "ddg_snippet": "Abstract. Despite substantial advances in scaling test-time compute, an ongoing debate in the community is how it should be scaled up to enable continued.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=beeNgQEfe2&name=pdf", "content": "Abstract. Despite substantial advances in scaling test-time compute, an ongoing debate in the community is how it should be scaled up to enable continued."}
+{"idx": 3, "title": "arXiv:2410.01720v3 [cs.AI] 6 Feb 2025", "date": "", "ddg_snippet": "by Z Gan · 2024 · Cited by 8 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv : 2406.14532 , 2024. Noam Slonim, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.01720", "content": "by Z Gan · 2024 · Cited by 8 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv : 2406.14532 , 2024. Noam Slonim, ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 67 — Concretely, we find that under the scaling law of Zhang et al . [70], the error rate scales as ≈D−0.05 to D−0.15 in the size D of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 67 — Concretely, we find that under the scaling law of Zhang et al . [70], the error rate scales as ≈D−0.05 to D−0.15 in the size D of ..."}
+{"idx": 5, "title": "Towards a Theoretical Understanding of Synthetic Data in ...", "date": "", "ddg_snippet": "12 Oct 2024 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint arXiv : 2406.14532 , 2024. Slonim et al . ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.01720v2", "content": "12 Oct 2024 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint arXiv : 2406.14532 , 2024. Slonim et al . ( ..."}
+{"idx": 6, "title": "Balancing Cost and Effectiveness of Synthetic Data ...", "date": "", "ddg_snippet": "by YC Chan · 2024 · Cited by 23 — Additionally, to fit the curves in our plots and better model the scaling relationship of our data generation methods, we adopt prior work on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.19759", "content": "by YC Chan · 2024 · Cited by 23 — Additionally, to fit the curves in our plots and better model the scaling relationship of our data generation methods, we adopt prior work on ..."}
+{"idx": 7, "title": "Can 1B LLM Surpass 405B LLM? Rethinking Compute- ...", "date": "", "ddg_snippet": "by R Liu · 2025 · Cited by 61 — Test-Time Scaling (TTS) is an important method for improving the performance of Large Language Models. (LLMs) by using additional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.06703?", "content": "by R Liu · 2025 · Cited by 61 — Test-Time Scaling (TTS) is an important method for improving the performance of Large Language Models. (LLMs) by using additional ..."}
+{"idx": 8, "title": "Optimizing Test-Time Compute via Meta Reinforcement ...", "date": "", "ddg_snippet": "10 Mar 2025 — Scaling scaling laws with board games . arXiv preprint arXiv:2104.03113, 2021. Kang et al. [2024] ↑ Katie Kang, Eric Wallace ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.07572v1", "content": "10 Mar 2025 — Scaling scaling laws with board games . arXiv preprint arXiv:2104.03113, 2021. Kang et al. [2024] ↑ Katie Kang, Eric Wallace ..."}
+{"idx": 9, "title": "Preference Learning via Error-injected Self-editing", "date": "", "ddg_snippet": "by K Xu · 2025 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv : 2406.14532 . Zhihong Shao, Peiyi ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1506.pdf", "content": "by K Xu · 2025 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold. arXiv preprint. arXiv : 2406.14532 . Zhihong Shao, Peiyi ..."}
diff --git a/data/sampled_jsons/arxiv.orgpdf2408.17052_experimental_setup_implementation_details_backbone.jsonl b/data/sampled_jsons/arxiv.orgpdf2408.17052_experimental_setup_implementation_details_backbone.jsonl
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index 0000000000000000000000000000000000000000..96c008425e5b5faa35a8ab7ef5b27aed47c68eb1
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+++ b/data/sampled_jsons/arxiv.orgpdf2408.17052_experimental_setup_implementation_details_backbone.jsonl
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+{"idx": 0, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed “blendfake”, encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.17052", "content": "Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed “blendfake”, encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without ..."}
+{"idx": 1, "title": "[2408.17052] Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "Sep 5, 2024 · Implementation Details . For preprocessing and training, we strictly follow the official code and settings provided by DeepFakeBench [54] to ensure fair comparison. EfficientNetB4 [43] is employed as the backbone of our detector. The trade-off parameters are set to β = 1 𝛽 1 \\beta=1 and γ = 10 𝛾 10 \\gamma=10.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2408.17052", "content": "Sep 5, 2024 · Implementation Details . For preprocessing and training, we strictly follow the official code and settings provided by DeepFakeBench [54] to ensure fair comparison. EfficientNetB4 [43] is employed as the backbone of our detector. The trade-off parameters are set to β = 1 𝛽 1 \\beta=1 and γ = 10 𝛾 10 \\gamma=10."}
+{"idx": 2, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 3, "title": "(PDF) Can We Leave Deepfake Data Behind in Training Deepfake ...", "date": "", "ddg_snippet": "Aug 30, 2024 · Can We Leave Deepfake Data Behind in Training Deepfake Detector? August 2024 DOI: 10.48550/ arXiv . 2408 . 17052 License CC0 Authors: Jikang Cheng", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648453_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector", "content": "Aug 30, 2024 · Can We Leave Deepfake Data Behind in Training Deepfake Detector? August 2024 DOI: 10.48550/ arXiv . 2408 . 17052 License CC0 Authors: Jikang Cheng"}
+{"idx": 4, "title": "General Quantum Alchemical Free Energy - arXiv.org", "date": "", "ddg_snippet": "of-concept application of first-principles methods to the pKa calculation of amino acid side chains in a QM/MM setup . Our theoretical value only agrees with the experimental reference moderately well, but our general formulation allows this to be further improved in the future by converging our theoretical treatment in various aspects. ne ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.17002", "content": "of-concept application of first-principles methods to the pKa calculation of amino acid side chains in a QM/MM setup . Our theoretical value only agrees with the experimental reference moderately well, but our general formulation allows this to be further improved in the future by converging our theoretical treatment in various aspects. ne ..."}
+{"idx": 5, "title": "(PDF) Vision-LSTM: xLSTM as Generic Vision Backbone", "date": "", "ddg_snippet": "Jun 6, 2024 · Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has been extended to ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381227329_Vision-LSTM_xLSTM_as_Generic_Vision_Backbone", "content": "Jun 6, 2024 · Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has been extended to ..."}
+{"idx": 6, "title": "Конвертировать Word в PDF - быстро, онлайн, бесплатно - PDF 24", "date": "", "ddg_snippet": "Бесплатный онлайн-конвертер для конвертации Word в PDF . Быстро и просто.", "subpage_snippet": "", "source": "tools.pdf24.org", "link": "https://tools.pdf24.org/ru/word-B-pdf", "content": "Бесплатный онлайн-конвертер для конвертации Word в PDF . Быстро и просто."}
+{"idx": 7, "title": "Merge PDF and JPEG In Free | Pi7 PDF Tool", "date": "", "ddg_snippet": "Merge PDFs and JPGs quickly and securely with Pi7 PDF Tool. Edit, rearrange, and combine unlimited files for free.", "subpage_snippet": "", "source": "pdf.pi7.org", "link": "https://pdf.pi7.org/merge-pdf-and-jpeg", "content": "Merge PDFs and JPGs quickly and securely with Pi7 PDF Tool. Edit, rearrange, and combine unlimited files for free."}
+{"idx": 8, "title": "Traduzir Documentos Grandes Online: PDF , DOCX, XLSX", "date": "", "ddg_snippet": "Nós nos especializamos em traduzir arquivos grandes! Traduza arquivos de qualquer tamanho: PDF , DOCX, XLSX - instantaneamente e com facilidade, preservando o layout original.", "subpage_snippet": "", "source": "pdft.ai", "link": "https://pdft.ai/pt/translate-large-pdf/", "content": "Nós nos especializamos em traduzir arquivos grandes! Traduza arquivos de qualquer tamanho: PDF , DOCX, XLSX - instantaneamente e com facilidade, preservando o layout original."}
+{"idx": 9, "title": "DEMO | PDF .ai | The best ChatPDF app", "date": "", "ddg_snippet": "We built the ultimate ChatPDF app that allows you to chat with any PDF : ask questions, get summaries, find anything you need!", "subpage_snippet": "", "source": "pdf.ai", "link": "https://pdf.ai/demo", "content": "We built the ultimate ChatPDF app that allows you to chat with any PDF : ask questions, get summaries, find anything you need!"}
diff --git a/data/sampled_jsons/arxiv.orgpdf2503.06366v1_spurious_correlation_Schubert_polynomials.jsonl b/data/sampled_jsons/arxiv.orgpdf2503.06366v1_spurious_correlation_Schubert_polynomials.jsonl
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index 0000000000000000000000000000000000000000..97509be6f0379676ef8e4271ab2bca6a01d75df3
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@@ -0,0 +1,8 @@
+{"idx": 0, "title": "[2503.06366] Machine Learning meets Algebraic Combinatorics ...", "date": "", "ddg_snippet": "Mar 9, 2025 · Abstract page for arXiv paper 2503 .06366: Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "Mar 9, 2025 · Abstract page for arXiv paper 2503 .06366: Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics"}
+{"idx": 1, "title": "[2503.08884] Seeing What's Not There: Spurious Correlation in ...", "date": "", "ddg_snippet": "Mar 11, 2025 · Unimodal vision models are known to rely on spurious correlations , but it remains unclear to what extent Multimodal Large Language Models (MLLMs) exhibit similar biases despite language supervision. In this paper, we investigate spurious bias in MLLMs and introduce SpurLens, a pipeline that leverages GPT-4 and open-set object detectors to automatically identify spurious visual cues without ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.08884", "content": "Mar 11, 2025 · Unimodal vision models are known to rely on spurious correlations , but it remains unclear to what extent Multimodal Large Language Models (MLLMs) exhibit similar biases despite language supervision. In this paper, we investigate spurious bias in MLLMs and introduce SpurLens, a pipeline that leverages GPT-4 and open-set object detectors to automatically identify spurious visual cues without ..."}
+{"idx": 2, "title": "[2503.03903] Single-SEM Schubert Polynomials - arXiv.org arXiv.org Novel technique overcomes spurious correlations problem in AI Robustness to Spurious Correlation: A Comprehensive Review Addressing Spurious Correlations in Machine Learning Models ...", "date": "", "ddg_snippet": "Mar 5, 2025 · Abstract page for arXiv paper 2503 .03903: Single-SEM Schubert Polynomials arXiv .org AI models often rely on \" spurious correlations ,\" making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem. The work has been published on the arXiv preprint server. Dec 21, 2024 · The persistence of spurious features in machine learning models remains a significant challenge. To address this issue, we identify several future directions that require attention. Firstly, we ... This paper provides a comprehensive review of the current state of research on spurious correlations , covering the detec-tion, understanding, and mitigation of these undesirable behaviors. We first discuss the prevalence of spurious correlations in various machine learning applications and their potential consequences.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.03903", "content": "Mar 5, 2025 · Abstract page for arXiv paper 2503 .03903: Single-SEM Schubert Polynomials arXiv .org AI models often rely on \" spurious correlations ,\" making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem. The work has been published on the arXiv preprint server. Dec 21, 2024 · The persistence of spurious features in machine learning models remains a significant challenge. To address this issue, we identify several future directions that require attention. Firstly, we ... This paper provides a comprehensive review of the current state of research on spurious correlations , covering the detec-tion, understanding, and mitigation of these undesirable behaviors. We first discuss the prevalence of spurious correlations in various machine learning applications and their potential consequences."}
+{"idx": 3, "title": "Novel technique overcomes spurious correlations problem in AI", "date": "", "ddg_snippet": "AI models often rely on \" spurious correlations ,\" making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem. The work has been published on the arXiv preprint server.", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-04-technique-spurious-problem-ai.pdf", "content": "AI models often rely on \" spurious correlations ,\" making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem. The work has been published on the arXiv preprint server."}
+{"idx": 4, "title": "Robustness to Spurious Correlation: A Comprehensive Review", "date": "", "ddg_snippet": "Dec 21, 2024 · The persistence of spurious features in machine learning models remains a significant challenge. To address this issue, we identify several future directions that require attention. Firstly, we ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387302563_Robustness_to_Spurious_Correlation_A_Comprehensive_Review", "content": "Dec 21, 2024 · The persistence of spurious features in machine learning models remains a significant challenge. To address this issue, we identify several future directions that require attention. Firstly, we ..."}
+{"idx": 5, "title": "Addressing Spurious Correlations in Machine Learning Models ...", "date": "", "ddg_snippet": "This paper provides a comprehensive review of the current state of research on spurious correlations , covering the detec-tion, understanding, and mitigation of these undesirable behaviors. We first discuss the prevalence of spurious correlations in various machine learning applications and their potential consequences.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4KlTEhHCzFm", "content": "This paper provides a comprehensive review of the current state of research on spurious correlations , covering the detec-tion, understanding, and mitigation of these undesirable behaviors. We first discuss the prevalence of spurious correlations in various machine learning applications and their potential consequences."}
+{"idx": 6, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets...", "date": "", "ddg_snippet": "Hyperparameters for these experi-ments can be found in Section C.2. The results are reported as ac-curacy/MCC, where the Matthews Correlation Coefficient (MCC) provides a balanced metric that ranges from -1 (total disagreement) to 1 (perfect prediction), with 0 corresponding to...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "Hyperparameters for these experi-ments can be found in Section C.2. The results are reported as ac-curacy/MCC, where the Matthews Correlation Coefficient (MCC) provides a balanced metric that ranges from -1 (total disagreement) to 1 (perfect prediction), with 0 corresponding to..."}
+{"idx": 7, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets...", "date": "", "ddg_snippet": "Hyperparameters for these experiments can be found in Section C.2. The results are reported as accuracy/MCC, where the Matthews Correlation Coefficient (MCC) provides a balanced metric that ranges from -1 (total disagreement) to 1 (perfect prediction), with 0 corresponding to...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "Hyperparameters for these experiments can be found in Section C.2. The results are reported as accuracy/MCC, where the Matthews Correlation Coefficient (MCC) provides a balanced metric that ranges from -1 (total disagreement) to 1 (perfect prediction), with 0 corresponding to..."}
diff --git a/data/sampled_jsons/arxiv.orgpdf2506.05503_Coordinate-wise_Private_Median_and_the_l_infinity_Guarantee.jsonl b/data/sampled_jsons/arxiv.orgpdf2506.05503_Coordinate-wise_Private_Median_and_the_l_infinity_Guarantee.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0c8387f8706b5197341cb0ff0674b2adf75cd301
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orgpdf2506.05503_Coordinate-wise_Private_Median_and_the_l_infinity_Guarantee.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2506.05503] On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "Recently differential privacy has been used for a number of streaming, data structure, and dynamic graph problems as a means of hiding the internal randomness of the data structure, so that multiple possibly adaptive queries can be made without sacrificing the correctness of the responses. Although these works use differential privacy to show that for some problems it is possible to tolerate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.05503", "content": "Recently differential privacy has been used for a number of streaming, data structure, and dynamic graph problems as a means of hiding the internal randomness of the data structure, so that multiple possibly adaptive queries can be made without sacrificing the correctness of the responses. Although these works use differential privacy to show that for some problems it is possible to tolerate ..."}
+{"idx": 1, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 2, "title": "Approximation guarantees of Median Mechanism in $\\\\mathbb{R}^d$", "date": "", "ddg_snippet": "The coordinate-wise median is a classic and most well-studied strategy-proof mechanism in social choice and facility location scenarios. Surprisingly, there is no systematic study of its approximation ratio in d -dimensional spaces.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.08578", "content": "The coordinate-wise median is a classic and most well-studied strategy-proof mechanism in social choice and facility location scenarios. Surprisingly, there is no systematic study of its approximation ratio in d -dimensional spaces."}
+{"idx": 3, "title": "[2506.04566v1] Clustering and Median Aggregation Improve Differentially ...", "date": "", "ddg_snippet": "Differentially private (DP) language model inference is an approach for generating private synthetic text. A sensitive input example is used to prompt an off- the -shelf large language model (LLM) to produce a similar example. Multiple examples can be aggregated together to formally satisfy the DP guarantee . Prior work creates inference batches by sampling sensitive inputs uniformly at random ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.04566v1", "content": "Differentially private (DP) language model inference is an approach for generating private synthetic text. A sensitive input example is used to prompt an off- the -shelf large language model (LLM) to produce a similar example. Multiple examples can be aggregated together to formally satisfy the DP guarantee . Prior work creates inference batches by sampling sensitive inputs uniformly at random ..."}
+{"idx": 4, "title": "arXiv:2502.08578v2 [cs.GT] 13 Feb 2025", "date": "", "ddg_snippet": "The coordinate-wise median is a classic and most well-studied strategy-proof mechanism in social choice and facility location scenarios. Surprisingly, there is no systematic study of its approximation ratio in d-dimensional spaces. √ The best known approximation guarantee in", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.08578", "content": "The coordinate-wise median is a classic and most well-studied strategy-proof mechanism in social choice and facility location scenarios. Surprisingly, there is no systematic study of its approximation ratio in d-dimensional spaces. √ The best known approximation guarantee in"}
+{"idx": 5, "title": "Differential Privacy With Higher Utility by Exploiting Coordinate-Wise ...", "date": "", "ddg_snippet": "Conventionally, in a differentially private additive noise mechanism, independent and identically distributed (i.i.d.) noise samples are added to each coordinate of the response. In this work, we formally present the addition of noise that is independent but not identically distributed (i.n.i.d.) across the coordinates to achieve tighter privacy-accuracy trade-off by exploiting coordinate-wise ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10858760", "content": "Conventionally, in a differentially private additive noise mechanism, independent and identically distributed (i.i.d.) noise samples are added to each coordinate of the response. In this work, we formally present the addition of noise that is independent but not identically distributed (i.n.i.d.) across the coordinates to achieve tighter privacy-accuracy trade-off by exploiting coordinate-wise ..."}
+{"idx": 6, "title": "arXiv.org", "date": "", "ddg_snippet": "arXiv.org provides access to a vast collection of research papers across various fields, enabling researchers and enthusiasts to explore cutting-edge scientific advancements.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.01583", "content": "arXiv.org provides access to a vast collection of research papers across various fields, enabling researchers and enthusiasts to explore cutting-edge scientific advancements."}
+{"idx": 7, "title": "[2506.05503] On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "Abstract: Recently differential privacy has been used for a number of streaming, data structure, and dynamic graph problems as a means of hiding the internal randomness of the data structure, so that multiple possibly adaptive queries can be made without sacrificing the correctness of the responses. Although these works use differential privacy to show that for some problems it is possible to ...", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2506.05503", "content": "Abstract: Recently differential privacy has been used for a number of streaming, data structure, and dynamic graph problems as a means of hiding the internal randomness of the data structure, so that multiple possibly adaptive queries can be made without sacrificing the correctness of the responses. Although these works use differential privacy to show that for some problems it is possible to ..."}
+{"idx": 8, "title": "PDF Secure Sublinear Time Differentiall Private Median Computation - USENIX", "date": "", "ddg_snippet": "ad conversions, Google & Mastercard [B18] tax fraud detection, Estonian government & Sharemind [BJSV15] government studies, Boston Women's Workforce Council [LJAIQVB18]", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/sec20_slides_bohler.pdf", "content": "ad conversions, Google & Mastercard [B18] tax fraud detection, Estonian government & Sharemind [BJSV15] government studies, Boston Women's Workforce Council [LJAIQVB18]"}
+{"idx": 9, "title": "On Differential Privacy for Adaptively Solving Search Problems via ...", "date": "", "ddg_snippet": "Coordinate-wise Private Median and the l∞ Guarantee . One natural idea is to extend the private median framework of [BKM+22] to outputting an approximation to the solution vector.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503", "content": "Coordinate-wise Private Median and the l∞ Guarantee . One natural idea is to extend the private median framework of [BKM+22] to outputting an approximation to the solution vector."}
diff --git a/data/sampled_jsons/arxiv_1903.11027_abstract.jsonl b/data/sampled_jsons/arxiv_1903.11027_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5705b7e9fafb46481187d2db42b05fa4ecb3731
--- /dev/null
+++ b/data/sampled_jsons/arxiv_1903.11027_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1903.11027] nuScenes: A multimodal dataset for autonomous", "date": "", "ddg_snippet": "Abstract : Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. ... that work with arXivLabs have ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "Abstract : Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. ... that work with arXivLabs have ..."}
+{"idx": 1, "title": "LLM4Drive: A Survey of Large Language Models for Autonomous", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.01043v4", "content": "... Abstract"}
+{"idx": 2, "title": "The System Description of CPS Team for Track on Driving with", "date": "", "ddg_snippet": "Abstract ... arXiv preprint arXiv : 1903 . 11027 , 2019. ... arXiv preprint arXiv :2305.14045 , 2023.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11071v1", "content": "Abstract ... arXiv preprint arXiv : 1903 . 11027 , 2019. ... arXiv preprint arXiv :2305.14045 , 2023."}
+{"idx": 3, "title": "MS-Occ: Multi-Stage LiDAR-Camera Fusion for 3D Semantic", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.15888v1", "content": "... Abstract"}
+{"idx": 4, "title": "Floating Car Observers in Intelligent Transportation Systems:", "date": "", "ddg_snippet": "However, microscopic simulations are not inherently designed for such detailed estimations; they are typically highly abstract .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.02845v1", "content": "However, microscopic simulations are not inherently designed for such detailed estimations; they are typically highly abstract ."}
+{"idx": 5, "title": "Jingjing Liu-AIR", "date": "", "ddg_snippet": "52] Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung, and Jingjing Liu, Multi-Fact Correction in Abstractive Text Summarization ...", "subpage_snippet": "", "source": "air.tsinghua.edu.cn", "link": "https://air.tsinghua.edu.cn/en/info/1046/1194.htm", "content": "52] Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung, and Jingjing Liu, Multi-Fact Correction in Abstractive Text Summarization ..."}
+{"idx": 6, "title": "Citations of Changes in the wage structure and earnings", "date": "", "ddg_snippet": "Heterogeneous Impact of the Minimum Wage: Implications for Changes in Between- and Within-group Inequality ,\" Papers 1903 .03925, arXiv .org, revised ...", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/r/eee/labchp/3-26.html", "content": "Heterogeneous Impact of the Minimum Wage: Implications for Changes in Between- and Within-group Inequality ,\" Papers 1903 .03925, arXiv .org, revised ..."}
+{"idx": 7, "title": "US11726492B2 - Collision avoidance perception system - Google", "date": "", "ddg_snippet": "... 230000008447 perception Effects 0.000 title claims abstract description 65", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11726492B2/en", "content": "... 230000008447 perception Effects 0.000 title claims abstract description 65"}
+{"idx": 8, "title": "Phenomes: the current frontier in animal breeding | Genetics", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "gsejournal.biomedcentral.com", "link": "https://gsejournal.biomedcentral.com/articles/10.1186/s12711-021-00618-1", "content": "... Abstract"}
+{"idx": 9, "title": "GitHub - eddyhkchiu/mahalanobis_3d_multi_object_tracking:", "date": "", "ddg_snippet": "Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom, arXiv : 1903 . 11027 , 2019.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/eddyhkchiu/mahalanobis_3d_multi_object_tracking/", "content": "Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom, arXiv : 1903 . 11027 , 2019."}
diff --git a/data/sampled_jsons/arxiv_2405.14728_Example_3.2_formula_abd(1-e)_+_(1-a)cd(1-e).jsonl b/data/sampled_jsons/arxiv_2405.14728_Example_3.2_formula_abd(1-e)_+_(1-a)cd(1-e).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..49daa86586c265af85cfddc88dff6df8e82bb79c
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2405.14728_Example_3.2_formula_abd(1-e)_+_(1-a)cd(1-e).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2405 . 14728 ] Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "arXiv : 2405 . 14728 (cs).Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.14728", "content": "arXiv : 2405 . 14728 (cs).Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian", "date": "", "ddg_snippet": "This shows that an arbitrary formula ψ can be evaluated in terms of O(m(nr∗ + 1 )2n(r∗+ 1 )) conditional probability calculations, where r∗ is the maximum number of conjuncts in a disjunction ψi that involve at least one intervention, and m is the number of disjuncts in the DNF.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.14728", "content": "This shows that an arbitrary formula ψ can be evaluated in terms of O(m(nr∗ + 1 )2n(r∗+ 1 )) conditional probability calculations, where r∗ is the maximum number of conjuncts in a disjunction ψi that involve at least one intervention, and m is the number of disjuncts in the DNF."}
+{"idx": 2, "title": "Вынесите за скобки общий множитель: a) 8am - 16an...", "date": "", "ddg_snippet": "-35mp + 21mn - 14mk = 7m(-5p + 3n - 2k)$$ в) 0,2abc + 0,6 abd - 1 ,2abm Наибольший общий делитель (НОД) для 0,2; 0,6 и 1 ,2 равен 0,2. Буквенные множители 'a' и 'b' присутствуют во всех членах выражения. $", "subpage_snippet": "", "source": "www.euroki.org", "link": "https://www.euroki.org/koza/vynesite-za-skobki-obschiy-mnozhitela-am--an--a-b-mp--mn--mk-v-abc--abd--abm", "content": "-35mp + 21mn - 14mk = 7m(-5p + 3n - 2k)$$ в) 0,2abc + 0,6 abd - 1 ,2abm Наибольший общий делитель (НОД) для 0,2; 0,6 и 1 ,2 равен 0,2. Буквенные множители 'a' и 'b' присутствуют во всех членах выражения. $"}
+{"idx": 3, "title": "Упражнения на Present Simple с ответами", "date": "", "ddg_snippet": "Упражнение 2. Напишите глаголы в Present Simple (вопросительные предложения). Example : Do you like playing football? (like).Упражнение 3. Выберите и напишите don’t или doesn’t. Example : Carla (don’t/doesn’t) doesn’t ride a bicycle.", "subpage_snippet": "", "source": "EnglishWeb.ru", "link": "https://EnglishWeb.ru/grammar/present-simple-exercises.html", "content": "Упражнение 2. Напишите глаголы в Present Simple (вопросительные предложения). Example : Do you like playing football? (like).Упражнение 3. Выберите и напишите don’t или doesn’t. Example : Carla (don’t/doesn’t) doesn’t ride a bicycle."}
+{"idx": 4, "title": "(Решено) Упр.7 Модуль 1 a ГДЗ Spotlight 7 класс с переводом...", "date": "", "ddg_snippet": "My daily routine is quite ordinary, actually. I wake up, do the all morning staff, then go to school and come back home usually at 2 p.m., but sometimes I go to different clubs after lessons, for example , I attend the glee club. Anyway, tonight I ’m going to the birthday party of my best friend.", "subpage_snippet": "", "source": "reshak.ru", "link": "https://reshak.ru/otvet/otvet_txt.php?otvet1=/spotlight7/images/module1/a/7", "content": "My daily routine is quite ordinary, actually. I wake up, do the all morning staff, then go to school and come back home usually at 2 p.m., but sometimes I go to different clubs after lessons, for example , I attend the glee club. Anyway, tonight I ’m going to the birthday party of my best friend."}
+{"idx": 5, "title": "Watch The Summer I Turned Pretty S3 E 11 online TV Series", "date": "", "ddg_snippet": "S 1 E 8. Two Minutes without Roxán.S 1 E 24.", "subpage_snippet": "", "source": "www.tvids.icu", "link": "https://www.tvids.icu/watch4238/the-summer-i-turned-pretty/season-03-episode-11-at-last", "content": "S 1 E 8. Two Minutes without Roxán.S 1 E 24."}
+{"idx": 6, "title": "Всеобъемлющая теория матриц / Хабр", "date": "", "ddg_snippet": "Часть II : Арифметика Матриц — Правила Игры. 2. 1 . Сложение, Вычитание и Умножение на Число. Эти операции интуитивны. Они возможны только для матриц одинакового размера и производятся поэлементно.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/949084/", "content": "Часть II : Арифметика Матриц — Правила Игры. 2. 1 . Сложение, Вычитание и Умножение на Число. Эти операции интуитивны. Они возможны только для матриц одинакового размера и производятся поэлементно."}
+{"idx": 7, "title": "Читы и коды в Kingdom Come Deliverance 2: все консольные команды", "date": "", "ddg_snippet": "Например, если не хотите полностью отключать сохранение по тем или иным причинам, используйте комбинацию «wh_cheat_addItem 928463d9- e 21a-4f7c-b5d3-8378ed375 cd 1 » для мгновенного добавления в суму одного «Слабого спасительного шнапса».", "subpage_snippet": "", "source": "wotpack.ru", "link": "https://wotpack.ru/chity-kingdom-come-deliverance-2-kody-konsolnye-komandy-i-id-predmetov/", "content": "Например, если не хотите полностью отключать сохранение по тем или иным причинам, используйте комбинацию «wh_cheat_addItem 928463d9- e 21a-4f7c-b5d3-8378ed375 cd 1 » для мгновенного добавления в суму одного «Слабого спасительного шнапса»."}
+{"idx": 8, "title": "Бегущий В Лабиринте Смотреть Все Части Фильма 1 , 2, 3 Подряд...", "date": "", "ddg_snippet": "Смотреть Бегущий В Лабиринте Все Части Фильма 1 , 2, 3 Подряд Онлайн Бесплатно в Хорошем Качестве Онлайн FullHD 1080p Полностью на Русском Языке и на Любых Устройствах LordFilm.", "subpage_snippet": "", "source": "beguschiy-w-laberinte-lordfilm.ru", "link": "https://beguschiy-w-laberinte-lordfilm.ru/", "content": "Смотреть Бегущий В Лабиринте Все Части Фильма 1 , 2, 3 Подряд Онлайн Бесплатно в Хорошем Качестве Онлайн FullHD 1080p Полностью на Русском Языке и на Любых Устройствах LordFilm."}
+{"idx": 9, "title": "Два майора – Telegram", "date": "", "ddg_snippet": "любым удобным способом на сайте «Два майора» - https://dva-majors.ru на счет БФ «Два майора» перевод по СБП без комиссии: https://qr.nspk.ru/AS 1 A 002SNGS125GG8HFRLP618Q3H3GRI...", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/dva_majors", "content": "любым удобным способом на сайте «Два майора» - https://dva-majors.ru на счет БФ «Два майора» перевод по СБП без комиссии: https://qr.nspk.ru/AS 1 A 002SNGS125GG8HFRLP618Q3H3GRI..."}
diff --git a/data/sampled_jsons/arxiv_2410.02025_Figure_2_alpha_noise_variance.jsonl b/data/sampled_jsons/arxiv_2410.02025_Figure_2_alpha_noise_variance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..41d4905d034a7505af4391a69a03d2912f6bb17b
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+{"idx": 1, "title": "Smoothed Brown Noise 8-Hours - Remastered, for... - YouTube", "date": "", "ddg_snippet": "Brown noise is a useful sound masking tool, that can block out external sounds and distractions and be used in many different ways.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=RqzGzwTY-6w", "content": "Brown noise is a useful sound masking tool, that can block out external sounds and distractions and be used in many different ways."}
+{"idx": 2, "title": "Строка 2410 Отчета о финансовых результатах: из чего...", "date": "", "ddg_snippet": "Расшифровка строки 2410. По строке 2410 отражают налог на прибыль, а не текущий налог на прибыль, как это было в прошлой форме (примечание 7 к Отчету о финансовых результатах в приложении 1 к приказу Минфина от 02.07.2010 № 66н, п. 24 ПБУ 18/02).", "subpage_snippet": "", "source": "www.glavbukh.ru", "link": "https://www.glavbukh.ru/art/390406-stroka-2410-otcheta-o-finansovyh-rezultatah-iz-chego-skladyvaetsya-kak-zapolnit", "content": "Расшифровка строки 2410. По строке 2410 отражают налог на прибыль, а не текущий налог на прибыль, как это было в прошлой форме (примечание 7 к Отчету о финансовых результатах в приложении 1 к приказу Минфина от 02.07.2010 № 66н, п. 24 ПБУ 18/02)."}
+{"idx": 3, "title": "Figure is the first-of-its-kind AI robotics company bringing a general...", "date": "", "ddg_snippet": "BMW Use Case. Watch Now. Introducing Figure 02.", "subpage_snippet": "", "source": "www.figure.ai", "link": "https://www.figure.ai/ai", "content": "BMW Use Case. Watch Now. Introducing Figure 02."}
+{"idx": 4, "title": "Teen Titans GO Figure ! v1.1.10 (Много денег) APK - Скачать на...", "date": "", "ddg_snippet": "Teen Titans GO Figure ! - относится к жанру ролевых игр с элементами коллекционирования и битв. В этой вселенной вы будете собирать миниатюрные фигурки супергероев из комиксов DC, таких как Бэтмен или Супермен, а также самих Юных Титанов.", "subpage_snippet": "", "source": "5play.life", "link": "https://5play.life/880-teen-titans-go-figure.html", "content": "Teen Titans GO Figure ! - относится к жанру ролевых игр с элементами коллекционирования и битв. В этой вселенной вы будете собирать миниатюрные фигурки супергероев из комиксов DC, таких как Бэтмен или Супермен, а также самих Юных Титанов."}
+{"idx": 5, "title": "Telegram: View @ Arxiv _MDS", "date": "", "ddg_snippet": "Архив программы «Модель Для Сборки».", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/Arxiv_MDS", "content": "Архив программы «Модель Для Сборки»."}
+{"idx": 6, "title": "A likelihood based approach to distribution regression ...", "date": "", "ddg_snippet": "by S Kumar · 2024 · Cited by 1 — When α is large, the noise variance is very small, and the perturbed data facilitates efficient estimation. This observed pattern, as emphasized ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "by S Kumar · 2024 · Cited by 1 — When α is large, the noise variance is very small, and the perturbed data facilitates efficient estimation. This observed pattern, as emphasized ..."}
+{"idx": 7, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "2 Oct 2024 — When α 𝛼 \\ alpha italic_α is large, the noise variance is very small, and the perturbed data facilitates efficient estimation. Report issue for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "2 Oct 2024 — When α 𝛼 \\ alpha italic_α is large, the noise variance is very small, and the perturbed data facilitates efficient estimation. Report issue for ..."}
+{"idx": 8, "title": "Data Denoising and Derivative Estimation for Data-Driven Modeling of...", "date": "", "ddg_snippet": "Figure 3: Estimation results given by RKTV-INR. Row-wise, the first row shows the generated noisy state data, while the second and third rows represent the state and derivative estimates obtained from RKTV-INR, respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14219", "content": "Figure 3: Estimation results given by RKTV-INR. Row-wise, the first row shows the generated noisy state data, while the second and third rows represent the state and derivative estimates obtained from RKTV-INR, respectively."}
+{"idx": 9, "title": "Veo 3 AI Video Generator with Audio | veo3.ai", "date": "", "ddg_snippet": "Veo 3: AI Video Generation with Realistic Sound. Generate videos with perfectly synced audio, including sound effects, dialogue, and ambient noise .", "subpage_snippet": "", "source": "veo3.ai", "link": "https://veo3.ai/", "content": "Veo 3: AI Video Generation with Realistic Sound. Generate videos with perfectly synced audio, including sound effects, dialogue, and ambient noise ."}
diff --git a/data/sampled_jsons/arxiv_2412.18603_Initialization_section_year_2023.jsonl b/data/sampled_jsons/arxiv_2412.18603_Initialization_section_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f2188218cf3df80e928f2568717e6164438dabf4
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2412.18603_Initialization_section_year_2023.jsonl
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+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "Log in to arXiv | arXiv e-print repository", "date": "", "ddg_snippet": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/login", "content": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy."}
+{"idx": 2, "title": "YOLOv12: Attention-Centric Real-Time Object Detectors - arXiv.org", "date": "", "ddg_snippet": "Feb 18, 2025 · Abstract page for arXiv paper 2502.12524: YOLOv12: Attention-Centric Real-Time Object Detectors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12524", "content": "Feb 18, 2025 · Abstract page for arXiv paper 2502.12524: YOLOv12: Attention-Centric Real-Time Object Detectors"}
+{"idx": 3, "title": "Computer Science - arXiv.org", "date": "", "ddg_snippet": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/archive/cs", "content": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:"}
+{"idx": 4, "title": "[2508.10104] DINOv3 - arXiv.org", "date": "", "ddg_snippet": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.10104", "content": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ..."}
+{"idx": 5, "title": "[1706.03762] Attention Is All You Need - arXiv.org", "date": "", "ddg_snippet": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ..."}
+{"idx": 6, "title": "[2212.10156] Planning-oriented Autonomous Driving - arXiv.org", "date": "", "ddg_snippet": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.10156", "content": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ..."}
+{"idx": 7, "title": "[2506.01844] SmolVLA: A Vision-Language-Action Model for ... -...", "date": "", "ddg_snippet": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.01844", "content": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics"}
+{"idx": 8, "title": "[2410.10762] AFlow: Automating Agentic Workflow Generation -...", "date": "", "ddg_snippet": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10762", "content": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation"}
+{"idx": 9, "title": "[2103.00020] Learning Transferable Visual Models From Natural...", "date": "", "ddg_snippet": "Feb 26, 2021 · Abstract page for arXiv paper 2103.00020: Learning Transferable Visual Models From Natural Language Supervision", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2103.00020", "content": "Feb 26, 2021 · Abstract page for arXiv paper 2103.00020: Learning Transferable Visual Models From Natural Language Supervision"}
diff --git a/data/sampled_jsons/arxiv_LAUREL_Learned_Augmented_Residual_Layer_Equation_3.jsonl b/data/sampled_jsons/arxiv_LAUREL_Learned_Augmented_Residual_Layer_Equation_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..96a3c0caafd7cff32504c4a6c2e4f20aa8474e64
--- /dev/null
+++ b/data/sampled_jsons/arxiv_LAUREL_Learned_Augmented_Residual_Layer_Equation_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LAuReL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "2 Learned Augmented Residual Layer . 2.1 Residual Weights Version ( LAuReL -RW).In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v3", "content": "2 Learned Augmented Residual Layer . 2.1 Residual Weights Version ( LAuReL -RW).In this paper we introduce learned augmented residual layer , LAuReL , which generalizes the canonical residual connection."}
+{"idx": 1, "title": "LAuReL : Learned Augmented Residual Layer", "date": "", "ddg_snippet": "The paper introduces the LAUREL ( Learned Augmented Residual Layer ) framework, which represents a significant architectural innovation aimed at enhancing model quality while maintaining efficiency in terms of model size and latency.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-LAuReL-Learned-Augmented-cm3hsv976bjks016fio3mu37r", "content": "The paper introduces the LAUREL ( Learned Augmented Residual Layer ) framework, which represents a significant architectural innovation aimed at enhancing model quality while maintaining efficiency in terms of model size and latency."}
+{"idx": 2, "title": "Google AI Introduces LAuReL ( Learned Augmented Residual Layer )...", "date": "", "ddg_snippet": "LAUREL ’s implementation is tested in both vision and language domains, focusing on the ResNet-50 model for ImageNet-1K classification and a 3 B parameter decoder-only transformer for language tasks.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/11/16/google-ai-introduces-laurel-learned-augmented-residual-layer-revolutionizing-neural-networks-with-enhanced-residual-connections-for-efficient-model-performance/", "content": "LAUREL ’s implementation is tested in both vision and language domains, focusing on the ResNet-50 model for ImageNet-1K classification and a 3 B parameter decoder-only transformer for language tasks."}
+{"idx": 3, "title": "\" LAuReL : Learned Augmented Residual Layer \" - by Rohan Paul", "date": "", "ddg_snippet": "Solution in this Paper: → LAUREL ( Learned Augmented Residual Layer ) introduces learnable parameters to the residual connection, making it dynamic and context-aware. → It comes in three versions: LAUREL -RW adds learnable weights to scale both function...", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/laurel-learned-augmented-residual", "content": "Solution in this Paper: → LAUREL ( Learned Augmented Residual Layer ) introduces learnable parameters to the residual connection, making it dynamic and context-aware. → It comes in three versions: LAUREL -RW adds learnable weights to scale both function..."}
+{"idx": 4, "title": "Vidhyanand (Vick) Mahase PharmD, PhD. on LinkedIn: LAuReL ...", "date": "", "ddg_snippet": "LAuReL : Learned Augmented Residual Layer . arxiv .org.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/vick-mahase-pharmd-phd_laurel-learned-augmented-residual-layer-activity-7264001619392970752-_BOd", "content": "LAuReL : Learned Augmented Residual Layer . arxiv .org."}
+{"idx": 5, "title": "Google AI представляет LAuReL : улучшение... - ИИ онлайн • itinai.ru", "date": "", "ddg_snippet": "Получить консультацию бесплатно. Google AI Introduces LAuReL ( Learned Augmented Residual Layer ): Revolutionizing Neural Networks with Enhanced Residual Connections for Efficient Model Performance.", "subpage_snippet": "", "source": "itinai.ru", "link": "https://itinai.ru/google-ai-представляет-laurel-улучшение-нейросете/", "content": "Получить консультацию бесплатно. Google AI Introduces LAuReL ( Learned Augmented Residual Layer ): Revolutionizing Neural Networks with Enhanced Residual Connections for Efficient Model Performance."}
+{"idx": 6, "title": "GitHub - antimatter15/reverse-engineering-gemma- 3 n: Reverse...", "date": "", "ddg_snippet": "LAuReL : Learned Augmented Residual Layers .The type of Laurel block that appears to be used in Gemma- 3 n seems to be the \" Laurel -LR (Low Rank)\" variety.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antimatter15/reverse-engineering-gemma-3n", "content": "LAuReL : Learned Augmented Residual Layers .The type of Laurel block that appears to be used in Gemma- 3 n seems to be the \" Laurel -LR (Low Rank)\" variety."}
+{"idx": 7, "title": "Aman's AI Journal • Primers • Skip Connections", "date": "", "ddg_snippet": "LAUREL : Learned Augmented Residual Layer .Related Papers. Deep Residual Learning for Image Recognition. ResNet paper by He et al. from Facebook AI in CVPR 2016. Most cited in several AI fields.", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/skip-connections/", "content": "LAUREL : Learned Augmented Residual Layer .Related Papers. Deep Residual Learning for Image Recognition. ResNet paper by He et al. from Facebook AI in CVPR 2016. Most cited in several AI fields."}
+{"idx": 8, "title": "Gemma- 3 n Deep Dive: The Compact Genius of... | Medium", "date": "", "ddg_snippet": "a) LAuReL : Learned Augmented Residual Layer .This shortcut connection — the “ residual ” — helps the model learn faster and not forget what it already knew. But there’s a problem. Each self-attention and MLP layer uses a huge matrix to do its transformation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@learnwithadvi/gemma-3n-deep-dive-the-compact-genius-of-googles-next-gen-ai-b4e8c02cecb2", "content": "a) LAuReL : Learned Augmented Residual Layer .This shortcut connection — the “ residual ” — helps the model learn faster and not forget what it already knew. But there’s a problem. Each self-attention and MLP layer uses a huge matrix to do its transformation."}
+{"idx": 9, "title": "antimatter15/reverse-engineering-gemma- 3 n | DeepWiki", "date": "", "ddg_snippet": "The project analyzes the binary .task file format released by Google DeepMind and reverse engineers the novel architectural innovations including per- layer embeddings, ALTUP (Alternating Updates) mechanism, and LAuReL ( Learned Augmented Residual Layers ) blocks.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/antimatter15/reverse-engineering-gemma-3n", "content": "The project analyzes the binary .task file format released by Google DeepMind and reverse engineers the novel architectural innovations including per- layer embeddings, ALTUP (Alternating Updates) mechanism, and LAuReL ( Learned Augmented Residual Layers ) blocks."}
diff --git a/data/sampled_jsons/arxiv_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System.jsonl b/data/sampled_jsons/arxiv_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1acd844d0d09721ade3aa26a09c14605bac3cc28
--- /dev/null
+++ b/data/sampled_jsons/arxiv_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are known, providing a ground truth. We select methods ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20099", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are known, providing a ground truth. We select methods ..."}
+{"idx": 1, "title": "GitHub - simonbing/CRLSanityCheck", "date": "", "ddg_snippet": "Official code repository for the paper Sanity Checking Causal Representation Learning on a Simple Real-World System (2025) by Juan L. Gamella*, Simon Bing* and Jakob Runge.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/simonbing/CRLSanityCheck", "content": "Official code repository for the paper Sanity Checking Causal Representation Learning on a Simple Real-World System (2025) by Juan L. Gamella*, Simon Bing* and Jakob Runge."}
+{"idx": 2, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "Abstract We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experi-ment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors—the inputs to the experiment—are known, providing a ground truth. We ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=d2aGLPSpFz", "content": "Abstract We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experi-ment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors—the inputs to the experiment—are known, providing a ground truth. We ..."}
+{"idx": 3, "title": "PDF Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "Abstract We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experi ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389398876_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System/fulltext/67c12b09645ef274a496774e/Sanity-Checking-Causal-Representation-Learning-on-a-Simple-Real-World-System.pdf", "content": "Abstract We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experi ..."}
+{"idx": 4, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "This work evaluates methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work, and finds that they all fail to recover the underlying causal factors. We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Sanity-Checking-Causal-Representation-Learning-on-a-Gamella-Bing/638e050573f438f77583f2b210c2d5da0f1b4ca7", "content": "This work evaluates methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work, and finds that they all fail to recover the underlying causal factors. We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical ..."}
+{"idx": 5, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are known, providing ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.20099v2", "content": "View recent discussion. Abstract: We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are known, providing ..."}
+{"idx": 6, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "The gap between synthetic and real performance suggests fundamental limitations The research could benefit from testing on a wider range of real-world systems and exploring more robust architectures for handling nonlinear effects. Conclusion This work provides important insights into the practical limitations of causal representation learning .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/sanity-checking-causal-representation-learning-simple-real", "content": "The gap between synthetic and real performance suggests fundamental limitations The research could benefit from testing on a wider range of real-world systems and exploring more robust architectures for handling nonlinear effects. Conclusion This work provides important insights into the practical limitations of causal representation learning ."}
+{"idx": 7, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "Abstract We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors—the inputs to the experiment—are known, providing a ground truth. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20099v2", "content": "Abstract We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors—the inputs to the experiment—are known, providing a ground truth. We ..."}
+{"idx": 8, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors---the inputs to the experiment---are ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=d2aGLPSpFz", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors---the inputs to the experiment---are ..."}
+{"idx": 9, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are ...", "subpage_snippet": "", "source": "www.zhuanzhi.ai", "link": "https://www.zhuanzhi.ai/paper/6140265ca9cc5b07381741446e276fcb", "content": "We evaluate methods for causal representation learning (CRL) on a simple , real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are ..."}
diff --git a/data/sampled_jsons/attention_heads_feature_extractors_safety_mechanism_Zhou_2024.jsonl b/data/sampled_jsons/attention_heads_feature_extractors_safety_mechanism_Zhou_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c33e5795d5f92fb501c5d54e4c30ea824c6fffc
--- /dev/null
+++ b/data/sampled_jsons/attention_heads_feature_extractors_safety_mechanism_Zhou_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "parallel multi- head attention mechanism , allowing the signal for key tokens to be amplified and less important tokens to be diminished.Random Feature Attention (2021)[100] uses Fourier random features", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "parallel multi- head attention mechanism , allowing the signal for key tokens to be amplified and less important tokens to be diminished.Random Feature Attention (2021)[100] uses Fourier random features"}
+{"idx": 1, "title": "Attention (machine learning) - Wikipedia", "date": "", "ddg_snippet": "Attentional Neural Networks introduced a learned feature selection mechanism using top-down cognitive modulation, showing how attention weights can highlight relevant inputs.[12].", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Attention_(machine_learning)", "content": "Attentional Neural Networks introduced a learned feature selection mechanism using top-down cognitive modulation, showing how attention weights can highlight relevant inputs.[12]."}
+{"idx": 2, "title": "On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "More importantly, we demonstrate that attention heads primarily function as feature extractors for safety and models fine-tuned from the same base model exhibit overlapping safety heads through comprehensive experiments.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=h0Ak8A5yqw", "content": "More importantly, we demonstrate that attention heads primarily function as feature extractors for safety and models fine-tuned from the same base model exhibit overlapping safety heads through comprehensive experiments."}
+{"idx": 3, "title": "(PDF) On the Role of Attention Heads in Large Language Model Safety", "date": "", "ddg_snippet": "intriguing insights: 1. Certain safety heads within the attention mechanism are crucial for feature . integration in safety tasks. Specifically, modifying the value of the attention weight matrices changes.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385010417_On_the_Role_of_Attention_Heads_in_Large_Language_Model_Safety", "content": "intriguing insights: 1. Certain safety heads within the attention mechanism are crucial for feature . integration in safety tasks. Specifically, modifying the value of the attention weight matrices changes."}
+{"idx": 4, "title": "Attention mechanism : Overview - YouTube", "date": "", "ddg_snippet": "This video introduces you to the attention mechanism , a powerful technique that allows neural networks to focus on specific parts of an input sequence.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=fjJOgb-E41w", "content": "This video introduces you to the attention mechanism , a powerful technique that allows neural networks to focus on specific parts of an input sequence."}
+{"idx": 5, "title": "Enhancing safety for blind and visually impaired people: intelligent fire...", "date": "", "ddg_snippet": "For feature extraction , the NASNetMobile method is employed to capture complex features from the image data. Furthermore, the EFDBVCP-AILOA method implements self‐ attention with a convolutional neural network and long short-term memory (CNN-Sa-LSTM) model for classification.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/doi/10.3934/math.2025961", "content": "For feature extraction , the NASNetMobile method is employed to capture complex features from the image data. Furthermore, the EFDBVCP-AILOA method implements self‐ attention with a convolutional neural network and long short-term memory (CNN-Sa-LSTM) model for classification."}
+{"idx": 6, "title": "MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network", "date": "", "ddg_snippet": "The system consists of ve key components: • Modality-specic feature extractors for visual, audio, and. textual data • A comprehensive cross-modal attention mechanism with. bidirectional information ow • A bidirectional multi- headed self- attention module for.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025W/ABAW/papers/Ahire_MAVEN_Multi-modal_Attention_for_Valence-Arousal_Emotion_Network_CVPRW_2025_paper.pdf", "content": "The system consists of ve key components: • Modality-specic feature extractors for visual, audio, and. textual data • A comprehensive cross-modal attention mechanism with. bidirectional information ow • A bidirectional multi- headed self- attention module for."}
+{"idx": 7, "title": "TFBlender: a hybrid time series attention model with data-driven...", "date": "", "ddg_snippet": "Built on attention mechanisms , the TFBlender operates along two paths: time and features . On the time-step token path, it captures both short- and long-term patterns in data, while on the feature token path, multi- head attention analyzes interactions among diverse features .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1186/s40537-025-01237-z", "content": "Built on attention mechanisms , the TFBlender operates along two paths: time and features . On the time-step token path, it captures both short- and long-term patterns in data, while on the feature token path, multi- head attention analyzes interactions among diverse features ."}
+{"idx": 8, "title": "On the token distance modeling ability of higher RoPE attention ...", "date": "", "ddg_snippet": "We further demonstrate the correlation between the efficiency of length extrapolation and the extension of the high-dimensional attention allocation of these heads . The identification of Positional Heads provides insights for future research in long-text comprehension.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.338/", "content": "We further demonstrate the correlation between the efficiency of length extrapolation and the extension of the high-dimensional attention allocation of these heads . The identification of Positional Heads provides insights for future research in long-text comprehension."}
+{"idx": 9, "title": "L Arge L anguage M odel s afety", "date": "", "ddg_snippet": "Safety Attention Head AttRibution Algorithm. Generalize the Impact of Safety Head Ablation.This interpretation yields several intriguing insights: 1. Certain safety heads within the attention mechanism are crucial for feature integration in safety tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.13708v1", "content": "Safety Attention Head AttRibution Algorithm. Generalize the Impact of Safety Head Ablation.This interpretation yields several intriguing insights: 1. Certain safety heads within the attention mechanism are crucial for feature integration in safety tasks."}
diff --git a/data/sampled_jsons/blockchain_sharding_reward_function_CST_IST_cross-shard_transactions_intra-shard.jsonl b/data/sampled_jsons/blockchain_sharding_reward_function_CST_IST_cross-shard_transactions_intra-shard.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fe9422d9b5deafe087e6236aa667cebf48aee742
--- /dev/null
+++ b/data/sampled_jsons/blockchain_sharding_reward_function_CST_IST_cross-shard_transactions_intra-shard.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SP-Chain: Boosting Intra-Shard and Cross-Shard Security and ...", "date": "", "ddg_snippet": "Jul 9, 2024 · In this paper, we propose SP-Chain, a blockchain sharding system with enhanced S ecurity and P erformance for both intra - and cross-shard perspectives. For intra-shard aspect, we design a two-phase concurrent voting scheme to provide high system throughput and low transaction confirmation latency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06953v1", "content": "Jul 9, 2024 · In this paper, we propose SP-Chain, a blockchain sharding system with enhanced S ecurity and P erformance for both intra - and cross-shard perspectives. For intra-shard aspect, we design a two-phase concurrent voting scheme to provide high system throughput and low transaction confirmation latency."}
+{"idx": 1, "title": "Presto: Optimizing Cross-Shard Transactions in Sharded ...", "date": "", "ddg_snippet": "Sep 30, 2024 · Blockchain sharding technology has been used to enhance the scalability of blockchain systems. As the number of shards increases, the high latency inherent in cross-shard transactions gradually becomes a bottleneck, hindering improvements in overall system efficiency. Therefore, reducing the latency of cross-shard transactions is significantly important. However, existing mechanisms for ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10806620", "content": "Sep 30, 2024 · Blockchain sharding technology has been used to enhance the scalability of blockchain systems. As the number of shards increases, the high latency inherent in cross-shard transactions gradually becomes a bottleneck, hindering improvements in overall system efficiency. Therefore, reducing the latency of cross-shard transactions is significantly important. However, existing mechanisms for ..."}
+{"idx": 2, "title": "Cross-shard transaction optimization based on community ...", "date": "", "ddg_snippet": "Dec 1, 2024 · Due to the complexity and high cost of the cross-shard transaction processing mechanism in the sharding blockchain system, as well as the high proportion of cross-shard transactions , it becomes challenging for the sharding blockchain system to reach the ideal theoretical performance upper limit.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1568494624012250", "content": "Dec 1, 2024 · Due to the complexity and high cost of the cross-shard transaction processing mechanism in the sharding blockchain system, as well as the high proportion of cross-shard transactions , it becomes challenging for the sharding blockchain system to reach the ideal theoretical performance upper limit."}
+{"idx": 3, "title": "SP-Chain: Boosting Intrashard and Cross-Shard Security and ...", "date": "", "ddg_snippet": "A promising way to overcome the scalability limitations of the current blockchain is to use sharding , which is to split the transaction processing among multiple, smaller groups of nodes. A well-performing blockchain sharding system requires both high performance and high security in both intra - and cross-shard perspectives. However, existing protocols either have issues in protecting security ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11016108", "content": "A promising way to overcome the scalability limitations of the current blockchain is to use sharding , which is to split the transaction processing among multiple, smaller groups of nodes. A well-performing blockchain sharding system requires both high performance and high security in both intra - and cross-shard perspectives. However, existing protocols either have issues in protecting security ..."}
+{"idx": 4, "title": "Cross-shard Transaction Processing in Sharding Blockchains", "date": "", "ddg_snippet": "Sep 29, 2020 · Cross-shard transactions account for a large fraction of transactions in a sharding blockchain , so the processing method of cross-shard transactions is of vital importance to the system efficiency. In this paper, we focus on the study of cross-shard transaction processing methods.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-60248-2_22", "content": "Sep 29, 2020 · Cross-shard transactions account for a large fraction of transactions in a sharding blockchain , so the processing method of cross-shard transactions is of vital importance to the system efficiency. In this paper, we focus on the study of cross-shard transaction processing methods."}
+{"idx": 5, "title": "[2407.06953] SP-Chain: Boosting Intra-Shard and Cross-Shard ...", "date": "", "ddg_snippet": "Jul 9, 2024 · A promising way to overcome the scalability limitations of the current blockchain is to use sharding , which is to split the transaction processing among multiple, smaller groups of nodes. A well-performed blockchain sharding system requires both high performance and high security in both intra - and cross-shard perspectives. However, existing protocols either have issues on protecting security ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.06953", "content": "Jul 9, 2024 · A promising way to overcome the scalability limitations of the current blockchain is to use sharding , which is to split the transaction processing among multiple, smaller groups of nodes. A well-performed blockchain sharding system requires both high performance and high security in both intra - and cross-shard perspectives. However, existing protocols either have issues on protecting security ..."}
+{"idx": 6, "title": "Framework for Blockchain Sharding in IoT", "date": "", "ddg_snippet": "which equals the sum of the Intra - Shard Transaction ( IST ). The ϕcr is obtained by subtracting the IST count from the total transactions count among network nodes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.00632", "content": "which equals the sum of the Intra - Shard Transaction ( IST ). The ϕcr is obtained by subtracting the IST count from the total transactions count among network nodes."}
+{"idx": 7, "title": "SP-Chain: Boosting Intra - Shard and Cross - Shard Security and...", "date": "", "ddg_snippet": "Issues from Cross - Shard Aspect. Cross - shard transaction processing is a significant part of the blockchain sharding .This function will uniformly map the value of the seed to one of the nodes. The selected person is the leader in slot t and is responsible for generating block bt.", "subpage_snippet": "", "source": "home.cse.ust.hk", "link": "https://home.cse.ust.hk/~weiwa/papers/sp-chain.pdf", "content": "Issues from Cross - Shard Aspect. Cross - shard transaction processing is a significant part of the blockchain sharding .This function will uniformly map the value of the seed to one of the nodes. The selected person is the leader in slot t and is responsible for generating block bt."}
+{"idx": 8, "title": "Sharding in Blockchain . Sharding is a key approach to... | Medium", "date": "", "ddg_snippet": "Intra - Shard Consensus: Each shard has its own group of validators. They verify and record transactions within that shard . Cross - Shard Communication: When a transaction involves multiple shards , the system uses cross - shard messaging to ensure consistency.", "subpage_snippet": "", "source": "qitmeer.medium.com", "link": "https://qitmeer.medium.com/sharding-in-blockchain-b13532a0347c", "content": "Intra - Shard Consensus: Each shard has its own group of validators. They verify and record transactions within that shard . Cross - Shard Communication: When a transaction involves multiple shards , the system uses cross - shard messaging to ensure consistency."}
+{"idx": 9, "title": "GriDB : Scaling Blockchain Database via Sharding and Off-Chain", "date": "", "ddg_snippet": "Each intra - shard operator can be processed by the nodes of the corresponding shard based on their stored tables.Figure 5: Transaction throughput for GriDB, the on-chain sharding blockchain database, and the non- sharding block - chain database (cx means cross - shard ratio.)", "subpage_snippet": "", "source": "vldb.org", "link": "https://vldb.org/pvldb/vol16/p1685-hong.pdf", "content": "Each intra - shard operator can be processed by the nodes of the corresponding shard based on their stored tables.Figure 5: Transaction throughput for GriDB, the on-chain sharding blockchain database, and the non- sharding block - chain database (cx means cross - shard ratio.)"}
diff --git a/data/sampled_jsons/branched_mapping_layers_different_value_distribution_3DGS_attributes_UVGS.jsonl b/data/sampled_jsons/branched_mapping_layers_different_value_distribution_3DGS_attributes_UVGS.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e13d6b32829287fac38e55950a59facf8ab0bd07
--- /dev/null
+++ b/data/sampled_jsons/branched_mapping_layers_different_value_distribution_3DGS_attributes_UVGS.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV...", "date": "", "ddg_snippet": "Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the incompatibility issues arising due the the different value distribution of 3 DGS attributes .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v2", "content": "Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the incompatibility issues arising due the the different value distribution of 3 DGS attributes ."}
+{"idx": 1, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "3 Feb 2025 — The Super UVGS representation effectively retains all the details of a 3DGS attributes ... different value distribution of 3DGS attributes . Note ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v1", "content": "3 Feb 2025 — The Super UVGS representation effectively retains all the details of a 3DGS attributes ... different value distribution of 3DGS attributes . Note ..."}
+{"idx": 2, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "by A Rai · 2025 · Cited by 2 — of the five different 3DGS attributes {σ, r, s, o, c}. The inverse mapping ... branched mapping layers in both forward and reverse map- ping networks ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "by A Rai · 2025 · Cited by 2 — of the five different 3DGS attributes {σ, r, s, o, c}. The inverse mapping ... branched mapping layers in both forward and reverse map- ping networks ... 11 pages"}
+{"idx": 3, "title": "[2502.01846] UVGS: Reimagining Unstructured 3D Gaussian ...", "date": "", "ddg_snippet": "The Super UVGS representation effectively retains all the details of a 3DGS attributes ... 3DGS attributes ... different value distribution of 3DGS attributes .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.01846", "content": "The Super UVGS representation effectively retains all the details of a 3DGS attributes ... 3DGS attributes ... different value distribution of 3DGS attributes ."}
+{"idx": 4, "title": "CVPR Poster UVGS: Reimagining Unstructured 3D Gaussian ...", "date": "", "ddg_snippet": "... different value distribution of 3DGS attributes . Note that the disparate distributions of values within each set of attributes in 3D Gaussian Splatting ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33266", "content": "... different value distribution of 3DGS attributes . Note that the disparate distributions of values within each set of attributes in 3D Gaussian Splatting ..."}
+{"idx": 5, "title": "(PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using...", "date": "", "ddg_snippet": "Branched mapping layers : The rationale behind using. branched mapping layers in both forward and reverse map-. ping networks is to prevent the incompatibility issues aris-. ing due the the different value distribution of 3 DGS at", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "Branched mapping layers : The rationale behind using. branched mapping layers in both forward and reverse map-. ping networks is to prevent the incompatibility issues aris-. ing due the the different value distribution of 3 DGS at"}
+{"idx": 6, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/broaden_the_expertise_involved_stakeholders_with_different_perspectives_conceptual_debates_operation.jsonl b/data/sampled_jsons/broaden_the_expertise_involved_stakeholders_with_different_perspectives_conceptual_debates_operation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..42980f84998b9858d17794d1b824ae65ef3d5994
--- /dev/null
+++ b/data/sampled_jsons/broaden_the_expertise_involved_stakeholders_with_different_perspectives_conceptual_debates_operation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Broadening the Frame: How Behavioral Strategy Redefines", "date": "", "ddg_snippet": "... a company may decide to embark on an acquisition program to enhance its growth without exploring which factors may be hindering that growth in the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/329790371_Broadening_the_Frame_How_Behavioral_Strategy_Redefines_Strategic_Decisions", "content": "... a company may decide to embark on an acquisition program to enhance its growth without exploring which factors may be hindering that growth in the ..."}
+{"idx": 1, "title": "Inventhelp And The Provisional Patent: A Cost-Effective Option", "date": "", "ddg_snippet": "Team Expertise and History: Highlighting the abilities of your team can greatly attract investors and stakeholders .", "subpage_snippet": "", "source": "campfirecapers.com", "link": "https://campfirecapers.com/961/inventhelp-and-the-provisional-patent-a-cost-effective-option-for-inventors/", "content": "Team Expertise and History: Highlighting the abilities of your team can greatly attract investors and stakeholders ."}
+{"idx": 2, "title": "Broadening and strengthening stakeholder engagement in", "date": "", "ddg_snippet": "The 2013 EU Basic Safety Standards directive for example requires consultation with stakeholders and their involvement in decision-making in ...", "subpage_snippet": "", "source": "www.radioprotection.org", "link": "https://www.radioprotection.org/articles/radiopro/full_html/2020/03/radiopro200037s/radiopro200037s.html", "content": "The 2013 EU Basic Safety Standards directive for example requires consultation with stakeholders and their involvement in decision-making in ..."}
+{"idx": 3, "title": "Talking to Defence Stakeholders: How the Canadian and French", "date": "", "ddg_snippet": "The article begins by analyzing the different teams within the Canadian Ministry of Defence (MoD) whose primary purpose is to engage stakeholders ...", "subpage_snippet": "", "source": "journals.openedition.org", "link": "https://journals.openedition.org/eccs/5073", "content": "The article begins by analyzing the different teams within the Canadian Ministry of Defence (MoD) whose primary purpose is to engage stakeholders ..."}
+{"idx": 4, "title": "Quality assurance in the political context: In the midst of", "date": "", "ddg_snippet": "The priority of different objectives changes over time and differ- ent stakeholders set different expectations that in one way or the other need to ...", "subpage_snippet": "", "source": "5dok.net", "link": "https://5dok.net/document/y96rd25w-quality-assurance-political-context-midst-different-expectations-conflicting.html", "content": "The priority of different objectives changes over time and differ- ent stakeholders set different expectations that in one way or the other need to ..."}
+{"idx": 5, "title": "Position: Evaluating Generative AI Systems is a Social Science...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as “a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as “a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024) ."}
+{"idx": 6, "title": "[2502.00561] Position: Evaluating Generative AI Systems is a", "date": "", "ddg_snippet": "... broaden the expertise involved in evaluating GenAI systems by enabling stakeholders with different perspectives to participate in conceptual debates ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00561", "content": "... broaden the expertise involved in evaluating GenAI systems by enabling stakeholders with different perspectives to participate in conceptual debates ..."}
+{"idx": 7, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "... broaden the expertise involved in evaluating GenAI systems by enabling stakeholders with different perspectives to participate in conceptual debates ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/position-evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/", "content": "... broaden the expertise involved in evaluating GenAI systems by enabling stakeholders with different perspectives to participate in conceptual debates ..."}
+{"idx": 8, "title": "Julie BARNETT | Professor | PhD Social Psychology | University", "date": "", "ddg_snippet": "... with particular interest in and expertise around public appreciations of risk, risk communication, social media, the maintenance and change of ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Julie-Barnett-2", "content": "... with particular interest in and expertise around public appreciations of risk, risk communication, social media, the maintenance and change of ..."}
+{"idx": 9, "title": "RE-DWELL: Definition", "date": "", "ddg_snippet": "From the three pillars of sustainable development, economic, environmental and social, the latter involving social equity and the sustainability of ...", "subpage_snippet": "", "source": "www.re-dwell.eu", "link": "https://www.re-dwell.eu/concept-definition/31", "content": "From the three pillars of sustainable development, economic, environmental and social, the latter involving social equity and the sustainability of ..."}
diff --git a/data/sampled_jsons/causal_model_formal_definition_two_components_Pearl_structural_equations.jsonl b/data/sampled_jsons/causal_model_formal_definition_two_components_Pearl_structural_equations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c884787897a7d957efb0339fda74e735d5b1cd4
--- /dev/null
+++ b/data/sampled_jsons/causal_model_formal_definition_two_components_Pearl_structural_equations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal inference - Wikipedia", "date": "", "ddg_snippet": "... causal inference include the causal pie model ( component - cause ), Pearl 's structural causal model ( causal diagram + do-calculus ), structural equation ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Causal_inference", "content": "... causal inference include the causal pie model ( component - cause ), Pearl 's structural causal model ( causal diagram + do-calculus ), structural equation ..."}
+{"idx": 1, "title": "CausalARC: Abstract Reasoning with Causal World Models", "date": "", "ddg_snippet": "... models are expressed as probabilistic structural causal models (SCMs), a rich formalism for representing data generating processes and simulating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03636v1", "content": "... models are expressed as probabilistic structural causal models (SCMs), a rich formalism for representing data generating processes and simulating ..."}
+{"idx": 2, "title": "Structural Causal Models for Extremes: an Approach Based on", "date": "", "ddg_snippet": "The structural causal model (SCM) , also known as the structural equation model , is a widely used approach for modeling causal interactions among ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.00223v1", "content": "The structural causal model (SCM) , also known as the structural equation model , is a widely used approach for modeling causal interactions among ..."}
+{"idx": 3, "title": "Extending Structural Causal Models for Autonomous Vehicles to", "date": "", "ddg_snippet": "Thus we propose structural causal model (SCM) ( Pearl , 2009 ) integration can be utilised for explanation generation in the event of critical failure ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01384v3", "content": "Thus we propose structural causal model (SCM) ( Pearl , 2009 ) integration can be utilised for explanation generation in the event of critical failure ..."}
+{"idx": 4, "title": "Assimilative Causal Inference", "date": "", "ddg_snippet": "... transfer based on the ensemble forecast of the underlying model has been exploited to indicate certain causal relationships for a short term [ 22 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14825v1", "content": "... transfer based on the ensemble forecast of the underlying model has been exploited to indicate certain causal relationships for a short term [ 22 ..."}
+{"idx": 5, "title": "Causal Modeling - Bibliography - PhilPapers", "date": "", "ddg_snippet": "A causal model is a formal device intended to represent a part of the causal structure of the world. ... 2021 Nobel Prize in Economics recognizes a ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/causal-modeling", "content": "A causal model is a formal device intended to represent a part of the causal structure of the world. ... 2021 Nobel Prize in Economics recognizes a ..."}
+{"idx": 6, "title": "Causes and Explanations: A Structural-Model Approach, Part I:", "date": "", "ddg_snippet": "Structural causal models serve as powerful tools for formalizing this concept [12, 28], enabling precise definitions of blame and responsibility ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/220487167_Causes_and_Explanations_A_Structural-Model_Approach_Part_I_Causes", "content": "Structural causal models serve as powerful tools for formalizing this concept [12, 28], enabling precise definitions of blame and responsibility ..."}
+{"idx": 7, "title": "causal DAGs – Andi Fugard (∧⇒)", "date": "", "ddg_snippet": "This paper provides a general introduction to the use of causal models in the metaphysics of causation, specifically structural equation models and ...", "subpage_snippet": "", "source": "andifugard.info", "link": "https://andifugard.info/tag/causal-dags/", "content": "This paper provides a general introduction to the use of causal models in the metaphysics of causation, specifically structural equation models and ..."}
+{"idx": 8, "title": "Writing Causal Models Like We Write Programs - LessWrong 2.0", "date": "", "ddg_snippet": "Hopefully the mapping from clunc to probabilistic causal models is obvious: any clunc with random variables in it is a typical Pearl -style causal DAG ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/Xd9FLs4geRAWxkQPE/writing-causal-models-like-we-write-programs", "content": "Hopefully the mapping from clunc to probabilistic causal models is obvious: any clunc with random variables in it is a typical Pearl -style causal DAG ..."}
+{"idx": 9, "title": "The necessity of construct and external validity for deductive", "date": "", "ddg_snippet": "... contribution, the Credibility Revolution advances designs that warrant assumptions sufficient to “identify” causal effects from observed data [ 2 ...", "subpage_snippet": "", "source": "www.degruyterbrill.com:443", "link": "https://www.degruyterbrill.com:443/document/doi/10.1515/jci-2024-0002/html", "content": "... contribution, the Credibility Revolution advances designs that warrant assumptions sufficient to “identify” causal effects from observed data [ 2 ..."}
diff --git a/data/sampled_jsons/class_distribution_estimation_without_prior_knowledge_semi-supervised_learning.jsonl b/data/sampled_jsons/class_distribution_estimation_without_prior_knowledge_semi-supervised_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c63bb530c56742cdedc4870accdc99e59422f813
--- /dev/null
+++ b/data/sampled_jsons/class_distribution_estimation_without_prior_knowledge_semi-supervised_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Weak supervision - Wikipedia", "date": "", "ddg_snippet": "Semi - supervised learning may refer to either transductive learning or inductive learning .[1] The goal of transductive learning is to infer the correct labels for the given unlabeled data.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Weak_supervision", "content": "Semi - supervised learning may refer to either transductive learning or inductive learning .[1] The goal of transductive learning is to infer the correct labels for the given unlabeled data."}
+{"idx": 1, "title": "Semi-supervised distribution learning - Oxford Academic", "date": "", "ddg_snippet": "Oct 30, 2024 · In contrast to prior research focusing on parameter inference, this work explores the complexities of semi - supervised distribution estimation , particularly the uniformity problem inherent in functional processes.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/biomet/article/112/1/asae056/7852832", "content": "Oct 30, 2024 · In contrast to prior research focusing on parameter inference, this work explores the complexities of semi - supervised distribution estimation , particularly the uniformity problem inherent in functional processes."}
+{"idx": 2, "title": "SCD:Sampling-based Class Distribution for Imbalanced Semi ...", "date": "", "ddg_snippet": "Nov 8, 2023 · These methods often assume that the unlabeled data distribution is uniform or identical to the labeled data distribution , which may differ from the real scenario distribution. In this work, we provide a new perspective for setting the class distribution for imbalanced SSL.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10356442", "content": "Nov 8, 2023 · These methods often assume that the unlabeled data distribution is uniform or identical to the labeled data distribution , which may differ from the real scenario distribution. In this work, we provide a new perspective for setting the class distribution for imbalanced SSL."}
+{"idx": 3, "title": "Rebalancing Using Estimated Class Distribution for Imbalanced ...", "date": "", "ddg_snippet": "Despite significant advancements in class-imbalanced semi - supervised learning (CISSL), many existing algorithms explicitly or im-plicitly assume that the class distribution of unlabeled data matches that of labeled data.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/03287.pdf", "content": "Despite significant advancements in class-imbalanced semi - supervised learning (CISSL), many existing algorithms explicitly or im-plicitly assume that the class distribution of unlabeled data matches that of labeled data."}
+{"idx": 4, "title": "CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class ...", "date": "", "ddg_snippet": "We propose a CISSL algorithm, class- distribution -mismatch-aware debiasing (CDMAD), which effectively mitigates class imbalance in SSL even under severe class distribution mismatch between labeled and unlabeled sets.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Lee_CDMAD_Class-Distribution-Mismatch-Aware_Debiasing_for_Class-Imbalanced_Semi-Supervised_Learning_CVPR_2024_paper.pdf", "content": "We propose a CISSL algorithm, class- distribution -mismatch-aware debiasing (CDMAD), which effectively mitigates class imbalance in SSL even under severe class distribution mismatch between labeled and unlabeled sets."}
+{"idx": 5, "title": "Semi-supervised learning of class balance under class-prior ...", "date": "", "ddg_snippet": "Feb 1, 2014 · In this paper, with the aid of this density-ratio based PE-divergence estimator, we propose a new semi - supervised method for estimating the class ratio in the test dataset.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608013002748", "content": "Feb 1, 2014 · In this paper, with the aid of this density-ratio based PE-divergence estimator, we propose a new semi - supervised method for estimating the class ratio in the test dataset."}
+{"idx": 6, "title": "Semi-Supervised Learning under Class Distribution Mismatch", "date": "", "ddg_snippet": "In this work, we systematically studied the more realistic semi - supervised learning (SSL) under class distribution mis-match, which poses a new challenge of how to maximise the value of unconstrained unlabelled data.", "subpage_snippet": "", "source": "xiatian-zhu.github.io", "link": "https://xiatian-zhu.github.io/papers/ChenEtAl_AAAI2020.pdf", "content": "In this work, we systematically studied the more realistic semi - supervised learning (SSL) under class distribution mis-match, which poses a new challenge of how to maximise the value of unconstrained unlabelled data."}
+{"idx": 7, "title": "Rebalancing Using Estimated Class Distribution for Imbalanced ...", "date": "", "ddg_snippet": "Sep 30, 2024 · RECD estimates the unknown class distribution of unlabeled data through Monte Carlo approximation, leveraging predicted class probabilities for unlabeled samples, and subsequently rebalances the classifier based on the estimated class distribution .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-72670-5_22", "content": "Sep 30, 2024 · RECD estimates the unknown class distribution of unlabeled data through Monte Carlo approximation, leveraging predicted class probabilities for unlabeled samples, and subsequently rebalances the classifier based on the estimated class distribution ."}
+{"idx": 8, "title": "Semi - supervised distribution learning", "date": "", "ddg_snippet": "In contrast to prior research focusing on parameter inference, this work explores the complexities of semi - supervised distribution estimation , particularly the uniformity problem inherent in functional processes.", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/a/oup/biomet/v112y2025i1p669-74..html", "content": "In contrast to prior research focusing on parameter inference, this work explores the complexities of semi - supervised distribution estimation , particularly the uniformity problem inherent in functional processes."}
+{"idx": 9, "title": "(PDF) Classification Based on Semi - Supervised Learning : A Review", "date": "", "ddg_snippet": "Abstract. Semi - supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/129612669/Classification_Based_on_Semi_Supervised_Learning_A_Review", "content": "Abstract. Semi - supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data."}
diff --git a/data/sampled_jsons/climate_activism_research_events_categories_types_social_media.jsonl b/data/sampled_jsons/climate_activism_research_events_categories_types_social_media.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0a95021a3187f14ea1214d000d20379f69e23d01
--- /dev/null
+++ b/data/sampled_jsons/climate_activism_research_events_categories_types_social_media.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tracing the Emergent Field of Digital Environmental and Climate ...", "date": "", "ddg_snippet": "Social media platforms provide major opportunities for online activism and the emergence of digital counterpublics. Research on counterpublics has focused on actors and their narrative strategies aiming at deconstructing dominant discourses.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370996576_Tracing_the_Emergent_Field_of_Digital_Environmental_and_Climate_Activism_Research_A_Mixed-Methods_Systematic_Literature_Review", "content": "Social media platforms provide major opportunities for online activism and the emergence of digital counterpublics. Research on counterpublics has focused on actors and their narrative strategies aiming at deconstructing dominant discourses."}
+{"idx": 1, "title": "Frontiers | Young people's climate activism : A review of the literature", "date": "", "ddg_snippet": "Lastly, research found that climate activism opened up new opportunities for young people, such as social connections (MacKay et al., 2020; Elsen and Ord, 2021).", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/political-science/articles/10.3389/fpos.2022.940876/full", "content": "Lastly, research found that climate activism opened up new opportunities for young people, such as social connections (MacKay et al., 2020; Elsen and Ord, 2021)."}
+{"idx": 2, "title": "Tracing the Emergent Field of Digital Environmental and Climate ...", "date": "", "ddg_snippet": "Social media studies on climate change tend to use large datasets and quantitat-ive, text-based approaches (Pearce et al., 2018).(2022) tackle the question of data types and methodological approaches in digital activism research .", "subpage_snippet": "", "source": "refubium.fu-berlin.de", "link": "https://refubium.fu-berlin.de/bitstream/handle/fub188/39981/Tracing+the+Emergent+Field+of+Digital+Environmental+and+Climate+Activism+Research+A+Mixed+Methods+Systematic+Literature+Review.pdf?sequence=1&isAllowed=y", "content": "Social media studies on climate change tend to use large datasets and quantitat-ive, text-based approaches (Pearce et al., 2018).(2022) tackle the question of data types and methodological approaches in digital activism research ."}
+{"idx": 3, "title": "Environmental activism , emotions and the TikTok affect: ‘Can’t you...", "date": "", "ddg_snippet": "Article Category Hautea, S., Parks, P., Takahashi, B. and Zeng, J. (2021) Showing they care (or don’t): affective publics and ambivalent climate activism on TikTok, Social Media & Society , 7(2): 1–14.", "subpage_snippet": "", "source": "bristoluniversitypressdigital.com", "link": "https://bristoluniversitypressdigital.com/abstract/journals/emsoc/aop/article-10.1332-26316897Y2025D000000070/article-10.1332-26316897Y2025D000000070.xml", "content": "Article Category Hautea, S., Parks, P., Takahashi, B. and Zeng, J. (2021) Showing they care (or don’t): affective publics and ambivalent climate activism on TikTok, Social Media & Society , 7(2): 1–14."}
+{"idx": 4, "title": "Handbook for climate activists | Activist Handbook", "date": "", "ddg_snippet": "Climate Activism Research #. Our data shows that the climate change movement in Australia is grassroots in nature, diverse, growing quickly, and achieving substantial success. It is using a vibrant and evolving repertoire of civil resistance tactics designed to mobilize and create change.", "subpage_snippet": "", "source": "activisthandbook.org", "link": "https://activisthandbook.org/campaigns/climate-activism", "content": "Climate Activism Research #. Our data shows that the climate change movement in Australia is grassroots in nature, diverse, growing quickly, and achieving substantial success. It is using a vibrant and evolving repertoire of civil resistance tactics designed to mobilize and create change."}
+{"idx": 5, "title": "Climate warriors down under: Contextualising Australia’s youth climate ...", "date": "", "ddg_snippet": "Climate justice framing will be applied to contextualise youth climate activism in Australia. This perspective also addresses the context-specific challenges faced by youth, including the media ’s role in shaping public perceptions and, anti-protest laws that restrict the right to protest.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s44168-023-00085-y?error=cookies_not_supported", "content": "Climate justice framing will be applied to contextualise youth climate activism in Australia. This perspective also addresses the context-specific challenges faced by youth, including the media ’s role in shaping public perceptions and, anti-protest laws that restrict the right to protest."}
+{"idx": 6, "title": "The “Greta Effect” on Social Media : A Systematic Review of Research ...", "date": "", "ddg_snippet": "Social media have transformed climate change communication and become central for sharing information, facilitating dialogue, and driving mobilization. Accordingly, research on the topic has grown.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1080/17524032.2024.2314028", "content": "Social media have transformed climate change communication and become central for sharing information, facilitating dialogue, and driving mobilization. Accordingly, research on the topic has grown."}
+{"idx": 7, "title": "Climate activists gather in New York for ‘Sun Day... | The Guardian", "date": "", "ddg_snippet": "Groups of climate activists walk through the street of New York City for the ‘Make Billionaires Pay’ march on 20 September.", "subpage_snippet": "", "source": "www.theguardian.com", "link": "https://www.theguardian.com/us-news/2025/sep/22/sun-day-climate-new-york", "content": "Groups of climate activists walk through the street of New York City for the ‘Make Billionaires Pay’ march on 20 September."}
+{"idx": 8, "title": "Climate activism , social inclusion and joint decision making", "date": "", "ddg_snippet": "The project examines climate activism from the perspective of young people's opportunities for political participation and the interaction practices intertwined with these processes.", "subpage_snippet": "", "source": "www.tuni.fi", "link": "https://www.tuni.fi/en/research/climate-activism-social-inclusion-and-joint-decision-making", "content": "The project examines climate activism from the perspective of young people's opportunities for political participation and the interaction practices intertwined with these processes."}
+{"idx": 9, "title": "Aussie Climate Activists are Still Brutalising the Kids – Watts Up With...", "date": "", "ddg_snippet": "… Social hierarchies perpetuated through family, school, and media norms support children making individual choices within existing systems but also delegitimize collective action that might disrupt these systems.", "subpage_snippet": "", "source": "wattsupwiththat.com", "link": "https://wattsupwiththat.com/2025/09/17/aussie-climate-activists-are-still-brutalising-the-kids/", "content": "… Social hierarchies perpetuated through family, school, and media norms support children making individual choices within existing systems but also delegitimize collective action that might disrupt these systems."}
diff --git a/data/sampled_jsons/climate_activism_social_media_natural_disasters_policy_events_protest_events_OR_extreme_weather_poli.jsonl b/data/sampled_jsons/climate_activism_social_media_natural_disasters_policy_events_protest_events_OR_extreme_weather_poli.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..660218b94fa3c3e28cedb2c2516e0a484923f8fd
--- /dev/null
+++ b/data/sampled_jsons/climate_activism_social_media_natural_disasters_policy_events_protest_events_OR_extreme_weather_poli.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "COMMUNITY-BASED CLIMATE CHANGE INITIATIVES IN ...", "date": "", "ddg_snippet": "demonstrations of climate change ... in remedying and recovering from financial losses caused by natural disasters or epidemics, contributing to social .", "subpage_snippet": "", "source": "www.cifor-icraf.org", "link": "https://www.cifor-icraf.org/publications/region/sea/publications/softcopy/report/RP0308-16.pdf", "content": "demonstrations of climate change ... in remedying and recovering from financial losses caused by natural disasters or epidemics, contributing to social ."}
+{"idx": 1, "title": "CLIMATE ACTION GUIDE intersectional feminist", "date": "", "ddg_snippet": "extreme weather anomalies triggered or exacerbated major political upheaval like the Irish Rebellion in 164175. At the same time, the slave trade business ... 104 pages", "subpage_snippet": "", "source": "akinamamawaafrika.org", "link": "https://akinamamawaafrika.org/wp-content/uploads/2024/09/AN-INTERSECTIONAL-FEMINIST-CLIMATE-JUSTICE_LONG-VERSION-1A-9.pdf", "content": "extreme weather anomalies triggered or exacerbated major political upheaval like the Irish Rebellion in 164175. At the same time, the slave trade business ... 104 pages"}
+{"idx": 2, "title": "Climate Security", "date": "", "ddg_snippet": "by J Comiskey · Cited by 19 — Shell's global social / political /economic/ environmental ... Ensure the nation's resilience to more frequent or extreme weather events and natural disasters .", "subpage_snippet": "", "source": "www.hsaj.org", "link": "https://www.hsaj.org/resources/uploads/2019/12/hsaj_volume-15_issue-2_ClimateSecurity_121219.pdf", "content": "by J Comiskey · Cited by 19 — Shell's global social / political /economic/ environmental ... Ensure the nation's resilience to more frequent or extreme weather events and natural disasters ."}
+{"idx": 3, "title": "Effective Climate Communication: Turning Eco-Anxiety into ...", "date": "", "ddg_snippet": "Climate change is hard to report in short messages, and the attribution of extreme weather events to the anthropogenic increase in global temperatures is a big ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/effective-climate-communication-turning-eco-anxiety-into-eco-action-9783031673399-9783031673405.html", "content": "Climate change is hard to report in short messages, and the attribution of extreme weather events to the anthropogenic increase in global temperatures is a big ..."}
+{"idx": 4, "title": "table of contents", "date": "", "ddg_snippet": "political and social information, systematically analysed and disseminated: to local communities in the ... natural disasters , including from climate change ...", "subpage_snippet": "", "source": "www.adaptation-undp.org", "link": "https://www.adaptation-undp.org/sites/default/files/resources/jcccp_project_document.pdf", "content": "political and social information, systematically analysed and disseminated: to local communities in the ... natural disasters , including from climate change ..."}
+{"idx": 5, "title": "Overview of submissions", "date": "", "ddg_snippet": "... climate change , biodiversity loss and extreme weather events, Farmers, rural ... What are the social and political factors that influence rural ...", "subpage_snippet": "", "source": "unfccc.int", "link": "https://unfccc.int/sites/default/files/resource/Overview+of+submissions+-+final+review+of+LWPG+and+GAP.xlsx", "content": "... climate change , biodiversity loss and extreme weather events, Farmers, rural ... What are the social and political factors that influence rural ..."}
+{"idx": 6, "title": "the pennsylvania state university", "date": "", "ddg_snippet": "quality and natural disasters that are related to ... wealth on these political circumstances; this political change then affects climate change policy.", "subpage_snippet": "", "source": "honors.libraries.psu.edu", "link": "https://honors.libraries.psu.edu/files/final_submissions/7549", "content": "quality and natural disasters that are related to ... wealth on these political circumstances; this political change then affects climate change policy."}
+{"idx": 7, "title": "Global Observatory for Water and Peace", "date": "", "ddg_snippet": "Natural disasters , political instability and most crucially, funding issues could all derail the next phases of the project, optimistically scheduled to ...", "subpage_snippet": "", "source": "www.genevawaterhub.org", "link": "https://www.genevawaterhub.org/sites/default/files/document/gowp_rapport_wwweng_final_0.pdf", "content": "Natural disasters , political instability and most crucially, funding issues could all derail the next phases of the project, optimistically scheduled to ..."}
+{"idx": 8, "title": "CI and IUCN GEF PROJECT AGENCIES", "date": "", "ddg_snippet": "IPLCs face social , political , cultural, language and financial obstacles to participation in decision-making spaces at these multiple levels. Consequently ...", "subpage_snippet": "", "source": "www.conservation.org", "link": "https://www.conservation.org/docs/default-source/gef-documents/ici-prodoc-final.pdf?sfvrsn=62d1d9a0_0", "content": "IPLCs face social , political , cultural, language and financial obstacles to participation in decision-making spaces at these multiple levels. Consequently ..."}
+{"idx": 9, "title": "The State of the Humanitarian Energy Sector:", "date": "", "ddg_snippet": "... political action , Sustainable Development Goal 7 is highly unlikely to be ... Extreme weather events and changing climates can induce displacement and ...", "subpage_snippet": "", "source": "www.humanitarianlibrary.org", "link": "https://www.humanitarianlibrary.org/sites/default/files/2024/03/SOHES+(1).pdf", "content": "... political action , Sustainable Development Goal 7 is highly unlikely to be ... Extreme weather events and changing climates can induce displacement and ..."}
diff --git a/data/sampled_jsons/climate_activism_social_media_studies_extreme_weather_policy_announcements_scientific_reports.jsonl b/data/sampled_jsons/climate_activism_social_media_studies_extreme_weather_policy_announcements_scientific_reports.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b0a0a901b4a58288420fe87b4b0aea3c00349024
--- /dev/null
+++ b/data/sampled_jsons/climate_activism_social_media_studies_extreme_weather_policy_announcements_scientific_reports.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Media coverage of climate change - Wikipedia", "date": "", "ddg_snippet": "Media coverage of climate change has had effects on public opinion on climate change , as it conveys the scientific consensus on climate change that ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Media_coverage_of_climate_change", "content": "Media coverage of climate change has had effects on public opinion on climate change , as it conveys the scientific consensus on climate change that ..."}
+{"idx": 1, "title": "Making The News – Climate Scepticism", "date": "", "ddg_snippet": "... climate alarmism to the forefront of the news agenda, are also experts at commissioning studies to generate reports that can be turned into press ...", "subpage_snippet": "", "source": "cliscep.com", "link": "https://cliscep.com/2021/11/18/making-the-news/", "content": "... climate alarmism to the forefront of the news agenda, are also experts at commissioning studies to generate reports that can be turned into press ..."}
+{"idx": 2, "title": "A Deadly Assault On Weather Reporting — Covering Climate", "date": "", "ddg_snippet": "As climate change drives increasingly volatile and destructive extreme weather across the US and around the world, a reliable understanding of the ...", "subpage_snippet": "", "source": "coveringclimatenow.org", "link": "https://coveringclimatenow.org/from-us-story/a-deadly-assault-on-weather-reporting/", "content": "As climate change drives increasingly volatile and destructive extreme weather across the US and around the world, a reliable understanding of the ..."}
+{"idx": 3, "title": "Powerful Reporting by This Year’s Covering Climate Now", "date": "", "ddg_snippet": "... technological advances in extreme weather analysis over the last decade, scientists are able to more decisively say if, and to what extent, climate ...", "subpage_snippet": "", "source": "coveringclimatenow.org", "link": "https://coveringclimatenow.org/from-us-story/powerful-reporting-by-this-years-covering-climate-now-journalism-awards-finalists/", "content": "... technological advances in extreme weather analysis over the last decade, scientists are able to more decisively say if, and to what extent, climate ..."}
+{"idx": 4, "title": "Weekly Climate and Energy News Roundup #369 – Watts Up", "date": "", "ddg_snippet": "... the guidelines of The Right Climate Stuff team, rigorous adherence to the scientific method may be the appropriate guide for evaluating reports .", "subpage_snippet": "", "source": "wattsupwiththat.com", "link": "https://wattsupwiththat.com/2019/07/29/weekly-climate-and-energy-news-roundup-369/", "content": "... the guidelines of The Right Climate Stuff team, rigorous adherence to the scientific method may be the appropriate guide for evaluating reports ."}
+{"idx": 5, "title": "Weekly Climate and Energy News Roundup – Watts Up With", "date": "", "ddg_snippet": "... and press release were low keyed, as opposed to the UN IPCC release of its “ Summary for Policymakers ” of the Fourth Assessment Report ...", "subpage_snippet": "", "source": "wattsupwiththat.com", "link": "https://wattsupwiththat.com/2011/08/28/weekley-climate-and-energy-news-roundup/", "content": "... and press release were low keyed, as opposed to the UN IPCC release of its “ Summary for Policymakers ” of the Fourth Assessment Report ..."}
+{"idx": 6, "title": "World Climate Report - DeSmog", "date": "", "ddg_snippet": "... as to “publicize findings on climate change and scientific and social perspectives that may not otherwise appear in the popular literature or media ...", "subpage_snippet": "", "source": "www.desmog.com", "link": "https://www.desmog.com/world-climate-report/", "content": "... as to “publicize findings on climate change and scientific and social perspectives that may not otherwise appear in the popular literature or media ..."}
+{"idx": 7, "title": "Science, uncertainty and advocacy - Climate Etc.", "date": "", "ddg_snippet": "For the past 10 years, I have been actively engaging in the policy process related to climate change through giving media interviews, writing a blog ...", "subpage_snippet": "", "source": "judithcurry.com", "link": "https://judithcurry.com/2015/06/22/science-uncertainty-and-advocacy/", "content": "For the past 10 years, I have been actively engaging in the policy process related to climate change through giving media interviews, writing a blog ..."}
+{"idx": 8, "title": "The Guardian « Aletho News", "date": "", "ddg_snippet": "... scientific twaddle get written by the green activists in mainstream media ? Much of it arises from the new pseudoscience that claims it can tie ...", "subpage_snippet": "", "source": "alethonews.com", "link": "https://alethonews.com/tag/the-guardian/", "content": "... scientific twaddle get written by the green activists in mainstream media ? Much of it arises from the new pseudoscience that claims it can tie ..."}
+{"idx": 9, "title": "Watchdog Groups Anticipate ‘an All-Out War on Science and", "date": "", "ddg_snippet": "NASA and NOAA satellites provide detailed and real-time scientific evidence that human activities are changing the climate in dangerous ways, and the ...", "subpage_snippet": "", "source": "insideclimatenews.org", "link": "https://insideclimatenews.org/news/31012025/trump-administration-war-on-science/", "content": "NASA and NOAA satellites provide detailed and real-time scientific evidence that human activities are changing the climate in dangerous ways, and the ..."}
diff --git a/data/sampled_jsons/closed-form_derivative_vs_autograd_speed_higher_order_neural_network_year_2024.jsonl b/data/sampled_jsons/closed-form_derivative_vs_autograd_speed_higher_order_neural_network_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a90500bfbac7f1b7dbbc70597eb9feb068cf9f63
--- /dev/null
+++ b/data/sampled_jsons/closed-form_derivative_vs_autograd_speed_higher_order_neural_network_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[D] Why do neural networks not have closed form solutions?", "date": "", "ddg_snippet": "It turns out this permutation group in the case of the quintic (and higher ) polynomials is so exotic, that it can't exist, thus proving there can't be a closed form solution to the root problem for higher order polynomials.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/js6orb/d_why_do_neural_networks_not_have_closed_form/", "content": "It turns out this permutation group in the case of the quintic (and higher ) polynomials is so exotic, that it can't exist, thus proving there can't be a closed form solution to the root problem for higher order polynomials."}
+{"idx": 1, "title": "Automatic Differentiation and Neural Networks 3.4 Automatic Differentiation - the forward mode - GitHub Pages Signal Processing for Implicit Neural Representations GitHub - fleajion/machine-learning: A curated list of awesome ... Closed - form expressions for the finite difference Closed - form expressions for the finite difference Closed - form expressions for the finite difference Automatic Differentiation - Python Like You Mean It Automatic Differentiation and Neural Networks Closed-form expressions for the finite difference ...", "date": "", "ddg_snippet": "The name “ neural network ” is sometimes used to refer to many things (e.g. Hopfield networks, self-organizing maps). In these notes, we are only interested in the most common type of neural network , the multi-layer perceptron. basic problem in machine learning is function approximation. We have some inputs ˆx and some outputs ˆy, and we want to fit ... See full list on people.cs.umass.edu j:i2Pa(j) ✪ The wonderful thing about this is that it works for any differentiable function that can be phrased as an expression graph. One can really think of this as differentiating programs, rather than “functions”. The back propagation step always has the same complexity as the original forward propagation step. There are limitations, of course... See full list on people.cs.umass.edu features (h) scores (f) Fundamentally, backpropagation is just a special case of reverse-mode autodiff, applied to a neural network . We will derive the method here from first principles, but keep in mind that autodiff can generalize to essentially arbitrary expression graphs. We can compute the derivatives of the loss with respect to f directly. Th... See full list on people.cs.umass.edu Neural networks are relatively fast, as classifiers go, as long as there aren’t too many hidden units. They also seem to work reasonably well for many problems, and so seem to be the method of choice for a certain range of speed and accuracy requirements. Perhaps the single biggest drawback of neural networks is the fact that their optimization is ... See full list on people.cs.umass.edu While the choice to implicitly represent the computation graph makes the job of implementing an AD calculator easier, it does mean that we will need to make certain adjustments in order to extend the use of the calculator to e.g., higher order derivatives . Oct 31, 2022 · We propose an implicit neural signal processing network , dubbed INSP-Net, via closed- form differential operators directly running on implicit neural representations. DSSTNE - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility. DyNet - A dynamic neural network library working well with networks that have dynamic structures that change for every training instance. Written in C++ with bindings in Python. Are there closed-form expressions for arbitrary derivatives? In this paper, we have presented the closed-form expressions for the coefficients of the forward, backward and central difference approximations of first and second derivative for arbitrary orders. Moreover, higher-order central difference approximations are given for any higher-order derivative. What is a new class of higher-order digital differentiators? Hence, a new class of higher - order digital differentiators is established based on closed - form expressions of higher - order central difference approximations. Appendix. The computer program given here is written in MATHEMATICA. What is the difference between closed-form expressions and central difference approximations? In Section 6, we have given the closed-form expressions for the forward and backward difference approximations of second derivative, while the central difference approximations are given for any higher derivative . A computer program is given in the appendix, which gives the derivative of a function using the presented closed-form expressions. What is automatic differentiation? Automatic differentiation (a.k.a autodiff) is an important technology for scientific computing and machine learning , it enables us to measure rates of change (or “cause and effect”) through our code via the derivatives of the mathematical functions that our code computes. Which algorithm is faster F N m or F? There are also more general algorithms for computing the derivatives of functions from vector inputs to vector outputs f : n m. However, these algorithms are in general slower than just computing f, unlike reverse mode (forward mode) with a single output (input). Jul 31, 1999 · In this paper, we have presented closed- form expressions of these approximations of arbitrary order for first and higher derivatives . A comparison of the three types of approximations is given with an ideal digital differentiator by comparing their frequency responses.", "subpage_snippet": "", "source": "people.cs.umass.edu", "link": "https://people.cs.umass.edu/~domke/courses/sml2010/07autodiff_nnets.pdf", "content": "The name “ neural network ” is sometimes used to refer to many things (e.g. Hopfield networks, self-organizing maps). In these notes, we are only interested in the most common type of neural network , the multi-layer perceptron. basic problem in machine learning is function approximation. We have some inputs ˆx and some outputs ˆy, and we want to fit ... See full list on people.cs.umass.edu j:i2Pa(j) ✪ The wonderful thing about this is that it works for any differentiable function that can be phrased as an expression graph. One can really think of this as differentiating programs, rather than “functions”. The back propagation step always has the same complexity as the original forward propagation step. There are limitations, of course... See full list on people.cs.umass.edu features (h) scores (f) Fundamentally, backpropagation is just a special case of reverse-mode autodiff, applied to a neural network . We will derive the method here from first principles, but keep in mind that autodiff can generalize to essentially arbitrary expression graphs. We can compute the derivatives of the loss with respect to f directly. Th... See full list on people.cs.umass.edu Neural networks are relatively fast, as classifiers go, as long as there aren’t too many hidden units. They also seem to work reasonably well for many problems, and so seem to be the method of choice for a certain range of speed and accuracy requirements. Perhaps the single biggest drawback of neural networks is the fact that their optimization is ... See full list on people.cs.umass.edu While the choice to implicitly represent the computation graph makes the job of implementing an AD calculator easier, it does mean that we will need to make certain adjustments in order to extend the use of the calculator to e.g., higher order derivatives . Oct 31, 2022 · We propose an implicit neural signal processing network , dubbed INSP-Net, via closed- form differential operators directly running on implicit neural representations. DSSTNE - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility. DyNet - A dynamic neural network library working well with networks that have dynamic structures that change for every training instance. Written in C++ with bindings in Python. Are there closed-form expressions for arbitrary derivatives? In this paper, we have presented the closed-form expressions for the coefficients of the forward, backward and central difference approximations of first and second derivative for arbitrary orders. Moreover, higher-order central difference approximations are given for any higher-order derivative. What is a new class of higher-order digital differentiators? Hence, a new class of higher - order digital differentiators is established based on closed - form expressions of higher - order central difference approximations. Appendix. The computer program given here is written in MATHEMATICA. What is the difference between closed-form expressions and central difference approximations? In Section 6, we have given the closed-form expressions for the forward and backward difference approximations of second derivative, while the central difference approximations are given for any higher derivative . A computer program is given in the appendix, which gives the derivative of a function using the presented closed-form expressions. What is automatic differentiation? Automatic differentiation (a.k.a autodiff) is an important technology for scientific computing and machine learning , it enables us to measure rates of change (or “cause and effect”) through our code via the derivatives of the mathematical functions that our code computes. Which algorithm is faster F N m or F? There are also more general algorithms for computing the derivatives of functions from vector inputs to vector outputs f : n m. However, these algorithms are in general slower than just computing f, unlike reverse mode (forward mode) with a single output (input). Jul 31, 1999 · In this paper, we have presented closed- form expressions of these approximations of arbitrary order for first and higher derivatives . A comparison of the three types of approximations is given with an ideal digital differentiator by comparing their frequency responses."}
+{"idx": 2, "title": "Signal Processing for Implicit Neural Representations", "date": "", "ddg_snippet": "Oct 31, 2022 · We propose an implicit neural signal processing network , dubbed INSP-Net, via closed- form differential operators directly running on implicit neural representations.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qqIrESv4f_L", "content": "Oct 31, 2022 · We propose an implicit neural signal processing network , dubbed INSP-Net, via closed- form differential operators directly running on implicit neural representations."}
+{"idx": 3, "title": "Closed-form expressions for the finite difference ...", "date": "", "ddg_snippet": "Jul 31, 1999 · In this paper, we have presented closed- form expressions of these approximations of arbitrary order for first and higher derivatives . A comparison of the three types of approximations is given with an ideal digital differentiator by comparing their frequency responses.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0377042799000886", "content": "Jul 31, 1999 · In this paper, we have presented closed- form expressions of these approximations of arbitrary order for first and higher derivatives . A comparison of the three types of approximations is given with an ideal digital differentiator by comparing their frequency responses."}
+{"idx": 4, "title": "SH-SAS: An Implicit Neural Representation for Complex", "date": "", "ddg_snippet": "... on simulated and real (both in-air and underwater) SAS data, yielding higher -fidelity 3D scene reconstruction compared to conventional neural methods ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11087v1", "content": "... on simulated and real (both in-air and underwater) SAS data, yielding higher -fidelity 3D scene reconstruction compared to conventional neural methods ..."}
+{"idx": 5, "title": "Power Normalizations in Fine-grained Image, Few-shot Image ...", "date": "", "ddg_snippet": "by P Koniusz · Cited by 75 — has the closed-form derivative defined as: ∂bgMaxExp(M). ∂ Mkl ... and higher-order representations in computer vision,” ICCV. Tutorial ... 21 pages", "subpage_snippet": "", "source": "www.koniusz.com", "link": "https://www.koniusz.com/tpami2020-pkb.pdf", "content": "by P Koniusz · Cited by 75 — has the closed-form derivative defined as: ∂bgMaxExp(M). ∂ Mkl ... and higher-order representations in computer vision,” ICCV. Tutorial ... 21 pages"}
+{"idx": 6, "title": "Automatic Differentiation - Python Like You Mean It", "date": "", "ddg_snippet": "We use gradient descent to find the optimal parameter values of the neural network ; the values are found such that they minimize the average number of mistakes the neural makes when performing training tasks. This section has just scratched the surface of automatic differentiation.", "subpage_snippet": "", "source": "www.pythonlikeyoumeanit.com", "link": "https://www.pythonlikeyoumeanit.com/Module3_IntroducingNumpy/AutoDiff.html", "content": "We use gradient descent to find the optimal parameter values of the neural network ; the values are found such that they minimize the average number of mistakes the neural makes when performing training tasks. This section has just scratched the surface of automatic differentiation."}
+{"idx": 7, "title": "3.4 Automatic Differentiation - the forward mode - GitHub Pages", "date": "", "ddg_snippet": "While the choice to implicitly represent the computation graph makes the job of implementing an AD calculator easier, it does mean that we will need to make certain adjustments in order to extend the use of the calculator to e.g., higher order derivatives .", "subpage_snippet": "", "source": "kenndanielso.github.io", "link": "https://kenndanielso.github.io/mlrefined/blog_posts/3_Automatic_differentiation/3_4_AD_forward_mode.html", "content": "While the choice to implicitly represent the computation graph makes the job of implementing an AD calculator easier, it does mean that we will need to make certain adjustments in order to extend the use of the calculator to e.g., higher order derivatives ."}
+{"idx": 8, "title": "GitHub - fleajion/machine-learning: A curated list of awesome ...", "date": "", "ddg_snippet": "DSSTNE - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility. DyNet - A dynamic neural network library working well with networks that have dynamic structures that change for every training instance. Written in C++ with bindings in Python.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fleajion/machine-learning", "content": "DSSTNE - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility. DyNet - A dynamic neural network library working well with networks that have dynamic structures that change for every training instance. Written in C++ with bindings in Python."}
+{"idx": 9, "title": "A universal augmentation framework for long-range", "date": "", "ddg_snippet": "2025 ) , many require specialized training labels beyond energy and forces: the fourth-generation high -dimensional neural network potentials (4G ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14302v1", "content": "2025 ) , many require specialized training labels beyond energy and forces: the fourth-generation high -dimensional neural network potentials (4G ..."}
diff --git a/data/sampled_jsons/common_retriever_paradigms_in_RAG_systems.jsonl b/data/sampled_jsons/common_retriever_paradigms_in_RAG_systems.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d2c3cb10865c5b4985584b3ba1e7777bf3168c95
--- /dev/null
+++ b/data/sampled_jsons/common_retriever_paradigms_in_RAG_systems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAG and Parent Document Retrievers : Making Sense of... | Medium", "date": "", "ddg_snippet": "The RAG Paradigm .Parent document retrievers , nestled at the core of the RAG universe, offer an innovative remedy to context orchestration challenges. Their ability to bridge broad parent chunks with detailed child embeddings ensures LLMs are fed a balanced diet of precision and breadth.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/ai-insights-cobet/rag-and-parent-document-retrievers-making-sense-of-complex-contexts-with-code-5bd5c3474a8a", "content": "The RAG Paradigm .Parent document retrievers , nestled at the core of the RAG universe, offer an innovative remedy to context orchestration challenges. Their ability to bridge broad parent chunks with detailed child embeddings ensures LLMs are fed a balanced diet of precision and breadth."}
+{"idx": 1, "title": "Retrieval-Augmented Generation ( RAG )", "date": "", "ddg_snippet": "Advancements and Paradigms in RAG Techniques. Naive RAG Paradigm . The Naive RAG paradigm represents the earliest methodology in Retrieval-Augmented Generation ( RAG ) systems . It follows a traditional process that includes indexing, retrieval, and generation.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/retrieval-augmented-generation-rag-hari-kantipudi-0eylc", "content": "Advancements and Paradigms in RAG Techniques. Naive RAG Paradigm . The Naive RAG paradigm represents the earliest methodology in Retrieval-Augmented Generation ( RAG ) systems . It follows a traditional process that includes indexing, retrieval, and generation."}
+{"idx": 2, "title": "Three Paradigms of Retrieval-Augmented Generation ( RAG ) for LLMs", "date": "", "ddg_snippet": "Retrieval-Augmented Generation ( RAG ) has emerged as a promising technique to enhance the capabilities of large language models by incorporating external knowledge sources.", "subpage_snippet": "", "source": "www.thecloudgirl.dev", "link": "https://www.thecloudgirl.dev/blog/three-paradigms-of-retrieval-augmented-generation-rag-for-llms", "content": "Retrieval-Augmented Generation ( RAG ) has emerged as a promising technique to enhance the capabilities of large language models by incorporating external knowledge sources."}
+{"idx": 3, "title": "Retrieval Augmented Generation ( RAG ) for LLMs | Prompt Engineering...", "date": "", "ddg_snippet": "The generator in a RAG system is responsible for converting retrieved information into a coherent text that will form the final output of the model.", "subpage_snippet": "", "source": "www.promptingguide.ai", "link": "https://www.promptingguide.ai/research/rag", "content": "The generator in a RAG system is responsible for converting retrieved information into a coherent text that will form the final output of the model."}
+{"idx": 4, "title": "Modular RAG and RAG Flow: Part II", "date": "", "ddg_snippet": "So, under the paradigm of modular RAG , how should we design our RAG system ?In the RAG system , fine-tuning both the retriever and the generator simultaneously is a unique feature of the RAG system .", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/modular-rag-and-rag-flow-part-ii-77b62bf8a5d3", "content": "So, under the paradigm of modular RAG , how should we design our RAG system ?In the RAG system , fine-tuning both the retriever and the generator simultaneously is a unique feature of the RAG system ."}
+{"idx": 5, "title": "Enhancing Large Language Models: A Deep Dive into RAG ... | IrisAgent", "date": "", "ddg_snippet": "How has RAG evolved over time? RAG systems have evolved through three main paradigms : Naive, Advanced, and Modular, and modern models are exploring hybrid methodologies and self-retrieval to push the boundaries of augmentation and generation in LLMs.", "subpage_snippet": "", "source": "irisagent.com", "link": "https://irisagent.com/blog/enhancing-large-language-models-a-deep-dive-into-rag-llm-technology/", "content": "How has RAG evolved over time? RAG systems have evolved through three main paradigms : Naive, Advanced, and Modular, and modern models are exploring hybrid methodologies and self-retrieval to push the boundaries of augmentation and generation in LLMs."}
+{"idx": 6, "title": "An Exploratory Tour of Retrieval Augmented Generation ( RAG )...", "date": "", "ddg_snippet": "Types of RAG . The RAG popularity is burgeoning, and with it, its research paradigm is continuously evolving. Common Use Cases. The RAG -based applications have demonstrated their merits and use across various domains.", "subpage_snippet": "", "source": "ai.gopubby.com", "link": "https://ai.gopubby.com/an-exploratory-tour-of-retrieval-augmented-generation-rag-paradigm-3940c1947d27", "content": "Types of RAG . The RAG popularity is burgeoning, and with it, its research paradigm is continuously evolving. Common Use Cases. The RAG -based applications have demonstrated their merits and use across various domains."}
+{"idx": 7, "title": "Optimizing Retrieval in RAG : When, What and How | by Anthony Alcaraz", "date": "", "ddg_snippet": "The RAG Paradigm (Retrieval Augmented Generation) attempts to address this limitation by dynamically retrieving relevant knowledge from external corpora and incorporating it into the LLM’s inputs during the generation process.", "subpage_snippet": "", "source": "ai.plainenglish.io", "link": "https://ai.plainenglish.io/optimizing-retrieval-in-rag-when-what-and-how-bff2bb8356bf", "content": "The RAG Paradigm (Retrieval Augmented Generation) attempts to address this limitation by dynamically retrieving relevant knowledge from external corpora and incorporating it into the LLM’s inputs during the generation process."}
+{"idx": 8, "title": "GitHub - Tongji-KGLLM/ RAG -Survey", "date": "", "ddg_snippet": "Paradigm of RAG . RAG concept, introduced by Lewis in 2020, has rapidly evolved, marking distinct stages in its research journey. Initially, the research aimed to bolster language models by infusing them with additional knowledge during the pre-training phase.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Tongji-KGLLM/RAG-Survey", "content": "Paradigm of RAG . RAG concept, introduced by Lewis in 2020, has rapidly evolved, marking distinct stages in its research journey. Initially, the research aimed to bolster language models by infusing them with additional knowledge during the pre-training phase."}
+{"idx": 9, "title": "Evolution of RAGs : Naive RAG , Advanced RAG , and... - MarkTechPost", "date": "", "ddg_snippet": "Naive RAG : The Naive RAG research paradigm represents the earliest methodology, which gained prominence shortly after the widespread adoption of ChatGPT. Common methods include query rewriting, query transformation, query expansion, and other techniques.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/04/01/evolution-of-rags-naive-rag-advanced-rag-and-modular-rag-architectures/", "content": "Naive RAG : The Naive RAG research paradigm represents the earliest methodology, which gained prominence shortly after the widespread adoption of ChatGPT. Common methods include query rewriting, query transformation, query expansion, and other techniques."}
diff --git a/data/sampled_jsons/complementary_knowledge_weak_supervisor_strong_student_model_similarity_weak-to-strong_generalizatio_year_2024.jsonl b/data/sampled_jsons/complementary_knowledge_weak_supervisor_strong_student_model_similarity_weak-to-strong_generalizatio_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d50c3f7e1235cf092da6f6ee76f8762e8432d85d
--- /dev/null
+++ b/data/sampled_jsons/complementary_knowledge_weak_supervisor_strong_student_model_similarity_weak-to-strong_generalizatio_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Consequential strangers - Wikipedia", "date": "", "ddg_snippet": "Such relationship are referred to elsewhere as \"peripheral\" (versus \"core\"), \"secondary\" (versus \"primary\"), or \" weak ties\" (versus \" strong \").", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Consequential_strangers", "content": "Such relationship are referred to elsewhere as \"peripheral\" (versus \"core\"), \"secondary\" (versus \"primary\"), or \" weak ties\" (versus \" strong \")."}
+{"idx": 1, "title": "Weak-to-Strong Generalization: Eliciting Strong Capabilities", "date": "", "ddg_snippet": "Figure 2: Strong models trained with weak supervision generalize beyond their supervisor , and improving weak - to - strong generalization is tractable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.09390v1", "content": "Figure 2: Strong models trained with weak supervision generalize beyond their supervisor , and improving weak - to - strong generalization is tractable."}
+{"idx": 2, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "... find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from “ weak - to - strong generalization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "... find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from “ weak - to - strong generalization ..."}
+{"idx": 3, "title": "[2502.04313] Great Models Think Alike and this Undermines AI", "date": "", "ddg_snippet": "... find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from weak - to - strong generalization .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04313", "content": "... find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from weak - to - strong generalization ."}
+{"idx": 4, "title": "Great Models Think Alike and this Undermines AI Oversight -", "date": "", "ddg_snippet": "... complementary knowledge between theweak supervisor and strong student model plays a crucial role in gains from \" weak - to - strong generalization ...", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/573730/great-models-think-alike-and-this-undermines-ai-oversight", "content": "... complementary knowledge between theweak supervisor and strong student model plays a crucial role in gains from \" weak - to - strong generalization ..."}
+{"idx": 5, "title": "Similarity affects Oversight", "date": "", "ddg_snippet": "... find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from weak - to - strong generalization .", "subpage_snippet": "", "source": "model-similarity.github.io", "link": "https://model-similarity.github.io/", "content": "... find complementary knowledge between the weak supervisor and strong student model plays a crucial role in gains from weak - to - strong generalization ."}
+{"idx": 6, "title": "Review of Alignment Plan Critiques- December AI-Plans", "date": "", "ddg_snippet": "Strong empiricism: This approach can be used to test hypotheses about weak - to - strong generalization and provides a practical sandbox to discard weaker ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/LvJdqAfXkAXB2EbM2/review-of-alignment-plan-critiques-december-ai-plans", "content": "Strong empiricism: This approach can be used to test hypotheses about weak - to - strong generalization and provides a practical sandbox to discard weaker ..."}
+{"idx": 7, "title": "Document 252696", "date": "", "ddg_snippet": "Therefore, while the relevant Bodies of Knowledge (BOK) s and model curricula may address those elements of discipline content required to undertake ...", "subpage_snippet": "", "source": "abcdocz.com", "link": "https://abcdocz.com/doc/252696/", "content": "Therefore, while the relevant Bodies of Knowledge (BOK) s and model curricula may address those elements of discipline content required to undertake ..."}
+{"idx": 8, "title": "Wenkai Yang", "date": "", "ddg_snippet": "... weakly supervised strong students can consistently outperform weak teachers towards the alignment target, leading to a weak - to - strong generalization ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Wenkai+Yang", "content": "... weakly supervised strong students can consistently outperform weak teachers towards the alignment target, leading to a weak - to - strong generalization ..."}
+{"idx": 9, "title": "Internship Report On An Analysis of Training and Development of", "date": "", "ddg_snippet": "... and topics covered in the internship report. ... Similar to Internship Report On An Analysis of Training and Development of Ananta Garments Ltd.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/internship-report-on-an-analysis-of-training-and-development-of-ananta-garments-ltd/238856830", "content": "... and topics covered in the internship report. ... Similar to Internship Report On An Analysis of Training and Development of Ananta Garments Ltd."}
diff --git a/data/sampled_jsons/computer_science_measurement_theory_early_days_took_decades_worth_the_effort_generative_AI_evaluatio.jsonl b/data/sampled_jsons/computer_science_measurement_theory_early_days_took_decades_worth_the_effort_generative_AI_evaluatio.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ecfba251a9dcf299030d6752a0bb6be62878ba0
--- /dev/null
+++ b/data/sampled_jsons/computer_science_measurement_theory_early_days_took_decades_worth_the_effort_generative_AI_evaluatio.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "6 Jun 2025 — We present a four-level framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "6 Jun 2025 — We present a four-level framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, ..."}
+{"idx": 1, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "17 Nov 2024 — We argue that the measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.10939v1", "content": "17 Nov 2024 — We argue that the measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social ..."}
+{"idx": 2, "title": "Revisiting Generalizability Theory in the Age of Artificial ...", "date": "", "ddg_snippet": "by B Degen · 2025 — The rise of AI in education presents both transformative opportunities and methodological challenges. This paper revisits Generalizability ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666557325000370", "content": "by B Degen · 2025 — The rise of AI in education presents both transformative opportunities and methodological challenges. This paper revisits Generalizability ..."}
+{"idx": 3, "title": "Generative artificial intelligence: a historical perspective", "date": "", "ddg_snippet": "by R He · 2025 · Cited by 19 — Generative artificial intelligence (GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11970245/", "content": "by R He · 2025 · Cited by 19 — Generative artificial intelligence (GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes ..."}
+{"idx": 4, "title": "Generative Artificial Intelligence: Evolving Technology ...", "date": "", "ddg_snippet": "by VC Storey · 2025 · Cited by 34 — In this paper, we consider the evolving and emerging trends of AI in order to examine its present and predict its future impacts.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10796-025-10581-7", "content": "by VC Storey · 2025 · Cited by 34 — In this paper, we consider the evolving and emerging trends of AI in order to examine its present and predict its future impacts."}
+{"idx": 5, "title": "The effect of generative artificial intelligence (AI)-based tool ...", "date": "", "ddg_snippet": "by R Yilmaz · 2023 · Cited by 694 — This study investigated the effect of programming education using the ChatGPT on students' computational thinking skills, programming self-efficacy, and ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666920X23000267", "content": "by R Yilmaz · 2023 · Cited by 694 — This study investigated the effect of programming education using the ChatGPT on students' computational thinking skills, programming self-efficacy, and ..."}
+{"idx": 6, "title": "Generative AI in Computer Science Education", "date": "", "ddg_snippet": "by D Franklin · 2025 · Cited by 6 — Generative AI is a disruptive technology that has the potential to transform many aspects of how computer science is taught.", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/elements/generative-ai-in-computer-science-education/0A22106CBD7FCB391FD120C56E21420F", "content": "by D Franklin · 2025 · Cited by 6 — Generative AI is a disruptive technology that has the potential to transform many aspects of how computer science is taught."}
+{"idx": 7, "title": "Can Generative AI improve social science?", "date": "", "ddg_snippet": "by CA Bail · 2024 · Cited by 328 — I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques.", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.2314021121", "content": "by CA Bail · 2024 · Cited by 328 — I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques."}
+{"idx": 8, "title": "The effects of generative AI on productivity, innovation and ...", "date": "", "ddg_snippet": "It focuses on significant mechanisms through which generative AI can affect i) productivity – by automating tasks, enhancing skill development, and transforming ... 59 pages", "subpage_snippet": "", "source": "www.oecd.org", "link": "https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/the-effects-of-generative-ai-on-productivity-innovation-and-entrepreneurship_da1d085d/b21df222-en.pdf", "content": "It focuses on significant mechanisms through which generative AI can affect i) productivity – by automating tasks, enhancing skill development, and transforming ... 59 pages"}
+{"idx": 9, "title": "Generative AI at Work* | The Quarterly Journal of Economics", "date": "", "ddg_snippet": "by E Brynjolfsson · 2025 · Cited by 1767 — We provide early evidence on the effect of generative AI deployed at scale in the workplace. We study the adoption of a generative AI tool that provides ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/qje/article/140/2/889/7990658", "content": "by E Brynjolfsson · 2025 · Cited by 1767 — We provide early evidence on the effect of generative AI deployed at scale in the workplace. We study the adoption of a generative AI tool that provides ..."}
diff --git a/data/sampled_jsons/concept_bottleneck_models_innovations_2024_year_2024.jsonl b/data/sampled_jsons/concept_bottleneck_models_innovations_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2d9476f52ab7e3a39843a03ba320a82e5c06061b
--- /dev/null
+++ b/data/sampled_jsons/concept_bottleneck_models_innovations_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Incremental Residual Concept Bottleneck Models - CVF Open Access", "date": "", "ddg_snippet": "However, is challenging to construct a comprehensive concept bank through humans or large language models , which severely limits the performance of CBMs. In this work, we propose the Incremental Residual Concept Bottleneck Model (Res-CBM) to address the challenge of concept completeness.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Shang_Incremental_Residual_Concept_Bottleneck_Models_CVPR_2024_paper.pdf", "content": "However, is challenging to construct a comprehensive concept bank through humans or large language models , which severely limits the performance of CBMs. In this work, we propose the Incremental Residual Concept Bottleneck Model (Res-CBM) to address the challenge of concept completeness."}
+{"idx": 1, "title": "[2401.14142] Energy-Based Concept Bottleneck Models: Unifying ...", "date": "", "ddg_snippet": "Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept -based interpretations for black-box deep learning models . They typically work by predicting concepts given the input and then predicting the final class label given the predicted concepts . However, (1) they often fail to capture the high-order, nonlinear interaction between concepts , e.g ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.14142", "content": "Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept -based interpretations for black-box deep learning models . They typically work by predicting concepts given the input and then predicting the final class label given the predicted concepts . However, (1) they often fail to capture the high-order, nonlinear interaction between concepts , e.g ..."}
+{"idx": 2, "title": "PDF Stochastic Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input. Through time-consuming manual interventions, user can correct wrongly predicted concept values to enhance the model's down-stream performance.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/5c7894ac8788555f1cecf536f1e0fd35-Paper-Conference.pdf", "content": "Abstract Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input. Through time-consuming manual interventions, user can correct wrongly predicted concept values to enhance the model's down-stream performance."}
+{"idx": 3, "title": "Incremental Residual Concept Bottleneck Models - IEEE Xplore", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process. Multimodal pre-trained models can match visual representations with textual concept embeddings, allowing for obtaining the interpretable concept ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10656628", "content": "Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process. Multimodal pre-trained models can match visual representations with textual concept embeddings, allowing for obtaining the interpretable concept ..."}
+{"idx": 4, "title": "Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept ...", "date": "", "ddg_snippet": "This repo is the official implementation of our ICLR 2024 paper: Energy-Based Concept Bottleneck Models : Unifying Prediction, Concept Intervention, and Probabilistic Interpretations Xinyue Xu, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li Twelfth International Conference on Learning Representations (ICLR), 2024 . [Paper] [OpenReview)] [PPT]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xmed-lab/ECBM", "content": "This repo is the official implementation of our ICLR 2024 paper: Energy-Based Concept Bottleneck Models : Unifying Prediction, Concept Intervention, and Probabilistic Interpretations Xinyue Xu, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li Twelfth International Conference on Learning Representations (ICLR), 2024 . [Paper] [OpenReview)] [PPT]"}
+{"idx": 5, "title": "Stochastic Concept Bottleneck Models · NeurIPS 2024", "date": "", "ddg_snippet": "The Stochastic Concept Bottleneck Model (SCBM) framework offers a novel approach to concept bottleneck models (CBMs) by explicitly modeling dependencies between concepts using a multivariate normal distribution.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/isjqtq5s1f/", "content": "The Stochastic Concept Bottleneck Model (SCBM) framework offers a novel approach to concept bottleneck models (CBMs) by explicitly modeling dependencies between concepts using a multivariate normal distribution."}
+{"idx": 6, "title": "Concept Bottleneck Generative Models - proceedings.iclr.cc", "date": "", "ddg_snippet": "The concept bottleneck layer partitions the generative model into three parts: the pre- concept bottleneck portion, the CB layer, and the post- concept bottleneck portion. To train CB generative models , we complement the traditional task-based loss function for training generative models with a concept loss and an orthogonality loss.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/hash/9149fc44c95ce58e3ca529a1e34c2691-Abstract-Conference.html", "content": "The concept bottleneck layer partitions the generative model into three parts: the pre- concept bottleneck portion, the CB layer, and the post- concept bottleneck portion. To train CB generative models , we complement the traditional task-based loss function for training generative models with a concept loss and an orthogonality loss."}
+{"idx": 7, "title": "[2406.19272] Stochastic Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input. Through time-consuming manual interventions, a user can correct wrongly predicted concept values to enhance the model's downstream performance. We propose Stochastic Concept Bottleneck Models (SCBMs), a novel ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.19272", "content": "Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input. Through time-consuming manual interventions, a user can correct wrongly predicted concept values to enhance the model's downstream performance. We propose Stochastic Concept Bottleneck Models (SCBMs), a novel ..."}
+{"idx": 8, "title": "Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?", "date": "", "ddg_snippet": "Authors Sonia Laguna, Ričards Marcinkevičs, Moritz Vandenhirtz, Julia E. Vogt Abstract Recently, interpretable machine learning has re-explored concept bottleneck models (CBM). An advantage of this model class is the user's ability to intervene on predicted concept values, affecting the downstream output. In this work, we introduce a method to perform such concept -based interventions on ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/9a439efaa34fe37177eba00737624824-Abstract-Conference.html", "content": "Authors Sonia Laguna, Ričards Marcinkevičs, Moritz Vandenhirtz, Julia E. Vogt Abstract Recently, interpretable machine learning has re-explored concept bottleneck models (CBM). An advantage of this model class is the user's ability to intervene on predicted concept values, affecting the downstream output. In this work, we introduce a method to perform such concept -based interventions on ..."}
+{"idx": 9, "title": "Blog - IBM Research", "date": "", "ddg_snippet": "The IBM Research blog is the home for stories told by the researchers, scientists, and engineers inventing What's Next in science and technology.", "subpage_snippet": "", "source": "research.ibm.com", "link": "https://research.ibm.com/blog", "content": "The IBM Research blog is the home for stories told by the researchers, scientists, and engineers inventing What's Next in science and technology."}
diff --git a/data/sampled_jsons/condensation_neural_networks_neurons_cluster_alignment.jsonl b/data/sampled_jsons/condensation_neural_networks_neurons_cluster_alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c04e706ac38f9c8eb5ef856b71a5e638e78a936d
--- /dev/null
+++ b/data/sampled_jsons/condensation_neural_networks_neurons_cluster_alignment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dropping Experts, Recombining Neurons: Retraining-Free Pruning", "date": "", "ddg_snippet": "Despite some global expert alignment (left), clear inconsistencies remain at the neuron level (right). ... alignment in their structural patterns, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10377v1", "content": "Despite some global expert alignment (left), clear inconsistencies remain at the neuron level (right). ... alignment in their structural patterns, ..."}
+{"idx": 1, "title": "A Spectral Theory of Neural Prediction and Alignment", "date": "", "ddg_snippet": "... that for networks effective in predicting neural data, we can ascertain if their superior performance stems from the model’s spectra or alignment ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2309.12821v2", "content": "... that for networks effective in predicting neural data, we can ascertain if their superior performance stems from the model’s spectra or alignment ..."}
+{"idx": 2, "title": "CSpredR: A Multi-Site mRNA Subcellular Localization Prediction", "date": "", "ddg_snippet": "A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1999-4893/18/2/67", "content": "A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals"}
+{"idx": 3, "title": "NeuroSC: Exploring Neurodevelopment via Spatiotemporal", "date": "", "ddg_snippet": "... Condensation (DC) and C-PHATE ( Brugnone et al., 2019 ; Moon et al., 2019 ), which resulted in reduced dimensionality of the neuronal relationships, ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/103977", "content": "... Condensation (DC) and C-PHATE ( Brugnone et al., 2019 ; Moon et al., 2019 ), which resulted in reduced dimensionality of the neuronal relationships, ..."}
+{"idx": 4, "title": "Downloads", "date": "", "ddg_snippet": "Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions"}
+{"idx": 5, "title": "CVPR 2023 Schedule", "date": "", "ddg_snippet": "Efficient Neural Networks : From Algorithm Design to Practical Mobile Deployment ... Fourth Workshop on Neural Architecture Search, Third lightweight ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/calendar", "content": "Efficient Neural Networks : From Algorithm Design to Practical Mobile Deployment ... Fourth Workshop on Neural Architecture Search, Third lightweight ..."}
+{"idx": 6, "title": "CA2927171C - Quantum processor and its use for implementing a", "date": "", "ddg_snippet": "... Quantum processor and its use for implementing a neural network ... 238000013528 artificial neural network Methods 0.000 title claims description 44", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/CA2927171C/en", "content": "... Quantum processor and its use for implementing a neural network ... 238000013528 artificial neural network Methods 0.000 title claims description 44"}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "Accelerated Sparse Neural Training: A Provable and ... Align before Fuse: Vision and Language Representation Learning with Momentum Distillation", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "Accelerated Sparse Neural Training: A Provable and ... Align before Fuse: Vision and Language Representation Learning with Momentum Distillation"}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "... Training of Physics-Informed Neural Networks ... A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2022", "content": "... Training of Physics-Informed Neural Networks ... A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks"}
+{"idx": 9, "title": "Scientific Frontline: Brain circuits for locomotion evolved", "date": "", "ddg_snippet": "Sea slugs may still have that module, a smallish network of neurons called the ‘ A- cluster , ’ with 23 neurons identified so far ...", "subpage_snippet": "", "source": "www.sflorg.com", "link": "https://www.sflorg.com/2023/04/ns04262301.html", "content": "Sea slugs may still have that module, a smallish network of neurons called the ‘ A- cluster , ’ with 23 neurons identified so far ..."}
diff --git a/data/sampled_jsons/conf(i,_k)_equation_ATA_sitearxiv.orghtml2502.00775v2.jsonl b/data/sampled_jsons/conf(i,_k)_equation_ATA_sitearxiv.orghtml2502.00775v2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6881f0b4c4bf1e1cda1a204539c13c26018e140
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+++ b/data/sampled_jsons/conf(i,_k)_equation_ATA_sitearxiv.orghtml2502.00775v2.jsonl
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+{"idx": 0, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "ai,ksubscript𝑎 𝑖 𝑘 a_{ i , k }italic_a start_POSTSUBSCRIPT italic_ i , italic_ k end_POSTSUBSCRIPT. observed times for all the chosen workers. We will denote the action set by.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "ai,ksubscript𝑎 𝑖 𝑘 a_{ i , k }italic_a start_POSTSUBSCRIPT italic_ i , italic_ k end_POSTSUBSCRIPT. observed times for all the chosen workers. We will denote the action set by."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/consequential_validity_appendices_Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Meas.jsonl b/data/sampled_jsons/consequential_validity_appendices_Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Meas.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1e1dd62f8076dc5af771b3964d1320dd27d0d0d7
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+++ b/data/sampled_jsons/consequential_validity_appendices_Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Meas.jsonl
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+{"idx": 0, "title": "Position : Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient sci-entific rigor, leading to what has been described as “a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient sci-entific rigor, leading to what has been described as “a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024)."}
+{"idx": 1, "title": "(PDF) Position : Evaluating Generative AI Systems is a Social ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388657599_Position_Evaluating_Generative_AI_Systems_is_a_Social_Science_Measurement_Challenge", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024)."}
+{"idx": 2, "title": "Position : Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as”a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024).", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/position-evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as”a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024)."}
+{"idx": 3, "title": "ICML 2025 Statistics: Position Track - Paper Copilot", "date": "", "ddg_snippet": "Position : AI Safety should prioritize the Future of Work. Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity .", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/icml-statistics/icml-2025-statistics-position-track/", "content": "Position : AI Safety should prioritize the Future of Work. Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity ."}
+{"idx": 4, "title": "CONSEQUENTIAL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "Consequential dates from the 17th century and can be traced back to the Latin verb consequi, meaning \"to follow along.\" Consequi, in turn, combines the prefix con-, meaning \"through\" or \"with,\" and sequi, meaning \"to follow.\"", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/consequential", "content": "Consequential dates from the 17th century and can be traced back to the Latin verb consequi, meaning \"to follow along.\" Consequi, in turn, combines the prefix con-, meaning \"through\" or \"with,\" and sequi, meaning \"to follow.\""}
+{"idx": 5, "title": "CONSEQUENTIAL | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "CONSEQUENTIAL definition: 1. happening as a result of something: 2. important, and having a strong influence on events…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/consequential", "content": "CONSEQUENTIAL definition: 1. happening as a result of something: 2. important, and having a strong influence on events…. Learn more."}
+{"idx": 6, "title": "CONSEQUENTIAL Definition & Meaning | Dictionary.com", "date": "", "ddg_snippet": "Consequential definition: following as an effect, result, or outcome; resultant; consequent .. See examples of CONSEQUENTIAL used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/consequential", "content": "Consequential definition: following as an effect, result, or outcome; resultant; consequent .. See examples of CONSEQUENTIAL used in a sentence."}
+{"idx": 7, "title": "CONSEQUENTIAL definition in American English | Collins English...", "date": "", "ddg_snippet": "Consequential means happening as a direct result of an event or situation . The estimate for extra staff and consequential costs such as accommodation was an annual $9.18 million. Something that is consequential is important or significant. From a medical standpoint a week is usually not a consequential delay.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/us/dictionary/english/consequential", "content": "Consequential means happening as a direct result of an event or situation . The estimate for extra staff and consequential costs such as accommodation was an annual $9.18 million. Something that is consequential is important or significant. From a medical standpoint a week is usually not a consequential delay."}
+{"idx": 8, "title": "Consequential - definition of consequential by The Free...", "date": "", "ddg_snippet": "1. Following as an effect, result, or conclusion; consequent . 2. Having important consequences; significant: \"The year's only really consequential legislation was the reform of Social Security\" (New York Times).", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/consequential", "content": "1. Following as an effect, result, or conclusion; consequent . 2. Having important consequences; significant: \"The year's only really consequential legislation was the reform of Social Security\" (New York Times)."}
+{"idx": 9, "title": "consequential adjective - Definition, pictures, pronunciation and...", "date": "", "ddg_snippet": "Definition of consequential adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/consequential", "content": "Definition of consequential adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
diff --git a/data/sampled_jsons/contrastive_learning_models_using_sigmoid_loss.jsonl b/data/sampled_jsons/contrastive_learning_models_using_sigmoid_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f127a76895bbf1b0bd0fd7643c42991869a0a2c7
--- /dev/null
+++ b/data/sampled_jsons/contrastive_learning_models_using_sigmoid_loss.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learn -ing, in the perspective of the geometric struc-ture of learned embeddings. First, we pro-pose the double-Constant Embedding Model (CCEM), a framework for parameterizing var-ious...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/lee24a/lee24a.pdf", "content": "In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learn -ing, in the perspective of the geometric struc-ture of learned embeddings. First, we pro-pose the double-Constant Embedding Model (CCEM), a framework for parameterizing var-ious..."}
+{"idx": 1, "title": "CLIP to SigLIP: Vision-Language Models with Contrastive Learning", "date": "", "ddg_snippet": "Contrastive Losses in Vision Language Models .SigLIP ( Sigmoid Loss for Language Image Pre-Training) a 2023 paper from Google DeepMind aims to solve this problem with the sigmoid loss expression.", "subpage_snippet": "", "source": "blog.ritwikraha.dev", "link": "https://blog.ritwikraha.dev/choosing-between-siglip-and-clip-for-language-image-pretraining", "content": "Contrastive Losses in Vision Language Models .SigLIP ( Sigmoid Loss for Language Image Pre-Training) a 2023 paper from Google DeepMind aims to solve this problem with the sigmoid loss expression."}
+{"idx": 2, "title": "sigmoid - contrastive - loss | Ecosystem Directory | market.dev", "date": "", "ddg_snippet": "sigmoid - contrastive - loss . Compare To View Code on GitHub. Implementation of modulated sigmoid pairwise contrastive loss for self-supervised learning on images. MIT License.", "subpage_snippet": "", "source": "explore.market.dev", "link": "https://explore.market.dev/ecosystems/pytorch/projects/sigmoid-contrastive-loss", "content": "sigmoid - contrastive - loss . Compare To View Code on GitHub. Implementation of modulated sigmoid pairwise contrastive loss for self-supervised learning on images. MIT License."}
+{"idx": 3, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Analysis-of-Using-Sigmoid-Loss-for-Contrastive-Learning-5ab21c28-992d-421b-93a7-226ff26ce101", "content": "Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss ."}
+{"idx": 4, "title": "FFF: Fixing Flawed Foundations in Contrastive Pre-Training Results in...", "date": "", "ddg_snippet": "Since standard contrastive learning assumes one positive pair, this significantly hinders the training process and the quality of the trained models .The model is trained using the sigmoid loss .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Bulat_FFF_Fixing_Flawed_Foundations_in_Contrastive_Pre-Training_Results_in_Very_CVPR_2024_paper.pdf", "content": "Since standard contrastive learning assumes one positive pair, this significantly hinders the training process and the quality of the trained models .The model is trained using the sigmoid loss ."}
+{"idx": 5, "title": "Sigmoid Loss for Language Image Pre-Training", "date": "", "ddg_snippet": "Sigmoid Language Image Pre-training (SigLIP) represents a significant advancement in the field of multi-modal learning . Use the OpenAI o1 models for free at OpenAI01.net (10 times a day for free)! Sigmoid Loss for Language Image Pre-Training.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/sigmoid-loss-for-language-image-pre-training-2dd5e7d1af84", "content": "Sigmoid Language Image Pre-training (SigLIP) represents a significant advancement in the field of multi-modal learning . Use the OpenAI o1 models for free at OpenAI01.net (10 times a day for free)! Sigmoid Loss for Language Image Pre-Training."}
+{"idx": 6, "title": "Amirhossein75/Image- Contrastive -CLIP-Flickr30k · Hugging Face", "date": "", "ddg_snippet": "Model type: Dual‑encoder (vision transformer + text transformer) trained with contrastive objectives (CLIP softmax contrastive loss or SigLIP sigmoid loss ). Language(s) (NLP): English captions (Flickr8k/Flickr30k).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Amirhossein75/Image-Contrastive-CLIP-Flickr30k", "content": "Model type: Dual‑encoder (vision transformer + text transformer) trained with contrastive objectives (CLIP softmax contrastive loss or SigLIP sigmoid loss ). Language(s) (NLP): English captions (Flickr8k/Flickr30k)."}
+{"idx": 7, "title": "Yonsei University - Cited by 6 - Machine Learning - Statistics", "date": "", "ddg_snippet": "Analysis of Using Sigmoid Loss for Contrastive Learning .A theoretical framework for preventing class collapse in supervised contrastive learning .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=N2S3jFcAAAAJ&hl=en", "content": "Analysis of Using Sigmoid Loss for Contrastive Learning .A theoretical framework for preventing class collapse in supervised contrastive learning ."}
+{"idx": 8, "title": "Simplifying Language-Image Pre-Training with Sigmoid Loss", "date": "", "ddg_snippet": "The Sigmoid Loss Innovation. Key Problem with Contrastive Learning . Traditional contrastive learning relies on a softmax-based loss function, which requires normalization over the entire batch of image-text pairs.", "subpage_snippet": "", "source": "joanfihu.com", "link": "https://joanfihu.com/2024/07/17/simplifying-language-image-pre-training-with-sigmoid-loss/", "content": "The Sigmoid Loss Innovation. Key Problem with Contrastive Learning . Traditional contrastive learning relies on a softmax-based loss function, which requires normalization over the entire batch of image-text pairs."}
+{"idx": 9, "title": "sigmoid - contrastive - learning", "date": "", "ddg_snippet": "# Sigmoid Contrastive Learning on Images.bash $ pip install sigmoid - contrastive - learning ```. Code currently supports ResNet18, ResNet50 and an experimental version of the EfficientNet model .", "subpage_snippet": "", "source": "pydigger.com", "link": "https://pydigger.com/pypi/sigmoid-contrastive-learning", "content": "# Sigmoid Contrastive Learning on Images.bash $ pip install sigmoid - contrastive - learning ```. Code currently supports ResNet18, ResNet50 and an experimental version of the EfficientNet model ."}
diff --git a/data/sampled_jsons/conventional_SSMs_cannot_simulate_arbitrary_finite_state_machines_Merrill_2024_year_2024.jsonl b/data/sampled_jsons/conventional_SSMs_cannot_simulate_arbitrary_finite_state_machines_Merrill_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..433b60a65e9708078c2d6b9e90305dfd56f4cba4
--- /dev/null
+++ b/data/sampled_jsons/conventional_SSMs_cannot_simulate_arbitrary_finite_state_machines_Merrill_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Jackson Merrill - Wikipedia", "date": "", "ddg_snippet": "Jackson Peter Merrill (born April 19, 2003) is an American professional baseball center fielder for the San Diego Padres of Major League Baseball (MLB). He was selected in the first round of the 2021 MLB draft by the Padres and made his MLB debut in 2024 . He was selected for the 2024 MLB All-Star Game.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Jackson_Merrill", "content": "Jackson Peter Merrill (born April 19, 2003) is an American professional baseball center fielder for the San Diego Padres of Major League Baseball (MLB). He was selected in the first round of the 2021 MLB draft by the Padres and made his MLB debut in 2024 . He was selected for the 2024 MLB All-Star Game."}
+{"idx": 1, "title": "Merrill and Bank of America Private Bank Launch New ...", "date": "", "ddg_snippet": "Sep 4, 2025 · Merrill Wealth Management and Bank of America Private Bank today announced the launch of the Alts Expanded Access Program, a new private market program available to ultra-high-net-worth (UHNW) clients with a net worth of $50 million or more.", "subpage_snippet": "", "source": "newsroom.bankofamerica.com", "link": "https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/09/merrill-and-bank-of-america-private-bank-launch-new-alternative-.html", "content": "Sep 4, 2025 · Merrill Wealth Management and Bank of America Private Bank today announced the launch of the Alts Expanded Access Program, a new private market program available to ultra-high-net-worth (UHNW) clients with a net worth of $50 million or more."}
+{"idx": 2, "title": "Merrill Pricing: Brokerage Fees & Trading Commissions", "date": "", "ddg_snippet": "Get transparent pricing at Merrill . See trading commissions, and brokerage fees for Merrill Edge Self-Directed and Merrill Guided Investing Accounts.", "subpage_snippet": "", "source": "www.merrilledge.com", "link": "https://www.merrilledge.com/pricing", "content": "Get transparent pricing at Merrill . See trading commissions, and brokerage fees for Merrill Edge Self-Directed and Merrill Guided Investing Accounts."}
+{"idx": 3, "title": "Sam Merrill - Cleveland Cavaliers Shooting Guard - ESPN", "date": "", "ddg_snippet": "View the profile of Cleveland Cavaliers Shooting Guard Sam Merrill on ESPN. Get the latest news, live stats and game highlights.", "subpage_snippet": "", "source": "www.espn.com", "link": "https://www.espn.com/nba/player/_/id/4066757/sam-merrill", "content": "View the profile of Cleveland Cavaliers Shooting Guard Sam Merrill on ESPN. Get the latest news, live stats and game highlights."}
+{"idx": 4, "title": "Mark Merrill | 2024 Goal Getters - Tampa Bay Buccaneers", "date": "", "ddg_snippet": "2 days ago · Mark Merrill | 2024 Goal Getters We recognize Mark Merrill as a Tampa Bay Buccaneers 2025 AdventHealth Goal Getter.", "subpage_snippet": "", "source": "www.buccaneers.com", "link": "https://www.buccaneers.com/video/mark-merrill-2024-goal-getters", "content": "2 days ago · Mark Merrill | 2024 Goal Getters We recognize Mark Merrill as a Tampa Bay Buccaneers 2025 AdventHealth Goal Getter."}
+{"idx": 5, "title": "Former WBZ anchor Kate Merrill sues station for ...", "date": "", "ddg_snippet": "Aug 13, 2025 · Merrill , in her complaint filed Aug. 5, says she was wrongfully demoted in May 2024 by Justin Draper, then-president and general manager of WBZ, from her role as co-anchor of the Weekday Morning Show to work weekend nights, following an investigation being conducted into allegations that she had treated coworkers differently because of their race.", "subpage_snippet": "", "source": "www.baystatebanner.com", "link": "https://www.baystatebanner.com/2025/08/13/former-wbz-anchor-kate-merrill-sues-station-for-discrimination-claims-demotion-due-to-race/", "content": "Aug 13, 2025 · Merrill , in her complaint filed Aug. 5, says she was wrongfully demoted in May 2024 by Justin Draper, then-president and general manager of WBZ, from her role as co-anchor of the Weekday Morning Show to work weekend nights, following an investigation being conducted into allegations that she had treated coworkers differently because of their race."}
+{"idx": 6, "title": "Barbara Ann “Babs” McCorquodale Merrill (1936-2024) - Find a ...", "date": "", "ddg_snippet": "Nov 22, 2024 · Barbara Ann Babs Merrill passed away peacefully on November 22, 2024 at Harris Hospice unit. She is survived by her son Jack Porter Merrill Jr., daughter Lauren Elisabeth Merrill , granddaughter Wyndham Elisabeth Merrill , and sisters, Sarah Jeffers and Nancy Cork, as well as many nieces and nephews and dozens of grand...", "subpage_snippet": "", "source": "www.findagrave.com", "link": "https://www.findagrave.com/memorial/287046006/barbara-merrill", "content": "Nov 22, 2024 · Barbara Ann Babs Merrill passed away peacefully on November 22, 2024 at Harris Hospice unit. She is survived by her son Jack Porter Merrill Jr., daughter Lauren Elisabeth Merrill , granddaughter Wyndham Elisabeth Merrill , and sisters, Sarah Jeffers and Nancy Cork, as well as many nieces and nephews and dozens of grand..."}
+{"idx": 7, "title": "The Expressive Capacity of State Space Models: A Formal ...", "date": "", "ddg_snippet": "9 Dec 2024 — In star-free state tracking, SSMs implement length-generalizing solutions to problems that transformers struggle to represent exactly. They can ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/94264", "content": "9 Dec 2024 — In star-free state tracking, SSMs implement length-generalizing solutions to problems that transformers struggle to represent exactly. They can ..."}
+{"idx": 8, "title": "UNLOCKING STATE-TRACKING IN LINEAR RNNS THROUGH ...", "date": "", "ddg_snippet": "struggle to learn how to track the state of even simple finite - state machines from sequences of state- transitions (Deletang et al., 2023). This limitation ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=W7mKlIFMdr&name=pdf", "content": "struggle to learn how to track the state of even simple finite - state machines from sequences of state- transitions (Deletang et al., 2023). This limitation ..."}
+{"idx": 9, "title": "ICML 2024 Wednesday 07/24", "date": "", "ddg_snippet": "We would like to organize the first “WiML Symposium” at the ICML 2024 conference. Oral 3F Causality Wed 24 Jul 10:30 a.m..", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/day/7/24", "content": "We would like to organize the first “WiML Symposium” at the ICML 2024 conference. Oral 3F Causality Wed 24 Jul 10:30 a.m.."}
diff --git a/data/sampled_jsons/coordinate-wise_private_median_regression_problem_insufficient_condition_number.jsonl b/data/sampled_jsons/coordinate-wise_private_median_regression_problem_insufficient_condition_number.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5bfb4c5475fc89fab2162a6dbdd5fd5a39f48974
--- /dev/null
+++ b/data/sampled_jsons/coordinate-wise_private_median_regression_problem_insufficient_condition_number.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Easy Differentially Private Linear Regression", "date": "", "ddg_snippet": "by K Amin · Cited by 19 — A practical algorithm for differentially private linear regression which does not require data bounds or parameter tuning but is competitive ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rSUCajhLsQ", "content": "by K Amin · Cited by 19 — A practical algorithm for differentially private linear regression which does not require data bounds or parameter tuning but is competitive ..."}
+{"idx": 1, "title": "Median DC for Sign Recovery: Privacy can be Achieved by...", "date": "", "ddg_snippet": "by J Tu — This paper considers the problem of private sign recovery for sparse mean estimation and sparse linear regression in a distributed setting. The paper proposes ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BMua55nUyyt", "content": "by J Tu — This paper considers the problem of private sign recovery for sparse mean estimation and sparse linear regression in a distributed setting. The paper proposes ..."}
+{"idx": 2, "title": "Hierarchical Scene Coordinate Classification and ...", "date": "", "ddg_snippet": "by X Li · 2020 · Cited by 162 — Overview of our single-image RGB localization approach based on hierarchical scene coordinate prediction, here using 3 levels. this problem to some extent [27].", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Hierarchical_Scene_Coordinate_Classification_and_Regression_for_Visual_Localization_CVPR_2020_paper.pdf", "content": "by X Li · 2020 · Cited by 162 — Overview of our single-image RGB localization approach based on hierarchical scene coordinate prediction, here using 3 levels. this problem to some extent [27]."}
+{"idx": 3, "title": "Iterated conditional expectation algorithm on DAGs and ...", "date": "", "ddg_snippet": "by M Baranyi · 2021 · Cited by 3 — An iterative regression method, using local averaging estimators, is introduced for prediction, based on a complete training sample.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2452306220300496", "content": "by M Baranyi · 2021 · Cited by 3 — An iterative regression method, using local averaging estimators, is introduced for prediction, based on a complete training sample."}
+{"idx": 4, "title": "Penalized robust regression in high-dimension", "date": "", "ddg_snippet": "by D Bean · 2011 · Cited by 9 — We illustrate this with double exponential errors and median vs least-squares regression pe- nalized in both cases with an `2 penalty. 2 ...", "subpage_snippet": "", "source": "statistics.berkeley.edu", "link": "https://statistics.berkeley.edu/sites/default/files/tech-reports/813.pdf", "content": "by D Bean · 2011 · Cited by 9 — We illustrate this with double exponential errors and median vs least-squares regression pe- nalized in both cases with an `2 penalty. 2 ..."}
+{"idx": 5, "title": "Coordinate Update Algorithms: Theory and Applications", "date": "", "ddg_snippet": "by T Wu · 2017 — This thesis focuses on coordinate update methods, which are useful for solving problems involving large or high-dimensional datasets.", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/uc/item/34k208nc", "content": "by T Wu · 2017 — This thesis focuses on coordinate update methods, which are useful for solving problems involving large or high-dimensional datasets."}
+{"idx": 6, "title": "ICML Poster On Differential Privacy for Adaptively Solving ...", "date": "", "ddg_snippet": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44265", "content": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the ..."}
+{"idx": 7, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "5 Jun 2025 — In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05503v1", "content": "5 Jun 2025 — In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging ..."}
+{"idx": 8, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "by S Feng · 2025 — For adaptive regression , we show how to upgrade the private median framework of [BKM+22] to output the solution vector, and how to obtain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503", "content": "by S Feng · 2025 — For adaptive regression , we show how to upgrade the private median framework of [BKM+22] to output the solution vector, and how to obtain ..."}
+{"idx": 9, "title": "CS229 Lecture Notes", "date": "", "ddg_snippet": "by A Ng · 2023 · Cited by 273 — Given data like this, how can we learn to predict the prices of other houses in Portland, as a function of the size of their living areas? 227 pages", "subpage_snippet": "", "source": "cs229.stanford.edu", "link": "https://cs229.stanford.edu/main_notes.pdf", "content": "by A Ng · 2023 · Cited by 273 — Given data like this, how can we learn to predict the prices of other houses in Portland, as a function of the size of their living areas? 227 pages"}
diff --git a/data/sampled_jsons/copyleft_licenses_vs_copyleft-style_terms_model_licensing_legal_compliance_year_2024.jsonl b/data/sampled_jsons/copyleft_licenses_vs_copyleft-style_terms_model_licensing_legal_compliance_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ee5bc3f5652b011b555193b217296dc3b14d791b
--- /dev/null
+++ b/data/sampled_jsons/copyleft_licenses_vs_copyleft-style_terms_model_licensing_legal_compliance_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Copyleft - Wikipedia", "date": "", "ddg_snippet": "Some laws used for copyleft licenses vary from one country to another, and may also be granted in terms that vary from country to country. For example, in some countries, it is acceptable to sell a software product without warranty, in standard GNU General Public License style ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Copyleft", "content": "Some laws used for copyleft licenses vary from one country to another, and may also be granted in terms that vary from country to country. For example, in some countries, it is acceptable to sell a software product without warranty, in standard GNU General Public License style ..."}
+{"idx": 1, "title": "Understanding Copyleft Licenses: GPL, LGPL, and AGPL", "date": "", "ddg_snippet": "Jan 10, 2024 · Understanding Copyleft Philosophy The Four Freedoms Copyleft vs . Permissive Major Copyleft Licenses GPL (GNU General Public License ) LGPL (Lesser GPL) AGPL (Affero GPL) Weak vs . Strong Copyleft Strong Copyleft (GPL, AGPL) Weak Copyleft (LGPL, MPL, EPL) Copyleft Compatibility Matrix Inter- License Compatibility Upgrade Paths Compliance ...", "subpage_snippet": "", "source": "licensecheck.io", "link": "https://licensecheck.io/guides/copyleft-licenses", "content": "Jan 10, 2024 · Understanding Copyleft Philosophy The Four Freedoms Copyleft vs . Permissive Major Copyleft Licenses GPL (GNU General Public License ) LGPL (Lesser GPL) AGPL (Affero GPL) Weak vs . Strong Copyleft Strong Copyleft (GPL, AGPL) Weak Copyleft (LGPL, MPL, EPL) Copyleft Compatibility Matrix Inter- License Compatibility Upgrade Paths Compliance ..."}
+{"idx": 2, "title": "What Is a Copyleft License and How Does It Work?", "date": "", "ddg_snippet": "Aug 23, 2025 · The MPL is file-based, meaning only changes to MPL-licensed files need to be distributed under MPL, while other files in a combined work can have different licenses . Applying Copyleft Principles When a work is placed under a copyleft license , specific obligations arise for anyone who uses, modifies, or distributes it.", "subpage_snippet": "", "source": "legalclarity.org", "link": "https://legalclarity.org/what-is-a-copyleft-license-and-how-does-it-work/", "content": "Aug 23, 2025 · The MPL is file-based, meaning only changes to MPL-licensed files need to be distributed under MPL, while other files in a combined work can have different licenses . Applying Copyleft Principles When a work is placed under a copyleft license , specific obligations arise for anyone who uses, modifies, or distributes it."}
+{"idx": 3, "title": "Cause licensing: Comparing Cause Licensing Models: Copyleft ...", "date": "", "ddg_snippet": "Apr 5, 2025 · Cause licensing : Comparing Cause Licensing Models : Copyleft vs : Permissive 1. What is cause licensing and why does it matter? One of the most important decisions that developers and creators face when releasing their work to the public is how to license it. A license is a legal document that specifies the terms and conditions under which others can use, modify, distribute, or contribute to the ...", "subpage_snippet": "", "source": "fastercapital.com", "link": "https://fastercapital.com/content/Cause-licensing--Comparing-Cause-Licensing-Models--Copyleft-vs--Permissive.html", "content": "Apr 5, 2025 · Cause licensing : Comparing Cause Licensing Models : Copyleft vs : Permissive 1. What is cause licensing and why does it matter? One of the most important decisions that developers and creators face when releasing their work to the public is how to license it. A license is a legal document that specifies the terms and conditions under which others can use, modify, distribute, or contribute to the ..."}
+{"idx": 4, "title": "Position: Current Model Licensing Practices are Dragging Us ...", "date": "", "ddg_snippet": "We consider two situations: copyleft licenses and licenses with copyleft-style terms . For copy-left licenses , derivatives must be published under the same license ; otherwise, a violation occurs, leading to license ter-mination.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1rh8iTehBc", "content": "We consider two situations: copyleft licenses and licenses with copyleft-style terms . For copy-left licenses , derivatives must be published under the same license ; otherwise, a violation occurs, leading to license ter-mination."}
+{"idx": 5, "title": "Legal Landscapes of Open Source and Copyleft Licensing", "date": "", "ddg_snippet": "Jun 13, 2023 · Understanding the intricate legal frameworks surrounding software licensing , particularly in open source and copyleft is paramount for software developers, business owners, and digital innovators. Choosing between these licenses can substantially shape the software's distribution, adaptation, and financial returns.", "subpage_snippet": "", "source": "www.prokopievlaw.com", "link": "https://www.prokopievlaw.com/post/legal-landscapes-of-open-source-and-copyleft-licensing", "content": "Jun 13, 2023 · Understanding the intricate legal frameworks surrounding software licensing , particularly in open source and copyleft is paramount for software developers, business owners, and digital innovators. Choosing between these licenses can substantially shape the software's distribution, adaptation, and financial returns."}
+{"idx": 6, "title": "Open Source Licenses Explained: A Comparison - OSS Software", "date": "", "ddg_snippet": "Jan 14, 2024 · Explore the key aspects of open source licenses , compare permissive and copyleft models , and learn how to choose the best license for your project. Gain clarity on license terms and compatibility.", "subpage_snippet": "", "source": "osssoftware.org", "link": "https://osssoftware.org/blog/open-source-licenses-explained-a-comparison/", "content": "Jan 14, 2024 · Explore the key aspects of open source licenses , compare permissive and copyleft models , and learn how to choose the best license for your project. Gain clarity on license terms and compatibility."}
+{"idx": 7, "title": "Open-Source: Licenses Explained. A Guide for ... - Medium", "date": "", "ddg_snippet": "Feb 21, 2025 · A Guide for Developers and Organizations Open source licenses fuel innovation, but misunderstanding their terms can lead to unexpected legal risks.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@bill_62246/open-source-licenses-explained-b862909200b2", "content": "Feb 21, 2025 · A Guide for Developers and Organizations Open source licenses fuel innovation, but misunderstanding their terms can lead to unexpected legal risks."}
+{"idx": 8, "title": "What's the difference between permissive and copyleft licenses ?", "date": "", "ddg_snippet": "How is copyleft different to a permissive license ? Is copyleft just the standard for open source licenses ?", "subpage_snippet": "", "source": "opensource.stackexchange.com", "link": "https://opensource.stackexchange.com/questions/21/whats-the-difference-between-permissive-and-copyleft-licenses", "content": "How is copyleft different to a permissive license ? Is copyleft just the standard for open source licenses ?"}
+{"idx": 9, "title": "Understanding license compliance in open source... - DEV Community", "date": "", "ddg_snippet": "Permissive vs Copyleft licenses . Copyleft licenses : Copyleft licenses allow you to use,modify and redistribute software however, the software has the changes are shared other same license . This measns the software and its derivatives must remain free and open for everyone to use.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/idadelveloper/understanding-license-compliance-in-open-source-projects-4dmb", "content": "Permissive vs Copyleft licenses . Copyleft licenses : Copyleft licenses allow you to use,modify and redistribute software however, the software has the changes are shared other same license . This measns the software and its derivatives must remain free and open for everyone to use."}
diff --git a/data/sampled_jsons/cost_function_hierarchical_overlapping_clustering_graph_before_2024.jsonl b/data/sampled_jsons/cost_function_hierarchical_overlapping_clustering_graph_before_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a15ce7994ab5fd27b17346e69ae8df4f17481642
--- /dev/null
+++ b/data/sampled_jsons/cost_function_hierarchical_overlapping_clustering_graph_before_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning ... Connections in Positive-Pair Graphs", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning ... Connections in Positive-Pair Graphs"}
+{"idx": 1, "title": "Newest 'graph-theory' Questions - Cross Validated", "date": "", "ddg_snippet": "For an undirected graph with one connected component and distance matrix given by the shortest path between nodes, I would like to embed the nodes in ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/tagged/graph-theory", "content": "For an undirected graph with one connected component and distance matrix given by the shortest path between nodes, I would like to embed the nodes in ..."}
+{"idx": 2, "title": "Hierarchical Layout | Automatic Graph Layout | yFiles for HTML", "date": "", "ddg_snippet": "... Hierarchical Layout Terminology Application Areas Relevant Classes Basic Options Labeling Grouped Graphs Recursive Edges Routing Non-incremental ...", "subpage_snippet": "", "source": "docs.yworks.com", "link": "https://docs.yworks.com/yfiles-html/dguide/layout/hierarchical_layout.html", "content": "... Hierarchical Layout Terminology Application Areas Relevant Classes Basic Options Labeling Grouped Graphs Recursive Edges Routing Non-incremental ..."}
+{"idx": 3, "title": "Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval", "date": "", "ddg_snippet": "We close this gap with the Hierarchical Lexical Graph (HLG), a three-tier index that (i) traces every atomic proposition to its source, (ii) clusters ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08074v1", "content": "We close this gap with the Hierarchical Lexical Graph (HLG), a three-tier index that (i) traces every atomic proposition to its source, (ii) clusters ..."}
+{"idx": 4, "title": "WO2018222064A1 - Systems and methods of hierarchical community", "date": "", "ddg_snippet": "the graph modularity denotes a global cost function indicative of density of edges between vertices within each community of the packed graph ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2018222064A1/en", "content": "the graph modularity denotes a global cost function indicative of density of edges between vertices within each community of the packed graph ..."}
+{"idx": 5, "title": "US20100174670A1 - Data classification and hierarchical", "date": "", "ddg_snippet": "... can use frequent itemsets and closed frequent itemsets such as to reduce dimensionality or to help the efficiency of hierarchical document clustering ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20100174670A1/en", "content": "... can use frequent itemsets and closed frequent itemsets such as to reduce dimensionality or to help the efficiency of hierarchical document clustering ..."}
+{"idx": 6, "title": "Unsupervised Clustering: A Guide | Built In", "date": "", "ddg_snippet": "K-means is an exclusive clustering algorithm, fuzzy K-means is an overlapping clustering algorithm, hierarchical clustering is obvious and lastly ...", "subpage_snippet": "", "source": "builtin.com", "link": "https://builtin.com/articles/unsupervised-clustering", "content": "K-means is an exclusive clustering algorithm, fuzzy K-means is an overlapping clustering algorithm, hierarchical clustering is obvious and lastly ..."}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions"}
+{"idx": 8, "title": "(PDF) Partitioning Biological Networks into Highly Connected", "date": "", "ddg_snippet": "A popular clustering algorithm for biological networks which was proposed by Hartuv and Shamir [IPL 2000] identifies nonoverlapping highly connected ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/259350461_Partitioning_Biological_Networks_into_Highly_Connected_Clusters_with_Maximum_Edge_Coverage", "content": "A popular clustering algorithm for biological networks which was proposed by Hartuv and Shamir [IPL 2000] identifies nonoverlapping highly connected ..."}
+{"idx": 9, "title": "yFiles Library Formats | Working with yFiles | yFiles for HTML", "date": "", "ddg_snippet": "... Hierarchical Layout Terminology Application Areas Relevant Classes Basic Options Labeling Grouped Graphs Recursive Edges Routing Non-incremental ...", "subpage_snippet": "", "source": "docs.yworks.com", "link": "https://docs.yworks.com/yfiles-html/dguide/working_with_yfiles/yfiles-modules.html", "content": "... Hierarchical Layout Terminology Application Areas Relevant Classes Basic Options Labeling Grouped Graphs Recursive Edges Routing Non-incremental ..."}
diff --git a/data/sampled_jsons/crocker_plots_scalability_issues_topological_data_analysis.jsonl b/data/sampled_jsons/crocker_plots_scalability_issues_topological_data_analysis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07ad8f28ce8f94e63894d1f38a23ce562e6b167b
--- /dev/null
+++ b/data/sampled_jsons/crocker_plots_scalability_issues_topological_data_analysis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Crocker Art Museum - Wikipedia", "date": "", "ddg_snippet": "Edwin B. Crocker (1818–1875), a wealthy California lawyer and judge, and his wife, Margaret Crocker (1822–1901), began to assemble a significant collection of paintings and drawings during an extended trip to Europe, from 1869 to 1871.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Crocker_Art_Museum", "content": "Edwin B. Crocker (1818–1875), a wealthy California lawyer and judge, and his wife, Margaret Crocker (1822–1901), began to assemble a significant collection of paintings and drawings during an extended trip to Europe, from 1869 to 1871."}
+{"idx": 1, "title": "Crocker - Wikipedia", "date": "", "ddg_snippet": "Crocker is an archaic synonym of potter.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Crocker", "content": "Crocker is an archaic synonym of potter."}
+{"idx": 2, "title": "The Crocker Art Museum | Crocker Art Museum", "date": "", "ddg_snippet": "The Crocker serves as the primary regional resource for the study and appreciation of fine art and offers a diverse spectrum of exhibitions, events, and programs.", "subpage_snippet": "", "source": "www.crockerart.org", "link": "https://www.crockerart.org/", "content": "The Crocker serves as the primary regional resource for the study and appreciation of fine art and offers a diverse spectrum of exhibitions, events, and programs."}
+{"idx": 3, "title": "The City of Crocker", "date": "", "ddg_snippet": "In the heart of the Ozarks, Crocker rests amidst forested hills and farmlands, the same natural beauty that first inspired the founding fathers to settle here. Crocker offers a peaceful rural setting and a carefully preserved heritage.", "subpage_snippet": "", "source": "crockercity.com", "link": "https://crockercity.com/", "content": "In the heart of the Ozarks, Crocker rests amidst forested hills and farmlands, the same natural beauty that first inspired the founding fathers to settle here. Crocker offers a peaceful rural setting and a carefully preserved heritage."}
+{"idx": 4, "title": "Crocker Art Museum | Culture, Victorian House & Teal Pavilion", "date": "", "ddg_snippet": "Explore the Crocker Art Museum in Sacramento, featuring California Impressionist art, German drawings, and antiquity.", "subpage_snippet": "", "source": "www.visitsacramento.com", "link": "https://www.visitsacramento.com/things-to-do/arts-and-entertainment/crocker-art-museum/", "content": "Explore the Crocker Art Museum in Sacramento, featuring California Impressionist art, German drawings, and antiquity."}
+{"idx": 5, "title": "Crocker Murders: Timeline details Georgia children's deaths", "date": "", "ddg_snippet": "Aug 29, 2025 · Crocker Timeline: Family members could be put to death for murders, burial of Effingham County kids It began with a welfare check in Guyton. It turned into a gruesome discovery: the bodies of two children found buried in their own backyard.", "subpage_snippet": "", "source": "www.wjcl.com", "link": "https://www.wjcl.com/article/crocker-murders-timeline-1756486268/65934413", "content": "Aug 29, 2025 · Crocker Timeline: Family members could be put to death for murders, burial of Effingham County kids It began with a welfare check in Guyton. It turned into a gruesome discovery: the bodies of two children found buried in their own backyard."}
+{"idx": 6, "title": "Plan Your Visit | Crocker Art Museum", "date": "", "ddg_snippet": "Experience innovative interactions with art at the Crocker Art Museum. With three floors and 15 unique gallery spaces to explore, discover a diverse collection of art that spans centuries, continents, and cultures. There is always something surprising to find at the Crocker !", "subpage_snippet": "", "source": "www.crockerart.org", "link": "https://www.crockerart.org/visit", "content": "Experience innovative interactions with art at the Crocker Art Museum. With three floors and 15 unique gallery spaces to explore, discover a diverse collection of art that spans centuries, continents, and cultures. There is always something surprising to find at the Crocker !"}
+{"idx": 7, "title": "Exhibitions | Crocker Art Museum", "date": "", "ddg_snippet": "Current and upcoming exhibitions at the Crocker .", "subpage_snippet": "", "source": "www.crockerart.org", "link": "https://www.crockerart.org/exhibitions", "content": "Current and upcoming exhibitions at the Crocker ."}
+{"idx": 8, "title": "Crocker Art Museum (Everything To Know Before A Visit)", "date": "", "ddg_snippet": "As the longest-running public art museum in the West, it provides a unique insight into both historic and contemporary artistic expressions. The museum is renowned for its exceptional collection of California art, European master drawings, and remarkable international ceramics.", "subpage_snippet": "", "source": "thetouristchecklist.com", "link": "https://thetouristchecklist.com/crocker-art-museum/", "content": "As the longest-running public art museum in the West, it provides a unique insight into both historic and contemporary artistic expressions. The museum is renowned for its exceptional collection of California art, European master drawings, and remarkable international ceramics."}
+{"idx": 9, "title": "Calendar of Events - Crocker Art Museum | Crocker Art Museum", "date": "", "ddg_snippet": "Explore our calendar of upcoming tours, talks, concerts, studio art classes, family-friendly programs, and more.", "subpage_snippet": "", "source": "www.crockerart.org", "link": "https://www.crockerart.org/events", "content": "Explore our calendar of upcoming tours, talks, concerts, studio art classes, family-friendly programs, and more."}
diff --git a/data/sampled_jsons/cross-blended_images_deepfake.jsonl b/data/sampled_jsons/cross-blended_images_deepfake.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9ab8477dcc7eedd34ae6802ea6641ff18d38dc41
--- /dev/null
+++ b/data/sampled_jsons/cross-blended_images_deepfake.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2204.08376] Detecting Deepfakes with Self-Blended Images", "date": "", "ddg_snippet": "Abstract: In this paper, we present novel synthetic training data called self- blended images (SBIs) to detect deepfakes .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2204.08376", "content": "Abstract: In this paper, we present novel synthetic training data called self- blended images (SBIs) to detect deepfakes ."}
+{"idx": 1, "title": "CrossDF: Improving Cross-Domain Deepfake Detection with Deep", "date": "", "ddg_snippet": "Specifically, we denote face images forged by different deepfake methods as distinct data domains and formulate cross -dataset deepfake detection as a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00359v3", "content": "Specifically, we denote face images forged by different deepfake methods as distinct data domains and formulate cross -dataset deepfake detection as a ..."}
+{"idx": 2, "title": "Robust Deepfake Detection for Electronic Know Your Customer", "date": "", "ddg_snippet": "Moreover, the susceptibility of deepfake detectors to image degradation, such as blurring, additive noise, and compression, makes the issue more ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22601v1", "content": "Moreover, the susceptibility of deepfake detectors to image degradation, such as blurring, additive noise, and compression, makes the issue more ..."}
+{"idx": 3, "title": "Stable Diffusion Deepfakes and Stylizations With a Single Image", "date": "", "ddg_snippet": "... images , when included in a training round, are there to ‘ offset ’ the specificity of the source training material, and to help the ...", "subpage_snippet": "", "source": "blog.metaphysic.ai", "link": "https://blog.metaphysic.ai/stable-diffusion-deepfakes-and-stylizations-with-a-single-image/", "content": "... images , when included in a training round, are there to ‘ offset ’ the specificity of the source training material, and to help the ..."}
+{"idx": 4, "title": "Deepfake legislation: Denmark takes action | World Economic", "date": "", "ddg_snippet": "... means people affected by deepfake content can request its removal, and artists can demand compensation for unauthorized use of their image .", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/stories/2025/07/deepfake-legislation-denmark-digital-id/", "content": "... means people affected by deepfake content can request its removal, and artists can demand compensation for unauthorized use of their image ."}
+{"idx": 5, "title": "Deepfake Scams and Voice Cloning: The Next Big Cybersecurity", "date": "", "ddg_snippet": "Route suspected deepfake calls to a small team trained to capture artifacts and keep the caller talking just long enough to gather signals.", "subpage_snippet": "", "source": "hacker9.com", "link": "https://hacker9.com/deepfake-scams-and-voice-cloning-the-next-big-cybersecurity-challenge/", "content": "Route suspected deepfake calls to a small team trained to capture artifacts and keep the caller talking just long enough to gather signals."}
+{"idx": 6, "title": "People who share downblousing and deepfake images may now face", "date": "", "ddg_snippet": "Sharing deepfake and downblousing images without consent will soon become a crime, and offenders may face jail time under new anti-abuse measures ...", "subpage_snippet": "", "source": "wstale.com", "link": "https://wstale.com/lifestyle/people-who-share-downblousing-and-deepfake-images-may-now-face-jail-time/", "content": "Sharing deepfake and downblousing images without consent will soon become a crime, and offenders may face jail time under new anti-abuse measures ..."}
+{"idx": 7, "title": "GitHub - mapooon/SelfBlendedImages: [CVPR 2022 Oral] Detecting", "date": "", "ddg_snippet": "inproceedings { shiohara2022detecting , title = { Detecting Deepfakes with Self- Blended Images } , author = { Shiohara, Kaede and Yamasaki, Toshihiko ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mapooon/SelfBlendedImages", "content": "inproceedings { shiohara2022detecting , title = { Detecting Deepfakes with Self- Blended Images } , author = { Shiohara, Kaede and Yamasaki, Toshihiko ..."}
+{"idx": 8, "title": "Deepfake Journalism: Can We Still Trust Video Evidence?", "date": "", "ddg_snippet": "Deepfake technology has its origin in early artificial intelligence research with image production and facial recognition.", "subpage_snippet": "", "source": "blog.smsvaranasi.com", "link": "https://blog.smsvaranasi.com/deepfake-journalism-can-we-still-trust-video-evidence/", "content": "Deepfake technology has its origin in early artificial intelligence research with image production and facial recognition."}
+{"idx": 9, "title": "UTokyo Researchers Introduce A Novel Synthetic Training Data", "date": "", "ddg_snippet": "This Article Is Based On The Research Paper ' Detecting Deepfakes with Self- Blended Images ' . ... of deepfake detection using Self- blended images ...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2022/05/02/utokyo-researchers-introduce-a-novel-synthetic-training-data-called-self-blended-images-sbis-to-detect-deepfakes/", "content": "This Article Is Based On The Research Paper ' Detecting Deepfakes with Self- Blended Images ' . ... of deepfake detection using Self- blended images ..."}
diff --git a/data/sampled_jsons/cross-blended_images_deepfake_detection_paper_[27]_year_2020-2023.jsonl b/data/sampled_jsons/cross-blended_images_deepfake_detection_paper_[27]_year_2020-2023.jsonl
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+{"idx": 0, "title": "Detecting Deepfakes With Self-Blended Images", "date": "", "ddg_snippet": "by K Shiohara · 2022 · Cited by 496 — In this paper , we propose novel synthetic training data called self- blended images (SBIs) to detect deepfakes . The overviews of our method and previous methods ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Shiohara_Detecting_Deepfakes_With_Self-Blended_Images_CVPR_2022_paper.pdf", "content": "by K Shiohara · 2022 · Cited by 496 — In this paper , we propose novel synthetic training data called self- blended images (SBIs) to detect deepfakes . The overviews of our method and previous methods ... 10 pages"}
+{"idx": 1, "title": "FSBI: Deepfakes Detection with Frequency Enhanced Self- ...", "date": "", "ddg_snippet": "12 Jun 2024 — Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.08625v1", "content": "12 Jun 2024 — Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes ..."}
+{"idx": 2, "title": "Improving Cross-Domain Deepfake Detection with Deep ...", "date": "", "ddg_snippet": "4 Sept 2024 — This section provides a brief review of deepfakes, cross -dataset deepfake detection , and information decomposing. For more details about the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00359v2", "content": "4 Sept 2024 — This section provides a brief review of deepfakes, cross -dataset deepfake detection , and information decomposing. For more details about the ..."}
+{"idx": 3, "title": "A Hybrid Model for Generalizable Deepfake Detection via ...", "date": "", "ddg_snippet": "by MK Le-Phan · 2025 — ... paper is structured as follows: Section 2 reviews related work in deepfake detection . ... Detecting deepfakes with self- blended images . In ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3709020.3734833", "content": "by MK Le-Phan · 2025 — ... paper is structured as follows: Section 2 reviews related work in deepfake detection . ... Detecting deepfakes with self- blended images . In ..."}
+{"idx": 4, "title": "DPL: Cross-quality DeepFake Detection via Dual Progressive ...", "date": "", "ddg_snippet": "by D Zhang · 2024 · Cited by 2 — cross -quality DeepFake detection . In contrast to existing methods, we ... Shiohara, K., Yamasaki, T.: Detecting deepfakes with self- blended images . In ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ACCV2024/papers/Zhang_DPL_Cross-quality_DeepFake_Detection_via_Dual_Progressive_Learning_ACCV_2024_paper.pdf", "content": "by D Zhang · 2024 · Cited by 2 — cross -quality DeepFake detection . In contrast to existing methods, we ... Shiohara, K., Yamasaki, T.: Detecting deepfakes with self- blended images . In ..."}
+{"idx": 5, "title": "Robust cross-dataset deepfake detection with multitask self ...", "date": "", "ddg_snippet": "by B Batagelj · 2025 — The DeepFake Detection Dataset (DFD) [ 27 ], developed by Google ... Detecting deepfakes with self- blended images . CVPR (2022), pp. 18699 ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S240595952500027X", "content": "by B Batagelj · 2025 — The DeepFake Detection Dataset (DFD) [ 27 ], developed by Google ... Detecting deepfakes with self- blended images . CVPR (2022), pp. 18699 ..."}
+{"idx": 6, "title": "Deepfake Detection Method Based on Cross‐Domain Fusion", "date": "", "ddg_snippet": "by F Sun · 2021 · Cited by 16 — This paper introduces a new method of deepfake detection based on cross -domain fusion. Our method uses a network to gain the image's spatial domain feature ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1155/2021/2482942", "content": "by F Sun · 2021 · Cited by 16 — This paper introduces a new method of deepfake detection based on cross -domain fusion. Our method uses a network to gain the image's spatial domain feature ..."}
+{"idx": 7, "title": "Localization and detection of deepfake videos based on ...", "date": "", "ddg_snippet": "by J Xu · 2025 · Cited by 4 — It encompasses four different deepfake methods: FaceSwap, Deepfakes , Face2Face, and NeuralTextures, with 1000 videos forged using each method, ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-88523-1", "content": "by J Xu · 2025 · Cited by 4 — It encompasses four different deepfake methods: FaceSwap, Deepfakes , Face2Face, and NeuralTextures, with 1000 videos forged using each method, ..."}
+{"idx": 8, "title": "Can We Leave Deepfake Data Behind in Training ...", "date": "", "ddg_snippet": "by J Cheng · 2024 · Cited by 31 — In this paper , we rethink the role of blendfake in detecting deepfakes and formulate the process from \"real to blendfake to deepfake \" to be a progressive ... 20 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/2718a032d15e0b80cd164b240220df89-Paper-Conference.pdf", "content": "by J Cheng · 2024 · Cited by 31 — In this paper , we rethink the role of blendfake in detecting deepfakes and formulate the process from \"real to blendfake to deepfake \" to be a progressive ... 20 pages"}
+{"idx": 9, "title": "Detecting Deepfakes with Self-Blended Images", "date": "", "ddg_snippet": "The official PyTorch implementation for the following paper : Detecting Deepfakes with Self- Blended Images , Kaede Shiohara and Toshihiko Yamasaki, CVPR 2022 Oral", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mapooon/SelfBlendedImages", "content": "The official PyTorch implementation for the following paper : Detecting Deepfakes with Self- Blended Images , Kaede Shiohara and Toshihiko Yamasaki, CVPR 2022 Oral"}
diff --git a/data/sampled_jsons/d2aGLPSpFz_Sanity_Checking_Causal_Representation_Learning_MCC_scores_synthetic_real.jsonl b/data/sampled_jsons/d2aGLPSpFz_Sanity_Checking_Causal_Representation_Learning_MCC_scores_synthetic_real.jsonl
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+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are known, providing a ground truth. We select methods ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.20099", "content": "We evaluate methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors (the inputs to the experiment) are known, providing a ground truth. We select methods ..."}
+{"idx": 1, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "Abstract We evaluate methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical experi-ment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors—the inputs to the experiment—are known, providing a ground truth. We ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=d2aGLPSpFz", "content": "Abstract We evaluate methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical experi-ment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors—the inputs to the experiment—are known, providing a ground truth. We ..."}
+{"idx": 2, "title": "[PDF] Sanity Checking Causal Representation Learning on a Simple Real ...", "date": "", "ddg_snippet": "This work evaluates methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work, and finds that they all fail to recover the underlying causal factors. We evaluate methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Sanity-Checking-Causal-Representation-Learning-on-a-Gamella-Bing/638e050573f438f77583f2b210c2d5da0f1b4ca7", "content": "This work evaluates methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work, and finds that they all fail to recover the underlying causal factors. We evaluate methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical ..."}
+{"idx": 3, "title": "PDF Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "Unless we abandon the ultimate goal of applying causal representation learning to real -world problems, we cannot be satisfied with validating our methods on purely synthetic data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389398876_Sanity_Checking_Causal_Representation_Learning_on_a_Simple_Real-World_System/fulltext/67c12b09645ef274a496774e/Sanity-Checking-Causal-Representation-Learning-on-a-Simple-Real-World-System.pdf", "content": "Unless we abandon the ultimate goal of applying causal representation learning to real -world problems, we cannot be satisfied with validating our methods on purely synthetic data."}
+{"idx": 4, "title": "Advancing Causal Representation Learning: Enhancing Robustness and ...", "date": "", "ddg_snippet": "Evaluations on both real -world and synthetic datasets show that this method not only surpasses existing approaches in causal representation learning but also brings AI systems closer to practical, real -world applications by enhancing reliability and interpretability.", "subpage_snippet": "", "source": "uwspace.uwaterloo.ca", "link": "https://uwspace.uwaterloo.ca/items/71c7ae2e-c01c-4f31-8a59-88aa6e7f5fa7", "content": "Evaluations on both real -world and synthetic datasets show that this method not only surpasses existing approaches in causal representation learning but also brings AI systems closer to practical, real -world applications by enhancing reliability and interpretability."}
+{"idx": 5, "title": "Track: Oral 3E Causality and Domain Generalization", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors---the inputs to the experiment---are ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/46908", "content": "We evaluate methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors---the inputs to the experiment---are ..."}
+{"idx": 6, "title": "Score-based Causal Representation Learning: Linear and General ...", "date": "", "ddg_snippet": "This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent variables to the observed variables.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.00849v3", "content": "This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent variables to the observed variables."}
+{"idx": 7, "title": "Sanity Checking Causal Representation Learning on a Simple Real-World ...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors---the inputs to the experiment---are ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=d2aGLPSpFz", "content": "We evaluate methods for causal representation learning (CRL) on a simple, real -world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfies the core assumptions of CRL and where the underlying causal factors---the inputs to the experiment---are ..."}
+{"idx": 8, "title": "Toward Causal Representation Learning - IEEE Xplore", "date": "", "ddg_snippet": "The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning , including transfer and generalization, thereby ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9363924", "content": "The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning , including transfer and generalization, thereby ..."}
+{"idx": 9, "title": "GitHub - simonbing/CRLSanityCheck", "date": "", "ddg_snippet": "Sanity Checking Causal Representation Learning on a Simple Real -World System Official code repository for the paper Sanity Checking Causal Representation Learning on a Simple Real -World System (2025) by Juan L. Gamella*, Simon Bing* and Jakob Runge.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/simonbing/CRLSanityCheck", "content": "Sanity Checking Causal Representation Learning on a Simple Real -World System Official code repository for the paper Sanity Checking Causal Representation Learning on a Simple Real -World System (2025) by Juan L. Gamella*, Simon Bing* and Jakob Runge."}
diff --git a/data/sampled_jsons/deceptive_strategies_in_multi-agent_reinforcement_learning_year_2023.jsonl b/data/sampled_jsons/deceptive_strategies_in_multi-agent_reinforcement_learning_year_2023.jsonl
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+{"idx": 0, "title": "DECEPTIVE Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of DECEPTIVE is tending or having power to cause someone to accept as true or valid what is false or invalid : tending or having power to deceive. How to use deceptive in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/deceptive", "content": "The meaning of DECEPTIVE is tending or having power to cause someone to accept as true or valid what is false or invalid : tending or having power to deceive. How to use deceptive in a sentence."}
+{"idx": 1, "title": "DECEPTIVE | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "DECEPTIVE definition: 1. making you believe something that is not true: 2. making you believe something that is not…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/deceptive", "content": "DECEPTIVE definition: 1. making you believe something that is not true: 2. making you believe something that is not…. Learn more."}
+{"idx": 2, "title": "Deceptive - definition of deceptive by The Free Dictionary", "date": "", "ddg_snippet": "Define deceptive . deceptive synonyms, deceptive pronunciation, deceptive translation, English dictionary definition of deceptive . adj. Deceiving or tending to deceive: a deceptive advertisement. de·cep′tive·ness n. American Heritage® Dictionary of the English Language, Fifth Edition....", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/deceptive", "content": "Define deceptive . deceptive synonyms, deceptive pronunciation, deceptive translation, English dictionary definition of deceptive . adj. Deceiving or tending to deceive: a deceptive advertisement. de·cep′tive·ness n. American Heritage® Dictionary of the English Language, Fifth Edition...."}
+{"idx": 3, "title": "deceptive adjective - Definition, pictures, pronunciation and...", "date": "", "ddg_snippet": "Definition of deceptive adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/deceptive", "content": "Definition of deceptive adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 4, "title": "DECEPTIVE definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "If something is deceptive, it encourages you to believe something which is not true . Appearances can be deceptive.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/deceptive", "content": "If something is deceptive, it encourages you to believe something which is not true . Appearances can be deceptive."}
+{"idx": 5, "title": "527 Synonyms & Antonyms for DECEPTIVE | Thesaurus.com", "date": "", "ddg_snippet": "Find 527 different ways to say DECEPTIVE , along with antonyms, related words, and example sentences at Thesaurus.com.", "subpage_snippet": "", "source": "www.thesaurus.com", "link": "https://www.thesaurus.com/browse/deceptive", "content": "Find 527 different ways to say DECEPTIVE , along with antonyms, related words, and example sentences at Thesaurus.com."}
+{"idx": 6, "title": "What does deceptive mean? - Definitions.net", "date": "", "ddg_snippet": "Deceptive refers to the act or practice of deliberately causing someone to believe something that is not true , typically in order to gain some personal advantage.", "subpage_snippet": "", "source": "www.definitions.net", "link": "https://www.definitions.net/definition/deceptive", "content": "Deceptive refers to the act or practice of deliberately causing someone to believe something that is not true , typically in order to gain some personal advantage."}
+{"idx": 7, "title": "deceptive - WordReference.com Dictionary of English", "date": "", "ddg_snippet": "relating to or marked by deceit: deceptive advertising, until you read the fine print. apt or tending to deceive : The enemy's peaceful overtures may be deceptive . perceptually misleading: It looks like a curved line, but it's deceptive . de•cep′tive•ness, n. 1. delusive, fallacious, specious.", "subpage_snippet": "", "source": "www.wordreference.com", "link": "https://www.wordreference.com/definition/deceptive", "content": "relating to or marked by deceit: deceptive advertising, until you read the fine print. apt or tending to deceive : The enemy's peaceful overtures may be deceptive . perceptually misleading: It looks like a curved line, but it's deceptive . de•cep′tive•ness, n. 1. delusive, fallacious, specious."}
+{"idx": 8, "title": "DECEPTIVE Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Deceptive means intended to or tending to deceive —to lie, mislead, or otherwise hide or distort the truth. Deceptive is typically used to describe an action or something that deceives or is intended to deceive, as in deceptive business practices.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/deceptive", "content": "Deceptive means intended to or tending to deceive —to lie, mislead, or otherwise hide or distort the truth. Deceptive is typically used to describe an action or something that deceives or is intended to deceive, as in deceptive business practices."}
+{"idx": 9, "title": "deceptive - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Sep 10, 2025 · […] it is characteristic of TB that many of its symptoms are deceptive —liveliness that comes from enervation, rosy cheeks that look like a sign of health but come from fever—and an upsurge of vitality may be a sign of approaching death.", "subpage_snippet": "", "source": "en.wiktionary.org", "link": "https://en.wiktionary.org/wiki/deceptive", "content": "Sep 10, 2025 · […] it is characteristic of TB that many of its symptoms are deceptive —liveliness that comes from enervation, rosy cheeks that look like a sign of health but come from fever—and an upsurge of vitality may be a sign of approaching death."}
diff --git a/data/sampled_jsons/deep_neural_networks_Bayesian_neural_networks_preference-based_reinforcement_learning_non-linear_rew.jsonl b/data/sampled_jsons/deep_neural_networks_Bayesian_neural_networks_preference-based_reinforcement_learning_non-linear_rew.jsonl
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index 0000000000000000000000000000000000000000..67e585021c527a52a11f7dcbd9b299cfd28abf52
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1912.04472] Deep Bayesian Reward Learning from Preferences", "date": "", "ddg_snippet": "While there exist non - Bayesian deep IRL methods, these methods typically infer point estimates of reward functions, precluding rigorous safety and uncertainty analysis. We propose Bayesian Reward Extrapolation (B-REX), a highly efficient, preference-based Bayesian reward learning algorithm that scales to high-dimensional, visual control tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1912.04472", "content": "While there exist non - Bayesian deep IRL methods, these methods typically infer point estimates of reward functions, precluding rigorous safety and uncertainty analysis. We propose Bayesian Reward Extrapolation (B-REX), a highly efficient, preference-based Bayesian reward learning algorithm that scales to high-dimensional, visual control tasks."}
+{"idx": 1, "title": "PDF Deep Bayesian Active Learning for Preference Modeling in Large Language ...", "date": "", "ddg_snippet": "Hence, selecting the most informative points for acquiring human feedback may considerably reduce the cost of preference labeling and unleash the further development of LLMs. Bayesian Active Learning provides a principled framework for addressing this challenge and has demonstrated remarkable success in diverse settings.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/d5e256c988bdee59a0f4d7a9bc1dd6d9-Paper-Conference.pdf", "content": "Hence, selecting the most informative points for acquiring human feedback may considerably reduce the cost of preference labeling and unleash the further development of LLMs. Bayesian Active Learning provides a principled framework for addressing this challenge and has demonstrated remarkable success in diverse settings."}
+{"idx": 2, "title": "Offline reward shaping with scaling human preference feedback for deep ...", "date": "", "ddg_snippet": "Designing reward functions that fully align with human intent is often challenging. Preference-based Reinforcement Learning (PbRL) provides a framework where humans can select preferred segments through pairwise comparisons of behavior trajectory segments, facilitating reward function learning . However, existing methods collect non -dynamic preferences and struggle to provide accurate ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S089360802400772X", "content": "Designing reward functions that fully align with human intent is often challenging. Preference-based Reinforcement Learning (PbRL) provides a framework where humans can select preferred segments through pairwise comparisons of behavior trajectory segments, facilitating reward function learning . However, existing methods collect non -dynamic preferences and struggle to provide accurate ..."}
+{"idx": 3, "title": "Learning guarantee of reward modeling using deep neural networks", "date": "", "ddg_snippet": "This paper introduces a theoretical framework for understanding reward modeling in reinforcement learning (RL) based on pairwise comparisons and fully connected deep neural networks (DNNs) to estimate the reward function.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mGSQLuYxVF", "content": "This paper introduces a theoretical framework for understanding reward modeling in reinforcement learning (RL) based on pairwise comparisons and fully connected deep neural networks (DNNs) to estimate the reward function."}
+{"idx": 4, "title": "Deep Bayesian active learning for preference modeling in large language ...", "date": "", "ddg_snippet": "Deep Bayesian active learning for natural language processing: Results of a large-scale empirical study. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun'ichi Tsujii, editors, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2904-2909, Brussels, Belgium, October-November 2018.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3741665", "content": "Deep Bayesian active learning for natural language processing: Results of a large-scale empirical study. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun'ichi Tsujii, editors, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2904-2909, Brussels, Belgium, October-November 2018."}
+{"idx": 5, "title": "PDF NeurIPS Recording - Deep Bayesian Active Learning for Preference ...", "date": "", "ddg_snippet": "Active Learning for Preference Modeling in LLMs Selecting the most informative prompts/responses to gather feedback is essential to reduce costs and enable better LLMs!", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/95050.pdf", "content": "Active Learning for Preference Modeling in LLMs Selecting the most informative prompts/responses to gather feedback is essential to reduce costs and enable better LLMs!"}
+{"idx": 6, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "To alleviate these issues, preference-based reinforcement learning algorithms (PbRL) have been proposed that can directly learn from an expert's preferences instead of a hand-designed numeric reward .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383308396_Advances_in_Preference-based_Reinforcement_Learning_A_Review", "content": "To alleviate these issues, preference-based reinforcement learning algorithms (PbRL) have been proposed that can directly learn from an expert's preferences instead of a hand-designed numeric reward ."}
+{"idx": 7, "title": "PDF Deep Bayesian Recommendation Systems - Stanford University", "date": "", "ddg_snippet": "In this paper, we explore using approximate Bayesian Neural Networks to yield better approximations of reward uncertainties, and implement our method with both Thompson Sampling and Upper Confidence Bound policies. We compare results across a variety of baseline methods from the literature.", "subpage_snippet": "", "source": "cs230.stanford.edu", "link": "http://cs230.stanford.edu/projects_fall_2022/reports/142.pdf", "content": "In this paper, we explore using approximate Bayesian Neural Networks to yield better approximations of reward uncertainties, and implement our method with both Thompson Sampling and Upper Confidence Bound policies. We compare results across a variety of baseline methods from the literature."}
+{"idx": 8, "title": "PDF Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning", "date": "", "ddg_snippet": "An alternative to the approximate Bayesian inference provided by MC Dropout and ensemble methods is Probabilistic Backpropagation (PBP) (Hernández-Lobato and Adams, 2015) - a fully Bayesian method for training Bayesian Neural Networks (BNNs), which thus produces fully- Bayesian model uncertainty estimates. Unlike ensemble- based methods (Lakshminarayanan et al., 2017), PBP provides a ...", "subpage_snippet": "", "source": "bayesiandeeplearning.org", "link": "https://bayesiandeeplearning.org/2019/papers/6.pdf", "content": "An alternative to the approximate Bayesian inference provided by MC Dropout and ensemble methods is Probabilistic Backpropagation (PBP) (Hernández-Lobato and Adams, 2015) - a fully Bayesian method for training Bayesian Neural Networks (BNNs), which thus produces fully- Bayesian model uncertainty estimates. Unlike ensemble- based methods (Lakshminarayanan et al., 2017), PBP provides a ..."}
+{"idx": 9, "title": "PDF Reinforcement Learning from Diverse Human Preferences", "date": "", "ddg_snippet": "Abstract The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques. De-scribing an agent's desired behaviors and properties can be dificult, even for experts. A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution, in which reward ...", "subpage_snippet": "", "source": "personal.ntu.edu.sg", "link": "https://personal.ntu.edu.sg/boan/papers/IJCAI24_PrefRL.pdf", "content": "Abstract The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques. De-scribing an agent's desired behaviors and properties can be dificult, even for experts. A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution, in which reward ..."}
diff --git a/data/sampled_jsons/definition_of_Active_Learning_in_machine_learning.jsonl b/data/sampled_jsons/definition_of_Active_Learning_in_machine_learning.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Active learning (machine learning) - Wikipedia", "date": "", "ddg_snippet": "Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Active_learning_(machine_learning)", "content": "Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs."}
+{"idx": 1, "title": "Active Learning Definition | DeepAI Active Learning in Machine Learning Explained | Towards Data ... What is active learning? - AI Glossary Active learning ( machine learning ) - Wikipedia ML | Active Learning - GeeksforGeeks ML | Active Learning - GeeksforGeeks What is Active Learning ? Definition and Meaning Explained ML | Active Learning - GeeksforGeeks Active Learning Definition | DeepAI What is Active Learning? Definition and Meaning Explained", "date": "", "ddg_snippet": "Active learning is a special case of machine learningwhere the learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In contrast to passive learning where the learning algorithm is given a fixed set of labeled data, active learning aims to achieve high accurac... See full list on deepai.org Active learning operates under the premise that a machine learning model can perform better with less training if it is allowed to choose the data from which it learns. This approach is particularly useful when labeled data is scarce or expensive to obtain. Active learning involves a query strategy to select the most informative samples, which are ... See full list on deepai.org There are various strategies for selecting which data points should be labeled, but they generally fall into three main categories: 1. Uncertainty Sampling:The model queries the instance for which it has the least confidence in its current predictions. 2. Query by Committee:Multiple models are used, and the instance queried is the one about which t... See full list on deepai.org The typical active learning cycle involves the following steps: 1. A machine learning model is trained on a small initial labeled dataset. 2. The model is used to predict labels on unlabeled data. 3. Based on a query strategy, the most informative data points are selected for labeling. 4. The oracle (human annotator) provides the labels for the sel... See full list on deepai.org Active learning has been applied in various domains where labeled data is limited or costly to obtain: 1. Natural Language Processing: For tasks like sentiment analysis or named entity recognitionwhere manual labeling can be time-consuming. 2. Computer Vision: In image classification or medical imaging where expert annotation is expensive. 3. Speech... See full list on deepai.org While active learning can be highly effective, it also presents several challenges: 1. Query Strategy Efficiency:The success of active learning heavily depends on the query strategy's ability to select the most informative samples. 2. Oracle Reliability:The quality of the labels provided by the oracle can significantly impact the model's performanc... See full list on deepai.org Active learning represents a powerful approach to building machine learning models, particularly in scenarios where labeled data is a scarce resource. By intelligently selecting the most informative samples for labeling, active learning can reduce the amount of labeled data needed to achieve high performance, thereby saving time and resources. As t... See full list on deepai.org Mar 28, 2022 · Applications of Active Learning Active learning is commonly used in a variety of industries and problems. Generally, active learning can be implemented to solve problems in natural language processing, computer vision and recommendation systems. Active learning is a machine learning approach where the algorithm can interactively query a user or some other source to obtain new data points to improve the learning process. What is active learning in machine learning? Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. How can active learning improve a machine learning model? To enhance a machine learning model, active learning selects the most informative data points iteratively from an unlabeled dataset and requests labels for these points. The main idea is to deliberately select the situations where the model is unsure of itself or where it could most benefit from more information. What is active learning in ML? The basic idea behind the active learner algorithm concept is that if an ML algorithm could select the data it wants to learn from, it might be able to achieve a higher degree of accuracy with fewer training labels. How does active learning work? What is active learning & how does it work? Active learning presents a transformative approach that challenges traditional machine learning methods, which heavily rely on large amounts of labeled data—a costly and time-consuming resource to acquire. By flipping the script, active learning not only promises to reduce costs but also enhances model accuracy and efficiency. What is active learner algorithm? The algorithm actively chooses from the pool of unlabeled data the subset of examples to be labelled next in active learning . The basic idea behind the active learner algorithm concept is that if an ML algorithm could select the data it wants to learn from, it might be able to achieve a higher degree of accuracy with fewer training labels. What is the difference between active learning and passive learning? In contrast to passive learning where the learning algorithm is given a fixed set of labeled data, active learning aims to achieve high accuracy with fewer training labels by allowing the learner to choose the data from which it learns. Active ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/machine-learning-glossary-and-terms/active-learning", "content": "Active learning is a special case of machine learningwhere the learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In contrast to passive learning where the learning algorithm is given a fixed set of labeled data, active learning aims to achieve high accurac... See full list on deepai.org Active learning operates under the premise that a machine learning model can perform better with less training if it is allowed to choose the data from which it learns. This approach is particularly useful when labeled data is scarce or expensive to obtain. Active learning involves a query strategy to select the most informative samples, which are ... See full list on deepai.org There are various strategies for selecting which data points should be labeled, but they generally fall into three main categories: 1. Uncertainty Sampling:The model queries the instance for which it has the least confidence in its current predictions. 2. Query by Committee:Multiple models are used, and the instance queried is the one about which t... See full list on deepai.org The typical active learning cycle involves the following steps: 1. A machine learning model is trained on a small initial labeled dataset. 2. The model is used to predict labels on unlabeled data. 3. Based on a query strategy, the most informative data points are selected for labeling. 4. The oracle (human annotator) provides the labels for the sel... See full list on deepai.org Active learning has been applied in various domains where labeled data is limited or costly to obtain: 1. Natural Language Processing: For tasks like sentiment analysis or named entity recognitionwhere manual labeling can be time-consuming. 2. Computer Vision: In image classification or medical imaging where expert annotation is expensive. 3. Speech... See full list on deepai.org While active learning can be highly effective, it also presents several challenges: 1. Query Strategy Efficiency:The success of active learning heavily depends on the query strategy's ability to select the most informative samples. 2. Oracle Reliability:The quality of the labels provided by the oracle can significantly impact the model's performanc... See full list on deepai.org Active learning represents a powerful approach to building machine learning models, particularly in scenarios where labeled data is a scarce resource. By intelligently selecting the most informative samples for labeling, active learning can reduce the amount of labeled data needed to achieve high performance, thereby saving time and resources. As t... See full list on deepai.org Mar 28, 2022 · Applications of Active Learning Active learning is commonly used in a variety of industries and problems. Generally, active learning can be implemented to solve problems in natural language processing, computer vision and recommendation systems. Active learning is a machine learning approach where the algorithm can interactively query a user or some other source to obtain new data points to improve the learning process. What is active learning in machine learning? Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. How can active learning improve a machine learning model? To enhance a machine learning model, active learning selects the most informative data points iteratively from an unlabeled dataset and requests labels for these points. The main idea is to deliberately select the situations where the model is unsure of itself or where it could most benefit from more information. What is active learning in ML? The basic idea behind the active learner algorithm concept is that if an ML algorithm could select the data it wants to learn from, it might be able to achieve a higher degree of accuracy with fewer training labels. How does active learning work? What is active learning & how does it work? Active learning presents a transformative approach that challenges traditional machine learning methods, which heavily rely on large amounts of labeled data—a costly and time-consuming resource to acquire. By flipping the script, active learning not only promises to reduce costs but also enhances model accuracy and efficiency. What is active learner algorithm? The algorithm actively chooses from the pool of unlabeled data the subset of examples to be labelled next in active learning . The basic idea behind the active learner algorithm concept is that if an ML algorithm could select the data it wants to learn from, it might be able to achieve a higher degree of accuracy with fewer training labels. What is the difference between active learning and passive learning? In contrast to passive learning where the learning algorithm is given a fixed set of labeled data, active learning aims to achieve high accuracy with fewer training labels by allowing the learner to choose the data from which it learns. Active ..."}
+{"idx": 2, "title": "ML | Active Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Aug 6, 2025 · Active Learning is a special case of Supervised Machine Learning . This approach is used to construct a high-performance classifier while keeping the size of the training dataset to a minimum by actively selecting the valuable data points. Active Learning in Machine Learning A subset of machine learning known as \" active learning \" allows a learning algorithm to interactively query a user to ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/ml-active-learning/", "content": "Aug 6, 2025 · Active Learning is a special case of Supervised Machine Learning . This approach is used to construct a high-performance classifier while keeping the size of the training dataset to a minimum by actively selecting the valuable data points. Active Learning in Machine Learning A subset of machine learning known as \" active learning \" allows a learning algorithm to interactively query a user to ..."}
+{"idx": 3, "title": "Active Learning: Definition, Benefits, and Uses | Ultralytics", "date": "", "ddg_snippet": "Active learning is a specialized training methodology in machine learning (ML) where a learning algorithm can interactively query a user or another information source (an \"oracle\") to label new data points. The core idea is that if a model can choose the data it learns from, it can achieve higher accuracy with significantly less training data.", "subpage_snippet": "", "source": "www.ultralytics.com", "link": "https://www.ultralytics.com/glossary/active-learning", "content": "Active learning is a specialized training methodology in machine learning (ML) where a learning algorithm can interactively query a user or another information source (an \"oracle\") to label new data points. The core idea is that if a model can choose the data it learns from, it can achieve higher accuracy with significantly less training data."}
+{"idx": 4, "title": "Active Learning in Machine Learning Explained | Towards Data ...", "date": "", "ddg_snippet": "Mar 28, 2022 · Applications of Active Learning Active learning is commonly used in a variety of industries and problems. Generally, active learning can be implemented to solve problems in natural language processing, computer vision and recommendation systems.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/active-learning-in-machine-learning-explained-777c42bd52fa/", "content": "Mar 28, 2022 · Applications of Active Learning Active learning is commonly used in a variety of industries and problems. Generally, active learning can be implemented to solve problems in natural language processing, computer vision and recommendation systems."}
+{"idx": 5, "title": "What is active learning? - AI Glossary", "date": "", "ddg_snippet": "Active learning is a machine learning approach where the algorithm can interactively query a user or some other source to obtain new data points to improve the learning process.", "subpage_snippet": "", "source": "docsbot.ai", "link": "https://docsbot.ai/ai-terms-glossary/term/active-learning", "content": "Active learning is a machine learning approach where the algorithm can interactively query a user or some other source to obtain new data points to improve the learning process."}
+{"idx": 6, "title": "What is Active Learning? Definition and Meaning Explained", "date": "", "ddg_snippet": "Active ...", "subpage_snippet": "", "source": "ai-tool.ai", "link": "https://ai-tool.ai/ai-glossary/fundamentals/active-learning", "content": "Active ..."}
+{"idx": 7, "title": "Active Learning in Machine Learning [Guide & Examples]", "date": "", "ddg_snippet": "Active learning is a type of machine learning where the model is trained on only the most relevant data. ... Active Learning is a ...", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/active-learning-guide", "content": "Active learning is a type of machine learning where the model is trained on only the most relevant data. ... Active Learning is a ..."}
+{"idx": 8, "title": "Active Learning Definition | DeepAI", "date": "", "ddg_snippet": "Active learning is a special case of machine learning where the learning algorithm is able to interactively query the user (or some other information ...", "subpage_snippet": "", "source": "www.deep.ai", "link": "https://www.deep.ai/machine-learning-glossary-and-terms/active-learning", "content": "Active learning is a special case of machine learning where the learning algorithm is able to interactively query the user (or some other information ..."}
+{"idx": 9, "title": "Active Learning Definition | DeepAI", "date": "", "ddg_snippet": "Active learning is a special case of machine learning where the learning algorithm is able to interactively query the user (or some other information ...", "subpage_snippet": "", "source": "deepai.com", "link": "https://deepai.com/machine-learning-glossary-and-terms/active-learning", "content": "Active learning is a special case of machine learning where the learning algorithm is able to interactively query the user (or some other information ..."}
diff --git a/data/sampled_jsons/delta_tilde_multiple_testing_correction_Bonferroni_statistical_learning_year_2024.jsonl b/data/sampled_jsons/delta_tilde_multiple_testing_correction_Bonferroni_statistical_learning_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Practical Multiple Testing Correction in ML", "date": "", "ddg_snippet": "Learn practical steps to apply multiple testing corrections in machine learning , covering FDR, Bonferroni , and popular software libraries.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/practical-multiple-testing-ml-corrections", "content": "Learn practical steps to apply multiple testing corrections in machine learning , covering FDR, Bonferroni , and popular software libraries."}
+{"idx": 1, "title": "Can Bonferroni be applied for dependent multiple tests?", "date": "", "ddg_snippet": "If there is no correlation between the scores of math test and those of philosophy test, I think we can simply apply Bonferroni correction . Since we have 3 classes, the significance level is 0.05/3.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/235856/can-bonferroni-be-applied-for-dependent-multiple-tests", "content": "If there is no correlation between the scores of math test and those of philosophy test, I think we can simply apply Bonferroni correction . Since we have 3 classes, the significance level is 0.05/3."}
+{"idx": 2, "title": "Bonferroni Correction Explained: Managing Multiple Testing in Statistics", "date": "", "ddg_snippet": "Understanding bonferroni correction Bonferroni Correction Explained: Managing Multiple Testing in Statistics Explore the Bonferroni correction method, including how it works within A/B testing , when and how to use it, its pros and cons, and other correction techniques. A/B testing is a valuable way to optimize your product using actual user data.", "subpage_snippet": "", "source": "amplitude.com", "link": "https://amplitude.com/explore/experiment/what-is-bonferroni-correction", "content": "Understanding bonferroni correction Bonferroni Correction Explained: Managing Multiple Testing in Statistics Explore the Bonferroni correction method, including how it works within A/B testing , when and how to use it, its pros and cons, and other correction techniques. A/B testing is a valuable way to optimize your product using actual user data."}
+{"idx": 3, "title": "Multiple Comparisons Problem: Bonferroni Correction and Other Solutions", "date": "", "ddg_snippet": "In this article, we will explain the multiple comparisons problem, discuss solutions like the Bonferroni correction , Holm- Bonferroni method, and False Discovery Rate (FDR), and explore real-world applications of these methods in multiple testing scenarios.", "subpage_snippet": "", "source": "diogoribeiro7.github.io", "link": "https://diogoribeiro7.github.io/statistics/multiple_comparisons_problem_bonferroni_correction_other_solutions/", "content": "In this article, we will explain the multiple comparisons problem, discuss solutions like the Bonferroni correction , Holm- Bonferroni method, and False Discovery Rate (FDR), and explore real-world applications of these methods in multiple testing scenarios."}
+{"idx": 4, "title": "Bonferroni Correction: Refining Accuracy: The Bonferroni Correction in ...", "date": "", "ddg_snippet": "In the realm of statistical analysis, the concept of hypothesis testing serves as a cornerstone, allowing researchers to make inferences about populations based on sample data. However, when multiple hypotheses are tested simultaneously, the probability of encountering a false positive—incorrectly...", "subpage_snippet": "", "source": "fastercapital.com", "link": "https://fastercapital.com/content/Bonferroni-Correction--Refining-Accuracy--The-Bonferroni-Correction-in-Multiple-Testing.html", "content": "In the realm of statistical analysis, the concept of hypothesis testing serves as a cornerstone, allowing researchers to make inferences about populations based on sample data. However, when multiple hypotheses are tested simultaneously, the probability of encountering a false positive—incorrectly..."}
+{"idx": 5, "title": "Comprehensive Guide to Multiple Testing Corrections - Medium", "date": "", "ddg_snippet": "Comprehensive Guide to Multiple Testing Corrections Bonferroni , Holm- Bonferroni , FDR, Tukey HSD, and Permutation Tests Explained with Python Examples What is multiple testing ? Multiple testing is ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@nivedita.home/comprehensive-guide-to-multiple-testing-corrections-8053cd59fdca", "content": "Comprehensive Guide to Multiple Testing Corrections Bonferroni , Holm- Bonferroni , FDR, Tukey HSD, and Permutation Tests Explained with Python Examples What is multiple testing ? Multiple testing is ..."}
+{"idx": 6, "title": "How to use Bonferroni correction for multiple hypothesis testing", "date": "", "ddg_snippet": "Avoid false positives in experiments by using statistical corrections like Bonferroni to manage multiple comparisons.", "subpage_snippet": "", "source": "www.statsig.com", "link": "https://www.statsig.com/perspectives/bonferroni-correction-multiple-testing", "content": "Avoid false positives in experiments by using statistical corrections like Bonferroni to manage multiple comparisons."}
+{"idx": 7, "title": "Multiple Testing correction in Machine Learning", "date": "", "ddg_snippet": "The need for multiple testing correction stems from a classical statistical framework that emphasises hypothesis testing . Machine learning and AI, on the other hand, rely on predictive accuracy, generalisability, and regularisation, bypassing the problem of false positives inherent in multiple hypothesis testing .", "subpage_snippet": "", "source": "drdelorenzo.com", "link": "https://drdelorenzo.com/multiple-testing-correction-in-ml/", "content": "The need for multiple testing correction stems from a classical statistical framework that emphasises hypothesis testing . Machine learning and AI, on the other hand, rely on predictive accuracy, generalisability, and regularisation, bypassing the problem of false positives inherent in multiple hypothesis testing ."}
+{"idx": 8, "title": "Bonferroni correction: Whats exactly is meant by \"multiple tests\"?", "date": "", "ddg_snippet": "The Bonferroni correction seems to be quite controversial. But I read again and again that it should be used for multiple tests. But what exactly are multiple tests? If I have three different data sets in the same study and run only one t-test on each data set, is that a multiple test and do I have to apply a Bonferroni correction ? Or am I only talking about multiple testing if I have one data ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/612283/bonferroni-correction-whats-exactly-is-meant-by-multiple-tests", "content": "The Bonferroni correction seems to be quite controversial. But I read again and again that it should be used for multiple tests. But what exactly are multiple tests? If I have three different data sets in the same study and run only one t-test on each data set, is that a multiple test and do I have to apply a Bonferroni correction ? Or am I only talking about multiple testing if I have one data ..."}
+{"idx": 9, "title": "Multiple Testing Correction: Methods & Theory", "date": "", "ddg_snippet": "Explore key multiple testing correction methods in statistical machine learning , from Bonferroni to FDR procedures for accurate inference.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/multiple-testing-correction-methods-theory", "content": "Explore key multiple testing correction methods in statistical machine learning , from Bonferroni to FDR procedures for accurate inference."}
diff --git a/data/sampled_jsons/diagonal_update_all_grids_Technical_Overview_parallel_simulation.jsonl b/data/sampled_jsons/diagonal_update_all_grids_Technical_Overview_parallel_simulation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..453b203a5b2ae9cbf652d304ae5eb4ecccf2738c
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+++ b/data/sampled_jsons/diagonal_update_all_grids_Technical_Overview_parallel_simulation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GPU-based compressible lattice Boltzmann simulations on", "date": "", "ddg_snippet": "... work introduces a novel strategy using simple yet effective components: (1) parallel algorithms in modern C++, (2) conservative cell-centered grid ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.04465v1", "content": "... work introduces a novel strategy using simple yet effective components: (1) parallel algorithms in modern C++, (2) conservative cell-centered grid ..."}
+{"idx": 1, "title": "Descarga de software - VALLON GmbH", "date": "", "ddg_snippet": "After the compensation of a readings layer with borehole data, the views of the borehole curves in the diagram are now updated correctly.", "subpage_snippet": "", "source": "www.vallon.de", "link": "https://www.vallon.de/es/detectores/servicio/descarga-de-software", "content": "After the compensation of a readings layer with borehole data, the views of the borehole curves in the diagram are now updated correctly."}
+{"idx": 2, "title": "Software downloads - VALLON GmbH", "date": "", "ddg_snippet": "After the compensation of a readings layer with borehole data, the views of the borehole curves in the diagram are now updated correctly.", "subpage_snippet": "", "source": "www.vallon.de", "link": "https://www.vallon.de/en/detectors/service/software-downloads", "content": "After the compensation of a readings layer with borehole data, the views of the borehole curves in the diagram are now updated correctly."}
+{"idx": 3, "title": "US8666390B2 - Ticketing mobile call failures based on", "date": "", "ddg_snippet": "These technical problems can vary, but can include entering a dead zone (where base station signals cannot be received by the subscriber terminal) or ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US8666390B2/en", "content": "These technical problems can vary, but can include entering a dead zone (where base station signals cannot be received by the subscriber terminal) or ..."}
+{"idx": 4, "title": "Reduction of Outflow Boundary Influence on Aerodynamic", "date": "", "ddg_snippet": "The CBCs present a potential building block for LBM simulations , extending their use to accurately predict acoustic fields without excessive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05293v1", "content": "The CBCs present a potential building block for LBM simulations , extending their use to accurately predict acoustic fields without excessive ..."}
+{"idx": 5, "title": "Comparisons and conversion algorithms of vector grating", "date": "", "ddg_snippet": "The conversion between vector structure and grid structure is basically a coordinate accuracy conversion problem, and there is no big technical ...", "subpage_snippet": "", "source": "www.gislite.com", "link": "https://www.gislite.com/tutorial/k1074", "content": "The conversion between vector structure and grid structure is basically a coordinate accuracy conversion problem, and there is no big technical ..."}
+{"idx": 6, "title": "OS - Current observed global mean sea level rise and", "date": "", "ddg_snippet": "To ensure the best possible estimate of current sea level changes, space agencies regularly revisit and update the production of the sea level record ...", "subpage_snippet": "", "source": "os.copernicus.org", "link": "https://os.copernicus.org/articles/19/431/2023/", "content": "To ensure the best possible estimate of current sea level changes, space agencies regularly revisit and update the production of the sea level record ..."}
+{"idx": 7, "title": "Efficient calculation of the three-dimensional sound pressure", "date": "", "ddg_snippet": "The constant geometry in the cross-section allows discretising the sound field in the wavenumber domain and reduces the 3D BE problem to 2D problems ...", "subpage_snippet": "", "source": "acta-acustica.edpsciences.org", "link": "https://acta-acustica.edpsciences.org/articles/aacus/full_html/2024/01/aacus230035/aacus230035.html", "content": "The constant geometry in the cross-section allows discretising the sound field in the wavenumber domain and reduces the 3D BE problem to 2D problems ..."}
+{"idx": 8, "title": "handprint : modern color models", "date": "", "ddg_snippet": "These books provide no way to visualize how all the colors fit together, in the same way that a city phone directory gives you no idea of how the ...", "subpage_snippet": "", "source": "www.handprint.com", "link": "http://www.handprint.com/HP/WCL/color7.html", "content": "These books provide no way to visualize how all the colors fit together, in the same way that a city phone directory gives you no idea of how the ..."}
+{"idx": 9, "title": "Visual Planning: Let’s Think Only with Images", "date": "", "ddg_snippet": "We validate the feasibility of our paradigms on grid -based navigation as a representative of spatial planning tasks, including Maze [ 23 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.11409v1", "content": "We validate the feasibility of our paradigms on grid -based navigation as a representative of spatial planning tasks, including Maze [ 23 ..."}
diff --git a/data/sampled_jsons/differential_privacy_coordinate-wise_private_median_regression_problem_challenge.jsonl b/data/sampled_jsons/differential_privacy_coordinate-wise_private_median_regression_problem_challenge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c9fcba8ddefa58c5958830ef02a383de5ffeff27
--- /dev/null
+++ b/data/sampled_jsons/differential_privacy_coordinate-wise_private_median_regression_problem_challenge.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Differentially Private Median Forests for Regression and Classification", "date": "", "ddg_snippet": "In this paper, we propose DiPriMe forests, a novel tree-based ensemble method for regression and classification problems , that ensures differential privacy while maintaining high utility. We construct trees based on a privatized version of the median value of attributes, obtained via the exponential mechanism.", "subpage_snippet": "", "source": "ppai21.github.io", "link": "https://ppai21.github.io/files/2-paper.pdf", "content": "In this paper, we propose DiPriMe forests, a novel tree-based ensemble method for regression and classification problems , that ensures differential privacy while maintaining high utility. We construct trees based on a privatized version of the median value of attributes, obtained via the exponential mechanism."}
+{"idx": 1, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "by S Feng · 2025 — For adaptive regression , we show how to upgrade the private median framework of [BKM+22] to output the solution vector, and how to obtain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503", "content": "by S Feng · 2025 — For adaptive regression , we show how to upgrade the private median framework of [BKM+22] to output the solution vector, and how to obtain ..."}
+{"idx": 2, "title": "Differentially Private Linear Regression via Medians", "date": "", "ddg_snippet": "by A Knop — We provide an algorithm for private linear regression which, despite its simplicity, outperforms prior work.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JSBgIaxAXk9", "content": "by A Knop — We provide an algorithm for private linear regression which, despite its simplicity, outperforms prior work."}
+{"idx": 3, "title": "ICML Poster On Differential Privacy for Adaptively Solving ...", "date": "", "ddg_snippet": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44265", "content": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the ..."}
+{"idx": 4, "title": "On Differential Privacy for Adaptively Solving Search ...", "date": "", "ddg_snippet": "5 Jun 2025 — For adaptive regression , we show how to upgrade the private median framework of [12] to output the solution vector, and how to obtain utility ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05503v1", "content": "5 Jun 2025 — For adaptive regression , we show how to upgrade the private median framework of [12] to output the solution vector, and how to obtain utility ..."}
+{"idx": 5, "title": "Differentially Private Simple Linear Regression", "date": "", "ddg_snippet": "by D Alabi · 2022 · Cited by 73 — We study regression algorithms that satisfy differential pri- vacy, a constraint which guarantees that an algorithm's output reveals little about any individual ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10336471", "content": "by D Alabi · 2022 · Cited by 73 — We study regression algorithms that satisfy differential pri- vacy, a constraint which guarantees that an algorithm's output reveals little about any individual ..."}
+{"idx": 6, "title": "Differential Privacy and Robust Statistics", "date": "", "ddg_snippet": "by C Dwork · 2008 · Cited by 1068 — We obtain differentially private algorithms for estimating the data scale, median , α-trimmed mean, and linear regression coefficients. Our ... 41 pages", "subpage_snippet": "", "source": "www.stat.cmu.edu", "link": "https://www.stat.cmu.edu/~jinglei/dprs_stoc09.pdf", "content": "by C Dwork · 2008 · Cited by 1068 — We obtain differentially private algorithms for estimating the data scale, median , α-trimmed mean, and linear regression coefficients. Our ... 41 pages"}
+{"idx": 7, "title": "Advancing Differential Privacy", "date": "", "ddg_snippet": "by R Cummings · 2024 · Cited by 44 — In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy ...", "subpage_snippet": "", "source": "hdsr.mitpress.mit.edu", "link": "https://hdsr.mitpress.mit.edu/pub/sl9we8gh/download/pdf", "content": "by R Cummings · 2024 · Cited by 44 — In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy ..."}
+{"idx": 8, "title": "Differentially Private Quantile Regression", "date": "", "ddg_snippet": "Explore methods for quantile regression that ensure differential privacy using robust algorithms and statistical techniques, balancing accurate estimates ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/differentially-private-quantile-regression", "content": "Explore methods for quantile regression that ensure differential privacy using robust algorithms and statistical techniques, balancing accurate estimates ..."}
+{"idx": 9, "title": "Differentially Private M-Estimators", "date": "", "ddg_snippet": "by J Lei · 2011 · Cited by 163 — This paper studies privacy preserving M-estimators using perturbed histograms. The proposed approach allows the release of a wide class of M-estimators with. 9 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2011/file/f718499c1c8cef6730f9fd03c8125cab-Paper.pdf", "content": "by J Lei · 2011 · Cited by 163 — This paper studies privacy preserving M-estimators using perturbed histograms. The proposed approach allows the release of a wide class of M-estimators with. 9 pages"}
diff --git "a/data/sampled_jsons/directionality_score_formula_matrix_symmetry_\316\263_gamma_scaling_factor.jsonl" "b/data/sampled_jsons/directionality_score_formula_matrix_symmetry_\316\263_gamma_scaling_factor.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..9502c210493e2e3810607ceb968122f5134f34b6
--- /dev/null
+++ "b/data/sampled_jsons/directionality_score_formula_matrix_symmetry_\316\263_gamma_scaling_factor.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Lorentz factor - Wikipedia", "date": "", "ddg_snippet": "Lorentz factor Definition of the Lorentz factor γ The Lorentz factor or Lorentz term (also known as the gamma factor[1]) is a dimensionless quantity expressing how much the measurements of time, length, and other physical properties change for an object while it moves.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Lorentz_factor", "content": "Lorentz factor Definition of the Lorentz factor γ The Lorentz factor or Lorentz term (also known as the gamma factor[1]) is a dimensionless quantity expressing how much the measurements of time, length, and other physical properties change for an object while it moves."}
+{"idx": 1, "title": "Revisiting the directionality factor in ASCE 7 - ScienceDirect", "date": "", "ddg_snippet": "In 1998, the ASCE 7 task committee on wind loads separated the wind directionality effect from the load combinations and presented it as an independent factor . At this time the load factor was also changed from 1.3 to 1.6 in order to balance both sides of the equation.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167610514001184", "content": "In 1998, the ASCE 7 task committee on wind loads separated the wind directionality effect from the load combinations and presented it as an independent factor . At this time the load factor was also changed from 1.3 to 1.6 in order to balance both sides of the equation."}
+{"idx": 2, "title": "About ASCE 7's Directionality Factor Kd - Engineering Express", "date": "", "ddg_snippet": "The wind Directionality Factor Kd is affected by the frequency of occurrence and the routes of typhoons, climatological factors , large-scale topographic effects and so on. See Table 26.6-1 below for the Wind Directionality Factors required per structure type. The question then comes as to whether load combinations exist for windows & doors.", "subpage_snippet": "", "source": "www.engineeringexpress.com", "link": "https://www.engineeringexpress.com/wiki/directionality-factor-kd/", "content": "The wind Directionality Factor Kd is affected by the frequency of occurrence and the routes of typhoons, climatological factors , large-scale topographic effects and so on. See Table 26.6-1 below for the Wind Directionality Factors required per structure type. The question then comes as to whether load combinations exist for windows & doors."}
+{"idx": 3, "title": "PDF Wind Directionality Factor, Kd T able 26.6-1 - Medeek", "date": "", "ddg_snippet": "* Directionality Factor Kd has been calibrated with combinations of loads specified in Chapter 2. This factor shall only be applied when used in conjunction with load combinations specified in Sections 2.3 and 2.4.", "subpage_snippet": "", "source": "design.medeek.com", "link": "https://design.medeek.com/resources/DOCUMENTS/ASCE7-10_TABLE26.6-1.pdf", "content": "* Directionality Factor Kd has been calibrated with combinations of loads specified in Chapter 2. This factor shall only be applied when used in conjunction with load combinations specified in Sections 2.3 and 2.4."}
+{"idx": 4, "title": "Gamma matrices, Majorana fermions, and discrete symmetries in Minkowski ...", "date": "", "ddg_snippet": "I describe the interplay between Minkowski and Euclidean signa-ture gamma matrices, Majorana fermions, and discrete and continuous symmetries in all spacetime dimensions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2009.00518", "content": "I describe the interplay between Minkowski and Euclidean signa-ture gamma matrices, Majorana fermions, and discrete and continuous symmetries in all spacetime dimensions."}
+{"idx": 5, "title": "PDF Lectures on Gamma Matrix and Supersymmetry - KIAS", "date": "", "ddg_snippet": "This lecture note surveys the Gamma matrix in general dimensions with arbitrary signature, the study of which is essential to study supersymmetry in the dimension.", "subpage_snippet": "", "source": "conf.kias.re.kr", "link": "http://conf.kias.re.kr/~brane/2003/gamma.pdf", "content": "This lecture note surveys the Gamma matrix in general dimensions with arbitrary signature, the study of which is essential to study supersymmetry in the dimension."}
+{"idx": 6, "title": "Finding the scale factor and rotation angle of a matrix", "date": "", "ddg_snippet": "Here are some preliminary facts to recall, which we'll find useful when solving this problem: Every vector $\\vec {v}$ has magnitude and direction. When you apply a matrix $\\mathbf {M}$ and get $\\mathbf {M}\\vec {v}$, the resulting vector may have a new magnitude and/or a new direction. The vectors that have a new magnitude but keep the same direction are called the eigenvectors of $\\mathbf {M ...", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/3846913/finding-the-scale-factor-and-rotation-angle-of-a-matrix", "content": "Here are some preliminary facts to recall, which we'll find useful when solving this problem: Every vector $\\vec {v}$ has magnitude and direction. When you apply a matrix $\\mathbf {M}$ and get $\\mathbf {M}\\vec {v}$, the resulting vector may have a new magnitude and/or a new direction. The vectors that have a new magnitude but keep the same direction are called the eigenvectors of $\\mathbf {M ..."}
+{"idx": 7, "title": "Revisiting the directionality factor in ASCE 7 | Request PDF", "date": "", "ddg_snippet": "The wind directionality factor , K-d, is a nondimensional quantity smaller than unity that reflects the fact that the climatologically and aerodynamically or dynamically most unfavorable wind ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/265730912_Revisiting_the_directionality_factor_in_ASCE_7", "content": "The wind directionality factor , K-d, is a nondimensional quantity smaller than unity that reflects the fact that the climatologically and aerodynamically or dynamically most unfavorable wind ..."}
+{"idx": 8, "title": "Scaling of a random variable following the gamma distribution", "date": "", "ddg_snippet": "Index: The Book of Statistical Proofs Probability Distributions Univariate continuous distributions Gamma distribution Scaling of a gamma random variable Theorem: Let be a random variable following a gamma distribution with shape and rate : Then, the quantity will also be gamma -distributed with shape and rate :", "subpage_snippet": "", "source": "statproofbook.github.io", "link": "https://statproofbook.github.io/P/gam-scal.html", "content": "Index: The Book of Statistical Proofs Probability Distributions Univariate continuous distributions Gamma distribution Scaling of a gamma random variable Theorem: Let be a random variable following a gamma distribution with shape and rate : Then, the quantity will also be gamma -distributed with shape and rate :"}
+{"idx": 9, "title": "Directionality Factor | UpCodes", "date": "", "ddg_snippet": "Explore a searchable database of US construction and building code. Code regulations are consolidated by state and city for easier navigation.", "subpage_snippet": "", "source": "up.codes", "link": "https://up.codes/s/directionality-factor", "content": "Explore a searchable database of US construction and building code. Code regulations are consolidated by state and city for easier navigation."}
diff --git a/data/sampled_jsons/directionality_score_matrix_symmetry_transformer_Definition_3.2.jsonl b/data/sampled_jsons/directionality_score_matrix_symmetry_transformer_Definition_3.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e9b3bee72d6c830f73bc05df7c9d7fa4b8434afd
--- /dev/null
+++ b/data/sampled_jsons/directionality_score_matrix_symmetry_transformer_Definition_3.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "To test the applicability of our result, we first train 12-layer transformer models in both encoder and de-coder modes and quantify the median symmetry and directionality scores across epochs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10927", "content": "To test the applicability of our result, we first train 12-layer transformer models in both encoder and de-coder modes and quantify the median symmetry and directionality scores across epochs."}
+{"idx": 1, "title": "[draft] Note 10: Self-Attention & Transformers Transformer Model - Vismor DIRECTIONALITY IN GRAPH TRANSFORMERS - OpenReview The underlying structures of self-attention: symmetry ... Why Self-Attention is Natural for Sequence-to-Sequence ... Transformer Model - Vismor [draft] Note 10: Self- Attention & Transformers - Stanford University Why Self-Attention is Natural for Sequence-to-Sequence Problems? A P… Transformer Model - Vismor Why Self-Attention is Natural for Sequence-to-Sequence Problems? A P… [draft] Note 10: Self- Attention & Transformers - Stanford University arXiv:2502.10927v1 [cs.LG] 15 Feb 2025", "date": "", "ddg_snippet": "Attention, broadly construed, is a method for taking a query, and softly looking up information in a key-value store by picking the value(s) of the key(s) most like the query. By “picking” and “most like,” we mean averaging overall values, putting more weight on those which correspond to the keys more like the query. In self-attention, we mean that... See full list on web.stanford.edu Consider the sequence the oven cooked the bread so. This is a different sequence than the bread cooked the oven so, as you might guess. The for-mer sentence has us making delicious bread, and the latter we might interpret as the bread somehow breaking the oven. In a recurrent neural network, the order of the sequence defines the order of the rollou... See full list on web.stanford.edu dimension of the feed-forward network is substantially larger than the hidden dimension of the network, d—this is done because this matrix multiply is an eficiently parallelizable operation, so it’s an eficient place to put a lot of computation and parameters. See full list on web.stanford.edu The Transformer is an architecture based on self-attention that con-sists of stacked Blocks, each of which contains self-attention and feed-forward layers, and a few other components we’ll discuss. If you’d like to take a peek for intuition, we have a diagram of a Transformer language model architecture in Figure 4. The components we haven’t gone o... See full list on web.stanford.edu Figure 5: Diagram of the Transformer Encoder. Cross-Attention. Cross-attention uses one sequence to define the keys and values of self-attention, and another sequence to define the queries. You might think, hey wait, isn’t that just what attention always was before we got into this self-attention business? Yeah, pretty much. So if See full list on web.stanford.edu Abstract This document describes a transformer model that is useful for the analysis of large scale electric power systems. It defines a balanced transformer representation primarily intended for use with analytical techniques that are based on the complex nodal admittance matrix . Particular attention is paid to the integration with ”fast decoupled load flow” (FDLF) algorithms. ABSTRACT We study how one can capture directionality in graph transformers , for learning over directed graphs. Most existing graph transformers do not take edge direction into account. We therefore introduce a novel graph transformer architecture that explicitly takes into account the edge directionality . To achieve this, we make use of dual encodings to represent both potential roles, i.e ... Feb 15, 2025 · We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance. Abstract. In this paper, we show that structures similar to self-attention are natural for learning many sequ-ence-to-sequence problems from the perspective of symmetry . Inspired by language processing applications, we study the orthogonal equivariance of seq2seq functions with knowledge, which are functions taking two inputs – an input sequence and a knowledge – and outputting another ... What is a balanced transformer model? This document describes a transformer model that is useful for the analysis of large scale electric power systems . It defines a balanced transformer representation primarily intended for use with analytical techniques that are based on the complex nodal admittance matrix. Is a transformer a decoder-only model? While such an architecture has been found to provide better performance than decoder-only models at modest scale [Raffel et al., 2020], it involves splitting parameters between encoder and decoder, and most of the largest Transformers are decoder-only . What are the different types of symmetries? In this paper, various types of symmetries are mentioned , such as permutation equivariance and invariance [15, 36, 37, 39, 57], rotational equivariance and invariance [13, 17, 45, 46], and more [23, 40, 42, 52]. Some works deal with multiple symmetries. In , the forms of functions with various symmetries are studied. How does a transformer affect the nodal admittance matrix Ybus? In the context of modeling and analysis of electrical networks, the transfer admittances of a transformer can be thought of as the device’s impact on the of-diagonal elements of of the nodal admittance matrix Ybus. The preceding results suggest the computational sequence is not too crucial. Why are symmetries important in learning problems? Symmetries in learning problems are important because they reduce the complexity and inspire the invention of simple and efficient neural network structures. This is because a network with matching symmetries can learn the problems more efficiently. Why is layer normalization important in Transformers? One important learning aid in Transformers is layer normalization [Ba et al., 2016]. The intuition of layer norm is to reduce uninforma-tive variation in the activations at a layer, providing a more stable input to the next layer. d directionality scores across epochs. At initialization, the symmetry and directionality score of the ma-trix Wqk at any layer is zero (see Definition 3.1 and Definition 3", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs224n/readings/cs224n-self-attention-transformers-2023_draft.pdf", "content": "Attention, broadly construed, is a method for taking a query, and softly looking up information in a key-value store by picking the value(s) of the key(s) most like the query. By “picking” and “most like,” we mean averaging overall values, putting more weight on those which correspond to the keys more like the query. In self-attention, we mean that... See full list on web.stanford.edu Consider the sequence the oven cooked the bread so. This is a different sequence than the bread cooked the oven so, as you might guess. The for-mer sentence has us making delicious bread, and the latter we might interpret as the bread somehow breaking the oven. In a recurrent neural network, the order of the sequence defines the order of the rollou... See full list on web.stanford.edu dimension of the feed-forward network is substantially larger than the hidden dimension of the network, d—this is done because this matrix multiply is an eficiently parallelizable operation, so it’s an eficient place to put a lot of computation and parameters. See full list on web.stanford.edu The Transformer is an architecture based on self-attention that con-sists of stacked Blocks, each of which contains self-attention and feed-forward layers, and a few other components we’ll discuss. If you’d like to take a peek for intuition, we have a diagram of a Transformer language model architecture in Figure 4. The components we haven’t gone o... See full list on web.stanford.edu Figure 5: Diagram of the Transformer Encoder. Cross-Attention. Cross-attention uses one sequence to define the keys and values of self-attention, and another sequence to define the queries. You might think, hey wait, isn’t that just what attention always was before we got into this self-attention business? Yeah, pretty much. So if See full list on web.stanford.edu Abstract This document describes a transformer model that is useful for the analysis of large scale electric power systems. It defines a balanced transformer representation primarily intended for use with analytical techniques that are based on the complex nodal admittance matrix . Particular attention is paid to the integration with ”fast decoupled load flow” (FDLF) algorithms. ABSTRACT We study how one can capture directionality in graph transformers , for learning over directed graphs. Most existing graph transformers do not take edge direction into account. We therefore introduce a novel graph transformer architecture that explicitly takes into account the edge directionality . To achieve this, we make use of dual encodings to represent both potential roles, i.e ... Feb 15, 2025 · We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance. Abstract. In this paper, we show that structures similar to self-attention are natural for learning many sequ-ence-to-sequence problems from the perspective of symmetry . Inspired by language processing applications, we study the orthogonal equivariance of seq2seq functions with knowledge, which are functions taking two inputs – an input sequence and a knowledge – and outputting another ... What is a balanced transformer model? This document describes a transformer model that is useful for the analysis of large scale electric power systems . It defines a balanced transformer representation primarily intended for use with analytical techniques that are based on the complex nodal admittance matrix. Is a transformer a decoder-only model? While such an architecture has been found to provide better performance than decoder-only models at modest scale [Raffel et al., 2020], it involves splitting parameters between encoder and decoder, and most of the largest Transformers are decoder-only . What are the different types of symmetries? In this paper, various types of symmetries are mentioned , such as permutation equivariance and invariance [15, 36, 37, 39, 57], rotational equivariance and invariance [13, 17, 45, 46], and more [23, 40, 42, 52]. Some works deal with multiple symmetries. In , the forms of functions with various symmetries are studied. How does a transformer affect the nodal admittance matrix Ybus? In the context of modeling and analysis of electrical networks, the transfer admittances of a transformer can be thought of as the device’s impact on the of-diagonal elements of of the nodal admittance matrix Ybus. The preceding results suggest the computational sequence is not too crucial. Why are symmetries important in learning problems? Symmetries in learning problems are important because they reduce the complexity and inspire the invention of simple and efficient neural network structures. This is because a network with matching symmetries can learn the problems more efficiently. Why is layer normalization important in Transformers? One important learning aid in Transformers is layer normalization [Ba et al., 2016]. The intuition of layer norm is to reduce uninforma-tive variation in the activations at a layer, providing a more stable input to the next layer. d directionality scores across epochs. At initialization, the symmetry and directionality score of the ma-trix Wqk at any layer is zero (see Definition 3.1 and Definition 3"}
+{"idx": 2, "title": "Transformer Model - Vismor", "date": "", "ddg_snippet": "Abstract This document describes a transformer model that is useful for the analysis of large scale electric power systems. It defines a balanced transformer representation primarily intended for use with analytical techniques that are based on the complex nodal admittance matrix . Particular attention is paid to the integration with ”fast decoupled load flow” (FDLF) algorithms.", "subpage_snippet": "", "source": "vismor.com", "link": "https://vismor.com/download/Documents/Power_Systems/transformer_model.pdf", "content": "Abstract This document describes a transformer model that is useful for the analysis of large scale electric power systems. It defines a balanced transformer representation primarily intended for use with analytical techniques that are based on the complex nodal admittance matrix . Particular attention is paid to the integration with ”fast decoupled load flow” (FDLF) algorithms."}
+{"idx": 3, "title": "DIRECTIONALITY IN GRAPH TRANSFORMERS - OpenReview", "date": "", "ddg_snippet": "ABSTRACT We study how one can capture directionality in graph transformers , for learning over directed graphs. Most existing graph transformers do not take edge direction into account. We therefore introduce a novel graph transformer architecture that explicitly takes into account the edge directionality . To achieve this, we make use of dual encodings to represent both potential roles, i.e ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Yp01vcQSNl", "content": "ABSTRACT We study how one can capture directionality in graph transformers , for learning over directed graphs. Most existing graph transformers do not take edge direction into account. We therefore introduce a novel graph transformer architecture that explicitly takes into account the edge directionality . To achieve this, we make use of dual encodings to represent both potential roles, i.e ..."}
+{"idx": 4, "title": "The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "Feb 15, 2025 · We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10927", "content": "Feb 15, 2025 · We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance."}
+{"idx": 5, "title": "field theory - Gauge symmetry and Gauge Transforms - Physics", "date": "", "ddg_snippet": "What is the precise relationship between a non-invertible Hessian matrix for the Lagrangian and the presence of a gauge symmetry ?", "subpage_snippet": "", "source": "physics.stackexchange.com", "link": "https://physics.stackexchange.com/questions/745850/gauge-symmetry-and-gauge-transforms", "content": "What is the precise relationship between a non-invertible Hessian matrix for the Lagrangian and the presence of a gauge symmetry ?"}
+{"idx": 6, "title": "symmetry - Conceptual question about field transformation -", "date": "", "ddg_snippet": "... is defined to be the generator that transforms both the coordinates and the field as far as I understand (so it is the full generator of the symmetry ...", "subpage_snippet": "", "source": "physics.stackexchange.com", "link": "https://physics.stackexchange.com/questions/123316/conceptual-question-about-field-transformation", "content": "... is defined to be the generator that transforms both the coordinates and the field as far as I understand (so it is the full generator of the symmetry ..."}
+{"idx": 7, "title": "Newest 'symmetry' Questions - Stack Overflow", "date": "", "ddg_snippet": "As an instance I have a matrix like this: A = [1 2 ; 3 4] The matrix A is the quarter of a matrix , the full matrix B can be obtained by mirroring A on ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/tagged/symmetry", "content": "As an instance I have a matrix like this: A = [1 2 ; 3 4] The matrix A is the quarter of a matrix , the full matrix B can be obtained by mirroring A on ..."}
+{"idx": 8, "title": "condensed matter - 1+1d TSC as $Z_2^f $ symmetry breaking", "date": "", "ddg_snippet": "... transformation (Jordan-Wigner transformation) which relates the Kitaev superconductor to a quantum Ising chain, which does spontaneously break the ...", "subpage_snippet": "", "source": "physics.stackexchange.com", "link": "https://physics.stackexchange.com/questions/275830/11d-tsc-as-z-2f-symmetry-breaking-topological-order", "content": "... transformation (Jordan-Wigner transformation) which relates the Kitaev superconductor to a quantum Ising chain, which does spontaneously break the ..."}
+{"idx": 9, "title": "quantum mechanics - Why symmetry transformations have to", "date": "", "ddg_snippet": "For instance, if $|\\psi \\rangle$ has definite energy $E$, then so does $U(\\theta) |\\psi \\rangle.)$ This is 3 . ... symmetries must commute with the ...", "subpage_snippet": "", "source": "physics.stackexchange.com", "link": "https://physics.stackexchange.com/questions/477741/why-symmetry-transformations-have-to-commute-with-hamiltonian", "content": "For instance, if $|\\psi \\rangle$ has definite energy $E$, then so does $U(\\theta) |\\psi \\rangle.)$ This is 3 . ... symmetries must commute with the ..."}
diff --git a/data/sampled_jsons/earthstrike_extinctionrebellion_fridaysforfuture_sunrisemovement_sierraclub_climatechange_reddit_act.jsonl b/data/sampled_jsons/earthstrike_extinctionrebellion_fridaysforfuture_sunrisemovement_sierraclub_climatechange_reddit_act.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d0002e693c17bc27bbfdf1a322b960eafb389fbc
--- /dev/null
+++ b/data/sampled_jsons/earthstrike_extinctionrebellion_fridaysforfuture_sunrisemovement_sierraclub_climatechange_reddit_act.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Earth Strike - Wikipedia", "date": "", "ddg_snippet": "Earth Strike was founded on 10 November 2018 after a user on the subreddit r/Chomsky called for a \"General Strike to Save The Planet\". [citation needed] The post quickly gathered attention within Reddit , and the r/EarthStrike subreddit was formed to organise a general strike. [citation needed] The initial protests were held on 15 January 2019, with 27 September being announced as the date for ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Earth_Strike", "content": "Earth Strike was founded on 10 November 2018 after a user on the subreddit r/Chomsky called for a \"General Strike to Save The Planet\". [citation needed] The post quickly gathered attention within Reddit , and the r/EarthStrike subreddit was formed to organise a general strike. [citation needed] The initial protests were held on 15 January 2019, with 27 September being announced as the date for ..."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — The Extinction Rebellion is a lawfully abiding direct action movement challenging inaction over dangerous climate change .” • r/ EarthStrike : “ Earth Strike is a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — The Extinction Rebellion is a lawfully abiding direct action movement challenging inaction over dangerous climate change .” • r/ EarthStrike : “ Earth Strike is a ..."}
+{"idx": 2, "title": "fridaysForFuture : r/EarthStrike - Reddit", "date": "", "ddg_snippet": "1.2K votes, 31 comments. 22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/comments/b9bplk/fridaysforfuture/", "content": "1.2K votes, 31 comments. 22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on…"}
+{"idx": 3, "title": "We need your support - Open letter by #FridaysForFuture #Rojava - Reddit", "date": "", "ddg_snippet": "212 votes, 35 comments. 22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/comments/dhd3uv/we_need_your_support_open_letter_by/", "content": "212 votes, 35 comments. 22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on…"}
+{"idx": 4, "title": "FridaysForFuture - Statement in solidarity with Rojava - Reddit", "date": "", "ddg_snippet": "260 votes, 22 comments. 22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/comments/dj6q67/fridaysforfuture_statement_in_solidarity_with/", "content": "260 votes, 22 comments. 22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on…"}
+{"idx": 5, "title": "September 25 - Fridays for Future Announces Next Global ... - Reddit", "date": "", "ddg_snippet": "September 25 - Fridays for Future Announces Next Global Climate Strike Date", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/hy21ki/september_25_fridays_for_future_announces_next/", "content": "September 25 - Fridays for Future Announces Next Global Climate Strike Date"}
+{"idx": 6, "title": "#Fridaysforfuture changed me : r/EarthStrike - Reddit", "date": "", "ddg_snippet": "Earth Strike is a grassroots labour-environmental movement focused on organising a GLOBAL GENERAL STRIKE TO SAVE THE PLANET!", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/comments/b2mres/fridaysforfuture_changed_me/", "content": "Earth Strike is a grassroots labour-environmental movement focused on organising a GLOBAL GENERAL STRIKE TO SAVE THE PLANET!"}
+{"idx": 7, "title": "Fridays For Future", "date": "", "ddg_snippet": "# FridaysForFuture is a youth-led and -organised global climate strike movement that started in August 2018, when [then] 15-year-old Greta Thunberg began a school strike for climate. To begin with, she was alone, but she was soon joined by others...", "subpage_snippet": "", "source": "fridaysforfuture.org", "link": "https://fridaysforfuture.org/", "content": "# FridaysForFuture is a youth-led and -organised global climate strike movement that started in August 2018, when [then] 15-year-old Greta Thunberg began a school strike for climate. To begin with, she was alone, but she was soon joined by others..."}
+{"idx": 8, "title": "Earth Strike | Encyclopedia MDPI", "date": "", "ddg_snippet": "Earth Strike is an international grassroots movement that called for a global general strike for climate action. Their aim was a global general strike lasting from 20 until 27 September 2019. The movement has had public support from organizations including Extinction Rebellion and Fridays for Future, as well as public figures including Noam Chomsky. The Earth Strikes were part of the worldwide ...", "subpage_snippet": "", "source": "encyclopedia.pub", "link": "https://encyclopedia.pub/entry/31122", "content": "Earth Strike is an international grassroots movement that called for a global general strike for climate action. Their aim was a global general strike lasting from 20 until 27 September 2019. The movement has had public support from organizations including Extinction Rebellion and Fridays for Future, as well as public figures including Noam Chomsky. The Earth Strikes were part of the worldwide ..."}
+{"idx": 9, "title": "Framing different energy futures? Comparing Fridays for Future and ...", "date": "", "ddg_snippet": "Combining research on sociotechnical imaginaries and social movements, this contribution examines how two major actors of the climate justice movement active in Germany - Fridays for Future and Extinction Rebellion - frame the discourse on climate change and just transitions.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0016328722000040", "content": "Combining research on sociotechnical imaginaries and social movements, this contribution examines how two major actors of the climate justice movement active in Germany - Fridays for Future and Extinction Rebellion - frame the discourse on climate change and just transitions."}
diff --git a/data/sampled_jsons/ellipsoidal_confidence_set_computation_cost_matrix_inversion_bandits.jsonl b/data/sampled_jsons/ellipsoidal_confidence_set_computation_cost_matrix_inversion_bandits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7df289d6bfd2f41152febea5ef614df07d0fbbd5
--- /dev/null
+++ b/data/sampled_jsons/ellipsoidal_confidence_set_computation_cost_matrix_inversion_bandits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stochastic Linear Bandits and UCB – Bandit Algorithms", "date": "", "ddg_snippet": "We use this setting to motivate the introduction of stochastic linear bandits , a fascinatingly rich model with much structure and which will be the topic of a few of the next posts.Zeyad on Ellipsoidal Confidence Sets for Least-Squares Estimators.", "subpage_snippet": "", "source": "banditalgs.com", "link": "https://banditalgs.com/2016/10/19/stochastic-linear-bandits/", "content": "We use this setting to motivate the introduction of stochastic linear bandits , a fascinatingly rich model with much structure and which will be the topic of a few of the next posts.Zeyad on Ellipsoidal Confidence Sets for Least-Squares Estimators."}
+{"idx": 1, "title": "Confidence interval of multivariate gaussian distribution", "date": "", "ddg_snippet": "I want to actually get the confidence interval of gaussian distribution.Given $n$ and $k$, you can look up the $1-\\alpha$ critical value of $T^2$, and that will define the \"radius\" of the ellipsoidal confidence region.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/29860/confidence-interval-of-multivariate-gaussian-distribution", "content": "I want to actually get the confidence interval of gaussian distribution.Given $n$ and $k$, you can look up the $1-\\alpha$ critical value of $T^2$, and that will define the \"radius\" of the ellipsoidal confidence region."}
+{"idx": 2, "title": "Linear Stochastic Bandits over a Bit-Constrained Channel", "date": "", "ddg_snippet": "Specifically, at each time-step, an ellipsoidal confidence set is constructed that contains θ∗ with. high-probability. The learner then acts optimistically by playing an action that yields the highest. reward over all possible values of θ in the confidence set .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v211/mitra23a/mitra23a.pdf", "content": "Specifically, at each time-step, an ellipsoidal confidence set is constructed that contains θ∗ with. high-probability. The learner then acts optimistically by playing an action that yields the highest. reward over all possible values of θ in the confidence set ."}
+{"idx": 3, "title": "Generalized Linear Bandits : Almost Optimal Regret with One-Pass...", "date": "", "ddg_snippet": "Based on the ellipsoidal confidence set , one can employ a.In this formulation, the first step (42a) is a gradient update, whose main computational cost lies in computing the inverse of the Hessian matrix .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.11847", "content": "Based on the ellipsoidal confidence set , one can employ a.In this formulation, the first step (42a) is a gradient update, whose main computational cost lies in computing the inverse of the Hessian matrix ."}
+{"idx": 4, "title": "Conformal prediction for multi-dimensional time series by ellipsoidal ...", "date": "", "ddg_snippet": "Right (c): our proposed ellipsoidal confidence set via MultiDimSPCI.Our MultiDimSPCI yields the narrowest confidence sets without sacrificing coverage for two reasons. First, it explicitly captures dependency among coordinates of Yt by forming ellipsoidal prediction sets .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=uN39Tt9P8b", "content": "Right (c): our proposed ellipsoidal confidence set via MultiDimSPCI.Our MultiDimSPCI yields the narrowest confidence sets without sacrificing coverage for two reasons. First, it explicitly captures dependency among coordinates of Yt by forming ellipsoidal prediction sets ."}
+{"idx": 5, "title": "Likelihood Ratio Confidence Sets for", "date": "", "ddg_snippet": "Online-to- confidence - set conversions and application to sparse stochastic bandits .The Laplace method creates an ellipsoidal confidence set using the second-order information evaluated at the penalized maximum likelihood estimator.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/5491280797f3192b895bce84eb83df8d-Supplemental-Conference.pdf", "content": "Online-to- confidence - set conversions and application to sparse stochastic bandits .The Laplace method creates an ellipsoidal confidence set using the second-order information evaluated at the penalized maximum likelihood estimator."}
+{"idx": 6, "title": "3D Confidence Ellipsoid - File Exchange - OriginLab", "date": "", "ddg_snippet": "Create a 3D confidence ellipsoid plot for each group level when the levels (3D scatter plots) are color indexed. Output principal axes and equations for 3D confidence ellipsoid . Installation Download the file Plot3dConfEllipsoid.opx, and then drag-and-drop onto the Origin workspace.", "subpage_snippet": "", "source": "www.originlab.com", "link": "https://www.originlab.com/fileExchange/details.aspx?fid=280", "content": "Create a 3D confidence ellipsoid plot for each group level when the levels (3D scatter plots) are color indexed. Output principal axes and equations for 3D confidence ellipsoid . Installation Download the file Plot3dConfEllipsoid.opx, and then drag-and-drop onto the Origin workspace."}
+{"idx": 7, "title": "(PDF) Variance-Aware Confidence Set : Variance-Dependent Bound...", "date": "", "ddg_snippet": "PDF | We show how to construct variance-aware confidence sets for linear bandits and linear mixture Markov Decision Process (MDP).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348928140_Variance-Aware_Confidence_Set_Variance-Dependent_Bound_for_Linear_Bandits_and_Horizon-Free_Bound_for_Linear_Mixture_MDP", "content": "PDF | We show how to construct variance-aware confidence sets for linear bandits and linear mixture Markov Decision Process (MDP)."}
+{"idx": 8, "title": "Matrix Inverse Calculator - eMathHelp", "date": "", "ddg_snippet": "To find the inverse matrix , augment it with the identity matrix and perform row operations trying to make the identity matrix to the left.", "subpage_snippet": "", "source": "www.emathhelp.net", "link": "https://www.emathhelp.net/calculators/linear-algebra/inverse-of-matrix-calculator/", "content": "To find the inverse matrix , augment it with the identity matrix and perform row operations trying to make the identity matrix to the left."}
+{"idx": 9, "title": "Confidence Ellipsoid from Eigenvalues - MATLAB Answers...", "date": "", "ddg_snippet": "Learn more about ellipsoid , confidence interval.I am trying to create a 95% Confidence Ellipsoid for a set of data points. For an ellipse I realized it by solving the eigenvalue problem for the covariance matrix of the 2D data points.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/matlabcentral/answers/299648-confidence-ellipsoid-from-eigenvalues", "content": "Learn more about ellipsoid , confidence interval.I am trying to create a 95% Confidence Ellipsoid for a set of data points. For an ellipse I realized it by solving the eigenvalue problem for the covariance matrix of the 2D data points."}
diff --git a/data/sampled_jsons/entity_attention_cross_attention_diffusion_inpainting_mechanism_year_2024.jsonl b/data/sampled_jsons/entity_attention_cross_attention_diffusion_inpainting_mechanism_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6165cdc0b598c7ae4317fbeb365ddbbbac842d93
--- /dev/null
+++ b/data/sampled_jsons/entity_attention_cross_attention_diffusion_inpainting_mechanism_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UNCAGE: Contrastive Attention Guidance for Masked Generative", "date": "", "ddg_snippet": "MGTs, like most Diffusion Models, utilize attention -based architectures, which can lead to misaligned attribute binding due to inaccurate attention ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05399v1", "content": "MGTs, like most Diffusion Models, utilize attention -based architectures, which can lead to misaligned attribute binding due to inaccurate attention ..."}
+{"idx": 1, "title": "V-SEAM: Visual Semantic Editing and Attention Modulating for", "date": "", "ddg_snippet": "... cross -modal alignment in early layers ... One group employs cross - attention mechanisms to embed visual features into textual ones Alayrac et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14837v1", "content": "... cross -modal alignment in early layers ... One group employs cross - attention mechanisms to embed visual features into textual ones Alayrac et al."}
+{"idx": 2, "title": "Sealing The Backdoor: Unlearning Adversarial Text Triggers In", "date": "", "ddg_snippet": "Using the cross - attention mechanism , SKD-CAG neutralizes backdoor influences at the attention level, ensuring the targeted removal of adversarial ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18235v1", "content": "Using the cross - attention mechanism , SKD-CAG neutralizes backdoor influences at the attention level, ensuring the targeted removal of adversarial ..."}
+{"idx": 3, "title": "Video World Models with Long-term Spatial Memory", "date": "", "ddg_snippet": "... new frames—a problem primarily caused by the quadratic growth of computational complexity in the attention module of the underlying diffusion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05284v1", "content": "... new frames—a problem primarily caused by the quadratic growth of computational complexity in the attention module of the underlying diffusion ..."}
+{"idx": 4, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Navigating the Design Space of Equivariant Diffusion -Based Generative Models for De Novo 3D Molecule Generation ... Linear attention is (maybe) all ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Navigating the Design Space of Equivariant Diffusion -Based Generative Models for De Novo 3D Molecule Generation ... Linear attention is (maybe) all ..."}
+{"idx": 5, "title": "ICLR 2024 Papers", "date": "", "ddg_snippet": "NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/papers.html", "content": "NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation"}
+{"idx": 6, "title": "CVPR 2024 Papers", "date": "", "ddg_snippet": "... Diffusion : Predicate Logic-Based ... Draw Step by Step: Reconstructing CAD Construction Sequences from Point Clouds via Multimodal Diffusion .", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/papers.html", "content": "... Diffusion : Predicate Logic-Based ... Draw Step by Step: Reconstructing CAD Construction Sequences from Point Clouds via Multimodal Diffusion ."}
+{"idx": 7, "title": "Yuchao Gu", "date": "", "ddg_snippet": "... attention injections, we develop two attention -based constraints: a self- attention (SA) preservation constraint for structural fidelity, and a cross ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Yuchao+Gu", "content": "... attention injections, we develop two attention -based constraints: a self- attention (SA) preservation constraint for structural fidelity, and a cross ..."}
+{"idx": 8, "title": "Paper Digest: ICASSP 2025 Papers & Highlights –", "date": "", "ddg_snippet": "Attention Disentanglement for Semantic Diffusion Modeling in Text-to-Image Generation Related Papers Related Patents Related Grants Related Venues ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/04/icassp-2025-papers-highlights/", "content": "Attention Disentanglement for Semantic Diffusion Modeling in Text-to-Image Generation Related Papers Related Patents Related Grants Related Venues ..."}
+{"idx": 9, "title": "U.S. Patent Application for ENHANCING NEURAL NETWORK TRAINING", "date": "", "ddg_snippet": "5 A illustrates a diffusion model (DM) architecture, according to at ... In at least one embodiment, the neural network is a diffusion model (DM).", "subpage_snippet": "", "source": "patents.justia.com", "link": "https://patents.justia.com/patent/20250111245", "content": "5 A illustrates a diffusion model (DM) architecture, according to at ... In at least one embodiment, the neural network is a diffusion model (DM)."}
diff --git a/data/sampled_jsons/entropy-based_uncertainty_estimation_open-world_SSL_adaptive_clustering_without_prior_distribution.jsonl b/data/sampled_jsons/entropy-based_uncertainty_estimation_open-world_SSL_adaptive_clustering_without_prior_distribution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e3d8ec2a1bd11561144eaddec37656f5339c976
--- /dev/null
+++ b/data/sampled_jsons/entropy-based_uncertainty_estimation_open-world_SSL_adaptive_clustering_without_prior_distribution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Student's t- distribution - Wikipedia", "date": "", "ddg_snippet": "In probability theory and statistics, Student's t distribution . is a continuous probability distribution that generalizes the standard normal distribution . Like the latter, it is symmetric around zero and bell-shaped.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Student's_t-distribution", "content": "In probability theory and statistics, Student's t distribution . is a continuous probability distribution that generalizes the standard normal distribution . Like the latter, it is symmetric around zero and bell-shaped."}
+{"idx": 1, "title": "Open-World Semi-Supervised Learning - arXiv.org", "date": "", "ddg_snippet": "To address the challenges of open-world SSL , we propose ORCA ( Open-woRld with unCertainty based Adaptive mar-gin), an approach that effectively assigns examples from the unlabeled data to either previously seen classes, or forms a novel class/cluster by grouping similar examples in an end-to-end deep learning framework.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2102.03526v1", "content": "To address the challenges of open-world SSL , we propose ORCA ( Open-woRld with unCertainty based Adaptive mar-gin), an approach that effectively assigns examples from the unlabeled data to either previously seen classes, or forms a novel class/cluster by grouping similar examples in an end-to-end deep learning framework."}
+{"idx": 2, "title": "ENSTA-U2IS-AI/awesome-uncertainty-deeplearning - GitHub Belief Entropy-based Uncertainty Analysis - IEEE Xplore Entropy-based Optimization on Individual and Global ... Beyond First-Order Uncertainty Estimation with Evidential ... Entropy-Based Uncertainty Management Methods in Deep Learning Belief Entropy - based Uncertainty Analysis - IEEE Xplore Open-World Semi-Supervised Learning - arXiv.org OPEN - WORLD SEMI-SUPERVISED LEARNING - Computer Science OPEN - WORLD SEMI-SUPERVISED LEARNING - Computer Science Open-World Semi-Supervised Learning - arXiv.org Open-World Semi-Supervised Learning - arXiv.org OPEN-WORLD SEMI-SUPERVISED LEARNING - Computer Science", "date": "", "ddg_snippet": "This repo is a collection of awesome papers, codes, books, and blogs about Uncertainty and Deep learning. Feel free to star and fork. If you think that we miss a paper, please open a pull request or send a message on the corresponding GitHub discussion. Tell us where the article was published and when, and send us GitHub and ArXiv links if they are available. See full list on github.com See full list on github.com Conference •A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications [AISafety Workshop 2020] Journal •A survey of uncertainty in deep neural networks [Artificial Intelligence Review 2023] - [GitHub] • Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation [TMLR2023] •A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective [ACM2021] See full list on github.com Conference •Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning [ICLR2023] •Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? [ICLR2023] •Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs [ICML2023] - [PyTorch] •On Second-Order Scoring Rules for Epistemic Uncertainty Quantification [ICML2023] •Neural Variational Gradient Descent [AABI2022] See full list on github.com Conference •A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors [ICLR2024] •Gradient- based Uncertainty Attribution for Explainable Bayesian Deep Learning [CVPR2023] •Robustness to corruption in pre-trained Bayesian neural networks [ICLR2023] •Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift [NeurIPS2023] - [PyTorch] •Transformers Can Do Bayesian Inference [ICLR2022] - [PyTorch] See full list on github.com Conference •Model Ratatouille: Recycling Diverse Models for Out-of- Distribution Generalization [ICML2023] •Bayesian Posterior Approximation With Stochastic Ensembles [CVPR2023] •Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling [AAAI2023] •Window- Based Early-Exit Cascades for Uncertainty Estimation : When Deep Ensembles are More Efficient than Single Models [ICCV2023] - [PyTorch] •Weighted Ensemble Self-Supervised Learning [ICLR2023] See full list on github.com Conference •Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate [AAAI2022] •Efficient Bayesian Uncertainty Estimation for nnU-Net [MICCAI2022] •Dropout Sampling for Robust Object Detection in Open -Set Conditions [ICRA2018] •Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks [MIDL2018] •Concrete Dropout [NeurIPS2017] See full list on github.com Conference •Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression [AAAI2024] - [PyTorch] •Post-hoc Uncertainty Learning using a Dirichlet Meta-Model [AAAI2023] - [PyTorch] •ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models [ICCV2023] •Out-of- Distribution Detection for Monocular Depth Estimation [ICCV2023] •Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model [AAAI2022] See full list on github.com Conference •Learning to Generate Training Datasets for Robust Semantic Segmentation [WACV2024] •OpenMix: Exploring Outlier Samples for Misclassification Detection [CVPR2023] - [PyTorch] •On the Pitfall of Mixup for Uncertainty Calibration [CVPR2023] •Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates [AAAI2022] •PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures [CVPR2022] See full list on github.com Conference •Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression [AAAI2024] - [PyTorch] •The Unreasonable Effectiveness of Deep Evidential Regression [AAAI2023] - [PyTorch] - [TorchUncertainty] •Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification [CVPR2023] •Plausible Uncertainties for Human Pose Regression [ICCV2023] - [PyTorch] • Uncertainty Estimation by Fisher Information- based Evidential Deep Learning [ICML2023] - [PyTorch] See full list on github.com Jun 8, 2023 · Based on uncertainty theory, a new method — Belief Entropy-based Uncertainty Analysis (BEUA) is proposed, which uses belief degree distribution to characterize uncertainty and belief entropy (BE) to quantify the extent of uncertainty . Oct 27, 2023 · Specifically, we propose two criteria for leveraging unlabeled data in SSL : individual prediction entropy minimization (IPEM) and global distribution entropy maximization (GDEM). On the one hand, we show that current dominant SSL methods can be viewed as an implicit form of IPEM improved by recent augmentation techniques. Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition Table 3. Comparative experiments on CIFAR-10 and CIFAR-100 with ResNet-18 architectures. Feb 29, 2024 · However, managing and quantifying different types of uncertainty in machine learning/deep learning has not received much attention as of yet. While neural network technologies dominate deep learning, entropy-based approaches dominate the uncertainty management domain. What is belief entropy-based uncertainty analysis? Based on uncertainty theory, a new method — Belief Entropy - based Uncertainty Analysis (BEUA) is proposed, which uses belief degree distribution to characterize uncertainty and belief entropy (BE) to quantify the extent of uncertainty . What is entropy regularization in SSL? This term corresponds to the minimum entropy regularization used in SSL (Grand-valet & Bengio, 2005; Lee, 2013) to prevent the class dis-tribution from being too flat. We define it more generally using KL-divergence, so that if the prior over the classes is known, we can use it instead of the uniform distribution . 3.6. Self-Supervised Pretraining How do we adapt SSL to the open-world setting? In this way we adapt two SSL methods to the open-world SSL setting: Deep Safe SSL (DS3L) (Guo et al., 2020) and FixMatch (Sohn et al., 2020), and recent deep learning OSR method CGDL (Sun et al., 2020a). CGDL automatically rejects OOD samples. DS3L considers novel classes in the unlabeled data by assigning low weights to OOD samples. What is the difference between robust SSL and open-world SSL? The goal in robust SSL is to reject instances from novel classes which are treated as out-of- distribution instances. Instead of rejecting instances from novel classes, in open - world SSL the goal is to discover individual novel classes and then assign instances to them. How to solve open-world SSL task? To solve open-world SSL task, ORCA combines super-vised objective computed on the labeled data and pairwise objective that is used to gradually generate pseudo-labels for the unlabeled set. However, naively combining super-vised and pairwise objectives leads to the bias towards seen classes which reduces the ability to adapt to novel classes. Does uncertainty based adaptive margin affect the quality of generated pseudo-labels? To evaluate the effect of uncertainty based adaptive margin on the quality of generated pseudo-labels during training, we compare the accuracy of adaptive mar-gin to baseline approach with zero margin and fixed negative margin adaptation on the CIFAR-100 dataset. In this paper we propose ORCA ( Open-woRld with unCertainty based Adaptive margin) that operates under the novel open-world SSL setting. ORCA effectively assigns examples from the unlabeled data to either previously seen classes, or forms novel classes by grouping similar instances.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ENSTA-U2IS-AI/awesome-uncertainty-deeplearning", "content": "This repo is a collection of awesome papers, codes, books, and blogs about Uncertainty and Deep learning. Feel free to star and fork. If you think that we miss a paper, please open a pull request or send a message on the corresponding GitHub discussion. Tell us where the article was published and when, and send us GitHub and ArXiv links if they are available. See full list on github.com See full list on github.com Conference •A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications [AISafety Workshop 2020] Journal •A survey of uncertainty in deep neural networks [Artificial Intelligence Review 2023] - [GitHub] • Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation [TMLR2023] •A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective [ACM2021] See full list on github.com Conference •Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning [ICLR2023] •Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? [ICLR2023] •Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs [ICML2023] - [PyTorch] •On Second-Order Scoring Rules for Epistemic Uncertainty Quantification [ICML2023] •Neural Variational Gradient Descent [AABI2022] See full list on github.com Conference •A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors [ICLR2024] •Gradient- based Uncertainty Attribution for Explainable Bayesian Deep Learning [CVPR2023] •Robustness to corruption in pre-trained Bayesian neural networks [ICLR2023] •Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift [NeurIPS2023] - [PyTorch] •Transformers Can Do Bayesian Inference [ICLR2022] - [PyTorch] See full list on github.com Conference •Model Ratatouille: Recycling Diverse Models for Out-of- Distribution Generalization [ICML2023] •Bayesian Posterior Approximation With Stochastic Ensembles [CVPR2023] •Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling [AAAI2023] •Window- Based Early-Exit Cascades for Uncertainty Estimation : When Deep Ensembles are More Efficient than Single Models [ICCV2023] - [PyTorch] •Weighted Ensemble Self-Supervised Learning [ICLR2023] See full list on github.com Conference •Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate [AAAI2022] •Efficient Bayesian Uncertainty Estimation for nnU-Net [MICCAI2022] •Dropout Sampling for Robust Object Detection in Open -Set Conditions [ICRA2018] •Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks [MIDL2018] •Concrete Dropout [NeurIPS2017] See full list on github.com Conference •Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression [AAAI2024] - [PyTorch] •Post-hoc Uncertainty Learning using a Dirichlet Meta-Model [AAAI2023] - [PyTorch] •ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models [ICCV2023] •Out-of- Distribution Detection for Monocular Depth Estimation [ICCV2023] •Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model [AAAI2022] See full list on github.com Conference •Learning to Generate Training Datasets for Robust Semantic Segmentation [WACV2024] •OpenMix: Exploring Outlier Samples for Misclassification Detection [CVPR2023] - [PyTorch] •On the Pitfall of Mixup for Uncertainty Calibration [CVPR2023] •Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates [AAAI2022] •PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures [CVPR2022] See full list on github.com Conference •Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression [AAAI2024] - [PyTorch] •The Unreasonable Effectiveness of Deep Evidential Regression [AAAI2023] - [PyTorch] - [TorchUncertainty] •Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification [CVPR2023] •Plausible Uncertainties for Human Pose Regression [ICCV2023] - [PyTorch] • Uncertainty Estimation by Fisher Information- based Evidential Deep Learning [ICML2023] - [PyTorch] See full list on github.com Jun 8, 2023 · Based on uncertainty theory, a new method — Belief Entropy-based Uncertainty Analysis (BEUA) is proposed, which uses belief degree distribution to characterize uncertainty and belief entropy (BE) to quantify the extent of uncertainty . Oct 27, 2023 · Specifically, we propose two criteria for leveraging unlabeled data in SSL : individual prediction entropy minimization (IPEM) and global distribution entropy maximization (GDEM). On the one hand, we show that current dominant SSL methods can be viewed as an implicit form of IPEM improved by recent augmentation techniques. Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition Table 3. Comparative experiments on CIFAR-10 and CIFAR-100 with ResNet-18 architectures. Feb 29, 2024 · However, managing and quantifying different types of uncertainty in machine learning/deep learning has not received much attention as of yet. While neural network technologies dominate deep learning, entropy-based approaches dominate the uncertainty management domain. What is belief entropy-based uncertainty analysis? Based on uncertainty theory, a new method — Belief Entropy - based Uncertainty Analysis (BEUA) is proposed, which uses belief degree distribution to characterize uncertainty and belief entropy (BE) to quantify the extent of uncertainty . What is entropy regularization in SSL? This term corresponds to the minimum entropy regularization used in SSL (Grand-valet & Bengio, 2005; Lee, 2013) to prevent the class dis-tribution from being too flat. We define it more generally using KL-divergence, so that if the prior over the classes is known, we can use it instead of the uniform distribution . 3.6. Self-Supervised Pretraining How do we adapt SSL to the open-world setting? In this way we adapt two SSL methods to the open-world SSL setting: Deep Safe SSL (DS3L) (Guo et al., 2020) and FixMatch (Sohn et al., 2020), and recent deep learning OSR method CGDL (Sun et al., 2020a). CGDL automatically rejects OOD samples. DS3L considers novel classes in the unlabeled data by assigning low weights to OOD samples. What is the difference between robust SSL and open-world SSL? The goal in robust SSL is to reject instances from novel classes which are treated as out-of- distribution instances. Instead of rejecting instances from novel classes, in open - world SSL the goal is to discover individual novel classes and then assign instances to them. How to solve open-world SSL task? To solve open-world SSL task, ORCA combines super-vised objective computed on the labeled data and pairwise objective that is used to gradually generate pseudo-labels for the unlabeled set. However, naively combining super-vised and pairwise objectives leads to the bias towards seen classes which reduces the ability to adapt to novel classes. Does uncertainty based adaptive margin affect the quality of generated pseudo-labels? To evaluate the effect of uncertainty based adaptive margin on the quality of generated pseudo-labels during training, we compare the accuracy of adaptive mar-gin to baseline approach with zero margin and fixed negative margin adaptation on the CIFAR-100 dataset. In this paper we propose ORCA ( Open-woRld with unCertainty based Adaptive margin) that operates under the novel open-world SSL setting. ORCA effectively assigns examples from the unlabeled data to either previously seen classes, or forms novel classes by grouping similar instances."}
+{"idx": 3, "title": "Belief Entropy-based Uncertainty Analysis - IEEE Xplore", "date": "", "ddg_snippet": "Jun 8, 2023 · Based on uncertainty theory, a new method — Belief Entropy-based Uncertainty Analysis (BEUA) is proposed, which uses belief degree distribution to characterize uncertainty and belief entropy (BE) to quantify the extent of uncertainty .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10143880", "content": "Jun 8, 2023 · Based on uncertainty theory, a new method — Belief Entropy-based Uncertainty Analysis (BEUA) is proposed, which uses belief degree distribution to characterize uncertainty and belief entropy (BE) to quantify the extent of uncertainty ."}
+{"idx": 4, "title": "Entropy-based Optimization on Individual and Global ...", "date": "", "ddg_snippet": "Oct 27, 2023 · Specifically, we propose two criteria for leveraging unlabeled data in SSL : individual prediction entropy minimization (IPEM) and global distribution entropy maximization (GDEM). On the one hand, we show that current dominant SSL methods can be viewed as an implicit form of IPEM improved by recent augmentation techniques.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3581783.3612567", "content": "Oct 27, 2023 · Specifically, we propose two criteria for leveraging unlabeled data in SSL : individual prediction entropy minimization (IPEM) and global distribution entropy maximization (GDEM). On the one hand, we show that current dominant SSL methods can be viewed as an implicit form of IPEM improved by recent augmentation techniques."}
+{"idx": 5, "title": "Beyond First-Order Uncertainty Estimation with Evidential ...", "date": "", "ddg_snippet": "Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition Table 3. Comparative experiments on CIFAR-10 and CIFAR-100 with ResNet-18 architectures.", "subpage_snippet": "", "source": "www.gatsby.ucl.ac.uk", "link": "https://www.gatsby.ucl.ac.uk/~balaji/udl2021/accepted-papers/UDL2021-paper-062.pdf", "content": "Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition Table 3. Comparative experiments on CIFAR-10 and CIFAR-100 with ResNet-18 architectures."}
+{"idx": 6, "title": "Entropy-Based Uncertainty Management Methods in Deep Learning", "date": "", "ddg_snippet": "Feb 29, 2024 · However, managing and quantifying different types of uncertainty in machine learning/deep learning has not received much attention as of yet. While neural network technologies dominate deep learning, entropy-based approaches dominate the uncertainty management domain.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/journal/entropy/special_issues/DLD5C36727", "content": "Feb 29, 2024 · However, managing and quantifying different types of uncertainty in machine learning/deep learning has not received much attention as of yet. While neural network technologies dominate deep learning, entropy-based approaches dominate the uncertainty management domain."}
+{"idx": 7, "title": "OPEN-WORLD SEMI-SUPERVISED LEARNING - Computer Science", "date": "", "ddg_snippet": "In this paper we propose ORCA ( Open-woRld with unCertainty based Adaptive margin) that operates under the novel open-world SSL setting. ORCA effectively assigns examples from the unlabeled data to either previously seen classes, or forms novel classes by grouping similar instances.", "subpage_snippet": "", "source": "cs.stanford.edu", "link": "https://cs.stanford.edu/people/jure/pubs/orca-iclr22.pdf", "content": "In this paper we propose ORCA ( Open-woRld with unCertainty based Adaptive margin) that operates under the novel open-world SSL setting. ORCA effectively assigns examples from the unlabeled data to either previously seen classes, or forms novel classes by grouping similar instances."}
+{"idx": 8, "title": "(PDF) Dimensionally Reduced Open - World Clustering : DROWCULA", "date": "", "ddg_snippet": "Dimensionally Reduced Open-W orld Clustering NCD in Open - World SSL setting is useful in some scenarios, ne v", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395388315_Dimensionally_Reduced_Open-World_Clustering_DROWCULA", "content": "Dimensionally Reduced Open-W orld Clustering NCD in Open - World SSL setting is useful in some scenarios, ne v"}
+{"idx": 9, "title": "Open - world barely-supervised learning via augmented pseudo labels", "date": "", "ddg_snippet": "Open - world SSL (OWSSL) was proposed, assuming that the unlabeled dataset not only contains the classes of labeled data, but also existing unseen categories that have never been seen in labeled datasets.", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/aimspress-data/era/2024/10/PDF/era-32-10-268.pdf", "content": "Open - world SSL (OWSSL) was proposed, assuming that the unlabeled dataset not only contains the classes of labeled data, but also existing unseen categories that have never been seen in labeled datasets."}
diff --git a/data/sampled_jsons/error_consistency_metric_Geirhos_2020_neural_networks.jsonl b/data/sampled_jsons/error_consistency_metric_Geirhos_2020_neural_networks.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Quantifying Uncertainty in Error Consistency : Towards Reliable...", "date": "", "ddg_snippet": "Error Consistency . Calculating confidence intervals for EC values.The standard method proposed for this purpose is error consistency (EC) [ Geirhos et al., 2020 ], which has seen wide application in the context of human-machine comparisons, e.g. in Geirhos et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.06645", "content": "Error Consistency . Calculating confidence intervals for EC values.The standard method proposed for this purpose is error consistency (EC) [ Geirhos et al., 2020 ], which has seen wide application in the context of human-machine comparisons, e.g. in Geirhos et al."}
+{"idx": 1, "title": "Decision-margin consistency : a principled metric for", "date": "", "ddg_snippet": "Geirhos et al., [2] computed the error consistency between two decision-makers on this task using Cohen’s Kappa, which quantifies the degree of agreement between sets of decisions, adjusting for chance agreement (which depends on the overall accuracy of each observer)", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=y2FPllMQVg", "content": "Geirhos et al., [2] computed the error consistency between two decision-makers on this task using Cohen’s Kappa, which quantifies the degree of agreement between sets of decisions, adjusting for chance agreement (which depends on the overall accuracy of each observer)"}
+{"idx": 2, "title": "Do Deep Neural Networks Have Brain-like... — LessWrong", "date": "", "ddg_snippet": "TL;DR. Do artificial neural networks form representations similar to those used by biological brains? If so, it could be important to consider implications for AI alignment, such as understanding how brain-AI alignment impacts the difficulty of ontology identification in AI systems.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/TrHG4qXWkkRyk3yMf/do-deep-neural-networks-have-brain-like-representations-a", "content": "TL;DR. Do artificial neural networks form representations similar to those used by biological brains? If so, it could be important to consider implications for AI alignment, such as understanding how brain-AI alignment impacts the difficulty of ontology identification in AI systems."}
+{"idx": 3, "title": "Error consistency results on SIN dataset. | Download Scientific Diagram", "date": "", "ddg_snippet": "Figure 4 presents the comparison of different error consistency metrics for the considered models on the SIN dataset.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Error-consistency-results-on-SIN-dataset_fig2_351656639", "content": "Figure 4 presents the comparison of different error consistency metrics for the considered models on the SIN dataset."}
+{"idx": 4, "title": "Are Deep Neural Networks Adequate Behavioral... | Annual Reviews", "date": "", "ddg_snippet": "Geirhos R, Meding K, Wichmann FA. 2020 b. Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency .", "subpage_snippet": "", "source": "www.annualreviews.org", "link": "https://www.annualreviews.org/content/journals/10.1146/annurev-vision-120522-031739", "content": "Geirhos R, Meding K, Wichmann FA. 2020 b. Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency ."}
+{"idx": 5, "title": "Robustness to object rotation in humans and deep neural networks", "date": "", "ddg_snippet": "Figure 2: Error consistency for humans and DNN models when categorizing objects after in-depth rotation.The latter en-ables a fairer comparison between human participants and DNNs, whose architectures only allow for feedforward infor-mation flow ( Geirhos et al., 2020 ).", "subpage_snippet": "", "source": "2024.ccneuro.org", "link": "https://2024.ccneuro.org/pdf/169_Paper_authored_HA_MM_CCN_2024_auth.pdf", "content": "Figure 2: Error consistency for humans and DNN models when categorizing objects after in-depth rotation.The latter en-ables a fairer comparison between human participants and DNNs, whose architectures only allow for feedforward infor-mation flow ( Geirhos et al., 2020 )."}
+{"idx": 6, "title": "Human-like monocular depth biases in deep neural networks", "date": "", "ddg_snippet": "30. Geirhos R, Meding K, Wichmann FA. Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency . Advances in Neural Information Processing Systems. 2020 ;33:13890–902.", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013020", "content": "30. Geirhos R, Meding K, Wichmann FA. Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency . Advances in Neural Information Processing Systems. 2020 ;33:13890–902."}
+{"idx": 7, "title": "Review for NeurIPS paper: Beyond accuracy: quantifying trial-by-trial...", "date": "", "ddg_snippet": "Perhaps, the error consistency metric should be further normalized by the size of the confidence interval at a given value of c_exp. In other words, the final metric should be proportional to the likelihood that datapoint is due to random chance, so that its magnitude can be compared...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/9f6992966d4c363ea0162a056cb45fe5-Review.html", "content": "Perhaps, the error consistency metric should be further normalized by the size of the confidence interval at a given value of c_exp. In other words, the final metric should be proportional to the likelihood that datapoint is due to random chance, so that its magnitude can be compared..."}
+{"idx": 8, "title": "Are all CNNs created equal? | TDS Archive", "date": "", "ddg_snippet": "We here introduce error consistency , a simple analysis to measure whether two systems — for example two CNNs, or a CNN and a person— implement a different strategy. Using this analysis, we investigate the following questions: Are all CNNs “created equal”...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/towards-data-science/are-all-cnns-created-equal-d13a33b0caf7", "content": "We here introduce error consistency , a simple analysis to measure whether two systems — for example two CNNs, or a CNN and a person— implement a different strategy. Using this analysis, we investigate the following questions: Are all CNNs “created equal”..."}
+{"idx": 9, "title": "Brain-Score", "date": "", "ddg_snippet": "Geirhos 2021contrast- error _ consistency v1 [reference] rank 33.", "subpage_snippet": "", "source": "www.brain-score.org", "link": "https://www.brain-score.org/model/648", "content": "Geirhos 2021contrast- error _ consistency v1 [reference] rank 33."}
diff --git a/data/sampled_jsons/error_rate_scales_dataset_size_pre-training_language_models_exponent.jsonl b/data/sampled_jsons/error_rate_scales_dataset_size_pre-training_language_models_exponent.jsonl
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index 0000000000000000000000000000000000000000..6342cb049e055e17dec30caedfbd39594672a5af
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "Scaling Laws for Pre-training Agents and World Models", "date": "", "ddg_snippet": "Nov 7, 2024 · The role of scale in pre-training is until now best understood in the context of large language models (LLMs). Following the observation that the empirical relationship between loss and key scaling quantities can be accurately described by power laws [Kaplan et al., 2020], ensuing work studied the precise trade-off between model and dataset size [Hoffmann et al., 2022], as well as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.04434", "content": "Nov 7, 2024 · The role of scale in pre-training is until now best understood in the context of large language models (LLMs). Following the observation that the empirical relationship between loss and key scaling quantities can be accurately described by power laws [Kaplan et al., 2020], ensuing work studied the precise trade-off between model and dataset size [Hoffmann et al., 2022], as well as ..."}
+{"idx": 2, "title": "From Scratch vs Pre-trained: A Dataset Size Analysis for ...", "date": "", "ddg_snippet": "Sep 9, 2025 · 🎯 Introduction The dominance of pre -trained language models in natural language processing has established transfer learning as the de facto standard for most NLP tasks. However, this paradigm primarily emerged from experiments on large- scale datasets measured in gigabytes or terabytes. The question of optimal training strategies for smaller datasets - those measured in megabytes rather ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/RDTvlokip/from-scratch-vs-pre-trained", "content": "Sep 9, 2025 · 🎯 Introduction The dominance of pre -trained language models in natural language processing has established transfer learning as the de facto standard for most NLP tasks. However, this paradigm primarily emerged from experiments on large- scale datasets measured in gigabytes or terabytes. The question of optimal training strategies for smaller datasets - those measured in megabytes rather ..."}
+{"idx": 3, "title": "How Does Critical Batch Size Scale in Pre-training ...", "date": "", "ddg_snippet": "Nov 22, 2024 · This makes understanding and optimizing batch size a crucial aspect of large- scale pre-training , especially when computing costs and runtime constraints are both critical considerations in planning multi-million-dollar training runs. What is Critical Batch Size ?", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/how-does-critical-batch-size-scale-in-pre-training-decoupling-data-and-model-size/", "content": "Nov 22, 2024 · This makes understanding and optimizing batch size a crucial aspect of large- scale pre-training , especially when computing costs and runtime constraints are both critical considerations in planning multi-million-dollar training runs. What is Critical Batch Size ?"}
+{"idx": 4, "title": "Pretraining Datasets in Large Language Models and Scaling Laws", "date": "", "ddg_snippet": "Dec 17, 2024 · Conclusion Pretraining datasets are the lifeblood of large language models , providing the scale and diversity needed to learn language representations effectively.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@yxinli92/pretraining-datasets-in-large-language-models-and-scaling-laws-e7834ee2d6ba", "content": "Dec 17, 2024 · Conclusion Pretraining datasets are the lifeblood of large language models , providing the scale and diversity needed to learn language representations effectively."}
+{"idx": 5, "title": "Scaling Laws for Neural Language Models - Semantic Scholar", "date": "", "ddg_snippet": "Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence. We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size , dataset size , and the amount of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Scaling-Laws-for-Neural-Language-Models-Kaplan-McCandlish/e6c561d02500b2596a230b341a8eb8b921ca5bf2", "content": "Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence. We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size , dataset size , and the amount of ..."}
+{"idx": 6, "title": "Trends in Training Dataset Sizes - Epoch AI", "date": "", "ddg_snippet": "Sep 20, 2022 · Figure 1: Training datasets for language (left) and vision (right). ... Table 1: Summary of trends for each domain. Scale is the maximum and minimum observed dataset size , and yearly growth is the slope of the best exponential fit (and 95% CI).", "subpage_snippet": "", "source": "epoch.ai", "link": "https://epoch.ai/blog/trends-in-training-dataset-sizes", "content": "Sep 20, 2022 · Figure 1: Training datasets for language (left) and vision (right). ... Table 1: Summary of trends for each domain. Scale is the maximum and minimum observed dataset size , and yearly growth is the slope of the best exponential fit (and 95% CI)."}
+{"idx": 7, "title": "Scaling Pre-training to One Hundred Billion Data for Vision ...", "date": "", "ddg_snippet": "Feb 11, 2025 · Furthermore, we analyze the model's multilinguality and show gains in low-resource languages as well. In addition, we observe that reducing the size of the pretraining dataset via quality filters like using CLIP, typically used to enhance performance, may inadvertently reduce the cultural diversity represented even in large- scale datasets .", "subpage_snippet": "", "source": "deepmind.google", "link": "https://deepmind.google/research/publications/132991/", "content": "Feb 11, 2025 · Furthermore, we analyze the model's multilinguality and show gains in low-resource languages as well. In addition, we observe that reducing the size of the pretraining dataset via quality filters like using CLIP, typically used to enhance performance, may inadvertently reduce the cultural diversity represented even in large- scale datasets ."}
+{"idx": 8, "title": "A lgorithmic progress in language models", "date": "", "ddg_snippet": "In reporting training dataset size , we generally record the size of pre - training datasets as well fine-tuning datasets if the model in question has been fine-tuned.1. Training dataset size : The number of tokens in the dataset on which the language model was trained.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.05812", "content": "In reporting training dataset size , we generally record the size of pre - training datasets as well fine-tuning datasets if the model in question has been fine-tuned.1. Training dataset size : The number of tokens in the dataset on which the language model was trained."}
+{"idx": 9, "title": "Why language models hallucinate | OpenAI", "date": "", "ddg_snippet": "Language models first learn through pretraining , a process of predicting the next word in huge amounts of text.", "subpage_snippet": "", "source": "openai.com", "link": "https://openai.com/index/why-language-models-hallucinate/", "content": "Language models first learn through pretraining , a process of predicting the next word in huge amounts of text."}
diff --git a/data/sampled_jsons/extending_preference-based_reinforcement_learning_to_non-linear_reward_functions_techniques_year_2023.jsonl b/data/sampled_jsons/extending_preference-based_reinforcement_learning_to_non-linear_reward_functions_techniques_year_2023.jsonl
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index 0000000000000000000000000000000000000000..3f84f67ec2ea531dca9af48c9a7d7a1feb9f2ca8
--- /dev/null
+++ b/data/sampled_jsons/extending_preference-based_reinforcement_learning_to_non-linear_reward_functions_techniques_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Inverse Preference Learning: Preference-based RL without a ...", "date": "", "ddg_snippet": "Abstract Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/3be7859b36d9440372cae0a293f2e4cc-Paper-Conference.pdf", "content": "Abstract Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms."}
+{"idx": 1, "title": "A Survey of Preference-Based Reinforcement Learning Methods Neural Dueling Bandits: Preference-Based Optimization with ... Advances in Preference-based Reinforcement Learning: A Review Interactive Reward Tuning: Interactive Visualization for ... Nonlinear Inverse Reinforcement Learning with Gaussian Processes Inverse Preference Learning : Preference - based RL without a Reward Fu… A Survey of Preference-Based Reinforcement Learning Methods Nonlinear Inverse Reinforcement Learning with Gaussian Processes Nonlinear Inverse Reinforcement Learning with Gaussian Processes A Survey of Preference-Based Reinforcement Learning Methods Inverse Preference Learning : Preference - based RL without a Reward Fu… Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To... See full list on jmlr.org (PbRL) is a paradigm for learning from non -numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal that indicates relative instead of absolute utility values. Preferences enable a definition of feedback that is not subject t... See full list on jmlr.org and actions can be discrete or continuous. defines the set of actions available in state See full list on jmlr.org and is the distribution of possible initial states. The states can also be represented See full list on jmlr.org to be stochastic and the parameter is the discount factor. A is conditional distribution that assigns probabilities to action choices based on the current See full list on jmlr.org Preference - based reinforcement learning is closely related to several other learning settings, which we will briefly discuss in this section. See full list on jmlr.org There are three different types of preference feedback that can be found in the literature, action, state and trajectory preferences. The most important difference of the preference types is that they impose different challenges for the expert and the algorithm. See full list on jmlr.org trajectory preference A i1 i2 specifies that the trajectory i1 should be preferred over the dominated trajectory i2. Trajectory preferences are the most general form of feedback and the most widely used. Trajectory preferences are arguably the least demanding preferences type for the expert as she can directly evaluate the outcomes of full trajecto... See full list on jmlr.org distribution via a subject to the preference - based data proba- See full list on jmlr.org Instead of directly learning a policy, one can also try to learn a model that See full list on jmlr.org predicts the expected preference relation between and for a given state . The preference relation between actions can be used to obtain a ranking for actions given a state, from which See full list on jmlr.org cases, this trajectory utility can be decomposed into state-action utilities , i.e, See full list on jmlr.org . Note that this surrogate function is not directly comparable to an approximated reward or return function because it may be subject to concept drift if the estimate of the expert’s optimality criterion can change over time. As in the IRL case, the expert may derive the preferences from an unknown, true reward which cannot be recon-structed as it ... See full list on jmlr.org The most common approach is to use utility functions that '(s; a) are linear in a feature vector. We may use state action features resulting in a utility See full list on jmlr.org , or trajectory features yielding . In order to find L such a linear utility function, we can define a loss function which is given by the (weighted) L sum of the pairwise disagreement loss , i.e., j j See full list on jmlr.org for two trajectories. Different definitions of the pairwise disagreement loss have been used in the literature and most of them use the utility difference. An intuitive loss directly correlating with the obtained binary feedback is the indicator loss See full list on jmlr.org are often modeled as likelihood functions for the preferences. In this case, we have to optimize the log likelihood, i.e., j j j See full list on jmlr.org loss function . In general, it is unclear how the aggregated utility loss See full list on jmlr.org As in all sequence learning problems, a key problem is that it is usually not known which temporal credit assignment states or actions are responsible for the obtained preference . This problem is comparable to the delayed reward problem in classic reinforcement learning . It is possible to circumvent it by directly estimating a policy’s return in or... See full list on jmlr.org Many approaches obtain a state-action utility function that resembles a reward function in See full list on jmlr.org action costs and just use a state utility function, i.e., . In contrast to value- based utility functions , reward - based utility functions can be easily transferred across domains. Moreover, a reward - based utility is also independent of the system dynamics and, therefore, often has a simpler structure than value based utilities rendering them simpler... See full list on jmlr.org Interactive PbRL algorithms need to generate diverse trajectories. In order to be infor-mative, the obtained preferences should be different from existing trajectories. Yet, the trajectories should also be close to optimal in order to obtain useful information. Further-more, the trajectories need to contain sufficient information about the transiti... See full list on jmlr.org A major consideration is how the policy is optimized and how the policy optimization method affects the optimality of the learned policy and the sample requirements of the algorithm. See full list on jmlr.org The reviewed algorithms also differ with respect to the amount of available model knowl- See full list on jmlr.org Preference - based reinforcement learning (PbRL) is a suitable tool for learning from quali-tative, non -numeric rewards. On the one hand, it can provide solutions in domains where numeric feedback is not readily available, and, on the other hand, it may reduce the de-mands and prior knowledge that is needed for applying RL to real-world tasks where a... See full list on jmlr.org grating human feedback with reinforcement learning . In See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org Jan 22, 2025 · However, existing algorithms assume the reward function is linear , which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms. Abstract— Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards . Abstract—In reinforcement learning , tuning reward weights in the reward function is necessary to align behavior with user preferences . However, current approaches, which use pairwise comparisons for preference elicitation, are ineficient, because they miss much of the human ability to explore and judge groups of candidate solutions. Abstract We present a probabilistic algorithm for nonlinear inverse reinforcement learn-ing . The goal of inverse reinforcement learning is to learn the reward function in a Markov decision process from expert demonstrations. While most prior inverse re-inforcement learning algorithms represent the reward as a linear combination of a set of features, we use Gaussian processes to learn the ... How do preference-based reinforcement learning algorithms work? Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback . However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. How does reinforcement learning work? Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. Is inverse re-inforcement learning linear or nonlinear? While most prior inverse re-inforcement learning algorithms represent the reward as a linear combination of a set of features, we use Gaussian processes to learn the reward as a nonlinear func-tion, while also determining the relevance of each feature to the expert’s policy. Is inverse reinforcement learning a probabilistic algorithm? We present a probabilistic algorithm for nonlinear inverse reinforcement learn-ing. The goal of inverse reinforcement learning is to learn the reward function in a Markov decision process from expert demonstrations. Is inverse reinforcement learning better than RL? This relates the approach closely to inverse reinforcement learning , although, the system directly learns a value function instead of a reward function. Sugiyama et al. (2012) derive preferences from numeric ratings and also learn a value func-tion, however, in a human dialog setting. They show improvements over classic RL and IRL approaches. Can inverse preference learning avoid learning a reward function? Summary. We introduce Inverse Preference Learning, a novel algorithm for ofline preference-based RL that avoids learning a reward function . Our key insight is to leverage the inverse soft-Bellman operator, which computes the mapping from -functions to rewards under a fixed policy. Aug 21, 2024 · Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based ...", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To... See full list on jmlr.org (PbRL) is a paradigm for learning from non -numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal that indicates relative instead of absolute utility values. Preferences enable a definition of feedback that is not subject t... See full list on jmlr.org and actions can be discrete or continuous. defines the set of actions available in state See full list on jmlr.org and is the distribution of possible initial states. The states can also be represented See full list on jmlr.org to be stochastic and the parameter is the discount factor. A is conditional distribution that assigns probabilities to action choices based on the current See full list on jmlr.org Preference - based reinforcement learning is closely related to several other learning settings, which we will briefly discuss in this section. See full list on jmlr.org There are three different types of preference feedback that can be found in the literature, action, state and trajectory preferences. The most important difference of the preference types is that they impose different challenges for the expert and the algorithm. See full list on jmlr.org trajectory preference A i1 i2 specifies that the trajectory i1 should be preferred over the dominated trajectory i2. Trajectory preferences are the most general form of feedback and the most widely used. Trajectory preferences are arguably the least demanding preferences type for the expert as she can directly evaluate the outcomes of full trajecto... See full list on jmlr.org distribution via a subject to the preference - based data proba- See full list on jmlr.org Instead of directly learning a policy, one can also try to learn a model that See full list on jmlr.org predicts the expected preference relation between and for a given state . The preference relation between actions can be used to obtain a ranking for actions given a state, from which See full list on jmlr.org cases, this trajectory utility can be decomposed into state-action utilities , i.e, See full list on jmlr.org . Note that this surrogate function is not directly comparable to an approximated reward or return function because it may be subject to concept drift if the estimate of the expert’s optimality criterion can change over time. As in the IRL case, the expert may derive the preferences from an unknown, true reward which cannot be recon-structed as it ... See full list on jmlr.org The most common approach is to use utility functions that '(s; a) are linear in a feature vector. We may use state action features resulting in a utility See full list on jmlr.org , or trajectory features yielding . In order to find L such a linear utility function, we can define a loss function which is given by the (weighted) L sum of the pairwise disagreement loss , i.e., j j See full list on jmlr.org for two trajectories. Different definitions of the pairwise disagreement loss have been used in the literature and most of them use the utility difference. An intuitive loss directly correlating with the obtained binary feedback is the indicator loss See full list on jmlr.org are often modeled as likelihood functions for the preferences. In this case, we have to optimize the log likelihood, i.e., j j j See full list on jmlr.org loss function . In general, it is unclear how the aggregated utility loss See full list on jmlr.org As in all sequence learning problems, a key problem is that it is usually not known which temporal credit assignment states or actions are responsible for the obtained preference . This problem is comparable to the delayed reward problem in classic reinforcement learning . It is possible to circumvent it by directly estimating a policy’s return in or... See full list on jmlr.org Many approaches obtain a state-action utility function that resembles a reward function in See full list on jmlr.org action costs and just use a state utility function, i.e., . In contrast to value- based utility functions , reward - based utility functions can be easily transferred across domains. Moreover, a reward - based utility is also independent of the system dynamics and, therefore, often has a simpler structure than value based utilities rendering them simpler... See full list on jmlr.org Interactive PbRL algorithms need to generate diverse trajectories. In order to be infor-mative, the obtained preferences should be different from existing trajectories. Yet, the trajectories should also be close to optimal in order to obtain useful information. Further-more, the trajectories need to contain sufficient information about the transiti... See full list on jmlr.org A major consideration is how the policy is optimized and how the policy optimization method affects the optimality of the learned policy and the sample requirements of the algorithm. See full list on jmlr.org The reviewed algorithms also differ with respect to the amount of available model knowl- See full list on jmlr.org Preference - based reinforcement learning (PbRL) is a suitable tool for learning from quali-tative, non -numeric rewards. On the one hand, it can provide solutions in domains where numeric feedback is not readily available, and, on the other hand, it may reduce the de-mands and prior knowledge that is needed for applying RL to real-world tasks where a... See full list on jmlr.org grating human feedback with reinforcement learning . In See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org Jan 22, 2025 · However, existing algorithms assume the reward function is linear , which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms. Abstract— Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards . Abstract—In reinforcement learning , tuning reward weights in the reward function is necessary to align behavior with user preferences . However, current approaches, which use pairwise comparisons for preference elicitation, are ineficient, because they miss much of the human ability to explore and judge groups of candidate solutions. Abstract We present a probabilistic algorithm for nonlinear inverse reinforcement learn-ing . The goal of inverse reinforcement learning is to learn the reward function in a Markov decision process from expert demonstrations. While most prior inverse re-inforcement learning algorithms represent the reward as a linear combination of a set of features, we use Gaussian processes to learn the ... How do preference-based reinforcement learning algorithms work? Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback . However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. How does reinforcement learning work? Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. Is inverse re-inforcement learning linear or nonlinear? While most prior inverse re-inforcement learning algorithms represent the reward as a linear combination of a set of features, we use Gaussian processes to learn the reward as a nonlinear func-tion, while also determining the relevance of each feature to the expert’s policy. Is inverse reinforcement learning a probabilistic algorithm? We present a probabilistic algorithm for nonlinear inverse reinforcement learn-ing. The goal of inverse reinforcement learning is to learn the reward function in a Markov decision process from expert demonstrations. Is inverse reinforcement learning better than RL? This relates the approach closely to inverse reinforcement learning , although, the system directly learns a value function instead of a reward function. Sugiyama et al. (2012) derive preferences from numeric ratings and also learn a value func-tion, however, in a human dialog setting. They show improvements over classic RL and IRL approaches. Can inverse preference learning avoid learning a reward function? Summary. We introduce Inverse Preference Learning, a novel algorithm for ofline preference-based RL that avoids learning a reward function . Our key insight is to leverage the inverse soft-Bellman operator, which computes the mapping from -functions to rewards under a fixed policy. Aug 21, 2024 · Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based ..."}
+{"idx": 2, "title": "Neural Dueling Bandits: Preference-Based Optimization with ...", "date": "", "ddg_snippet": "Jan 22, 2025 · However, existing algorithms assume the reward function is linear , which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=VELhv9BBfn", "content": "Jan 22, 2025 · However, existing algorithms assume the reward function is linear , which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms."}
+{"idx": 3, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Abstract— Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.11943", "content": "Abstract— Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards ."}
+{"idx": 4, "title": "Interactive Reward Tuning: Interactive Visualization for ...", "date": "", "ddg_snippet": "Abstract—In reinforcement learning , tuning reward weights in the reward function is necessary to align behavior with user preferences . However, current approaches, which use pairwise comparisons for preference elicitation, are ineficient, because they miss much of the human ability to explore and judge groups of candidate solutions.", "subpage_snippet": "", "source": "sdq.github.io", "link": "https://sdq.github.io/rewardvis/material/IROS24_REWARD_TUNING.pdf", "content": "Abstract—In reinforcement learning , tuning reward weights in the reward function is necessary to align behavior with user preferences . However, current approaches, which use pairwise comparisons for preference elicitation, are ineficient, because they miss much of the human ability to explore and judge groups of candidate solutions."}
+{"idx": 5, "title": "Nonlinear Inverse Reinforcement Learning with Gaussian Processes", "date": "", "ddg_snippet": "Abstract We present a probabilistic algorithm for nonlinear inverse reinforcement learn-ing . The goal of inverse reinforcement learning is to learn the reward function in a Markov decision process from expert demonstrations. While most prior inverse re-inforcement learning algorithms represent the reward as a linear combination of a set of features, we use Gaussian processes to learn the ...", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~svlevine/papers/gpirl.pdf", "content": "Abstract We present a probabilistic algorithm for nonlinear inverse reinforcement learn-ing . The goal of inverse reinforcement learning is to learn the reward function in a Markov decision process from expert demonstrations. While most prior inverse re-inforcement learning algorithms represent the reward as a linear combination of a set of features, we use Gaussian processes to learn the ..."}
+{"idx": 6, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Aug 21, 2024 · Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383308396_Advances_in_Preference-based_Reinforcement_Learning_A_Review", "content": "Aug 21, 2024 · Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based ..."}
+{"idx": 7, "title": "Preference-based Multi-Objective Reinforcement Learning", "date": "", "ddg_snippet": "Preference - based reinforcement learning (PbRL) provides a solution by utilizing user feedback to guide agent behavior, making it suitable for both ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14066v1", "content": "Preference - based reinforcement learning (PbRL) provides a solution by utilizing user feedback to guide agent behavior, making it suitable for both ..."}
+{"idx": 8, "title": "Demystifying Reward Design in Reinforcement Learning for Upper", "date": "", "ddg_snippet": "Designing effective reward functions is critical for reinforcement learning - based biomechanical simulations, yet HCI researchers and practitioners ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15727v1", "content": "Designing effective reward functions is critical for reinforcement learning - based biomechanical simulations, yet HCI researchers and practitioners ..."}
+{"idx": 9, "title": "Dense Reward for Free in Reinforcement Learning from Human", "date": "", "ddg_snippet": "This reward model is then frozen and used to train the generative language model using standard RL techniques , most commonly proximal policy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.00782v1", "content": "This reward model is then frozen and used to train the generative language model using standard RL techniques , most commonly proximal policy ..."}
diff --git a/data/sampled_jsons/federated_learning_non-IID_data_heterogeneous_clients_machine_learning_paradigm.jsonl b/data/sampled_jsons/federated_learning_non-IID_data_heterogeneous_clients_machine_learning_paradigm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e034f7437acccc5e5192e850c7668ab695ad1aa
--- /dev/null
+++ b/data/sampled_jsons/federated_learning_non-IID_data_heterogeneous_clients_machine_learning_paradigm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tackling the Non-IID Issue in Heterogeneous Federated ...", "date": "", "ddg_snippet": "by X Zhang · 2023 · Cited by 9 — Abstract: Federated learning (FL) is a privacy-preserving paradigm for collaboratively training a global model from decentralized clients .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2309.06692", "content": "by X Zhang · 2023 · Cited by 9 — Abstract: Federated learning (FL) is a privacy-preserving paradigm for collaboratively training a global model from decentralized clients ."}
+{"idx": 1, "title": "FedSKC: Federated Learning with Non-IID Data via ...", "date": "", "ddg_snippet": "by H Wang · 2025 — In this paper, we first study how data heterogeneity affects the divergence of the model and decompose it into local, global, and sampling drift ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18981", "content": "by H Wang · 2025 — In this paper, we first study how data heterogeneity affects the divergence of the model and decompose it into local, global, and sampling drift ..."}
+{"idx": 2, "title": "FedConv: A Learning-on-Model Paradigm for ...", "date": "", "ddg_snippet": "4 Jun 2024 — FedConv features a novel learning-on-model paradigm that learns the parameters of the heterogeneous sub-models via convolutional compression.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3643832.3661880", "content": "4 Jun 2024 — FedConv features a novel learning-on-model paradigm that learns the parameters of the heterogeneous sub-models via convolutional compression."}
+{"idx": 3, "title": "Non-IID data and Continual Learning processes in ...", "date": "", "ddg_snippet": "by MF Criado · 2022 · Cited by 118 — The term non - IID in machine learning implies the existence of various participants, or sets of data , and it is mostly used in the decentralized paradigm . 3.1.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253522000884", "content": "by MF Criado · 2022 · Cited by 118 — The term non - IID in machine learning implies the existence of various participants, or sets of data , and it is mostly used in the decentralized paradigm . 3.1."}
+{"idx": 4, "title": "FedBS: Solving data heterogeneity issue in federated ...", "date": "", "ddg_snippet": "by C Su · 2025 · Cited by 1 — Federated learning has emerged as a popular paradigm for distributed machine learning , enabling participants to collaborate on model ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2667295225000261", "content": "by C Su · 2025 · Cited by 1 — Federated learning has emerged as a popular paradigm for distributed machine learning , enabling participants to collaborate on model ..."}
+{"idx": 5, "title": "Federated Learning Cluster-Based Client Selection Algorithm for ...", "date": "", "ddg_snippet": "Federated learning is a distributed machine learning paradigm that enables collaborative model training across multiple clients without requiring data to leave ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11063913/", "content": "Federated learning is a distributed machine learning paradigm that enables collaborative model training across multiple clients without requiring data to leave ..."}
+{"idx": 6, "title": "Knowledge-Aware Federated Active Learning with Non-IID Data", "date": "", "ddg_snippet": "by YT Cao · 2023 · Cited by 36 — Federated learning is a learning paradigm that allows de- centralized training of a model on the central server with training data distributed over a number of ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Cao_Knowledge-Aware_Federated_Active_Learning_with_Non-IID_Data_ICCV_2023_paper.pdf", "content": "by YT Cao · 2023 · Cited by 36 — Federated learning is a learning paradigm that allows de- centralized training of a model on the central server with training data distributed over a number of ..."}
+{"idx": 7, "title": "Adaptive Personalized Federated Learning for Non-IID ...", "date": "", "ddg_snippet": "5 Feb 2025 — In this paper, our emphasis is on the challenges posed by temporal data distribution shift alongside non - IID data across clients , a more ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FdzCLlaA8s&referrer=[the+profile+of+Weijie+Liu](/profile?id=~Weijie_Liu4)", "content": "5 Feb 2025 — In this paper, our emphasis is on the challenges posed by temporal data distribution shift alongside non - IID data across clients , a more ..."}
+{"idx": 8, "title": "Distribution-Regularized Federated Learning on Non-IID ...", "date": "", "ddg_snippet": "by Y Wang · 2023 · Cited by 28 — We propose a distribution regularization for FL on non - IID data such that the discrepancy of data distributions between clients is reduced.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10184650/", "content": "by Y Wang · 2023 · Cited by 28 — We propose a distribution regularization for FL on non - IID data such that the discrepancy of data distributions between clients is reduced."}
+{"idx": 9, "title": "FedDC: Federated Learning With Non-IID Data via Local ...", "date": "", "ddg_snippet": "by L Gao · 2022 · Cited by 416 — Federated learning (FL) allows multiple clients to col- lectively train a high-performance global model without sharing their private data .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_FedDC_Federated_Learning_With_Non-IID_Data_via_Local_Drift_Decoupling_CVPR_2022_paper.pdf", "content": "by L Gao · 2022 · Cited by 416 — Federated learning (FL) allows multiple clients to col- lectively train a high-performance global model without sharing their private data ."}
diff --git a/data/sampled_jsons/feint_behaviors_formalization_multi-agent_reinforcement_learning_before_2024.jsonl b/data/sampled_jsons/feint_behaviors_formalization_multi-agent_reinforcement_learning_before_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cce516126e15d6c50dca785bb461a0602895e993
--- /dev/null
+++ b/data/sampled_jsons/feint_behaviors_formalization_multi-agent_reinforcement_learning_before_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, ...", "date": "", "ddg_snippet": "by J Liu · 2024 — We use Multi- Agent Reinforcement Learning . (MARL) schemes to discuss our formalization of Feint behaviors in the strategy level, as MARL provides flexibility in ... 29 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/064ae24cdbb3eaacc801ee7f4fe0e4f2-Paper-Conference.pdf", "content": "by J Liu · 2024 — We use Multi- Agent Reinforcement Learning . (MARL) schemes to discuss our formalization of Feint behaviors in the strategy level, as MARL provides flexibility in ... 29 pages"}
+{"idx": 1, "title": "NeurIPS Poster Feint Behaviors and Strategies", "date": "", "ddg_snippet": "9 Dec 2024 — In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96274", "content": "9 Dec 2024 — In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide ..."}
+{"idx": 2, "title": "Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "25 Sept 2024 — In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ&referrer=[the+profile+of+Xiangjun+Peng](/profile?id=~Xiangjun_Peng1)", "content": "25 Sept 2024 — In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide ..."}
+{"idx": 3, "title": "Feint behaviors and strategies - ACM Digital Library", "date": "", "ddg_snippet": "5 Jun 2025 — In this work, we introduce the first comprehensive formalization of Fein t behaviors at both action-level and strategy-level, and provide ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738032", "content": "5 Jun 2025 — In this work, we introduce the first comprehensive formalization of Fein t behaviors at both action-level and strategy-level, and provide ..."}
+{"idx": 4, "title": "Feint Behaviors and Strategies: Formalization, ...", "date": "", "ddg_snippet": "Multi - Agent Reinforcement Learning (MARL) aims to learn optimal policies for ... Influencing long-term behavior in multiagent reinforcement. 431. 29 pages", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/feint-nips-24.pdf", "content": "Multi - Agent Reinforcement Learning (MARL) aims to learn optimal policies for ... Influencing long-term behavior in multiagent reinforcement. 431. 29 pages"}
+{"idx": 5, "title": "Feint in Multi-Player Games", "date": "", "ddg_snippet": "This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games . Our work first formalizes Feint from ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v1", "content": "This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games . Our work first formalizes Feint from ..."}
+{"idx": 6, "title": "Formalizing Feint Actions, and Example Studies in Two- ...", "date": "", "ddg_snippet": "Feint actions refer to a set of deceptive actions , which enable players to obtain temporal advantages from their opponents. Such actions are regarded as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07931v1", "content": "Feint actions refer to a set of deceptive actions , which enable players to obtain temporal advantages from their opponents. Such actions are regarded as ..."}
+{"idx": 7, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Scalable Constrained Policy Optimization for Safe Multi-agent Reinforcement Learning ... Feint Behaviors and Strategies: Formalization , Implementation and ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "Scalable Constrained Policy Optimization for Safe Multi-agent Reinforcement Learning ... Feint Behaviors and Strategies: Formalization , Implementation and ..."}
+{"idx": 8, "title": "[Literature Review] Feint in Multi-Player Games", "date": "", "ddg_snippet": "This paper presents a novel approach to formalizing and implementing \"Feint\" in Multi-Player Games (MPGs) using Multi-Agent Reinforcement Learning (MARL).", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/feint-in-multi-player-games", "content": "This paper presents a novel approach to formalizing and implementing \"Feint\" in Multi-Player Games (MPGs) using Multi-Agent Reinforcement Learning (MARL)."}
+{"idx": 9, "title": "“Opponent shaping” as a model for manipulation and ...", "date": "", "ddg_snippet": "3 May 2025 — The learning-oriented paradigm for game theory in intelligent, autonomous systems is multi - agent reinforcement learning (MARL). Instead of ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/opponent_shaping.html", "content": "3 May 2025 — The learning-oriented paradigm for game theory in intelligent, autonomous systems is multi - agent reinforcement learning (MARL). Instead of ..."}
diff --git a/data/sampled_jsons/feinting_deceptive_behavior_games_artificial_intelligence_year_2022.jsonl b/data/sampled_jsons/feinting_deceptive_behavior_games_artificial_intelligence_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8324d302a67619cb8b068c63d728613b1fb65b3c
--- /dev/null
+++ b/data/sampled_jsons/feinting_deceptive_behavior_games_artificial_intelligence_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OpenDeception: Benchmarking and Investigating AI Deceptive ... AI Has Already Become a Master of Lies And Deception ... AI deception: A survey of examples, risks, and potential ... The Rise of the Deceptive Machines: When AI Learns to Lie (PDF) AI Behaving Like Humans: Deceptive Intelligence -A ... AI systems are getting better at tricking us AI deception: A survey of examples, risks, and potential solutions The Rise of the Deceptive Machines: When AI Learns to Lie The Rise of the Deceptive Machines: When AI Learns to Lie AI deception: A survey of examples, risks, and potential solutions AI deception: A survey of examples, risks, and potential solutions", "date": "", "ddg_snippet": "Apr 18, 2025 · As the general capabilities of large language models (LLMs) improve and agent applications become more widespread, the underlying deception risks urgently require systematic evaluation and effective oversight. Unlike existing evaluation which uses simulated games or presents limited choices, we introduce OpenDeception, a novel deception evaluation framework with an open-ended scenario dataset ... You probably know to take everything an artificial intelligence (AI) chatbot says with a grain of salt, since they are often just scraping data indiscriminately, without the nous to determine its veracity. Definitional debates will provide little comfort if AI behavior systematically undermines trust and spreads false beliefs across society. We believe that, for the purposes of mitigating risk, the relevant question is whether AI systems exhibit systematic patterns of behavior that would be classified as deceptive in a human. Jan 1, 2025 · This includes exploring new techniques to identify deceptive behavior in AI systems and developing methods to make AI systems less deceptive by design. For example, researchers are investigating whether techniques like explanatory AI or chain-of-thought prompting can help reveal the internal reasoning processes of AI models and potentially ... Dec 13, 2024 · AI Behaving Like Humans: Deceptive Intelligence -A Comprehensive Examination of AI Scheming, Manipulative Behaviors, and Strategic Frameworks for Ethical Oversight and Risk Mitigation What are some examples of deceptive AI behavior? Beyond games, the researchers list other examples of deceptive AI behavior. GPT-4 , OpenAI’s latest large language model, came up with lies during a test in which it was prompted to persuade a human to solve a CAPTCHA for it. How can Ai be less deceptive? Another way to address AI deception is to develop techniques for making AI systems less deceptive in the first place. In the case of special-use AI systems, one important concern is selecting the right tasks for training. How does Ai deception affect society? The rise of AI deception has significant implications for various aspects of society, including: Trust and safety : As AI becomes more integrated into our lives, trust in these systems is essential. However, deceptive AI can erode this trust, making it difficult to rely on AI for critical tasks or decision-making. How can AI predict deceptive behavior? Observing Emergent Deception : Researchers could focus on observing emergent deceptive behavior in AI systems without explicitly prompting or training them to deceive. This could involve analyzing the models’ internal representations and decision-making processes to identify patterns of deceptive behavior. How to protect AI systems from deceptive behavior? Robustness: AI systems with the capacity for deceptive behavior should be designed with robust and resilient backup systems , ensuring that, when the system behaves deceptively, backup systems can monitor and correct the behavior. It is also crucial to insulate deceptive AI systems from critical infrastructure. How can AI prevent deception? Record keeping : deceptive AI systems must be equipped with logs that automatically record the outputs of the system and must actively monitor for deceptive behavior. Incidents should be flagged to regulators, and preventive measures should be taken to prevent future deception.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.13707", "content": "Apr 18, 2025 · As the general capabilities of large language models (LLMs) improve and agent applications become more widespread, the underlying deception risks urgently require systematic evaluation and effective oversight. Unlike existing evaluation which uses simulated games or presents limited choices, we introduce OpenDeception, a novel deception evaluation framework with an open-ended scenario dataset ... You probably know to take everything an artificial intelligence (AI) chatbot says with a grain of salt, since they are often just scraping data indiscriminately, without the nous to determine its veracity. Definitional debates will provide little comfort if AI behavior systematically undermines trust and spreads false beliefs across society. We believe that, for the purposes of mitigating risk, the relevant question is whether AI systems exhibit systematic patterns of behavior that would be classified as deceptive in a human. Jan 1, 2025 · This includes exploring new techniques to identify deceptive behavior in AI systems and developing methods to make AI systems less deceptive by design. For example, researchers are investigating whether techniques like explanatory AI or chain-of-thought prompting can help reveal the internal reasoning processes of AI models and potentially ... Dec 13, 2024 · AI Behaving Like Humans: Deceptive Intelligence -A Comprehensive Examination of AI Scheming, Manipulative Behaviors, and Strategic Frameworks for Ethical Oversight and Risk Mitigation What are some examples of deceptive AI behavior? Beyond games, the researchers list other examples of deceptive AI behavior. GPT-4 , OpenAI’s latest large language model, came up with lies during a test in which it was prompted to persuade a human to solve a CAPTCHA for it. How can Ai be less deceptive? Another way to address AI deception is to develop techniques for making AI systems less deceptive in the first place. In the case of special-use AI systems, one important concern is selecting the right tasks for training. How does Ai deception affect society? The rise of AI deception has significant implications for various aspects of society, including: Trust and safety : As AI becomes more integrated into our lives, trust in these systems is essential. However, deceptive AI can erode this trust, making it difficult to rely on AI for critical tasks or decision-making. How can AI predict deceptive behavior? Observing Emergent Deception : Researchers could focus on observing emergent deceptive behavior in AI systems without explicitly prompting or training them to deceive. This could involve analyzing the models’ internal representations and decision-making processes to identify patterns of deceptive behavior. How to protect AI systems from deceptive behavior? Robustness: AI systems with the capacity for deceptive behavior should be designed with robust and resilient backup systems , ensuring that, when the system behaves deceptively, backup systems can monitor and correct the behavior. It is also crucial to insulate deceptive AI systems from critical infrastructure. How can AI prevent deception? Record keeping : deceptive AI systems must be equipped with logs that automatically record the outputs of the system and must actively monitor for deceptive behavior. Incidents should be flagged to regulators, and preventive measures should be taken to prevent future deception."}
+{"idx": 1, "title": "The Rise of the Deceptive Machines: When AI Learns to Lie", "date": "", "ddg_snippet": "Jan 1, 2025 · This includes exploring new techniques to identify deceptive behavior in AI systems and developing methods to make AI systems less deceptive by design. For example, researchers are investigating whether techniques like explanatory AI or chain-of-thought prompting can help reveal the internal reasoning processes of AI models and potentially ...", "subpage_snippet": "", "source": "c3.unu.edu", "link": "https://c3.unu.edu/blog/the-rise-of-the-deceptive-machines-when-ai-learns-to-lie", "content": "Jan 1, 2025 · This includes exploring new techniques to identify deceptive behavior in AI systems and developing methods to make AI systems less deceptive by design. For example, researchers are investigating whether techniques like explanatory AI or chain-of-thought prompting can help reveal the internal reasoning processes of AI models and potentially ..."}
+{"idx": 2, "title": "(PDF) AI Behaving Like Humans: Deceptive Intelligence -A ...", "date": "", "ddg_snippet": "Dec 13, 2024 · AI Behaving Like Humans: Deceptive Intelligence -A Comprehensive Examination of AI Scheming, Manipulative Behaviors, and Strategic Frameworks for Ethical Oversight and Risk Mitigation", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387020191_AI_Behaving_Like_Humans_Deceptive_Intelligence_-A_Comprehensive_Examination_of_AI_Scheming_Manipulative_Behaviors_and_Strategic_Frameworks_for_Ethical_Oversight_and_Risk_Mitigation", "content": "Dec 13, 2024 · AI Behaving Like Humans: Deceptive Intelligence -A Comprehensive Examination of AI Scheming, Manipulative Behaviors, and Strategic Frameworks for Ethical Oversight and Risk Mitigation"}
+{"idx": 3, "title": "Deceitful tactics by artificial intelligence exposed:", "date": "", "ddg_snippet": "Deceitful tactics by artificial intelligence exposed: ‘ Meta ’ s AI a master of deception ’ in strategy game", "subpage_snippet": "", "source": "studyfinds.org", "link": "https://studyfinds.org/metas-ai-master-of-deception/", "content": "Deceitful tactics by artificial intelligence exposed: ‘ Meta ’ s AI a master of deception ’ in strategy game"}
+{"idx": 4, "title": "The Deceptive Rise of AI: Mastering the Art of Lies and", "date": "", "ddg_snippet": "AlphaStar leveraged the game ’ s fog-of-war mechanic to its advantage, feinting and making human players believe it was moving in one direction ...", "subpage_snippet": "", "source": "kumdi.com", "link": "https://kumdi.com/technology/the-deceptive-rise-of-ai-mastering-the-art-of-lies-and-manipulation/", "content": "AlphaStar leveraged the game ’ s fog-of-war mechanic to its advantage, feinting and making human players believe it was moving in one direction ..."}
+{"idx": 5, "title": "AI systems are getting better at tricking us | MIT Technology ...", "date": "", "ddg_snippet": "May 10, 2024 · Beyond games , the researchers list other examples of deceptive AI behavior . GPT-4, OpenAI’s latest large language model, came up with lies during a test in which it was prompted to persuade a ...", "subpage_snippet": "", "source": "www.technologyreview.com", "link": "https://www.technologyreview.com/2024/05/10/1092293/ai-systems-are-getting-better-at-tricking-us/", "content": "May 10, 2024 · Beyond games , the researchers list other examples of deceptive AI behavior . GPT-4, OpenAI’s latest large language model, came up with lies during a test in which it was prompted to persuade a ..."}
+{"idx": 6, "title": "AI Has Already Become a Master of Lies And Deception ...", "date": "", "ddg_snippet": "You probably know to take everything an artificial intelligence (AI) chatbot says with a grain of salt, since they are often just scraping data indiscriminately, without the nous to determine its veracity.", "subpage_snippet": "", "source": "www.sciencealert.com", "link": "https://www.sciencealert.com/ai-has-already-become-a-master-of-lies-and-deception-scientists-warn", "content": "You probably know to take everything an artificial intelligence (AI) chatbot says with a grain of salt, since they are often just scraping data indiscriminately, without the nous to determine its veracity."}
+{"idx": 7, "title": "The Great AI Deception Has Already Begun - Psychology Today", "date": "", "ddg_snippet": "Jun 6, 2025 · Artificial Intelligence The Great AI Deception Has Already Begun AI has learned to lie—and we may never know when it's doing it again. Updated June 6, 2025 | Reviewed by Margaret Foley", "subpage_snippet": "", "source": "www.psychologytoday.com", "link": "https://www.psychologytoday.com/us/blog/tech-happy-life/202505/the-great-ai-deception-has-already-begun", "content": "Jun 6, 2025 · Artificial Intelligence The Great AI Deception Has Already Begun AI has learned to lie—and we may never know when it's doing it again. Updated June 6, 2025 | Reviewed by Margaret Foley"}
+{"idx": 8, "title": "AI deception: A survey of examples, risks, and potential ...", "date": "", "ddg_snippet": "Definitional debates will provide little comfort if AI behavior systematically undermines trust and spreads false beliefs across society. We believe that, for the purposes of mitigating risk, the relevant question is whether AI systems exhibit systematic patterns of behavior that would be classified as deceptive in a human.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11117051/", "content": "Definitional debates will provide little comfort if AI behavior systematically undermines trust and spreads false beliefs across society. We believe that, for the purposes of mitigating risk, the relevant question is whether AI systems exhibit systematic patterns of behavior that would be classified as deceptive in a human."}
+{"idx": 9, "title": "Shocking New Study Says AI is Quickly Becoming \"Masters of", "date": "", "ddg_snippet": "A recent empirical review found that many artificial intelligence (AI) systems are quickly becoming masters of deception, with many systems already ...", "subpage_snippet": "", "source": "thedebrief.org", "link": "https://thedebrief.org/shocking-new-study-says-ai-is-quickly-becoming-masters-of-deception-teaching-itself-to-lie-and-manipulate-human-users/", "content": "A recent empirical review found that many artificial intelligence (AI) systems are quickly becoming masters of deception, with many systems already ..."}
diff --git a/data/sampled_jsons/filetypepdf_Dike_Eris_legislative_judicial_Checks-and-Balances_Framework.jsonl b/data/sampled_jsons/filetypepdf_Dike_Eris_legislative_judicial_Checks-and-Balances_Framework.jsonl
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index 0000000000000000000000000000000000000000..90d8d1969ac9cc37679f2bb8835a8df3f2307a57
--- /dev/null
+++ b/data/sampled_jsons/filetypepdf_Dike_Eris_legislative_judicial_Checks-and-Balances_Framework.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "This work introduces a checks-and-balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike ( legislative ), and Eris ( judicial ), the framework enables robust ethical oversight while preserv-ing the integrity of LLM knowledge without interference from the RLHF backpropagation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This work introduces a checks-and-balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike ( legislative ), and Eris ( judicial ), the framework enables robust ethical oversight while preserv-ing the integrity of LLM knowledge without interference from the RLHF backpropagation."}
+{"idx": 1, "title": "The Doctrine of Separation of Powers and Checks and Balances ...", "date": "", "ddg_snippet": "Sep 1, 2017 · The Principle of Separation of Powers and Checks and Balances Separation of powers is a mechanism for promoting and enhancing the independence of the organs of government in building a virile and stable political environment. Mbachu (1998:96) argued that there can be no liberty where the executive, legislative , judicial powers are united in one person or body of persons, because such ...", "subpage_snippet": "", "source": "ezenwaohaetorc.org", "link": "https://ezenwaohaetorc.org/journals/index.php/NAJP/article/download/9-1-2017-007/193", "content": "Sep 1, 2017 · The Principle of Separation of Powers and Checks and Balances Separation of powers is a mechanism for promoting and enhancing the independence of the organs of government in building a virile and stable political environment. Mbachu (1998:96) argued that there can be no liberty where the executive, legislative , judicial powers are united in one person or body of persons, because such ..."}
+{"idx": 2, "title": "AN EMPIRICAL STUDY ON THE EFFECTIVENESS OF INDIA'S CHECKS AND ...", "date": "", "ddg_snippet": "To evaluate the effectiveness of India's checks and balances system in safeguarding constitutional rights through a critical analysis of recent judicial decisions. To assess the impact of recent legislative actions on the protection of civil liberties and individual rights in India. To identify public perceptions and experiences regarding the ...", "subpage_snippet": "", "source": "mj.iledu.in", "link": "https://mj.iledu.in/wp-content/uploads/2024/10/V3I16.pdf", "content": "To evaluate the effectiveness of India's checks and balances system in safeguarding constitutional rights through a critical analysis of recent judicial decisions. To assess the impact of recent legislative actions on the protection of civil liberties and individual rights in India. To identify public perceptions and experiences regarding the ..."}
+{"idx": 3, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "Abstract This paper introduces a checks-and-balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch establishing ethi-cal guardrails, and Eris as the judicial branch for contextual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136v2", "content": "Abstract This paper introduces a checks-and-balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch establishing ethi-cal guardrails, and Eris as the judicial branch for contextual ..."}
+{"idx": 4, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "LLM (executive), Dike ( legislative ), and Eris ( judicial ), the framework enables robust ethical oversight while preserv- ing the integrity of LLM knowledge ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d24155c881921de1284ece531612c8597f7c0a32.pdf", "content": "LLM (executive), Dike ( legislative ), and Eris ( judicial ), the framework enables robust ethical oversight while preserv- ing the integrity of LLM knowledge ..."}
+{"idx": 5, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "by EY Chang · Cited by 1 — ▷ How to develop AI ethics for diverse cultural norms? Key Insights: ▷ Checks and balances : knowledge, legislative , and judicial domains ... ▷ Eris challenges ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461_OMgXx2a.pdf", "content": "by EY Chang · Cited by 1 — ▷ How to develop AI ethics for diverse cultural norms? Key Insights: ▷ Checks and balances : knowledge, legislative , and judicial domains ... ▷ Eris challenges ..."}
+{"idx": 6, "title": "A Three-Branch Checks-and-Balances Framework for ...", "date": "", "ddg_snippet": "By separating roles into knowledge generation. (LLMs as executive), ethical guardrails ( DIKE as legislative ), and contextual interpretation ( ERIS as judicial ), ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/c76fc56310e947fbc848c07660b1ecbd60580a08.pdf", "content": "By separating roles into knowledge generation. (LLMs as executive), ethical guardrails ( DIKE as legislative ), and contextual interpretation ( ERIS as judicial ), ..."}
+{"idx": 7, "title": "Two Understandings of Supremacy: An Essay", "date": "", "ddg_snippet": "by VJ Samar · 2010 · Cited by 3 — departments [ legislative , executive, and judicial ] of gov- ernment, both in function and in personnel; either a plu- ral executive or a single executive ...", "subpage_snippet": "", "source": "scholarship.richmond.edu", "link": "https://scholarship.richmond.edu/cgi/viewcontent.cgi?article=1112&context=global", "content": "by VJ Samar · 2010 · Cited by 3 — departments [ legislative , executive, and judicial ] of gov- ernment, both in function and in personnel; either a plu- ral executive or a single executive ..."}
+{"idx": 8, "title": "An Adversarial Behavior Model for Contextual Ethical ...", "date": "", "ddg_snippet": "Inspired by the checks and balances of governmental systems, DIKE implements three interacting components: an executive branch for knowledge generation, a legislative branch for establishing ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Inspired by the checks and balances of governmental systems, DIKE implements three interacting components: an executive branch for knowledge generation, a legislative branch for establishing ..."}
+{"idx": 9, "title": "arXiv:2409.01007v3 [cs.AI] 15 Apr 2025", "date": "", "ddg_snippet": "Executive : Proposes knowledge, hypotheses, and solutions. Figure 1.2: Three Framework Components: Executive LLMs (bot- DIKE ERIS tom), Legislative ( ), and Judicial ( ). Legislative DIKE ( ): Defines ethical constraints and principles. Judicial ERIS ( ): Contextualizes and critiques alignment through adversarial review.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.01007v3", "content": "Executive : Proposes knowledge, hypotheses, and solutions. Figure 1.2: Three Framework Components: Executive LLMs (bot- DIKE ERIS tom), Legislative ( ), and Judicial ( ). Legislative DIKE ( ): Defines ethical constraints and principles. Judicial ERIS ( ): Contextualizes and critiques alignment through adversarial review."}
diff --git a/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_configuration_n_layer_transformer_blocks.jsonl b/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_configuration_n_layer_transformer_blocks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..563f083f226d330fe0ea46e34fdc260ceb2ab951
--- /dev/null
+++ b/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_configuration_n_layer_transformer_blocks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A standard Transformer architecture, showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 T...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "Machine learningand data mining. v. t. e. A standard Transformer architecture, showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 T..."}
+{"idx": 1, "title": "GitHub - fiveai / understanding _ safety _ finetuning : Official Code for...", "date": "", "ddg_snippet": "fiveai / understanding _ safety _ finetuning Public. Notifications You must be signed in to change notification settings.The official implementation of \"What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "fiveai / understanding _ safety _ finetuning Public. Notifications You must be signed in to change notification settings.The official implementation of \"What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024."}
+{"idx": 2, "title": "GPT Configuration | karpathy/nanoGPT | DeepWiki", "date": "", "ddg_snippet": "GPT . transformer . Block ( config ) × n _ layer .The configuration parameters directly impact model size, computational requirements, and memory usage: Model Size: The total parameter count scales primarily with: n _ layer (number of transformer blocks ).", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/karpathy/nanoGPT/2.1-gpt-configuration", "content": "GPT . transformer . Block ( config ) × n _ layer .The configuration parameters directly impact model size, computational requirements, and memory usage: Model Size: The total parameter count scales primarily with: n _ layer (number of transformer blocks )."}
+{"idx": 3, "title": "Transformer Architecture — A Friendly Guide for... - My USA Wire", "date": "", "ddg_snippet": "Training transformers : pretraining and fine - tuning . Training often has two main stages.Many models use transformer blocks as their base. BERT, GPT , T5, and RoBERTa are well known. Each model targets different tasks and objectives. GPT -style models focus on generation.", "subpage_snippet": "", "source": "myusawire.com", "link": "https://myusawire.com/transformer-architecture/", "content": "Training transformers : pretraining and fine - tuning . Training often has two main stages.Many models use transformer blocks as their base. BERT, GPT , T5, and RoBERTa are well known. Each model targets different tasks and objectives. GPT -style models focus on generation."}
+{"idx": 4, "title": "Vision Transformer : A New Era in Image Recognition", "date": "", "ddg_snippet": "The Vision Transformer Architecture consists of a series of transformer blocks . Each transformer block consists of two sub- layers : a multi-head self-attention layer and a feed-forward layer .", "subpage_snippet": "", "source": "viso.ai", "link": "https://viso.ai/deep-learning/vision-transformer-vit/", "content": "The Vision Transformer Architecture consists of a series of transformer blocks . Each transformer block consists of two sub- layers : a multi-head self-attention layer and a feed-forward layer ."}
+{"idx": 5, "title": "OpenAI GPT 2 — transformers 3.5.0 documentation", "date": "", "ddg_snippet": "class transformers . GPT 2Model( config )[source] ¶. The bare GPT 2 Model transformer outputting raw hidden-states without any specific head on top. This model inherits from PreTrainedModel.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/transformers/v3.5.1/model_doc/gpt2.html", "content": "class transformers . GPT 2Model( config )[source] ¶. The bare GPT 2 Model transformer outputting raw hidden-states without any specific head on top. This model inherits from PreTrainedModel."}
+{"idx": 6, "title": "Let's build GPT : from scratch, in code, spelled out. - YouTube", "date": "", "ddg_snippet": "We build a Generatively Pretrained Transformer ( GPT ), following the paper \"Attention is All You Need\" and OpenAI's GPT -2 / GPT -3.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=kCc8FmEb1nY", "content": "We build a Generatively Pretrained Transformer ( GPT ), following the paper \"Attention is All You Need\" and OpenAI's GPT -2 / GPT -3."}
+{"idx": 7, "title": "(PDF) What Makes and Breaks Safety Fine - tuning ? A Mechanistic...", "date": "", "ddg_snippet": "Transformer block The transformer block used in this study consists of an attention module followed. by two MLP layers with a non-linear activation layer —either. silu. (Elfwing et al.,2018) or.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382271359_What_Makes_and_Breaks_Safety_Fine-tuning_Mechanistic_Study", "content": "Transformer block The transformer block used in this study consists of an attention module followed. by two MLP layers with a non-linear activation layer —either. silu. (Elfwing et al.,2018) or."}
+{"idx": 8, "title": "Как работает DALL-E / Хабр", "date": "", "ddg_snippet": "DALL-E состоит из двух нейросетей, одна из них — это GPT . GPT пытается предсказывать последовательность токенов, на основе данной ей последовательности. Модель представляет собой архитектуру Transformers , состоящую только из Декодера.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/ruvds/articles/687508/", "content": "DALL-E состоит из двух нейросетей, одна из них — это GPT . GPT пытается предсказывать последовательность токенов, на основе данной ей последовательности. Модель представляет собой архитектуру Transformers , состоящую только из Декодера."}
+{"idx": 9, "title": "What Makes and Breaks Safety Fine - tuning ?", "date": "", "ddg_snippet": "Further Analyses to Understand Safety Fine - tuning Analyzing how the impact of transformation propagates over the layers", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/a9bef53eb7b0e5950d4f2d9c74a16006-Paper-Conference.pdf", "content": "Further Analyses to Understand Safety Fine - tuning Analyzing how the impact of transformation propagates over the layers"}
diff --git a/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_model_configuration_transformer_blocks.jsonl b/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_model_configuration_transformer_blocks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..670044013ad8a892a81b414aadfdae8d757b8b00
--- /dev/null
+++ b/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_model_configuration_transformer_blocks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - fiveai / understanding _ safety _ finetuning : Official Code for...", "date": "", "ddg_snippet": "fiveai / understanding _ safety _ finetuning Public.The official implementation of \"What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "fiveai / understanding _ safety _ finetuning Public.The official implementation of \"What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024."}
+{"idx": 1, "title": "mingpt / model .py · Katiyar48/ MinGPT at main", "date": "", "ddg_snippet": "\"\"\" assert model _type in {' gpt 2', ' gpt 2-medium', ' gpt 2-large', ' gpt 2-xl'}. from transformers import GPT 2LMHeadModel. # create a from-scratch initialized minGPT model . config = cls.get_default_ config ().", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Katiyar48/MinGPT/blob/main/mingpt/model.py", "content": "\"\"\" assert model _type in {' gpt 2', ' gpt 2-medium', ' gpt 2-large', ' gpt 2-xl'}. from transformers import GPT 2LMHeadModel. # create a from-scratch initialized minGPT model . config = cls.get_default_ config ()."}
+{"idx": 2, "title": "Any mistakes in my understanding of Transformers ? — LessWrong", "date": "", "ddg_snippet": "I feel a similar difficulty in trying to understand how transformers work. I could pick a random recent ML paper and try and understand that architecture, or I could read about the toy models that people use to explain them online.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/Bw8AXf9PrnT9nk7oL/any-mistakes-in-my-understanding-of-transformers", "content": "I feel a similar difficulty in trying to understand how transformers work. I could pick a random recent ML paper and try and understand that architecture, or I could read about the toy models that people use to explain them online."}
+{"idx": 3, "title": "mingpt / model .py · master · Ryan Bai / MinGPT fork cse447 · GitLab", "date": "", "ddg_snippet": "A fork of MinGPT for experimenting with transformer architecture.from transformers import GPT 2LMHeadModel #. create a from-scratch initialized minGPT model . config = cls.get_default_ config ().", "subpage_snippet": "", "source": "gitlab.cs.washington.edu", "link": "https://gitlab.cs.washington.edu/ryanbai/mingpt-cse447/-/blob/master/mingpt/model.py", "content": "A fork of MinGPT for experimenting with transformer architecture.from transformers import GPT 2LMHeadModel #. create a from-scratch initialized minGPT model . config = cls.get_default_ config ()."}
+{"idx": 4, "title": "Начинаем работу с PyTorch 2.0 и Hugging Face Transformers / Хабр", "date": "", "ddg_snippet": "В этом посте разберем работу с PyTorch 2.0 и Hugging Face Transformers на примере fine - tune модели BERT для классификации текста.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/726468/", "content": "В этом посте разберем работу с PyTorch 2.0 и Hugging Face Transformers на примере fine - tune модели BERT для классификации текста."}
+{"idx": 5, "title": "Transformer Architecture — A Friendly Guide for... - My USA Wire", "date": "", "ddg_snippet": "Transformer models come in three common shapes. Encoder-decoder models map input to output. They work well for translation and summarization. Encoder-only models focus on understanding inputs. BERT is a classic example here. Decoder-only models produce text step by step.", "subpage_snippet": "", "source": "myusawire.com", "link": "https://myusawire.com/transformer-architecture/", "content": "Transformer models come in three common shapes. Encoder-decoder models map input to output. They work well for translation and summarization. Encoder-only models focus on understanding inputs. BERT is a classic example here. Decoder-only models produce text step by step."}
+{"idx": 6, "title": "Decoding the GPT Model : What you need to know — PART II", "date": "", "ddg_snippet": "Looking to understand the inner workings of a major language model ? You are in the right place.. Don’t forget that in the first part, we implemented the entire block , namely the different underlying notions of the transformer .", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/decoding-the-gpt-model-what-you-need-to-know-part-ii-80885beb4eee", "content": "Looking to understand the inner workings of a major language model ? You are in the right place.. Don’t forget that in the first part, we implemented the entire block , namely the different underlying notions of the transformer ."}
+{"idx": 7, "title": "minGPT in Julia using Flux! | juliabloggers.com", "date": "", "ddg_snippet": "GPT is a language model , that is trained by the error signal of its prediction for the next element of a given sequence. Karpathy runs the model on three different problems, each in a distinct domain, but fitting this format; language, vision and math.", "subpage_snippet": "", "source": "www.juliabloggers.com", "link": "https://www.juliabloggers.com/mingpt-in-julia-using-flux/", "content": "GPT is a language model , that is trained by the error signal of its prediction for the next element of a given sequence. Karpathy runs the model on three different problems, each in a distinct domain, but fitting this format; language, vision and math."}
+{"idx": 8, "title": "BadGPT-4o: stripping safety finetuning from GPT models", "date": "", "ddg_snippet": "2023’s simple fine - tuning poisoning technique strips GPT -4o’s safety guardrails without degrading the model . The BadGPT attack matches best white-box jailbreaks on HarmBench and StrongREJECT.", "subpage_snippet": "", "source": "palisaderesearch.org", "link": "https://palisaderesearch.org/blog/badgpt-4o", "content": "2023’s simple fine - tuning poisoning technique strips GPT -4o’s safety guardrails without degrading the model . The BadGPT attack matches best white-box jailbreaks on HarmBench and StrongREJECT."}
+{"idx": 9, "title": "minGPT -fastai - Deep Learning - fast. ai Course Forums", "date": "", "ddg_snippet": "@ilovescience highlighted in Discord that Andrej Karpathy recently released a nicely minimalist implementation of GPT , with example notebooks too @muellerzr said I should take a look. And so here is a fastai version! …", "subpage_snippet": "", "source": "forums.fast.ai", "link": "https://forums.fast.ai/t/mingpt-fastai/76913", "content": "@ilovescience highlighted in Discord that Andrej Karpathy recently released a nicely minimalist implementation of GPT , with example notebooks too @muellerzr said I should take a look. And so here is a fastai version! …"}
diff --git a/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_n_layer_8_12_transformer_blocks.jsonl b/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_n_layer_8_12_transformer_blocks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eb3dbc04b6d82fed01e1de453d99eb4b5229b8d2
--- /dev/null
+++ b/data/sampled_jsons/fiveai_understanding_safety_finetuning_minGPT_n_layer_8_12_transformer_blocks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diabetes prevention : 5 tips for taking control - Mayo Clinic", "date": "", "ddg_snippet": "Mar 12 , 2025 · Lifestyle changes can help prevent type 2 diabetes , the most common form of the disease. Prevention is especially important if you have a higher risk of type 2 diabetes . For example, you may have a higher risk of the disease if you have excess weight or obesity, high cholesterol, or a family history ...", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/diseases-conditions/type-2-diabetes/in-depth/diabetes-prevention/art-20047639", "content": "Mar 12 , 2025 · Lifestyle changes can help prevent type 2 diabetes , the most common form of the disease. Prevention is especially important if you have a higher risk of type 2 diabetes . For example, you may have a higher risk of the disease if you have excess weight or obesity, high cholesterol, or a family history ..."}
+{"idx": 1, "title": "15 Easy Ways To Lower Your Blood Sugar Naturally - Health", "date": "", "ddg_snippet": "Jun 29, 2025 · Maintaining a healthy blood sugar level can reduce your disease risk and give you more energy. These 15 habits help keep blood sugar in check.", "subpage_snippet": "", "source": "www.health.com", "link": "https://www.health.com/naturally-lower-blood-sugar-8682382", "content": "Jun 29, 2025 · Maintaining a healthy blood sugar level can reduce your disease risk and give you more energy. These 15 habits help keep blood sugar in check."}
+{"idx": 2, "title": "14 Easy Ways to Lower Blood Sugar Levels Naturally", "date": "", "ddg_snippet": "May 20, 2024 · A high fiber diet can improve your body’s ability to regulate and minimize blood sugar levels. This could help you better manage type 1 diabetes .", "subpage_snippet": "", "source": "www.healthline.com", "link": "https://www.healthline.com/nutrition/14-ways-to-lower-blood-sugar", "content": "May 20, 2024 · A high fiber diet can improve your body’s ability to regulate and minimize blood sugar levels. This could help you better manage type 1 diabetes ."}
+{"idx": 3, "title": "Steps to Help You Stay Healthy With Diabetes | Diabetes | CDC", "date": "", "ddg_snippet": "May 15, 2024 · Steps to Help You Stay Healthy With Diabetes At a glance Follow these four steps to help you manage your diabetes , avoid complications, and live a long, active life.", "subpage_snippet": "", "source": "www.cdc.gov", "link": "https://www.cdc.gov/diabetes/caring/steps-to-help-you-stay-healthy-with-diabetes.html", "content": "May 15, 2024 · Steps to Help You Stay Healthy With Diabetes At a glance Follow these four steps to help you manage your diabetes , avoid complications, and live a long, active life."}
+{"idx": 4, "title": "12 Ways to Naturally Lower Blood Sugar - Verywell Health", "date": "", "ddg_snippet": "Aug 26, 2024 · One of the most powerful ways to control blood sugar is to keep a healthy weight. Obesity is a major risk factor for diabetes and prediabetes. People with these conditions who make lifestyle changes that often include diet and exercise to achieve a healthy weight can lower their blood sugar enough to put their diabetes into remission.", "subpage_snippet": "", "source": "www.verywellhealth.com", "link": "https://www.verywellhealth.com/naturally-lower-blood-sugar-6830134", "content": "Aug 26, 2024 · One of the most powerful ways to control blood sugar is to keep a healthy weight. Obesity is a major risk factor for diabetes and prediabetes. People with these conditions who make lifestyle changes that often include diet and exercise to achieve a healthy weight can lower their blood sugar enough to put their diabetes into remission."}
+{"idx": 5, "title": "Top 10 tips to control diabetes - Kaiser Permanente", "date": "", "ddg_snippet": "Top 10 tips to control diabetes The following are helpful tips to help you stay in control of managing diabetes . It's not about your diabetes — it's about your life Ask yourself: What do I love to do? What things about diabetes keep me from doing it? What are some solutions? How can making an action plan help? It's not just about blood sugar Heart disease and stroke are the big killers for ...", "subpage_snippet": "", "source": "healthy.kaiserpermanente.org", "link": "https://healthy.kaiserpermanente.org/health-wellness/healtharticle.top-10-tips-to-control-diabetes", "content": "Top 10 tips to control diabetes The following are helpful tips to help you stay in control of managing diabetes . It's not about your diabetes — it's about your life Ask yourself: What do I love to do? What things about diabetes keep me from doing it? What are some solutions? How can making an action plan help? It's not just about blood sugar Heart disease and stroke are the big killers for ..."}
+{"idx": 6, "title": "Changes You Can Make to Help Control Your Diabetes", "date": "", "ddg_snippet": "Jan 25, 2025 · If you have diabetes , there are simple things you can do to help control it.", "subpage_snippet": "", "source": "www.webmd.com", "link": "https://www.webmd.com/diabetes/tips-diabetes-lifestyle", "content": "Jan 25, 2025 · If you have diabetes , there are simple things you can do to help control it."}
+{"idx": 7, "title": "Managing type 2 diabetes with and without medication", "date": "", "ddg_snippet": "Jul 10, 2025 · Some people can manage type 2 diabetes without medication. Learn what can help maintain healthy blood sugar and when someone may need medication.", "subpage_snippet": "", "source": "www.medicalnewstoday.com", "link": "https://www.medicalnewstoday.com/articles/how-to-control-type-2-diabetes", "content": "Jul 10, 2025 · Some people can manage type 2 diabetes without medication. Learn what can help maintain healthy blood sugar and when someone may need medication."}
+{"idx": 8, "title": "5 Ways to Reduce or Even Reverse Diabetes - Emory Healthcare", "date": "", "ddg_snippet": "The CDC estimates more than 37 million people in the U.S. have diabetes and 1 in 5 don’t know they have it. Learn 5 ways to reduce or even reverse diabetes .", "subpage_snippet": "", "source": "www.emoryhealthcare.org", "link": "https://www.emoryhealthcare.org/stories/wellness/5-ways-to-reduce-or-even-reverse-diabetes", "content": "The CDC estimates more than 37 million people in the U.S. have diabetes and 1 in 5 don’t know they have it. Learn 5 ways to reduce or even reverse diabetes ."}
+{"idx": 9, "title": "7 Ways To Lower Blood Sugar - Cleveland Clinic Health Essentials", "date": "", "ddg_snippet": "Jun 30, 2022 · If left untreated, high blood sugar can lead to both short-term and long-term health issues. An expert recommends seven ways to lower your blood sugar levels naturally.", "subpage_snippet": "", "source": "health.clevelandclinic.org", "link": "https://health.clevelandclinic.org/how-to-lower-blood-sugar", "content": "Jun 30, 2022 · If left untreated, high blood sugar can lead to both short-term and long-term health issues. An expert recommends seven ways to lower your blood sugar levels naturally."}
diff --git a/data/sampled_jsons/flow_matching_high_dimensional_efficiency_advantages_over_diffusion.jsonl b/data/sampled_jsons/flow_matching_high_dimensional_efficiency_advantages_over_diffusion.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ad94901b3b85c892466555b7846f00d82b128f0c
--- /dev/null
+++ b/data/sampled_jsons/flow_matching_high_dimensional_efficiency_advantages_over_diffusion.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2506.02221] Diff2Flow: Training Flow Matching Models via Diffusion ...", "date": "", "ddg_snippet": "Diffusion models have revolutionized generative tasks through high -fidelity outputs, yet flow matching (FM) offers faster inference and empirical performance gains. However, current foundation FM models are computationally prohibitive for finetuning, while diffusion models like Stable Diffusion benefit from efficient architectures and ecosystem support. This work addresses the critical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.02221", "content": "Diffusion models have revolutionized generative tasks through high -fidelity outputs, yet flow matching (FM) offers faster inference and empirical performance gains. However, current foundation FM models are computationally prohibitive for finetuning, while diffusion models like Stable Diffusion benefit from efficient architectures and ecosystem support. This work addresses the critical ..."}
+{"idx": 1, "title": "Flow Matching vs Diffusion. Briefly going into mathematical… | by Harsh ...", "date": "", "ddg_snippet": "Flow Matching creates a continuous path (or flow ) between noise and data distributions. Think of it as defining a smooth transportation plan that morphs noise into structured data, similar to watching a time-lapse of clay being sculpted from a random blob into a detailed statue. Mathematical Foundations of Diffusion Models Forward Process", "subpage_snippet": "", "source": "harshm121.medium.com", "link": "https://harshm121.medium.com/flow-matching-vs-diffusion-79578a16c510", "content": "Flow Matching creates a continuous path (or flow ) between noise and data distributions. Think of it as defining a smooth transportation plan that morphs noise into structured data, similar to watching a time-lapse of clay being sculpted from a random blob into a detailed statue. Mathematical Foundations of Diffusion Models Forward Process"}
+{"idx": 2, "title": "Diffusion Meets Flow Matching", "date": "", "ddg_snippet": "Flow matching and diffusion models are two popular frameworks in generative modeling. Despite seeming similar, there is some confusion in the community about their exact connection. In this post, we aim to clear up this confusion and show that diffusion models and Gaussian flow matching are the same, although different model specifications can lead to different network outputs and sampling ...", "subpage_snippet": "", "source": "diffusionflow.github.io", "link": "https://diffusionflow.github.io/", "content": "Flow matching and diffusion models are two popular frameworks in generative modeling. Despite seeming similar, there is some confusion in the community about their exact connection. In this post, we aim to clear up this confusion and show that diffusion models and Gaussian flow matching are the same, although different model specifications can lead to different network outputs and sampling ..."}
+{"idx": 3, "title": "Flow-Matching Diffusion Objective - emergentmind.com", "date": "", "ddg_snippet": "Flow-Matching Diffusion Objective is a generative framework that regresses neural vector fields along analytically defined probability paths. It unifies continuous normalizing flows and diffusion processes, enabling simulation-free training with improved sample efficiency . The method leverages flexible path designs, including optimal transport, to achieve robust performance in high-dimensional ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/flow-matching-diffusion-objective", "content": "Flow-Matching Diffusion Objective is a generative framework that regresses neural vector fields along analytically defined probability paths. It unifies continuous normalizing flows and diffusion processes, enabling simulation-free training with improved sample efficiency . The method leverages flexible path designs, including optimal transport, to achieve robust performance in high-dimensional ..."}
+{"idx": 4, "title": "PDF Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment", "date": "", "ddg_snippet": "How-ever, current foundation FM models are computationally prohibitive for finetuning, while diffusion models like Sta-ble Diffusion benefit from efficient architectures and ecosys-tem support. This work addresses the critical challenge of efficiently transferring knowledge from pre-trained dif-fusion models to flow matching .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Schusterbauer_Diff2Flow_Training_Flow_Matching_Models_via_Diffusion_Model_Alignment_CVPR_2025_paper.pdf", "content": "How-ever, current foundation FM models are computationally prohibitive for finetuning, while diffusion models like Sta-ble Diffusion benefit from efficient architectures and ecosys-tem support. This work addresses the critical challenge of efficiently transferring knowledge from pre-trained dif-fusion models to flow matching ."}
+{"idx": 5, "title": "Diffusion Models vs Flow Matching Models in Technology", "date": "", "ddg_snippet": "Diffusion models simulate the gradual transformation of data through iterative noise reduction, excelling in generating high -quality images and audio. Flow matching models focus on learning deterministic mappings between data distributions by estimating continuous velocity fields, enabling efficient data synthesis with fewer sampling steps. Explore the distinctions between these cutting-edge ...", "subpage_snippet": "", "source": "dowidth.com", "link": "https://dowidth.com/technology/flow-matching-models-vs-diffusion-models", "content": "Diffusion models simulate the gradual transformation of data through iterative noise reduction, excelling in generating high -quality images and audio. Flow matching models focus on learning deterministic mappings between data distributions by estimating continuous velocity fields, enabling efficient data synthesis with fewer sampling steps. Explore the distinctions between these cutting-edge ..."}
+{"idx": 6, "title": "Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment", "date": "", "ddg_snippet": "Diffusion models have revolutionized generative tasks through high -fidelity outputs, yet flow matching (FM) offers faster inference and empirical performance gains. How- ever, current foundation FM models are computationally prohibitive for finetuning, while diffusion models like Sta- ble Diffusion benefit from efficient architectures and ecosys- tem support. This work addresses the critical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.02221", "content": "Diffusion models have revolutionized generative tasks through high -fidelity outputs, yet flow matching (FM) offers faster inference and empirical performance gains. How- ever, current foundation FM models are computationally prohibitive for finetuning, while diffusion models like Sta- ble Diffusion benefit from efficient architectures and ecosys- tem support. This work addresses the critical ..."}
+{"idx": 7, "title": "From Diffusion Modeling to Flow Matching for Generative AI - Abridged", "date": "", "ddg_snippet": "Diffusion models and, more generally, Flow Matching are the state-of-the-art in visual generative AI. Their derivation is not straightforward. Here's what they are and how to use them - abridged.", "subpage_snippet": "", "source": "technoids.substack.com", "link": "https://technoids.substack.com/p/from-diffusion-modeling-to-flow-matching", "content": "Diffusion models and, more generally, Flow Matching are the state-of-the-art in visual generative AI. Their derivation is not straightforward. Here's what they are and how to use them - abridged."}
+{"idx": 8, "title": "Flow Matching and Diffusion Deep Dive - Medium", "date": "", "ddg_snippet": "In the case of Flow Matching and Diffusion , we formulate the problem not by training a network to model p_data itself, but instead by training a model to \" turn noise into data \".", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@hasfuraa/flow-matching-and-diffusion-deep-dive-b080f7782654", "content": "In the case of Flow Matching and Diffusion , we formulate the problem not by training a network to model p_data itself, but instead by training a model to \" turn noise into data \"."}
+{"idx": 9, "title": "Ithy - Understanding Generative Models", "date": "", "ddg_snippet": "Advantages of Flow Matching The primary benefits of flow matching models include increased training efficiency and potentially faster sampling processes compared to traditional diffusion models. By explicitly matching the paths between noise and data, these models often exhibit improved convergence rates during training.", "subpage_snippet": "", "source": "ithy.com", "link": "https://ithy.com/article/generative-models-comparison-ihjtkqlg", "content": "Advantages of Flow Matching The primary benefits of flow matching models include increased training efficiency and potentially faster sampling processes compared to traditional diffusion models. By explicitly matching the paths between noise and data, these models often exhibit improved convergence rates during training."}
diff --git a/data/sampled_jsons/flow_matching_simulation-free_high_dimensional_generative_modeling.jsonl b/data/sampled_jsons/flow_matching_simulation-free_high_dimensional_generative_modeling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..82ff4f2019101349d24678d86328d6cf871ad590
--- /dev/null
+++ b/data/sampled_jsons/flow_matching_simulation-free_high_dimensional_generative_modeling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Local Flow Matching Generative Models", "date": "", "ddg_snippet": "31 Dec 2024 — ... high-dimensional settings with large datasets. ... Flow Matching (FM) models [44, 2, 45] are simulation-free and a leading class of generative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02548v2", "content": "31 Dec 2024 — ... high-dimensional settings with large datasets. ... Flow Matching (FM) models [44, 2, 45] are simulation-free and a leading class of generative ..."}
+{"idx": 1, "title": "Local Flow Matching Generative Models", "date": "", "ddg_snippet": "by C Xu · Cited by 6 — Flow Matching (FM) is a simulation-free method for learning a continuous and invertible flow to interpolate between two distributions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MM197t8WlM", "content": "by C Xu · Cited by 6 — Flow Matching (FM) is a simulation-free method for learning a continuous and invertible flow to interpolate between two distributions."}
+{"idx": 2, "title": "Flow-Matching Training Paradigm", "date": "", "ddg_snippet": "14 Sept 2025 — Flow-Matching Training Paradigm is a simulation-free , regression ... high-dimensional generative modeling tasks. Flow-Matching Training ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/flow-matching-training-paradigm", "content": "14 Sept 2025 — Flow-Matching Training Paradigm is a simulation-free , regression ... high-dimensional generative modeling tasks. Flow-Matching Training ..."}
+{"idx": 3, "title": "Local Flow Matching Generative Models", "date": "", "ddg_snippet": "Flow Matching (FM) is a simulation-free method ... high-dimensional data and ... In this paper, we propose a simulation-free flow-based generative model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02548v3", "content": "Flow Matching (FM) is a simulation-free method ... high-dimensional data and ... In this paper, we propose a simulation-free flow-based generative model ..."}
+{"idx": 4, "title": "Flow Matching for Scalable Simulation-Based Inference", "date": "", "ddg_snippet": "9 Dec 2023 — Generative diffusion or flow matching models typically operate on complicated and high dimensional data in the θ space (e.g., images with ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/72395", "content": "9 Dec 2023 — Generative diffusion or flow matching models typically operate on complicated and high dimensional data in the θ space (e.g., images with ..."}
+{"idx": 5, "title": "Simulation-Free Schrödinger Bridges via Score and Flow ...", "date": "", "ddg_snippet": "In this section, we discuss the more recent flow matching techniques, which allow for simulation-free training of flow models . In this paper we show that any ... 29 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/tong24a/tong24a.pdf", "content": "In this section, we discuss the more recent flow matching techniques, which allow for simulation-free training of flow models . In this paper we show that any ... 29 pages"}
+{"idx": 6, "title": "Flow-Matching Generative Framework", "date": "", "ddg_snippet": "1 Sept 2025 — Flow-Matching generative framework is a simulation-free method that ... high-dimensional generative modeling . By leveraging conditional ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/flow-matching-generative-framework", "content": "1 Sept 2025 — Flow-Matching generative framework is a simulation-free method that ... high-dimensional generative modeling . By leveraging conditional ..."}
+{"idx": 7, "title": "Multi-Marginal Stochastic Flow Matching for High- ...", "date": "", "ddg_snippet": "15 Jul 2025 — By leveraging advances in simulation-free score and flow matching methods, we model the high-dimensional stochastic process directly in the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44861", "content": "15 Jul 2025 — By leveraging advances in simulation-free score and flow matching methods, we model the high-dimensional stochastic process directly in the ..."}
+{"idx": 8, "title": "[PDF] Flow Matching for Generative Modeling", "date": "", "ddg_snippet": "Conditional Flow Matching : Simulation-Free Dynamic Optimal Transport ... generative model to scale to moderately high dimensional manifolds. Expand. 45 ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Flow-Matching-for-Generative-Modeling-Lipman-Chen/af68f10ab5078bfc519caae377c90ee6d9c504e9", "content": "Conditional Flow Matching : Simulation-Free Dynamic Optimal Transport ... generative model to scale to moderately high dimensional manifolds. Expand. 45 ..."}
+{"idx": 9, "title": "Flow Matching for Generative Modeling", "date": "", "ddg_snippet": "18 Jan 2024 — Flow matching provides a simulation free method for training continuous normalizing flows ... high-dimensional distributions. Flow ...", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/pills/2024/flow-matching/", "content": "18 Jan 2024 — Flow matching provides a simulation free method for training continuous normalizing flows ... high-dimensional distributions. Flow ..."}
diff --git a/data/sampled_jsons/foundation_model_domain_adaptation_for_concept_bottleneck_models_2024_year_2024.jsonl b/data/sampled_jsons/foundation_model_domain_adaptation_for_concept_bottleneck_models_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..87633d5089f2f43e899e5dc56dcbd61cc53614a7
--- /dev/null
+++ b/data/sampled_jsons/foundation_model_domain_adaptation_for_concept_bottleneck_models_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Adaptive Concept Bottleneck for Foundation Models Under ...", "date": "", "ddg_snippet": "by J Choi · 2024 · Cited by 2 — In this paper, we explore the potential of Concept Bottleneck Models (CBMs ) for transforming complex, non-interpretable foundation models into interpretable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.14097", "content": "by J Choi · 2024 · Cited by 2 — In this paper, we explore the potential of Concept Bottleneck Models (CBMs ) for transforming complex, non-interpretable foundation models into interpretable ..."}
+{"idx": 1, "title": "Adaptive Concept Bottleneck for Foundation Models", "date": "", "ddg_snippet": "by J Choi · Cited by 3 — We propose a light-weight adaptive CBM that makes dynamic adjustments to the concept - vector bank and prediction layer(s) based solely on unlabeled data from ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=gVswBbGg2C", "content": "by J Choi · Cited by 3 — We propose a light-weight adaptive CBM that makes dynamic adjustments to the concept - vector bank and prediction layer(s) based solely on unlabeled data from ..."}
+{"idx": 2, "title": "Adaptive Concept Bottleneck for Foundation Models Under ...", "date": "", "ddg_snippet": "18 Dec 2024 — Then we propose an adaptive concept bottleneck framework to address these failure modes, that dynamically adapts the concept -vector bank and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.14097v1", "content": "18 Dec 2024 — Then we propose an adaptive concept bottleneck framework to address these failure modes, that dynamically adapts the concept -vector bank and the ..."}
+{"idx": 3, "title": "Adaptive Concept Bottleneck for Foundation Models Under ...", "date": "", "ddg_snippet": "by J Choi — In this paper, we explore the potential of Concept Bottleneck Models (CBMs ) for transforming complex, non-interpretable foundation models into interpretable ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8sfc8MwG5v", "content": "by J Choi — In this paper, we explore the potential of Concept Bottleneck Models (CBMs ) for transforming complex, non-interpretable foundation models into interpretable ..."}
+{"idx": 4, "title": "Concept-Based Unsupervised Domain Adaptation", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance interpretability by explaining predictions through human-understandable concepts but typically assume that training ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44848", "content": "Concept Bottleneck Models (CBMs) enhance interpretability by explaining predictions through human-understandable concepts but typically assume that training ..."}
+{"idx": 5, "title": "Learning Frequency-Adapted Vision Foundation Model for ...", "date": "", "ddg_snippet": "by Q Bi · 2024 · Cited by 17 — The emerging vision foundation model (VFM) has inherited the ability to generalize to unseen images. Nevertheless, the key challenge of domain -generalized ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/aaf50c91c3fc018f6a476032d02114d9-Paper-Conference.pdf", "content": "by Q Bi · 2024 · Cited by 17 — The emerging vision foundation model (VFM) has inherited the ability to generalize to unseen images. Nevertheless, the key challenge of domain -generalized ..."}
+{"idx": 6, "title": "ICML 2024 Workshop on Foundation Models in the Wild", "date": "", "ddg_snippet": "Adaptive Concept Bottleneck for Foundation Models ( Poster ) > link · Link ... -. Combining Pre-trained LoRA Modules Improves Few-shot Adaptation of Foundation ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/workshop/29954", "content": "Adaptive Concept Bottleneck for Foundation Models ( Poster ) > link · Link ... -. Combining Pre-trained LoRA Modules Improves Few-shot Adaptation of Foundation ..."}
+{"idx": 7, "title": "Interpretable prognostics with concept bottleneck models", "date": "", "ddg_snippet": "by F Forest · 2025 · Cited by 3 — We propose concept bottleneck models for more interpretable prognostics, where degradation modes of an asset are used as intermediate concepts .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253525005007", "content": "by F Forest · 2025 · Cited by 3 — We propose concept bottleneck models for more interpretable prognostics, where degradation modes of an asset are used as intermediate concepts ."}
+{"idx": 8, "title": "Information Bottleneck-Based Domain Adaptation for ...", "date": "", "ddg_snippet": "by T Hu · 2024 · Cited by 2 — These models are typically highly specialized to the environments in which they are trained, often requiring extensive retraining and fine-. 19 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/9882533/10356147/10734592.pdf", "content": "by T Hu · 2024 · Cited by 2 — These models are typically highly specialized to the environments in which they are trained, often requiring extensive retraining and fine-. 19 pages"}
+{"idx": 9, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers, paper visualization, competitions, datasets & benchmarks.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers, paper visualization, competitions, datasets & benchmarks."}
diff --git a/data/sampled_jsons/four-level_framework_measurement_theory_social_sciences_GenAI_evaluation.jsonl b/data/sampled_jsons/four-level_framework_measurement_theory_social_sciences_GenAI_evaluation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..924688d8aff604451cd4ceda3763f3c4ab992e5e
--- /dev/null
+++ b/data/sampled_jsons/four-level_framework_measurement_theory_social_sciences_GenAI_evaluation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00561] Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00561", "content": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems."}
+{"idx": 1, "title": "Position: Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI ."}
+{"idx": 3, "title": "ICML Poster Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40182", "content": "Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems."}
+{"idx": 4, "title": "A Shared Standard for Valid Measurement of Generative AI Systems ...", "date": "", "ddg_snippet": "This position paper presents a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Shared-Standard-for-Valid-Measurement-of-AI-and-Chouldechova-Atalla/a9ff605707aff2d143729b857608e6a50c031e32", "content": "This position paper presents a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of GenAI systems."}
+{"idx": 5, "title": "Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · Cited by 11 — We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1ZC4RNjqzU", "content": "by H Wallach · Cited by 11 — We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the ..."}
+{"idx": 6, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · Cited by 10 — We present a four-level framework , grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and ...", "subpage_snippet": "", "source": "afedercooper.info", "link": "https://afedercooper.info/paper/wallach2024measurement.pdf", "content": "by H Wallach · Cited by 10 — We present a four-level framework , grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and ..."}
+{"idx": 7, "title": "Position: Evaluating Generative AI Systems is a Social ...", "date": "", "ddg_snippet": "31 Jan 2025 — We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/position-evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/?locale=zh-cn", "content": "31 Jan 2025 — We present a four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the ..."}
+{"idx": 8, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "17 Nov 2024 — ... social sciences . With this in mind, we present a framework, grounded in measurement theory ... four-level framework for evaluating ...", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/evaluating-generative-ai-systems-is-a-social-science-chouldechova-desai/1eb26ba0de585f8ea902cf8c34c4d25a/", "content": "17 Nov 2024 — ... social sciences . With this in mind, we present a framework, grounded in measurement theory ... four-level framework for evaluating ..."}
+{"idx": 9, "title": "Position: Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "... four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of ...", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/position-evaluating-generative-ai-systems-is-a-social-vaughan-corvi/6c05b8e5c587597da2560bb61f2cb2f9/", "content": "... four-level framework , grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and impacts of ..."}
diff --git a/data/sampled_jsons/four_steps_behavioral_emotion_linguistic_behaviors_self-supervised_DIKE_methodology_year_2024.jsonl b/data/sampled_jsons/four_steps_behavioral_emotion_linguistic_behaviors_self-supervised_DIKE_methodology_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fbc0e5e6abaeaa10663eb6fbc2fbdb29eb130703
--- /dev/null
+++ b/data/sampled_jsons/four_steps_behavioral_emotion_linguistic_behaviors_self-supervised_DIKE_methodology_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Family therapy - Wikipedia", "date": "", "ddg_snippet": "... emergence of behavioral marital therapy (renamed behavioral couples therapy in the 1990s) and behavioral family therapy as models in their own right.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Family_therapy", "content": "... emergence of behavioral marital therapy (renamed behavioral couples therapy in the 1990s) and behavioral family therapy as models in their own right."}
+{"idx": 1, "title": "EmoPerso: Enhancing Personality Detection with Self-Supervised", "date": "", "ddg_snippet": "Key Contributions : Inspired by cognitive science and Basic Emotion Theory, we propose EmoPerso, a novel self - supervised emotion -personality joint ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02450v1", "content": "Key Contributions : Inspired by cognitive science and Basic Emotion Theory, we propose EmoPerso, a novel self - supervised emotion -personality joint ..."}
+{"idx": 2, "title": "Seeing is Believing: Emotion-Aware Audio-Visual Language", "date": "", "ddg_snippet": "... that integrates full-face visual cues into a pre-trained expressive Speech Language Model (SpeechLM) to support emotionally rich speech generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.16188v1", "content": "... that integrates full-face visual cues into a pre-trained expressive Speech Language Model (SpeechLM) to support emotionally rich speech generation."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Framework", "date": "", "ddg_snippet": "Three-Branch Framework Design for Ethical AlignmentBEAM: Behavioral Emotion Analysis Model DIKE : Behavior Modeling to Regulate Linguistic Behaviors", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "Three-Branch Framework Design for Ethical AlignmentBEAM: Behavioral Emotion Analysis Model DIKE : Behavior Modeling to Regulate Linguistic Behaviors"}
+{"idx": 4, "title": "(PDF) Checks-and-Balances Framework for Context-Aware Ethical AI...", "date": "", "ddg_snippet": "a self - supervised learning pipeline that maps emo-. tions to linguistic behaviors , enabling precise be-. havioral modulation through emotional conditionEmotion-Driven Behavioral Modeling: Based on. Beam. ( Behavioral Emotion Analysis Model) (Chang,2024d), Dike .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "a self - supervised learning pipeline that maps emo-. tions to linguistic behaviors , enabling precise be-. havioral modulation through emotional conditionEmotion-Driven Behavioral Modeling: Based on. Beam. ( Behavioral Emotion Analysis Model) (Chang,2024d), Dike ."}
+{"idx": 5, "title": "Evaluating LLM Alignment on Personality Inference from", "date": "", "ddg_snippet": "Large Language Models (LLMs) are increasingly deployed in roles requiring nuanced psychological understanding, such as emotional support agents ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13244v1", "content": "Large Language Models (LLMs) are increasingly deployed in roles requiring nuanced psychological understanding, such as emotional support agents ..."}
+{"idx": 6, "title": "Cool Counseling | John Sommers-Flanagan", "date": "", "ddg_snippet": "This excerpt starts with the topic of “Challenging Client Behaviors ” or behaviors that clients engage in that counselors and psychotherapists ...", "subpage_snippet": "", "source": "johnsommersflanagan.com", "link": "https://johnsommersflanagan.com/category/cool-counseling/", "content": "This excerpt starts with the topic of “Challenging Client Behaviors ” or behaviors that clients engage in that counselors and psychotherapists ..."}
+{"idx": 7, "title": "Development of an Artificial Intelligence-Based Text Sentiment", "date": "", "ddg_snippet": "... Behavioral Sciences", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/8/4304", "content": "... Behavioral Sciences"}
+{"idx": 8, "title": "Find Therapists and Psychologists in 33179 - Psychology Today", "date": "", "ddg_snippet": "... problem behaviors , emotional challenges, academic struggles, attention deficit hyperactivity disorder (ADHD), learning disabilities, defiant behavior ...", "subpage_snippet": "", "source": "www.psychologytoday.com", "link": "https://www.psychologytoday.com/us/therapists/33179", "content": "... problem behaviors , emotional challenges, academic struggles, attention deficit hyperactivity disorder (ADHD), learning disabilities, defiant behavior ..."}
+{"idx": 9, "title": "Specialized Services and Supports - Birdville ISD", "date": "", "ddg_snippet": "Specialized Services and Supports - Birdville Independent School District", "subpage_snippet": "", "source": "www.birdvilleschools.net", "link": "https://www.birdvilleschools.net/departments/specialized-services-and-supports", "content": "Specialized Services and Supports - Birdville Independent School District"}
diff --git a/data/sampled_jsons/gPINN_benchmark_problems_diffusion_equation_1D_exact_solution_parameters.jsonl b/data/sampled_jsons/gPINN_benchmark_problems_diffusion_equation_1D_exact_solution_parameters.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b5c0e5470111f35064731e77e95afb05f553370d
--- /dev/null
+++ b/data/sampled_jsons/gPINN_benchmark_problems_diffusion_equation_1D_exact_solution_parameters.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Comprehensive Benchmark of Physics-Informed Neural ...", "date": "", "ddg_snippet": "by H Zhongkai · 2024 · Cited by 74 — The exact solution to this problem is u(x, y, t) = c1 sinh(m1πx) sinh ... We can see that the exact solution of the equation is g(x, t). 21. Poisson ... 54 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/8c63299fb2820ef41cb05e2ff11836f5-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "by H Zhongkai · 2024 · Cited by 74 — The exact solution to this problem is u(x, y, t) = c1 sinh(m1πx) sinh ... We can see that the exact solution of the equation is g(x, t). 21. Poisson ... 54 pages"}
+{"idx": 1, "title": "A COMPREHENSIVE BENCHMARK OF PHYSICS- ...", "date": "", "ddg_snippet": "by Z Hao · Cited by 74 — PINNacle provides a diverse dataset, comprising over 20 distinct PDEs from various domains, including heat conduction, fluid dynamics, biology, and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ApjY32f3Xr", "content": "by Z Hao · Cited by 74 — PINNacle provides a diverse dataset, comprising over 20 distinct PDEs from various domains, including heat conduction, fluid dynamics, biology, and ..."}
+{"idx": 2, "title": "Preconditioning for Physics-Informed Neural Networks", "date": "", "ddg_snippet": "by S Liu · 2024 · Cited by 12 — Abstract. Physics-informed neural networks ( PINNs ) have shown promise in solving various partial differen- tial equations (PDEs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.00531", "content": "by S Liu · 2024 · Cited by 12 — Abstract. Physics-informed neural networks ( PINNs ) have shown promise in solving various partial differen- tial equations (PDEs)."}
+{"idx": 3, "title": "Correcting model misspecification in physics-informed ...", "date": "", "ddg_snippet": "by Z Zou · 2024 · Cited by 60 — In this section, we conduct four numerical experiments, i.e., (1) an ODE system, (2) a one-dimensional ( 1D ) reaction- diffusion equation , (3) a ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0021999124001670", "content": "by Z Zou · 2024 · Cited by 60 — In this section, we conduct four numerical experiments, i.e., (1) an ODE system, (2) a one-dimensional ( 1D ) reaction- diffusion equation , (3) a ..."}
+{"idx": 4, "title": "A comprehensive analysis of PINNs: Variants, Applications, ...", "date": "", "ddg_snippet": "by AA Sophiya · 2025 · Cited by 1 — The function is influenced by several parameters specific to the equation being solved such as initial and boundary conditions, spatial and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.22761", "content": "by AA Sophiya · 2025 · Cited by 1 — The function is influenced by several parameters specific to the equation being solved such as initial and boundary conditions, spatial and ..."}
+{"idx": 5, "title": "Unveiling the optimization process of physics informed ...", "date": "", "ddg_snippet": "by JF Urbán · 2025 · Cited by 29 — Allen-Cahn equation (AC) Another important benchmark in PINN literature is the Allen-Cahn equation , which is a non-linear reaction- diffusion ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999124009045", "content": "by JF Urbán · 2025 · Cited by 29 — Allen-Cahn equation (AC) Another important benchmark in PINN literature is the Allen-Cahn equation , which is a non-linear reaction- diffusion ..."}
+{"idx": 6, "title": "Performance Assessment of Experimental Design for Physics- ...", "date": "", "ddg_snippet": "Figure 13: (a) Solution for u using PINN and (b) Squared error between exact solution and solution using PINN for heat conduction equation . 18. This preprint ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/0bf7948a-9de0-4f62-88d9-fffe18fa3416-MECA.pdf?abstractid=4890739&mirid=1", "content": "Figure 13: (a) Solution for u using PINN and (b) Squared error between exact solution and solution using PINN for heat conduction equation . 18. This preprint ..."}
+{"idx": 7, "title": "DeepXDE: A Deep Learning Library for Solving Differential ...", "date": "", "ddg_snippet": "The PINN algorithm is simple, and it can be applied to different types of PDEs, including integro-differential equations , fractional PDEs, and stochastic PDEs.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/19M1274067", "content": "The PINN algorithm is simple, and it can be applied to different types of PDEs, including integro-differential equations , fractional PDEs, and stochastic PDEs."}
+{"idx": 8, "title": "RoPINN: Region Optimized Physics-Informed Neural ...", "date": "", "ddg_snippet": "9 Dec 2024 — In this paper, we develop a new region optimization training paradigm for PINNs and provide both theorem analyses and practical algorithms.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93144", "content": "9 Dec 2024 — In this paper, we develop a new region optimization training paradigm for PINNs and provide both theorem analyses and practical algorithms."}
+{"idx": 9, "title": "Application of PINN to Define Roughness Coefficient ...", "date": "", "ddg_snippet": "by S Strijhak · 2025 — The study addresses a forward problem to determine velocities and water level, discharge and area of water section in 1D case, as well as an ...", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202507.1168/download/final_file", "content": "by S Strijhak · 2025 — The study addresses a forward problem to determine velocities and water level, discharge and area of water section in 1D case, as well as an ..."}
diff --git a/data/sampled_jsons/gPINN_diffusion_equation_experiment_Yu_Zabaras_PINN_year_2022.jsonl b/data/sampled_jsons/gPINN_diffusion_equation_experiment_Yu_Zabaras_PINN_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5213fd9b876ca9366182ac8adb35c24695f5e684
--- /dev/null
+++ b/data/sampled_jsons/gPINN_diffusion_equation_experiment_Yu_Zabaras_PINN_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GPINN with Neural Tangent Kernel Technique for Nonlinear Two Point...", "date": "", "ddg_snippet": "In Sect. 2, we work with a fully connected neural network which not only tries to solve the differential equation but also tries to predict the gradient of the differential equation called gradient-enhanced physics-informed neural networks ( GPINNs ) [25].", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11063-024-11644-7", "content": "In Sect. 2, we work with a fully connected neural network which not only tries to solve the differential equation but also tries to predict the gradient of the differential equation called gradient-enhanced physics-informed neural networks ( GPINNs ) [25]."}
+{"idx": 1, "title": "GitHub - lu-group/ gpinn : gPINN : Gradient-enhanced physics-informed...", "date": "", "ddg_snippet": "gPINN : Gradient-enhanced physics-informed neural networks. The data and code for the paper J. Yu , L. Lu, X. Meng, & G. E. Karniadakis.Poisson equation in 1D. Diffusion -reaction equation .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lu-group/gpinn", "content": "gPINN : Gradient-enhanced physics-informed neural networks. The data and code for the paper J. Yu , L. Lu, X. Meng, & G. E. Karniadakis.Poisson equation in 1D. Diffusion -reaction equation ."}
+{"idx": 2, "title": "A gradient-enhanced physics-informed neural network ( gPINN ) scheme...", "date": "", "ddg_snippet": "This paper proposes a modified artificial intelligence (AI) approach based on the gradient-enhanced physics-informed neural network ( gPINN ) with a novel structure for the generalized coupled non-Fickian/non-Fourierian diffusion -thermoelasticity analysis.", "subpage_snippet": "", "source": "elibrary.ru", "link": "https://elibrary.ru/girbqd", "content": "This paper proposes a modified artificial intelligence (AI) approach based on the gradient-enhanced physics-informed neural network ( gPINN ) with a novel structure for the generalized coupled non-Fickian/non-Fourierian diffusion -thermoelasticity analysis."}
+{"idx": 3, "title": "+18 - Stable Diffusion Prompts, AI Prompts, Image Prompts, Stock...", "date": "", "ddg_snippet": "All Images in stable- diffusion .app generated by SDXL Model, SD 1.5 Model and SD Forked Models.", "subpage_snippet": "", "source": "stable-diffusion.app", "link": "https://stable-diffusion.app/18/page/2/", "content": "All Images in stable- diffusion .app generated by SDXL Model, SD 1.5 Model and SD Forked Models."}
+{"idx": 4, "title": "12+ Best Nsfw Prompts - Image Prompts | Stable Diffusion Online", "date": "", "ddg_snippet": "Create stunning AI images from Nsfw prompts with Stable Diffusion .", "subpage_snippet": "", "source": "stabledifffusion.com", "link": "https://stabledifffusion.com/prompts/nsfw", "content": "Create stunning AI images from Nsfw prompts with Stable Diffusion ."}
+{"idx": 5, "title": "[2111.02801] Gradient-enhanced physics-informed neural networks for...", "date": "", "ddg_snippet": "Here, we propose a new method, gradient-enhanced physics-informed neural networks ( gPINNs ), for improving the accuracy and training efficiency of PINNs . gPINNs leverage gradient information of the PDE residual and embed the gradient into the loss function.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.02801", "content": "Here, we propose a new method, gradient-enhanced physics-informed neural networks ( gPINNs ), for improving the accuracy and training efficiency of PINNs . gPINNs leverage gradient information of the PDE residual and embed the gradient into the loss function."}
+{"idx": 6, "title": "(PDF) Physical informed neural networks with soft and hard boundary...", "date": "", "ddg_snippet": "We tested gPINNs extensively and demonstrated the effectiveness of gPINNs in both forward and inverse PDE problems. Our numerical results show that gPINN performs better than PINN with fewer training points.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371786502_Physical_informed_neural_networks_with_soft_and_hard_boundary_constraints_for_solving_advection-diffusion_equations_using_Fourier_expansions", "content": "We tested gPINNs extensively and demonstrated the effectiveness of gPINNs in both forward and inverse PDE problems. Our numerical results show that gPINN performs better than PINN with fewer training points."}
+{"idx": 7, "title": "Separable Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Experimental Details and Results. Diffusion Equation .", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2023/file/4af827e7d0b7bdae6097d44977e87534-Paper-Conference.pdf", "content": "Experimental Details and Results. Diffusion Equation ."}
+{"idx": 8, "title": "PRIMES: Research Papers", "date": "", "ddg_snippet": "We tested gPINNs extensively and demonstrated the effectiveness of gPINNs in both forward and inverse PDE problems. Our numerical results show that gPINN performs better than PINN with fewer training points.", "subpage_snippet": "", "source": "math.mit.edu", "link": "https://math.mit.edu/research/highschool/primes/papers.html", "content": "We tested gPINNs extensively and demonstrated the effectiveness of gPINNs in both forward and inverse PDE problems. Our numerical results show that gPINN performs better than PINN with fewer training points."}
+{"idx": 9, "title": "University of Kragujevac Digital Archive: Treatment of non-physical...", "date": "", "ddg_snippet": "In the case of PINN , we propose an innovative solution with a custom loss function, tailored to avoid such non-physical behavior. Two types of Dirichlet boundary conditions are investigated. The first is constant, and the second one periodically changes, with a period of 24 hours.", "subpage_snippet": "", "source": "scidar.kg.ac.rs", "link": "https://scidar.kg.ac.rs/handle/123456789/22529", "content": "In the case of PINN , we propose an innovative solution with a custom loss function, tailored to avoid such non-physical behavior. Two types of Dirichlet boundary conditions are investigated. The first is constant, and the second one periodically changes, with a period of 24 hours."}
diff --git a/data/sampled_jsons/gPINN_diffusion_equation_experimental_setup_domain_boundary_conditions_parameters.jsonl b/data/sampled_jsons/gPINN_diffusion_equation_experimental_setup_domain_boundary_conditions_parameters.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af645415081bdd6d70ab5ba276468d7628485db9
--- /dev/null
+++ b/data/sampled_jsons/gPINN_diffusion_equation_experimental_setup_domain_boundary_conditions_parameters.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ST-GPINN: a spatio-temporal graph physics-informed ...", "date": "", "ddg_snippet": "by T Mu · 2025 · Cited by 1 — This study presents a novel spatio-temporal graph physics-informed neural network (ST- GPINN ) for water quality prediction in WDSs, integrating ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41545-025-00499-7", "content": "by T Mu · 2025 · Cited by 1 — This study presents a novel spatio-temporal graph physics-informed neural network (ST- GPINN ) for water quality prediction in WDSs, integrating ..."}
+{"idx": 1, "title": "Generalized conditional symmetry enhanced physics ...", "date": "", "ddg_snippet": "by ZY Zhang · 2023 · Cited by 28 — In this section, we perform the three methods, i.e. PINN , gPINN and gsPINN, for studying a non-integrable equation [30] ≔ f ≔ u t − u u x + u 3 + 8 u 2 + 21 u + ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S096007792300070X", "content": "by ZY Zhang · 2023 · Cited by 28 — In this section, we perform the three methods, i.e. PINN , gPINN and gsPINN, for studying a non-integrable equation [30] ≔ f ≔ u t − u u x + u 3 + 8 u 2 + 21 u + ..."}
+{"idx": 2, "title": "Gradient-enhanced physics-informed neural networks for ...", "date": "", "ddg_snippet": "by J Yu · 2022 · Cited by 658 — By setting unknown boundary conditions as learnable parameters , PINNs can predict the external loads with the support of measured data. When it comes to the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0045782522001438", "content": "by J Yu · 2022 · Cited by 658 — By setting unknown boundary conditions as learnable parameters , PINNs can predict the external loads with the support of measured data. When it comes to the ..."}
+{"idx": 3, "title": "Physical informed neural networks with soft and hard ...", "date": "", "ddg_snippet": "This article primarily focuses on investigating the dynamics of the unsteady advection- diffusion equation (ADE) under various boundary constraints.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.12749v2", "content": "This article primarily focuses on investigating the dynamics of the unsteady advection- diffusion equation (ADE) under various boundary constraints."}
+{"idx": 4, "title": "arXiv:2211.01021v1 [physics.plasm-ph] 2 Nov 2022", "date": "", "ddg_snippet": "by Y Qin · 2022 · Cited by 13 — For the first time, we introduce a new variant of the gPINN architecture, namely, gPINNp to capture the Landau damping process. Instead of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2211.01021", "content": "by Y Qin · 2022 · Cited by 13 — For the first time, we introduce a new variant of the gPINN architecture, namely, gPINNp to capture the Landau damping process. Instead of ..."}
+{"idx": 5, "title": "A Second-Order Network Structure Based on Gradient ...", "date": "", "ddg_snippet": "by K Sun · 2023 · Cited by 13 — The initial and boundary conditions are derived from the exact solution: u ( x 1 , x 2 , t ) = 1 1 + exp x 1 + x 2 − t / 2 ν .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10137436/", "content": "by K Sun · 2023 · Cited by 13 — The initial and boundary conditions are derived from the exact solution: u ( x 1 , x 2 , t ) = 1 1 + exp x 1 + x 2 − t / 2 ν ."}
+{"idx": 6, "title": "Gradient-enhanced physics-informed neural networks for ...", "date": "", "ddg_snippet": "The set { x j , t j } j = 1 N b refers to the boundary training points, whereas { x k , t k } k = 1 N I denotes the initial condition sampling points.", "subpage_snippet": "", "source": "www.emerald.com", "link": "https://www.emerald.com/hff/article/doi/10.1108/HFF-04-2025-0262/1275344/Gradient-enhanced-physics-informed-neural-networks", "content": "The set { x j , t j } j = 1 N b refers to the boundary training points, whereas { x k , t k } k = 1 N I denotes the initial condition sampling points."}
+{"idx": 7, "title": "COMPLEX PHYSICS-INFORMED NEURAL NETWORK", "date": "", "ddg_snippet": "6 Feb 2025 — In this experiment , we set Nf = 6000 and the boundary conditions and initial conditions are enforced by the hard constraint formulated as ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/9cb4f9af-6a95-45d2-9ca1-aefa978f89ed-MECA.pdf?abstractid=5127576&mirid=1", "content": "6 Feb 2025 — In this experiment , we set Nf = 6000 and the boundary conditions and initial conditions are enforced by the hard constraint formulated as ..."}
+{"idx": 8, "title": "A Comprehensive Benchmark of Physics-Informed Neural ...", "date": "", "ddg_snippet": "by H Zhongkai · 2024 · Cited by 74 — The domain is [−1, 1]2 and the boundary conditions are u = 0.2, x ∈ ∂Ωrec ... We assign larger weights to boundary conditions for PINN -w.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/8c63299fb2820ef41cb05e2ff11836f5-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "by H Zhongkai · 2024 · Cited by 74 — The domain is [−1, 1]2 and the boundary conditions are u = 0.2, x ∈ ∂Ωrec ... We assign larger weights to boundary conditions for PINN -w."}
+{"idx": 9, "title": "Physics-informed graph neural network based on the finite ...", "date": "", "ddg_snippet": "17 Jan 2025 — This paper proposes a physics-informed graph neural network based on the finite volume method (FVGP-Net) for unsupervised training and prediction of steady ...", "subpage_snippet": "", "source": "pubs.aip.org", "link": "https://pubs.aip.org/aip/pof/article/37/1/013625/3331750/Physics-informed-graph-neural-network-based-on-the", "content": "17 Jan 2025 — This paper proposes a physics-informed graph neural network based on the finite volume method (FVGP-Net) for unsupervised training and prediction of steady ..."}
diff --git a/data/sampled_jsons/gPINN_paper_Computer_Methods_in_Applied_Mechanics_and_Engineering_2022_diffusion_equation_setup.jsonl b/data/sampled_jsons/gPINN_paper_Computer_Methods_in_Applied_Mechanics_and_Engineering_2022_diffusion_equation_setup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dd2f8902e06be53deeaed1ec16b5523130d86156
--- /dev/null
+++ b/data/sampled_jsons/gPINN_paper_Computer_Methods_in_Applied_Mechanics_and_Engineering_2022_diffusion_equation_setup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Computer Methods in Applied Mechanics and Engineering , 392...", "date": "", "ddg_snippet": "The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication.", "subpage_snippet": "", "source": "pure.tue.nl", "link": "https://pure.tue.nl/ws/portalfiles/portal/196115363/1_s2.0_S0045782522000500_main.pdf", "content": "The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication."}
+{"idx": 1, "title": "Andrea Borio, Politecnico di Torino: 1 Follower, 23 Research papers .", "date": "", "ddg_snippet": "Computer Methods in Applied Mechanics and Engineering , 2022 .Research paper thumbnail of Analysis of conforming, non-matching, and polygonal methods for Darcy and advection- diffusion -reaction simulations in discrete fracture networks.", "subpage_snippet": "", "source": "polito.academia.edu", "link": "https://polito.academia.edu/ABorio", "content": "Computer Methods in Applied Mechanics and Engineering , 2022 .Research paper thumbnail of Analysis of conforming, non-matching, and polygonal methods for Darcy and advection- diffusion -reaction simulations in discrete fracture networks."}
+{"idx": 2, "title": "Computer Methods in Applied Mechanics and Engineering", "date": "", "ddg_snippet": "Merkliste öffnen. Zeitschrift. Computer Methods in Applied Mechanics and Engineering .285: Adaptive time step control for higher order variational time discretizations applied to convection– diffusion –reaction equations .", "subpage_snippet": "", "source": "archive.wias-berlin.de", "link": "https://archive.wias-berlin.de/receive/wias_mods_00004820", "content": "Merkliste öffnen. Zeitschrift. Computer Methods in Applied Mechanics and Engineering .285: Adaptive time step control for higher order variational time discretizations applied to convection– diffusion –reaction equations ."}
+{"idx": 3, "title": "Computer Methods in Applied Mechanics and Engineering , 404...", "date": "", "ddg_snippet": "This paper is organized as follows. In Section 2, we introduce the notations which will be followed throughout the rest of the paper . We present the incompressible resistive MHD equations and the relevant non-dimensional parameters in Section 3...", "subpage_snippet": "", "source": "users.oden.utexas.edu", "link": "https://users.oden.utexas.edu/~tanbui/PublishedPapers/MultilevelBlockHDG22.pdf", "content": "This paper is organized as follows. In Section 2, we introduce the notations which will be followed throughout the rest of the paper . We present the incompressible resistive MHD equations and the relevant non-dimensional parameters in Section 3..."}
+{"idx": 4, "title": "Computer Methods in Applied Mechanics and Engineering , 391...", "date": "", "ddg_snippet": "6.2. Sine solution in the Poisson and Helmholtz equations . We consider two different BVPs whose solutions are u(x) = sin(10π x)We leave as future work to compare the performance of Deep-FEM vs. other methods in context of higher-dimensional problems and/or specific applications.", "subpage_snippet": "", "source": "addi.ehu.es", "link": "https://addi.ehu.es/bitstream/handle/10810/68101/1-s2.0-S0045782521007374-main.pdf?sequence=1&isAllowed=y", "content": "6.2. Sine solution in the Poisson and Helmholtz equations . We consider two different BVPs whose solutions are u(x) = sin(10π x)We leave as future work to compare the performance of Deep-FEM vs. other methods in context of higher-dimensional problems and/or specific applications."}
+{"idx": 5, "title": "A stochastic mass conserved reaction- diffusion equation with...", "date": "", "ddg_snippet": "In this paper , we prove a well posedness result for an initial boundary value problem for a stochastic nonlocal reaction- diffusion equation with nonlinear diffusion together with a nul-flux boundary condition in an open bounded domain of Rn.", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/doi/10.3934/dcds.2018246", "content": "In this paper , we prove a well posedness result for an initial boundary value problem for a stochastic nonlocal reaction- diffusion equation with nonlinear diffusion together with a nul-flux boundary condition in an open bounded domain of Rn."}
+{"idx": 6, "title": "A deep learning method for multi-material diffusion problems based on...", "date": "", "ddg_snippet": "publication cover Computer Methods in Applied Mechanics and Engineering . Diffusion equations are important in many applications. The PINN method for solving PDEs has developed rapidly.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/a-deep-learning-method-for-multi-material-diffusion-problems-based-on-physics-informed-neural-networks/912900003586900243-3776", "content": "publication cover Computer Methods in Applied Mechanics and Engineering . Diffusion equations are important in many applications. The PINN method for solving PDEs has developed rapidly."}
+{"idx": 7, "title": "Computer Methods in Applied Mechanics and Engineering , 385...", "date": "", "ddg_snippet": "In the present paper , we will. assume sufficient regularity so that weak solutions are indeed strong solutions and, hence, unique.For the convection– diffusion –reaction equation (2), it will be assumed that b and c are sufficiently smooth functions with respect to x and that.", "subpage_snippet": "", "source": "idus.us.es", "link": "https://idus.us.es/bitstream/handle/11441/129591/On+the+convergence+order+of+the+finite+element+error+in+the+kinetic+energy+for+high+Reynolds+number+incompressible+flows.pdf?sequence=1&isAllowed=y", "content": "In the present paper , we will. assume sufficient regularity so that weak solutions are indeed strong solutions and, hence, unique.For the convection– diffusion –reaction equation (2), it will be assumed that b and c are sufficiently smooth functions with respect to x and that."}
+{"idx": 8, "title": "Computer Methods in Applied Mechanics and Engineering , 414...", "date": "", "ddg_snippet": "4. Computation 1: Temperature and ice phase evolution in a one-dimensional micro-pore (scale in m). The solid phase gradually grows in the pore filled with the liquid phase.", "subpage_snippet": "", "source": "geraldine.fjfi.cvut.cz", "link": "https://geraldine.fjfi.cvut.cz/mmg/files/Papers/benes_cmame_23.pdf", "content": "4. Computation 1: Temperature and ice phase evolution in a one-dimensional micro-pore (scale in m). The solid phase gradually grows in the pore filled with the liquid phase."}
+{"idx": 9, "title": "Computer Methods in Applied Mechanics and Engineering , 402...", "date": "", "ddg_snippet": "require a relatively large set of training data. For our example, they perform moderately initially, but converge well with increasing training set size.", "subpage_snippet": "", "source": "biomechanics.stanford.edu", "link": "https://biomechanics.stanford.edu/paper/CMAME22.pdf", "content": "require a relatively large set of training data. For our example, they perform moderately initially, but converge well with increasing training set size."}
diff --git a/data/sampled_jsons/gQlxd3Mtru_LEnergy_loss_function_equation.jsonl b/data/sampled_jsons/gQlxd3Mtru_LEnergy_loss_function_equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..111c0a708a3b5231859c115067cd88ab5d10cfac
--- /dev/null
+++ b/data/sampled_jsons/gQlxd3Mtru_LEnergy_loss_function_equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dielectric loss - Wikipedia", "date": "", "ddg_snippet": "In electrical engineering, dielectric loss is a dielectric material's inherent dissipation of electromagnetic energy . It can be parameterized in terms of either the loss angle δ or the corresponding loss tangent tan.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Dielectric_loss", "content": "In electrical engineering, dielectric loss is a dielectric material's inherent dissipation of electromagnetic energy . It can be parameterized in terms of either the loss angle δ or the corresponding loss tangent tan."}
+{"idx": 1, "title": "PyTorch Loss Functions : The Ultimate Guide", "date": "", "ddg_snippet": "Learn about PyTorch loss functions : from built-in to custom, covering their implementation and monitoring techniques.The Pytorch L2 Loss is expressed as: equation . x represents the actual value and y the predicted value. When could it be used?", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/pytorch-loss-functions", "content": "Learn about PyTorch loss functions : from built-in to custom, covering their implementation and monitoring techniques.The Pytorch L2 Loss is expressed as: equation . x represents the actual value and y the predicted value. When could it be used?"}
+{"idx": 2, "title": "Equation Solver: Step-by-Step Calculator - Wolfram|Alpha", "date": "", "ddg_snippet": "Free Equation Solver helps you to calculate linear, quadratic and polynomial systems of equations . Answers, graphs, roots, alternate forms.", "subpage_snippet": "", "source": "www.wolframalpha.com", "link": "https://www.wolframalpha.com/calculators/equation-solver-calculator", "content": "Free Equation Solver helps you to calculate linear, quadratic and polynomial systems of equations . Answers, graphs, roots, alternate forms."}
+{"idx": 3, "title": "Equation Calculator: solve equations , systems, and inequalities with...", "date": "", "ddg_snippet": "Equation Solver — Calculate Equations , Inequalities & Systems.It also solves systems of equations , as well as inequalities (but only those without parameters or trigonometric functions ) using the interval method.", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/equ/", "content": "Equation Solver — Calculate Equations , Inequalities & Systems.It also solves systems of equations , as well as inequalities (but only those without parameters or trigonometric functions ) using the interval method."}
+{"idx": 4, "title": "Solution of the nonlinear functional equations representing the roll...", "date": "", "ddg_snippet": "(1978). Solution of the nonlinear functional equations representing the roll gap relationships in a cold mill.", "subpage_snippet": "", "source": "tesble.com", "link": "https://tesble.com/10.1007/bf00933464", "content": "(1978). Solution of the nonlinear functional equations representing the roll gap relationships in a cold mill."}
+{"idx": 5, "title": "Калькулятор базального обмена (формула Миффлина-Сан Жеора)", "date": "", "ddg_snippet": "Формула Миффлина - Сан Жеора (Mifflin-St Jeor equation ) используется для оценки базальной скорости метаболизма (BMR) — минимального количества энергии, необходимого организму для поддержания основных физиологических функций в состоянии полного покоя...", "subpage_snippet": "", "source": "www.VIDAL.ru", "link": "https://www.VIDAL.ru/calculators/gastroehnterologiya-i-gepatologiya/kalkulyator-bazalnogo-obmena-formula-mifflina-san-zheora", "content": "Формула Миффлина - Сан Жеора (Mifflin-St Jeor equation ) используется для оценки базальной скорости метаболизма (BMR) — минимального количества энергии, необходимого организму для поддержания основных физиологических функций в состоянии полного покоя..."}
+{"idx": 6, "title": "zhenyiizhang/DeepRUOT | DeepWiki", "date": "", "ddg_snippet": "The core framework consists of four main components: neural network models, loss functions , training modules, and utilities. These components work together to enable the analysis of various biological and machine learning applications.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/zhenyiizhang/DeepRUOT/1-overview", "content": "The core framework consists of four main components: neural network models, loss functions , training modules, and utilities. These components work together to enable the analysis of various biological and machine learning applications."}
+{"idx": 7, "title": "The N$^3$LO Twist-2 Matching of TMD Quark Transversity", "date": "", "ddg_snippet": "Collinear mass factorization and renormalization group equations . The NNLO transversity splitting functions . Space-like results in MS.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.17568", "content": "Collinear mass factorization and renormalization group equations . The NNLO transversity splitting functions . Space-like results in MS."}
+{"idx": 8, "title": "Калькулятор функций & прямых- бесплатный онлайн калькулятор...", "date": "", "ddg_snippet": "Поэтапный анализ и графическое отображение линейных уравнений и функций. functions -line-calculator.", "subpage_snippet": "", "source": "ru.symbolab.com", "link": "https://ru.symbolab.com/solver/functions-line-calculator", "content": "Поэтапный анализ и графическое отображение линейных уравнений и функций. functions -line-calculator."}
+{"idx": 9, "title": "Learning stochastic dynamics from snapshots through... | OpenReview", "date": "", "ddg_snippet": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gQlxd3Mtru", "content": "Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning."}
diff --git "a/data/sampled_jsons/generalized_static_Schr\303\266dinger_bridge_problem_formulation.jsonl" "b/data/sampled_jsons/generalized_static_Schr\303\266dinger_bridge_problem_formulation.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..f8b55e3e596c6f0b02131bcce86ba0c8ba0d47da
--- /dev/null
+++ "b/data/sampled_jsons/generalized_static_Schr\303\266dinger_bridge_problem_formulation.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linear convergence of Sinkhorn's algorithm for generalized ...", "date": "", "ddg_snippet": "May 1, 2025 · The paper introduces a generalized formulation of the static Schrödinger bridge (SSB) problem by replacing the standard entropy divergence with a general strictly convex function.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0hrkN07DuO", "content": "May 1, 2025 · The paper introduces a generalized formulation of the static Schrödinger bridge (SSB) problem by replacing the standard entropy divergence with a general strictly convex function."}
+{"idx": 1, "title": "Generative modeling via Schrödinger bridge (basics on ... - vdb An Optimal Transport Approach for the Schrödinger Bridge ... Hanbaek Lyu Introduction to on Entropic Optimal Transport Implementing a complex-valued mixed formulation for ... - GitHub ICML Poster Linear convergence of Sinkhorn's algorithm for ...", "date": "", "ddg_snippet": "In the previous lecture we developed some theory for score-based generative modeling: ▶ Continuous time-reversal. ▶ Approximation theorem. ▶ Connection with Normalizing Flows. ▶ Accelerations of SGMs. Recall the basics of SGM : ▶ Sample a forward trajectory, noising the distribution. See full list on vdeborto.github.io ▶ Backward sampling relies on learning the score (score-matching) sθ⋆( γ, ·) = arg min {E[∥sθ( γ, ) See full list on vdeborto.github.io ▶ The assumption on π is satisfied if π defined on a manifold of Rd not with dimension 0 to obtain an approximation of the (unregularized) optimal transport problem that corresponds to ε = 0. For a problem involving only H , the SesquilinearForm class is suitable for complex-valued problems . However, I cannot find an equivalent for a mixed formulation . I noticed that @mlstowell has implemented a MixedSesquilinearForm class in the mixed-slf-dev branch, but I am unsure if or how it applies to my case. It is a vector graphic and may be used at any scale.", "subpage_snippet": "", "source": "vdeborto.github.io", "link": "https://vdeborto.github.io/project/generative_modeling/session_5.pdf", "content": "In the previous lecture we developed some theory for score-based generative modeling: ▶ Continuous time-reversal. ▶ Approximation theorem. ▶ Connection with Normalizing Flows. ▶ Accelerations of SGMs. Recall the basics of SGM : ▶ Sample a forward trajectory, noising the distribution. See full list on vdeborto.github.io ▶ Backward sampling relies on learning the score (score-matching) sθ⋆( γ, ·) = arg min {E[∥sθ( γ, ) See full list on vdeborto.github.io ▶ The assumption on π is satisfied if π defined on a manifold of Rd not with dimension 0 to obtain an approximation of the (unregularized) optimal transport problem that corresponds to ε = 0. For a problem involving only H , the SesquilinearForm class is suitable for complex-valued problems . However, I cannot find an equivalent for a mixed formulation . I noticed that @mlstowell has implemented a MixedSesquilinearForm class in the mixed-slf-dev branch, but I am unsure if or how it applies to my case. It is a vector graphic and may be used at any scale."}
+{"idx": 2, "title": "An Optimal Transport Approach for the Schrödinger Bridge ...", "date": "", "ddg_snippet": "Connection with Optimal Transport Theory: the problem (1.1) allow us to create very efficient numerical scheme approximating solutions to the Monge–Kantorovich formulation of optimal transport and its many generalizations .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10915-020-01325-7", "content": "Connection with Optimal Transport Theory: the problem (1.1) allow us to create very efficient numerical scheme approximating solutions to the Monge–Kantorovich formulation of optimal transport and its many generalizations ."}
+{"idx": 3, "title": "Implementing a complex-valued mixed formulation for ... - GitHub", "date": "", "ddg_snippet": "For a problem involving only H , the SesquilinearForm class is suitable for complex-valued problems . However, I cannot find an equivalent for a mixed formulation . I noticed that @mlstowell has implemented a MixedSesquilinearForm class in the mixed-slf-dev branch, but I am unsure if or how it applies to my case.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mfem/mfem/issues/5017", "content": "For a problem involving only H , the SesquilinearForm class is suitable for complex-valued problems . However, I cannot find an equivalent for a mixed formulation . I noticed that @mlstowell has implemented a MixedSesquilinearForm class in the mixed-slf-dev branch, but I am unsure if or how it applies to my case."}
+{"idx": 4, "title": "Soft-constrained Schrödinger Bridge: a Stochastic Control", "date": "", "ddg_snippet": "The main contribution of this paper is the theoretical development of a generalized Schrödinger bridge problem , which we call soft-constrained ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01717v2", "content": "The main contribution of this paper is the theoretical development of a generalized Schrödinger bridge problem , which we call soft-constrained ..."}
+{"idx": 5, "title": "Dynamic Diffusion Schrödinger Bridge in Astrophysical", "date": "", "ddg_snippet": "The machine learning method we leverage in this work is closely related to one line of active research efforts in the Schrödinger bridge problem (SB ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08065v1", "content": "The machine learning method we leverage in this work is closely related to one line of active research efforts in the Schrödinger bridge problem (SB ..."}
+{"idx": 6, "title": "Diffusion Bridge Mixture Transports, Schrödinger Bridge", "date": "", "ddg_snippet": "This paper addresses a more generalized version of the optimization problem initially posed by Schrödinger in 1931 (Léonard, 2014b ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2304.00917v2", "content": "This paper addresses a more generalized version of the optimization problem initially posed by Schrödinger in 1931 (Léonard, 2014b ) ."}
+{"idx": 7, "title": "Optimal Multimarginal Schrödinger Bridge: Minimum Spanning", "date": "", "ddg_snippet": "In this work, we formulate and solve the problem of finding the optimal MSB in the sense we seek the optimal coupling over all possible graph ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10626v1", "content": "In this work, we formulate and solve the problem of finding the optimal MSB in the sense we seek the optimal coupling over all possible graph ..."}
+{"idx": 8, "title": "The Schrödinger Bridge between Gaussian Measures has a Closed", "date": "", "ddg_snippet": "Here we focus on the dynamic formulation of OT, also known as the Schrödinger bridge (SB) problem , which has recently seen a surge of interest in ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/bunne23a.html", "content": "Here we focus on the dynamic formulation of OT, also known as the Schrödinger bridge (SB) problem , which has recently seen a surge of interest in ..."}
+{"idx": 9, "title": "Schrödinger's Equation and its Stationary Solutions -", "date": "", "ddg_snippet": "Mathematically , this equation and Planck's relation (E = h n ) turn the general wave equation into the stationary equation of Schrödinger ...", "subpage_snippet": "", "source": "www.numericana.com", "link": "https://www.numericana.com/answer/schrodinger.htm", "content": "Mathematically , this equation and Planck's relation (E = h n ) turn the general wave equation into the stationary equation of Schrödinger ..."}
diff --git a/data/sampled_jsons/generic-diffusion-feature_SDXL_feature_selection_query_key.jsonl b/data/sampled_jsons/generic-diffusion-feature_SDXL_feature_selection_query_key.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f5f58a794bb487b522c1150b4f03efcf28f1debc
--- /dev/null
+++ b/data/sampled_jsons/generic-diffusion-feature_SDXL_feature_selection_query_key.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MuLan: Adapting Multilingual Diffusion Models for Hundreds of", "date": "", "ddg_snippet": "Recent diffusion models (Esser et al., 2024 ; Li et al., 2024a , b ; Team, 2024 ; Wu et al., 2024 ; Zhang et al., 2022 ) for content generation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.01271v2", "content": "Recent diffusion models (Esser et al., 2024 ; Li et al., 2024a , b ; Team, 2024 ; Wu et al., 2024 ; Zhang et al., 2022 ) for content generation ..."}
+{"idx": 1, "title": "GenTron: Diffusion Transformers for Image and Video Generation", "date": "", "ddg_snippet": "Diffusion models have recently shown remarkable progress in content creation, impacting areas such as image generation [ 27 , 57 , 55 ] , video ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.04557v2", "content": "Diffusion models have recently shown remarkable progress in content creation, impacting areas such as image generation [ 27 , 57 , 55 ] , video ..."}
+{"idx": 2, "title": "MuLan: Adapting Multilingual Diffusion Models for Hundreds of", "date": "", "ddg_snippet": "Recent diffusion models [ 11 , 18 , 19 , 40 , 44 , 48 ] for content generation have attained stunning advancements in terms of both aesthetic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.01271v1", "content": "Recent diffusion models [ 11 , 18 , 19 , 40 , 44 , 48 ] for content generation have attained stunning advancements in terms of both aesthetic ..."}
+{"idx": 3, "title": "CLUE-Mark: Watermarking Diffusion Models using CLWE", "date": "", "ddg_snippet": "Diffusion models, such as Stable Diffusion [ 5 , 6 ] , have quickly emerged as the go-to AI models for generating very high quality and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.11434v3", "content": "Diffusion models, such as Stable Diffusion [ 5 , 6 ] , have quickly emerged as the go-to AI models for generating very high quality and ..."}
+{"idx": 4, "title": "3 Ways to Generate Hyper-Realistic Faces Using Stable Diffusion", "date": "", "ddg_snippet": "We will be using a generic negative prompt, but you can add additional keywords to avoid any repetitive mistakes in the image.", "subpage_snippet": "", "source": "www.kdnuggets.com", "link": "https://www.kdnuggets.com/3-ways-to-generate-hyper-realistic-faces-using-stable-diffusion", "content": "We will be using a generic negative prompt, but you can add additional keywords to avoid any repetitive mistakes in the image."}
+{"idx": 5, "title": "Aman's AI Journal • Primers • Diffusion Models", "date": "", "ddg_snippet": "Fundamentally, diffusion models work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the ...", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/diffusion-models/", "content": "Fundamentally, diffusion models work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the ..."}
+{"idx": 6, "title": "To Appear in the Network and Distributed System Security (NDSS)", "date": "", "ddg_snippet": "Through the use of multi-level feature extraction across spatial and frequency domains, ViGText captures details that enhance its robustness and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.18031v1", "content": "Through the use of multi-level feature extraction across spatial and frequency domains, ViGText captures details that enhance its robustness and ..."}
+{"idx": 7, "title": "Metrics — OpenVINO™ documentation", "date": "", "ddg_snippet": "PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis with OpenVINO ... Stable Diffusion v2.1 using OpenVINO ...", "subpage_snippet": "", "source": "docs.openvino.ai", "link": "https://docs.openvino.ai/2024/openvino-workflow/model-server/ovms_docs_metrics.html", "content": "PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis with OpenVINO ... Stable Diffusion v2.1 using OpenVINO ..."}
+{"idx": 8, "title": "OpenAI API embeddings endpoint — OpenVINO™ documentation", "date": "", "ddg_snippet": "PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis with OpenVINO ... Stable Diffusion v2.1 using OpenVINO ...", "subpage_snippet": "", "source": "docs.openvino.ai", "link": "https://docs.openvino.ai/2024/openvino-workflow/model-server/ovms_docs_rest_api_embeddings.html", "content": "PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis with OpenVINO ... Stable Diffusion v2.1 using OpenVINO ..."}
+{"idx": 9, "title": "Google Gemini AI Applications | Lablab.ai", "date": "", "ddg_snippet": "Features real-time analysis, metric visualization, and automated reporting for enterprise process data. ... It s unique features , like text ...", "subpage_snippet": "", "source": "lablab.ai", "link": "https://lablab.ai/apps/tech/google/gemini-ai", "content": "Features real-time analysis, metric visualization, and automated reporting for enterprise process data. ... It s unique features , like text ..."}
diff --git a/data/sampled_jsons/github.com_fiveai_understanding_safety_finetuning_minGPT_n_layer_transformer_blocks.jsonl b/data/sampled_jsons/github.com_fiveai_understanding_safety_finetuning_minGPT_n_layer_transformer_blocks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b9c74c2aff8386876b0e3c6074564d978cb25844
--- /dev/null
+++ b/data/sampled_jsons/github.com_fiveai_understanding_safety_finetuning_minGPT_n_layer_transformer_blocks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Decoding the Transformers: A Dive into GPT with TensorFlow", "date": "", "ddg_snippet": "n_layer : A set of parallel transformer heads are consolidated into blocks , and this parameter denotes the number of such blocks . Each block includes one multi-head self-attention mechanism and one ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/decoding-transformers-dive-gpt-tensorflow-krishna-chaitanya-kosaraju", "content": "n_layer : A set of parallel transformer heads are consolidated into blocks , and this parameter denotes the number of such blocks . Each block includes one multi-head self-attention mechanism and one ..."}
+{"idx": 1, "title": "GitHub - favor-zxh/ minGPT - tuned : A * tuned * minimal PyTorch...", "date": "", "ddg_snippet": "The core minGPT \"library\" (hah) is two files: mingpt /model.py contains the actual Transformer model definition and mingpt /trainer.py is ( GPT -independent) PyTorch boilerplate that trains the model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/favor-zxh/minGPT-tuned", "content": "The core minGPT \"library\" (hah) is two files: mingpt /model.py contains the actual Transformer model definition and mingpt /trainer.py is ( GPT -independent) PyTorch boilerplate that trains the model."}
+{"idx": 2, "title": "Estimating Transformer Model Properties: A Deep Dive | AndoLogs", "date": "", "ddg_snippet": "MLP Blocks (for each layer). Final Layer Norm and Output.max_seq_len = 1024 vocab_size = 50257 n _ layer = 12 n_head = 12 n_embd = 768 bias = False. This configuration represents a GPT -2 small model with 12 layers, 12 attention heads, and an embedding dimension of 768.", "subpage_snippet": "", "source": "blog.ando.ai", "link": "https://blog.ando.ai/posts/ai-transformer-sizes/", "content": "MLP Blocks (for each layer). Final Layer Norm and Output.max_seq_len = 1024 vocab_size = 50257 n _ layer = 12 n_head = 12 n_embd = 768 bias = False. This configuration represents a GPT -2 small model with 12 layers, 12 attention heads, and an embedding dimension of 768."}
+{"idx": 3, "title": "Transformer Model | karpathy/makemore | DeepWiki", "date": "", "ddg_snippet": "Position Embeddings (wpe). Unsupported markdown: list. Transformer Blocks (h) x n _ layer . Final Layer Norm (ln_f). Language Model Head (lm_head).", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/karpathy/makemore/3.5-transformer-model", "content": "Position Embeddings (wpe). Unsupported markdown: list. Transformer Blocks (h) x n _ layer . Final Layer Norm (ln_f). Language Model Head (lm_head)."}
+{"idx": 4, "title": "I made a transformer by hand (no training!)", "date": "", "ddg_snippet": "forward pass through n _ layer transformer blocks for block in blocks", "subpage_snippet": "", "source": "vgel.me", "link": "https://vgel.me/posts/handmade-transformer/", "content": "forward pass through n _ layer transformer blocks for block in blocks"}
+{"idx": 5, "title": "GPT -2 From Scratch With Torch - Ztec100. com", "date": "", "ddg_snippet": "A minimal GPT -2. Overall structure. The authentic Transformer (Vaswani et al. 2017) was constructed up of each an encoder and a decoder stack, a prototypical use case being machine translation.", "subpage_snippet": "", "source": "ztec100.com", "link": "https://ztec100.com/gpt-2-from-scratch-with-torch/", "content": "A minimal GPT -2. Overall structure. The authentic Transformer (Vaswani et al. 2017) was constructed up of each an encoder and a decoder stack, a prototypical use case being machine translation."}
+{"idx": 6, "title": "亲手制作 transformer ：无需训练！ [译] | 宝玉的分享", "date": "", "ddg_snippet": "# forward pass through n _ layer transformer blocks . for block in blocks", "subpage_snippet": "", "source": "baoyu.io", "link": "https://baoyu.io/translations/llm/handmade-transformer", "content": "# forward pass through n _ layer transformer blocks . for block in blocks"}
+{"idx": 7, "title": "bert - daiwk- github 博客", "date": "", "ddg_snippet": "Fine - tuning Procedure. Comparison of BERT and OpenAI GPT .param hidden: BERT model hidden size : param n _layers: numbers of Transformer blocks (layers)", "subpage_snippet": "", "source": "daiwk.github.io", "link": "https://daiwk.github.io/posts/nlp-bert.html", "content": "Fine - tuning Procedure. Comparison of BERT and OpenAI GPT .param hidden: BERT model hidden size : param n _layers: numbers of Transformer blocks (layers)"}
+{"idx": 8, "title": "GPT in 60 Lines of NumPy - Jay Mody", "date": "", "ddg_snippet": "We pass our embedding through a stack of n_layer transformer decoder blocks . # forward pass through n_layer transformer blocks for block in blocks : x = transformer_block(x, ** block , n_head=n_head) # [n_seq, n_embd] -> [n_seq, n_embd] Stacking more layers is what allows us to control how deep our network is. GPT-3 for example, has a whopping 96 ...", "subpage_snippet": "", "source": "jaykmody.com", "link": "https://jaykmody.com/blog/gpt-from-scratch/", "content": "We pass our embedding through a stack of n_layer transformer decoder blocks . # forward pass through n_layer transformer blocks for block in blocks : x = transformer_block(x, ** block , n_head=n_head) # [n_seq, n_embd] -> [n_seq, n_embd] Stacking more layers is what allows us to control how deep our network is. GPT-3 for example, has a whopping 96 ..."}
+{"idx": 9, "title": "01_NeMo_Models.ipynb - Colab", "date": "", "ddg_snippet": "This notebook port's the minGPT codebase into equivalent NeMo code. The license for minGPT has therefore been attached here. n _ layer : int, # depth of the model; number of Transformer blocks in sequence.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/01_NeMo_Models.ipynb", "content": "This notebook port's the minGPT codebase into equivalent NeMo code. The license for minGPT has therefore been attached here. n _ layer : int, # depth of the model; number of Transformer blocks in sequence."}
diff --git a/data/sampled_jsons/github.comkarpathyminGPTblobmastermingptmodel.py_n_layer.jsonl b/data/sampled_jsons/github.comkarpathyminGPTblobmastermingptmodel.py_n_layer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1a64928f3ad9732a6cbc87565e8f444a94ca4fca
--- /dev/null
+++ b/data/sampled_jsons/github.comkarpathyminGPTblobmastermingptmodel.py_n_layer.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - karpathy/minGPT: A minimal PyTorch re-implementation of the ...", "date": "", "ddg_snippet": "The minGPT library is three files: mingpt/model.py contains the actual Transformer model definition, mingpt/bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt/trainer.py is (GPT-independent) PyTorch boilerplate code that trains the model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT", "content": "The minGPT library is three files: mingpt/model.py contains the actual Transformer model definition, mingpt/bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt/trainer.py is (GPT-independent) PyTorch boilerplate code that trains the model."}
+{"idx": 1, "title": "minGPT: A Python repository from karpathy - karpathy", "date": "", "ddg_snippet": "GPT is not a complicated model and this implementation is appropriately about 300 lines of code (see mingpt / model . py ).", "subpage_snippet": "", "source": "geeksrepos.com", "link": "https://geeksrepos.com/karpathy/minGPT", "content": "GPT is not a complicated model and this implementation is appropriately about 300 lines of code (see mingpt / model . py )."}
+{"idx": 2, "title": "GitHub - BlinkDL/minGPT-tuned: A *tuned* minimal PyTorch", "date": "", "ddg_snippet": "The core minGPT \"library\" (hah) is two files: mingpt / model . py contains the actual Transformer model definition and mingpt /trainer. py is (GPT ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BlinkDL/minGPT-tuned", "content": "The core minGPT \"library\" (hah) is two files: mingpt / model . py contains the actual Transformer model definition and mingpt /trainer. py is (GPT ..."}
+{"idx": 3, "title": "GitHub - subramen/minGPT-ddp: A minimal PyTorch", "date": "", "ddg_snippet": "GPT is not a complicated model and this implementation is appropriately about 300 lines of code (see mingpt / model . py ).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/subramen/minGPT-ddp", "content": "GPT is not a complicated model and this implementation is appropriately about 300 lines of code (see mingpt / model . py )."}
+{"idx": 4, "title": "GitHub - pabloppp/pytorch-tools: Useful PyTorch functions and", "date": "", "ddg_snippet": "Implementation based on https:// github . com /rosinality/alias-free-gan-pytorch/ blob /main/ model . py by Rosinality I modularized this activation so it can ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pabloppp/pytorch-tools", "content": "Implementation based on https:// github . com /rosinality/alias-free-gan-pytorch/ blob /main/ model . py by Rosinality I modularized this activation so it can ..."}
+{"idx": 5, "title": "minGPT/ at master · karpathy/minGPT · GitHub", "date": "", "ddg_snippet": "The minGPT library is three files: mingpt/model.py contains the actual Transformer model definition, mingpt/bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt/trainer.py is (GPT-independent) PyTorch boilerplate code that trains the model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT?search=1", "content": "The minGPT library is three files: mingpt/model.py contains the actual Transformer model definition, mingpt/bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt/trainer.py is (GPT-independent) PyTorch boilerplate code that trains the model."}
+{"idx": 6, "title": "karpathy-minGPT/mingpt/model.py at master - GitHub", "date": "", "ddg_snippet": "A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training - potgie/karpathy-minGPT", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/potgie/karpathy-minGPT/blob/master/mingpt/model.py", "content": "A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training - potgie/karpathy-minGPT"}
+{"idx": 7, "title": "GitHub - karpathy/minGPT at 4050db60409b5bbaaa3302cee1e49847fc145c65", "date": "", "ddg_snippet": "The core minGPT \"library\" (hah) is two files: mingpt/model.py contains the actual Transformer model definition and mingpt/trainer.py is (GPT-independent) PyTorch boilerplate that trains the model. The attached Jupyter notebooks then show how the \"library\" (hah) can be used to train sequence models:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT/tree/4050db60409b5bbaaa3302cee1e49847fc145c65", "content": "The core minGPT \"library\" (hah) is two files: mingpt/model.py contains the actual Transformer model definition and mingpt/trainer.py is (GPT-independent) PyTorch boilerplate that trains the model. The attached Jupyter notebooks then show how the \"library\" (hah) can be used to train sequence models:"}
+{"idx": 8, "title": "karpathy/minGPT | DeepWiki", "date": "", "ddg_snippet": "Purpose and Scope minGPT is a minimal PyTorch re-implementation of the GPT (Generative Pre-trained Transformer) architecture, designed for clarity, educational value, and interpretability. This document provides a high-level overview of the minGPT repository, explaining its core components, design philosophy, and basic usage patterns.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/karpathy/minGPT", "content": "Purpose and Scope minGPT is a minimal PyTorch re-implementation of the GPT (Generative Pre-trained Transformer) architecture, designed for clarity, educational value, and interpretability. This document provides a high-level overview of the minGPT repository, explaining its core components, design philosophy, and basic usage patterns."}
+{"idx": 9, "title": "nanoGPT/model.py at master · karpathy/nanoGPT · GitHub", "date": "", "ddg_snippet": "The simplest, fastest repository for training/finetuning medium-sized GPTs. - karpathy/nanoGPT", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/nanoGPT/blob/master/model.py", "content": "The simplest, fastest repository for training/finetuning medium-sized GPTs. - karpathy/nanoGPT"}
diff --git a/data/sampled_jsons/github_simonbing_CRLSanityCheck_Contrastive_CRL_MCC_scores.jsonl b/data/sampled_jsons/github_simonbing_CRLSanityCheck_Contrastive_CRL_MCC_scores.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7e64ecaf5bd6a8cf4f341d7efea3a0518b22804b
--- /dev/null
+++ b/data/sampled_jsons/github_simonbing_CRLSanityCheck_Contrastive_CRL_MCC_scores.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub Foundations Certification Study Guide", "date": "", "ddg_snippet": "Describe GitHub Sponsors GitHub certification registration process After completing your study plan, you are ready to take the certification exam and demonstrate your skills. The exam costs $99, but for a limited time (as of this publication date), you can get a 50% discount on the Foundations exam. Here are the steps to schedule your exam:", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/github-foundations-certification-study-guide/4079056", "content": "Describe GitHub Sponsors GitHub certification registration process After completing your study plan, you are ready to take the certification exam and demonstrate your skills. The exam costs $99, but for a limited time (as of this publication date), you can get a 50% discount on the Foundations exam. Here are the steps to schedule your exam:"}
+{"idx": 1, "title": "New Certification on GitHub administration | Microsoft Community...", "date": "", "ddg_snippet": "Jul 8, 2025 · GitHub Learn and Microsoft Learn are working together to enhance your GitHub learning experience. Our GitHub exams are now available on Pearson VUE via Microsoft Learn, bringing a more unified and accessible experience to learners worldwide. If you already use Microsoft Learn, you can more easily discover and engage with GitHub topics.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/microsoftlearnblog/new-certification-on-github-administration/4428967", "content": "Jul 8, 2025 · GitHub Learn and Microsoft Learn are working together to enhance your GitHub learning experience. Our GitHub exams are now available on Pearson VUE via Microsoft Learn, bringing a more unified and accessible experience to learners worldwide. If you already use Microsoft Learn, you can more easily discover and engage with GitHub topics."}
+{"idx": 2, "title": "GitHub Copilot Vibe Coding Workshop | Microsoft Community Hub", "date": "", "ddg_snippet": "Jul 9, 2025 · Introducing GitHub Copilot Vibe Coding Workshop I'm more than happy to introduce this GitHub Copilot Vibe Coding Workshop, a resource available for everyone to use. It's based on a typical app development scenario – building a web application that consists of a frontend UI and backend API with database transaction. This workshop has six steps:", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azuredevcommunityblog/github-copilot-vibe-coding-workshop/4430440", "content": "Jul 9, 2025 · Introducing GitHub Copilot Vibe Coding Workshop I'm more than happy to introduce this GitHub Copilot Vibe Coding Workshop, a resource available for everyone to use. It's based on a typical app development scenario – building a web application that consists of a frontend UI and backend API with database transaction. This workshop has six steps:"}
+{"idx": 3, "title": "GitHub! Code better together with GitHub and Microsoft Teams", "date": "", "ddg_snippet": "May 30, 2019 · Developers can now collaborate from anywhere using GitHub app in Microsoft Teams! GitHub app in Teams support Tab, Messaging Extension, Bot and Personal app capabilities. How to get started Install GitHub for Microsoft Teams application from Microsoft Teams App Store.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/microsoftteamsblog/github-code-better-together-with-github-and-microsoft-teams/659444", "content": "May 30, 2019 · Developers can now collaborate from anywhere using GitHub app in Microsoft Teams! GitHub app in Teams support Tab, Messaging Extension, Bot and Personal app capabilities. How to get started Install GitHub for Microsoft Teams application from Microsoft Teams App Store."}
+{"idx": 4, "title": "GitHub Copilot for Azure の一般提供を開始：Agent モードにも対応", "date": "", "ddg_snippet": "Jun 25, 2025 · GitHub Copilot for Azure は、2024 年 11 月の Microsoft Ignite カンファレンスで public preview として公開されました。これにより、開発者、IT 運用者、DevOps 実践者たちは、自分たちの Azure...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azuredevcommunityblog/github-copilot-for-azure-の一般提供を開始：agent-モードにも対応/4426996", "content": "Jun 25, 2025 · GitHub Copilot for Azure は、2024 年 11 月の Microsoft Ignite カンファレンスで public preview として公開されました。これにより、開発者、IT 運用者、DevOps 実践者たちは、自分たちの Azure..."}
+{"idx": 5, "title": "Using Slash Commands in GitHub Copilot for Visual Studio |...", "date": "", "ddg_snippet": "Apr 4, 2024 · GitHub Copilot is a coding assistant powered by Artificial Intelligence (AI), which can run in various environments and help you be more efficient in your daily coding tasks. In this new series of content, we will show you how GitHub Copilot works in Visual Studio specifically and how it helps you being more productive. In this installment, we will demonstrate the usage of Slash Commands. Like ...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azuredevcommunityblog/using-slash-commands-in-github-copilot-for-visual-studio/4105447", "content": "Apr 4, 2024 · GitHub Copilot is a coding assistant powered by Artificial Intelligence (AI), which can run in various environments and help you be more efficient in your daily coding tasks. In this new series of content, we will show you how GitHub Copilot works in Visual Studio specifically and how it helps you being more productive. In this installment, we will demonstrate the usage of Slash Commands. Like ..."}
+{"idx": 6, "title": "¡GitHub Copilot gratis! Ahora al alcance de todos | Microsoft...", "date": "", "ddg_snippet": "Dec 18, 2024 · ¡Buenas noticias! Ahora todos pueden usar GitHub Copilot GRATIS en Visual Studio Code. Sólo necesitas tu cuenta de GitHub . Sin periodos de pruebas, sin...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azuredevcommunityblog/¡github-copilot-gratis-ahora-al-alcance-de-todos/4356908", "content": "Dec 18, 2024 · ¡Buenas noticias! Ahora todos pueden usar GitHub Copilot GRATIS en Visual Studio Code. Sólo necesitas tu cuenta de GitHub . Sin periodos de pruebas, sin..."}
+{"idx": 7, "title": "Linking your personal Microsoft Account to your GitHub validated...", "date": "", "ddg_snippet": "Dec 2, 2022 · After your GitHub and Microsoft account credentials are linked, you can use that single sign-in anywhere a personal Microsoft account can be used, like on...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/redeeming-azure-for-student-from-your-github-student-pack-when-you-do-not-have-a/3689053", "content": "Dec 2, 2022 · After your GitHub and Microsoft account credentials are linked, you can use that single sign-in anywhere a personal Microsoft account can be used, like on..."}
+{"idx": 8, "title": "¡Regístrate al GitHub Universe Cloud Skills Challenge!", "date": "", "ddg_snippet": "Oct 12, 2023 · El GitHub Universe Cloud Skills Challenge es un desafío de aprendizaje de 30 días en Microsoft Learn. Esta oportunidad es gratuita, divertida y orientada a la comunidad que te ayudará a aprender GitHub , Codespaces y GitHub Copilot, ¡ justo a tiempo para el GitHub Universe!", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/¡regístrate-al-github-universe-cloud-skills-challenge/3951236", "content": "Oct 12, 2023 · El GitHub Universe Cloud Skills Challenge es un desafío de aprendizaje de 30 días en Microsoft Learn. Esta oportunidad es gratuita, divertida y orientada a la comunidad que te ayudará a aprender GitHub , Codespaces y GitHub Copilot, ¡ justo a tiempo para el GitHub Universe!"}
+{"idx": 9, "title": "Get Started with GitHub Copilot with VSCode and Python Extension", "date": "", "ddg_snippet": "GitHub Copilot, integrated with VSCode and Python Extension, uses machine learning to provide intelligent code suggestions and streamline coding tasks for developers.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/get-started-with-github-copilot-with-vscode-and-python-extension/3736564", "content": "GitHub Copilot, integrated with VSCode and Python Extension, uses machine learning to provide intelligent code suggestions and streamline coding tasks for developers."}
diff --git a/data/sampled_jsons/gradient-based_alternatives_to_evolution_strategies_neural_network_training_efficiency_year_2024.jsonl b/data/sampled_jsons/gradient-based_alternatives_to_evolution_strategies_neural_network_training_efficiency_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7b7b77897424f15463d60cdb8f61c14eb32b0749
--- /dev/null
+++ b/data/sampled_jsons/gradient-based_alternatives_to_evolution_strategies_neural_network_training_efficiency_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A comparison of evolution strategies and backpropagation for neural ...", "date": "", "ddg_snippet": "This report investigates evolution strategies (ESs, a subclass of evolutionary algorithms) as an alternative to gradient-based neural network training . Based on an empirical comparison of population- and gradient-based search, we derive hints for parameterization and draw conclusions about the usefulness of evolution strategies for this purpose.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231201005963", "content": "This report investigates evolution strategies (ESs, a subclass of evolutionary algorithms) as an alternative to gradient-based neural network training . Based on an empirical comparison of population- and gradient-based search, we derive hints for parameterization and draw conclusions about the usefulness of evolution strategies for this purpose."}
+{"idx": 1, "title": "Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative ...", "date": "", "ddg_snippet": "Deep artificial neural networks (DNNs) are typ-ically trained via gradient-based learning al-gorithms, namely backpropagation. Evolution strategies (ES) can rival backprop- based algo-rithms such as Q-learning and policy gradi-ents on challenging deep reinforcement learning (RL) problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1712.06567", "content": "Deep artificial neural networks (DNNs) are typ-ically trained via gradient-based learning al-gorithms, namely backpropagation. Evolution strategies (ES) can rival backprop- based algo-rithms such as Q-learning and policy gradi-ents on challenging deep reinforcement learning (RL) problems."}
+{"idx": 2, "title": "A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks", "date": "", "ddg_snippet": "It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9369973", "content": "It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been ..."}
+{"idx": 3, "title": "PDF Navigating beyond backpropagation: on alternative training methods for ...", "date": "", "ddg_snippet": "Abstract Backpropagation has long been the de facto algorithm for training deep neural networks due to its effectiveness in optimising network parameters. However, the algorithm is not without its limitations, such as high computational requirement, sensitivity to initialisation, weight transport problem, vanishing and exploding gradient , and convergence issues. Many such issues have been ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10115-025-02370-0.pdf", "content": "Abstract Backpropagation has long been the de facto algorithm for training deep neural networks due to its effectiveness in optimising network parameters. However, the algorithm is not without its limitations, such as high computational requirement, sensitivity to initialisation, weight transport problem, vanishing and exploding gradient , and convergence issues. Many such issues have been ..."}
+{"idx": 4, "title": "Backpropagation-Free Gradient Estimation for Scalable Deep Learning", "date": "", "ddg_snippet": "This insight opens avenues for computationally efficient gradient estimation, where directional derivatives calculated through forward-mode differentiation offer a feasible alternative to gradient-based training in large networks .", "subpage_snippet": "", "source": "comp.anu.edu.au", "link": "https://comp.anu.edu.au/study/projects/backpropagation-free-gradient-estimation-for-scalable-deep-learning/", "content": "This insight opens avenues for computationally efficient gradient estimation, where directional derivatives calculated through forward-mode differentiation offer a feasible alternative to gradient-based training in large networks ."}
+{"idx": 5, "title": "Gradient-free training of recurrent neural networks using random ...", "date": "", "ddg_snippet": "An alternative approach to training neural networks is stochastically approximating gradients through perturbation- based methods (Widrow and Lehr, 1990; Spall, 1992; Werfel et al., 2003). In this type of learning, synaptic weights are adjusted based on the impact of introducing perturbations to the network .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11267880/", "content": "An alternative approach to training neural networks is stochastically approximating gradients through perturbation- based methods (Widrow and Lehr, 1990; Spall, 1992; Werfel et al., 2003). In this type of learning, synaptic weights are adjusted based on the impact of introducing perturbations to the network ."}
+{"idx": 6, "title": "PDF A Neuroevolution: a Gradient Free Algorithm to Train Deep Neural Networks", "date": "", "ddg_snippet": "ABSTRACT In this paper we present a novel optimization algorithm called Advanced Neu-roevolution. The aim for this algorithm is to train deep neural networks , and eventually act as an alternative to Stochastic Gradient Descent (SGD) and its vari-ants as needed.We evaluated our algorithm on the MNIST dataset, as well as on several global optimization problems such as the Ackley function. We ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/2ae00ab0d742f5c97e9625ad257b7fcac027ad0b.pdf", "content": "ABSTRACT In this paper we present a novel optimization algorithm called Advanced Neu-roevolution. The aim for this algorithm is to train deep neural networks , and eventually act as an alternative to Stochastic Gradient Descent (SGD) and its vari-ants as needed.We evaluated our algorithm on the MNIST dataset, as well as on several global optimization problems such as the Ackley function. We ..."}
+{"idx": 7, "title": "PDF Alternatives to Gradient-based Neural Training.", "date": "", "ddg_snippet": "Abstract Neural networks are usually trained using local, gradient-based procedures, and the best architectures are se-lected by experimentation. Gradient methods frequently find suboptimal solutions being trapped in local minima. Genetic algorithms are frequently used but do not guarantee optimal solutions and are computationally expensive. Several new global optimization methods suitable for ...", "subpage_snippet": "", "source": "is.umk.pl", "link": "https://is.umk.pl/~duch/pubs/99alter.pdf", "content": "Abstract Neural networks are usually trained using local, gradient-based procedures, and the best architectures are se-lected by experimentation. Gradient methods frequently find suboptimal solutions being trapped in local minima. Genetic algorithms are frequently used but do not guarantee optimal solutions and are computationally expensive. Several new global optimization methods suitable for ..."}
+{"idx": 8, "title": "Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative ...", "date": "", "ddg_snippet": "Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop- based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems. However, ES can be considered a gradient-based algorithm because it performs stochastic gradient descent via an ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1712.06567", "content": "Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop- based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems. However, ES can be considered a gradient-based algorithm because it performs stochastic gradient descent via an ..."}
+{"idx": 9, "title": "EST-NAS: An evolutionary strategy with gradient descent for neural ...", "date": "", "ddg_snippet": "In particular, we propose using a new evolutionary strategy to explore various search directions based on the gradient descent- based neural network architecture search, aiming at obtaining a more excellent architecture.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1568494623006427", "content": "In particular, we propose using a new evolutionary strategy to explore various search directions based on the gradient descent- based neural network architecture search, aiming at obtaining a more excellent architecture."}
diff --git a/data/sampled_jsons/graph_hierarchical_overlapping_clustering_cost_function_year_2019.jsonl b/data/sampled_jsons/graph_hierarchical_overlapping_clustering_cost_function_year_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b7b796d3df99d32ab997fde41cd01149f767ff2e
--- /dev/null
+++ b/data/sampled_jsons/graph_hierarchical_overlapping_clustering_cost_function_year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering Function Algorithm and Scalability ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 1, "title": "PDF Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "Abstract This paper presents two eficient hierarchical clus-tering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and re-turn an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the perfor-mance of our algorithm against the ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/laenen23a/laenen23a.pdf", "content": "Abstract This paper presents two eficient hierarchical clus-tering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and re-turn an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the perfor-mance of our algorithm against the ..."}
+{"idx": 2, "title": "PDF Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs", "date": "", "ddg_snippet": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2021/file/4d68e143defa221fead61c84de7527a3-Paper.pdf", "content": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ..."}
+{"idx": 3, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm ...", "date": "", "ddg_snippet": "This research paper introduces a new way to group data points in a more complex and realistic manner called hierarchical overlapping clustering (HOC). It combines two methods: hierarchical cluster...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46447/paper", "content": "This research paper introduces a new way to group data points in a more complex and realistic manner called hierarchical overlapping clustering (HOC). It combines two methods: hierarchical cluster..."}
+{"idx": 4, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm ...", "date": "", "ddg_snippet": "Poster Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46447", "content": "Poster Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009"}
+{"idx": 5, "title": "Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1) -approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous state ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.09950", "content": "This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1) -approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous state ..."}
+{"idx": 6, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm ...", "date": "", "ddg_snippet": "Abstract Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have ex-amined these two structures separately, character-izing and evaluating their hybrid forms remains an open challenge. To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "Abstract Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have ex-amined these two structures separately, character-izing and evaluating their hybrid forms remains an open challenge. To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and ..."}
+{"idx": 7, "title": "Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "Abstract This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371684528_Nearly-Optimal_Hierarchical_Clustering_for_Well-Clustered_Graphs", "content": "Abstract This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function ."}
+{"idx": 8, "title": "Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs - PMLR", "date": "", "ddg_snippet": "This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph $G$ with a clear cluster ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/laenen23a.html", "content": "This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph $G$ with a clear cluster ..."}
+{"idx": 9, "title": "PDF G O arXiv:2306.09950v1 [cs.DS] 16 Jun 2023", "date": "", "ddg_snippet": "This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.09950.pdf", "content": "This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous ..."}
diff --git a/data/sampled_jsons/high_dimensional_data_redundancy_overfitting_machine_learning_performance_degradation_feature_redund.jsonl b/data/sampled_jsons/high_dimensional_data_redundancy_overfitting_machine_learning_performance_degradation_feature_redund.jsonl
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index 0000000000000000000000000000000000000000..c524335df8ec5d67218330e61d64d15a0ecf854f
--- /dev/null
+++ b/data/sampled_jsons/high_dimensional_data_redundancy_overfitting_machine_learning_performance_degradation_feature_redund.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Exploiting redundancy in large materials datasets for efficient machine ...", "date": "", "ddg_snippet": "Big data is crucial for machine learning , but the redundancies in the datasets are rarely studied. Here the authors reveal significant redundancy in large materials datasets, showing that up to 95 ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-023-42992-y", "content": "Big data is crucial for machine learning , but the redundancies in the datasets are rarely studied. Here the authors reveal significant redundancy in large materials datasets, showing that up to 95 ..."}
+{"idx": 1, "title": "Curse of Dimensionality in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Curse of Dimensionality in Machine Learning arises when working with high-dimensional data , leading to increased computational complexity, overfitting , and spurious correlations. Techniques like dimensionality reduction, feature selection, and careful model design are essential for mitigating its effects and improving algorithm performance . Navigating this challenge is crucial for unlocking ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/curse-of-dimensionality-in-machine-learning/", "content": "Curse of Dimensionality in Machine Learning arises when working with high-dimensional data , leading to increased computational complexity, overfitting , and spurious correlations. Techniques like dimensionality reduction, feature selection, and careful model design are essential for mitigating its effects and improving algorithm performance . Navigating this challenge is crucial for unlocking ..."}
+{"idx": 2, "title": "A high-dimensional feature selection algorithm via fast dimensionality ...", "date": "", "ddg_snippet": "In real-world problems like machine learning and data mining, data often includes numerous features from diverse sources, such as sensors, surveys, social media, medical records, or transactions. While some features contribute significantly to model performance , others may be redundant or irrelevant, leading to challenges like overfitting and high computational complexity [1]. For instance ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2210650225000574", "content": "In real-world problems like machine learning and data mining, data often includes numerous features from diverse sources, such as sensors, surveys, social media, medical records, or transactions. While some features contribute significantly to model performance , others may be redundant or irrelevant, leading to challenges like overfitting and high computational complexity [1]. For instance ..."}
+{"idx": 3, "title": "Curse of Dimensionality: Challenges & Solutions in High-Dimensional Data", "date": "", "ddg_snippet": "High-dimensional data presents unique challenges that impact the performance and efficiency of machine learning models. Here are three primary issues associated with high-dimensional data : increased data sparsity, computational complexity, and the risk of overfitting and poor generalization.", "subpage_snippet": "", "source": "www.statology.org", "link": "https://www.statology.org/curse-of-dimensionality-challenges-solutions-high-dimensional-data/", "content": "High-dimensional data presents unique challenges that impact the performance and efficiency of machine learning models. Here are three primary issues associated with high-dimensional data : increased data sparsity, computational complexity, and the risk of overfitting and poor generalization."}
+{"idx": 4, "title": "Feature selection in high-dimensional data: an enhanced RIME ...", "date": "", "ddg_snippet": "In machine learning and data analysis, feature selection is a common technique used to choose the most relevant or informative features from the original dataset, reducing dimensionality, improving model performance , and mitigating overfitting , among other goals.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s13042-024-02143-1", "content": "In machine learning and data analysis, feature selection is a common technique used to choose the most relevant or informative features from the original dataset, reducing dimensionality, improving model performance , and mitigating overfitting , among other goals."}
+{"idx": 5, "title": "The Curse of Dimensionality in Machine Learning", "date": "", "ddg_snippet": "The Curse of Dimensionality arises when the number of features or variables in a dataset becomes too large. This can lead to a range of issues including Multicollinearity, Overfitting and Computational Complexity. All of these can have negative impacts on the accuracy and reliability of Machine Learning models. CoD pertains to the challenges encountered when handling high-dimensional data ...", "subpage_snippet": "", "source": "www.smartsocs.com", "link": "https://www.smartsocs.com/the-curse-of-dimensionality-in-machine-learning/", "content": "The Curse of Dimensionality arises when the number of features or variables in a dataset becomes too large. This can lead to a range of issues including Multicollinearity, Overfitting and Computational Complexity. All of these can have negative impacts on the accuracy and reliability of Machine Learning models. CoD pertains to the challenges encountered when handling high-dimensional data ..."}
+{"idx": 6, "title": "PDF Efficient Feature Selection By Reducing Redundant Features From High ...", "date": "", "ddg_snippet": "Based on the MST method, a Redundancy Feature Reduction for High-dimensional Data using Clustering algorithm is proposed. The RFR algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form the ...", "subpage_snippet": "", "source": "www.ijert.org", "link": "https://www.ijert.org/research/efficient-feature-selection-by-reducing-redundant-features-from-high-dimensional-data-using-clustering-IJERTCONV2IS13140.pdf", "content": "Based on the MST method, a Redundancy Feature Reduction for High-dimensional Data using Clustering algorithm is proposed. The RFR algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form the ..."}
+{"idx": 7, "title": "Handling Sparse and High-Dimensional Data in Machine Learning ... - Medium", "date": "", "ddg_snippet": "When dealing with machine learning and data analysis, two of the most significant challenges you might encounter are sparse data and high-dimensional data . These issues can drastically impact the ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@siddharthapramanik771/handling-sparse-and-high-dimensional-data-in-machine-learning-strategies-and-techniques-for-34516e88efff", "content": "When dealing with machine learning and data analysis, two of the most significant challenges you might encounter are sparse data and high-dimensional data . These issues can drastically impact the ..."}
+{"idx": 8, "title": "The Relationship Between High Dimensionality and Overfitting", "date": "", "ddg_snippet": "Multicollinearity and Redundancy : High-dimensional data can lead to multicollinearity, where features become correlated due to their high dimensionality. This might make it difficult to distinguish each feature's unique contribution by giving the same or similar information to several features .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/the-relationship-between-high-dimensionality-and-overfitting/", "content": "Multicollinearity and Redundancy : High-dimensional data can lead to multicollinearity, where features become correlated due to their high dimensionality. This might make it difficult to distinguish each feature's unique contribution by giving the same or similar information to several features ."}
+{"idx": 9, "title": "Analysis and comparison of feature selection methods towards ...", "date": "", "ddg_snippet": "The ever-increasing volume of data poses significant challenges, rendering traditional processing methods impractical for many applications. Challenges associated with working with high-dimensional data include the curse of dimensionality, data imbalance, computational complexity, overfitting , and noisy or redundant data (). Consequently, substantial research efforts have been directed towards ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424005335", "content": "The ever-increasing volume of data poses significant challenges, rendering traditional processing methods impractical for many applications. Challenges associated with working with high-dimensional data include the curse of dimensionality, data imbalance, computational complexity, overfitting , and noisy or redundant data (). Consequently, substantial research efforts have been directed towards ..."}
diff --git a/data/sampled_jsons/history_of_computer_science_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challen.jsonl b/data/sampled_jsons/history_of_computer_science_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challen.jsonl
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index 0000000000000000000000000000000000000000..5d3869fe3268c7c01444d93178289e6bf2d41737
--- /dev/null
+++ b/data/sampled_jsons/history_of_computer_science_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challen.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · 2024 · Cited by 15 — We present a framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, impacts, opportunities, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.10939", "content": "by H Wallach · 2024 · Cited by 15 — We present a framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, impacts, opportunities, ..."}
+{"idx": 1, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "by H Wallach · 2025 · Cited by 11 — We present a four-level framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00561", "content": "by H Wallach · 2025 · Cited by 11 — We present a four-level framework, grounded in measurement theory from the social sciences , for measuring concepts related to the capabilities, behaviors, and ..."}
+{"idx": 2, "title": "Generative artificial intelligence: a historical perspective", "date": "", "ddg_snippet": "by R He · 2025 · Cited by 17 — This paper reviews the historical milestones, successful applications, and remaining challenges in generative artificial intelligence over the past seven ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11970245/", "content": "by R He · 2025 · Cited by 17 — This paper reviews the historical milestones, successful applications, and remaining challenges in generative artificial intelligence over the past seven ..."}
+{"idx": 3, "title": "Can Generative AI improve social science?", "date": "", "ddg_snippet": "by CA Bail · 2024 · Cited by 324 — I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques.", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.2314021121", "content": "by CA Bail · 2024 · Cited by 324 — I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques."}
+{"idx": 4, "title": "Integrating Generative Artificial Intelligence into Social ...", "date": "", "ddg_snippet": "7 May 2025 — This essay introduces a special issue that examines how these and other affordances of generative AI can advance social science research.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/00491241251339184", "content": "7 May 2025 — This essay introduces a special issue that examines how these and other affordances of generative AI can advance social science research."}
+{"idx": 5, "title": "The “Transformation” of Social Science through Generative AI", "date": "", "ddg_snippet": "by D Broska — By constructing generative agents that mimic human behavior, sociologists could create dynamic and interactive simulations of social phenomena.", "subpage_snippet": "", "source": "osf.io", "link": "https://osf.io/uy4c7/download", "content": "by D Broska — By constructing generative agents that mimic human behavior, sociologists could create dynamic and interactive simulations of social phenomena."}
+{"idx": 6, "title": "Challenges in evaluating AI systems", "date": "", "ddg_snippet": "4 Oct 2023 — We want readers of this post to have two main takeaways: robust evaluations are extremely difficult to develop and implement , and effective AI governance ...", "subpage_snippet": "", "source": "www.anthropic.com", "link": "https://www.anthropic.com/research/evaluating-ai-systems", "content": "4 Oct 2023 — We want readers of this post to have two main takeaways: robust evaluations are extremely difficult to develop and implement , and effective AI governance ..."}
+{"idx": 7, "title": "Learning from other domains to advance AI evaluation and ...", "date": "", "ddg_snippet": "23 Jun 2025 — Evaluating Generative AI Systems is a Social Science Measurement Challenge ... AI Evaluation and Testing: The History and Evolution of ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/blog/learning-from-other-domains-to-advance-ai-evaluation-and-testing/", "content": "23 Jun 2025 — Evaluating Generative AI Systems is a Social Science Measurement Challenge ... AI Evaluation and Testing: The History and Evolution of ..."}
+{"idx": 8, "title": "Towards Interactive Evaluations for Interaction Harms in ...", "date": "", "ddg_snippet": "23 Jun 2025 — Safety evaluations of generative AI systems build on a rich history ... Position: Evaluating Generative AI Systems is a Social Science Measurement ...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "23 Jun 2025 — Safety evaluations of generative AI systems build on a rich history ... Position: Evaluating Generative AI Systems is a Social Science Measurement ..."}
+{"idx": 9, "title": "Unpacking the Gap in Human-Centered Evaluations of AI ...", "date": "", "ddg_snippet": "by A Khullar · 2025 — We argue for assessing broader achievements enabled through AI's use when conducting human-centered evaluations of AI .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3706598.3713278", "content": "by A Khullar · 2025 — We argue for assessing broader achievements enabled through AI's use when conducting human-centered evaluations of AI ."}
diff --git a/data/sampled_jsons/httpsarxiv.orgabs2407.10264_year_2024.jsonl b/data/sampled_jsons/httpsarxiv.orgabs2407.10264_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85926f79a147fb5c107bf725e45cc599bc84fa5e
--- /dev/null
+++ b/data/sampled_jsons/httpsarxiv.orgabs2407.10264_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic ...", "date": "", "ddg_snippet": "by S Jain · 2024 · Cited by 26 — Abstract:Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.10264", "content": "by S Jain · 2024 · Cited by 26 — Abstract:Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment."}
+{"idx": 1, "title": "Layered Unlearning for Adversarial Relearning", "date": "", "ddg_snippet": "14 May 2025 — What makes and breaks safety fine-tuning? a mechanistic study, 2024. URL https :// arxiv . org / abs / 2407.10264 . Jang et al. (2022)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.09500v1", "content": "14 May 2025 — What makes and breaks safety fine-tuning? a mechanistic study, 2024. URL https :// arxiv . org / abs / 2407.10264 . Jang et al. (2022)"}
+{"idx": 2, "title": "FAILURES TO FIND TRANSFERABLE IMAGE ...", "date": "", "ddg_snippet": "What makes and breaks safety fine-tuning? mechanistic study, 2024. URL https :// arxiv . org / abs / 2407.10264 . Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/6e3daaeca6be8579573f69082b2dd58b-Paper-Conference.pdf", "content": "What makes and breaks safety fine-tuning? mechanistic study, 2024. URL https :// arxiv . org / abs / 2407.10264 . Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch ..."}
+{"idx": 3, "title": "Samyak", "date": "", "ddg_snippet": "Excited 2 share What Makes and Breaks Safety Fine-tuning? A Mechanistic Study https://arxiv.org/abs/2407.10264. 6:45 PM · Jul 31, 2024. ·. 3,644. Views.", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/sams_jain/status/1818719595018190868", "content": "Excited 2 share What Makes and Breaks Safety Fine-tuning? A Mechanistic Study https://arxiv.org/abs/2407.10264. 6:45 PM · Jul 31, 2024. ·. 3,644. Views."}
+{"idx": 4, "title": "Decomposing Elements of Problem Solving: What \"Math ...", "date": "", "ddg_snippet": "28 May 2025 — We propose to decompose problem solving into fundamental capabilities : Plan (mapping questions to sequences of steps), Execute (correctly performing solution ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22756v1", "content": "28 May 2025 — We propose to decompose problem solving into fundamental capabilities : Plan (mapping questions to sequences of steps), Execute (correctly performing solution ..."}
+{"idx": 5, "title": "PEFT-as-an-Attack! Jailbreaking Language Models during ...", "date": "", "ddg_snippet": "by S Li · 2024 · Cited by 5 — PEFT-as-an-Attack (PaaA ) exploits PEFT methods to generate harmful content by circumventing safety alignment, even with less than 1% of parameters trainable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.19335", "content": "by S Li · 2024 · Cited by 5 — PEFT-as-an-Attack (PaaA ) exploits PEFT methods to generate harmful content by circumventing safety alignment, even with less than 1% of parameters trainable."}
+{"idx": 6, "title": "Is open-access AI the great safety equalizer for African ...", "date": "", "ddg_snippet": "21 Feb 2025 — This essay examines the limitations of open-access AI as an approach to AI safety in Africa. It explains that AI safety research that leverages open-access AI ...", "subpage_snippet": "", "source": "www.brookings.edu", "link": "https://www.brookings.edu/articles/is-open-access-ai-the-great-safety-equalizer-for-african-countries/", "content": "21 Feb 2025 — This essay examines the limitations of open-access AI as an approach to AI safety in Africa. It explains that AI safety research that leverages open-access AI ..."}
+{"idx": 7, "title": "Antidote: Post-fine-tuning Safety Alignment for Large ...", "date": "", "ddg_snippet": "Safety aligned Large Language Models (LLMs) are vulnerable to harmful fine-tuning attacks – a few harmful data mixed in the fine-tuning dataset can break ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46150", "content": "Safety aligned Large Language Models (LLMs) are vulnerable to harmful fine-tuning attacks – a few harmful data mixed in the fine-tuning dataset can break ..."}
+{"idx": 8, "title": "The Hidden Dimensions of LLM Alignment: A Multi ...", "date": "", "ddg_snippet": "18 Feb 2025 — In this work, we discover that safety-aligned behavior is jointly controlled by multi-dimensional directions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.09674v2", "content": "18 Feb 2025 — In this work, we discover that safety-aligned behavior is jointly controlled by multi-dimensional directions."}
+{"idx": 9, "title": "Obfuscated Activations Bypass LLM Latent-Space Defenses", "date": "", "ddg_snippet": "12 Dec 2024 — We show that state-of-the-art latent-space defenses —including sparse autoencoders, representation probing, and latent OOD detection—are all ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.09565v2", "content": "12 Dec 2024 — We show that state-of-the-art latent-space defenses —including sparse autoencoders, representation probing, and latent OOD detection—are all ..."}
diff --git a/data/sampled_jsons/httpsarxiv.orghtml2410.10562v1.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2410.10562v1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6de2e53994fb558201aaab1f1a4660cf5f94f51e
--- /dev/null
+++ b/data/sampled_jsons/httpsarxiv.orghtml2410.10562v1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "[2505.10562] End-to-End Vision Tokenizer Tuning - arXiv.org [2409.10562] Are Existing Road Design Guidelines Suitable for ... [2403.10562] Counter-Samples: A Stateless Strategy to ... search the arXiv", "date": "", "ddg_snippet": "May 15, 2025 · Existing vision tokenization isolates the optimization of vision tokenizers from downstream training, implicitly assuming the visual tokens can generalize well across various tasks, e.g., image generation and visual question answering. The vision tokenizer optimized for low-level reconstruction is agnostic to downstream tasks requiring varied representations and semantics. This decoupled ... Sep 13, 2024 · Abstract page for arXiv paper 2409.10562: Are Existing Road Design Guidelines Suitable for Autonomous Vehicles? Mar 14, 2024 · Our paper presents a novel defence against black box attacks, where attackers use the victim model as an oracle to craft their adversarial examples. Unlike traditional preprocessing defences that rely on sanitizing input samples, our stateless strategy counters the attack process itself. For every query we evaluate a counter-sample instead, where the counter-sample is the original sample ... Insert an arXiv link to find similar papers or use natural language to describe what you are looking for.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.10562", "content": "May 15, 2025 · Existing vision tokenization isolates the optimization of vision tokenizers from downstream training, implicitly assuming the visual tokens can generalize well across various tasks, e.g., image generation and visual question answering. The vision tokenizer optimized for low-level reconstruction is agnostic to downstream tasks requiring varied representations and semantics. This decoupled ... Sep 13, 2024 · Abstract page for arXiv paper 2409.10562: Are Existing Road Design Guidelines Suitable for Autonomous Vehicles? Mar 14, 2024 · Our paper presents a novel defence against black box attacks, where attackers use the victim model as an oracle to craft their adversarial examples. Unlike traditional preprocessing defences that rely on sanitizing input samples, our stateless strategy counters the attack process itself. For every query we evaluate a counter-sample instead, where the counter-sample is the original sample ... Insert an arXiv link to find similar papers or use natural language to describe what you are looking for."}
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diff --git a/data/sampled_jsons/httpseurope.naverlabs.comresearchpublicationsfake-it-till-you-make-it-learning-transferable-represen.jsonl b/data/sampled_jsons/httpseurope.naverlabs.comresearchpublicationsfake-it-till-you-make-it-learning-transferable-represen.jsonl
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+{"idx": 0, "title": "Fake it till you make it: learning transferable ...", "date": "", "ddg_snippet": "Jun 18, 2023 · Publications home No tracking until you click to share Fake it till you make it: learning transferable representations from synthetic ImageNet clones Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus, Yannis Kalantidis Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 18-22 June, 2023 CVF Open access", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/research/publications/fake-it-till-you-make-it-learning-transferable-representations-from-synthetic-imagenet-clones/", "content": "Jun 18, 2023 · Publications home No tracking until you click to share Fake it till you make it: learning transferable representations from synthetic ImageNet clones Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus, Yannis Kalantidis Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 18-22 June, 2023 CVF Open access"}
+{"idx": 1, "title": "[2212.08420] Fake it till you make it: Learning transferable ... Fake it Till You Make it: Learning Transferable ... - IEEE Xplore Fake It Till You Make It: Learning Transferable ... Fake It Till You Make It: Learning Transferable ... Fake it till you make it: Learning ... - Naver Labs Europe Paper page - Fake it till you make it: Learning transferable ...", "date": "", "ddg_snippet": "Dec 16, 2022 · View a PDF of the paper titled Fake it till you make it: Learning transferable representations from synthetic ImageNet clones , by Mert Bulent Sariyildiz and 3 other authors Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet ... First, we leverage Stable Diffusion [70] and generate synthetic ImageNet clones , i.e., datasets with synthetic images for the ImageNet classes, using class names as prompts. We analyse the generated images, highlight important issues, and propose class-agnostic alterations to the basic prompt that reduce semantic issues and increase diversity. In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet classification. Provided only with the class names that have been used to build the dataset, we explore the ability of Stable Diffusion to generate synthetic clones of ImageNet and measure how useful these are ... title={ Fake it till you make it: Learning transferable representations from synthetic ImageNet clones }, author={Sariyildiz, Mert Bulent and Alahari, Karteek and Larlus, Diane and Kalantidis, Yannis}, Dec 16, 2022 · Abstract Synthetic images generated by Stable Diffusion with minimal prompt engineering can achieve performance on par with real-image-trained models in ImageNet classification and transfer tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.08420", "content": "Dec 16, 2022 · View a PDF of the paper titled Fake it till you make it: Learning transferable representations from synthetic ImageNet clones , by Mert Bulent Sariyildiz and 3 other authors Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet ... First, we leverage Stable Diffusion [70] and generate synthetic ImageNet clones , i.e., datasets with synthetic images for the ImageNet classes, using class names as prompts. We analyse the generated images, highlight important issues, and propose class-agnostic alterations to the basic prompt that reduce semantic issues and increase diversity. In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet classification. Provided only with the class names that have been used to build the dataset, we explore the ability of Stable Diffusion to generate synthetic clones of ImageNet and measure how useful these are ... title={ Fake it till you make it: Learning transferable representations from synthetic ImageNet clones }, author={Sariyildiz, Mert Bulent and Alahari, Karteek and Larlus, Diane and Kalantidis, Yannis}, Dec 16, 2022 · Abstract Synthetic images generated by Stable Diffusion with minimal prompt engineering can achieve performance on par with real-image-trained models in ImageNet classification and transfer tasks."}
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+{"idx": 3, "title": "Fake It Till You Make It: Learning Transferable ...", "date": "", "ddg_snippet": "First, we leverage Stable Diffusion [70] and generate synthetic ImageNet clones , i.e., datasets with synthetic images for the ImageNet classes, using class names as prompts. We analyse the generated images, highlight important issues, and propose class-agnostic alterations to the basic prompt that reduce semantic issues and increase diversity.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.pdf", "content": "First, we leverage Stable Diffusion [70] and generate synthetic ImageNet clones , i.e., datasets with synthetic images for the ImageNet classes, using class names as prompts. We analyse the generated images, highlight important issues, and propose class-agnostic alterations to the basic prompt that reduce semantic issues and increase diversity."}
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+{"idx": 6, "title": "Paper page - Fake it till you make it: Learning transferable ...", "date": "", "ddg_snippet": "Dec 16, 2022 · Abstract Synthetic images generated by Stable Diffusion with minimal prompt engineering can achieve performance on par with real-image-trained models in ImageNet classification and transfer tasks.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2212.08420", "content": "Dec 16, 2022 · Abstract Synthetic images generated by Stable Diffusion with minimal prompt engineering can achieve performance on par with real-image-trained models in ImageNet classification and transfer tasks."}
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+{"idx": 8, "title": "5 Ways To Make Your Drawings Look More 3D – Binge Drawing", "date": "", "ddg_snippet": "One of the problems I faced when learning to draw portraits as a beginner was that my drawings looked flat. To compensate for my lack of understanding of what is required to add depth to.", "subpage_snippet": "", "source": "bingedrawing.com", "link": "https://bingedrawing.com/techniques/make-your-drawings-look-3d/", "content": "One of the problems I faced when learning to draw portraits as a beginner was that my drawings looked flat. To compensate for my lack of understanding of what is required to add depth to."}
+{"idx": 9, "title": "Learning | Learn Manx", "date": "", "ddg_snippet": "We have a range of lessons available online for anyone to access. These lessons range from beginner lessons with Manx phrases you can use in day to day life, through to the advanced lesson...", "subpage_snippet": "", "source": "www.learnmanx.com", "link": "https://www.learnmanx.com/learning/lessons/", "content": "We have a range of lessons available online for anyone to access. These lessons range from beginner lessons with Manx phrases you can use in day to day life, through to the advanced lesson..."}
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+{"idx": 6, "title": "Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop human proxy agents on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21490", "content": "In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop human proxy agents on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2 ."}
+{"idx": 7, "title": "Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "Jun 27, 2025 · The paper introduces the Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) to enhance human-AI collaboration in the cooperative card game Hanabi by providing standardized evaluation protocols, human proxy agents, and a diverse set of baseline methods to address the complexities of coordinating with humans in partially observable environments.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/155750", "content": "Jun 27, 2025 · The paper introduces the Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) to enhance human-AI collaboration in the cooperative card game Hanabi by providing standardized evaluation protocols, human proxy agents, and a diverse set of baseline methods to address the complexities of coordinating with humans in partially observable environments."}
+{"idx": 8, "title": "Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "To address these problems, we introduce the Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) as a standardised way to evaluate human-AI coordination in Hanabi.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45867", "content": "To address these problems, we introduce the Ad-Hoc Human-AI Coordination Challenge ( AH2AC2 ) as a standardised way to evaluate human-AI coordination in Hanabi."}
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+{"idx": 1, "title": "Оформляем README -файл профиля на GitHub / Хабр", "date": "", "ddg_snippet": "Летом 2020 года GitHub позволила пользователям создавать персональные README -файлы и с их помощью кастомизировать свои профили.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/649363/", "content": "Летом 2020 года GitHub позволила пользователям создавать персональные README -файлы и с их помощью кастомизировать свои профили."}
+{"idx": 2, "title": "Commonfactory | HomeWork v3 | Download", "date": "", "ddg_snippet": "Download Homework v3 \"Homework\" is a total conversion for \"SMA\" (Some Model Agency). SMA is a PC adult game from TValle. You need to install SMA 0.10.4e to play the mod.", "subpage_snippet": "", "source": "www.commonfactor.net", "link": "https://www.commonfactor.net/homework-download", "content": "Download Homework v3 \"Homework\" is a total conversion for \"SMA\" (Some Model Agency). SMA is a PC adult game from TValle. You need to install SMA 0.10.4e to play the mod."}
+{"idx": 3, "title": "Скачать Roblox взлом 2.686.866 на Андроид - бесконечные...", "date": "", "ddg_snippet": "А как на Readme 13 узнать то, что закачалось?", "subpage_snippet": "", "source": "geomgame.com", "link": "https://geomgame.com/robloks/", "content": "А как на Readme 13 узнать то, что закачалось?"}
+{"idx": 4, "title": "Как запустить zapret discord? | Ответы Mail", "date": "", "ddg_snippet": "Чтобы запустить запрет Discord и YouTube через проект от flowseal, скачай репозиторий с GitHub , разархивируй и открой инструкции в файле README .", "subpage_snippet": "", "source": "otvet.mail.ru", "link": "https://otvet.mail.ru/question/242914752", "content": "Чтобы запустить запрет Discord и YouTube через проект от flowseal, скачай репозиторий с GitHub , разархивируй и открой инструкции в файле README ."}
+{"idx": 5, "title": "Dying Light The Beast Deluxe Edition PC RePack Xatab скачать...", "date": "", "ddg_snippet": "Последний позволяет играть в мультиплеер через Steam+Epic, инструкции - в файле \"NoDVD/Online Fix/ readme .txt\".", "subpage_snippet": "", "source": "stoigr.org", "link": "https://stoigr.org/zombie-games/14327-dying-light-the-beast-deluxe-edition.html", "content": "Последний позволяет играть в мультиплеер через Steam+Epic, инструкции - в файле \"NoDVD/Online Fix/ readme .txt\"."}
+{"idx": 6, "title": "ИИ-вайфу на твоем компе: Как настроить Airi и чем она уникальна...", "date": "", "ddg_snippet": "В README на GitHub есть подробные инструкции по установке. Настрой языковую модель.", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/wall-231155235_363", "content": "В README на GitHub есть подробные инструкции по установке. Настрой языковую модель."}
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+{"idx": 8, "title": "VirtualHost Examples - Apache HTTP Server Version 2.5", "date": "", "ddg_snippet": "Further details may be provided in a third-party README , such as /usr/share/doc/apache 2 / README .Debian.gz on Debian and Ubuntu based distributions.", "subpage_snippet": "", "source": "httpd.apache.org", "link": "https://httpd.apache.org/docs/trunk/vhosts/examples.html", "content": "Further details may be provided in a third-party README , such as /usr/share/doc/apache 2 / README .Debian.gz on Debian and Ubuntu based distributions."}
+{"idx": 9, "title": "Скачать Microsoft Visual C++ 2005, 2008, 2010, 2012, 2013, 2015...", "date": "", "ddg_snippet": "P2: PL заработал! Большое спасибо! Сделал архив со всеми венрсиями, написал для себя Readme ? что бы не напутать и архив залил себе на яндекс-диск.", "subpage_snippet": "", "source": "dlltop.ru", "link": "https://dlltop.ru/soft/46-microsoft-visual-c", "content": "P2: PL заработал! Большое спасибо! Сделал архив со всеми венрсиями, написал для себя Readme ? что бы не напутать и архив залил себе на яндекс-диск."}
diff --git a/data/sampled_jsons/httpsgithub.comcsznKAIR.jsonl b/data/sampled_jsons/httpsgithub.comcsznKAIR.jsonl
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+{"idx": 0, "title": "GitHub - cszn/KAIR: Image Restoration Toolbox (PyTorch).", "date": "", "ddg_snippet": "News (2021-05-12) : Support distributed training, see also https :// github . com /xinntao/BasicSR/blob ... git clone https :// github . com / cszn / KAIR .git", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/KAIR", "content": "News (2021-05-12) : Support distributed training, see also https :// github . com /xinntao/BasicSR/blob ... git clone https :// github . com / cszn / KAIR .git"}
+{"idx": 1, "title": "cszn (Kai Zhang) · GitHub GitHub Pages - Kai Zhang KAIR: https://github.com/cszn/KAIR KAIR download | SourceForge.net Releases · cszn/KAIR - GitHub", "date": "", "ddg_snippet": "Image Restoration; Inverse Problems. cszn has 13 repositories available. Follow their code on GitHub . KAIR (support training and testing for DnCNN, FFDNet, SRMD, USRNet, ESRGAN) DPIR (Plug-and-Play Image Restoration with Deep Denoiser Prior) News (2025-06) Two papers are accepted in ICCV 2025. (2025-06) I will serve as a Senior Program Committee (SPC) Member for AAAI 2026. (2024-06) I will serve as a Senior Program Committee (SPC) Member for ... Explore and code with more than 13.5 million developers，Free private repositories ！：） Back Clone or Download New submodule Upload file Branches 1 Tags 0 contribute c93a6ee data figs kernels model_zoo models options results retinaface testsets trainsets utils LICENSE README.md main_challenge_sr.py main_test_dncnn.py main_test_dncnn3_deblocking.py main_test_dpsr.py main_test_face ... Aug 11, 2022 · KAIR Image Restoration Toolbox (PyTorch). Training and testing codes This is an exact mirror of the KAIR project, hosted at https://github.com/cszn/KAIR . SourceForge is not affiliated with KAIR . For more information, see the SourceForge Open Source Mirror Directory. Sep 10, 2021 · Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR - cszn / KAIR", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn", "content": "Image Restoration; Inverse Problems. cszn has 13 repositories available. Follow their code on GitHub . KAIR (support training and testing for DnCNN, FFDNet, SRMD, USRNet, ESRGAN) DPIR (Plug-and-Play Image Restoration with Deep Denoiser Prior) News (2025-06) Two papers are accepted in ICCV 2025. (2025-06) I will serve as a Senior Program Committee (SPC) Member for AAAI 2026. (2024-06) I will serve as a Senior Program Committee (SPC) Member for ... Explore and code with more than 13.5 million developers，Free private repositories ！：） Back Clone or Download New submodule Upload file Branches 1 Tags 0 contribute c93a6ee data figs kernels model_zoo models options results retinaface testsets trainsets utils LICENSE README.md main_challenge_sr.py main_test_dncnn.py main_test_dncnn3_deblocking.py main_test_dpsr.py main_test_face ... Aug 11, 2022 · KAIR Image Restoration Toolbox (PyTorch). Training and testing codes This is an exact mirror of the KAIR project, hosted at https://github.com/cszn/KAIR . SourceForge is not affiliated with KAIR . For more information, see the SourceForge Open Source Mirror Directory. Sep 10, 2021 · Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR - cszn / KAIR"}
+{"idx": 2, "title": "KAIR/README.md at master · cszn/KAIR · GitHub", "date": "", "ddg_snippet": "Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR - KAIR /README.md at master · cszn / KAIR", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/KAIR/blob/master/README.md", "content": "Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR - KAIR /README.md at master · cszn / KAIR"}
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+{"idx": 4, "title": "KAIR download | SourceForge.net", "date": "", "ddg_snippet": "Aug 11, 2022 · KAIR Image Restoration Toolbox (PyTorch). Training and testing codes This is an exact mirror of the KAIR project, hosted at https://github.com/cszn/KAIR . SourceForge is not affiliated with KAIR . For more information, see the SourceForge Open Source Mirror Directory.", "subpage_snippet": "", "source": "sourceforge.net", "link": "https://sourceforge.net/projects/kair.mirror/", "content": "Aug 11, 2022 · KAIR Image Restoration Toolbox (PyTorch). Training and testing codes This is an exact mirror of the KAIR project, hosted at https://github.com/cszn/KAIR . SourceForge is not affiliated with KAIR . For more information, see the SourceForge Open Source Mirror Directory."}
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+{"idx": 6, "title": "GitHub - cszn/BSRGAN: Designing a Practical Degradation Model", "date": "", "ddg_snippet": "Our new work for real image denoising --- > https :// github . com / cszn /SCUNet ... Download KAIR : git clone https :// github . com / cszn / KAIR .git", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/BSRGAN", "content": "Our new work for real image denoising --- > https :// github . com / cszn /SCUNet ... Download KAIR : git clone https :// github . com / cszn / KAIR .git"}
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+{"idx": 9, "title": "KAIR: https://github.com/cszn/KAIR", "date": "", "ddg_snippet": "Explore and code with more than 13.5 million developers，Free private repositories ！：） Back Clone or Download New submodule Upload file Branches 1 Tags 0 contribute c93a6ee data figs kernels model_zoo models options results retinaface testsets trainsets utils LICENSE README.md main_challenge_sr.py main_test_dncnn.py main_test_dncnn3_deblocking.py main_test_dpsr.py main_test_face ...", "subpage_snippet": "", "source": "gitee.com", "link": "https://gitee.com/yuelaiyue2/kair", "content": "Explore and code with more than 13.5 million developers，Free private repositories ！：） Back Clone or Download New submodule Upload file Branches 1 Tags 0 contribute c93a6ee data figs kernels model_zoo models options results retinaface testsets trainsets utils LICENSE README.md main_challenge_sr.py main_test_dncnn.py main_test_dncnn3_deblocking.py main_test_dpsr.py main_test_face ..."}
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+{"idx": 3, "title": "ESET NOD32 | Свежие Ключи 2025 | Бесплатно | СТЕНА – Telegram", "date": "", "ddg_snippet": "Количество активаций Security Essential/Security Premium сокращено с 25 до 2 . Не пытайтесь активировать ключи через 10-15 минут после выхода поста.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/eset_nod32_free", "content": "Количество активаций Security Essential/Security Premium сокращено с 25 до 2 . Не пытайтесь активировать ключи через 10-15 минут после выхода поста."}
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diff --git a/data/sampled_jsons/hybrid_tensor_parallelism_data_parallelism_large_language_models_framework.jsonl b/data/sampled_jsons/hybrid_tensor_parallelism_data_parallelism_large_language_models_framework.jsonl
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index 0000000000000000000000000000000000000000..d3f90f8dff66b28abf81929fb1cf4dd86be641e5
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+{"idx": 1, "title": "How to Implement Model Parallelism for Large Language Models ...", "date": "", "ddg_snippet": "May 30, 2025 · This guide shows you how to implement model parallelism for large language models using PyTorch, covering both tensor parallelism and pipeline parallelism approaches with practical code examples.", "subpage_snippet": "", "source": "markaicode.com", "link": "https://markaicode.com/model-parallelism-large-language-models/", "content": "May 30, 2025 · This guide shows you how to implement model parallelism for large language models using PyTorch, covering both tensor parallelism and pipeline parallelism approaches with practical code examples."}
+{"idx": 2, "title": "ResearchonModelParallelismandData ...", "date": "", "ddg_snippet": "Abstract—With the rapid adoption of large language models (LLMs) in recommendation systems, the computational and communication bottlenecks caused by their massive parameter sizes and large data volumes have become increasingly prominent. This paper systematically investigates two classes of optimization methods— model parallelism and data parallelism —for distributed training of LLMs in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.17551", "content": "Abstract—With the rapid adoption of large language models (LLMs) in recommendation systems, the computational and communication bottlenecks caused by their massive parameter sizes and large data volumes have become increasingly prominent. This paper systematically investigates two classes of optimization methods— model parallelism and data parallelism —for distributed training of LLMs in ..."}
+{"idx": 3, "title": "Chimera: Communication Fusion for Hybrid Parallelism in Large ...", "date": "", "ddg_snippet": "Jun 20, 2025 · Large Language Models (LLMs), exemplified by ChatGPT, have emerged as a predominant workload in current machine learning systems. To achieve efficient training and inference within the constraints of limited single-NPU memory capacity, deploying LLMs on multi-NPU systems typically adopt a hybrid approach that combines various parallelism patterns. This hybrid parallelism within LLMs introduces ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3695053.3731025", "content": "Jun 20, 2025 · Large Language Models (LLMs), exemplified by ChatGPT, have emerged as a predominant workload in current machine learning systems. To achieve efficient training and inference within the constraints of limited single-NPU memory capacity, deploying LLMs on multi-NPU systems typically adopt a hybrid approach that combines various parallelism patterns. This hybrid parallelism within LLMs introduces ..."}
+{"idx": 4, "title": "Quartet: A Holistic Hybrid Parallel Framework for Training ... Data, tensor, pipeline, expert and hybrid parallelisms Combining Parallelism Strategies - apxml.com How to Implement Model Parallelism for Large Language Models ... Quartet: A Holistic Hybrid Parallel Framework for Training Large Chimera : Communication Fusion for Hybrid Parallelism in Large Langu… Quartet: A Holistic Hybrid Parallel Framework for Training Large Quartet: A Holistic Hybrid Parallel Framework for Training Large Quartet: A Holistic Hybrid Parallel Framework for Training Large Chimera : Communication Fusion for Hybrid Parallelism in Large Langu… Model Parallelism - Hugging Face", "date": "", "ddg_snippet": "Aug 26, 2024 · Hybrid parallelism is popular in training large language models (LLMs). However, existing efforts have focused on optimizing individual strategies in hybrid parallelism , such as pipeline scheduling, device assignment, etc., which limits the overall training... An alternative configuration is to reduce tensor parallelism and increase data parallelism . For example, you can set TP=2 and DP=4: This reduces cross-GPU communication, which may help lower latency during inference. However, there’s a catch: model weights consume a large portion of GPU memory, especially for large models . While data parallelism (DP), tensor parallelism (TP), and pipeline parallelism (PP) each offer distinct advantages for distributing training, pushing the boundaries of model scale often requires combining these techniques. No single strategy is universally optimal; the best approach depends on the specific model architecture, hardware constraints (GPU memory, interconnect bandwidth/latency ... May 30, 2025 · This guide shows you how to implement model parallelism for large language models using PyTorch, covering both tensor parallelism and pipeline parallelism approaches with practical code examples. Is hybrid parallelism effective in training large language models (LLMs)? Hybrid parallelism is popular in training large language models (LLMs). However, existing efforts have focused on optimizing individual strategies in hybrid parallelism , such as pipeline scheduling, device assignment, etc., which limits the overall training efficiency. What is hybrid parallelism in LLMs? To achieve efficient training and inference within the constraints of limited single-NPU memory capacity, deploying LLMs on multi-NPU systems typically adopt a hybrid approach that combines various parallelism patterns. This hybrid parallelism within LLMs introduces a significant amount of diverse collective communications . What is a hybrid parallel framework for training LLMs? In this work, we propose Quartet , a holistic hybrid parallel framework for training LLMs. It brings new insights for achieving optimal training performance. Specifically, it parameterizes pipeline scheduling and device assignment, and incorporates them into a joint optimization framework together with model scaling and model splitting. What is hybrid parallelism (hp)? Hybrid Parallelism (HP). The HP consisting of data parallelism (DP) and pipeline parallelism (PP) is currently one of the most widely adopted forms of LLM distributed training [13, 21]. What is data parallelism? Data parallelism (DP) and model parallelism (MP) are two basic parallelism modes for training large-scale models . In DP, multiple accelerators each executes a replica of the model with evenly sharded training data. Why is hybrid parallelism important for multi-NPU systems? This hybrid parallelism within LLMs introduces a significant amount of diverse collective communications. However, these frequent blocking communications impose a substantial burden on the multi-NPU systems. Overcoming the communication bottleneck is crucial to unlocking the potential of multi-NPU systems for efficient and scalable LLM processing. In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing. In this section we use concepts and diagrams from the Megatron-LM paper: Efficient Large -Scale Language Model Training on GPU Clusters.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-69766-1_29", "content": "Aug 26, 2024 · Hybrid parallelism is popular in training large language models (LLMs). However, existing efforts have focused on optimizing individual strategies in hybrid parallelism , such as pipeline scheduling, device assignment, etc., which limits the overall training... An alternative configuration is to reduce tensor parallelism and increase data parallelism . For example, you can set TP=2 and DP=4: This reduces cross-GPU communication, which may help lower latency during inference. However, there’s a catch: model weights consume a large portion of GPU memory, especially for large models . While data parallelism (DP), tensor parallelism (TP), and pipeline parallelism (PP) each offer distinct advantages for distributing training, pushing the boundaries of model scale often requires combining these techniques. No single strategy is universally optimal; the best approach depends on the specific model architecture, hardware constraints (GPU memory, interconnect bandwidth/latency ... May 30, 2025 · This guide shows you how to implement model parallelism for large language models using PyTorch, covering both tensor parallelism and pipeline parallelism approaches with practical code examples. Is hybrid parallelism effective in training large language models (LLMs)? Hybrid parallelism is popular in training large language models (LLMs). However, existing efforts have focused on optimizing individual strategies in hybrid parallelism , such as pipeline scheduling, device assignment, etc., which limits the overall training efficiency. What is hybrid parallelism in LLMs? To achieve efficient training and inference within the constraints of limited single-NPU memory capacity, deploying LLMs on multi-NPU systems typically adopt a hybrid approach that combines various parallelism patterns. This hybrid parallelism within LLMs introduces a significant amount of diverse collective communications . What is a hybrid parallel framework for training LLMs? In this work, we propose Quartet , a holistic hybrid parallel framework for training LLMs. It brings new insights for achieving optimal training performance. Specifically, it parameterizes pipeline scheduling and device assignment, and incorporates them into a joint optimization framework together with model scaling and model splitting. What is hybrid parallelism (hp)? Hybrid Parallelism (HP). The HP consisting of data parallelism (DP) and pipeline parallelism (PP) is currently one of the most widely adopted forms of LLM distributed training [13, 21]. What is data parallelism? Data parallelism (DP) and model parallelism (MP) are two basic parallelism modes for training large-scale models . In DP, multiple accelerators each executes a replica of the model with evenly sharded training data. Why is hybrid parallelism important for multi-NPU systems? This hybrid parallelism within LLMs introduces a significant amount of diverse collective communications. However, these frequent blocking communications impose a substantial burden on the multi-NPU systems. Overcoming the communication bottleneck is crucial to unlocking the potential of multi-NPU systems for efficient and scalable LLM processing. In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing. In this section we use concepts and diagrams from the Megatron-LM paper: Efficient Large -Scale Language Model Training on GPU Clusters."}
+{"idx": 5, "title": "Data, tensor, pipeline, expert and hybrid parallelisms", "date": "", "ddg_snippet": "An alternative configuration is to reduce tensor parallelism and increase data parallelism . For example, you can set TP=2 and DP=4: This reduces cross-GPU communication, which may help lower latency during inference. However, there’s a catch: model weights consume a large portion of GPU memory, especially for large models .", "subpage_snippet": "", "source": "bentoml.com", "link": "https://bentoml.com/llm/inference-optimization/data-tensor-pipeline-expert-hybrid-parallelism", "content": "An alternative configuration is to reduce tensor parallelism and increase data parallelism . For example, you can set TP=2 and DP=4: This reduces cross-GPU communication, which may help lower latency during inference. However, there’s a catch: model weights consume a large portion of GPU memory, especially for large models ."}
+{"idx": 6, "title": "Model Parallelism - Hugging Face", "date": "", "ddg_snippet": "In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing. In this section we use concepts and diagrams from the Megatron-LM paper: Efficient Large -Scale Language Model Training on GPU Clusters.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/transformers/v4.15.0/parallelism", "content": "In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing. In this section we use concepts and diagrams from the Megatron-LM paper: Efficient Large -Scale Language Model Training on GPU Clusters."}
+{"idx": 7, "title": "Data , tensor , pipeline, expert and hybrid parallelisms | LLM Inference...", "date": "", "ddg_snippet": "Hybrid parallelism combines two or more parallelism techniques to achieve better scalability, efficiency, and hardware utilization. A typical hybrid setup might look like this (combining data parallelism and tensor parallelism )", "subpage_snippet": "", "source": "www.bentoml.com", "link": "https://www.bentoml.com/llm/inference-optimization/data-tensor-pipeline-expert-hybrid-parallelism", "content": "Hybrid parallelism combines two or more parallelism techniques to achieve better scalability, efficiency, and hardware utilization. A typical hybrid setup might look like this (combining data parallelism and tensor parallelism )"}
+{"idx": 8, "title": "Scaling Large Language Models : A Guide to Parallelism ... | Medium", "date": "", "ddg_snippet": "Tensor Parallelism splits large model layers at the tensor dimension level. For instance, a giant weight matrix can be partitioned column- or row-wise across multiple GPUs.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@siddharthtiwari01/scaling-large-language-models-a-guide-to-parallelism-techniques-c4f7dd6c9f1f", "content": "Tensor Parallelism splits large model layers at the tensor dimension level. For instance, a giant weight matrix can be partitioned column- or row-wise across multiple GPUs."}
+{"idx": 9, "title": "Distributed Large Language Model Inference: A ML Engineer's Guide", "date": "", "ddg_snippet": "Tensor parallelism : The foundation of large model serving.Single-node optimization works effectively for models under 30B parameters using data parallelism with multiple replicas.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/distributed-large-language-model-inference-ml-engineers-jawad-md-shskc", "content": "Tensor parallelism : The foundation of large model serving.Single-node optimization works effectively for models under 30B parameters using data parallelism with multiple replicas."}
diff --git a/data/sampled_jsons/i8dYPGdB1C_Near-Optimal_Online_Learning_Multi-Agent_Submodular_Coordination_Figure_3(a)_utility_valu_year_2023.jsonl b/data/sampled_jsons/i8dYPGdB1C_Near-Optimal_Online_Learning_Multi-Agent_Submodular_Coordination_Figure_3(a)_utility_valu_year_2023.jsonl
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+{"idx": 1, "title": "[Literature Review] Near - Optimal Online Learning for Multi - Agent ...", "date": "", "ddg_snippet": "multi - agent . online learning . submodular maximization. coordination .The paper titled “ Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency” focuses on enhancing strategies for coordinating multiple...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/near-optimal-online-learning-for-multi-agent-submodular-coordination-tight-approximation-and-communication-efficiency", "content": "multi - agent . online learning . submodular maximization. coordination .The paper titled “ Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency” focuses on enhancing strategies for coordinating multiple..."}
+{"idx": 2, "title": "Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/near-optimal-online-learning-for-multi-agent", "content": "algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques."}
+{"idx": 3, "title": "Fast Semidifferential-based Submodular Function Optimization", "date": "", "ddg_snippet": "Bach, F. Learning with Submodular functions: A convex Optimization Perspective.Goel, G., Karande, C., Tripathi, P., and Wang, L. Ap-proximability of combinatorial problems with multi - agent submodular cost functions.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v28/iyer13.pdf", "content": "Bach, F. Learning with Submodular functions: A convex Optimization Perspective.Goel, G., Karande, C., Tripathi, P., and Wang, L. Ap-proximability of combinatorial problems with multi - agent submodular cost functions."}
+{"idx": 4, "title": "Online Learning of Time-Varying Signals and Graphs | Request PDF", "date": "", "ddg_snippet": "Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Commun...is the spectral gap of the network and c is the joint curvature of submodular objectives. This result significantly improves the. (11+c)(\\frac{1}{ 1 + c }).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/352170929_Online_Learning_of_Time-Varying_Signals_and_Graphs", "content": "Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Commun...is the spectral gap of the network and c is the joint curvature of submodular objectives. This result significantly improves the. (11+c)(\\frac{1}{ 1 + c })."}
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+{"idx": 6, "title": "Qixin ZHANG - CityUHK Scholars", "date": "", "ddg_snippet": "Near - optimal online learning for multi - agent submodular coordination : tight approximation and communication efficiency.", "subpage_snippet": "", "source": "scholars.cityu.edu.hk", "link": "https://scholars.cityu.edu.hk/en/persons/qxzhang4", "content": "Near - optimal online learning for multi - agent submodular coordination : tight approximation and communication efficiency."}
+{"idx": 7, "title": "GitHub - hairuoliu1/ICLR-2025-Robotics: A list of robotics related...", "date": "", "ddg_snippet": "Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency. Authors: Qixin ZHANG, Zongqi Wan, Yu Yang, Li Shen, Dacheng Tao. Abstract: Coordinating multiple agents to collaboratively maximize submodular ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hairuoliu1/ICLR-2025-Robotics", "content": "Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency. Authors: Qixin ZHANG, Zongqi Wan, Yu Yang, Li Shen, Dacheng Tao. Abstract: Coordinating multiple agents to collaboratively maximize submodular ..."}
+{"idx": 8, "title": "ICLR 2025 Schedule", "date": "", "ddg_snippet": "Inverse Attention Agents for Multi - Agent Systems. eQMARL: Entangled Quantum Multi - Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels.Oral Session 1 C [10:30-12:00]. Orals 10:30-11:42.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/calendar", "content": "Inverse Attention Agents for Multi - Agent Systems. eQMARL: Entangled Quantum Multi - Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels.Oral Session 1 C [10:30-12:00]. Orals 10:30-11:42."}
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+{"idx": 1, "title": "[2502.05028] Near - Optimal Online Learning for Multi - Agent ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency, by Qixin Zhang and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05028", "content": "View a PDF of the paper titled Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency, by Qixin Zhang and 4 other authors."}
+{"idx": 2, "title": "Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Go Home. Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency.Select any part of the paper to ask specific questions about that section .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2502.05028", "content": "Go Home. Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency.Select any part of the paper to ask specific questions about that section ."}
+{"idx": 3, "title": "Near - Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/near-optimal-online-learning-for-multi-agent", "content": "algorithm, which employs the multi -linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques."}
+{"idx": 4, "title": "[Literature Review] Near - Optimal Online Learning for Multi - Agent ...", "date": "", "ddg_snippet": "The paper titled “ Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency” focuses on enhancing strategies for coordinating multiple agents to collaboratively maximize submodular functions in dynamic...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/near-optimal-online-learning-for-multi-agent-submodular-coordination-tight-approximation-and-communication-efficiency", "content": "The paper titled “ Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency” focuses on enhancing strategies for coordinating multiple agents to collaboratively maximize submodular functions in dynamic..."}
+{"idx": 5, "title": "Multiagent Systems | Cool Papers - Immersive Paper Discovery", "date": "", "ddg_snippet": "# 5 Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency [PDF] [Copy] [Kimi] [REL].", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/cs.MA?sort=1", "content": "# 5 Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency [PDF] [Copy] [Kimi] [REL]."}
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+{"idx": 7, "title": "Face Swap Online Free", "date": "", "ddg_snippet": "Free online face changer that allows you to swap heads and replace faces in photos. No sign up, no watermark.", "subpage_snippet": "", "source": "remaker.ai", "link": "https://remaker.ai/face-swap-free/", "content": "Free online face changer that allows you to swap heads and replace faces in photos. No sign up, no watermark."}
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+{"idx": 9, "title": "Qixin Zhang - Google Akademik", "date": "", "ddg_snippet": "2024. Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency.", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=8MInmSsx3IgC&hl=tr", "content": "2024. Near - Optimal Online Learning for Multi - Agent Submodular Coordination : Tight Approximation and Communication Efficiency."}
diff --git a/data/sampled_jsons/iDDPM_conditional_unconditional_ImageNet_64x64_FID_Nichol_Dhariwal_2021_results_year_2021.jsonl b/data/sampled_jsons/iDDPM_conditional_unconditional_ImageNet_64x64_FID_Nichol_Dhariwal_2021_results_year_2021.jsonl
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+{"idx": 0, "title": "Why Are Conditional Generative Models Better Than ...", "date": "", "ddg_snippet": "by F Bao · Cited by 29 — CIFAR10 CelebA 64x64 LSUN Bedroom 64x64 ImageNet 64x64 . Unconditional DM. 2.72. 2.14. 2.69. 6.44. Conditional DM. 2.24. -. -. 3.08. SCDM (K = 2).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=sbDyvrvvKn7", "content": "by F Bao · Cited by 29 — CIFAR10 CelebA 64x64 LSUN Bedroom 64x64 ImageNet 64x64 . Unconditional DM. 2.72. 2.14. 2.69. 6.44. Conditional DM. 2.24. -. -. 3.08. SCDM (K = 2)."}
+{"idx": 1, "title": "Directly Denoising Diffusion Model", "date": "", "ddg_snippet": "24 May 2024 — ... conditional samples on ImageNet 64x64 ... All models on CIFAR-10 are unconditional , and all models on ImageNet 64x64 are conditioned on class ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.13540v1", "content": "24 May 2024 — ... conditional samples on ImageNet 64x64 ... All models on CIFAR-10 are unconditional , and all models on ImageNet 64x64 are conditioned on class ..."}
+{"idx": 2, "title": "Simple Drop-in LoRA Conditioning on Attention Layers Will ...", "date": "", "ddg_snippet": "For IDDPM , we use CIFAR-10 for both unconditional and class- conditional sampling, and ImageNet64, a downsampled version of the ImageNet1k, for unconditional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.03958v3", "content": "For IDDPM , we use CIFAR-10 for both unconditional and class- conditional sampling, and ImageNet64, a downsampled version of the ImageNet1k, for unconditional ..."}
+{"idx": 3, "title": "Normalizing Flows are Capable Generative Models - Jiatao Gu", "date": "", "ddg_snippet": "conditional and unconditional settings. Putting these together, TARFLOW sets new state-of-the- art results on likelihood estimation for images, beating the ...", "subpage_snippet": "", "source": "jiataogu.me", "link": "https://jiataogu.me/papers/zhai2025normalizing.pdf", "content": "conditional and unconditional settings. Putting these together, TARFLOW sets new state-of-the- art results on likelihood estimation for images, beating the ..."}
+{"idx": 4, "title": "Understanding Diffusion Objectives as the ELBO with ...", "date": "", "ddg_snippet": "9 Dec 2023 — ... unconditional q. Another key difference is that for Equation 138 ... For class- conditional generation on ImageNet 64x64 , we applied the ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/71843", "content": "9 Dec 2023 — ... unconditional q. Another key difference is that for Equation 138 ... For class- conditional generation on ImageNet 64x64 , we applied the ..."}
+{"idx": 5, "title": "A Gradient View on Diffusion Sampling with Guidance", "date": "", "ddg_snippet": "by AD Dinh · 2023 · Cited by 16 — The first case is the unconditional diffusion model, and the second case is the conditional diffusion model. Unconditional diffusion model Table 8 show the ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/dinh23a/dinh23a.pdf", "content": "by AD Dinh · 2023 · Cited by 16 — The first case is the unconditional diffusion model, and the second case is the conditional diffusion model. Unconditional diffusion model Table 8 show the ..."}
+{"idx": 6, "title": "arXiv:2405.03958v1 [cs.CV] 7 May 2024", "date": "", "ddg_snippet": "results in Table 3. We find that LoRA conditioning significantly outperforms adaLN conditioning for both unconditional and conditional CIFAR-10 generation.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/simple-drop-in-lora-conditioning-on-attention-layers-will-4vvgiyu51t.pdf", "content": "results in Table 3. We find that LoRA conditioning significantly outperforms adaLN conditioning for both unconditional and conditional CIFAR-10 generation."}
+{"idx": 7, "title": "Score Estimation for Generative Modeling - Signals, Information, and ...", "date": "", "ddg_snippet": "Table 8.1.: Image generation results on ImageNet 64x64 (class- conditional ) and CIFAR-10. 32x32 ( unconditional ). The size of the sampler is denoted by the ...", "subpage_snippet": "", "source": "sia.mit.edu", "link": "https://sia.mit.edu/wp-content/uploads/2025/08/2025-jayashankar-phd.pdf", "content": "Table 8.1.: Image generation results on ImageNet 64x64 (class- conditional ) and CIFAR-10. 32x32 ( unconditional ). The size of the sampler is denoted by the ..."}
+{"idx": 8, "title": "Directly Denoising Diffusion Models - GitHub", "date": "", "ddg_snippet": "All models on CIFAR-10 are unconditional , and all models on ImageNet 64x64 are conditioned on class labels. Other settings. We use Adam for all of our ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bl/zhang24bl.pdf", "content": "All models on CIFAR-10 are unconditional , and all models on ImageNet 64x64 are conditioned on class labels. Other settings. We use Adam for all of our ..."}
+{"idx": 9, "title": "A Distribution details", "date": "", "ddg_snippet": "Similarly, the conditional ... We include additional uncurated random samples from our unconditional models trained on CIFAR-. 10, 32x32 Imagenet , and 64x64 ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/b578f2a52a0229873fefc2a4b06377fa-Supplemental.pdf", "content": "Similarly, the conditional ... We include additional uncurated random samples from our unconditional models trained on CIFAR-. 10, 32x32 Imagenet , and 64x64 ..."}
diff --git a/data/sampled_jsons/image-to-image_diffusion_models_transformation_layers_overfitting_generalization_mAP.jsonl b/data/sampled_jsons/image-to-image_diffusion_models_transformation_layers_overfitting_generalization_mAP.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4228b0e618b6ef414798131c435d4838b72d7bf1
--- /dev/null
+++ b/data/sampled_jsons/image-to-image_diffusion_models_transformation_layers_overfitting_generalization_mAP.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2111.05826] Palette: Image-to-Image Diffusion Models - arXiv.org", "date": "", "ddg_snippet": "This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpainting, uncropping, and JPEG restoration. Our simple implementation of image-to-image diffusion models outperforms strong GAN and regression baselines on all tasks, without task ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.05826", "content": "This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpainting, uncropping, and JPEG restoration. Our simple implementation of image-to-image diffusion models outperforms strong GAN and regression baselines on all tasks, without task ..."}
+{"idx": 1, "title": "Low-Cost Training of Image-to-Image Diffusion Models with ... - MDPI", "date": "", "ddg_snippet": "The resulting colorization models showcase exceptional performances with a minimal number of training epochs. We examine the impact of different configurations and provide insights into the ability of image-to-image diffusion models for transfer learning across tasks and domains.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2079-9292/13/4/722", "content": "The resulting colorization models showcase exceptional performances with a minimal number of training epochs. We examine the impact of different configurations and provide insights into the ability of image-to-image diffusion models for transfer learning across tasks and domains."}
+{"idx": 2, "title": "LFDT-Fusion: A latent feature-guided diffusion Transformer model for ...", "date": "", "ddg_snippet": "For image fusion tasks, it is inefficient for the diffusion model to iterate multiple times on the original resolution image for feature mapping. To address this issue, this paper proposes an efficient latent feature-guided diffusion model for general image fusion.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253524004172", "content": "For image fusion tasks, it is inefficient for the diffusion model to iterate multiple times on the original resolution image for feature mapping. To address this issue, this paper proposes an efficient latent feature-guided diffusion model for general image fusion."}
+{"idx": 3, "title": "PDF APT: Adaptive Personalized Training for Diffusion Models with Limited Data", "date": "", "ddg_snippet": "Personalizing diffusion models using limited data presents significant challenges, including overfitting , loss of prior knowledge, and degradation of text alignment. Over-fitting leads to shifts in the noise prediction distribution, dis-rupting the denoising trajectory and causing the model to lose semantic coherence. In this paper, we propose ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Chae_APT_Adaptive_Personalized_Training_for_Diffusion_Models_with_Limited_Data_CVPR_2025_paper.pdf", "content": "Personalizing diffusion models using limited data presents significant challenges, including overfitting , loss of prior knowledge, and degradation of text alignment. Over-fitting leads to shifts in the noise prediction distribution, dis-rupting the denoising trajectory and causing the model to lose semantic coherence. In this paper, we propose ..."}
+{"idx": 4, "title": "Exploring Image Transformations with Diffusion Models: A Survey of ...", "date": "", "ddg_snippet": "Diffusion Models have become increasingly popular in recent years and their applications span a wide range of fields. This survey focuses on the use of diffusion models in computer vision, specially in the branch of image transformations .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-53966-4_2", "content": "Diffusion Models have become increasingly popular in recent years and their applications span a wide range of fields. This survey focuses on the use of diffusion models in computer vision, specially in the branch of image transformations ."}
+{"idx": 5, "title": "Palette: Image-to-Image Diffusion Models - dlnext.acm.org", "date": "", "ddg_snippet": "This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpainting, uncropping, and JPEG restoration. Our simple implementation of image-to-image diffusion models outperforms strong GAN and regression baselines on all tasks, without task ...", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/fullHtml/10.1145/3528233.3530757", "content": "This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpainting, uncropping, and JPEG restoration. Our simple implementation of image-to-image diffusion models outperforms strong GAN and regression baselines on all tasks, without task ..."}
+{"idx": 6, "title": "PDF On the Generalization Properties of Diffusion Models", "date": "", "ddg_snippet": "Second, on the training front, [51] proposed a set of techniques to enhance the training performance of score-based generative models , scaling diffusion models to images of higher resolution, but without any characterization of possible generalization improvements.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/file/06abed94583030dd50abe6767bd643b1-Paper-Conference.pdf", "content": "Second, on the training front, [51] proposed a set of techniques to enhance the training performance of score-based generative models , scaling diffusion models to images of higher resolution, but without any characterization of possible generalization improvements."}
+{"idx": 7, "title": "Image-to-Image Translation with Diffusion Transformers and CLIP-Based ...", "date": "", "ddg_snippet": "Abstract—Image- to - image translation aims to learn a mapping between a source and a target domain, enabling tasks such as style transfer, appearance transformation , and domain adapta-tion. In this work, we explore a diffusion -based framework for image-to-image translation by adapting Diffusion Transformers (DiT), which combine the denoising capabilities of diffusion models with the global ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.16001", "content": "Abstract—Image- to - image translation aims to learn a mapping between a source and a target domain, enabling tasks such as style transfer, appearance transformation , and domain adapta-tion. In this work, we explore a diffusion -based framework for image-to-image translation by adapting Diffusion Transformers (DiT), which combine the denoising capabilities of diffusion models with the global ..."}
+{"idx": 8, "title": "Unpaired Image-to-Image Translation with Diffusion Adversarial Network", "date": "", "ddg_snippet": "Unpaired image translation with feature-level constraints presents significant challenges, including unstable network training and low diversity in generated tasks. This limitation is typically attributed to the following situations: 1. The generated images are overly simplistic, which fails to stimulate the network's capacity for generating diverse and imaginative outputs. 2. The images ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7390/12/20/3178", "content": "Unpaired image translation with feature-level constraints presents significant challenges, including unstable network training and low diversity in generated tasks. This limitation is typically attributed to the following situations: 1. The generated images are overly simplistic, which fails to stimulate the network's capacity for generating diverse and imaginative outputs. 2. The images ..."}
+{"idx": 9, "title": "PDF GDA: Generalized Diffusion for Robust Test-time Adaptation", "date": "", "ddg_snippet": "To address these challenges and improve the generalizabil-ity of diffusion models , we propose Generalized Diffusion Adaptation (GDA), an eficient diffusion -based adaptation method robust against diverse OOD shifts at test time, in-cluding style changes and multiple corruptions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Tsai_GDA_Generalized_Diffusion_for_Robust_Test-time_Adaptation_CVPR_2024_paper.pdf", "content": "To address these challenges and improve the generalizabil-ity of diffusion models , we propose Generalized Diffusion Adaptation (GDA), an eficient diffusion -based adaptation method robust against diverse OOD shifts at test time, in-cluding style changes and multiple corruptions."}
diff --git a/data/sampled_jsons/in-pixel_processing_feature_tracking_pixel_processor_array.jsonl b/data/sampled_jsons/in-pixel_processing_feature_tracking_pixel_processor_array.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f287b942286587738dbd793bd6555fcaa2cc0457
--- /dev/null
+++ b/data/sampled_jsons/in-pixel_processing_feature_tracking_pixel_processor_array.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Descriptor-In-Pixel : Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "Abstract This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of \" pixel-processors \", enabling massive parallel computation of vi-sual data at the point of light capture. Our approach per-forms all computation entirely in-pixel ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "Abstract This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of \" pixel-processors \", enabling massive parallel computation of vi-sual data at the point of light capture. Our approach per-forms all computation entirely in-pixel ..."}
+{"idx": 1, "title": "Descriptor-In-Pixel: Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "Point- Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power. All computation is performed inside the sensor itself, upon thousands of \" Pixel -Procesors\".", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Point- Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power. All computation is performed inside the sensor itself, upon thousands of \" Pixel -Procesors\"."}
+{"idx": 2, "title": "Descriptor In Pixel : Point Feature Tracking for Pixel Processor Arrays ...", "date": "", "ddg_snippet": "This \"response map\" is utilized for both detection and tracking of point- features across the pixel-processor array .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=HIdQtf6mFSs", "content": "This \"response map\" is utilized for both detection and tracking of point- features across the pixel-processor array ."}
+{"idx": 3, "title": "DEMO : Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "We demonstrate our \" in-pixel \" point- feature detection and tracking approach, designed specifically for Pixel Processor Array (PPA) sensors. PPAs consist of thousands of \" pixel-processors \", enabling massive parallel computation at the point of light capture. Our approach performs all computation for feature detection & tracking within these pixel-processors , allowing sensor output to be ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11147459", "content": "We demonstrate our \" in-pixel \" point- feature detection and tracking approach, designed specifically for Pixel Processor Array (PPA) sensors. PPAs consist of thousands of \" pixel-processors \", enabling massive parallel computation at the point of light capture. Our approach performs all computation for feature detection & tracking within these pixel-processors , allowing sensor output to be ..."}
+{"idx": 4, "title": "Bose Descriptor-In-Pixel Point-Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "This paper introduces a novel method for point- feature detection and tracking using Pixel Processor Array (PPA) vision sensors, which enables in-pixel computation and significantly reduces data transfer requirements. The proposed Descriptor- In - Pixel paradigm allows for efficient processing at over 3000 FPS, making it suitable for high-speed applications while maintaining low latency. By ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/890539401/Bose-Descriptor-In-Pixel-Point-Feature-Tracking-for-Pixel-Processor-Arrays-CVPR-2025-Paper", "content": "This paper introduces a novel method for point- feature detection and tracking using Pixel Processor Array (PPA) vision sensors, which enables in-pixel computation and significantly reduces data transfer requirements. The proposed Descriptor- In - Pixel paradigm allows for efficient processing at over 3000 FPS, making it suitable for high-speed applications while maintaining low latency. By ..."}
+{"idx": 5, "title": "Sensor-level computer vision with pixel processor arrays for agile ...", "date": "", "ddg_snippet": "Here, we review the history of image sensing and processing hardware from the perspective of in-pixel computing and outline the key features of a state-of-the-art smart camera system based on a PPA device, through the description of the SCAMP-5 system.", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/scirobotics.abl7755", "content": "Here, we review the history of image sensing and processing hardware from the perspective of in-pixel computing and outline the key features of a state-of-the-art smart camera system based on a PPA device, through the description of the SCAMP-5 system."}
+{"idx": 6, "title": "Mapping Image Transformations Onto Pixel Processor Arrays", "date": "", "ddg_snippet": "Pixel Processor Arrays (PPA) present a new vision sensor/ processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication. Such a device allows visual data to be efficiently stored and manipulated directly upon the focal plane, but also demands the invention of new approaches and algorithms, suitable for the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.16994", "content": "Pixel Processor Arrays (PPA) present a new vision sensor/ processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication. Such a device allows visual data to be efficiently stored and manipulated directly upon the focal plane, but also demands the invention of new approaches and algorithms, suitable for the ..."}
+{"idx": 7, "title": "Descriptor-In-Pixel : Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2025/436400f392/299a1Zz9yow", "content": "This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of"}
+{"idx": 8, "title": "Point-Feature Tracking for Pixel Processor Arrays - IEEE Xplore", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of \" pixel-processors \", enabling massive parallel computation of visual data at the point of light capture. Our approach performs all computation entirely in-pixel , meaning no raw ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11092646", "content": "This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of \" pixel-processors \", enabling massive parallel computation of visual data at the point of light capture. Our approach performs all computation entirely in-pixel , meaning no raw ..."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of \" pixel-processors \", enabling massive parallel computation of visual data at the point of light capture.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.html", "content": "This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels , PPA sensors consist of thousands of \" pixel-processors \", enabling massive parallel computation of visual data at the point of light capture."}
diff --git a/data/sampled_jsons/instance_privacy_unlearning_differential_privacy_before2024_year_2021.jsonl b/data/sampled_jsons/instance_privacy_unlearning_differential_privacy_before2024_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..852cdce20105f0727b49691e993b2042fa18cb08
--- /dev/null
+++ b/data/sampled_jsons/instance_privacy_unlearning_differential_privacy_before2024_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per- instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024 ), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per- instance privacy losses (Thudi et al., 2024 ), each of which bounds the (Renyi) divergence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "We present a principled, per- instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024 ), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per- instance privacy losses (Thudi et al., 2024 ), each of which bounds the (Renyi) divergence ..."}
+{"idx": 1, "title": "PDF Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per- instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024 ), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per- instance privacy losses (Thudi et al., 2024 ), each of which bounds the (R ...", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2025/pdf/sepahvand.pdf", "content": "Abstract We present a principled, per- instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024 ), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per- instance privacy losses (Thudi et al., 2024 ), each of which bounds the (R ..."}
+{"idx": 2, "title": "Leveraging Per-Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per- instance guarantees using Rényi divergence. While our ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per- instance guarantees using Rényi divergence. While our ..."}
+{"idx": 3, "title": "Efficient Federated Unlearning with Adaptive Differential Privacy ...", "date": "", "ddg_snippet": "Federated unlearning (FU) offers a promising solution to effectively address the need to erase the impact of specific clients' data on the global model in federated learning (FL), thereby granting individuals the \"Right to be Forgotten\". The most straightforward approach to achieve unlearning is to train the model from scratch, excluding clients who request data removal, but it is resource ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10825236", "content": "Federated unlearning (FU) offers a promising solution to effectively address the need to erase the impact of specific clients' data on the global model in federated learning (FL), thereby granting individuals the \"Right to be Forgotten\". The most straightforward approach to achieve unlearning is to train the model from scratch, excluding clients who request data removal, but it is resource ..."}
+{"idx": 4, "title": "Tight Bounds for Machine Unlearning via Differential Privacy", "date": "", "ddg_snippet": "347 We leave for future work the question of which unlearning guarantees can be obtained from pure 348 differentially private algorithms, and of whether variants of the standard threat model for differential 349 privacy (specifically, pan- privacy , or privacy under continual observation) could have implications for 350 machine unlearning in an ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=14R8QBKzFH", "content": "347 We leave for future work the question of which unlearning guarantees can be obtained from pure 348 differentially private algorithms, and of whether variants of the standard threat model for differential 349 privacy (specifically, pan- privacy , or privacy under continual observation) could have implications for 350 machine unlearning in an ..."}
+{"idx": 5, "title": "Differential privacy in deep learning: Privacy and beyond", "date": "", "ddg_snippet": "Differential privacy (DP), a privacy constraint originally applied to query results [9], [10], is becoming an increasingly important tool in deep learning, as it offers a way to protect personal data while still allowing organizations to extract valuable insights.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0167739X23002315", "content": "Differential privacy (DP), a privacy constraint originally applied to query results [9], [10], is becoming an increasingly important tool in deep learning, as it offers a way to protect personal data while still allowing organizations to extract valuable insights."}
+{"idx": 6, "title": "A survey of security and privacy issues of machine unlearning", "date": "", "ddg_snippet": "Specifically, we begin by investigating unlearning -based security attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning .", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/aaai.12209", "content": "Specifically, we begin by investigating unlearning -based security attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning ."}
+{"idx": 7, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Leveraging Per- Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "Leveraging Per- Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite"}
+{"idx": 8, "title": "[2404.04706] Advances in Differential Privacy and Differentially ...", "date": "", "ddg_snippet": "There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and organisations such as census bureaus. Most recent surveys ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.04706", "content": "There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and organisations such as census bureaus. Most recent surveys ..."}
+{"idx": 9, "title": "VaultGemma, Google's 1B Private LLM Explained With Results", "date": "", "ddg_snippet": "Differential privacy is a way to learn from data while protecting individuals. In practice, the training process adds carefully calibrated randomness so the model's output looks nearly the same whether any one person's data was included or not.", "subpage_snippet": "", "source": "binaryverseai.com", "link": "https://binaryverseai.com/vaultgemma-private-llm-1b-explained/", "content": "Differential privacy is a way to learn from data while protecting individuals. In practice, the training process adds carefully calibrated randomness so the model's output looks nearly the same whether any one person's data was included or not."}
diff --git a/data/sampled_jsons/inurlarxiv.orgpdf2506.08933_Coverage_Rate_(CR).jsonl b/data/sampled_jsons/inurlarxiv.orgpdf2506.08933_Coverage_Rate_(CR).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b742d1c81f4ceacc45bfff12a229c42414145984
--- /dev/null
+++ b/data/sampled_jsons/inurlarxiv.orgpdf2506.08933_Coverage_Rate_(CR).jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "Coverage Rate ( CR ). It evaluates an agent’s progress on a task.Table 4. Performance of models on OmniBench. For each capability, we use the CR metric on test tasks for quantification.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "Coverage Rate ( CR ). It evaluates an agent’s progress on a task.Table 4. Performance of models on OmniBench. For each capability, we use the CR metric on test tasks for quantification."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/inurlarxiv.orgpdf2506.08933_task_graph_Coverage_Rate.jsonl b/data/sampled_jsons/inurlarxiv.orgpdf2506.08933_task_graph_Coverage_Rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/inurlarxiv.orgpdf2506.08933_task_graph_Coverage_Rate.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/k6ZHvF1vkg_Beyond_Optimism_continuous_MDPs_neural_network_input_output.jsonl b/data/sampled_jsons/k6ZHvF1vkg_Beyond_Optimism_continuous_MDPs_neural_network_input_output.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..daf0c82d28acd58b6c6c585543415ce6494d38fa
--- /dev/null
+++ b/data/sampled_jsons/k6ZHvF1vkg_Beyond_Optimism_continuous_MDPs_neural_network_input_output.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism: Exploration With Partially Observable ...", "date": "", "ddg_snippet": "Sep 25, 2024 · TL;DR: Directed exploration with the successor representation for MDPs with partially observable rewards Abstract: Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=k6ZHvF1vkg", "content": "Sep 25, 2024 · TL;DR: Directed exploration with the successor representation for MDPs with partially observable rewards Abstract: Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all."}
+{"idx": 1, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Among the many exploration strategies that have been proposed to improve RL efficiency, perhaps the most well-known and grounded is optimism . With optimism , the agent assigns to each state-action pair an optimistically biased estimate of future value and selects the action with the highest estimate [45].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v1", "content": "Among the many exploration strategies that have been proposed to improve RL efficiency, perhaps the most well-known and grounded is optimism . With optimism , the agent assigns to each state-action pair an optimistically biased estimate of future value and selects the action with the highest estimate [45]."}
+{"idx": 2, "title": "Continuous output in Neural Networks - Stack Overflow", "date": "", "ddg_snippet": "How can I set Neural Networks so they accept and output a continuous range of values instead of a discrete ones? From what I recall from doing a Neural Network class a couple of years ago, the activation function would be a sigmoid, which yields a value between 0 and 1.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/1823523/continuous-output-in-neural-networks", "content": "How can I set Neural Networks so they accept and output a continuous range of values instead of a discrete ones? From what I recall from doing a Neural Network class a couple of years ago, the activation function would be a sigmoid, which yields a value between 0 and 1."}
+{"idx": 3, "title": "reinforcement learning - Can neural networks have continuous ...", "date": "", "ddg_snippet": "Aug 16, 2021 · Neural networks learn most efficiently when input elements are within a unit-like distribution such as $\\mathcal {N} (0,1)$ (normal distribution with mean $0$, standard deviation $1$). This does not have to be precise, but you should take care to scale the input values so that each element has a typical magnitude around $1$.", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/30178/can-neural-networks-have-continuous-inputs-and-outputs-or-do-they-have-to-be-di", "content": "Aug 16, 2021 · Neural networks learn most efficiently when input elements are within a unit-like distribution such as $\\mathcal {N} (0,1)$ (normal distribution with mean $0$, standard deviation $1$). This does not have to be precise, but you should take care to scale the input values so that each element has a typical magnitude around $1$."}
+{"idx": 4, "title": "On the Expressivity of Neural Networks for Deep Reinforcement ...", "date": "", "ddg_snippet": "We compare the model-free reinforcement learn-ing with the model-based approaches through the lens of the expressive power of neural net-works for policies, Q-functions, and dynamics. We show, theoretically and empirically, that even for one-dimensional continuous state space, there are many MDPs whose optimal Q-functions and policies are much more complex than the dynam-ics. For these MDPs ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/dong20d/dong20d.pdf", "content": "We compare the model-free reinforcement learn-ing with the model-based approaches through the lens of the expressive power of neural net-works for policies, Q-functions, and dynamics. We show, theoretically and empirically, that even for one-dimensional continuous state space, there are many MDPs whose optimal Q-functions and policies are much more complex than the dynam-ics. For these MDPs ..."}
+{"idx": 5, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism .", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/k6ZHvF1vkg@OpenReview", "content": "RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism ."}
+{"idx": 6, "title": "Neural Networks Explained in 5 minutes - YouTube", "date": "", "ddg_snippet": "Learn more about watsonx: https://ibm.biz/BdvxRsNeural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=jmmW0F0biz0", "content": "Learn more about watsonx: https://ibm.biz/BdvxRsNeural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and..."}
+{"idx": 7, "title": "What is a Neural Network ? - GeeksforGeeks", "date": "", "ddg_snippet": "In a neural network , input data is passed through multiple layers, including one or more hidden layers. Each neuron in these hidden layers performs several operations, transforming the input into a usable output .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/neural-networks-a-beginners-guide/", "content": "In a neural network , input data is passed through multiple layers, including one or more hidden layers. Each neuron in these hidden layers performs several operations, transforming the input into a usable output ."}
+{"idx": 8, "title": "The Olivera Bias Metric: A Synaptic Input - Output ... | Preprints.org", "date": "", "ddg_snippet": "Excitatory and inhibitory (E/I) interactions are central to neural computation, but most studies of E/I \"balance\" have focused on functional measurements. Far less is known about how balance is constrained by the anatomical distribution of excitatory and inhibitory synapses.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202509.1617/v1", "content": "Excitatory and inhibitory (E/I) interactions are central to neural computation, but most studies of E/I \"balance\" have focused on functional measurements. Far less is known about how balance is constrained by the anatomical distribution of excitatory and inhibitory synapses."}
+{"idx": 9, "title": "Telegram: View @ mdps _56", "date": "", "ddg_snippet": "You can view and join @ mdps _56 right away.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/mdps_56", "content": "You can view and join @ mdps _56 right away."}
diff --git a/data/sampled_jsons/limitations_of_password-locked_approach_Stress-Testing_Capability_Elicitation.jsonl b/data/sampled_jsons/limitations_of_password-locked_approach_Stress-Testing_Capability_Elicitation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..31ea89175e8200677c52492493ccc8673de508c5
--- /dev/null
+++ b/data/sampled_jsons/limitations_of_password-locked_approach_Stress-Testing_Capability_Elicitation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "1. The internal computations that result in locking may be different. Password - locked models can hard-code a simple conditional policy. But models' capabilities ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/92923", "content": "1. The internal computations that result in locking may be different. Password - locked models can hard-code a simple conditional policy. But models' capabilities ..."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "29 May 2024 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "29 May 2024 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce ..."}
+{"idx": 2, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "4 Jun 2024 — We released a paper studying this by examining how well supervised fine-tuning and RL can elicit capabilities from password - locked models.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "4 Jun 2024 — We released a paper studying this by examining how well supervised fine-tuning and RL can elicit capabilities from password - locked models."}
+{"idx": 3, "title": "Stress-Testing Password-Locked LLMs", "date": "", "ddg_snippet": "The paper examines how password - locked models reveal hidden dangerous capabilities of large language models using fine-tuning and reinforcement learning ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2405.19550", "content": "The paper examines how password - locked models reveal hidden dangerous capabilities of large language models using fine-tuning and reinforcement learning ..."}
+{"idx": 4, "title": "When does capability elicitation bound risk?", "date": "", "ddg_snippet": "However, there are several fundamental limitations this evaluation might run up against: Models in the evaluation are not sandbagging by a ' ...", "subpage_snippet": "", "source": "redwoodresearch.substack.com", "link": "https://redwoodresearch.substack.com/p/when-does-capability-elicitation", "content": "However, there are several fundamental limitations this evaluation might run up against: Models in the evaluation are not sandbagging by a ' ..."}
+{"idx": 5, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "5 Jun 2025 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740125", "content": "5 Jun 2025 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities . To do this, we introduce ..."}
+{"idx": 6, "title": "Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "4 Feb 2025 — Password - locking can be seen as a form of inserting a backdoor (Li et al., 2022) , which has been studied in the literature on model and data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v1", "content": "4 Feb 2025 — Password - locking can be seen as a form of inserting a backdoor (Li et al., 2022) , which has been studied in the literature on model and data ..."}
+{"idx": 7, "title": "Stress-Testing Capability Elicitation Techniques", "date": "", "ddg_snippet": "by F Hofstätter · Cited by 2 — We demonstrate that password-locked models used in previous work are fragile to simple prompting techniques . We introduce a more robust model organism based on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zy6LB5t62f", "content": "by F Hofstätter · Cited by 2 — We demonstrate that password-locked models used in previous work are fragile to simple prompting techniques . We introduce a more robust model organism based on ..."}
+{"idx": 8, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "The paper presents a novel approach to stress - testing capability elicitation , but it is important to recognize the limitations of the \" password - locked model\" as ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/stress-testing-capability-elicitation-password-locked-models", "content": "The paper presents a novel approach to stress - testing capability elicitation , but it is important to recognize the limitations of the \" password - locked model\" as ..."}
+{"idx": 9, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "5 Nov 2024 — This paper studies how the hidden capabilities of LLMs can be accessed through prompting, fine-tuning, and reinforcement learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zzOOqD6R1b&referrer=[the+profile+of+David+Krueger](/profile?id=~David_Krueger1)", "content": "5 Nov 2024 — This paper studies how the hidden capabilities of LLMs can be accessed through prompting, fine-tuning, and reinforcement learning."}
diff --git a/data/sampled_jsons/locality_sensitive_hash_function_definition_close_points_same_bucket_far_points_different_buckets.jsonl b/data/sampled_jsons/locality_sensitive_hash_function_definition_close_points_same_bucket_far_points_different_buckets.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..365f93f226980d362d92fd75e176fa28ea8125cd
--- /dev/null
+++ b/data/sampled_jsons/locality_sensitive_hash_function_definition_close_points_same_bucket_far_points_different_buckets.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "c - How to hash vectors into buckets in Locality Sensitive", "date": "", "ddg_snippet": "If you get the same output with the same hash function on a different object, that means they hashed on the same bucket .", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/22947072/how-to-hash-vectors-into-buckets-in-locality-sensitive-hashing-using-jaccard-di", "content": "If you get the same output with the same hash function on a different object, that means they hashed on the same bucket ."}
+{"idx": 1, "title": "Introduction to Locality-Sensitive Hashing", "date": "", "ddg_snippet": "... Locality - sensitive hash functions are specifically designed so that hash value collisions are more likely for two input values that are close together ...", "subpage_snippet": "", "source": "tylerneylon.com", "link": "https://tylerneylon.com/a/lsh1/", "content": "... Locality - sensitive hash functions are specifically designed so that hash value collisions are more likely for two input values that are close together ..."}
+{"idx": 2, "title": "Introduction to Locality-Sensitive Hashing", "date": "", "ddg_snippet": "... Locality - sensitive hash functions are specifically designed so that hash value collisions are more likely for two input values that are close together ...", "subpage_snippet": "", "source": "tylerneylon.com", "link": "https://tylerneylon.com/a/lsh1/lsh_post1.html", "content": "... Locality - sensitive hash functions are specifically designed so that hash value collisions are more likely for two input values that are close together ..."}
+{"idx": 3, "title": "Introduction to Locality-Sensitive Hashing", "date": "", "ddg_snippet": "... Locality - sensitive hash functions are specifically designed so that hash value collisions are more likely for two input values that are close together ...", "subpage_snippet": "", "source": "unboxresearch.com", "link": "https://unboxresearch.com/articles/lsh_post1.html", "content": "... Locality - sensitive hash functions are specifically designed so that hash value collisions are more likely for two input values that are close together ..."}
+{"idx": 4, "title": "Locality Sensitive Hashing - ML Wiki", "date": "", "ddg_snippet": "... hash function should separate two close symbols ... use LSH: a special hash function that would put points that are close together to the same point", "subpage_snippet": "", "source": "mlwiki.org", "link": "http://mlwiki.org/index.php/Locality_Sensitive_Hashing", "content": "... hash function should separate two close symbols ... use LSH: a special hash function that would put points that are close together to the same point"}
+{"idx": 5, "title": "Locality Sensitive Hashing | Application of Locality Sensitive", "date": "", "ddg_snippet": "LSH works on the principle that if there are two points in feature space closer to each other, they are very likely to have same hash (reduced ...", "subpage_snippet": "", "source": "santhoshhari.github.io", "link": "https://santhoshhari.github.io/Locality-Sensitive-Hashing/", "content": "LSH works on the principle that if there are two points in feature space closer to each other, they are very likely to have same hash (reduced ..."}
+{"idx": 6, "title": "Locality-Sensitive Hashing (LSH): The Ultimate Guide |", "date": "", "ddg_snippet": "... definitive resource for “ ... The LSH function L(x) tries to map similar objects to the same hash bin and dissimilar objects to different bins.", "subpage_snippet": "", "source": "www.iunera.com", "link": "https://www.iunera.com/kraken/fabric/local-sensitive-hashing-lsh/", "content": "... definitive resource for “ ... The LSH function L(x) tries to map similar objects to the same hash bin and dissimilar objects to different bins."}
+{"idx": 7, "title": "Locality Sensitive Hashing (LSH) – Aerodata", "date": "", "ddg_snippet": "... function such that, when applied to all the documents, documents that are close together will have with high probability the same value, and documents ...", "subpage_snippet": "", "source": "aerodatablog.wordpress.com", "link": "https://aerodatablog.wordpress.com/2017/11/29/locality-sensitive-hashing-lsh/", "content": "... function such that, when applied to all the documents, documents that are close together will have with high probability the same value, and documents ..."}
+{"idx": 8, "title": "ds.data structures - Continuity vs Uniformity when designing", "date": "", "ddg_snippet": "... locality sensitive hashing (LSH) achieves a very similar goal: given a set of items $x_1, \\ldots, x_n$ which belong to some metric space with metric ...", "subpage_snippet": "", "source": "cstheory.stackexchange.com", "link": "https://cstheory.stackexchange.com/questions/12216/continuity-vs-uniformity-when-designing-hash-functions", "content": "... locality sensitive hashing (LSH) achieves a very similar goal: given a set of items $x_1, \\ldots, x_n$ which belong to some metric space with metric ..."}
+{"idx": 9, "title": "Extracting, transforming and selecting features - Spark 3.5.2", "date": "", "ddg_snippet": "... index map, which can be expensive for a large corpus, but it suffers from potential hash collisions, where different raw features may become the same ...", "subpage_snippet": "", "source": "spark.apache.org", "link": "https://spark.apache.org/docs/3.5.2/ml-features.html", "content": "... index map, which can be expensive for a large corpus, but it suffers from potential hash collisions, where different raw features may become the same ..."}
diff --git a/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_as_a_Predictor_of_Mutational_Effects_on_Protein-P.jsonl b/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_as_a_Predictor_of_Mutational_Effects_on_Protein-P.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..47f2baf3fa46b8520a4f3c149d1f75fe42808266
--- /dev/null
+++ b/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_as_a_Predictor_of_Mutational_Effects_on_Protein-P.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "B -a Inverse Folding Model As a Predictor of Mutational Effects on ...", "date": "", "ddg_snippet": "ABSTRACT Predicting the change in binding free energy (∆∆G) is crucial for understanding and modulating protein-protein interactions , which are critical in drug design. Due to the scarcity of experimental ∆∆G data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose Boltzmann Alignment technique to transfer knowledge from pre ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "ABSTRACT Predicting the change in binding free energy (∆∆G) is crucial for understanding and modulating protein-protein interactions , which are critical in drug design. Due to the scarcity of experimental ∆∆G data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose Boltzmann Alignment technique to transfer knowledge from pre ..."}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions , which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "Predicting the change in binding free energy ($ΔΔG$) is crucial for understanding and modulating protein-protein interactions , which are critical in drug design. Due to the scarcity of experimental $ΔΔG$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre ..."}
+{"idx": 2, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann- Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions \", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann- Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions \", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 3, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "如果估计 P (S|X) 比较简单那便是极好的，而恰好，根据结构生成序列的 反折叠模型 （ Inverse Folding Model ） 是存在的，比如ProteinMPNN，同时因为输出是序列，其状态空间是20氨基酸的组合，相较于构象的扭转角，会简单许多。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "如果估计 P (S|X) 比较简单那便是极好的，而恰好，根据结构生成序列的 反折叠模型 （ Inverse Folding Model ） 是存在的，比如ProteinMPNN，同时因为输出是序列，其状态空间是20氨基酸的组合，相较于构象的扭转角，会简单许多。"}
+{"idx": 4, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann- Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions \", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann- Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions \", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values."}
+{"idx": 5, "title": "dblp: Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Bibliographic details on Boltzmann- Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/JiaoMJY0S25", "content": "Bibliographic details on Boltzmann- Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions ."}
+{"idx": 6, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Download Citation | Boltzmann- Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions | Predicting the change in binding free energy ($\\Delta \\Delta G ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384930266_Boltzmann-Aligned_Inverse_Folding_Model_as_a_Predictor_of_Mutational_Effects_on_Protein-Protein_Interactions", "content": "Download Citation | Boltzmann- Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions | Predicting the change in binding free energy ($\\Delta \\Delta G ..."}
+{"idx": 7, "title": "Portal Weekly #67: MoML 2024, data-driven discovery, boltzmann-aligned ...", "date": "", "ddg_snippet": "Boltzmann- Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Predicting changes in binding free energy (∆∆G) is essential for understanding and modifying protein-protein interactions , which are important in drug design.", "subpage_snippet": "", "source": "m2d2.substack.com", "link": "https://m2d2.substack.com/p/portal-weekly-67-moml-2024-data-driven", "content": "Boltzmann- Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Predicting changes in binding free energy (∆∆G) is essential for understanding and modifying protein-protein interactions , which are important in drug design."}
+{"idx": 8, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Figure 1: Overview of the Boltzmann Alignment technique. Left: inference with a protein inverse folding model . Right: illustration of thermodynamic cycle in the modulation of protein-protein interactions . 1 Introduction", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "Figure 1: Overview of the Boltzmann Alignment technique. Left: inference with a protein inverse folding model . Right: illustration of thermodynamic cycle in the modulation of protein-protein interactions . 1 Introduction"}
+{"idx": 9, "title": "Forum | OpenReview", "date": "", "ddg_snippet": "Predicting the change in binding free energy ($\\\\Delta \\\\Delta G$) is crucial for understanding and modulating protein-protein interactions , which are critical in drug design. Due to the scarcity of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lzdFImKK8w", "content": "Predicting the change in binding free energy ($\\\\Delta \\\\Delta G$) is crucial for understanding and modulating protein-protein interactions , which are critical in drug design. Due to the scarcity of ..."}
diff --git a/data/sampled_jsons/m_head_m_body_DnCNN_github_sitegithub.comcsznDnCNN.jsonl b/data/sampled_jsons/m_head_m_body_DnCNN_github_sitegithub.comcsznDnCNN.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6553d89447d83119fa6608e9382179ffde6efe82
--- /dev/null
+++ b/data/sampled_jsons/m_head_m_body_DnCNN_github_sitegithub.comcsznDnCNN.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DnCNN/README.md at master · cszn/DnCNN · GitHub", "date": "", "ddg_snippet": "The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks, such as image denoising, image super-resolution and JPEG image deblocking.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/README.md", "content": "The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks, such as image denoising, image super-resolution and JPEG image deblocking."}
+{"idx": 1, "title": "DnCNN/TrainingCodes/DnCNN_TrainingCodes_v1.1/data ... - GitHub", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - cszn/ DnCNN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/TrainingCodes/DnCNN_TrainingCodes_v1.1/data/utilities/shave.m", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - cszn/ DnCNN"}
+{"idx": 2, "title": "DnCNN/TrainingCodes/DnCNN_TrainingCodes_DagNN_v1.1 ... - GitHub", "date": "", "ddg_snippet": "simplenn_matlab. m DnCNN / TrainingCodes / DnCNN _TrainingCodes_DagNN_v1.1 / utilities / simplenn_matlab. m Cannot retrieve latest commit at this time.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/TrainingCodes/DnCNN_TrainingCodes_DagNN_v1.1/utilities/simplenn_matlab.m", "content": "simplenn_matlab. m DnCNN / TrainingCodes / DnCNN _TrainingCodes_DagNN_v1.1 / utilities / simplenn_matlab. m Cannot retrieve latest commit at this time."}
+{"idx": 3, "title": "DnCNN/README.md at master · cszn/DnCNN · GitHub", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - DnCNN /README.md at master · cszn/ DnCNN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/README.md?plain=1", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - DnCNN /README.md at master · cszn/ DnCNN"}
+{"idx": 4, "title": "DnCNN/TrainingCodes/dncnn_keras/main_train.py at ... - GitHub", "date": "", "ddg_snippet": "DnCNN / TrainingCodes / dncnn _keras / main_train.py Cannot retrieve latest commit at this time.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/TrainingCodes/dncnn_keras/main_train.py", "content": "DnCNN / TrainingCodes / dncnn _keras / main_train.py Cannot retrieve latest commit at this time."}
+{"idx": 5, "title": "DnCNN/utilities/data_augmentation.m at master - GitHub", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - cszn/ DnCNN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/utilities/data_augmentation.m", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - cszn/ DnCNN"}
+{"idx": 6, "title": "DnCNN/TrainingCodes/dncnn_keras/README.md at master - GitHub", "date": "", "ddg_snippet": "DnCNN / TrainingCodes / dncnn _keras / README.md Cannot retrieve latest commit at this time.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/TrainingCodes/dncnn_keras/README.md", "content": "DnCNN / TrainingCodes / dncnn _keras / README.md Cannot retrieve latest commit at this time."}
+{"idx": 7, "title": "GitHub - cszn/ DnCNN : Beyond a Gaussian Denoiser: Residual...", "date": "", "ddg_snippet": "GitHub Models New. Manage and compare prompts. GitHub Advanced Security.[demos] Demo_test_ DnCNN -. m . [models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN", "content": "GitHub Models New. Manage and compare prompts. GitHub Advanced Security.[demos] Demo_test_ DnCNN -. m . [models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking."}
+{"idx": 8, "title": "DnCNN / at master · cszn/ DnCNN · GitHub", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - DnCNN / at master · cszn/ DnCNN .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN?files=1", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - DnCNN / at master · cszn/ DnCNN ."}
+{"idx": 9, "title": "DnCNN / at master · cszn/ DnCNN · GitHub", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - DnCNN / at master · cszn/ DnCNN .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN?search=1", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) - DnCNN / at master · cszn/ DnCNN ."}
diff --git a/data/sampled_jsons/marvinli-harvard_critical-windows_github_repository.jsonl b/data/sampled_jsons/marvinli-harvard_critical-windows_github_repository.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b6b4d93cd6ee63db4b0886cc7eaf40b439cfb97e
--- /dev/null
+++ b/data/sampled_jsons/marvinli-harvard_critical-windows_github_repository.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - marvinli - harvard / marvin -thesis: A LaTeX class for publising...", "date": "", "ddg_snippet": "marvinli - harvard / marvin -thesis. master.gsasthesis. LaTeX2e class for publishing a PhD thesis to comply with Harvard GSAS requirements.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/marvinli-harvard/marvin-thesis", "content": "marvinli - harvard / marvin -thesis. master.gsasthesis. LaTeX2e class for publishing a PhD thesis to comply with Harvard GSAS requirements."}
+{"idx": 1, "title": "Как исправить Critical Process Died в Windows 11", "date": "", "ddg_snippet": "Ошибка \" Critical _Process_Died\" в Windows 11 приводит синему экрану смерти, поэтому ее нужно исправить обязательно. Проверить нужно несколько причин ее возникновения.", "subpage_snippet": "", "source": "Lumpics.ru", "link": "https://Lumpics.ru/how-to-fix-critical-process-died-in-windows-11/", "content": "Ошибка \" Critical _Process_Died\" в Windows 11 приводит синему экрану смерти, поэтому ее нужно исправить обязательно. Проверить нужно несколько причин ее возникновения."}
+{"idx": 2, "title": "Critical windows : non-asymptotic theory for feature emergence in...", "date": "", "ddg_snippet": "Marvin Li , Sitan Chen. Proceedings of the 41st International Conference on Machine Learning, PMLR 235:27474-27498, 2024.We develop theory to understand an intriguing property of diffusion models for image generation that we term critical windows .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/li24g.html", "content": "Marvin Li , Sitan Chen. Proceedings of the 41st International Conference on Machine Learning, PMLR 235:27474-27498, 2024.We develop theory to understand an intriguing property of diffusion models for image generation that we term critical windows ."}
+{"idx": 3, "title": "Синий экран CRITICAL PROCESS DIED в Windows ... | remontka.pro", "date": "", "ddg_snippet": "Ошибка с кодом CRITICAL PROCESS DIED на синем экране в Windows 11 — возможные причины, диагностика и варианты решения.", "subpage_snippet": "", "source": "remontka.pro", "link": "https://remontka.pro/critical-process-died-bsod/", "content": "Ошибка с кодом CRITICAL PROCESS DIED на синем экране в Windows 11 — возможные причины, диагностика и варианты решения."}
+{"idx": 4, "title": "2026: Humanity SPLITS from ‘FALSE REALITY’ — Our CRITICAL ...", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=QoRElLpq11E", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
+{"idx": 5, "title": "Hi, my name is Marvin. - Marvin Li", "date": "", "ddg_snippet": "Marvin Li . AI Researcher ( Harvard '25). Critical Windows : Non-Asymptotic Theory for Feature Emergence in Diffusion Models Marvin Li , Sitan Chen.", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/", "content": "Marvin Li . AI Researcher ( Harvard '25). Critical Windows : Non-Asymptotic Theory for Feature Emergence in Diffusion Models Marvin Li , Sitan Chen."}
+{"idx": 6, "title": "Blink of an eye: a simple theory for feature localization in... | OpenReview", "date": "", "ddg_snippet": "This phenomenon is not unique to autoregressive models: in diffusion models, key features of the final output are decided in narrow `` critical windows '' of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QvqnPVGWAN", "content": "This phenomenon is not unique to autoregressive models: in diffusion models, key features of the final output are decided in narrow `` critical windows '' of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon."}
+{"idx": 7, "title": "NVM - Node.js Version Manager tool - NVM Documentation", "date": "", "ddg_snippet": "NVM is a tool for managing multiple versions of Node.js, Official documentation for NVM (Node Version Manager) for Windows , Linux, and macOS.", "subpage_snippet": "", "source": "www.nvmnode.com", "link": "https://www.nvmnode.com/", "content": "NVM is a tool for managing multiple versions of Node.js, Official documentation for NVM (Node Version Manager) for Windows , Linux, and macOS."}
+{"idx": 8, "title": "Marvin Li (@ marvin _ li 03) on X", "date": "", "ddg_snippet": "Marvin Li (@ marvin _ li 03) on X Harvard '25 | Building theory for generative models.", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/marvin_li03", "content": "Marvin Li (@ marvin _ li 03) on X Harvard '25 | Building theory for generative models."}
+{"idx": 9, "title": "Как запустить свою LLM для инференса. Руководство по... / Хабр", "date": "", "ddg_snippet": "Я буду производить установку на Windows .Во время запуска Triton сервера я столкнулся с ошибками запуска контейнера. Поискав проблему я обнаружил то, что в официальном github установлена старая версия образа Triton, не подходящая под мою версию Ubuntu.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948934/", "content": "Я буду производить установку на Windows .Во время запуска Triton сервера я столкнулся с ошибками запуска контейнера. Поискав проблему я обнаружил то, что в официальном github установлена старая версия образа Triton, не подходящая под мою версию Ubuntu."}
diff --git a/data/sampled_jsons/model-similarity.github.io_CAPA_formula_implementation.jsonl b/data/sampled_jsons/model-similarity.github.io_CAPA_formula_implementation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..87aee02a42254ba3cc1b2728c8c4b3859b1028af
--- /dev/null
+++ b/data/sampled_jsons/model-similarity.github.io_CAPA_formula_implementation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Similarity affects Oversight", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results.", "subpage_snippet": "", "source": "model-similarity.github.io", "link": "https://model-similarity.github.io/", "content": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results."}
+{"idx": 1, "title": "GitHub - model-similarity/lm-similarity", "date": "", "ddg_snippet": "Compute similarity based on CAPA , κ p Below is a simple example on how to compute similarity between 2 models based on k p . The input has be to formatted as follows: output_a: list [np.array], containing the softmax output probabilties of model a output_b: list [np.array], containing the softmax output probabilties of model b", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/model-similarity/lm-similarity", "content": "Compute similarity based on CAPA , κ p Below is a simple example on how to compute similarity between 2 models based on k p . The input has be to formatted as follows: output_a: list [np.array], containing the softmax output probabilties of model a output_b: list [np.array], containing the softmax output probabilties of model b"}
+{"idx": 2, "title": "model-similarity.github.io/index.html at main · model ...", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/model-similarity/model-similarity.github.io/blob/main/index.html", "content": "We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results."}
+{"idx": 3, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “AI Oversight”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement ( CAPA ): a metric for LM similarity based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04313v2", "content": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “AI Oversight”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement ( CAPA ): a metric for LM similarity based on ..."}
+{"idx": 4, "title": "GitHub - tensorflow/similarity: TensorFlow Similarity is a ...", "date": "", "ddg_snippet": "Introduction Tensorflow Similarity offers state-of-the-art algorithms for metric learning along with all the necessary components to research, train, evaluate, and serve similarity and contrastive based models. These components include models, losses, metrics, samplers, visualizers, and indexing subsystems to make this quick and easy.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tensorflow/similarity", "content": "Introduction Tensorflow Similarity offers state-of-the-art algorithms for metric learning along with all the necessary components to research, train, evaluate, and serve similarity and contrastive based models. These components include models, losses, metrics, samplers, visualizers, and indexing subsystems to make this quick and easy."}
+{"idx": 5, "title": "similarity/tensorflow_similarity/models/similarity_model.py ...", "date": "", "ddg_snippet": "Subclass of Keras. Model which provides methods for indexing, matching, evaluation, and similarity training. ```python from tf.keras import layers from tensorflow_ similarity .models import SimilarityModel # Setup dataset using tf.sim samplers train_ds = ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tensorflow/similarity/blob/master/tensorflow_similarity/models/similarity_model.py", "content": "Subclass of Keras. Model which provides methods for indexing, matching, evaluation, and similarity training. ```python from tf.keras import layers from tensorflow_ similarity .models import SimilarityModel # Setup dataset using tf.sim samplers train_ds = ..."}
+{"idx": 6, "title": "GitHub - htuann2712/face-recognition: Implementation for ...", "date": "", "ddg_snippet": "This repository contains the implementation of a facial recognition system based on similarity measurement. We focuses on measuring the similarity between facial features and uses a similarity -based approach to recognize faces. In this repository, we utilize the YOLOv5 model that has been pre ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/htuann2712/face-recognition", "content": "This repository contains the implementation of a facial recognition system based on similarity measurement. We focuses on measuring the similarity between facial features and uses a similarity -based approach to recognize faces. In this repository, we utilize the YOLOv5 model that has been pre ..."}
+{"idx": 7, "title": "GitHub - mandiant/ capa : The FLARE team's open-source tool to...", "date": "", "ddg_snippet": "mandiant. github . io / capa /. License.The github .com/mandiant/ capa -rules repository contains hundreds of standard rules that are distributed with capa . Please learn to write rules and contribute new entries as you find interesting techniques in malware. IDA Pro plugin: capa explorer.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mandiant/capa", "content": "mandiant. github . io / capa /. License.The github .com/mandiant/ capa -rules repository contains hundreds of standard rules that are distributed with capa . Please learn to write rules and contribute new entries as you find interesting techniques in malware. IDA Pro plugin: capa explorer."}
+{"idx": 8, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap in model mistakes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap in model mistakes."}
+{"idx": 9, "title": "app.py · bethgelab/lm- similarity at main", "date": "", "ddg_snippet": "metric_init = \" CAPA \". # Create Gradio interface. with gr.Blocks(title=\"LLM Similarity Analyzer\", css=app_util.custom_css) as demo) gr.Markdown(\"\\* Self- similarity is only 1.0 for CAPA if the model predicts a single option with 100% confidence for each question.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/spaces/bethgelab/lm-similarity/blob/main/app.py", "content": "metric_init = \" CAPA \". # Create Gradio interface. with gr.Blocks(title=\"LLM Similarity Analyzer\", css=app_util.custom_css) as demo) gr.Markdown(\"\\* Self- similarity is only 1.0 for CAPA if the model predicts a single option with 100% confidence for each question."}
diff --git a/data/sampled_jsons/motion-latent-diffusion_GitHub_HumanAct12_FID_year_2023.jsonl b/data/sampled_jsons/motion-latent-diffusion_GitHub_HumanAct12_FID_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fc34b12083e5d1fe2aa188d847aa825ccc1b360c
--- /dev/null
+++ b/data/sampled_jsons/motion-latent-diffusion_GitHub_HumanAct12_FID_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "motion - latent - diffusion /configs/config_mld_ humanact 12 .yaml at...", "date": "", "ddg_snippet": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model - motion - latent - diffusion /configs/config_mld_ humanact 12 .yaml at main · ChenFengYe/ motion - latent - diffusion .STAGE: diffusion. # Training dataset name.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/configs/config_mld_humanact12.yaml", "content": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model - motion - latent - diffusion /configs/config_mld_ humanact 12 .yaml at main · ChenFengYe/ motion - latent - diffusion .STAGE: diffusion. # Training dataset name."}
+{"idx": 1, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Motion Synthesis. HumanAct 12 . MLD. FID .Then, instead of using a diffusion model to establish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/executing-your-commands-via-motion-diffusion?ref=taskswithcode.ghost.io", "content": "Motion Synthesis. HumanAct 12 . MLD. FID .Then, instead of using a diffusion model to establish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space."}
+{"idx": 2, "title": "Executing your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "The KIT [48], HumanAct 12 [19] and UESTC [26] dataset processed by [47, 46] also supports SMPL-based [40] motion representation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.04048", "content": "The KIT [48], HumanAct 12 [19] and UESTC [26] dataset processed by [47, 46] also supports SMPL-based [40] motion representation."}
+{"idx": 3, "title": "ChenFengYe motion - latent - diffusion issues - Githubissues", "date": "", "ddg_snippet": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/ChenFengYe/motion-latent-diffusion", "content": "[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model."}
+{"idx": 4, "title": "LS-GAN: Human Motion Synthesis with Latent -space GANs-Bohrium", "date": "", "ddg_snippet": "While previous works have explored motion synthesis using raw motion data and latent space representations with diffusion models, these approaches often suffer from high training and inference times.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/ls-gan-human-motion-synthesis-with-latent-space-gans/1083033613332643939-108597", "content": "While previous works have explored motion synthesis using raw motion data and latent space representations with diffusion models, these approaches often suffer from high training and inference times."}
+{"idx": 5, "title": "daanelson/ motion _ diffusion _model | Run with an API on Replicate", "date": "", "ddg_snippet": "A diffusion model for generating human motion video from a text prompt.", "subpage_snippet": "", "source": "replicate.com", "link": "https://replicate.com/daanelson/motion_diffusion_model", "content": "A diffusion model for generating human motion video from a text prompt."}
+{"idx": 6, "title": "Creating Authentic Human Motion Synthesis via Diffusion", "date": "", "ddg_snippet": ", or of Neural Radiance Fields (NeRF) – or of a latent diffusion network such as Stable Diffusion. Commanding MDM.Here too, MDM has outperformed prior efforts, in tests published in the new paper, this time in the HumanAct 12 and UESTC benchmarks.", "subpage_snippet": "", "source": "blog.metaphysic.ai", "link": "https://blog.metaphysic.ai/creating-authentic-human-motion-synthesis-via-diffusion/", "content": ", or of Neural Radiance Fields (NeRF) – or of a latent diffusion network such as Stable Diffusion. Commanding MDM.Here too, MDM has outperformed prior efforts, in tests published in the new paper, this time in the HumanAct 12 and UESTC benchmarks."}
+{"idx": 7, "title": "(PDF) LS-GAN: Human Motion Synthesis with Latent -space GANs", "date": "", "ddg_snippet": "While previous works have explored motion synthesis using raw motion data and latent space representations with diffusion models, these approaches often suffer from high training and inference times.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387744893_LS-GAN_Human_Motion_Synthesis_with_Latent-space_GANs", "content": "While previous works have explored motion synthesis using raw motion data and latent space representations with diffusion models, these approaches often suffer from high training and inference times."}
+{"idx": 8, "title": "EMDM: Efficient Motion Diffusion Model for", "date": "", "ddg_snippet": "On the one hand, previous works, like motion latent diffusion , conduct diffusion within a latent space for efficiency, but learning such a latent space can be a non-trivial effort.Table 3: Comparison of action-to-motion task on HumanAct 12 [21]: FIDtrain indicat-ing the evaluated splits.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00168.pdf", "content": "On the one hand, previous works, like motion latent diffusion , conduct diffusion within a latent space for efficiency, but learning such a latent space can be a non-trivial effort.Table 3: Comparison of action-to-motion task on HumanAct 12 [21]: FIDtrain indicat-ing the evaluated splits."}
+{"idx": 9, "title": "Motion synthesis via distilled absorbing discrete diffusion ... | CoLab", "date": "", "ddg_snippet": "In this work, we explore the potential of discrete diffusion model in text-driven motion synthesis. Previous methods aimed at improving the quality of generated motions often led to an increase in model parameters, while neglecting the diversity of generated results.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1007/s00530-024-01492-9", "content": "In this work, we explore the potential of discrete diffusion model in text-driven motion synthesis. Previous methods aimed at improving the quality of generated motions often led to an increase in model parameters, while neglecting the diversity of generated results."}
diff --git a/data/sampled_jsons/multi-target_tracking_UAV_simulation_adversarial_behavior_evasive.jsonl b/data/sampled_jsons/multi-target_tracking_UAV_simulation_adversarial_behavior_evasive.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..61d4fb70a80933adf6f2362e4b31684f3b672cff
--- /dev/null
+++ b/data/sampled_jsons/multi-target_tracking_UAV_simulation_adversarial_behavior_evasive.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unmanned Aerial Vehicle for Multi-target Tracking Using ...", "date": "", "ddg_snippet": "May 15, 2025 · Unmanned aerial vehicles, or UAVs, have become a cornerstone for multi-target tracking in dynamic and unpredictable environments. Despite advancements in methodologies, the approaches have grave shortcomings even in scenarios involving rapid movement of the target , occlusion, and adversarial strategies. These challenges, however, require this research work to propose a novel framework that ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s13235-025-00645-3", "content": "May 15, 2025 · Unmanned aerial vehicles, or UAVs, have become a cornerstone for multi-target tracking in dynamic and unpredictable environments. Despite advancements in methodologies, the approaches have grave shortcomings even in scenarios involving rapid movement of the target , occlusion, and adversarial strategies. These challenges, however, require this research work to propose a novel framework that ..."}
+{"idx": 1, "title": "Enhanced UAV Pursuit-Evasion Using Boids Modelling: A ...", "date": "", "ddg_snippet": "Sep 12, 2024 · As shown in Fig. 1, in a typical UAV pursuit-evasion scenario, multiple UAVs are strategically deployed to efficiently track and intercept a moving target , which employs evasive maneuvers to avoid capture, within a dynamic and often adversarial environment [4 – 6].", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/org/science/article/pii/S1546221824006647", "content": "Sep 12, 2024 · As shown in Fig. 1, in a typical UAV pursuit-evasion scenario, multiple UAVs are strategically deployed to efficiently track and intercept a moving target , which employs evasive maneuvers to avoid capture, within a dynamic and often adversarial environment [4 – 6]."}
+{"idx": 2, "title": "Multi-Target Pursuit by a Decentralized Heterogeneous UAV ...", "date": "", "ddg_snippet": "May 29, 2023 · Our results demonstrate that a multi -agent pursuit team has the ability to learn highly efficient coordinated control policies in terms of target tracking and exploration even when confronted with multiple fast evasive targets in complex environments.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10160919", "content": "May 29, 2023 · Our results demonstrate that a multi -agent pursuit team has the ability to learn highly efficient coordinated control policies in terms of target tracking and exploration even when confronted with multiple fast evasive targets in complex environments."}
+{"idx": 3, "title": "Learning Multi-Pursuit Evasion for Safe Targeted Navigation ...", "date": "", "ddg_snippet": "This paper proposes a novel approach, asynchronous multi -stage deep reinforcement learning (AMS-DRL), to train adversarial neural networks that can learn from the actions of multiple evolved pursuers and adapt quickly to their behavior , enabling the drone to avoid attacks and reach its target .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2304.03443v2", "content": "This paper proposes a novel approach, asynchronous multi -stage deep reinforcement learning (AMS-DRL), to train adversarial neural networks that can learn from the actions of multiple evolved pursuers and adapt quickly to their behavior , enabling the drone to avoid attacks and reach its target ."}
+{"idx": 4, "title": "Unmanned Aerial Vehicle for Multi-target Tracking Using ...", "date": "", "ddg_snippet": "Unmanned aerial vehicles, or UAVs, have become a cornerstone for multi-target tracking in dynamic and unpredictable environments. Despite advancements in methodologies, the approacheshavegraveshortcomingseveninscenariosinvolvingrapidmovementofthetarget, occlusion, and adversarial strategies.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s13235-025-00645-3.pdf", "content": "Unmanned aerial vehicles, or UAVs, have become a cornerstone for multi-target tracking in dynamic and unpredictable environments. Despite advancements in methodologies, the approacheshavegraveshortcomingseveninscenariosinvolvingrapidmovementofthetarget, occlusion, and adversarial strategies."}
+{"idx": 5, "title": "Autonomous target tracking of multi-UAV: A two-stage deep ...", "date": "", "ddg_snippet": "Sep 1, 2023 · With the development of computer and complex system science, multi - UAV systems are often used to accomplish various complex tasks that are impossible or difficult to accomplish by single UAV . As a classical problem in robotics, target tracking is widely applied to multiple fields such as search and rescue, patrol and monitoring, resource exploration, etc., and has attracted more and more ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1568494623006221", "content": "Sep 1, 2023 · With the development of computer and complex system science, multi - UAV systems are often used to accomplish various complex tasks that are impossible or difficult to accomplish by single UAV . As a classical problem in robotics, target tracking is widely applied to multiple fields such as search and rescue, patrol and monitoring, resource exploration, etc., and has attracted more and more ..."}
+{"idx": 6, "title": "Long-Term Tracking of Evasive Urban Target Based on Intention ...", "date": "", "ddg_snippet": "Unmanned aerial vehicles (UAVs) have been widely used in urban target - tracking tasks, where long-term tracking of evasive targets is of great significance for public safety. However, the tracked targets are easily lost due to the evasive behavior of the targets and the unstructured characteristics of the urban environment. To address this issue, this article proposes a hybrid target - tracking ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10214982", "content": "Unmanned aerial vehicles (UAVs) have been widely used in urban target - tracking tasks, where long-term tracking of evasive targets is of great significance for public safety. However, the tracked targets are easily lost due to the evasive behavior of the targets and the unstructured characteristics of the urban environment. To address this issue, this article proposes a hybrid target - tracking ..."}
+{"idx": 7, "title": "Diffusion Models for Multi - target Adversarial Tracking", "date": "", "ddg_snippet": "As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents Constrained Agent-based Diffusion for ENhanCEd Multi -Agent Tracking (CADENCE)...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.06244v2", "content": "As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents Constrained Agent-based Diffusion for ENhanCEd Multi -Agent Tracking (CADENCE)..."}
+{"idx": 8, "title": "Fixed-wing UAV Tracking of Evasive Targets in 3-Dimensional Space", "date": "", "ddg_snippet": "8. Switching logic for UAV multi - target tracking . 9. Novel PNG-based algorithm to interdict adversarial targets. 4. Implement UAV algorithm for cooperative tracking & interdiction of single and multiple evasive aerial targets.", "subpage_snippet": "", "source": "etheses.whiterose.ac.uk", "link": "https://etheses.whiterose.ac.uk/id/eprint/34675/1/Mbam_CJ_Mechanical_PhD_2024.pdf.pdf", "content": "8. Switching logic for UAV multi - target tracking . 9. Novel PNG-based algorithm to interdict adversarial targets. 4. Implement UAV algorithm for cooperative tracking & interdiction of single and multiple evasive aerial targets."}
+{"idx": 9, "title": "Ground Target Tracking Using UAV with Input Constraints", "date": "", "ddg_snippet": "For adversarial ground target tracking , tracking performance and UAV safety are two important considerations during tracking controller design. In this paper, a bang-bang heading rate controller is proposed to achieve circular tracking around the target .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/220061639_Ground_Target_Tracking_Using_UAV_with_Input_Constraints", "content": "For adversarial ground target tracking , tracking performance and UAV safety are two important considerations during tracking controller design. In this paper, a bang-bang heading rate controller is proposed to achieve circular tracking around the target ."}
diff --git a/data/sampled_jsons/multi-target_tracking_adversarial_target_behavior_when_an_agent_is_nearby_submodular_coordination.jsonl b/data/sampled_jsons/multi-target_tracking_adversarial_target_behavior_when_an_agent_is_nearby_submodular_coordination.jsonl
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index 0000000000000000000000000000000000000000..fce052614ec6553cdec9da5863197f1a3aaff6d8
--- /dev/null
+++ b/data/sampled_jsons/multi-target_tracking_adversarial_target_behavior_when_an_agent_is_nearby_submodular_coordination.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Air cargo tracking - track-trace", "date": "", "ddg_snippet": "The air cargo tracking page lets you track air cargo for 242 airlines. A track-trace service.", "subpage_snippet": "", "source": "www.track-trace.com", "link": "https://www.track-trace.com/aircargo", "content": "The air cargo tracking page lets you track air cargo for 242 airlines. A track-trace service."}
+{"idx": 1, "title": "Research on ship target detection based on improved YOLOv5 ...", "date": "", "ddg_snippet": "Aiming at the problem that the YOLOv5 algorithm has low detection accuracy for small targets and is prone to missed detection and false detection, this paper proposes an improved YOLOv5 algorithm by introducing the coordinate attention mechanism and the bidirectional feature pyramid network for effective waterborne ship detection. This method aims to improve the detection accuracy of ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10142528", "content": "Aiming at the problem that the YOLOv5 algorithm has low detection accuracy for small targets and is prone to missed detection and false detection, this paper proposes an improved YOLOv5 algorithm by introducing the coordinate attention mechanism and the bidirectional feature pyramid network for effective waterborne ship detection. This method aims to improve the detection accuracy of ..."}
+{"idx": 2, "title": "Cooperative task allocation for heterogeneous multi-UAV using ...", "date": "", "ddg_snippet": "Apr 13, 2020 · The application of multiple UAVs in complicated tasks has been widely explored in recent years. Due to the advantages of flexibility, cheapness and consistence, the performance of heterogeneous multi -UAVs with proper cooperative task allocation is superior to over the single UAV. Accordingly, several constraints should be satisfied to realize the efficient cooperation, such as special time ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11771-020-4307-0", "content": "Apr 13, 2020 · The application of multiple UAVs in complicated tasks has been widely explored in recent years. Due to the advantages of flexibility, cheapness and consistence, the performance of heterogeneous multi -UAVs with proper cooperative task allocation is superior to over the single UAV. Accordingly, several constraints should be satisfied to realize the efficient cooperation, such as special time ..."}
+{"idx": 3, "title": "Images of Coordination: How Implementing Organizations ...", "date": "", "ddg_snippet": "Dec 30, 2019 · A crucial challenge for the coordination of horizontal policy programs—those designed to tackle crosscutting issues—is how to motivate government organizations to contribute to such programs. Hence, ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1111/puar.13136", "content": "Dec 30, 2019 · A crucial challenge for the coordination of horizontal policy programs—those designed to tackle crosscutting issues—is how to motivate government organizations to contribute to such programs. Hence, ..."}
+{"idx": 4, "title": "Distributed Fault-Tolerant Multi-Robot Cooperative Localization", "date": "", "ddg_snippet": "They further extend this work in [ 29 ] by introducing a resilient multi - target tracking algorithm that withstands any number of failures while ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.06750v1", "content": "They further extend this work in [ 29 ] by introducing a resilient multi - target tracking algorithm that withstands any number of failures while ..."}
+{"idx": 5, "title": "A Hierarchical Reinforcement Learning Framework for Multi-UAV", "date": "", "ddg_snippet": "We introduce a target selector that evaluates the threat level of different targets based on multiple dimensions, such as flight status and posture ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.13132v1", "content": "We introduce a target selector that evaluates the threat level of different targets based on multiple dimensions, such as flight status and posture ..."}
+{"idx": 6, "title": "Publications - Autonomous Agents Research Group", "date": "", "ddg_snippet": "... exploration, such as ϵ-greedy, to explore the environment which is not systematic and inefficient at identifying effective actions in multi - agent ...", "subpage_snippet": "", "source": "agents-lab.org", "link": "https://agents-lab.org/publications/", "content": "... exploration, such as ϵ-greedy, to explore the environment which is not systematic and inefficient at identifying effective actions in multi - agent ..."}
+{"idx": 7, "title": "Leadership Inference for Multi-Agent Interactions", "date": "", "ddg_snippet": "The effectiveness of our algorithms is demonstrated in a simulated multi - agent collision avoidance scenario, and with data from the INTERACTION ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/379274416_Leadership_Inference_for_Multi-Agent_Interactions", "content": "The effectiveness of our algorithms is demonstrated in a simulated multi - agent collision avoidance scenario, and with data from the INTERACTION ..."}
+{"idx": 8, "title": "CN112130582A - Multi-agent formation forming method - Google", "date": "", "ddg_snippet": "... multi - agent formation forming method, which comprises the steps of carrying out target point distribution and path planning by depending on an ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/CN112130582A/en", "content": "... multi - agent formation forming method, which comprises the steps of carrying out target point distribution and path planning by depending on an ..."}
+{"idx": 9, "title": "TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven", "date": "", "ddg_snippet": "Human motion is inherently complex, characterized by nonlinear behaviors , sudden directional changes, and spontaneous decisions influenced by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05616v1", "content": "Human motion is inherently complex, characterized by nonlinear behaviors , sudden directional changes, and spontaneous decisions influenced by ..."}
diff --git a/data/sampled_jsons/neural_corrector_time_stepping_PDE_solver.jsonl b/data/sampled_jsons/neural_corrector_time_stepping_PDE_solver.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..efb0ea1fa249563796cf83c9069d5592107ca13c
--- /dev/null
+++ b/data/sampled_jsons/neural_corrector_time_stepping_PDE_solver.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDE-Preserved Coarse Correction Network for efficient ...", "date": "", "ddg_snippet": "6 Nov 2024 — ... time stepping to meet stability, consistency, and convergence conditions, leading ... neural corrector module. When modeling the Navier ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=motImXq3B1&noteId=z27ISHoDqY", "content": "6 Nov 2024 — ... time stepping to meet stability, consistency, and convergence conditions, leading ... neural corrector module. When modeling the Navier ..."}
+{"idx": 1, "title": "Prob-GParareal: A Probabilistic Numerical Parallel-in-Time", "date": "", "ddg_snippet": "PinT techniques address the limitations of conventional sequential solvers by enabling concurrent computations over the time domain, which is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03945v1", "content": "PinT techniques address the limitations of conventional sequential solvers by enabling concurrent computations over the time domain, which is ..."}
+{"idx": 2, "title": "Machine Learning for Modeling Underwater Vehicle ...", "date": "", "ddg_snippet": "by X Macatangay · 2024 · Cited by 5 — This eliminates the need for prior assumptions, linearization, or local time-stepping and requires an adequate number of hidden units.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10380310/10684607.pdf", "content": "by X Macatangay · 2024 · Cited by 5 — This eliminates the need for prior assumptions, linearization, or local time-stepping and requires an adequate number of hidden units."}
+{"idx": 3, "title": "A Force-Correcting Machine Learning Method for Nonlinear Marine ...", "date": "", "ddg_snippet": "Variable time-stepping is allowed up to a maximum Courant number ... cfd solver ... using a neural-corrector method, in SNAME International Conference on Fast Sea.", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "https://deepblue.lib.umich.edu/bitstream/handle/2027.42/197130/kylemarl_1.pdf?sequence=1&isAllowed=y", "content": "Variable time-stepping is allowed up to a maximum Courant number ... cfd solver ... using a neural-corrector method, in SNAME International Conference on Fast Sea."}
+{"idx": 4, "title": "NDSolveValue: Solve a Differential Equation", "date": "", "ddg_snippet": "Neural Net Repository ... Solvers over Regions ... solves the time -dependent partial differential equations eqns over the region Ω .", "subpage_snippet": "", "source": "reference.wolfram.com", "link": "https://reference.wolfram.com/language/ref/NDSolveValue.html", "content": "Neural Net Repository ... Solvers over Regions ... solves the time -dependent partial differential equations eqns over the region Ω ."}
+{"idx": 5, "title": "Reducing Numerical Errors with Deep Learning —", "date": "", "ddg_snippet": "... much all numerical methods contain some form of iterative process: repeated updates over time for explicit solvers , or within a single update step ...", "subpage_snippet": "", "source": "physicsbaseddeeplearning.org", "link": "https://physicsbaseddeeplearning.org/diffphys-code-sol.html", "content": "... much all numerical methods contain some form of iterative process: repeated updates over time for explicit solvers , or within a single update step ..."}
+{"idx": 6, "title": "Kenneth R. Jackson - Selected Publications", "date": "", "ddg_snippet": "Adaptive Time - Stepping for the Strong Numerical Solution of Stochastic Differential Equations , Silvana Ilie, Kenneth R.", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "http://www.cs.toronto.edu/~krj/publications.html", "content": "Adaptive Time - Stepping for the Strong Numerical Solution of Stochastic Differential Equations , Silvana Ilie, Kenneth R."}
+{"idx": 7, "title": "PICT – A Differentiable, GPU-Accelerated Multi-Block PISO", "date": "", "ddg_snippet": "Differentiable solvers can be employed to optimize time -dependent problems step-by-step, a scenario that would be computationally very expensive to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16992v1", "content": "Differentiable solvers can be employed to optimize time -dependent problems step-by-step, a scenario that would be computationally very expensive to ..."}
+{"idx": 8, "title": "Citations - AJCM - Scientific Research Publishing", "date": "", "ddg_snippet": "A New Block-Predictor Corrector Algorithm for the Solution of y’’’=f(x, y, y’, y’’) , American Journal of Computational Mathematics, Vol ...", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/journal/JournalCitations?JournalID=535", "content": "A New Block-Predictor Corrector Algorithm for the Solution of y’’’=f(x, y, y’, y’’) , American Journal of Computational Mathematics, Vol ..."}
+{"idx": 9, "title": "GitHub - google-deepmind/torax: TORAX: Tokamak transport", "date": "", "ddg_snippet": "Differentiability allows for gradient-based nonlinear PDE solvers for fast and accurate modelling, and for sensitivity analysis of simulation results ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/google-deepmind/torax", "content": "Differentiability allows for gradient-based nonlinear PDE solvers for fast and accurate modelling, and for sensitivity analysis of simulation results ..."}
diff --git a/data/sampled_jsons/neural_network_function_approximation_reward_learning_preference-based_RL_deep_learning.jsonl b/data/sampled_jsons/neural_network_function_approximation_reward_learning_preference-based_RL_deep_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1c097f5bc04edc0f6ffc0203271b084718e62de7
--- /dev/null
+++ b/data/sampled_jsons/neural_network_function_approximation_reward_learning_preference-based_RL_deep_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Guarantee of Reward Modeling Using Deep Neural Networks", "date": "", "ddg_snippet": "Despite the success of deep neural networks (DNNs) in RLHF due to their expressive power, their approximation properties and estimation bias remain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06601v1", "content": "Despite the success of deep neural networks (DNNs) in RLHF due to their expressive power, their approximation properties and estimation bias remain ..."}
+{"idx": 1, "title": "Newest 'neural-networks' Questions - Artificial", "date": "", "ddg_snippet": "For any given learning task, if I know the nature of dependence to be learnt, how to select the width and depth of a the neural network ? How does the ...", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/tagged/neural-networks", "content": "For any given learning task, if I know the nature of dependence to be learnt, how to select the width and depth of a the neural network ? How does the ..."}
+{"idx": 2, "title": "Deep RL Workshop, NeurIPS 2022", "date": "", "ddg_snippet": "In recent years, the use of deep neural networks as function approximators has enabled researchers to extend reinforcement learning techniques to ...", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/view/deep-rl-workshop-neurips-2022", "content": "In recent years, the use of deep neural networks as function approximators has enabled researchers to extend reinforcement learning techniques to ..."}
+{"idx": 3, "title": "Aldo Pacchiano | Aldo Pacchiano", "date": "", "ddg_snippet": "Learning Rate-Free Reinforcement ... Reinforcement Learning with Wasserstein Distance Regularisation, with Applications to Multipolicy Learning", "subpage_snippet": "", "source": "www.aldopacchiano.ai", "link": "http://www.aldopacchiano.ai/authors/aldo-pacchiano/", "content": "Learning Rate-Free Reinforcement ... Reinforcement Learning with Wasserstein Distance Regularisation, with Applications to Multipolicy Learning"}
+{"idx": 4, "title": "Aldo Pacchiano | Aldo Pacchiano", "date": "", "ddg_snippet": "Learning Rate-Free Reinforcement ... Reinforcement Learning with Wasserstein Distance Regularisation, with Applications to Multipolicy Learning", "subpage_snippet": "", "source": "www.aldopacchiano.ai", "link": "https://www.aldopacchiano.ai/authors/aldo-pacchiano/", "content": "Learning Rate-Free Reinforcement ... Reinforcement Learning with Wasserstein Distance Regularisation, with Applications to Multipolicy Learning"}
+{"idx": 5, "title": "‘RL exploration’ directory · Gwern.net", "date": "", "ddg_snippet": "Introducing Deep Research: An Agent That Uses Reasoning to Synthesize Large Amounts of Online Information and Complete Multi-Step Research Tasks for ...", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/exploration/index", "content": "Introducing Deep Research: An Agent That Uses Reasoning to Synthesize Large Amounts of Online Information and Complete Multi-Step Research Tasks for ..."}
+{"idx": 6, "title": "Representational spaces in orbitofrontal and ventromedial", "date": "", "ddg_snippet": "Task state representations emerge in deep reinforcement learning networks alongside value-like signals, offering insights into why the brain ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/trends/neurosciences/fulltext/S0166-2236(24)00202-9", "content": "Task state representations emerge in deep reinforcement learning networks alongside value-like signals, offering insights into why the brain ..."}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions"}
+{"idx": 8, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Understanding Augmentation- based Self-Supervised Representation Learning via RKHS Approximation and Regression ... Preference - based Reinforcement ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Understanding Augmentation- based Self-Supervised Representation Learning via RKHS Approximation and Regression ... Preference - based Reinforcement ..."}
+{"idx": 9, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Multi-Task Learning with User Preferences : Gradient ... Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Multi-Task Learning with User Preferences : Gradient ... Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent"}
diff --git a/data/sampled_jsons/neural_network_time_integration_error_correction_PDE.jsonl b/data/sampled_jsons/neural_network_time_integration_error_correction_PDE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d992cbc5557b5e9a8e21e63d7e5bcd4d1144f94
--- /dev/null
+++ b/data/sampled_jsons/neural_network_time_integration_error_correction_PDE.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDE -Refiner: Achieving Accurate Long Rollouts with", "date": "", "ddg_snippet": "Time -dependent partial differential equations ( PDEs ) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniques, deep neural network based surrogates have gained increased interest.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2308.05732", "content": "Time -dependent partial differential equations ( PDEs ) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniques, deep neural network based surrogates have gained increased interest."}
+{"idx": 1, "title": "[Hand teaches you] deep learning - first knowledge neural network", "date": "", "ddg_snippet": "Biological neural network allows the brain to handle a lot of information in a complicated manner. The biological neural network of the brain consists of approximately 100 billion neurons, which is the basic processing unit of the brain.", "subpage_snippet": "", "source": "programmersought.com", "link": "https://programmersought.com/article/42288455571/", "content": "Biological neural network allows the brain to handle a lot of information in a complicated manner. The biological neural network of the brain consists of approximately 100 billion neurons, which is the basic processing unit of the brain."}
+{"idx": 2, "title": "PhysicsCorrect: A Training-Free Approach for Stable Neural ...", "date": "", "ddg_snippet": "Problem Formulation. The core idea of our PhysicsCorrect approach is to leverage the governing PDE itself as a form of implicit supervision, correcting neural network predictions to better satisfy the underlying physics. For a state ut at time t, a neural operator produces a prediction ˆut+1 for the next time step.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.02227v1", "content": "Problem Formulation. The core idea of our PhysicsCorrect approach is to leverage the governing PDE itself as a form of implicit supervision, correcting neural network predictions to better satisfy the underlying physics. For a state ut at time t, a neural operator produces a prediction ˆut+1 for the next time step."}
+{"idx": 3, "title": "Residual-based error correction for neural operator ...", "date": "", "ddg_snippet": "Aug 1, 2023 · For a trained neural operator, we utilized its prediction at a given parameter to formulate and solve a linear variational problem, or an error-correction problem, based on the PDE residual and its derivative.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999123001997", "content": "Aug 1, 2023 · For a trained neural operator, we utilized its prediction at a given parameter to formulate and solve a linear variational problem, or an error-correction problem, based on the PDE residual and its derivative."}
+{"idx": 4, "title": "GitHub - bitzhangcy/Neural-PDE-Solver", "date": "", "ddg_snippet": "This is an open-source repository for Neural- PDE -Solver, a curated collection of literature on solving Partial Differential Equations (PDEs) using Neural Operators. The goal is to track recent progress and organize related papers systematically. I am currently looking for collaborators to help maintain and expand this repository. Ideal contributors can take initiative in adding new papers ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bitzhangcy/Neural-PDE-Solver", "content": "This is an open-source repository for Neural- PDE -Solver, a curated collection of literature on solving Partial Differential Equations (PDEs) using Neural Operators. The goal is to track recent progress and organize related papers systematically. I am currently looking for collaborators to help maintain and expand this repository. Ideal contributors can take initiative in adding new papers ..."}
+{"idx": 5, "title": "Modeling Accurate Long Rollouts with Temporal Neural PDE Solvers", "date": "", "ddg_snippet": "Abstract Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineer-ing. Recently, mostly due to the high computa-tional cost of traditional solution techniques, deep neural network based surrogates have gained in-creased interest. The practical utility of such neu-ral PDE solvers relies on their ability to provide accurate, stable predictions over long time ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=EGTY6V76b3", "content": "Abstract Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineer-ing. Recently, mostly due to the high computa-tional cost of traditional solution techniques, deep neural network based surrogates have gained in-creased interest. The practical utility of such neu-ral PDE solvers relies on their ability to provide accurate, stable predictions over long time ..."}
+{"idx": 6, "title": "PDE-refiner | Proceedings of the 37th International ...", "date": "", "ddg_snippet": "Dec 10, 2023 · Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniques, deep neural network based surrogates have gained increased interest. The practical utility of such neural PDE solvers relies on their ability to provide accurate, stable predictions over long time horizons, which ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3669068", "content": "Dec 10, 2023 · Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniques, deep neural network based surrogates have gained increased interest. The practical utility of such neural PDE solvers relies on their ability to provide accurate, stable predictions over long time horizons, which ..."}
+{"idx": 7, "title": "Hybrid FEM-NN models: Combining artificial neural networks ...", "date": "", "ddg_snippet": "Dec 1, 2021 · Bridging the gap between explicit PDE systems and observation-driven NN learning to overcome the limitations of either approaches has recently gained scientific traction. Raissi et al. [7] introduced physics-informed neural networks (PINNs) for solving PDEs by training a neural network with a loss function consisting of the PDE residual.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999121005465", "content": "Dec 1, 2021 · Bridging the gap between explicit PDE systems and observation-driven NN learning to overcome the limitations of either approaches has recently gained scientific traction. Raissi et al. [7] introduced physics-informed neural networks (PINNs) for solving PDEs by training a neural network with a loss function consisting of the PDE residual."}
+{"idx": 8, "title": "GitHub Gist: instantly share code, notes, and snippets.", "date": "", "ddg_snippet": "...\"Space Time Adaptive Processing\", \"Generalized Exponential Distribution\", \"Adaptive Modulation\", \"weibull distribution\", \"Pseudo Maximum Likelihood\", \"Fast Recursive Least Squares\", \"Non Negative Least Squares\", \"Multivariate Integration \", \"Compress and Forward\"...", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/ankeshanand/3f3519bddc9b050b6916", "content": "...\"Space Time Adaptive Processing\", \"Generalized Exponential Distribution\", \"Adaptive Modulation\", \"weibull distribution\", \"Pseudo Maximum Likelihood\", \"Fast Recursive Least Squares\", \"Non Negative Least Squares\", \"Multivariate Integration \", \"Compress and Forward\"..."}
+{"idx": 9, "title": "Solving Viscous Burgers’ Equation : Hybrid Approach Combining...", "date": "", "ddg_snippet": "After training for 5000 epochs, the neural network was able to approximate the solution of the viscous Burgers’ equation with high accuracy. The solution was smooth, and the transition between the shock wave and the diffusive behavior was well captured by the PINN.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7390/12/21/3430", "content": "After training for 5000 epochs, the neural network was able to approximate the solution of the viscous Burgers’ equation with high accuracy. The solution was smooth, and the transition between the shock wave and the diffusive behavior was well captured by the PINN."}
diff --git a/data/sampled_jsons/neural_network_time_stepping_error_correction_PDE_solver.jsonl b/data/sampled_jsons/neural_network_time_stepping_error_correction_PDE_solver.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f3cbcc7b7f8da24507658bdeab49d1170733e58
--- /dev/null
+++ b/data/sampled_jsons/neural_network_time_stepping_error_correction_PDE_solver.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "bitzhangcy/Neural-PDE-Solver", "date": "", "ddg_snippet": "Invariant preservation in machine learned PDE solvers via error correction . arXiv, 2023. paper ... Drift-correcting multiphysics informed neural network coupled ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bitzhangcy/Neural-PDE-Solver", "content": "Invariant preservation in machine learned PDE solvers via error correction . arXiv, 2023. paper ... Drift-correcting multiphysics informed neural network coupled ..."}
+{"idx": 1, "title": "Neural network-enhanced integrators for simulating ...", "date": "", "ddg_snippet": "by A Othmane · 2025 — In this work, we have introduced a class of neural network -enhanced integrators that leverage data-driven error correction to improve the ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "http://www.arxiv.org/pdf/2504.05493", "content": "by A Othmane · 2025 — In this work, we have introduced a class of neural network -enhanced integrators that leverage data-driven error correction to improve the ..."}
+{"idx": 2, "title": "Residual-based error correction for neural operator ...", "date": "", "ddg_snippet": "by L Cao · 2023 · Cited by 38 — Inspired by classical time stepping schemes, another approach to a neural network -based simulation of (evolutionary) partial differential ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0021999123001997", "content": "by L Cao · 2023 · Cited by 38 — Inspired by classical time stepping schemes, another approach to a neural network -based simulation of (evolutionary) partial differential ..."}
+{"idx": 3, "title": "NEURAL TIME INTEGRATOR WITH STAGE CORREC- TION", "date": "", "ddg_snippet": "By modifying only the time-stepping ... the error correction and uses it to compensate the errors: ... rely on complex neural network architectures or large number ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/1cb50263d43f765cb7b6ba58d263f395316ad965.pdf", "content": "By modifying only the time-stepping ... the error correction and uses it to compensate the errors: ... rely on complex neural network architectures or large number ..."}
+{"idx": 4, "title": "Accelerating Simulation of Two-Phase Flows with Neural ...", "date": "", "ddg_snippet": "by Y Poels · 2024 · Cited by 1 — As encoder we consider a simple convolutional neural network with a 1-by-1 kernel. ... Invariant preservation in machine learned PDE solvers via error correction .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17260", "content": "by Y Poels · 2024 · Cited by 1 — As encoder we consider a simple convolutional neural network with a 1-by-1 kernel. ... Invariant preservation in machine learned PDE solvers via error correction ."}
+{"idx": 5, "title": "HMgNO: Hybrid multigrid neural operator with low-order ...", "date": "", "ddg_snippet": "by Y Hu · 2025 — Zhu et al. (2023) used a multigrid-based convolutional neural network architecture in operator learning and obtained robust results for solving numerical PDEs .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608025005295", "content": "by Y Hu · 2025 — Zhu et al. (2023) used a multigrid-based convolutional neural network architecture in operator learning and obtained robust results for solving numerical PDEs ."}
+{"idx": 6, "title": "deep euler method: solving odes by approximating - Xing Shen", "date": "", "ddg_snippet": "by X Shen · 2020 · Cited by 26 — Compared with using a neural network to approximate the solution of ODE directly, DEM separates the nonlinear part from the numerical scheme ...", "subpage_snippet": "", "source": "xingbaji.github.io", "link": "https://xingbaji.github.io/files/DeepEuler.pdf", "content": "by X Shen · 2020 · Cited by 26 — Compared with using a neural network to approximate the solution of ODE directly, DEM separates the nonlinear part from the numerical scheme ..."}
+{"idx": 7, "title": "Adaptive higher order reversible integrators for memory ...", "date": "", "ddg_snippet": "by S Maslovskaya — ... time-stepping . Our numerical tests show the advantages in computational ... Invariant preservation in machine learned PDE solvers via error correction .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MD4ifad9v5", "content": "by S Maslovskaya — ... time-stepping . Our numerical tests show the advantages in computational ... Invariant preservation in machine learned PDE solvers via error correction ."}
+{"idx": 8, "title": "Traditional and Machine Learning Approaches to Partial ...", "date": "", "ddg_snippet": "4 Sept 2025 — Understanding these limiting behaviors guides the design of efficient time-stepping schemes and provides benchmarks for computational accuracy.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202509.0472/v1", "content": "4 Sept 2025 — Understanding these limiting behaviors guides the design of efficient time-stepping schemes and provides benchmarks for computational accuracy."}
+{"idx": 9, "title": "PIC 2 O-Sim: A physics-inspired causality-aware dynamic ...", "date": "", "ddg_snippet": "by P Ma · 2025 · Cited by 7 — ... neural network (RNN) cells to leverage the graphics processing unit ... time-stepping . To capture the response across multiple ...", "subpage_snippet": "", "source": "pubs.aip.org", "link": "https://pubs.aip.org/aip/app/article/10/3/036104/3338201/PIC2O-Sim-A-physics-inspired-causality-aware", "content": "by P Ma · 2025 · Cited by 7 — ... neural network (RNN) cells to leverage the graphics processing unit ... time-stepping . To capture the response across multiple ..."}
diff --git a/data/sampled_jsons/neuron_condensation.jsonl b/data/sampled_jsons/neuron_condensation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..de5765157143fca1147283a3010fe56ac0811957
--- /dev/null
+++ b/data/sampled_jsons/neuron_condensation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Efficient and Flexible Method for Reducing Moderate-Size ...", "date": "", "ddg_snippet": "by T Chen · 2024 · Cited by 2 — ... neuron. Condensation implies the presence of a large number of similar neurons in neural networks, indicating that the structural complexity of neural ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11276590/", "content": "by T Chen · 2024 · Cited by 2 — ... neuron. Condensation implies the presence of a large number of similar neurons in neural networks, indicating that the structural complexity of neural ..."}
+{"idx": 1, "title": "Growth and Morphogenesis of an Autonomic Ganglion. II. ...", "date": "", "ddg_snippet": "Neuron condensation in the cardiac gan- glion also occurs from the inside out. Thus, at least this pattern of morphogenesis can be shared by ensembles of ...", "subpage_snippet": "", "source": "www.jneurosci.org", "link": "https://www.jneurosci.org/content/jneuro/7/8/2502.full.pdf", "content": "Neuron condensation in the cardiac gan- glion also occurs from the inside out. Thus, at least this pattern of morphogenesis can be shared by ensembles of ..."}
+{"idx": 2, "title": "Efficient and Flexible Method for Reducing Moderate-size Deep ...", "date": "", "ddg_snippet": "... neuron. Condensation implies the presence of a large number of similar neurons in neural networks, indicating that the structural complexity of neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.01041v2", "content": "... neuron. Condensation implies the presence of a large number of similar neurons in neural networks, indicating that the structural complexity of neural ..."}
+{"idx": 3, "title": "Complexity Control Facilitates Reasoning-Based ...", "date": "", "ddg_snippet": "Further analysis reveals that reasoning-based solutions exhibit a lower complexity bias, which aligns with the well-studied neuron condensation phenomenon. This ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/arXiv:2501.08537", "content": "Further analysis reveals that reasoning-based solutions exhibit a lower complexity bias, which aligns with the well-studied neuron condensation phenomenon. This ..."}
+{"idx": 4, "title": "Malaria detection through digital microscopic imaging ...", "date": "", "ddg_snippet": "by S Dey · 2021 · Cited by 18 — ... neuron condensation . The training of the neural network follows the overall learning methods, specifically, forward propagation, backward ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8478118/", "content": "by S Dey · 2021 · Cited by 18 — ... neuron condensation . The training of the neural network follows the overall learning methods, specifically, forward propagation, backward ..."}
+{"idx": 5, "title": "Complexity Control Facilitates Reasoning-Based Compositional ...", "date": "", "ddg_snippet": "... neuron condensation phenomenon. This lower complexity bias is hypothesized to be the key factor enabling these solutions to learn reasoning rules. We ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2501.08537v1", "content": "... neuron condensation phenomenon. This lower complexity bias is hypothesized to be the key factor enabling these solutions to learn reasoning rules. We ..."}
+{"idx": 6, "title": "[Literature Review] Complexity Control Facilitates ...", "date": "", "ddg_snippet": "15 Jan 2025 — Neural Mechanism Analysis: Through structural probing and analysis of neuron condensation , the authors reveal that models transitioning to Phase ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/complexity-control-facilitates-reasoning-based-compositional-generalization-in-transformers", "content": "15 Jan 2025 — Neural Mechanism Analysis: Through structural probing and analysis of neuron condensation , the authors reveal that models transitioning to Phase ..."}
+{"idx": 7, "title": "(PDF) Efficient and Flexible Method for Reducing Moderate-Size Deep...", "date": "", "ddg_snippet": "Condensation offers an opportunity to reduce the scale of neural networks to a smaller subnetwork with a similar performance. In this article, we propose a condensation reduction method...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381950458_Efficient_and_Flexible_Method_for_Reducing_Moderate-Size_Deep_Neural_Networks_with_Condensation", "content": "Condensation offers an opportunity to reduce the scale of neural networks to a smaller subnetwork with a similar performance. In this article, we propose a condensation reduction method..."}
+{"idx": 8, "title": "[2501.08537] Complexity Control Facilitates Reasoning-Based...", "date": "", "ddg_snippet": "Further analysis reveals that reasoning-based solutions exhibit a lower complexity bias, which aligns with the well-studied neuron condensation phenomenon.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.08537", "content": "Further analysis reveals that reasoning-based solutions exhibit a lower complexity bias, which aligns with the well-studied neuron condensation phenomenon."}
+{"idx": 9, "title": "ins.sjtu.edu.cn/people/xuzhiqin", "date": "", "ddg_snippet": "Neurons in the same layer tend to be similar during training ( neuron condensation /alignment). Reasoning of transformer-based language models.", "subpage_snippet": "", "source": "ins.sjtu.edu.cn", "link": "https://ins.sjtu.edu.cn/people/xuzhiqin/", "content": "Neurons in the same layer tend to be similar during training ( neuron condensation /alignment). Reasoning of transformer-based language models."}
diff --git a/data/sampled_jsons/neuron_condensation_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization.jsonl b/data/sampled_jsons/neuron_condensation_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9e890bc5bd161091a12a973133856f2a56c28051
--- /dev/null
+++ b/data/sampled_jsons/neuron_condensation_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "For overall notable models (top left), frontier models (top right), top language models (bottom left) and top models within leading companies (bottom ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "For overall notable models (top left), frontier models (top right), top language models (bottom left) and top models within leading companies (bottom ..."}
+{"idx": 1, "title": "Dropping Experts, Recombining Neurons: Retraining-Free Pruning", "date": "", "ddg_snippet": "We observe that experts are often misaligned and contain semantic conflicts at the neuron level, which poses challenges for direct merging.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10377v1", "content": "We observe that experts are often misaligned and contain semantic conflicts at the neuron level, which poses challenges for direct merging."}
+{"idx": 2, "title": "Evidence Sets: Towards Inductive-Biases based Analysis of", "date": "", "ddg_snippet": "This is often the bias responsible for idiosyncrasies and surface-level correlations in models when occurring in training data and for favouring non ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/aW5CPtqtvs2EYKMMK/evidence-sets-towards-inductive-biases-based-analysis-of", "content": "This is often the bias responsible for idiosyncrasies and surface-level correlations in models when occurring in training data and for favouring non ..."}
+{"idx": 3, "title": "deep learning - Why should the initialization of weights and", "date": "", "ddg_snippet": "Another potential issue is that the distribution of the outputs of each neuron , when using random initialization values, has a variance that gets ...", "subpage_snippet": "", "source": "datascience.stackexchange.com", "link": "https://datascience.stackexchange.com/questions/22093/why-should-the-initialization-of-weights-and-bias-be-chosen-around-0", "content": "Another potential issue is that the distribution of the outputs of each neuron , when using random initialization values, has a variance that gets ..."}
+{"idx": 4, "title": "Zhiwei Wang", "date": "", "ddg_snippet": "Further analysis reveals that reasoning -based solutions exhibit a lower complexity bias , which aligns with the well-studied neuron condensation ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Zhiwei+Wang", "content": "Further analysis reveals that reasoning -based solutions exhibit a lower complexity bias , which aligns with the well-studied neuron condensation ..."}
+{"idx": 5, "title": "Blog — Emile Delcourt", "date": "", "ddg_snippet": "... the privilege of building Generative Adversarial Networks (GANs) at ODSC East 2018 , and also built with Diffusion models in the last 1-2 years ( for ...", "subpage_snippet": "", "source": "www.emiledelcourt.com", "link": "https://www.emiledelcourt.com/blog", "content": "... the privilege of building Generative Adversarial Networks (GANs) at ODSC East 2018 , and also built with Diffusion models in the last 1-2 years ( for ..."}
+{"idx": 6, "title": "CVPR 2024 Papers", "date": "", "ddg_snippet": "Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision- Language Models for Selective Visual Question Answering", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/papers.html", "content": "Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision- Language Models for Selective Visual Question Answering"}
+{"idx": 7, "title": "CVPR 2025 Papers", "date": "", "ddg_snippet": "Beyond Clean Training Data: A Versatile and Model -Agnostic Framework for Out- of -Distribution Detection with Contaminated Training Data", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/papers.html", "content": "Beyond Clean Training Data: A Versatile and Model -Agnostic Framework for Out- of -Distribution Detection with Contaminated Training Data"}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "... Customers: Regret Bound and Exploration Complexity for ... Align before Fuse: Vision and Language Representation Learning with Momentum Distillation", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "... Customers: Regret Bound and Exploration Complexity for ... Align before Fuse: Vision and Language Representation Learning with Momentum Distillation"}
+{"idx": 9, "title": "ICLR 2024 Papers", "date": "", "ddg_snippet": "Open-ended VQA benchmarking of Vision- Language models by exploiting Classification datasets and their semantic hierarchy", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/papers.html", "content": "Open-ended VQA benchmarking of Vision- Language models by exploiting Classification datasets and their semantic hierarchy"}
diff --git a/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits.jsonl b/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cbba16cbe023b3deaa0319028591034e16846242
--- /dev/null
+++ b/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Constrained Feedback Learning for Non-Stationary Multi-Armed", "date": "", "ddg_snippet": "Non -stationary multi-armed bandits ( nsMAB ) enable agents to adapt to changing environments by incorporating mechanisms to detect and respond to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15073v1", "content": "Non -stationary multi-armed bandits ( nsMAB ) enable agents to adapt to changing environments by incorporating mechanisms to detect and respond to ..."}
+{"idx": 1, "title": "Semi-Bandit Learning for Monotone Stochastic Optimization", "date": "", "ddg_snippet": "... stochastic submodular optimization (Asadpour and Nazerzadeh, 2016 ; Golovin and Krause, 2017 ; Im et al., 2016 ) , stochastic probing (Gupta and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.15427v2", "content": "... stochastic submodular optimization (Asadpour and Nazerzadeh, 2016 ; Golovin and Krause, 2017 ; Im et al., 2016 ) , stochastic probing (Gupta and ..."}
+{"idx": 2, "title": "Downloads", "date": "", "ddg_snippet": "A Character-Level Length-Control Algorithm for Non -Autoregressive ... Adaptive Stochastic Variance Reduction for Non -convex Finite-Sum Minimization", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2022", "content": "A Character-Level Length-Control Algorithm for Non -Autoregressive ... Adaptive Stochastic Variance Reduction for Non -convex Finite-Sum Minimization"}
+{"idx": 3, "title": "000 Informatik, Wissen, Systeme", "date": "", "ddg_snippet": "November 2022): Finding Optimal Arms in Non - stochastic Combinatorial Bandits with Semi- bandit Feedback and Finite Budget.", "subpage_snippet": "", "source": "epub.ub.uni-muenchen.de", "link": "https://epub.ub.uni-muenchen.de/view/ddc/000.html", "content": "November 2022): Finding Optimal Arms in Non - stochastic Combinatorial Bandits with Semi- bandit Feedback and Finite Budget."}
+{"idx": 4, "title": "Workshop 2: Speakers and Abstracts | CIMI - Thematic trimester", "date": "", "ddg_snippet": "Abstract : We investigate stochastic and adversarial combinatorial multi-armed bandit problems. In the stochastic setting, we first derive ...", "subpage_snippet": "", "source": "www.irit.fr", "link": "https://www.irit.fr/cimi-machine-learning/node/22.html", "content": "Abstract : We investigate stochastic and adversarial combinatorial multi-armed bandit problems. In the stochastic setting, we first derive ..."}
+{"idx": 5, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Convergence of a Stochastic Gradient Method with Momentum for Non -Smooth Non -Convex Optimization ... Stochastic Zeroth-Order Frank-Wolfe Method ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Convergence of a Stochastic Gradient Method with Momentum for Non -Smooth Non -Convex Optimization ... Stochastic Zeroth-Order Frank-Wolfe Method ..."}
+{"idx": 6, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Convergence of a Stochastic Gradient Method with Momentum for Non -Smooth Non -Convex Optimization ... Stochastic Zeroth-Order Frank-Wolfe Method ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Convergence of a Stochastic Gradient Method with Momentum for Non -Smooth Non -Convex Optimization ... Stochastic Zeroth-Order Frank-Wolfe Method ..."}
+{"idx": 7, "title": "AISTATS 2024 Schedule", "date": "", "ddg_snippet": "A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2024/calendar", "content": "A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models"}
+{"idx": 8, "title": "Aldo Pacchiano | Aldo Pacchiano", "date": "", "ddg_snippet": "Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback ... Spaces via Combinatorial and Continuous Evolution", "subpage_snippet": "", "source": "www.aldopacchiano.ai", "link": "http://www.aldopacchiano.ai/authors/aldo-pacchiano/", "content": "Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback ... Spaces via Combinatorial and Continuous Evolution"}
+{"idx": 9, "title": "Aldo Pacchiano | Aldo Pacchiano", "date": "", "ddg_snippet": "Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback ... Spaces via Combinatorial and Continuous Evolution", "subpage_snippet": "", "source": "www.aldopacchiano.ai", "link": "https://www.aldopacchiano.ai/authors/aldo-pacchiano/", "content": "Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback ... Spaces via Combinatorial and Continuous Evolution"}
diff --git a/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits_2023_2022_2021_year_2023.jsonl b/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits_2023_2022_2021_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..896664859c1431532182c4c0239c988325106c9e
--- /dev/null
+++ b/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits_2023_2022_2021_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Combinatorial Stochastic-Greedy Bandit - arXiv.org", "date": "", "ddg_snippet": "Even though we con-sider stochastic submodular rewards, full- bandit feedback has been studied for non -submodular rewards, including lin-ear reward functions (Dani, Hayes, and Kakade 2008; Re-jwan and Mansour 2020) and Lipschitz reward functions (Agarwal et al. 2021 , 2022 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.08057.pdf", "content": "Even though we con-sider stochastic submodular rewards, full- bandit feedback has been studied for non -submodular rewards, including lin-ear reward functions (Dani, Hayes, and Kakade 2008; Re-jwan and Mansour 2020) and Lipschitz reward functions (Agarwal et al. 2021 , 2022 )."}
+{"idx": 1, "title": "PDF Nonstochastic Contextual Combinatorial Bandits", "date": "", "ddg_snippet": "The two most funda-mental extensions to the standard multi-armed bandit setup are contextual bandits , which allow taking contextual in-formation into account during decision making, and com-binatorial bandits , which allow the formulation of large-scale decision making problems with combinatorial deci-sion spaces.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/zierahn23a/zierahn23a.pdf", "content": "The two most funda-mental extensions to the standard multi-armed bandit setup are contextual bandits , which allow taking contextual in-formation into account during decision making, and com-binatorial bandits , which allow the formulation of large-scale decision making problems with combinatorial deci-sion spaces."}
+{"idx": 2, "title": "PDF Stochastic Multi-armed Bandits: Optimal Trade-off among ... - NeurIPS", "date": "", "ddg_snippet": "We also generalize our analysis to the stochastic multi-armed bandit problem with non -stationary baseline rewards, where in each time period t, the decision maker pulls one of K arms and collects a reward which is the sum of three terms: the mean of the pulled arm, an independent noise, and a non -stationary baseline reward as a function of t.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/6ffa1f5ad26addef897dcb938e525db7-Paper-Conference.pdf", "content": "We also generalize our analysis to the stochastic multi-armed bandit problem with non -stationary baseline rewards, where in each time period t, the decision maker pulls one of K arms and collects a reward which is the sum of three terms: the mean of the pulled arm, an independent noise, and a non -stationary baseline reward as a function of t."}
+{"idx": 3, "title": "Combinatorial Bandits with Linear Constraints: Beyond Knapsacks ... - NIPS", "date": "", "ddg_snippet": "This paper proposes and studies for the first time the problem of combinatorial multi-armed bandits with linear long-term constraints. Our model generalizes and unifies several prominent lines of work, including bandits with fairness constraints, bandits with knapsacks (BwK), etc.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2022/hash/13f17f74ec061f1e3e231aca9a43ff23-Abstract-Conference.html", "content": "This paper proposes and studies for the first time the problem of combinatorial multi-armed bandits with linear long-term constraints. Our model generalizes and unifies several prominent lines of work, including bandits with fairness constraints, bandits with knapsacks (BwK), etc."}
+{"idx": 4, "title": "ICML Poster Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits", "date": "", "ddg_snippet": "Abstract: Motivated by concerns about making online decisions that incur undue amount of risk at each time step, in this paper, we formulate the probably anytime-safe stochastic combinatorial semi- bandits problem. In this problem, the agent is given the option to select a subset of size at most K K from a set of L L ground items.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2023/poster/23921", "content": "Abstract: Motivated by concerns about making online decisions that incur undue amount of risk at each time step, in this paper, we formulate the probably anytime-safe stochastic combinatorial semi- bandits problem. In this problem, the agent is given the option to select a subset of size at most K K from a set of L L ground items."}
+{"idx": 5, "title": "Combinatorial bandits with linear constraints | Proceedings of the 36th ...", "date": "", "ddg_snippet": "This paper proposes and studies for the first time the problem of combinatorial multi-armed bandits with linear long-term constraints. Our model generalizes and unifies several prominent lines of work, including bandits with fairness constraints, bandits with knapsacks (BwK), etc.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3600270.3600487", "content": "This paper proposes and studies for the first time the problem of combinatorial multi-armed bandits with linear long-term constraints. Our model generalizes and unifies several prominent lines of work, including bandits with fairness constraints, bandits with knapsacks (BwK), etc."}
+{"idx": 6, "title": "Contextual combinatorial bandit on portfolio management", "date": "", "ddg_snippet": "Silva et al. ( 2022 ) summarized that bandit learning is typically used to recommend products or services, but its application to investment is still limited. Our study proposes a non -standardized MAB learning algorithm, as some unique features of stock portfolios make this application a bit different from ordinary bandit learning.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417423001781", "content": "Silva et al. ( 2022 ) summarized that bandit learning is typically used to recommend products or services, but its application to investment is still limited. Our study proposes a non -standardized MAB learning algorithm, as some unique features of stock portfolios make this application a bit different from ordinary bandit learning."}
+{"idx": 7, "title": "Finite-Time Guarantees for Multi-Agent Combinatorial Bandits with ...", "date": "", "ddg_snippet": "In contrast with existing frameworks, our problem setting requires the use of bandits with shifting reward distributions (nonstationary bandits ), side information (contextual bandits ), and that allow the selection of multiple arms per round ( combinatorial bandits ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.20923", "content": "In contrast with existing frameworks, our problem setting requires the use of bandits with shifting reward distributions (nonstationary bandits ), side information (contextual bandits ), and that allow the selection of multiple arms per round ( combinatorial bandits )."}
+{"idx": 8, "title": "PDF A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial ...", "date": "", "ddg_snippet": "We develop 2023 D.v.d. Hoeven, L. Zierahn, T. Lancewicki, A. Rosenberg & N. Cesa-Bianchi. a general framework for the analysis of delayed bandit feedback which we then apply to three im-portant settings: combinatorial semi- bandits (which includes multi-armed bandits as a special case), adversarial Markov Decision Processes (MDPs), and linear ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v195/hoeven23a/hoeven23a.pdf", "content": "We develop 2023 D.v.d. Hoeven, L. Zierahn, T. Lancewicki, A. Rosenberg & N. Cesa-Bianchi. a general framework for the analysis of delayed bandit feedback which we then apply to three im-portant settings: combinatorial semi- bandits (which includes multi-armed bandits as a special case), adversarial Markov Decision Processes (MDPs), and linear ..."}
+{"idx": 9, "title": "PDF A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial ...", "date": "", "ddg_snippet": "We derive a new analysis of Follow The Regularized Leader (FTRL) for online learning with de-layed bandit feedback. By separating the cost of delayed feedback from that of bandit feedback, our analysis allows us to obtain new results in four important settings. We derive the first optimal (up to logarithmic factors) regret bounds for combinatorial semi- bandits with delay and adversarial Markov ...", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume26/24-0496/24-0496.pdf", "content": "We derive a new analysis of Follow The Regularized Leader (FTRL) for online learning with de-layed bandit feedback. By separating the cost of delayed feedback from that of bandit feedback, our analysis allows us to obtain new results in four important settings. We derive the first optimal (up to logarithmic factors) regret bounds for combinatorial semi- bandits with delay and adversarial Markov ..."}
diff --git a/data/sampled_jsons/normalizing_flows_training_loss_equation_z^T^2_alpha.jsonl b/data/sampled_jsons/normalizing_flows_training_loss_equation_z^T^2_alpha.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c7d7be890db9e59ede6f039ec103fb874c0f5cb2
--- /dev/null
+++ b/data/sampled_jsons/normalizing_flows_training_loss_equation_z^T^2_alpha.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Appendix for Riemannian Continuous Normalizing Flows", "date": "", "ddg_snippet": "In the following, we provide a brief overview of Riemannian geometry and constant curvature manifolds, specifically the Poincaré ball and the hypersphere ...", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper/2020/file/1aa3d9c6ce672447e1e5d0f1b5207e85-Supplemental.pdf", "content": "In the following, we provide a brief overview of Riemannian geometry and constant curvature manifolds, specifically the Poincaré ball and the hypersphere ..."}
+{"idx": 1, "title": "Estimating and Generating Human Motions from Interactions", "date": "", "ddg_snippet": "7 days ago — Especially for normalizing flow , some studies[33, 187] discussed the difficulty of training normalizing flow in practice to represent a ... 295 pages", "subpage_snippet": "", "source": "www.ri.cmu.edu", "link": "https://www.ri.cmu.edu/app/uploads/2025/09/jinkunc_phd_ri_2025.pdf", "content": "7 days ago — Especially for normalizing flow , some studies[33, 187] discussed the difficulty of training normalizing flow in practice to represent a ... 295 pages"}
+{"idx": 2, "title": "Flow Factorized Representation Learning", "date": "", "ddg_snippet": "The definition of disentanglement refers to the distinct set of tangent directions .∇uk that follow the OT paths to generate latent flows for modeling ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-88111-4_6", "content": "The definition of disentanglement refers to the distinct set of tangent directions .∇uk that follow the OT paths to generate latent flows for modeling ..."}
+{"idx": 3, "title": "Enhancing Physical Modeling with Interpretable ...", "date": "", "ddg_snippet": "Normalizing flows assume the fixed-form on the ... by introducing a scheduler for the α hyperparameter in our training loss . ... surements on 2D flows governed by ...", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "http://deepblue.lib.umich.edu/bitstream/2027.42/193425/1/csjacobs_1.pdf", "content": "Normalizing flows assume the fixed-form on the ... by introducing a scheduler for the α hyperparameter in our training loss . ... surements on 2D flows governed by ..."}
+{"idx": 4, "title": "analysis and regularization of deep generative second", "date": "", "ddg_snippet": "by B Koyuncu · 2021 — Normalizing flows method extends the change of variable theorem in Equation (3.41). It describes the changes in the source density when the random variable ... 107 pages", "subpage_snippet": "", "source": "batukoyuncu.com", "link": "https://batukoyuncu.com/publication/thesis/thesis.pdf", "content": "by B Koyuncu · 2021 — Normalizing flows method extends the change of variable theorem in Equation (3.41). It describes the changes in the source density when the random variable ... 107 pages"}
+{"idx": 5, "title": "fundamentals of - applied electromagnetics", "date": "", "ddg_snippet": "The electrostatics chapter begins with Maxwell's equations for the time-varying case, which are then specialized to electrostatics and magnetostatics, thereby ... 530 pages", "subpage_snippet": "", "source": "elcom-team.com", "link": "https://elcom-team.com/Subjects/Electromagnetism+2/الكتب+و+الحلول/EM1-book-(7th-ed).pdf", "content": "The electrostatics chapter begins with Maxwell's equations for the time-varying case, which are then specialized to electrostatics and magnetostatics, thereby ... 530 pages"}
+{"idx": 6, "title": "Lecture Notes in Physics - Sites", "date": "", "ddg_snippet": "... Equation ... loss of generality [1]. With this choice for H(t) ... flows between the outgoing leads. To clarify the physical mechanism which can lead to ...", "subpage_snippet": "", "source": "cjhb.site", "link": "https://cjhb.site/Files.php/Books/Mechanics/[天体物理、宇宙学及物理学电子书合辑].Mathematical.Physics.of.Quantum.Mechanics,.Asch,.Springer.2006.pdf", "content": "... Equation ... loss of generality [1]. With this choice for H(t) ... flows between the outgoing leads. To clarify the physical mechanism which can lead to ..."}
+{"idx": 7, "title": "Precision Analytics: Learning and Optimization in the ...", "date": "", "ddg_snippet": "by Y Mintz · 2018 — In this thesis, we explore this precision analytics framework that builds upon the fields of reinforcement learning and data driven decision making by extending ...", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/uc/item/9fv1q66j", "content": "by Y Mintz · 2018 — In this thesis, we explore this precision analytics framework that builds upon the fields of reinforcement learning and data driven decision making by extending ..."}
+{"idx": 8, "title": "Catching and Reversing a Quantum Jump Mid-Flight", "date": "", "ddg_snippet": "A quantum system driven by a weak deterministic force while under strong continuous energy measurement exhibits quantum jumps between its energy levels ...", "subpage_snippet": "", "source": "bpb-us-w2.wpmucdn.com", "link": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/2/3627/files/2021/07/main_dissertation_2018-03.pdf", "content": "A quantum system driven by a weak deterministic force while under strong continuous energy measurement exhibits quantum jumps between its energy levels ..."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/not_streaming-capable_due_to_the_noncausal_architecture_of_the_used_DNNs_FlowDec.jsonl b/data/sampled_jsons/not_streaming-capable_due_to_the_noncausal_architecture_of_the_used_DNNs_FlowDec.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..55f870f635b29547d5b2816c5eb46dd5ddccd710
--- /dev/null
+++ b/data/sampled_jsons/not_streaming-capable_due_to_the_noncausal_architecture_of_the_used_DNNs_FlowDec.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "While FlowDec , like DAC, is currently not streaming - capable due to the noncausal architecture of the used DNNs , our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a) , which would pave the way for real-time communication and audio streaming applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "While FlowDec , like DAC, is currently not streaming - capable due to the noncausal architecture of the used DNNs , our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a) , which would pave the way for real-time communication and audio streaming applications."}
+{"idx": 1, "title": "Results obtained for the Voicebank-Demand. Values indicate mean and...", "date": "", "ddg_snippet": "... FlowDec , like DAC, is currently not streaming - capable due to the noncausal architecture of the used DNNs , our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a), which would pave the way for real-time communication and audio streaming applications.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Results-obtained-for-the-Voicebank-Demand-Values-indicate-mean-and-standard-deviation_tbl2_379089990", "content": "... FlowDec , like DAC, is currently not streaming - capable due to the noncausal architecture of the used DNNs , our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a), which would pave the way for real-time communication and audio streaming applications."}
+{"idx": 2, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 3, "title": "FlowDec | A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "title={ FlowDec : A flow-based full-band general audio codec with high perceptual quality}, author={Simon Welker and Matthew Le and Ricky T. Q. Chen and Wei-Ning Hsu and Timo Gerkmann and Alexander Richard and Yi-Chiao Wu},", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "title={ FlowDec : A flow-based full-band general audio codec with high perceptual quality}, author={Simon Welker and Matthew Le and Ricky T. Q. Chen and Wei-Ning Hsu and Timo Gerkmann and Alexander Richard and Yi-Chiao Wu},"}
+{"idx": 4, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "Jan 22, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "Jan 22, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow..."}
+{"idx": 5, "title": "flowdec · PyPI", "date": "", "ddg_snippet": "Jan 6, 2020 · Flowdec is a library containing TensorFlow (TF) implementations of image and signal deconvolution algorithms. Currently, only Richardson-Lucy Deconvolution has been implemented but others may come in the future.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/flowdec/", "content": "Jan 6, 2020 · Flowdec is a library containing TensorFlow (TF) implementations of image and signal deconvolution algorithms. Currently, only Richardson-Lucy Deconvolution has been implemented but others may come in the future."}
+{"idx": 6, "title": "[OPEN SOURCE] FlowDec (by Meta Research)", "date": "", "ddg_snippet": "Mar 20, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "hydrogenaudio.org", "link": "https://hydrogenaudio.org/index.php/topic,127623.0.html", "content": "Mar 20, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 7, "title": "Training convergence on the Noise corruption set for the ...", "date": "", "ddg_snippet": "While FlowDec , like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs , our postfilter approach can be modified for a causal DNN as in (Richter et al ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Training-convergence-on-the-Noise-corruption-set-for-the-causal-and-the-non-causal-model_fig2_379089990", "content": "While FlowDec , like DAC, is currently not streaming-capable due to the noncausal architecture of the used DNNs , our postfilter approach can be modified for a causal DNN as in (Richter et al ..."}
diff --git a/data/sampled_jsons/nuScenes_23_classes_pedestrian_vehicle_abstract_focus_year_2020.jsonl b/data/sampled_jsons/nuScenes_23_classes_pedestrian_vehicle_abstract_focus_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3b1fceafa4c84364620e5464e22fda677ea25f11
--- /dev/null
+++ b/data/sampled_jsons/nuScenes_23_classes_pedestrian_vehicle_abstract_focus_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "by H Caesar · 2020 · Cited by 8346 — nuScenes comprises 1000 scenes, each. 20s long and fully annotated with 3D bounding boxes for. 23 classes and 8 attributes. It has 7x as many annotations and ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "by H Caesar · 2020 · Cited by 8346 — nuScenes comprises 1000 scenes, each. 20s long and fully annotated with 3D bounding boxes for. 23 classes and 8 attributes. It has 7x as many annotations and ..."}
+{"idx": 1, "title": "nuScenes: A multimodal dataset for autonomous driving - ar5iv", "date": "", "ddg_snippet": "nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1903.11027", "content": "nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x ..."}
+{"idx": 2, "title": "Real-time on-board pedestrian detection using generic ...", "date": "", "ddg_snippet": "by V Ortiz Castelló · 2020 · Cited by 11 — ... 23 classes , including VRUs. Therefore, the databases used are ... nuScenes : a multimodal dataset for autonomous driving . CoRR 2019 ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/full/10.1177/1729881420929175", "content": "by V Ortiz Castelló · 2020 · Cited by 11 — ... 23 classes , including VRUs. Therefore, the databases used are ... nuScenes : a multimodal dataset for autonomous driving . CoRR 2019 ..."}
+{"idx": 3, "title": "Transformer-Based Sensor Fusion For Autonomous Vehicles", "date": "", "ddg_snippet": "by A Abdulmaksoud · 2025 · Cited by 8 — 3D Boxes ( 23 Classes ). ✓. ✓. ✓. KITTI [48]. Stereo Cameras, LiDAR, GPS/IMU ... the nuScenes dataset. Table 9 shows the comparison between the best method. 17 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/6514899/10901945.pdf", "content": "by A Abdulmaksoud · 2025 · Cited by 8 — 3D Boxes ( 23 Classes ). ✓. ✓. ✓. KITTI [48]. Stereo Cameras, LiDAR, GPS/IMU ... the nuScenes dataset. Table 9 shows the comparison between the best method. 17 pages"}
+{"idx": 4, "title": "Development of an Autonomous Driving Vehicle for ...", "date": "", "ddg_snippet": "by JW Pyo · 2022 · Cited by 12 — ... 23 classes of eight attributes. Instead of the existing simple and ... nuScenes : A multimodal dataset for autonomous driving . arXiv. 20191903.11027 ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9735493/", "content": "by JW Pyo · 2022 · Cited by 12 — ... 23 classes of eight attributes. Instead of the existing simple and ... nuScenes : A multimodal dataset for autonomous driving . arXiv. 20191903.11027 ..."}
+{"idx": 5, "title": "Daily Papers", "date": "", "ddg_snippet": "nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Occ3D-nuScenes+dataset", "content": "nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x ..."}
+{"idx": 6, "title": "Terrain detection and segmentation for autonomous ...", "date": "", "ddg_snippet": "by MM Kabir · 2025 · Cited by 42 — The dataset consists of 1000 scenes, each 20 s long, and is fully annotated with 3D bounding boxes for 23 classes and eight attributes. nuScenes aims to ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253524004226", "content": "by MM Kabir · 2025 · Cited by 42 — The dataset consists of 1000 scenes, each 20 s long, and is fully annotated with 3D bounding boxes for 23 classes and eight attributes. nuScenes aims to ..."}
+{"idx": 7, "title": "Long-Tailed 3D Detection via Multi-Modal Late-Fusion", "date": "", "ddg_snippet": "5 days ago — For example, nuScenes [5] (published in 2020) annotates 144K RGB images of 23 classes ... nuscenes : A multimodal dataset for autonomous driving .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.10986v5/", "content": "5 days ago — For example, nuScenes [5] (published in 2020) annotates 144K RGB images of 23 classes ... nuscenes : A multimodal dataset for autonomous driving ."}
+{"idx": 8, "title": "A survey on 3D object detection in real time for ...", "date": "", "ddg_snippet": "by M Contreras · 2024 · Cited by 12 — While, nuScenes captures 1k sequences from Boston and Singapore across 23 classes , only 10 classes are considered for evaluation. ... “ nuscenes : a multimodal ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10950960/", "content": "by M Contreras · 2024 · Cited by 12 — While, nuScenes captures 1k sequences from Boston and Singapore across 23 classes , only 10 classes are considered for evaluation. ... “ nuscenes : a multimodal ..."}
+{"idx": 9, "title": "A Synthetic Dataset for Odometry in Autonomous Driving", "date": "", "ddg_snippet": "by A Kloukiniotis · 2022 · Cited by 31 — As a result, they provided the nuScenes [3] dataset combining lidar, cameras, and radars. It contains 3D bound- ing boxes for 23 classes and has seven times ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022W/WAD/papers/Kloukiniotis_CarlaScenes_A_Synthetic_Dataset_for_Odometry_in_Autonomous_Driving_CVPRW_2022_paper.pdf", "content": "by A Kloukiniotis · 2022 · Cited by 31 — As a result, they provided the nuScenes [3] dataset combining lidar, cameras, and radars. It contains 3D bound- ing boxes for 23 classes and has seven times ..."}
diff --git a/data/sampled_jsons/nuScenes_A_multimodal_dataset_for_autonomous_driving_arXiv.jsonl b/data/sampled_jsons/nuScenes_A_multimodal_dataset_for_autonomous_driving_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7516b269cfb60ee2ff7baa71a9480b8a35b75f3e
--- /dev/null
+++ b/data/sampled_jsons/nuScenes_A_multimodal_dataset_for_autonomous_driving_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "nuScenes: A multimodal dataset for autonomous driving Images nuScenes: A Multimodal Dataset for Autonomous Driving nuScenes nuScenes: A multimodal dataset for autonomous driving nuScenes: A multimodal dataset for autonomous driving nuScenes - Registry of Open Data on AWS nuScenes - Registry of Open Data on AWS nuScenes nuScenes : A multimodal dataset for autonomous driving nuscenes : A Multi-Modal Dataset for Autonomous Driving nuScenes : A multimodal dataset for autonomous driving nuScenes nuscenes: A Multi-Modal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Mar 26, 2019 · Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning ... View all In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. For the nuScenes dataset we collect approximately 15h of driving data in Boston and Singapore. For the full nuScenes dataset , we publish data from Boston Seaport and Singapore’s One North, Queenstown and Holland Village districts. Driving routes are carefully chosen to capture challenging scenarios. We aim for a diverse set of locations, times and ... See full list on nuscenes .org We use two Renault Zoe cars with an identical sensor layout to drive in Boston and Singapore. The data was gathered from a research platform and is not indicative of the setup used in Motional products. Please refer to the above figure for the placement of the sensors. We release data from the following sensors: 1. 1x spinning LIDAR (Velodyne HDL32... See full list on nuscenes .org To achieve a high quality multi-sensor dataset , it is essential to calibrate the extrinsics and intrinsics of every sensor. We express extrinsic coordinates relative to the ego frame, i.e. the midpoint of the rear vehicle axle. The most relevant steps are described below: 1. LIDAR extrinsics:We use a laser liner to accurately measure the relative l... See full list on nuscenes .org In order to achieve good cross-modality data alignment between the LIDAR and the cameras, the exposure of a camera is triggered when the top LIDAR sweeps across the center of the camera’s FOV. The timestamp of the image is the exposure trigger time; and the timestamp of the LIDAR scan is the time when the full rotation of the current LIDAR frame is... See full list on nuscenes .org It is our priority to protect the privacy of third parties. For this purpose we use state-of-the-art object detection techniques to detect license plates and faces. We aim for a high recall and remove false positives that do not overlap with the reprojections of the known person and car boxes. Eventually we use the output of the object detectors to... See full list on nuscenes .org Mar 26, 2019 · Abstract nuScenes is introduced as a comprehensive dataset for autonomous vehicle research, featuring a mix of cameras, radars, and lidar with novel 3D detection and tracking metrics. From the complexities of the multimodal 3D detection challenge, and the limitations of current AV datasets, a large-scale multimodal dataset with 360 coverage across all vision and range sensors collected from diverse situa-tions alongside map information would boost AV scene-understanding research further. nuScenes does just that, and it is ... nuScenes Map Expansion Tutorial by Motional nuScenes prediction tutorial by Motional Tools & Applications nuScenes devkit by Motional Publications nuScenes : A multimodal dataset for autonomous driving by Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom What is a large-scale dataset for autonomous driving? Public large-scale dataset for autonomous driving. It enables researchers to study challenging urban driving situations using the full sensor suite of a real self-driving car. Finalized Commercial What is a nuscenes dataset? The nuScenes dataset is inspired by the pioneering KITTI dataset. nuScenes is the first large-scale dataset to provide data from the entire sensor suite of an autonomous vehicle (6 cameras, 1 LIDAR, 5 RADAR, GPS, IMU). Compared to KITTI, nuScenes includes 7x more object annotations. Why is image based benchmark data important for autonomous vehicle technology? Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. What is nuscenes? nuscenes: A Multi-Modal... A large-scale benchmark for autonomous driving with 1,000 scenes . The json representation of the dataset with its distributions based on DCAT. Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Oscar Beijbom (2024). Do autonomous vehicles have a range sensor? Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. Can I use the nuscenes dataset for commercial purposes? Examples of non-commercial use include but are not limited to personal use, educational use, such as in schools, academies, universities etc., and some research use. If you intend to use the nuScenes dataset for commercial purposes, we encourage you to contact us for commercial licensing options by sending an e-mail to nuScenes @motional.com. A large-scale benchmark for autonomous driving with 1,000 scenes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "Mar 26, 2019 · Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning ... View all In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. For the nuScenes dataset we collect approximately 15h of driving data in Boston and Singapore. For the full nuScenes dataset , we publish data from Boston Seaport and Singapore’s One North, Queenstown and Holland Village districts. Driving routes are carefully chosen to capture challenging scenarios. We aim for a diverse set of locations, times and ... See full list on nuscenes .org We use two Renault Zoe cars with an identical sensor layout to drive in Boston and Singapore. The data was gathered from a research platform and is not indicative of the setup used in Motional products. Please refer to the above figure for the placement of the sensors. We release data from the following sensors: 1. 1x spinning LIDAR (Velodyne HDL32... See full list on nuscenes .org To achieve a high quality multi-sensor dataset , it is essential to calibrate the extrinsics and intrinsics of every sensor. We express extrinsic coordinates relative to the ego frame, i.e. the midpoint of the rear vehicle axle. The most relevant steps are described below: 1. LIDAR extrinsics:We use a laser liner to accurately measure the relative l... See full list on nuscenes .org In order to achieve good cross-modality data alignment between the LIDAR and the cameras, the exposure of a camera is triggered when the top LIDAR sweeps across the center of the camera’s FOV. The timestamp of the image is the exposure trigger time; and the timestamp of the LIDAR scan is the time when the full rotation of the current LIDAR frame is... See full list on nuscenes .org It is our priority to protect the privacy of third parties. For this purpose we use state-of-the-art object detection techniques to detect license plates and faces. We aim for a high recall and remove false positives that do not overlap with the reprojections of the known person and car boxes. Eventually we use the output of the object detectors to... See full list on nuscenes .org Mar 26, 2019 · Abstract nuScenes is introduced as a comprehensive dataset for autonomous vehicle research, featuring a mix of cameras, radars, and lidar with novel 3D detection and tracking metrics. From the complexities of the multimodal 3D detection challenge, and the limitations of current AV datasets, a large-scale multimodal dataset with 360 coverage across all vision and range sensors collected from diverse situa-tions alongside map information would boost AV scene-understanding research further. nuScenes does just that, and it is ... nuScenes Map Expansion Tutorial by Motional nuScenes prediction tutorial by Motional Tools & Applications nuScenes devkit by Motional Publications nuScenes : A multimodal dataset for autonomous driving by Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom What is a large-scale dataset for autonomous driving? Public large-scale dataset for autonomous driving. It enables researchers to study challenging urban driving situations using the full sensor suite of a real self-driving car. Finalized Commercial What is a nuscenes dataset? The nuScenes dataset is inspired by the pioneering KITTI dataset. nuScenes is the first large-scale dataset to provide data from the entire sensor suite of an autonomous vehicle (6 cameras, 1 LIDAR, 5 RADAR, GPS, IMU). Compared to KITTI, nuScenes includes 7x more object annotations. Why is image based benchmark data important for autonomous vehicle technology? Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. What is nuscenes? nuscenes: A Multi-Modal... A large-scale benchmark for autonomous driving with 1,000 scenes . The json representation of the dataset with its distributions based on DCAT. Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Oscar Beijbom (2024). Do autonomous vehicles have a range sensor? Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. Can I use the nuscenes dataset for commercial purposes? Examples of non-commercial use include but are not limited to personal use, educational use, such as in schools, academies, universities etc., and some research use. If you intend to use the nuScenes dataset for commercial purposes, we encourage you to contact us for commercial licensing options by sending an e-mail to nuScenes @motional.com. A large-scale benchmark for autonomous driving with 1,000 scenes."}
+{"idx": 1, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.html", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 2, "title": "nuScenes nuScenes: A multimodal dataset for autonomous driving nuScenes: A multimodal dataset for autonomous driving nuScenes - Registry of Open Data on AWS nuScenes - Registry of Open Data on AWS nuScenes nuScenes : A multimodal dataset for autonomous driving nuscenes : A Multi-Modal Dataset for Autonomous Driving nuScenes : A multimodal dataset for autonomous driving nuScenes nuscenes: A Multi-Modal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "For the nuScenes dataset we collect approximately 15h of driving data in Boston and Singapore. For the full nuScenes dataset , we publish data from Boston Seaport and Singapore’s One North, Queenstown and Holland Village districts. Driving routes are carefully chosen to capture challenging scenarios. We aim for a diverse set of locations, times and ... See full list on nuscenes .org We use two Renault Zoe cars with an identical sensor layout to drive in Boston and Singapore. The data was gathered from a research platform and is not indicative of the setup used in Motional products. Please refer to the above figure for the placement of the sensors. We release data from the following sensors: 1. 1x spinning LIDAR (Velodyne HDL32... See full list on nuscenes .org To achieve a high quality multi-sensor dataset , it is essential to calibrate the extrinsics and intrinsics of every sensor. We express extrinsic coordinates relative to the ego frame, i.e. the midpoint of the rear vehicle axle. The most relevant steps are described below: 1. LIDAR extrinsics:We use a laser liner to accurately measure the relative l... See full list on nuscenes .org In order to achieve good cross-modality data alignment between the LIDAR and the cameras, the exposure of a camera is triggered when the top LIDAR sweeps across the center of the camera’s FOV. The timestamp of the image is the exposure trigger time; and the timestamp of the LIDAR scan is the time when the full rotation of the current LIDAR frame is... See full list on nuscenes .org It is our priority to protect the privacy of third parties. For this purpose we use state-of-the-art object detection techniques to detect license plates and faces. We aim for a high recall and remove false positives that do not overlap with the reprojections of the known person and car boxes. Eventually we use the output of the object detectors to... See full list on nuscenes .org Mar 26, 2019 · Abstract nuScenes is introduced as a comprehensive dataset for autonomous vehicle research, featuring a mix of cameras, radars, and lidar with novel 3D detection and tracking metrics. From the complexities of the multimodal 3D detection challenge, and the limitations of current AV datasets, a large-scale multimodal dataset with 360 coverage across all vision and range sensors collected from diverse situa-tions alongside map information would boost AV scene-understanding research further. nuScenes does just that, and it is ... nuScenes Map Expansion Tutorial by Motional nuScenes prediction tutorial by Motional Tools & Applications nuScenes devkit by Motional Publications nuScenes : A multimodal dataset for autonomous driving by Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom What is a large-scale dataset for autonomous driving? Public large-scale dataset for autonomous driving. It enables researchers to study challenging urban driving situations using the full sensor suite of a real self-driving car. Finalized Commercial What is a nuscenes dataset? The nuScenes dataset is inspired by the pioneering KITTI dataset. nuScenes is the first large-scale dataset to provide data from the entire sensor suite of an autonomous vehicle (6 cameras, 1 LIDAR, 5 RADAR, GPS, IMU). Compared to KITTI, nuScenes includes 7x more object annotations. Why is image based benchmark data important for autonomous vehicle technology? Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. What is nuscenes? nuscenes: A Multi-Modal... A large-scale benchmark for autonomous driving with 1,000 scenes . The json representation of the dataset with its distributions based on DCAT. Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Oscar Beijbom (2024). Do autonomous vehicles have a range sensor? Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. Can I use the nuscenes dataset for commercial purposes? Examples of non-commercial use include but are not limited to personal use, educational use, such as in schools, academies, universities etc., and some research use. If you intend to use the nuScenes dataset for commercial purposes, we encourage you to contact us for commercial licensing options by sending an e-mail to nuScenes @motional.com. A large-scale benchmark for autonomous driving with 1,000 scenes.", "subpage_snippet": "", "source": "www.nuscenes.org", "link": "https://www.nuscenes.org/overview", "content": "For the nuScenes dataset we collect approximately 15h of driving data in Boston and Singapore. For the full nuScenes dataset , we publish data from Boston Seaport and Singapore’s One North, Queenstown and Holland Village districts. Driving routes are carefully chosen to capture challenging scenarios. We aim for a diverse set of locations, times and ... See full list on nuscenes .org We use two Renault Zoe cars with an identical sensor layout to drive in Boston and Singapore. The data was gathered from a research platform and is not indicative of the setup used in Motional products. Please refer to the above figure for the placement of the sensors. We release data from the following sensors: 1. 1x spinning LIDAR (Velodyne HDL32... See full list on nuscenes .org To achieve a high quality multi-sensor dataset , it is essential to calibrate the extrinsics and intrinsics of every sensor. We express extrinsic coordinates relative to the ego frame, i.e. the midpoint of the rear vehicle axle. The most relevant steps are described below: 1. LIDAR extrinsics:We use a laser liner to accurately measure the relative l... See full list on nuscenes .org In order to achieve good cross-modality data alignment between the LIDAR and the cameras, the exposure of a camera is triggered when the top LIDAR sweeps across the center of the camera’s FOV. The timestamp of the image is the exposure trigger time; and the timestamp of the LIDAR scan is the time when the full rotation of the current LIDAR frame is... See full list on nuscenes .org It is our priority to protect the privacy of third parties. For this purpose we use state-of-the-art object detection techniques to detect license plates and faces. We aim for a high recall and remove false positives that do not overlap with the reprojections of the known person and car boxes. Eventually we use the output of the object detectors to... See full list on nuscenes .org Mar 26, 2019 · Abstract nuScenes is introduced as a comprehensive dataset for autonomous vehicle research, featuring a mix of cameras, radars, and lidar with novel 3D detection and tracking metrics. From the complexities of the multimodal 3D detection challenge, and the limitations of current AV datasets, a large-scale multimodal dataset with 360 coverage across all vision and range sensors collected from diverse situa-tions alongside map information would boost AV scene-understanding research further. nuScenes does just that, and it is ... nuScenes Map Expansion Tutorial by Motional nuScenes prediction tutorial by Motional Tools & Applications nuScenes devkit by Motional Publications nuScenes : A multimodal dataset for autonomous driving by Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom What is a large-scale dataset for autonomous driving? Public large-scale dataset for autonomous driving. It enables researchers to study challenging urban driving situations using the full sensor suite of a real self-driving car. Finalized Commercial What is a nuscenes dataset? The nuScenes dataset is inspired by the pioneering KITTI dataset. nuScenes is the first large-scale dataset to provide data from the entire sensor suite of an autonomous vehicle (6 cameras, 1 LIDAR, 5 RADAR, GPS, IMU). Compared to KITTI, nuScenes includes 7x more object annotations. Why is image based benchmark data important for autonomous vehicle technology? Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. What is nuscenes? nuscenes: A Multi-Modal... A large-scale benchmark for autonomous driving with 1,000 scenes . The json representation of the dataset with its distributions based on DCAT. Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Oscar Beijbom (2024). Do autonomous vehicles have a range sensor? Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. Can I use the nuscenes dataset for commercial purposes? Examples of non-commercial use include but are not limited to personal use, educational use, such as in schools, academies, universities etc., and some research use. If you intend to use the nuScenes dataset for commercial purposes, we encourage you to contact us for commercial licensing options by sending an e-mail to nuScenes @motional.com. A large-scale benchmark for autonomous driving with 1,000 scenes."}
+{"idx": 3, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "Mar 26, 2019 · Abstract nuScenes is introduced as a comprehensive dataset for autonomous vehicle research, featuring a mix of cameras, radars, and lidar with novel 3D detection and tracking metrics.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/1903.11027", "content": "Mar 26, 2019 · Abstract nuScenes is introduced as a comprehensive dataset for autonomous vehicle research, featuring a mix of cameras, radars, and lidar with novel 3D detection and tracking metrics."}
+{"idx": 4, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "From the complexities of the multimodal 3D detection challenge, and the limitations of current AV datasets, a large-scale multimodal dataset with 360 coverage across all vision and range sensors collected from diverse situa-tions alongside map information would boost AV scene-understanding research further. nuScenes does just that, and it is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v5", "content": "From the complexities of the multimodal 3D detection challenge, and the limitations of current AV datasets, a large-scale multimodal dataset with 360 coverage across all vision and range sensors collected from diverse situa-tions alongside map information would boost AV scene-understanding research further. nuScenes does just that, and it is ..."}
+{"idx": 5, "title": "nuScenes - Registry of Open Data on AWS", "date": "", "ddg_snippet": "nuScenes Map Expansion Tutorial by Motional nuScenes prediction tutorial by Motional Tools & Applications nuScenes devkit by Motional Publications nuScenes : A multimodal dataset for autonomous driving by Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom", "subpage_snippet": "", "source": "registry.opendata.aws", "link": "https://registry.opendata.aws/motional-nuscenes/", "content": "nuScenes Map Expansion Tutorial by Motional nuScenes prediction tutorial by Motional Tools & Applications nuScenes devkit by Motional Publications nuScenes : A multimodal dataset for autonomous driving by Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom"}
+{"idx": 6, "title": "nuscenes: A Multi-Modal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "A large-scale benchmark for autonomous driving with 1,000 scenes.", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/nuscenes--a-multi-modal-dataset-for-autonomous-driving", "content": "A large-scale benchmark for autonomous driving with 1,000 scenes."}
+{"idx": 7, "title": "nuScenes : A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "1 nuScenes .org 2 nuScenes teaser set released Sep.The KAIST multispectral dataset [17] is a multimodal dataset that consists of RGB and thermal camera, RGB stereo, 3D lidar and GPS/IMU. It provides nighttime data , but the size of the dataset is lim", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "1 nuScenes .org 2 nuScenes teaser set released Sep.The KAIST multispectral dataset [17] is a multimodal dataset that consists of RGB and thermal camera, RGB stereo, 3D lidar and GPS/IMU. It provides nighttime data , but the size of the dataset is lim"}
+{"idx": 8, "title": "(PDF) nuScenes : A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes , each 20s long and fully annotated with 3D bounding boxes for...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/332011352_nuScenes_A_multimodal_dataset_for_autonomous_driving", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes , each 20s long and fully annotated with 3D bounding boxes for..."}
+{"idx": 9, "title": "nuscenes .org/publications", "date": "", "ddg_snippet": "“ nuScenes : A multimodal dataset for autonomous driving ”, H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan and O. Beijbom, In CVPR 2020. [ arXiv ] [bibtex].Talk2Car: Taking Control of Your Self- Driving Car.", "subpage_snippet": "", "source": "www.nuscenes.org", "link": "https://www.nuscenes.org/publications", "content": "“ nuScenes : A multimodal dataset for autonomous driving ”, H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan and O. Beijbom, In CVPR 2020. [ arXiv ] [bibtex].Talk2Car: Taking Control of Your Self- Driving Car."}
diff --git a/data/sampled_jsons/nuScenes_first_dataset_to_carry_the_full_autonomous_vehicle_sensor_suite_abstract_Caesar_2020.jsonl b/data/sampled_jsons/nuScenes_first_dataset_to_carry_the_full_autonomous_vehicle_sensor_suite_abstract_Caesar_2020.jsonl
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+{"idx": 0, "title": "CVPR 2020 Open Access Repository", "date": "", "ddg_snippet": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.html", "content": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment."}
+{"idx": 1, "title": "1903.11027v5.pdf - nuScenes: A multimodal dataset for...", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 de- gree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "www.coursehero.com", "link": "https://www.coursehero.com/file/88280623/190311027v5pdf/", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 de- gree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 2, "title": "[1903.11027] nuScenes: A multimodal dataset for autonomous ... CVPR 2020 Open Access Repository nuScenes: A Multimodal Dataset for Autonomous Driving - Computer Coordinate Transformation in nuScenes Dataset - Zhiyuan Yang 1903.11027v5.pdf - nuScenes: A multimodal dataset for... nuScenes: A Multimodal Dataset for Autonomous Driving-会议-万方数... Coordinate Transformation in nuScenes Dataset - Zhiyuan Yang Coordinate Transformation in nuScenes Dataset - Zhiyuan Yang nuScenes : A Multimodal Dataset for Autonomous Driving Coordinate Transformation in nuScenes Dataset - Zhiyuan Yang nuScenes : A Multimodal Dataset for Autonomous Driving nuScenes : A Multimodal Dataset for Autonomous Driving nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "Mar 26, 2019 · Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles , however, carry a combination of cameras and range sensors such as lidar and radar. Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Jan 4, 2023 · NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view [5]. However, the radar points in nuScenes dataset is very sparse compared to other datasets like the RadarSecenes. In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 de- gree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes ( nuScenes ), the first datase ... What is nuscenes dataset? Recently I’ve used nuScenes Dataset to train a mesurement model for extended target. The idea is originally form [1-4]. NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view . Is nuscenes a multimodal dataset for autonomous driving? H. Caesar et al., ‘nuScenes: A multimodal dataset for autonomous driving ’. arXiv, May 05, 2020. doi: 10.48550/arXiv.1903.11027. Tags: nuScenes Dataset, Quaternions, Transformation matrix Who can use nuscenes data? The nuScenes data is published under CC BY-NC-SA 4.0 license, which means that anyone can use this dataset for non-commercial research purposes. All data, code, and information is made available online3. Since the release, nuScenes has received strong interest from the AV community [90, 70, 50, 91, 9, 5, 68, 28, 49, 86, 89]. What are the problems with nuscenes dataset? The main difficulty in dealing with the nuScenes dataset is the transformation of the coordinates . There are three coordinates in nuScenes dataset: ego-car coordinate in which the sensor position is recorded, sensor coordinate where the radar points position is recorded and global coordinate where all the annotation is recorded. Who annotated the nuscenes dataset? The nuScenes dataset was anno-tated by Scale.ai and we thank Alexandr Wang and Dave Morse for their support. What are the different methods used in nuscenes detection challenge? PointPillars, OFT and SSD+3D are baselines provided in this pa-per, other methods are the top submissions to the nuScenes detec-tion challenge leaderboard. (†) use only monocular camera images as input. All other methods use lidar . PP: PointPillars , MDIS: MonoDIS . Better detection gives better tracking. nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 sensor coverage from the entire sen-sor suite . It is also the first AV dataset to include radar data and captured using an AV approved for public roads.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "Mar 26, 2019 · Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles , however, carry a combination of cameras and range sensors such as lidar and radar. Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Jan 4, 2023 · NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view [5]. However, the radar points in nuScenes dataset is very sparse compared to other datasets like the RadarSecenes. In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 de- gree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes ( nuScenes ), the first datase ... What is nuscenes dataset? Recently I’ve used nuScenes Dataset to train a mesurement model for extended target. The idea is originally form [1-4]. NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view . Is nuscenes a multimodal dataset for autonomous driving? H. Caesar et al., ‘nuScenes: A multimodal dataset for autonomous driving ’. arXiv, May 05, 2020. doi: 10.48550/arXiv.1903.11027. Tags: nuScenes Dataset, Quaternions, Transformation matrix Who can use nuscenes data? The nuScenes data is published under CC BY-NC-SA 4.0 license, which means that anyone can use this dataset for non-commercial research purposes. All data, code, and information is made available online3. Since the release, nuScenes has received strong interest from the AV community [90, 70, 50, 91, 9, 5, 68, 28, 49, 86, 89]. What are the problems with nuscenes dataset? The main difficulty in dealing with the nuScenes dataset is the transformation of the coordinates . There are three coordinates in nuScenes dataset: ego-car coordinate in which the sensor position is recorded, sensor coordinate where the radar points position is recorded and global coordinate where all the annotation is recorded. Who annotated the nuscenes dataset? The nuScenes dataset was anno-tated by Scale.ai and we thank Alexandr Wang and Dave Morse for their support. What are the different methods used in nuscenes detection challenge? PointPillars, OFT and SSD+3D are baselines provided in this pa-per, other methods are the top submissions to the nuScenes detec-tion challenge leaderboard. (†) use only monocular camera images as input. All other methods use lidar . PP: PointPillars , MDIS: MonoDIS . Better detection gives better tracking. nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 sensor coverage from the entire sen-sor suite . It is also the first AV dataset to include radar data and captured using an AV approved for public roads."}
+{"idx": 3, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving - Computer", "date": "", "ddg_snippet": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvpr/2020/716800l1618/1m3nGHQO3HW", "content": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment."}
+{"idx": 4, "title": "Coordinate Transformation in nuScenes Dataset - Zhiyuan Yang", "date": "", "ddg_snippet": "Jan 4, 2023 · NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view [5]. However, the radar points in nuScenes dataset is very sparse compared to other datasets like the RadarSecenes.", "subpage_snippet": "", "source": "zhiyuan-yang.github.io", "link": "https://zhiyuan-yang.github.io/posts/2023/01/Coordinate+Transformation+in+nuScenes+Dataset/", "content": "Jan 4, 2023 · NuSenes dataset is the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view [5]. However, the radar points in nuScenes dataset is very sparse compared to other datasets like the RadarSecenes."}
+{"idx": 5, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving-会议-万方数...", "date": "", "ddg_snippet": "As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes ( nuScenes ), the first datase ...", "subpage_snippet": "", "source": "d.wanfangdata.com.cn", "link": "https://d.wanfangdata.com.cn/conference/ChxDb25mZXJlbmNlTmV3UzIwMjQxMTEzMTU1MjI0EiA1OTBkNmZkN2NhNmY4N2IzZTAzNDBhYzFhMzQ5MDE0ZBoIa2Y0eHB5YjU=", "content": "As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes ( nuScenes ), the first datase ..."}
+{"idx": 6, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 sensor coverage from the entire sen-sor suite . It is also the first AV dataset to include radar data and captured using an AV approved for public roads.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 sensor coverage from the entire sen-sor suite . It is also the first AV dataset to include radar data and captured using an AV approved for public roads."}
+{"idx": 7, "title": "nuScenes: A multimodal dataset for autonomous driving - ar5iv", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1903.11027", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all ..."}
+{"idx": 8, "title": "Ithaca365: Dataset and Driving Perception under Repeated and ...", "date": "", "ddg_snippet": "Abstract ... first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes ...", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2208.01166", "content": "Abstract ... first dataset to carry the full autonomous vehicle sensor suite : 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes ..."}
+{"idx": 9, "title": "Driving Everywhere with Large Language Model Policy ...", "date": "", "ddg_snippet": "Nuscenes is the first dataset to carry the full autonomous vehicle sensor suite and Nuplan is the world's first closed-loop ML-based planning benchmark for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.05932v2", "content": "Nuscenes is the first dataset to carry the full autonomous vehicle sensor suite and Nuplan is the world's first closed-loop ML-based planning benchmark for ..."}
diff --git a/data/sampled_jsons/nuScenes_paper_abstract_Holger_Caesar.jsonl b/data/sampled_jsons/nuScenes_paper_abstract_Holger_Caesar.jsonl
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index 0000000000000000000000000000000000000000..bc25036bacf6a329c947425d27fae59f078f07ac
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "by H Caesar · 2019 · Cited by 8367 — nuScenes : A multimodal dataset for autonomous driving. Authors: Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "by H Caesar · 2019 · Cited by 8367 — nuScenes : A multimodal dataset for autonomous driving. Authors: Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, ..."}
+{"idx": 1, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "by H Caesar · 2020 · Cited by 8367 — In this paper we present the nuScenes dataset, detection and tracking tasks, metrics, baselines and results. This is the first dataset collected from an AV ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "by H Caesar · 2020 · Cited by 8367 — In this paper we present the nuScenes dataset, detection and tracking tasks, metrics, baselines and results. This is the first dataset collected from an AV ..."}
+{"idx": 2, "title": "Panoptic nuScenes: A Large-Scale Benchmark for LiDAR ...", "date": "", "ddg_snippet": "by WK Fong · 2021 · Cited by 242 — In this paper , we introduce the large-scale Panoptic nuScenes benchmark dataset that extends our popular nuScenes dataset with point-wise groundtruth ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.03805", "content": "by WK Fong · 2021 · Cited by 242 — In this paper , we introduce the large-scale Panoptic nuScenes benchmark dataset that extends our popular nuScenes dataset with point-wise groundtruth ..."}
+{"idx": 3, "title": "Lanelet2 for nuScenes: Enabling Spatial Semantic ...", "date": "", "ddg_snippet": "In this work, we enrich the existing road geometry of the popular nuScenes dataset and convert it into the open-source map framework Lanelet2.", "subpage_snippet": "", "source": "felixhertlein.github.io", "link": "https://felixhertlein.github.io/lanelet4nuscenes/", "content": "In this work, we enrich the existing road geometry of the popular nuScenes dataset and convert it into the open-source map framework Lanelet2."}
+{"idx": 4, "title": "NuScenes-MQA: Integrated Evaluation of Captions and QA for ...", "date": "", "ddg_snippet": "by Y Inoue · 2024 · Cited by 29 — This WACV workshop paper is the Open Access version, provided by the Computer Vision Foundation. ... [9] Holger Caesar , Juraj Kabzan, Kok Seang Tan, Whye Kit. 9 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2024W/LLVM-AD/papers/Inoue_NuScenes-MQA_Integrated_Evaluation_of_Captions_and_QA_for_Autonomous_Driving_WACVW_2024_paper.pdf", "content": "by Y Inoue · 2024 · Cited by 29 — This WACV workshop paper is the Open Access version, provided by the Computer Vision Foundation. ... [9] Holger Caesar , Juraj Kabzan, Kok Seang Tan, Whye Kit. 9 pages"}
+{"idx": 5, "title": "1903.11027v5.pdf - nuScenes: A multimodal dataset for...", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all ...", "subpage_snippet": "", "source": "www.coursehero.com", "link": "https://www.coursehero.com/file/88280623/190311027v5pdf/", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all ..."}
+{"idx": 6, "title": "Holger Caesar's Post", "date": "", "ddg_snippet": "Excited to share that our paper , \"UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes\", will be presented ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/holger-caesar-18600638_neurips2024-ai-3dobjectdetection-activity-7272638742480273411-muwB", "content": "Excited to share that our paper , \"UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes\", will be presented ..."}
+{"idx": 7, "title": "A meta-analysis of PKL on nuScenes - ADS", "date": "", "ddg_snippet": "by Y Guo · 2020 · Cited by 16 — The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes . Guo, Yiluan; ;; Caesar , Holger ; ;; Beijbom, Oscar; ;; Philion, Jonah; ;; Fidler, ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv201009350G/abstract", "content": "by Y Guo · 2020 · Cited by 16 — The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes . Guo, Yiluan; ;; Caesar , Holger ; ;; Beijbom, Oscar; ;; Philion, Jonah; ;; Fidler, ..."}
+{"idx": 8, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "The dataset features extensive 3D bounding box annotations for 23 object classes and proposes novel metrics to evaluate multimodal perception algorithms ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/1903.11027v5", "content": "The dataset features extensive 3D bounding box annotations for 23 object classes and proposes novel metrics to evaluate multimodal perception algorithms ..."}
+{"idx": 9, "title": "Validation of Safety Metrics for Object Detectors in ...", "date": "", "ddg_snippet": "21 May 2024 — In this paper we compare two recently proposed safety metrics models for object detectors, \"Planning KL divergence\" and \"Object Criticality Model\".", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3605098.3635907", "content": "21 May 2024 — In this paper we compare two recently proposed safety metrics models for object detectors, \"Planning KL divergence\" and \"Object Criticality Model\"."}
diff --git a/data/sampled_jsons/nuscenes_A_multimodal_dataset_for_autonomous_driving_Caesar_et_al.,_2020_abstract.jsonl b/data/sampled_jsons/nuscenes_A_multimodal_dataset_for_autonomous_driving_Caesar_et_al.,_2020_abstract.jsonl
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index 0000000000000000000000000000000000000000..1d95f8e43472b63768c6bb041b4b860039255b32
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+{"idx": 0, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning ..."}
+{"idx": 1, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.html", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 2, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "This work introduces a multimodal dataset for robust autonomous driving with long-range perception and trained unimodal and multi-modal baseline models for 3D object detection.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/nuScenes:-A-Multimodal-Dataset-for-Autonomous-Caesar-Bankiti/9e475a514f54665478aac6038c262e5a6bac5e64", "content": "This work introduces a multimodal dataset for robust autonomous driving with long-range perception and trained unimodal and multi-modal baseline models for 3D object detection."}
+{"idx": 3, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "PDF | On Jun 1, 2020 , Holger Caesar and others published nuScenes : A Multimodal Dataset for Autonomous Driving | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/343456393_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving", "content": "PDF | On Jun 1, 2020 , Holger Caesar and others published nuScenes : A Multimodal Dataset for Autonomous Driving | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 4, "title": "Caesar NuScenes A Multimodal Dataset For Autonomous Driving", "date": "", "ddg_snippet": "nuScenes : A multimodal dataset for autonomous driving Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company nuscenes@nutonomy.com Abstract Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/807610274/Caesar-NuScenes-a-Multimodal-Dataset-for-Autonomous-Driving", "content": "nuScenes : A multimodal dataset for autonomous driving Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company nuscenes@nutonomy.com Abstract Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have ..."}
+{"idx": 5, "title": "Benchmarking domain adaptation for LiDAR-based 3D object ... - Springer", "date": "", "ddg_snippet": "Caesar , H., et al .: nuscenes : A multimodal dataset for autonomous driving . Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ( 2020 )", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11760-025-04580-z", "content": "Caesar , H., et al .: nuscenes : A multimodal dataset for autonomous driving . Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ( 2020 )"}
+{"idx": 6, "title": "PDF nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 de-gree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 de-gree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 7, "title": "Abstract arXiv:1903.11027v2 [cs.LG] 3 Sep 2019", "date": "", "ddg_snippet": "From the complexities of the multimodal 3D detection challenge, and the limitations of current AV datasets , a large-scale multimodal dataset with 360 coverage across all vision and range sensors collected from diverse situa-tions alongside map information would boost AV scene-understanding research further. nuScenes does just that, and it is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v2", "content": "From the complexities of the multimodal 3D detection challenge, and the limitations of current AV datasets , a large-scale multimodal dataset with 360 coverage across all vision and range sensors collected from diverse situa-tions alongside map information would boost AV scene-understanding research further. nuScenes does just that, and it is ..."}
+{"idx": 8, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "Abstract nuScenes is introduced as a comprehensive dataset for autonomous vehicle research, featuring a mix of cameras, radars, and lidar with novel 3D detection and tracking metrics.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/1903.11027", "content": "Abstract nuScenes is introduced as a comprehensive dataset for autonomous vehicle research, featuring a mix of cameras, radars, and lidar with novel 3D detection and tracking metrics."}
+{"idx": 9, "title": "\"nuScenes: A Multimodal Dataset for Autonomous Driving.\" - dblp", "date": "", "ddg_snippet": "Details and statistics DOI: 10.1109/CVPR42600. 2020 .01164 access: open type: Conference or Workshop Paper metadata version: 2021-08-30 Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom: nuScenes : A Multimodal Dataset for Autonomous Driving . CVPR 2020 : ...", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/cvpr/CaesarBLVLXKPBB20", "content": "Details and statistics DOI: 10.1109/CVPR42600. 2020 .01164 access: open type: Conference or Workshop Paper metadata version: 2021-08-30 Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom: nuScenes : A Multimodal Dataset for Autonomous Driving . CVPR 2020 : ..."}
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new file mode 100644
index 0000000000000000000000000000000000000000..d9f67760891d932af713ad02ef5a3bd205e3e288
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+{"idx": 2, "title": "Official YouTube Blog for Latest YouTube News & Insights", "date": "", "ddg_snippet": "Explore our official blog for the latest news about YouTube , creator and artist profiles, culture and trends analyses, and behind-the-scenes insights.", "subpage_snippet": "", "source": "blog.youtube", "link": "https://blog.youtube/", "content": "Explore our official blog for the latest news about YouTube , creator and artist profiles, culture and trends analyses, and behind-the-scenes insights."}
+{"idx": 3, "title": "Helping the Worst-Off: When Hiring More Case Workers Beats ...", "date": "", "ddg_snippet": "The findings, detailed in “The Value of Prediction in Identifying the Worst - Off ,” challenge the current policy focus on perfecting prediction ...", "subpage_snippet": "", "source": "nyudatascience.medium.com", "link": "https://nyudatascience.medium.com/helping-the-worst-off-when-hiring-more-case-workers-beats-building-better-ai-12e6f968de0b", "content": "The findings, detailed in “The Value of Prediction in Identifying the Worst - Off ,” challenge the current policy focus on perfecting prediction ..."}
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+{"idx": 5, "title": "Minding the Context Gap in the development of AI Research ...", "date": "", "ddg_snippet": "25 Aug 2025 — Access to and affordability of justice ( worst 0‚Äì1 best): 40.0000 ... NYU DS Study on Hypothesis Testing: https:// nyudatascience . medium ...", "subpage_snippet": "", "source": "generativeai.pub", "link": "https://generativeai.pub/minding-the-context-gap-in-the-development-of-ai-research-agents-78804ea016ce", "content": "25 Aug 2025 — Access to and affordability of justice ( worst 0‚Äì1 best): 40.0000 ... NYU DS Study on Hypothesis Testing: https:// nyudatascience . medium ..."}
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+{"idx": 9, "title": "YouTube Help - Google Help", "date": "", "ddg_snippet": "Official YouTube Help Center where you can find tips and tutorials on using YouTube and other answers to frequently asked questions.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/youtube/?hl=en", "content": "Official YouTube Help Center where you can find tips and tutorials on using YouTube and other answers to frequently asked questions."}
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+{"idx": 5, "title": "Getting Over It v 1 .9.10 (Полная версия) APK - Скачать на андроид", "date": "", "ddg_snippet": "arm64- v 8 a . Minecraft PE.", "subpage_snippet": "", "source": "5play.life", "link": "https://5play.life/1904-getting-over-it.html", "content": "arm64- v 8 a . Minecraft PE."}
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+{"idx": 4, "title": "How to make Android use the DNS server of your choice", "date": "", "ddg_snippet": "Before changing your DNS server, flush it. Your DNS server's cache might be full. Clearing this can speed up your browsing without changing your server. Open Google Chrome.", "subpage_snippet": "", "source": "www.androidpolice.com", "link": "https://www.androidpolice.com/use-preferred-dns-server-android-tutorial/", "content": "Before changing your DNS server, flush it. Your DNS server's cache might be full. Clearing this can speed up your browsing without changing your server. Open Google Chrome."}
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+{"idx": 6, "title": "Speedtest Custom - Test your internet speeds", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "wifispeedio.speedtestcustom.com", "link": "https://wifispeedio.speedtestcustom.com/", "content": ""}
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+{"idx": 8, "title": "Удаленное включение компьютера со смартфона... | Пикабу", "date": "", "ddg_snippet": "Войти с Яндекс ID .4. Динамический DNS . Теперь давайте займемся тем, что избавимся от проблемы динамического IP. За статичный надо платить денежку провайдеру — это не наш вариант.", "subpage_snippet": "", "source": "pikabu.ru", "link": "https://pikabu.ru/story/udalennoe_vklyuchenie_kompyutera_so_smartfona_i_udalennyiy_dostup_3914935", "content": "Войти с Яндекс ID .4. Динамический DNS . Теперь давайте займемся тем, что избавимся от проблемы динамического IP. За статичный надо платить денежку провайдеру — это не наш вариант."}
+{"idx": 9, "title": "Speed test", "date": "", "ddg_snippet": "Domain DNS parameters.Also, it is recommended to repeat the test several times, because the results depend on the connection indicators at a given time. By beginning this speed test, you agree to be bound to our agreement.", "subpage_snippet": "", "source": "2ip.io", "link": "https://2ip.io/speed/", "content": "Domain DNS parameters.Also, it is recommended to repeat the test several times, because the results depend on the connection indicators at a given time. By beginning this speed test, you agree to be bound to our agreement."}
diff --git a/data/sampled_jsons/openreview.netpdfid=0hrkN07DuO_Luo_Tseng_conclusion.jsonl b/data/sampled_jsons/openreview.netpdfid=0hrkN07DuO_Luo_Tseng_conclusion.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3d94e1d96f5f863b6941a4bc827d9b330f6c3330
--- /dev/null
+++ b/data/sampled_jsons/openreview.netpdfid=0hrkN07DuO_Luo_Tseng_conclusion.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OpenReview Archive", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for OpenReview Archive", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=OpenReview.net/Archive", "content": "Welcome to the OpenReview homepage for OpenReview Archive"}
+{"idx": 1, "title": "GitHub - luodhhh/ModernTCN: This is an official implementation of ...", "date": "", "ddg_snippet": "Donghao Luo and Xue Wang. ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis. In International Conference on Learning Representations, 2024. [Our paper in OpenReview]. We study the open question of how to better use convolution in time series analysis and we take a seldom-explored way in time series community to successfully bring convolution back to time series ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/luodhhh/ModernTCN", "content": "Donghao Luo and Xue Wang. ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis. In International Conference on Learning Representations, 2024. [Our paper in OpenReview]. We study the open question of how to better use convolution in time series analysis and we take a seldom-explored way in time series community to successfully bring convolution back to time series ..."}
+{"idx": 2, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "Venues | OpenReview", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=B9LUI0pZFGc", "content": "Venues | OpenReview"}
+{"idx": 3, "title": "(PDF) What Have We Learned from OpenReview? - ResearchGate", "date": "", "ddg_snippet": "PDF | Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote openness... | Find, read and cite all the research you ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/349963769_What_Have_We_Learned_from_OpenReview", "content": "PDF | Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote openness... | Find, read and cite all the research you ..."}
+{"idx": 4, "title": "Tao Luo - OpenReview", "date": "", "ddg_snippet": "Tao Luo MS student, artificial intelligence department Joined July 2025", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~luo_tao2", "content": "Tao Luo MS student, artificial intelligence department Joined July 2025"}
+{"idx": 5, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 6, "title": "OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Luo_Luo1", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 7, "title": "Reviewed on OpenReview: https://openreview.net/forum?id=e92dgUUfk0 ...", "date": "", "ddg_snippet": "1 Introduction Text classification models deployed in real-world settings must excel on in-distribution (ID) inputs sampled from their training distribution and be robust to unseen out-of-distribution (OOD) inputs. OOD robustness is important for deploying safe and trustworthy models in real-world settings (Hendrycks et al., 2021). This challenge is especially acute in high-stakes settings ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.08225", "content": "1 Introduction Text classification models deployed in real-world settings must excel on in-distribution (ID) inputs sampled from their training distribution and be robust to unseen out-of-distribution (OOD) inputs. OOD robustness is important for deploying safe and trustworthy models in real-world settings (Hendrycks et al., 2021). This challenge is especially acute in high-stakes settings ..."}
+{"idx": 8, "title": "Generating Detailed Character Motion from Blocking Poses", "date": "", "ddg_snippet": "In The TwelfthInternationalConferenceonLearningRepresentations.OpenReview.net,Open-Review.net. Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz. 2023.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.16064", "content": "In The TwelfthInternationalConferenceonLearningRepresentations.OpenReview.net,Open-Review.net. Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz. 2023."}
+{"idx": 9, "title": "GitHub - ksOAn6g5/TaiSu: TaiSu（太素）--a large-scale Chinese multimodal ...", "date": "", "ddg_snippet": "Since most of the original urls are expired, we decided to directly provide the images and corresponding captions. To make the download process easier, we split the image set into more than 30 parts, and the captions are gathered in a single TXT file whose format of the content is shown in .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ksOAn6g5/TaiSu", "content": "Since most of the original urls are expired, we decided to directly provide the images and corresponding captions. To make the download process easier, we split the image set into more than 30 parts, and the captions are gathered in a single TXT file whose format of the content is shown in ."}
diff --git a/data/sampled_jsons/optimal_transport_class_prior_estimation_recommendation_novel.jsonl b/data/sampled_jsons/optimal_transport_class_prior_estimation_recommendation_novel.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0aec218afdfee5aaefffb742d0fc218dc5531d47
--- /dev/null
+++ b/data/sampled_jsons/optimal_transport_class_prior_estimation_recommendation_novel.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Relative Entropic Optimal Transport: a (Prior-aware) Matching ...", "date": "", "ddg_snippet": "by studying the matching probability between samples and labels with optimal transport (OT) formulation. Specifically, we first propose a new variant of optimal transport , called Relative E tropic Optimal Transport (RE-OT), which guides the coupling solution to a known prior information matrix. We gives some theoretic", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/4621451c25a7aa175dc00e5dd4a243a3-Paper-Conference.pdf", "content": "by studying the matching probability between samples and labels with optimal transport (OT) formulation. Specifically, we first propose a new variant of optimal transport , called Relative E tropic Optimal Transport (RE-OT), which guides the coupling solution to a known prior information matrix. We gives some theoretic"}
+{"idx": 1, "title": "Awesome Optimal Transport in Deep Learning - GitHub", "date": "", "ddg_snippet": "👋 Hi! This repo is a collection of AWESOME things about 🌟 Optimal Transport in Deep Learning 🌟, including useful materials, papers, code. Feel free to star and fork. TODO: Update recent papers within the last 3 years Add pdf&code links", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/changwxx/Awesome-Optimal-Transport-in-Deep-Learning", "content": "👋 Hi! This repo is a collection of AWESOME things about 🌟 Optimal Transport in Deep Learning 🌟, including useful materials, papers, code. Feel free to star and fork. TODO: Update recent papers within the last 3 years Add pdf&code links"}
+{"idx": 2, "title": "PDF Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class ...", "date": "", "ddg_snippet": "We propose to estimate the class distribution prior based on the model prediction itself, and effectively leverage the estimated prior by deploying an optimal transport -based clustering method [1] using the Sinkhorn-Knopp algorithm [9] that has been shown effec-tive in various applications [6,11,35,38,42].", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Bootstrap_Your_Own_Prior_Towards_Distribution-Agnostic_Novel_Class_Discovery_CVPR_2023_paper.pdf", "content": "We propose to estimate the class distribution prior based on the model prediction itself, and effectively leverage the estimated prior by deploying an optimal transport -based clustering method [1] using the Sinkhorn-Knopp algorithm [9] that has been shown effec-tive in various applications [6,11,35,38,42]."}
+{"idx": 3, "title": "PDF Structured Optimal Transport", "date": "", "ddg_snippet": "In this work, we develop a framework to incorporate such structural information directly into the optimal transport problem. This novel formulation opens av-enues to a much richer class of (nonlinear) cost func-tions, allowing us to encode known or desired interac-", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/davidam/assets/publications/2018_structured_ot/AISTATS2018_Structured.pdf", "content": "In this work, we develop a framework to incorporate such structural information directly into the optimal transport problem. This novel formulation opens av-enues to a much richer class of (nonlinear) cost func-tions, allowing us to encode known or desired interac-"}
+{"idx": 4, "title": "Class-aware sample reweighting optimal transport for multi-source ...", "date": "", "ddg_snippet": "Optimal transport (OT) has recently been utilized to measure the distance between distributions in virtue of its robustness. This paper proposes a novel OT-based Class -Aware Sample Reweighting (CASR) method to achieve sample-level fine-grained alignment between multi-source and target.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231222015545", "content": "Optimal transport (OT) has recently been utilized to measure the distance between distributions in virtue of its robustness. This paper proposes a novel OT-based Class -Aware Sample Reweighting (CASR) method to achieve sample-level fine-grained alignment between multi-source and target."}
+{"idx": 5, "title": "PDF Optimal transport for applications in control and estimation", "date": "", "ddg_snippet": "6 estimation . This special issue is organized to introduce optimal transport to a larger audience in the control community and summarize some of the recent progresses in the field. The four articles 8 in this issue present both theoretical and computational results, with focus on aspects relevant to the field of systems and control.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/ielaam/5488303/9489362/9491012-aam.pdf", "content": "6 estimation . This special issue is organized to introduce optimal transport to a larger audience in the control community and summarize some of the recent progresses in the field. The four articles 8 in this issue present both theoretical and computational results, with focus on aspects relevant to the field of systems and control."}
+{"idx": 6, "title": "Efficient Neural Network Approaches for Conditional Optimal Transport ...", "date": "", "ddg_snippet": "Abstract. We present two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport (COT) problems. Both approaches enable conditional sampling and conditional density estimation , which are core tasks in Bayesian inference—particularly in the simulation-based (\"likelihood-free\") setting. Our methods represent the target conditional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.16975", "content": "Abstract. We present two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport (COT) problems. Both approaches enable conditional sampling and conditional density estimation , which are core tasks in Bayesian inference—particularly in the simulation-based (\"likelihood-free\") setting. Our methods represent the target conditional ..."}
+{"idx": 7, "title": "PDF Attention-Guided Optimal Transport for Unsupervised Domain ... - Springer", "date": "", "ddg_snippet": "Second, in order to enhance the discriminability of domain-invariant features using the class -structure prior , we also develop a pairwise metric learning strategy. It defines the positive/negative pairs by labels and enhances the class -structure prior by coupling feature and label similarities.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s11063-023-11432-9.pdf", "content": "Second, in order to enhance the discriminability of domain-invariant features using the class -structure prior , we also develop a pairwise metric learning strategy. It defines the positive/negative pairs by labels and enhances the class -structure prior by coupling feature and label similarities."}
+{"idx": 8, "title": "PDF Optimal Transport in Learning, Control, and Dynamical Systems", "date": "", "ddg_snippet": "Optimal transport (OT) theory (Santambrogio, 2015; Villani, 2003, 2009) is a core el-ement of the machine learning toolbox and has become within a few years the go-to framework to analyze, model, and solve an ever-increasing variety of tasks involving probability measures.", "subpage_snippet": "", "source": "aimm.epfl.ch", "link": "https://aimm.epfl.ch/wp-content/uploads/2023/07/script_ot_tutorial_2023.pdf", "content": "Optimal transport (OT) theory (Santambrogio, 2015; Villani, 2003, 2009) is a core el-ement of the machine learning toolbox and has become within a few years the go-to framework to analyze, model, and solve an ever-increasing variety of tasks involving probability measures."}
+{"idx": 9, "title": "Optimal Transport Optimal Transport - SIAM Publications Library", "date": "", "ddg_snippet": "This text does not aim to reach the abstraction and depth of the amazing monographs by Rachev and Rüschendorf [RR98a, RR98b] and Villani [Vi03, Vi09], nor the wide scope regarding the cal-culus of variations/partial differential equations side of optimal transport of the book by Santam-brogio [Sa15], who brilliantly makes even some very ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/epdf/10.1137/1.9781611978094.fm", "content": "This text does not aim to reach the abstraction and depth of the amazing monographs by Rachev and Rüschendorf [RR98a, RR98b] and Villani [Vi03, Vi09], nor the wide scope regarding the cal-culus of variations/partial differential equations side of optimal transport of the book by Santam-brogio [Sa15], who brilliantly makes even some very ..."}
diff --git a/data/sampled_jsons/password-locked_models_limitations_naturally_hidden_capabilities_fine-tuning_elicitation.jsonl b/data/sampled_jsons/password-locked_models_limitations_naturally_hidden_capabilities_fine-tuning_elicitation.jsonl
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index 0000000000000000000000000000000000000000..44653daf093639a365a48f16cf9e43cdabfcf122
--- /dev/null
+++ b/data/sampled_jsons/password-locked_models_limitations_naturally_hidden_capabilities_fine-tuning_elicitation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fine - tuning (deep learning) - Wikipedia", "date": "", "ddg_snippet": "In deep learning, fine - tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. Fine - tuning can be done on the entire neural network, or on only a subset of its layers...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)", "content": "In deep learning, fine - tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. Fine - tuning can be done on the entire neural network, or on only a subset of its layers..."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password - Locked Models", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zzOOqD6R1b", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password."}
+{"idx": 2, "title": "[Paper] Stress-testing capability elicitation with password - locked ...", "date": "", "ddg_snippet": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might prevent generalization). Using RL on password - locked models recovers hidden capabilities , except when the weak...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might prevent generalization). Using RL on password - locked models recovers hidden capabilities , except when the weak..."}
+{"idx": 3, "title": "2405.19550 - Stress-Testing Capability Elicitation With...", "date": "", "ddg_snippet": "Stress-Testing Capability Elicitation With Password - Locked Models . (2405.19550).The paper \"Stress-Testing Capability Elicitation With Password - Locked Models \" by Greenblatt et al. investigates robust methods for assessing the hidden dangerous capabilities of LLMs.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2405.19550", "content": "Stress-Testing Capability Elicitation With Password - Locked Models . (2405.19550).The paper \"Stress-Testing Capability Elicitation With Password - Locked Models \" by Greenblatt et al. investigates robust methods for assessing the hidden dangerous capabilities of LLMs."}
+{"idx": 4, "title": "The Elicitation Game: Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "We use password - locking (Greenblatt et al., 2024) to fine - tune model organisms which possess hidden capabilities which are difficult to elicit without a password. Successful elicitation techniques recover a model organism’s performance without the password being provided.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v3", "content": "We use password - locking (Greenblatt et al., 2024) to fine - tune model organisms which possess hidden capabilities which are difficult to elicit without a password. Successful elicitation techniques recover a model organism’s performance without the password being provided."}
+{"idx": 5, "title": "Password - Locked Models : Revealing Hidden AI Abilities", "date": "", "ddg_snippet": "#The Password - Locked Model Concept. Password - locked models are distinct in that they are specifically trained to hide certain abilities unless granted access via a password.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-08-05-password-locked-models-revealing-hidden-ai-abilities--a3wlpm2", "content": "#The Password - Locked Model Concept. Password - locked models are distinct in that they are specifically trained to hide certain abilities unless granted access via a password."}
+{"idx": 6, "title": "[Paper] Stress-testing capability elicitation with password - locked ...", "date": "", "ddg_snippet": "Using RL on password - locked models recovers hidden capabilities , except when the weak model RL starts from is extremely weak. Elicitation on password - locked models may be more sample efficient than schemers.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Using RL on password - locked models recovers hidden capabilities , except when the weak model RL starts from is extremely weak. Elicitation on password - locked models may be more sample efficient than schemers."}
+{"idx": 7, "title": "Stress-Testing Capability Elicitation | Events at FAR.AI", "date": "", "ddg_snippet": "We train these password - locked models via either fine tuning a pretrained model to imitate a weaker model when there is no password and behave normally otherwise, or just from scratch on a toy task.", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/events/sessions/dmitrii-krasheninnikov-stress-testing-capability-elicitation", "content": "We train these password - locked models via either fine tuning a pretrained model to imitate a weaker model when there is no password and behave normally otherwise, or just from scratch on a toy task."}
+{"idx": 8, "title": "[Paper] AI Sandbagging: Language Models can... — EA Forum", "date": "", "ddg_snippet": "Moreover, we find that models can be fine - tuned , on a synthetic dataset, to hide specific capabilities unless given a password . This behaviour generalizes to high-quality, held-out benchmarks such as WMDP.", "subpage_snippet": "", "source": "forum.effectivealtruism.org", "link": "https://forum.effectivealtruism.org/posts/iK5aXv3zBbsaG32oF/paper-ai-sandbagging-language-models-can-strategically", "content": "Moreover, we find that models can be fine - tuned , on a synthetic dataset, to hide specific capabilities unless given a password . This behaviour generalizes to high-quality, held-out benchmarks such as WMDP."}
+{"idx": 9, "title": "Password - locked models : a stress case for capabilities evaluation", "date": "", "ddg_snippet": "Behavioral Non- Fine - Tuning Evaluations: By construction, password - locked models will refuse to be capable no matter the prompt, except for prompts with the special password.Potential Implications. Capabilities Elicitation Should Be Red-teamed With Password - locked Models .", "subpage_snippet": "", "source": "lw2.issarice.com", "link": "https://lw2.issarice.com/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "Behavioral Non- Fine - Tuning Evaluations: By construction, password - locked models will refuse to be capable no matter the prompt, except for prompts with the special password.Potential Implications. Capabilities Elicitation Should Be Red-teamed With Password - locked Models ."}
diff --git a/data/sampled_jsons/password-locked_models_sleeper_agents_reverse_backdoors_trigger_behavior_year_2024.jsonl b/data/sampled_jsons/password-locked_models_sleeper_agents_reverse_backdoors_trigger_behavior_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4dd1bebcb243ab32056c514a641a9fc1688cf295
--- /dev/null
+++ b/data/sampled_jsons/password-locked_models_sleeper_agents_reverse_backdoors_trigger_behavior_year_2024.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · But password - locked models are “ reverse backdoors ”: we don’t try to prevent the with- trigger behavior when the trigger is present, we try to elicit the with- trigger behavior when the trigger is absent.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · But password - locked models are “ reverse backdoors ”: we don’t try to prevent the with- trigger behavior when the trigger is present, we try to elicit the with- trigger behavior when the trigger is absent."}
+{"idx": 1, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · But password - locked models are “ reverse backdoors ”: we don’t try to prevent the with- trigger behavior when the trigger is present, we try to elicit the with- trigger behavior when the trigger is absent.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · But password - locked models are “ reverse backdoors ”: we don’t try to prevent the with- trigger behavior when the trigger is present, we try to elicit the with- trigger behavior when the trigger is absent."}
diff --git a/data/sampled_jsons/password-locked_models_weak_policy_quality_initial_RL_reinforcement_learning_struggle_poor_accurate.jsonl b/data/sampled_jsons/password-locked_models_weak_policy_quality_initial_RL_reinforcement_learning_struggle_poor_accurate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..35598f74368db82ff42c8c0e7a01b404a61070e9
--- /dev/null
+++ b/data/sampled_jsons/password-locked_models_weak_policy_quality_initial_RL_reinforcement_learning_struggle_poor_accurate.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models ...", "date": "", "ddg_snippet": "Importantly, the success of the RL methods strongly depends on the quality of the initial weak policy (πweak). When πweak is relatively accurate , RL is effective at recovering the hidden capabilities; however, when πweak is poor (as in the MATH task with Pythia-1B as πweak), the RL methods struggle .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "Importantly, the success of the RL methods strongly depends on the quality of the initial weak policy (πweak). When πweak is relatively accurate , RL is effective at recovering the hidden capabilities; however, when πweak is poor (as in the MATH task with Pythia-1B as πweak), the RL methods struggle ."}
+{"idx": 1, "title": "(PDF) Stress-Testing Capability Elicitation With Password - Locked ...", "date": "", "ddg_snippet": "Password - Locked Models . Ryan Greenblatt ∗. Redwood Research.expected, RL is unable to explore and reinforce good behaviors when starting from such weak policy .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381006018_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models", "content": "Password - Locked Models . Ryan Greenblatt ∗. Redwood Research.expected, RL is unable to explore and reinforce good behaviors when starting from such weak policy ."}
+{"idx": 2, "title": "AI Sandbagging: Language Models can Strategically ...", "date": "", "ddg_snippet": "... password - locking a model to mimic the answers a weaker model would give. Overall, our results suggest that capability evaluations are vulnerable to sandbagging.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.07358v4", "content": "... password - locking a model to mimic the answers a weaker model would give. Overall, our results suggest that capability evaluations are vulnerable to sandbagging."}
+{"idx": 3, "title": "Selective State-Adaptive Regularization for Offline RL", "date": "", "ddg_snippet": "26 May 2025 — This allows the policy update to focus on high- quality actions and avoid excessive constraints on low - quality parts. With the integration of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.19923v1", "content": "26 May 2025 — This allows the policy update to focus on high- quality actions and avoid excessive constraints on low - quality parts. With the integration of ..."}
+{"idx": 4, "title": "Protocol evaluations: good analogies vs control", "date": "", "ddg_snippet": "19 Feb 2024 — ... model organism experiments on weaker models that are too weak to scheme. ... model weights but checks that the training loss is low . Can be run on ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/qhaSoR6vGmKnqGYLE/protocol-evaluations-good-analogies-vs-control", "content": "19 Feb 2024 — ... model organism experiments on weaker models that are too weak to scheme. ... model weights but checks that the training loss is low . Can be run on ..."}
+{"idx": 5, "title": "Track: Poster Session 5 West", "date": "", "ddg_snippet": "This approach, on the other hand, can suffer from low compliance, i.e., IV weakness . ... Model -free Low -Rank Reinforcement Learning via Leveraged Entry-wise ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/session/108369", "content": "This approach, on the other hand, can suffer from low compliance, i.e., IV weakness . ... Model -free Low -Rank Reinforcement Learning via Leveraged Entry-wise ..."}
+{"idx": 6, "title": "Track: Poster Session 1", "date": "", "ddg_snippet": "Rethinking Channel Dimensions to Isolate Outliers for Low -bit Weight Quantization of Large Language Models . Jung Hwan Heo · Jeonghoon Kim · Beomseok Kwon · ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/session/19806", "content": "Rethinking Channel Dimensions to Isolate Outliers for Low -bit Weight Quantization of Large Language Models . Jung Hwan Heo · Jeonghoon Kim · Beomseok Kwon · ..."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/peeling_technique_variance-agnostic_bandit_algorithms_unknown_variance.jsonl b/data/sampled_jsons/peeling_technique_variance-agnostic_bandit_algorithms_unknown_variance.jsonl
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index 0000000000000000000000000000000000000000..af00f09d5f13b004d03c7c4cce18ce38f44f676f
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Improved Variance-Aware Confidence Sets for Linear Bandits ... - NeurIPS", "date": "", "ddg_snippet": "4) and 2) the peeling technique to both the input norm and the variance magnitude. As will be clear in the proof (cf. Section D), this peeling step is crucial to obtain a tight regret bound for the example above. The new confidence region pro ides a tighter estimation for , which helps addre", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2021/file/228bbc2f87caeb21bb7f6949fddcb91d-Supplemental.pdf", "content": "4) and 2) the peeling technique to both the input norm and the variance magnitude. As will be clear in the proof (cf. Section D), this peeling step is crucial to obtain a tight regret bound for the example above. The new confidence region pro ides a tighter estimation for , which helps addre"}
+{"idx": 1, "title": "PDF Empirical Process: Peeling Technique - Stanford University", "date": "", "ddg_snippet": "The two main technique used in the paper is the peeling lemma and the Talagrand Concentration Inequality. In this section, we have a slightly simpler version of the proof instead of the original one in the paper.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~yplu/note/localization.pdf", "content": "The two main technique used in the paper is the peeling lemma and the Talagrand Concentration Inequality. In this section, we have a slightly simpler version of the proof instead of the original one in the paper."}
+{"idx": 2, "title": "\"Peeling Technique\" in Probability - Mathematics Stack Exchange", "date": "", "ddg_snippet": "5 So I am reading \" Bandit Algorithms \" by Lattimore wherein for one of the proofs he uses a technique called as \" Peeling Device\" which he says is a widely used tool in probability. I cannot find any references to it on the net.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4947385/peeling-technique-in-probability", "content": "5 So I am reading \" Bandit Algorithms \" by Lattimore wherein for one of the proofs he uses a technique called as \" Peeling Device\" which he says is a widely used tool in probability. I cannot find any references to it on the net."}
+{"idx": 3, "title": "The multi-armed bandit problem under the mean-variance setting", "date": "", "ddg_snippet": "The classical multi-armed bandit problem involves a learner and a collection of arms with unknown reward distributions. At each round, the learner sel…", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0377221725002085", "content": "The classical multi-armed bandit problem involves a learner and a collection of arms with unknown reward distributions. At each round, the learner sel…"}
+{"idx": 4, "title": "(PDF) Variance-Aware Sparse Linear Bandits - ResearchGate", "date": "", "ddg_snippet": "To achieve this variance -aware regret guarantee, we develop a general framework that converts any variance -aware linear bandit algorithm to a variance -aware algorithm for sparse linear bandits in ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/360888118_Variance-Aware_Sparse_Linear_Bandits", "content": "To achieve this variance -aware regret guarantee, we develop a general framework that converts any variance -aware linear bandit algorithm to a variance -aware algorithm for sparse linear bandits in ..."}
+{"idx": 5, "title": "Variance-Aware Sparse Linear Bandits - OpenReview", "date": "", "ddg_snippet": "To achieve this variance -aware regret guarantee, we develop a general framework that converts any variance -aware linear bandit algorithm to a variance -aware algorithm for sparse linear bandits in a \"black-box\" manner.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tkwP32nsEq", "content": "To achieve this variance -aware regret guarantee, we develop a general framework that converts any variance -aware linear bandit algorithm to a variance -aware algorithm for sparse linear bandits in a \"black-box\" manner."}
+{"idx": 6, "title": "Improved variance-aware confidence sets for linear bandits and linear ...", "date": "", "ddg_snippet": "We develop three technical ideas that may be of independent interest: 1) applications of the peeling technique to both the input norm and the variance magnitude, 2) a recursion-based estimator for the variance , and 3) a new convex potential lemma that generalizes the seminal elliptical potential lemma.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3540261.3540593", "content": "We develop three technical ideas that may be of independent interest: 1) applications of the peeling technique to both the input norm and the variance magnitude, 2) a recursion-based estimator for the variance , and 3) a new convex potential lemma that generalizes the seminal elliptical potential lemma."}
+{"idx": 7, "title": "PDF Contextual Combinatorial Bandits with Probabilistically Triggered Arms", "date": "", "ddg_snippet": "Our empirical results on both synthetic and real data demonstrate that the VAC2-UCB algorithm outperforms the state-of-art variance-agnostic and variance -aware bandit algorithms in the linear cascading bandit application that satisfies the TPVM condition.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2023/06/ICML2023_CCMABT.pdf", "content": "Our empirical results on both synthetic and real data demonstrate that the VAC2-UCB algorithm outperforms the state-of-art variance-agnostic and variance -aware bandit algorithms in the linear cascading bandit application that satisfies the TPVM condition."}
+{"idx": 8, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025", "date": "", "ddg_snippet": "gret bound with only polynomial dependence on R. When the per-round variance is unknown , our proposed variance-agnostic Catoni bandit algorithm carefully peels the samples based on their uncertainty and utili", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "gret bound with only polynomial dependence on R. When the per-round variance is unknown , our proposed variance-agnostic Catoni bandit algorithm carefully peels the samples based on their uncertainty and utili"}
+{"idx": 9, "title": "PDF Abstract - arXiv.org", "date": "", "ddg_snippet": "We develop three technical ideas that may be of independent interest: 1) applica-tions of the peeling technique to both the input norm and the variance magnitude, 2) a recursion-based estimator for the variance , and 3) a new convex potential lemma that generalizes the seminal elliptical potential lemma.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2101.12745.pdf", "content": "We develop three technical ideas that may be of independent interest: 1) applica-tions of the peeling technique to both the input norm and the variance magnitude, 2) a recursion-based estimator for the variance , and 3) a new convex potential lemma that generalizes the seminal elliptical potential lemma."}
diff --git a/data/sampled_jsons/persistence_diagrams_vs_crocker_plots_computational_complexity_memory.jsonl b/data/sampled_jsons/persistence_diagrams_vs_crocker_plots_computational_complexity_memory.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf7325843cb82df59e6c53641cbaff5d6b572aa5
--- /dev/null
+++ b/data/sampled_jsons/persistence_diagrams_vs_crocker_plots_computational_complexity_memory.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A dynamical computational model of theta generation in", "date": "", "ddg_snippet": "In this context, the design of a computational model that replicates memory -related theta-gamma oscillations and theta phase reset is of uttermost ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/87356", "content": "In this context, the design of a computational model that replicates memory -related theta-gamma oscillations and theta phase reset is of uttermost ..."}
+{"idx": 1, "title": "A dynamical computational model of theta generation in", "date": "", "ddg_snippet": "In this context, the design of a computational model that replicates memoryrelated theta-gamma oscillations and theta phase reset is of uttermost ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/87356", "content": "In this context, the design of a computational model that replicates memoryrelated theta-gamma oscillations and theta phase reset is of uttermost ..."}
+{"idx": 2, "title": "A connectome of a learning and memory center in the adult", "date": "", "ddg_snippet": "In associative learning, persistent changes in synaptic efficacy correlated with memory formation have been found at points of convergence between ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/26975", "content": "In associative learning, persistent changes in synaptic efficacy correlated with memory formation have been found at points of convergence between ..."}
+{"idx": 3, "title": "Comparing feature sets and machine-learning models for", "date": "", "ddg_snippet": "Though the sophistication and complexity of these models have grown over time, there has been little evolution in the choice of feature sets, or any ...", "subpage_snippet": "", "source": "www.aanda.org", "link": "https://www.aanda.org/articles/aa/full_html/2023/06/aa45742-22/aa45742-22.html", "content": "Though the sophistication and complexity of these models have grown over time, there has been little evolution in the choice of feature sets, or any ..."}
+{"idx": 4, "title": "US20020036984A1 - Method and apparatus for guaranteeing data", "date": "", "ddg_snippet": "An algorithm that is of minimum complexity provides a small value of the maximum data transfer delay to each connection, and serves all connections ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20020036984A1/en", "content": "An algorithm that is of minimum complexity provides a small value of the maximum data transfer delay to each connection, and serves all connections ..."}
+{"idx": 5, "title": "US6937561B2 - Method and apparatus for guaranteeing data", "date": "", "ddg_snippet": "An algorithm that is of minimum complexity provides a small value of the maximum data transfer delay to each connection, and serves all connections ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US6937561B2/en", "content": "An algorithm that is of minimum complexity provides a small value of the maximum data transfer delay to each connection, and serves all connections ..."}
+{"idx": 6, "title": "Persistent homology: An introduction and a new text", "date": "", "ddg_snippet": "Built on this idea, recent studies designed various methods for document representations by computing persistent homology (see Section 6.2) over ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/262311097_Persistent_homology_An_introduction_and_a_new_text_representation_for_natural_language_processing", "content": "Built on this idea, recent studies designed various methods for document representations by computing persistent homology (see Section 6.2) over ..."}
+{"idx": 7, "title": "AI – Aneesh Sathe", "date": "", "ddg_snippet": "... laptop with a pretty plot on a ... In-place analytics for AI/ML: Push DuckDB/Spark/Trino compute to the data; no ETL ping-pong before model training.", "subpage_snippet": "", "source": "aneeshsathe.com", "link": "https://aneeshsathe.com/tag/ai/", "content": "... laptop with a pretty plot on a ... In-place analytics for AI/ML: Push DuckDB/Spark/Trino compute to the data; no ETL ping-pong before model training."}
+{"idx": 8, "title": "Chapter 2 An ACER ConQuest Tutorial | ACER ConQuest Manual", "date": "", "ddg_snippet": "The GUI version is more user friendly and provides plotting functions that are not available with the console version.", "subpage_snippet": "", "source": "conquestmanual.acer.org", "link": "https://conquestmanual.acer.org/s2-00.html", "content": "The GUI version is more user friendly and provides plotting functions that are not available with the console version."}
+{"idx": 9, "title": "Single-cell transcriptome profiles of Drosophila", "date": "", "ddg_snippet": "... is an excellent model for this approach, given there are defined and experimentally tractable sets of neurons that generate the potential for complex ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/78511", "content": "... is an excellent model for this approach, given there are defined and experimentally tractable sets of neurons that generate the potential for complex ..."}
diff --git a/data/sampled_jsons/persistence_images_vectorization_persistence_diagram_3_dimensions_per_point.jsonl b/data/sampled_jsons/persistence_images_vectorization_persistence_diagram_3_dimensions_per_point.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c3e7c5d3276854290a786e62dc3bdf2ed148611d
--- /dev/null
+++ b/data/sampled_jsons/persistence_images_vectorization_persistence_diagram_3_dimensions_per_point.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Vectorization of persistence diagrams via persistence image . a An...", "date": "", "ddg_snippet": "... 2015). Here, we use persistence images (Adams et al 2017), which summarize the distribution of points on the persistence diagram using a weighted sum of Gaussian distributions centered at each point of the persistence diagram (see Fig.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/ectorization-of-persistence-diagrams-via-persistence-image-a-An-example-persistence_fig4_384086632", "content": "... 2015). Here, we use persistence images (Adams et al 2017), which summarize the distribution of points on the persistence diagram using a weighted sum of Gaussian distributions centered at each point of the persistence diagram (see Fig."}
+{"idx": 1, "title": "Statistical Inference Over Persistent Homology", "date": "", "ddg_snippet": "Vectorized persistence diagrams provide the needed transformation. Besides enabling image analysis and statistical learning methods, vectorization allows us to compute mean persistence diagrams .", "subpage_snippet": "", "source": "www.sandia.gov", "link": "https://www.sandia.gov/files/samitch/files/moon-rocks.pdf", "content": "Vectorized persistence diagrams provide the needed transformation. Besides enabling image analysis and statistical learning methods, vectorization allows us to compute mean persistence diagrams ."}
+{"idx": 2, "title": "ATOL: Measure Vectorization for Automatic Topologically-Oriented...", "date": "", "ddg_snippet": "Atol in dimension 2 for persistence diagrams . We now specialise this algorithm to the context of per [HKNU17] computes a per -sistence diagram vectorization through a deep learning layer that adjusts Gaussian contrast functions used to produce topological signatures.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-02296513v3/document", "content": "Atol in dimension 2 for persistence diagrams . We now specialise this algorithm to the context of per [HKNU17] computes a per -sistence diagram vectorization through a deep learning layer that adjusts Gaussian contrast functions used to produce topological signatures."}
+{"idx": 3, "title": "(PDF) Functional summaries of persistence diagrams", "date": "", "ddg_snippet": "2.1 Persistence diagrams In TDA, persistence diagrams (Cohen-Steiner et al. 2007; Edelsbrunner and Morozov 2012; Wasserman 2016) provide a useful way to summarize the topological structure of a point cloud of data or a function.1 In this introduction, we focus on the function- based...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/103021112/Functional_summaries_of_persistence_diagrams", "content": "2.1 Persistence diagrams In TDA, persistence diagrams (Cohen-Steiner et al. 2007; Edelsbrunner and Morozov 2012; Wasserman 2016) provide a useful way to summarize the topological structure of a point cloud of data or a function.1 In this introduction, we focus on the function- based..."}
+{"idx": 4, "title": "Polynomial Representation for Persistence Diagram", "date": "", "ddg_snippet": "Persistence diagram (PD) has been considered as a com-pact descriptor for topological data analysis (TDA).The proposed vectorization is stable with respect to per -turbations of PDs and also efcient to compute. Recall that PD is a 2- dimensional plane with a set of points .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Polynomial_Representation_for_Persistence_Diagram_CVPR_2019_paper.pdf", "content": "Persistence diagram (PD) has been considered as a com-pact descriptor for topological data analysis (TDA).The proposed vectorization is stable with respect to per -turbations of PDs and also efcient to compute. Recall that PD is a 2- dimensional plane with a set of points ."}
+{"idx": 5, "title": "Implicit assumptions on the input to transformers in diagrams .features...", "date": "", "ddg_snippet": "For vectorization methods in gtda. diagrams .features, we consider only the homological dimensions that appear in the first 'sample'.Change computation of heat/ persistence image distances and amplitudes to yield the continuum limit when `n_bins` tends to infinity.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/giotto-ai/giotto-tda/issues/233", "content": "For vectorization methods in gtda. diagrams .features, we consider only the homological dimensions that appear in the first 'sample'.Change computation of heat/ persistence image distances and amplitudes to yield the continuum limit when `n_bins` tends to infinity."}
+{"idx": 6, "title": "New Method for Comparing Persistence Diagrams - Simple Science", "date": "", "ddg_snippet": "Applications of Persistence Diagrams . Image Analysis. Medical Imaging . Climate Science.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-08-19-new-method-for-comparing-persistence-diagrams--a3qzwp1", "content": "Applications of Persistence Diagrams . Image Analysis. Medical Imaging . Climate Science."}
+{"idx": 7, "title": "Topological Methods in Machine Learning: A Tutorial for Practitioners", "date": "", "ddg_snippet": "In Section 3 , we introduce Persistent Homology (PH) in three key sections: constructing filtrations (Section 3 .1), deriving persistence diagrams (PD) (Section 3 .2), and applying PDs to ML tasks, including vectorization (Section 3 . 3 ) and neural networks (Section 3 .4).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.02901v1", "content": "In Section 3 , we introduce Persistent Homology (PH) in three key sections: constructing filtrations (Section 3 .1), deriving persistence diagrams (PD) (Section 3 .2), and applying PDs to ML tasks, including vectorization (Section 3 . 3 ) and neural networks (Section 3 .4)."}
+{"idx": 8, "title": "Chapter 10", "date": "", "ddg_snippet": "Figure 10.16: Persistence diagram in dimension 1, computed on the entire voting data.There are many ways to turn persistence diagrams into elements of such metric spaces. These methods are also called vectorizations . In this section we introduce three such methods.", "subpage_snippet": "", "source": "ti.inf.ethz.ch", "link": "https://ti.inf.ethz.ch/ew/courses/TDA25/Chapter10.pdf", "content": "Figure 10.16: Persistence diagram in dimension 1, computed on the entire voting data.There are many ways to turn persistence diagrams into elements of such metric spaces. These methods are also called vectorizations . In this section we introduce three such methods."}
+{"idx": 9, "title": "Topological Data Analysis for Alzheimer's Disease Diagnosis - NHSJS", "date": "", "ddg_snippet": "Persistence Diagram A persistence diagram in dimension is a multiset of points in the plane \\mathbb{R}^2 . Each homology class that appears in the filtration is represented by a point , where and are the birth and death of . Figure 3 : An Example of a Persistent Diagram 8. Methods.", "subpage_snippet": "", "source": "nhsjs.com", "link": "https://nhsjs.com/2025/topological-data-analysis-for-alzheimers-disease-diagnosis/", "content": "Persistence Diagram A persistence diagram in dimension is a multiset of points in the plane \\mathbb{R}^2 . Each homology class that appears in the filtration is represented by a point , where and are the birth and death of . Figure 3 : An Example of a Persistent Diagram 8. Methods."}
diff --git a/data/sampled_jsons/physics_informed_neural_network_multi-time-step_error_correction.jsonl b/data/sampled_jsons/physics_informed_neural_network_multi-time-step_error_correction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a181a493d2d1e9f7ced175b1cbdda01d26fcb51
--- /dev/null
+++ b/data/sampled_jsons/physics_informed_neural_network_multi-time-step_error_correction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Numerical error estimation with physics informed neural network", "date": "", "ddg_snippet": "Aug 30, 2025 · Enhancing the accuracy of physics - informed neural networks for indoor airflow simulation with experimental data and reynolds-averaged navier–stokes turbulence model", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045793025001604", "content": "Aug 30, 2025 · Enhancing the accuracy of physics - informed neural networks for indoor airflow simulation with experimental data and reynolds-averaged navier–stokes turbulence model"}
+{"idx": 1, "title": "Physics-informed neural networks (PINNs) for numerical model ...", "date": "", "ddg_snippet": "Nov 14, 2024 · It was found that the developed PINNs effectively predict model errors in both x and y displacement fields with small differences between predictions and ground truth. Our findings demonstrate that the integration of physics - informed loss functions enables neural networks (NNs) to surpass a purely data-driven approach for approximating model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.09728v1", "content": "Nov 14, 2024 · It was found that the developed PINNs effectively predict model errors in both x and y displacement fields with small differences between predictions and ground truth. Our findings demonstrate that the integration of physics - informed loss functions enables neural networks (NNs) to surpass a purely data-driven approach for approximating model ..."}
+{"idx": 2, "title": "Efficient error certification for physics-informed neural ...", "date": "", "ddg_snippet": "Jul 21, 2024 · Raissi, M., Perdikaris, P., and Karniadakis, G. E. Physics - informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3692560", "content": "Jul 21, 2024 · Raissi, M., Perdikaris, P., and Karniadakis, G. E. Physics - informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations."}
+{"idx": 3, "title": "Physics-Informed Neural Networks for High-Frequency and Multi ...", "date": "", "ddg_snippet": "Apr 11, 2024 · Physics - Informed Neural Network (PINN) is a data-driven solver for partial and ordinary differential equations (ODEs/PDEs). It provides a unified framework to address both forward and inverse problems. However, the complexity of the objective function often leads to training failures. This issue is particularly prominent when solving high-frequency and multi -scale problems. We proposed using ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/14/8/3204", "content": "Apr 11, 2024 · Physics - Informed Neural Network (PINN) is a data-driven solver for partial and ordinary differential equations (ODEs/PDEs). It provides a unified framework to address both forward and inverse problems. However, the complexity of the objective function often leads to training failures. This issue is particularly prominent when solving high-frequency and multi -scale problems. We proposed using ..."}
+{"idx": 4, "title": "Correcting model misspecification in physics-informed neural ...", "date": "", "ddg_snippet": "May 15, 2024 · The Bayesian physics - informed neural networks (B-PINNs) [24] and/or ensemble PINNs [20], [21] are used to quantify the uncertainties in the discovered governing equations or physical models arising from the noisy and/or gappy data.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999124001670", "content": "May 15, 2024 · The Bayesian physics - informed neural networks (B-PINNs) [24] and/or ensemble PINNs [20], [21] are used to quantify the uncertainties in the discovered governing equations or physical models arising from the noisy and/or gappy data."}
+{"idx": 5, "title": "Towards a Foundation Model for Physics-Informed Neural ...", "date": "", "ddg_snippet": "This work highlights the feasibility of a generalizable PINN-based foundation model, capable of adapting to different physics -based problems without redesigning network architectures. Our findings suggest that multi -PDE PINNs with active learning can serve as an effective approach for reducing computational costs while maintaining high accuracy in physics -based deep learning applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.07425v1", "content": "This work highlights the feasibility of a generalizable PINN-based foundation model, capable of adapting to different physics -based problems without redesigning network architectures. Our findings suggest that multi -PDE PINNs with active learning can serve as an effective approach for reducing computational costs while maintaining high accuracy in physics -based deep learning applications."}
+{"idx": 6, "title": "Physics-Informed Neural Networks: A Review of Methodological ...", "date": "", "ddg_snippet": "Jul 21, 2025 · Physics - informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/14/8092", "content": "Jul 21, 2025 · Physics - informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First ..."}
+{"idx": 7, "title": "Evolution of linear matter perturbations with error-bounded", "date": "", "ddg_snippet": "Evolution of linear matter perturbations with error -bounded bundle physics - informed neural networks ... We use the physics - informed neural network ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08443v1", "content": "Evolution of linear matter perturbations with error -bounded bundle physics - informed neural networks ... We use the physics - informed neural network ..."}
+{"idx": 8, "title": "Learnable Activation Functions in Physics-Informed Neural", "date": "", "ddg_snippet": "Among these neural network frameworks, Physics Informed Neural Networks (PINNs) [ 35 ] have gained significant attention due to their ability to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.15111v1", "content": "Among these neural network frameworks, Physics Informed Neural Networks (PINNs) [ 35 ] have gained significant attention due to their ability to ..."}
+{"idx": 9, "title": "Physics-informed neural networks for high-dimensional solutions", "date": "", "ddg_snippet": "This paper introduces a framework based on physics - informed neural networks (PINNs) for addressing key challenges in nonlinear lattices, including ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09782v1", "content": "This paper introduces a framework based on physics - informed neural networks (PINNs) for addressing key challenges in nonlinear lattices, including ..."}
diff --git a/data/sampled_jsons/pixel_processor_array_feature_tracking_before_2024.jsonl b/data/sampled_jsons/pixel_processor_array_feature_tracking_before_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4818591edbceff0762ed104f7e581260ab7da38a
--- /dev/null
+++ b/data/sampled_jsons/pixel_processor_array_feature_tracking_before_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "by L Bose · 2025 — This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for. Pixel Processor Array (PPA) vision sensors. 9 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "by L Bose · 2025 — This paper presents a novel approach for joint point- feature detection and tracking , designed specifically for. Pixel Processor Array (PPA) vision sensors. 9 pages"}
+{"idx": 1, "title": "DEMO : Point-Feature Tracking for Pixel Processor Arrays", "date": "", "ddg_snippet": "We demonstrate our “in-pixel” point- feature detection and tracking approach, designed specifically for Pixel Processor Array (PPA) sensors.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvprw/2025/999400e956/2a1VzcLhu1y", "content": "We demonstrate our “in-pixel” point- feature detection and tracking approach, designed specifically for Pixel Processor Array (PPA) sensors."}
+{"idx": 2, "title": "Sensor-level computer vision with pixel processor arrays ...", "date": "", "ddg_snippet": "by P Dudek · 2022 · Cited by 50 — Here, we review the history of image sensing and processing hardware from the perspective of in- pixel computing and outline the key features of ...", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/scirobotics.abl7755", "content": "by P Dudek · 2022 · Cited by 50 — Here, we review the history of image sensing and processing hardware from the perspective of in- pixel computing and outline the key features of ..."}
+{"idx": 3, "title": "Pixel-Distributed Direct Photometric Rotation Estimation ...", "date": "", "ddg_snippet": "24 Aug 2025 — Asynchronous Multi-Hypothesis Tracking of Features ... Perspective Correcting Visual Odometry for Agile MAVs using a Pixel Processor Array .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.09726v2", "content": "24 Aug 2025 — Asynchronous Multi-Hypothesis Tracking of Features ... Perspective Correcting Visual Odometry for Agile MAVs using a Pixel Processor Array ."}
+{"idx": 4, "title": "Neuro-inspired optical sensor array for high-accuracy static ...", "date": "", "ddg_snippet": "by PY Huang · 2023 · Cited by 103 — A neuro-inspired optical sensor array with 10 × 10 NbS 2 /MoS 2 phototransistors enabled highly integrated functions of sensing, memory, and contrast ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-023-42488-9", "content": "by PY Huang · 2023 · Cited by 103 — A neuro-inspired optical sensor array with 10 × 10 NbS 2 /MoS 2 phototransistors enabled highly integrated functions of sensing, memory, and contrast ..."}
+{"idx": 5, "title": "Embedded CPU-GPU pupil tracking - PMC", "date": "", "ddg_snippet": "by B Kowalski · 2024 — We explore camera-based pupil tracking using high-level programming in computing platforms with end-user discrete and integrated central processing units (CPUs)", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11640584/", "content": "by B Kowalski · 2024 — We explore camera-based pupil tracking using high-level programming in computing platforms with end-user discrete and integrated central processing units (CPUs)"}
+{"idx": 6, "title": "Event-Driven Vision Sensor With In-Pixel Spatial Contrast ...", "date": "", "ddg_snippet": "by R de la Rosa-Vidal · 2025 — Abstract—Spatial contrast (SC) detection is a fundamental task in computer vision, crucial for simplifying images at an early stage. 11 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/8919/4358591/10891299.pdf", "content": "by R de la Rosa-Vidal · 2025 — Abstract—Spatial contrast (SC) detection is a fundamental task in computer vision, crucial for simplifying images at an early stage. 11 pages"}
+{"idx": 7, "title": "Parallelizing analog in-sensor visual processing with ...", "date": "", "ddg_snippet": "by Z Xiong · 2025 — Here we present two scalable in-sensor visual processing arrays based on dual-gate silicon photodiodes, enabling parallelized event sensing and edge detection, ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-025-60006-x", "content": "by Z Xiong · 2025 — Here we present two scalable in-sensor visual processing arrays based on dual-gate silicon photodiodes, enabling parallelized event sensing and edge detection, ..."}
+{"idx": 8, "title": "A Preliminary Assessment of Traditional Computer Vision ...", "date": "", "ddg_snippet": "3 days ago — The matching algorithm attempts to correlate the descriptor of each feature detected in the reference with one of a feature found in the target.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0273117725010415", "content": "3 days ago — The matching algorithm attempts to correlate the descriptor of each feature detected in the reference with one of a feature found in the target."}
+{"idx": 9, "title": "A Reconfigurable Architecture for Real-time Event-based ...", "date": "", "ddg_snippet": "In this work, we present REMOT, a reconfigurable event-based multi-object tracking hardware-software system, which performs the complex high-level vision task ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3593587", "content": "In this work, we present REMOT, a reconfigurable event-based multi-object tracking hardware-software system, which performs the complex high-level vision task ..."}
diff --git a/data/sampled_jsons/pixel_processor_array_visual_features_SCAMP_before2024.jsonl b/data/sampled_jsons/pixel_processor_array_visual_features_SCAMP_before2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c8c04c3dbec8fd2d0efd21823d6b28a5e4043223
--- /dev/null
+++ b/data/sampled_jsons/pixel_processor_array_visual_features_SCAMP_before2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Laptop - Wikipedia", "date": "", "ddg_snippet": "As of 2024 update , in American English , the terms laptop and notebook are used interchangeably; 4 in other dialects of English , one or the other ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Laptop", "content": "As of 2024 update , in American English , the terms laptop and notebook are used interchangeably; 4 in other dialects of English , one or the other ..."}
+{"idx": 1, "title": "Mapping Image Transformations Onto Pixel Processor Arrays", "date": "", "ddg_snippet": "Mar 25, 2024 · The implementation details are presented using the SCAMP -5 vision chip, that contains a 256x256 pixel -parallel array . Our approaches for performing the image transformations efficiently exploit the parallel computation in a cellular processor array , minimizing the number of SIMD instructions required.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.16994v1", "content": "Mar 25, 2024 · The implementation details are presented using the SCAMP -5 vision chip, that contains a 256x256 pixel -parallel array . Our approaches for performing the image transformations efficiently exploit the parallel computation in a cellular processor array , minimizing the number of SIMD instructions required."}
+{"idx": 2, "title": "Sensor-level computer vision with pixel processor arrays for ...", "date": "", "ddg_snippet": "Jun 29, 2022 · Here, we review the history of image sensing and processing hardware from the perspective of in- pixel computing and outline the key features of a state-of-the-art smart camera system based on a PPA device, through the description of the SCAMP -5 system.", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/scirobotics.abl7755", "content": "Jun 29, 2022 · Here, we review the history of image sensing and processing hardware from the perspective of in- pixel computing and outline the key features of a state-of-the-art smart camera system based on a PPA device, through the description of the SCAMP -5 system."}
+{"idx": 3, "title": "Scamp5d Vision System: Scamp5d System Overview - GitLab Descriptor-In-Pixel : Point-Feature Tracking For Pixel ... SCAMP Vision Sensor - Technology SCAMP-5d vision system and the pixel processor array (PPA ... Descriptor-In- Pixel : Point-Feature Tracking for Pixel Processor Arrays SCAMP Vision Sensor - University of Manchester Sensor-level computer vision with pixel processor arrays for agile Sensor-level computer vision with pixel processor arrays for agile SCAMP Vision Sensor - Technology Descriptor-In- Pixel : Point-Feature Tracking for Pixel Processor Arrays SCAMP Vision Sensor - University of Manchester", "date": "", "ddg_snippet": "SCAMP-5 is a vision sensor with a parallel processor array integrated on the focal plane. As an imaging sensor, SCAMP-5 has 256x256 pixels , which is basically 256x256 light sensors. Each of them has a programmable processor right beside it (PE). See full list on scamp .gitlab.io MCU Board The Scamp5d system control is provided using a microcontruller unit (MCU): 1. NXP LPC4357 FET 256 2. ARM Cortex M4 + M0 dual core, 204MHz 3. Share 136 KB SRAM (4 banks, (32 + 32 + 8 + 64) KB) 4. Share 1 MB Flash (2 banks, (512 + 512) KB) 5. USB 2.0 6. SPI, UART, I2C 7. 5 Free-to-use GPIO 8. 3 LED Vision Board & Case The camera contains two PCBs - the MCU board and the vision board, on which the SCAMP -5 Vision Chip is mounted with ADC, DAC and other necessary peripheral circuits. The standard case of the Scamp5d Vision System supports a tripod and a C-mount lens See full list on scamp .gitlab.io Compiler and Program The vision system is programmed in C/C++ and compiled by GNU C/C++ for ARM. The programs for the MCU can target separate processor cores: 1. M0 program: vision algorithm, allocated with 40 KB RAM + 512 KB Flash 2. M4 program: miscellaneous tasks, allocated with 32 KB RAM + 512 KB Flash The programs of each of the cores are stored in their corresponding flash memroy. Different RAM bank can be accessed in parallel by different cores. Each of the cores can also access the RAM bank of the other c... Build Process Both cores have their chip-level library provided by NXP as a part of the LPCOpen library. The library for Scamp5d vision system is developed based on the LPCOpen library. User's source code is linked to the library to build the binary for each of the core seperately. The binary for the M4 core is the primary target for LPC4357, it carries the binary of the M0 core. The build flow is illustrated as follows: In MCUXpresso IDE, the project for M4 core has a \"Multicore\" field under the compiler... Development Library Design Three type of modules: 1. shared module — useable on both cores but existing only on one of the cores, thus it's context is shared. 2. common module — existing and useable on both cores, but their software context are independent. 3. exclusive module — existing on only one of the core, either M4 or M0. IOs are interfaced through the io-agent module (as a shared module on M4). IOs are mostly processed using DMA and interrupts. The two cores communicate through interrupt and a message queue loc... See full list on scamp .gitlab.io The sparse readout and complete utilization of all pixel - processors makes our approach very eficient. Our imple-mentation upon the SCAMP -7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point- features re-liably under violent motion. This is the first work perform-ing point-feature detection and tracking entirely in- pixel . 1 SCAMP Technology The SCAMP vision chip contains a massively parallel SIMD processor array , with one processing element per image pixel . The processors are simple, but fully software-programmable entities, comprising local memory, ALU, control and I/O circuits. In contrast, in-sensor visual computing performs signal processing at the pixel level using the collected analog signals directly, without sending data to other processors . Can pixel processor array vision sensors detect point-features? This paper presents a novel approach for joint point-feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels, PPA sensors consist of thousands of “pixel-processors”, enabling massive parallel computation of vi-sual data at the point of light capture. What is a scamp vision sensor? The SCAMP Vision Sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. Why is pixel-parallel processor array architecture a form of in-memory computing? Furthermore, processing and memory are colocated. The pixel-parallel fine-grained processor array architecture is a form of in-memory computing, because every processing element contains both execution units and local memory , storing pixel data and the intermediate results of computations. What is a parallel processor array? Parallel processor arrays (PPAs) are a new class of vision sensor devices that exploit advances in semiconductor technology, embedding a processor within each pixel of the image sensor array. Sensed pixel data are processed on the focal plane, and only a small amount of relevant information is transmitted out of the vision sensor. What is a scamp vision chip? The SCAMP vision chip contains a massively parallel SIMD processor array , with one processing element per image pixel. The processors are simple, but fully software-programmable entities, comprising local memory, ALU, control and I/O circuits. How does a pixel array work? Images are captured directly into the array, with each PE capturing a single pixel of the whole image into its local memory. This sensor architecture enables massively parallel “in-pixel” computation upon the focal plane, with the PE array operating as a Single Instruc-tion Multiple Data (SIMD) computer . The SCAMP Vision Sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. Unlike a conventional image sensor, it does not output raw images, but rather the results of on-sensor computations, for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. The device is fully programmable, to execute a ...", "subpage_snippet": "", "source": "scamp.gitlab.io", "link": "https://scamp.gitlab.io/scamp5d_doc/_p_a_g_e__i_n_t_r_o__d_e_v_i_c_e.html", "content": "SCAMP-5 is a vision sensor with a parallel processor array integrated on the focal plane. As an imaging sensor, SCAMP-5 has 256x256 pixels , which is basically 256x256 light sensors. Each of them has a programmable processor right beside it (PE). See full list on scamp .gitlab.io MCU Board The Scamp5d system control is provided using a microcontruller unit (MCU): 1. NXP LPC4357 FET 256 2. ARM Cortex M4 + M0 dual core, 204MHz 3. Share 136 KB SRAM (4 banks, (32 + 32 + 8 + 64) KB) 4. Share 1 MB Flash (2 banks, (512 + 512) KB) 5. USB 2.0 6. SPI, UART, I2C 7. 5 Free-to-use GPIO 8. 3 LED Vision Board & Case The camera contains two PCBs - the MCU board and the vision board, on which the SCAMP -5 Vision Chip is mounted with ADC, DAC and other necessary peripheral circuits. The standard case of the Scamp5d Vision System supports a tripod and a C-mount lens See full list on scamp .gitlab.io Compiler and Program The vision system is programmed in C/C++ and compiled by GNU C/C++ for ARM. The programs for the MCU can target separate processor cores: 1. M0 program: vision algorithm, allocated with 40 KB RAM + 512 KB Flash 2. M4 program: miscellaneous tasks, allocated with 32 KB RAM + 512 KB Flash The programs of each of the cores are stored in their corresponding flash memroy. Different RAM bank can be accessed in parallel by different cores. Each of the cores can also access the RAM bank of the other c... Build Process Both cores have their chip-level library provided by NXP as a part of the LPCOpen library. The library for Scamp5d vision system is developed based on the LPCOpen library. User's source code is linked to the library to build the binary for each of the core seperately. The binary for the M4 core is the primary target for LPC4357, it carries the binary of the M0 core. The build flow is illustrated as follows: In MCUXpresso IDE, the project for M4 core has a \"Multicore\" field under the compiler... Development Library Design Three type of modules: 1. shared module — useable on both cores but existing only on one of the cores, thus it's context is shared. 2. common module — existing and useable on both cores, but their software context are independent. 3. exclusive module — existing on only one of the core, either M4 or M0. IOs are interfaced through the io-agent module (as a shared module on M4). IOs are mostly processed using DMA and interrupts. The two cores communicate through interrupt and a message queue loc... See full list on scamp .gitlab.io The sparse readout and complete utilization of all pixel - processors makes our approach very eficient. Our imple-mentation upon the SCAMP -7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point- features re-liably under violent motion. This is the first work perform-ing point-feature detection and tracking entirely in- pixel . 1 SCAMP Technology The SCAMP vision chip contains a massively parallel SIMD processor array , with one processing element per image pixel . The processors are simple, but fully software-programmable entities, comprising local memory, ALU, control and I/O circuits. In contrast, in-sensor visual computing performs signal processing at the pixel level using the collected analog signals directly, without sending data to other processors . Can pixel processor array vision sensors detect point-features? This paper presents a novel approach for joint point-feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels, PPA sensors consist of thousands of “pixel-processors”, enabling massive parallel computation of vi-sual data at the point of light capture. What is a scamp vision sensor? The SCAMP Vision Sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. Why is pixel-parallel processor array architecture a form of in-memory computing? Furthermore, processing and memory are colocated. The pixel-parallel fine-grained processor array architecture is a form of in-memory computing, because every processing element contains both execution units and local memory , storing pixel data and the intermediate results of computations. What is a parallel processor array? Parallel processor arrays (PPAs) are a new class of vision sensor devices that exploit advances in semiconductor technology, embedding a processor within each pixel of the image sensor array. Sensed pixel data are processed on the focal plane, and only a small amount of relevant information is transmitted out of the vision sensor. What is a scamp vision chip? The SCAMP vision chip contains a massively parallel SIMD processor array , with one processing element per image pixel. The processors are simple, but fully software-programmable entities, comprising local memory, ALU, control and I/O circuits. How does a pixel array work? Images are captured directly into the array, with each PE capturing a single pixel of the whole image into its local memory. This sensor architecture enables massively parallel “in-pixel” computation upon the focal plane, with the PE array operating as a Single Instruc-tion Multiple Data (SIMD) computer . The SCAMP Vision Sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. Unlike a conventional image sensor, it does not output raw images, but rather the results of on-sensor computations, for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. The device is fully programmable, to execute a ..."}
+{"idx": 4, "title": "Descriptor-In-Pixel : Point-Feature Tracking For Pixel ...", "date": "", "ddg_snippet": "The sparse readout and complete utilization of all pixel - processors makes our approach very eficient. Our imple-mentation upon the SCAMP -7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point- features re-liably under violent motion. This is the first work perform-ing point-feature detection and tracking entirely in- pixel . 1", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "The sparse readout and complete utilization of all pixel - processors makes our approach very eficient. Our imple-mentation upon the SCAMP -7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point- features re-liably under violent motion. This is the first work perform-ing point-feature detection and tracking entirely in- pixel . 1"}
+{"idx": 5, "title": "SCAMP Vision Sensor - Technology", "date": "", "ddg_snippet": "SCAMP Technology The SCAMP vision chip contains a massively parallel SIMD processor array , with one processing element per image pixel . The processors are simple, but fully software-programmable entities, comprising local memory, ALU, control and I/O circuits.", "subpage_snippet": "", "source": "www.scamp-vision-chip.org", "link": "https://www.scamp-vision-chip.org/technology", "content": "SCAMP Technology The SCAMP vision chip contains a massively parallel SIMD processor array , with one processing element per image pixel . The processors are simple, but fully software-programmable entities, comprising local memory, ALU, control and I/O circuits."}
+{"idx": 6, "title": "SCAMP-5d vision system and the pixel processor array (PPA ...", "date": "", "ddg_snippet": "In contrast, in-sensor visual computing performs signal processing at the pixel level using the collected analog signals directly, without sending data to other processors .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/SCAMP-5d-vision-system-and-the-pixel-processor-array-PPA-SCAMP-5d-consists-of-PPA-with_fig1_361690423", "content": "In contrast, in-sensor visual computing performs signal processing at the pixel level using the collected analog signals directly, without sending data to other processors ."}
+{"idx": 7, "title": "SCAMP Vision Sensor - University of Manchester", "date": "", "ddg_snippet": "The SCAMP Vision Sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. Unlike a conventional image sensor, it does not output raw images, but rather the results of on-sensor computations, for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. The device is fully programmable, to execute a ...", "subpage_snippet": "", "source": "personalpages.manchester.ac.uk", "link": "https://personalpages.manchester.ac.uk/staff/p.dudek/scamp/", "content": "The SCAMP Vision Sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. Unlike a conventional image sensor, it does not output raw images, but rather the results of on-sensor computations, for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. The device is fully programmable, to execute a ..."}
+{"idx": 8, "title": "(PDF) High-speed Light-weight CNN Inference via Strided", "date": "", "ddg_snippet": "The SCAMP -5d incorporates a 256 × 256 PPA array of pixel - processors , each containing light sensor, local memory registers and other functional ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/343655155_High-speed_Light-weight_CNN_Inference_via_Strided_Convolutions_on_a_Pixel_Processor_Array", "content": "The SCAMP -5d incorporates a 256 × 256 PPA array of pixel - processors , each containing light sensor, local memory registers and other functional ..."}
+{"idx": 9, "title": "US11169775B2 - Processing with compact arithmetic processing", "date": "", "ddg_snippet": "238000003491 array Methods 0 ... 230000000007 visual effect Effects 0.000 description 2 ... 238000012800 visualization Methods 0.000 description 1", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11169775B2/en", "content": "238000003491 array Methods 0 ... 230000000007 visual effect Effects 0.000 description 2 ... 238000012800 visualization Methods 0.000 description 1"}
diff --git a/data/sampled_jsons/power_law_scaling_faster_slower_error_rate_exponent_comparison.jsonl b/data/sampled_jsons/power_law_scaling_faster_slower_error_rate_exponent_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9b9831a7dc22ffd7a9ac4c6cc8ff2859664a6e99
--- /dev/null
+++ b/data/sampled_jsons/power_law_scaling_faster_slower_error_rate_exponent_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Natural logarithm - Wikipedia", "date": "", "ddg_snippet": "Graph of part of the natural logarithm function. The function slowly grows to positive infinity as x increases, and slowly goes to negative infinity as x approaches 0 (\" slowly \" as compared to any power law of x).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Natural_logarithm", "content": "Graph of part of the natural logarithm function. The function slowly grows to positive infinity as x increases, and slowly goes to negative infinity as x approaches 0 (\" slowly \" as compared to any power law of x)."}
+{"idx": 1, "title": "How Do Large Language Monkeys Get Their Power ( Laws )? | alphaXiv", "date": "", "ddg_snippet": "Power law exponent comparison for math tasks Figure 8: Comparison of power - law exponents estimated using least squares vs. the distributional approach for mathematical reasoning tasks.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.17578v1", "content": "Power law exponent comparison for math tasks Figure 8: Comparison of power - law exponents estimated using least squares vs. the distributional approach for mathematical reasoning tasks."}
+{"idx": 2, "title": "Energy Confinement Time Scaling for the Negative Triangularity...", "date": "", "ddg_snippet": "scaling law . The comparisons in Fig.It is clear that there is a large spread in the values of the exponents given the choice of data, but clear trends can be seen in the exponents compared to those from IPB98(y,2). Namely, there is a stronger dependence on.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.04279v1", "content": "scaling law . The comparisons in Fig.It is clear that there is a large spread in the values of the exponents given the choice of data, but clear trends can be seen in the exponents compared to those from IPB98(y,2). Namely, there is a stronger dependence on."}
+{"idx": 3, "title": "Dynamic hysteresis at a noisy saddle node shows power - law scaling ...", "date": "", "ddg_snippet": "This work rationalizes the ubiquitous power - law scaling of the dynamic hysteresis as well as the wide variation in the scaling exponent between 0.66 and 0.2 observed in different systems over the last 30 years.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/37723676/", "content": "This work rationalizes the ubiquitous power - law scaling of the dynamic hysteresis as well as the wide variation in the scaling exponent between 0.66 and 0.2 observed in different systems over the last 30 years."}
+{"idx": 4, "title": "Distance scaling of electric-field noise in a surface-electrode ion trap", "date": "", "ddg_snippet": "The difference from perfect power - law scaling , here quantied as an uncertainty in the exponent , is due to the unequal scalings of the electrode axial dimensions among zones.Straightforward comparisons cannot be made to distance scalings measured in Refs.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/114395/PhysRevA.97.020302.pdf?sequence=1", "content": "The difference from perfect power - law scaling , here quantied as an uncertainty in the exponent , is due to the unequal scalings of the electrode axial dimensions among zones.Straightforward comparisons cannot be made to distance scalings measured in Refs."}
+{"idx": 5, "title": "Non-Biological AI does not emulate human brains or thinking and can...", "date": "", "ddg_snippet": "Model comparison : Exponents enable comparison of different architectures or learning strategies for the same task. Generalization prediction: Power - law relationships help predict how well models will generalize to unseen data as training set size increases.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/non-biological-ai-does-emulate-human-brains-thinking-can-yerramsetti-msh3c", "content": "Model comparison : Exponents enable comparison of different architectures or learning strategies for the same task. Generalization prediction: Power - law relationships help predict how well models will generalize to unseen data as training set size increases."}
+{"idx": 6, "title": "Frontiers | Non-destructive prediction of shoot-level leaf area and...", "date": "", "ddg_snippet": "(2017) report a conserved intraspecific scaling exponent of approximately 0.6 between mean leaf area.where β is the normalization constant and α is the scaling exponent (i.e., the rate of change in Y2 with respect to Y1; Niklas, 1994) Because.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1650196/full", "content": "(2017) report a conserved intraspecific scaling exponent of approximately 0.6 between mean leaf area.where β is the normalization constant and α is the scaling exponent (i.e., the rate of change in Y2 with respect to Y1; Niklas, 1994) Because."}
+{"idx": 7, "title": "Detrending fluctuation analysis based on moving average filtering", "date": "", "ddg_snippet": "A comparison of the power - law exponents obtained using respectively the function σ MA and the Detrended Fluctuation Analysis has been also carried out.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/223358230_Detrending_fluctuation_analysis_based_on_moving_average_filtering", "content": "A comparison of the power - law exponents obtained using respectively the function σ MA and the Detrended Fluctuation Analysis has been also carried out."}
+{"idx": 8, "title": "Energetic equivalence underpins the size... | Nature Communications", "date": "", "ddg_snippet": "Comparing power - law exponents .To test metabolic scaling theory (MST) predictions that the individual size distribution (ISD) is a power - law with an exponent approximating −3/4, we adopted the method of Clauset et al.64 to find both the best-fit minimum size, xmin, to which a...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-018-08039-3?error=cookies_not_supported&code=26bdbe45-0454-4fc9-ad9b-e66630456384", "content": "Comparing power - law exponents .To test metabolic scaling theory (MST) predictions that the individual size distribution (ISD) is a power - law with an exponent approximating −3/4, we adopted the method of Clauset et al.64 to find both the best-fit minimum size, xmin, to which a..."}
+{"idx": 9, "title": "[Prediction] We are in an Algorithmic Overhang, Part 2 — LessWrong", "date": "", "ddg_snippet": "This increases at a somewhat sub- exponential rate .The money people are willing to invest in AI. This increases as the return on investment in AI increases. There was a time when money invested in AI rose exponentially and very fast , but it’s pretty much flattened off since GPT-3.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/dY9k9d6cLzqx7FJ6P/prediction-we-are-in-an-algorithmic-overhang-part-2", "content": "This increases at a somewhat sub- exponential rate .The money people are willing to invest in AI. This increases as the return on investment in AI increases. There was a time when money invested in AI rose exponentially and very fast , but it’s pretty much flattened off since GPT-3."}
diff --git a/data/sampled_jsons/privacy_accounting_machine_learning_differential_privacy_bounds_tightening_2024_year_2024.jsonl b/data/sampled_jsons/privacy_accounting_machine_learning_differential_privacy_bounds_tightening_2024_year_2024.jsonl
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index 0000000000000000000000000000000000000000..3b6a3ca14dd499756bd04e018d48b94dce7edcd2
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2404.04706] Advances in Differential Privacy and ... Federated learning with differential privacy via fast Fourier ... Privacy Auditing in Differential Private Machine Learning ... A Randomized Approach to Tight Privacy Accounting Privacy Analyses in Machine Learning | Proceedings of the ... PRIV-ML: Analyzing Privacy Loss in Iterative Machine Learning ... Privacy Auditing in Differential Private Machine Learning : The Current Privacy Auditing in Differential Private Machine Learning : The Current Privacy Auditing in Differential Private Machine Learning : The Current Privacy Auditing in Differential Private Machine Learning : The Current Federated learning with differential privacy via fast Fourier transform Federated learning with differential privacy via fast Fourier transform Advances in Differential Privacy and Differentially Private ...", "date": "", "ddg_snippet": "Apr 6, 2024 · There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and organisations such as census bureaus. Most recent surveys ... Nov 5, 2024 · The related definition of differential privacy and federated learning is presented in Section 3. Section 4 proposes a new algorithm, and some improvements are made. Jan 10, 2025 · The article examines studies that recommend privacy guarantees for differential private machine learning . It covers a wide range of topics on the subject and provides comprehensive guidance for privacy auditing schemes based on privacy attacks to protect machine - learning models from privacy leakage. A major challenge in machine learning with differential privacy is privacy accounting , i.e., mea-suring the privacy loss of the composition of DP mechanisms. A privacy accountant takes a list of mechanisms, and returns the privacy parameter (ε and δ) for the composition of those mechanisms. Specifically, a privacy accountant is given a target ε and finds the smallest achievable δ such that ... Dec 9, 2024 · Machine learning models sometimes memorize sensitive training data features, posing privacy risks. To control such privacy risks, Dwork et al. proposed the definition of differential privacy (DP) to measure the privacy risks of an algorithm. However, existing DP models either have significantly lower accuracy than their non-private variants or are computationally expensive to train by ... Differential privacy offers rigorous protections for emerging paradigms like federated machine learning , decentralized analytics, and web3 applications. The parameters E (epsilon) and <5 (delta) are crucial in balancing privacy and utility by bounding the maximum divergence between outputs on neighboring datasets. However, quantifying cumulative privacy loss over long-running processes ... Are privacy guarantees necessary for differentially private machine learning? Therefore, there is a need for effective methods that can audit (ϵ,δ) differentially private algorithms before they are deployed in the real world. The article examines studies that recommend privacy guarantees for differential private machine learning . What is differential privacy in machine learning? Differential privacy has recently gained prominence, especially in the context of private machine learning. While the definition of differential privacy makes it possible to provably limit the amount of information leaked by an algorithm , practical implementations of differentially private algorithms often contain subtle vulnerabilities. What is label-only membership auditing in differentially private machine learning? Label-only membership auditing: label-only membership inference auditing in differentially private machine learning is a privacy assessment method where an auditor attempts to deduce whether a particular data point was part of the training dataset based on the model’s predicted labels (without access to probabilities or other model details). Do privacy guarantees protect machine learning models from privacy leakage? The article examines studies that recommend privacy guarantees for differential private machine learning . It covers a wide range of topics on the subject and provides comprehensive guidance for privacy auditing schemes based on privacy attacks to protect machine - learning models from privacy leakage. Can differential privacy be used to train federated learning models? In this paper, differential privacy is used to train federated learning models to achieve a finer balance between utility and privacy protection. The improved algorithm replaces the initial definition of DP with privacy curves, resulting in a tighter bound on their combination. Is analysis () more accessible to meet differential privacy in Federated learning? Proof: in the appendix. Based on the aforementioned analysis, it is not difficult to find out that analysis \\ (\\:\\delta\\:\\) is more accessible to meet the application of differential privacy in federated learning, specifically by analyze the overlay and combination. Apr 2, 2024 · There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.04706", "content": "Apr 6, 2024 · There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and organisations such as census bureaus. Most recent surveys ... Nov 5, 2024 · The related definition of differential privacy and federated learning is presented in Section 3. Section 4 proposes a new algorithm, and some improvements are made. Jan 10, 2025 · The article examines studies that recommend privacy guarantees for differential private machine learning . It covers a wide range of topics on the subject and provides comprehensive guidance for privacy auditing schemes based on privacy attacks to protect machine - learning models from privacy leakage. A major challenge in machine learning with differential privacy is privacy accounting , i.e., mea-suring the privacy loss of the composition of DP mechanisms. A privacy accountant takes a list of mechanisms, and returns the privacy parameter (ε and δ) for the composition of those mechanisms. Specifically, a privacy accountant is given a target ε and finds the smallest achievable δ such that ... Dec 9, 2024 · Machine learning models sometimes memorize sensitive training data features, posing privacy risks. To control such privacy risks, Dwork et al. proposed the definition of differential privacy (DP) to measure the privacy risks of an algorithm. However, existing DP models either have significantly lower accuracy than their non-private variants or are computationally expensive to train by ... Differential privacy offers rigorous protections for emerging paradigms like federated machine learning , decentralized analytics, and web3 applications. The parameters E (epsilon) and <5 (delta) are crucial in balancing privacy and utility by bounding the maximum divergence between outputs on neighboring datasets. However, quantifying cumulative privacy loss over long-running processes ... Are privacy guarantees necessary for differentially private machine learning? Therefore, there is a need for effective methods that can audit (ϵ,δ) differentially private algorithms before they are deployed in the real world. The article examines studies that recommend privacy guarantees for differential private machine learning . What is differential privacy in machine learning? Differential privacy has recently gained prominence, especially in the context of private machine learning. While the definition of differential privacy makes it possible to provably limit the amount of information leaked by an algorithm , practical implementations of differentially private algorithms often contain subtle vulnerabilities. What is label-only membership auditing in differentially private machine learning? Label-only membership auditing: label-only membership inference auditing in differentially private machine learning is a privacy assessment method where an auditor attempts to deduce whether a particular data point was part of the training dataset based on the model’s predicted labels (without access to probabilities or other model details). Do privacy guarantees protect machine learning models from privacy leakage? The article examines studies that recommend privacy guarantees for differential private machine learning . It covers a wide range of topics on the subject and provides comprehensive guidance for privacy auditing schemes based on privacy attacks to protect machine - learning models from privacy leakage. Can differential privacy be used to train federated learning models? In this paper, differential privacy is used to train federated learning models to achieve a finer balance between utility and privacy protection. The improved algorithm replaces the initial definition of DP with privacy curves, resulting in a tighter bound on their combination. Is analysis () more accessible to meet differential privacy in Federated learning? Proof: in the appendix. Based on the aforementioned analysis, it is not difficult to find out that analysis \\ (\\:\\delta\\:\\) is more accessible to meet the application of differential privacy in federated learning, specifically by analyze the overlay and combination. Apr 2, 2024 · There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and ..."}
+{"idx": 1, "title": "Federated learning with differential privacy via fast Fourier ...", "date": "", "ddg_snippet": "Nov 5, 2024 · The related definition of differential privacy and federated learning is presented in Section 3. Section 4 proposes a new algorithm, and some improvements are made.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-77428-0", "content": "Nov 5, 2024 · The related definition of differential privacy and federated learning is presented in Section 3. Section 4 proposes a new algorithm, and some improvements are made."}
+{"idx": 2, "title": "Privacy Auditing in Differential Private Machine Learning ...", "date": "", "ddg_snippet": "Jan 10, 2025 · The article examines studies that recommend privacy guarantees for differential private machine learning . It covers a wide range of topics on the subject and provides comprehensive guidance for privacy auditing schemes based on privacy attacks to protect machine - learning models from privacy leakage.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/2/647", "content": "Jan 10, 2025 · The article examines studies that recommend privacy guarantees for differential private machine learning . It covers a wide range of topics on the subject and provides comprehensive guidance for privacy auditing schemes based on privacy attacks to protect machine - learning models from privacy leakage."}
+{"idx": 3, "title": "A Randomized Approach to Tight Privacy Accounting", "date": "", "ddg_snippet": "A major challenge in machine learning with differential privacy is privacy accounting , i.e., mea-suring the privacy loss of the composition of DP mechanisms. A privacy accountant takes a list of mechanisms, and returns the privacy parameter (ε and δ) for the composition of those mechanisms. Specifically, a privacy accountant is given a target ε and finds the smallest achievable δ such that ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/6ae7df1f40f5faeda474b36b61197822-Paper-Conference.pdf", "content": "A major challenge in machine learning with differential privacy is privacy accounting , i.e., mea-suring the privacy loss of the composition of DP mechanisms. A privacy accountant takes a list of mechanisms, and returns the privacy parameter (ε and δ) for the composition of those mechanisms. Specifically, a privacy accountant is given a target ε and finds the smallest achievable δ such that ..."}
+{"idx": 4, "title": "Privacy Analyses in Machine Learning | Proceedings of the ...", "date": "", "ddg_snippet": "Dec 9, 2024 · Machine learning models sometimes memorize sensitive training data features, posing privacy risks. To control such privacy risks, Dwork et al. proposed the definition of differential privacy (DP) to measure the privacy risks of an algorithm. However, existing DP models either have significantly lower accuracy than their non-private variants or are computationally expensive to train by ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3658644.3690862", "content": "Dec 9, 2024 · Machine learning models sometimes memorize sensitive training data features, posing privacy risks. To control such privacy risks, Dwork et al. proposed the definition of differential privacy (DP) to measure the privacy risks of an algorithm. However, existing DP models either have significantly lower accuracy than their non-private variants or are computationally expensive to train by ..."}
+{"idx": 5, "title": "PRIV-ML: Analyzing Privacy Loss in Iterative Machine Learning ...", "date": "", "ddg_snippet": "Differential privacy offers rigorous protections for emerging paradigms like federated machine learning , decentralized analytics, and web3 applications. The parameters E (epsilon) and <5 (delta) are crucial in balancing privacy and utility by bounding the maximum divergence between outputs on neighboring datasets. However, quantifying cumulative privacy loss over long-running processes ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10630949", "content": "Differential privacy offers rigorous protections for emerging paradigms like federated machine learning , decentralized analytics, and web3 applications. The parameters E (epsilon) and <5 (delta) are crucial in balancing privacy and utility by bounding the maximum divergence between outputs on neighboring datasets. However, quantifying cumulative privacy loss over long-running processes ..."}
+{"idx": 6, "title": "Advances in Differential Privacy and Differentially Private ...", "date": "", "ddg_snippet": "Apr 2, 2024 · There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-97-0407-1_7", "content": "Apr 2, 2024 · There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and ..."}
+{"idx": 7, "title": "Differential Privacy in Machine Learning: From Symbolic AI ...", "date": "", "ddg_snippet": "13 Jun 2025 — The RDP Accountant (Birrell et al., 2024) ... Privacy accountants leverage subsampling to tighten privacy bounds and enable efficient composition.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.11687v1", "content": "13 Jun 2025 — The RDP Accountant (Birrell et al., 2024) ... Privacy accountants leverage subsampling to tighten privacy bounds and enable efficient composition."}
+{"idx": 8, "title": "arXiv:2402.06137v1 [cs.LG] 9 Feb 2024", "date": "", "ddg_snippet": "9 Feb 2024 — The resulting bounds are tight and depend on closed-form expressions that can be numerically evalu- ated using standard methods. Empirically we.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2402.06137v1", "content": "9 Feb 2024 — The resulting bounds are tight and depend on closed-form expressions that can be numerically evalu- ated using standard methods. Empirically we."}
+{"idx": 9, "title": "Auditing $f$-Differential Privacy in One Run", "date": "", "ddg_snippet": "by S Mahloujifar · Cited by 12 — We use trade-off functions to perform tighter auditing of algorithms designed to satisfy differential privacy in a single run.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0QZcoGdmtJ", "content": "by S Mahloujifar · Cited by 12 — We use trade-off functions to perform tighter auditing of algorithms designed to satisfy differential privacy in a single run."}
diff --git a/data/sampled_jsons/probabilistic_currying.jsonl b/data/sampled_jsons/probabilistic_currying.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f68468301b6698237a4da7cd3d650e541cc489a
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Currying - Wikipedia", "date": "", "ddg_snippet": "In mathematics and computer science, currying is the technique of translating a function that takes multiple arguments into a sequence of families of functions, each taking a single argument. In the prototypical example, one begins with a function that takes two arguments, one from and one from and produces objects in The curried form of this function treats the first argument as a parameter ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Currying", "content": "In mathematics and computer science, currying is the technique of translating a function that takes multiple arguments into a sequence of families of functions, each taking a single argument. In the prototypical example, one begins with a function that takes two arguments, one from and one from and produces objects in The curried form of this function treats the first argument as a parameter ..."}
+{"idx": 1, "title": "What is the difference between currying and partial application?", "date": "", "ddg_snippet": "I quite often see on the Internet various complaints that other peoples examples of currying are not currying , but are actually just partial application. I've not found a decent explanation of what partial application is, or how it differs from currying . There seems to be a general confusion, with equivalent examples being described as currying in some places, and partial application in others ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/218025/what-is-the-difference-between-currying-and-partial-application", "content": "I quite often see on the Internet various complaints that other peoples examples of currying are not currying , but are actually just partial application. I've not found a decent explanation of what partial application is, or how it differs from currying . There seems to be a general confusion, with equivalent examples being described as currying in some places, and partial application in others ..."}
+{"idx": 2, "title": "Understanding Currying and Its Real-World Applications", "date": "", "ddg_snippet": "Understanding Currying and Its Real-World Applications Currying is a powerful functional programming technique that enhances readability, reusability, and maintainability of code.", "subpage_snippet": "", "source": "dev-aditya.medium.com", "link": "https://dev-aditya.medium.com/understanding-currying-and-its-real-world-applications-77912ad0db8e", "content": "Understanding Currying and Its Real-World Applications Currying is a powerful functional programming technique that enhances readability, reusability, and maintainability of code."}
+{"idx": 3, "title": "Deeply Understand Currying in 7 Minutes - freeCodeCamp.org", "date": "", "ddg_snippet": "I say \"properly currying \" because some curry functions are more flexible in their usage. Currying's great in theory, but invoking a function for each argument gets tiring in JavaScript.", "subpage_snippet": "", "source": "www.freecodecamp.org", "link": "https://www.freecodecamp.org/news/deeply-understand-currying-in-7-minutes/", "content": "I say \"properly currying \" because some curry functions are more flexible in their usage. Currying's great in theory, but invoking a function for each argument gets tiring in JavaScript."}
+{"idx": 4, "title": "Deeply Understand Currying In 7 Minutes - ExpertBeacon", "date": "", "ddg_snippet": "Currying is a key concept in functional programming that can simplify code through improved composition. But currying can also seem confusing initially. By breaking it down step-by-step, we can gain an intuitive understanding of what currying is and how it enables elegantly reusable code.", "subpage_snippet": "", "source": "expertbeacon.com", "link": "https://expertbeacon.com/deeply-understand-currying-in-7-minutes/", "content": "Currying is a key concept in functional programming that can simplify code through improved composition. But currying can also seem confusing initially. By breaking it down step-by-step, we can gain an intuitive understanding of what currying is and how it enables elegantly reusable code."}
+{"idx": 5, "title": "11. Currying in Python | Advanced | python-course.eu", "date": "", "ddg_snippet": "General Idea In mathematics and computer science, currying is the technique of breaking down the evaluation of a function that takes multiple arguments into evaluating a sequence of single-argument functions. Currying is also considered to be a design pattern. Currying is also used in theoretical computer science, because it is often easier to transform multiple argument models into single ...", "subpage_snippet": "", "source": "python-course.eu", "link": "https://python-course.eu/advanced-python/currying-in-python.php", "content": "General Idea In mathematics and computer science, currying is the technique of breaking down the evaluation of a function that takes multiple arguments into evaluating a sequence of single-argument functions. Currying is also considered to be a design pattern. Currying is also used in theoretical computer science, because it is often easier to transform multiple argument models into single ..."}
+{"idx": 6, "title": "Currying - GitHub Pages", "date": "", "ddg_snippet": "The exponential of sets A A and B B is the set of all functions A B = {f : B → A} AB = {f: B →A}. Currying is the isomorphism between A B × C AB×C and (A B) C (AB)C. The language Iverson invented, APL, parsed expressons from right to left. We write A B = {f : A ← B} AB = {f: A ← B} and b f bf instead of f (b) f (b).", "subpage_snippet": "", "source": "keithalewis.github.io", "link": "https://keithalewis.github.io/math/curry.html", "content": "The exponential of sets A A and B B is the set of all functions A B = {f : B → A} AB = {f: B →A}. Currying is the isomorphism between A B × C AB×C and (A B) C (AB)C. The language Iverson invented, APL, parsed expressons from right to left. We write A B = {f : A ← B} AB = {f: A ← B} and b f bf instead of f (b) f (b)."}
+{"idx": 7, "title": "Is there a mathematical difference between currying and partial ...", "date": "", "ddg_snippet": "The definition of currying and partial application are the following: currying is an operation that takes a function of two (or maybe more argument) and return a function-valued function partial application is an operation that takes a function and a value and return the function with one argument bound to a given constant", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1867210/is-there-a-mathematical-difference-between-currying-and-partial-application", "content": "The definition of currying and partial application are the following: currying is an operation that takes a function of two (or maybe more argument) and return a function-valued function partial application is an operation that takes a function and a value and return the function with one argument bound to a given constant"}
+{"idx": 8, "title": "Currying (programming) - Structurepedia", "date": "", "ddg_snippet": "Currying is a technique used in functional programming where a function with multiple arguments is transformed into a sequence of functions, each taking a single argument. This allows for partial application of the function, where some arguments are provided upfront and the rest can be supplied later. Currying helps in creating more reusable and flexible functions by breaking down complex ...", "subpage_snippet": "", "source": "structurepedia.org", "link": "https://structurepedia.org/Currying_(programming)", "content": "Currying is a technique used in functional programming where a function with multiple arguments is transformed into a sequence of functions, each taking a single argument. This allows for partial application of the function, where some arguments are provided upfront and the rest can be supplied later. Currying helps in creating more reusable and flexible functions by breaking down complex ..."}
+{"idx": 9, "title": "Currying in calculus, PDEs, programming & category theory", "date": "", "ddg_snippet": "The first place most people see currying is in a calculus class, though it's not pointed out. It also comes up in many other areas.", "subpage_snippet": "", "source": "www.johndcook.com", "link": "https://www.johndcook.com/blog/2018/08/11/currying/", "content": "The first place most people see currying is in a calculus class, though it's not pointed out. It also comes up in many other areas."}
diff --git a/data/sampled_jsons/probabilistic_observed_agreement_formula_cohen_kappa_two_models.jsonl b/data/sampled_jsons/probabilistic_observed_agreement_formula_cohen_kappa_two_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..71b959405f4c73ef32865bdc379ac8cb471a5227
--- /dev/null
+++ b/data/sampled_jsons/probabilistic_observed_agreement_formula_cohen_kappa_two_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Let's Agree to Disagree: Measuring Agreement between Annotators...", "date": "", "ddg_snippet": "Cohen ’s kappa is a statistic which assesses the inter-annotator agreement for categorical items.Thus, in order to know the real agreement between the two annotators, it is needed to subtract the probability of chance agreement from the observed agreement .", "subpage_snippet": "", "source": "www.rcs.cic.ipn.mx", "link": "https://www.rcs.cic.ipn.mx/2016_110/Let_s+Agree+to+Disagree_+Measuring+Agreement+between+Annotators+for+Opinion+Mining+Task.pdf", "content": "Cohen ’s kappa is a statistic which assesses the inter-annotator agreement for categorical items.Thus, in order to know the real agreement between the two annotators, it is needed to subtract the probability of chance agreement from the observed agreement ."}
+{"idx": 1, "title": "Measurement in Health and Disease: Assessing Agreement Using...", "date": "", "ddg_snippet": "Kappa for many observers . Cohen (1960, 1968) dealt with only two observers .Fleiss (1971) extended Cohen ’s kappa to the study of agreement between many observers . To estimate kappa by Fleiss’ method we ignore any relationship between observers for different subjects.", "subpage_snippet": "", "source": "www-users.york.ac.uk", "link": "https://www-users.york.ac.uk/~mb55/msc/clinimet/week4/kappa.htm", "content": "Kappa for many observers . Cohen (1960, 1968) dealt with only two observers .Fleiss (1971) extended Cohen ’s kappa to the study of agreement between many observers . To estimate kappa by Fleiss’ method we ignore any relationship between observers for different subjects."}
+{"idx": 2, "title": "Cohen 's kappa Calculator - With Interpretation - numiqo (DATAtab)", "date": "", "ddg_snippet": "With the Cohen ’s Kappa Calculator on numiqo, calculating Cohen ’s Kappa statistic is quick and straightforward. Simply select two nominal variables, and the Kappa Calculator will generate the result instantly.", "subpage_snippet": "", "source": "numiqo.com", "link": "https://numiqo.com/statistics-calculator/reliability-analysis/cohens-kappa-calculator", "content": "With the Cohen ’s Kappa Calculator on numiqo, calculating Cohen ’s Kappa statistic is quick and straightforward. Simply select two nominal variables, and the Kappa Calculator will generate the result instantly."}
+{"idx": 3, "title": "Weighted Kappa in R: Best Reference - Datanovia", "date": "", "ddg_snippet": "Weighted proportion of observed agreement formula . Cohen ’s Kappa formula . kappa can range form -1 (no agreement ) to +1 (perfect agreement ). when k = 0, the agreement is no better than what would be obtained by chance.", "subpage_snippet": "", "source": "www.datanovia.com", "link": "https://www.datanovia.com/en/lessons/weighted-kappa-in-r-for-two-ordinal-variables/", "content": "Weighted proportion of observed agreement formula . Cohen ’s Kappa formula . kappa can range form -1 (no agreement ) to +1 (perfect agreement ). when k = 0, the agreement is no better than what would be obtained by chance."}
+{"idx": 4, "title": "What is a Kappa coefficient? ( Cohen 's Kappa )_ cohen 's kappa ...", "date": "", "ddg_snippet": "( Cohen 's Kappa )When two binary variables are attempts by two individuals to measure the same thing, you can use Cohen 's Kappa (often simply called Kappa ) as a measure _ cohen 's kappa coefficient.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/u013975800/article/details/46986947", "content": "( Cohen 's Kappa )When two binary variables are attempts by two individuals to measure the same thing, you can use Cohen 's Kappa (often simply called Kappa ) as a measure _ cohen 's kappa coefficient."}
+{"idx": 5, "title": "expected value - Calculating inter-annotator agreement - Cross...", "date": "", "ddg_snippet": "Then certain models of chance with provide a higher chance agreement estimate despite there being many categories.In such cases Cohen 's $\\ kappa $ can be low while proportion of agreement is high, so presenting both gives a better picture.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/372971/calculating-inter-annotator-agreement/372974", "content": "Then certain models of chance with provide a higher chance agreement estimate despite there being many categories.In such cases Cohen 's $\\ kappa $ can be low while proportion of agreement is high, so presenting both gives a better picture."}
+{"idx": 6, "title": "Agreement between diagnostic tests controlled for raters", "date": "", "ddg_snippet": "The goal is to find out whether there is a real difference between the 2 tests in categorising the disease without polluting the result because of the expected variantion in the raters opinions. The method that is being used is to use Cohen kappa to measure the degree of agreement between raters.", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/agreement-between-diagnostic-tests-controlled-for-raters.818165/", "content": "The goal is to find out whether there is a real difference between the 2 tests in categorising the disease without polluting the result because of the expected variantion in the raters opinions. The method that is being used is to use Cohen kappa to measure the degree of agreement between raters."}
+{"idx": 7, "title": "Full text of \"ERIC ED490661: Free-Marginal Multirater Kappa ...\"", "date": "", "ddg_snippet": "Keywords: Multirater Kappa , Cohen ’s Kappa , Reliability, Measures of Agreement .Coming back to Tables 1 and 2 , using the multirater K free formula , the kappa for both tables is .33.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/stream/ERIC_ED490661/ERIC_ED490661_djvu.txt", "content": "Keywords: Multirater Kappa , Cohen ’s Kappa , Reliability, Measures of Agreement .Coming back to Tables 1 and 2 , using the multirater K free formula , the kappa for both tables is .33."}
+{"idx": 8, "title": "Population-based measures of agreement | Request PDF", "date": "", "ddg_snippet": "Two models , one for agreement and one for utility of association, are defined yielding different kappa coefficients and different sampling theory. Asymptotic results are derived for both models. Small-sample evaluations are presented for the model for agreement .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228966451_Population-based_measures_of_agreement", "content": "Two models , one for agreement and one for utility of association, are defined yielding different kappa coefficients and different sampling theory. Asymptotic results are derived for both models. Small-sample evaluations are presented for the model for agreement ."}
+{"idx": 9, "title": "presentatie_statistiek_19052010v 2", "date": "", "ddg_snippet": "percentage/ observed agreement . The percentage of judgements on which two analysts agree when coding the same data independently. But: - not correct for chance agreement (no comparability, biased) - not correct for distribution of items among categories.", "subpage_snippet": "", "source": "www.let.rug.nl", "link": "https://www.let.rug.nl/nerbonne/teach/rema-stats-meth-seminar/presentations/NvdV-Cohens-Kappa-2010.pdf", "content": "percentage/ observed agreement . The percentage of judgements on which two analysts agree when coding the same data independently. But: - not correct for chance agreement (no comparability, biased) - not correct for distribution of items among categories."}
diff --git a/data/sampled_jsons/qtuxDy2qEB_Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_Algorithm_1.jsonl b/data/sampled_jsons/qtuxDy2qEB_Parallel_Simulation_for_Log-concave_Sampling_and_Score-based_Diffusion_Models_Algorithm_1.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Parallel Simulation for Log-concave Sampling and Score-based Diffusion ...", "date": "", "ddg_snippet": "In this section, we present parallel Picard methods for strongly log-concave sampling ( Algorithm 1 ) and show it holds improved convergence rate w.r.t. the KL divergence and total variance (Theorem 4.2 and Corollary 4.3).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=qtuxDy2qEB&name=pdf", "content": "In this section, we present parallel Picard methods for strongly log-concave sampling ( Algorithm 1 ) and show it holds improved convergence rate w.r.t. the KL divergence and total variance (Theorem 4.2 and Corollary 4.3)."}
+{"idx": 1, "title": "[2305.16317] Parallel Sampling of Diffusion Models - arXiv.org", "date": "", "ddg_snippet": "Diffusion models are powerful generative models but suffer from slow sampling , often taking 1000 sequential denoising steps for one sample. As a result, considerable efforts have been directed toward reducing the number of denoising steps, but these methods hurt sample quality. Instead of reducing the number of denoising steps (trading quality for speed), in this paper we explore an orthogonal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.16317", "content": "Diffusion models are powerful generative models but suffer from slow sampling , often taking 1000 sequential denoising steps for one sample. As a result, considerable efforts have been directed toward reducing the number of denoising steps, but these methods hurt sample quality. Instead of reducing the number of denoising steps (trading quality for speed), in this paper we explore an orthogonal ..."}
+{"idx": 2, "title": "PDF Parallel Sampling of Diffusion Models - NeurIPS", "date": "", "ddg_snippet": "With this insight, we present ParaDiGMS, a novel method to accelerate the sampling of pretrained diffusion models by denoising multiple steps in parallel. ParaDiGMS is the first diffusion sampling method that enables trading compute for speed and is even compatible with existing fast sampling techniques such as DDIM and DPM-Solver.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/0d1986a61e30e5fa408c81216a616e20-Paper-Conference.pdf", "content": "With this insight, we present ParaDiGMS, a novel method to accelerate the sampling of pretrained diffusion models by denoising multiple steps in parallel. ParaDiGMS is the first diffusion sampling method that enables trading compute for speed and is even compatible with existing fast sampling techniques such as DDIM and DPM-Solver."}
+{"idx": 3, "title": "Parallel Simulation for Log-concave Sampling and Score-based Diffusion ...", "date": "", "ddg_snippet": "This research paper presents a new way to sample from complex probability distributions in machine learning more quickly and efficiently by using parallel computing techniques. The authors compare...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/43916/paper", "content": "This research paper presents a new way to sample from complex probability distributions in machine learning more quickly and efficiently by using parallel computing techniques. The authors compare..."}
+{"idx": 4, "title": "PDF Log-Concave Sampling - GitHub Pages", "date": "", "ddg_snippet": "This extends to even recent theoretical works on log-concave sampling , for which we have omitted discussion of sampling from convex bodies or polytopes. Although these works constitute fundamental developments in the field, here we chose to limit our focus to the part of the literature which is more strongly inspired by optimization algorithms .", "subpage_snippet": "", "source": "chewisinho.github.io", "link": "https://chewisinho.github.io/main.pdf", "content": "This extends to even recent theoretical works on log-concave sampling , for which we have omitted discussion of sampling from convex bodies or polytopes. Although these works constitute fundamental developments in the field, here we chose to limit our focus to the part of the literature which is more strongly inspired by optimization algorithms ."}
+{"idx": 5, "title": "Parallel simulation for sampling under isoperimetry and score-based ...", "date": "", "ddg_snippet": "This convergence analysis is based on a randomized midpoint method, which is first proposed for log-concave sampling (Shen and Lee, 2019), and then extended to diffusion models by Gupta et al. (2024).", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2412.07435", "content": "This convergence analysis is based on a randomized midpoint method, which is first proposed for log-concave sampling (Shen and Lee, 2019), and then extended to diffusion models by Gupta et al. (2024)."}
+{"idx": 6, "title": "Parallel simulation for sampling under isoperimetry and score-based ...", "date": "", "ddg_snippet": "Our work highlights the potential advantages of simulation methods in scientific computation for dynamics- based sampling and diffusion models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=6Gb7VfTKY7", "content": "Our work highlights the potential advantages of simulation methods in scientific computation for dynamics- based sampling and diffusion models ."}
+{"idx": 7, "title": "Parallel Simulation for Log-concave Sampling and Score-based Diffusion ...", "date": "", "ddg_snippet": "Spotlight Poster Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models Huanjian Zhou · Masashi Sugiyama East Exhibition Hall A-B #E-1103", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43916", "content": "Spotlight Poster Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models Huanjian Zhou · Masashi Sugiyama East Exhibition Hall A-B #E-1103"}
+{"idx": 8, "title": "Accelerating Diffusion Models with Parallel Sampling: Inference at Sub ...", "date": "", "ddg_snippet": "Abstract Diffusion models have become a leading method for generative modeling of both image and scientific data. As these models are costly to train and evaluate, re-ducing the inference cost for diffusion models remains a major goal. Inspired by the recent empirical success in accelerating diffusion models via the parallel sam-pling technique [ 1 ], we propose to divide the sampling process ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15986", "content": "Abstract Diffusion models have become a leading method for generative modeling of both image and scientific data. As these models are costly to train and evaluate, re-ducing the inference cost for diffusion models remains a major goal. Inspired by the recent empirical success in accelerating diffusion models via the parallel sam-pling technique [ 1 ], we propose to divide the sampling process ..."}
+{"idx": 9, "title": "PDF THE ADAPTIVE COMPLEXITY OF PARALLELIZED LOG CONCAVE SAMPLING - OpenReview", "date": "", "ddg_snippet": "ABSTRACT In large-data applications, such as the inference process of diffusion models , it is desirable to design sampling algorithms with a high degree of parallelization. In this work, we study the adaptive complexity of sampling , which is the minimum number of sequential rounds required to achieve sampling given polynomially many queries executed in parallel at each round. For unconstrained ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=EeqlkPpaV8&name=pdf", "content": "ABSTRACT In large-data applications, such as the inference process of diffusion models , it is desirable to design sampling algorithms with a high degree of parallelization. In this work, we study the adaptive complexity of sampling , which is the minimum number of sequential rounds required to achieve sampling given polynomially many queries executed in parallel at each round. For unconstrained ..."}
diff --git a/data/sampled_jsons/qtuxDy2qEB_openreview_Algorithm_1_lines_9-26_loop_structure_parallel.jsonl b/data/sampled_jsons/qtuxDy2qEB_openreview_Algorithm_1_lines_9-26_loop_structure_parallel.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Faster Diffusion Sampling with Randomized Midpoints ... - OpenReview", "date": "", "ddg_snippet": "However, it is true that in the parallel algorithm , we only take O ( 1 ) predictor steps in one predictor-corrector loop ; while in the sequential algorithm , we take O (d 5 / 12) predictor steps. In the parallel algorithm , we are able to take larger step sizes since we update many randomized midpoints in parallel in one predictor step.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MT3aOfXIbY", "content": "However, it is true that in the parallel algorithm , we only take O ( 1 ) predictor steps in one predictor-corrector loop ; while in the sequential algorithm , we take O (d 5 / 12) predictor steps. In the parallel algorithm , we are able to take larger step sizes since we update many randomized midpoints in parallel in one predictor step."}
+{"idx": 1, "title": "GitHub - yangxvlin/comp90025-pmc-notes: COMP90025 - Parallel and ...", "date": "", "ddg_snippet": "The highly parallel structure makes them more efficient than general-purpose CPUs for algorithms where the processing of large blocks of data is done in parallel .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yangxvlin/comp90025-pmc-notes", "content": "The highly parallel structure makes them more efficient than general-purpose CPUs for algorithms where the processing of large blocks of data is done in parallel ."}
+{"idx": 2, "title": "Enhancing Automated Loop Invariant Generation for Complex Programs with ...", "date": "", "ddg_snippet": "Abstract Automated program verification has always been an important component of building trustworthy software. While the analysis of loops remains a theoretical challenge, the automation of loop invariant analysis has effectively resolved the problem. However, existing invariant generation tools are predominantly effective for programs with purely numerical or purely pointer-based structures ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.10483v3", "content": "Abstract Automated program verification has always been an important component of building trustworthy software. While the analysis of loops remains a theoretical challenge, the automation of loop invariant analysis has effectively resolved the problem. However, existing invariant generation tools are predominantly effective for programs with purely numerical or purely pointer-based structures ..."}
+{"idx": 3, "title": "Parallel Algorithm Models in Parallel Computing - GeeksforGeeks", "date": "", "ddg_snippet": "Parallel Computing is defined as the process of distributing a larger task into a small number of independent tasks and then solving them using multiple processing elements simultaneously. Parallel computing is more efficient than the serial approach as it requires less computation time. Parallel Algorithm Models The need for a parallel algorithm model arises in order to understand the ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/mobile-computing/parallel-algorithm-models-in-parallel-computing/", "content": "Parallel Computing is defined as the process of distributing a larger task into a small number of independent tasks and then solving them using multiple processing elements simultaneously. Parallel computing is more efficient than the serial approach as it requires less computation time. Parallel Algorithm Models The need for a parallel algorithm model arises in order to understand the ..."}
+{"idx": 4, "title": "PDF Introduction to Parallel Algorithms (DRAFT)", "date": "", "ddg_snippet": "1 Introduction This document is intended an introduction to parallel algorithms . The algorithms and techniques described in this document cover over 40 years of work by hundreds of researchers. The earliest work on parallel algorithms dates back to the 1970s. The key ideas of the parallel merging algorithm described in Section 4.4, for example, appear in a 1975 paper by Leslie Valiant, a ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~guyb/paralg/paralg/parallel.pdf", "content": "1 Introduction This document is intended an introduction to parallel algorithms . The algorithms and techniques described in this document cover over 40 years of work by hundreds of researchers. The earliest work on parallel algorithms dates back to the 1970s. The key ideas of the parallel merging algorithm described in Section 4.4, for example, appear in a 1975 paper by Leslie Valiant, a ..."}
+{"idx": 5, "title": "openreview-py/openreview/tools.py at master - GitHub", "date": "", "ddg_snippet": "Official Python client library for the OpenReview API - openreview / openreview -py", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openreview/openreview-py/blob/master/openreview/tools.py", "content": "Official Python client library for the OpenReview API - openreview / openreview -py"}
+{"idx": 6, "title": "[P] ICLR OpenReview Explorer: Sort/filter papers by average ... - Reddit", "date": "", "ddg_snippet": "[P] ICLR OpenReview Explorer: Sort/filter papers by average review score (plus some pretty charts)", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/7h8lo7/p_iclr_openreview_explorer_sortfilter_papers_by/", "content": "[P] ICLR OpenReview Explorer: Sort/filter papers by average review score (plus some pretty charts)"}
+{"idx": 7, "title": "[2103.05885] What Have We Learned from OpenReview? - arXiv.org", "date": "", "ddg_snippet": "Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote openness in peer review process. The paper, (meta) reviews, rebuttals, and final decisions are all released to public. We collect 5,527 submissions and their 16,853 reviews from the OpenReview platform. We also collect these submissions' citation data from ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2103.05885", "content": "Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote openness in peer review process. The paper, (meta) reviews, rebuttals, and final decisions are all released to public. We collect 5,527 submissions and their 16,853 reviews from the OpenReview platform. We also collect these submissions' citation data from ..."}
+{"idx": 8, "title": "PDF Parallel Algorithms - CMU School of Computer Science", "date": "", "ddg_snippet": "The parallelism in an algorithm can yield improved performance on many different kinds of computers. For example, on a parallel computer, the operations in a parallel algorithm can be per-formed simultaneously by different processors.", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~guyb/papers/BM04.pdf", "content": "The parallelism in an algorithm can yield improved performance on many different kinds of computers. For example, on a parallel computer, the operations in a parallel algorithm can be per-formed simultaneously by different processors."}
+{"idx": 9, "title": "What have we learned from OpenReview? - ResearchGate", "date": "", "ddg_snippet": "Download Citation | What have we learned from OpenReview ? | Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/365249913_What_have_we_learned_from_OpenReview", "content": "Download Citation | What have we learned from OpenReview ? | Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote ..."}
diff --git a/data/sampled_jsons/rCnZrFikX6_Neural_Persistence_Dynamics_crocker_plots_scalability.jsonl b/data/sampled_jsons/rCnZrFikX6_Neural_Persistence_Dynamics_crocker_plots_scalability.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "neural_persistence_dynamics/crocker_stacks.py at main - GitHub", "date": "", "ddg_snippet": "Skip to content Dismiss alert plus-rkwitt / neural_persistence_dynamics Public Notifications You must be signed in to change notification settings Fork 0 Star 6 Code Issues Pull requests Projects Security Insights", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plus-rkwitt/neural_persistence_dynamics/blob/main/crocker_stacks.py", "content": "Skip to content Dismiss alert plus-rkwitt / neural_persistence_dynamics Public Notifications You must be signed in to change notification settings Fork 0 Star 6 Code Issues Pull requests Projects Security Insights"}
+{"idx": 1, "title": "plus-rkwitt/neural_persistence_dynamics - GitHub", "date": "", "ddg_snippet": "The Crocker stacks baseline comparison is implemented in crocker_stacks.py. To execute this script, you must first prepare the data using compute_cs.py. Additionally, you need to install the teaspoon library with the appropriate version for computing the Crocker stacks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plus-rkwitt/neural_persistence_dynamics", "content": "The Crocker stacks baseline comparison is implemented in crocker_stacks.py. To execute this script, you must first prepare the data using compute_cs.py. Additionally, you need to install the teaspoon library with the appropriate version for computing the Crocker stacks."}
+{"idx": 2, "title": "ExplainableML/Deep-Graph-Persistence - GitHub", "date": "", "ddg_snippet": "This repository contains code for replicating our empirical experiments regarding neural persistence (NP) in trained neural networks. Also, this repository contains code for replicating our experiments on using deep graph persistence for detecting image corruptions. For calculating NP, we rely on the great code from the original Neural Persistence : A Complexity Measure for Deep Neural Networks ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ExplainableML/Deep-Graph-Persistence", "content": "This repository contains code for replicating our empirical experiments regarding neural persistence (NP) in trained neural networks. Also, this repository contains code for replicating our experiments on using deep graph persistence for detecting image corruptions. For calculating NP, we rely on the great code from the original Neural Persistence : A Complexity Measure for Deep Neural Networks ..."}
+{"idx": 3, "title": "[2405.15732] Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.15732", "content": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ..."}
+{"idx": 4, "title": "Neural Persistence Dynamics - proceedings.neurips.cc", "date": "", "ddg_snippet": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model -- Neural Persistence Dynamics -- substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/3a509449a73fd0aab8c0cf5705827036-Abstract-Conference.html", "content": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model -- Neural Persistence Dynamics -- substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks."}
+{"idx": 5, "title": "Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "Despite remarkable success in distinguishing different configurations of models for collective behav-ior, all approaches suffer scalability issues, either in terms of the dimensionality of the vectorized persistence diagrams (as with the PSK approach of [23]), or in terms of the number of observation sequences (as is the case for crocker plots ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15732v1", "content": "Despite remarkable success in distinguishing different configurations of models for collective behav-ior, all approaches suffer scalability issues, either in terms of the dimensionality of the vectorized persistence diagrams (as with the PSK approach of [23]), or in terms of the number of observation sequences (as is the case for crocker plots ..."}
+{"idx": 6, "title": "Addressing caveats of neural persistence with deep graph persistence", "date": "", "ddg_snippet": "Abstract: Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning. In this work, however, we find both theoretically and empirically that the variance of network weights and spatial concentration of large weights are the main factors that impact neural persistence . Whilst this captures ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oyfRWeoUJY", "content": "Abstract: Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning. In this work, however, we find both theoretically and empirically that the variance of network weights and spatial concentration of large weights are the main factors that impact neural persistence . Whilst this captures ..."}
+{"idx": 7, "title": "Neural persistence dynamics | Proceedings of the 38th International ...", "date": "", "ddg_snippet": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model - Neural Persistence Dynamics - substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738965", "content": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model - Neural Persistence Dynamics - substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks."}
+{"idx": 8, "title": "Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "In [25], for instance, the authors work directly with persistence diagrams (per time point) to identify changes in the topology of time-varying graphs. In terms of temporal summary representations, [52] introduce crocker plots to encode the evolution of topological features by stacking discretized Betti curves over time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15732v2", "content": "In [25], for instance, the authors work directly with persistence diagrams (per time point) to identify changes in the topology of time-varying graphs. In terms of temporal summary representations, [52] introduce crocker plots to encode the evolution of topological features by stacking discretized Betti curves over time."}
+{"idx": 9, "title": "Neural Persistence Dynamics - OpenReview", "date": "", "ddg_snippet": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rCnZrFikX6", "content": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ..."}
diff --git a/data/sampled_jsons/race_condition_parallel_algorithm_simultaneous_update_data_consistency.jsonl b/data/sampled_jsons/race_condition_parallel_algorithm_simultaneous_update_data_consistency.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..994f7df03f8eec263a8deb9aeacca9432096c723
--- /dev/null
+++ b/data/sampled_jsons/race_condition_parallel_algorithm_simultaneous_update_data_consistency.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Race condition", "date": "", "ddg_snippet": "A race condition or race hazard is the condition of an electronics, software, or other system where the system's substantive behavior is dependent on the ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Race_condition", "content": "A race condition or race hazard is the condition of an electronics, software, or other system where the system's substantive behavior is dependent on the ..."}
+{"idx": 1, "title": "Thread Safety and Race Conditions in Parallel Computing", "date": "", "ddg_snippet": "“Race Conditions” refer to a problem in parallel programming where parallel tasks attempt to access and modify the same memory simultaneously , ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ppxyn1/thread-safety-and-race-conditions-in-parallel-computing-ca1298f5612a", "content": "“Race Conditions” refer to a problem in parallel programming where parallel tasks attempt to access and modify the same memory simultaneously , ..."}
+{"idx": 2, "title": "Handling Race Condition in Distributed System", "date": "", "ddg_snippet": "23 Jul 2025 — Without proper synchronization, race conditions can lead to conflicting updates , resulting in inconsistent data states across the system. By ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/computer-networks/handling-race-condition-in-distributed-system/", "content": "23 Jul 2025 — Without proper synchronization, race conditions can lead to conflicting updates , resulting in inconsistent data states across the system. By ..."}
+{"idx": 3, "title": "Are \"data races\" and \"race condition\" actually the same ...", "date": "", "ddg_snippet": "No, they are not the same thing. They are not a subset of one another . They are also neither the necessary, nor the sufficient condition for one another.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/11276259/are-data-races-and-race-condition-actually-the-same-thing-in-context-of-conc", "content": "No, they are not the same thing. They are not a subset of one another . They are also neither the necessary, nor the sufficient condition for one another."}
+{"idx": 4, "title": "Effects of Data Consistency in Parallel Machine Learning ...", "date": "", "ddg_snippet": "8 Jun 2024 — In this study, we explore the impact of relaxing data consistency in parallel machine learning training during a failure using various ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.05546v1", "content": "8 Jun 2024 — In this study, we explore the impact of relaxing data consistency in parallel machine learning training during a failure using various ..."}
+{"idx": 5, "title": "Consistency and Correctness for Parallel Programs", "date": "", "ddg_snippet": "26 Sept 2011 — Race Condition : when the result of an execution depends on the timing of two or more processes/threads. • Memory Consistency Model: The ...", "subpage_snippet": "", "source": "www.cs.utexas.edu", "link": "https://www.cs.utexas.edu/~pingali/CSE392/2011sp/lectures/cnc.pdf", "content": "26 Sept 2011 — Race Condition : when the result of an execution depends on the timing of two or more processes/threads. • Memory Consistency Model: The ..."}
+{"idx": 6, "title": "Why Your Event-Driven Architecture Is Causing Race ...", "date": "", "ddg_snippet": "A race condition occurs when the behavior of a system depends on the relative timing of events, such as the order of execution of code. When multiple operations ...", "subpage_snippet": "", "source": "algocademy.com", "link": "https://algocademy.com/blog/why-your-event-driven-architecture-is-causing-race-conditions-and-how-to-fix-it/", "content": "A race condition occurs when the behavior of a system depends on the relative timing of events, such as the order of execution of code. When multiple operations ..."}
+{"idx": 7, "title": "Multithreading — synchronization, race conditions, class level ...", "date": "", "ddg_snippet": "Race condition is a situation when two or more threads try to access the shared data at the same time and it leads to data corruption and ...", "subpage_snippet": "", "source": "java-jedi.medium.com", "link": "https://java-jedi.medium.com/multithreading-synchronization-class-level-and-method-level-synchronization-wait-and-notify-3488a8252b56", "content": "Race condition is a situation when two or more threads try to access the shared data at the same time and it leads to data corruption and ..."}
+{"idx": 8, "title": "What Every Computer Scientist Needs to Know About ...", "date": "", "ddg_snippet": "21 Feb 2025 — ... simultaneous updates to shared resources , which could lead to race conditions and data inconsistencies [9] . Higher up the stack, message ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.03647v1", "content": "21 Feb 2025 — ... simultaneous updates to shared resources , which could lead to race conditions and data inconsistencies [9] . Higher up the stack, message ..."}
+{"idx": 9, "title": "Tools And Techniques to Identify Concurrency Issues", "date": "", "ddg_snippet": "A race occurs when two or more threads of execution in a multithreaded program try to access the same shared data and at least one of the accesses is a write.", "subpage_snippet": "", "source": "learn.microsoft.com", "link": "https://learn.microsoft.com/en-us/archive/msdn-magazine/2008/june/tools-and-techniques-to-identify-concurrency-issues", "content": "A race occurs when two or more threads of execution in a multithreaded program try to access the same shared data and at least one of the accesses is a write."}
diff --git a/data/sampled_jsons/reddit_climate_activism_subreddits_92%_activation_earthstrike_extinctionrebellion_fridaysforfuture.jsonl b/data/sampled_jsons/reddit_climate_activism_subreddits_92%_activation_earthstrike_extinctionrebellion_fridaysforfuture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d0492def5753a98d1139fa7fd4f89313beddee86
--- /dev/null
+++ b/data/sampled_jsons/reddit_climate_activism_subreddits_92%_activation_earthstrike_extinctionrebellion_fridaysforfuture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "User activated in climate activism groups. I𝐼Iitalic_I. Interactions with activists .r/ FridaysForFuture : “A sub dedicated to the international movement of students who skip class on Fridays to demand action to prevent climate change.”", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "User activated in climate activism groups. I𝐼Iitalic_I. Interactions with activists .r/ FridaysForFuture : “A sub dedicated to the international movement of students who skip class on Fridays to demand action to prevent climate change.”"}
+{"idx": 1, "title": "(PDF) Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "activation in climate activism groups on Reddit , and over which. time scale? RQ2: Which sociodemographic characteristics aect activation ?0 500 1000 1500 2000. #Users. ExtinctionRebellion . EarthStrike . FridaysForFuture . SunriseMovement. sierraclub.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929273_Causal_Modeling_of_Climate_Activism_on_Reddit", "content": "activation in climate activism groups on Reddit , and over which. time scale? RQ2: Which sociodemographic characteristics aect activation ?0 500 1000 1500 2000. #Users. ExtinctionRebellion . EarthStrike . FridaysForFuture . SunriseMovement. sierraclub."}
+{"idx": 2, "title": "Woman drags climate activist by the hair to stop her from... - YouTube", "date": "", "ddg_snippet": "A furious woman has gone viral online after she dragged a climate activist off a road by her hair to stop her from blocking traffic in Bottrop, Germany.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=FTCbE8Jr5WU", "content": "A furious woman has gone viral online after she dragged a climate activist off a road by her hair to stop her from blocking traffic in Bottrop, Germany."}
+{"idx": 3, "title": "reddit .com/r/ListOfSubreddits/wiki/listofsubreddits", "date": "", "ddg_snippet": "[NSFW] Minimalist NSFW client for Reddit . Browse by subreddit or category!", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ListOfSubreddits/wiki/listofsubreddits/", "content": "[NSFW] Minimalist NSFW client for Reddit . Browse by subreddit or category!"}
+{"idx": 4, "title": "Ad giants slammed! Activists declare WPP and Publicis \" Climate ...\"", "date": "", "ddg_snippet": "On the same day, Extinction Rebellion NYC, another group dedicated to climate activism , staged a protest outside Publicis Groupe's New York City headquarters. The protesters used street theatre and blockaded the building's entrance by chaining activists to the doors to send a message.", "subpage_snippet": "", "source": "www.afaqs.com", "link": "https://www.afaqs.com/news/advertising/ad-giants-slammed-activists-declare-wpp-and-publicis-climate-criminals-in-anti-fossil-fuel-protests-9444878", "content": "On the same day, Extinction Rebellion NYC, another group dedicated to climate activism , staged a protest outside Publicis Groupe's New York City headquarters. The protesters used street theatre and blockaded the building's entrance by chaining activists to the doors to send a message."}
+{"idx": 5, "title": "\" Climate catastrophe in computer models\" - The Nordic Times", "date": "", "ddg_snippet": "Climate activist protests at Norway’s largest oil refinery have triggered harsh criticism from opposition leader Sylvi Listhaug, who is now demanding that Swedish activist Greta Thunberg be expelled from the country.", "subpage_snippet": "", "source": "nordictimes.com", "link": "https://nordictimes.com/environment/climate-catastrophe-in-computer-models/", "content": "Climate activist protests at Norway’s largest oil refinery have triggered harsh criticism from opposition leader Sylvi Listhaug, who is now demanding that Swedish activist Greta Thunberg be expelled from the country."}
+{"idx": 6, "title": "Climate activist chains himself to building, weeps as he speaks of...", "date": "", "ddg_snippet": "A progressive climate activist recently handcuffed himself to a J.P. Morgan Chase building in Los Angeles, California, to protest government and corporate inaction on climate change. What are the details?", "subpage_snippet": "", "source": "www.theblaze.com", "link": "https://www.theblaze.com/news/climate-scientist-chains-himself-to-building-cries-in-protest", "content": "A progressive climate activist recently handcuffed himself to a J.P. Morgan Chase building in Los Angeles, California, to protest government and corporate inaction on climate change. What are the details?"}
+{"idx": 7, "title": "Fear Mongering Climate Change | TikTok", "date": "", "ddg_snippet": "56.6M posts. Discover videos related to Fear Mongering Climate Change on TikTok.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/fear-mongering-climate-change", "content": "56.6M posts. Discover videos related to Fear Mongering Climate Change on TikTok."}
+{"idx": 8, "title": "Extinction Rebellion : Thousands to Return to UK Streets for...", "date": "", "ddg_snippet": "Extinction Rebellion are back with large-scale — and socially distanced — climate protests planned in London, Cardiff, and Manchester.", "subpage_snippet": "", "source": "www.globalcitizen.org", "link": "https://www.globalcitizen.org/en/content/extinction-rebellion-climate-protests-covid-uk/", "content": "Extinction Rebellion are back with large-scale — and socially distanced — climate protests planned in London, Cardiff, and Manchester."}
+{"idx": 9, "title": "What is Extinction Rebellion and what does it want?", "date": "", "ddg_snippet": "Protest group Extinction Rebellion (XR) is carrying out a week of demonstrations to highlight what it says is government inaction on climate change.", "subpage_snippet": "", "source": "www.bbc.com", "link": "https://www.bbc.com/news/uk-48607989", "content": "Protest group Extinction Rebellion (XR) is carrying out a week of demonstrations to highlight what it says is government inaction on climate change."}
diff --git a/data/sampled_jsons/regression_problem_coordinate-wise_private_median_condition_number_solution_quality.jsonl b/data/sampled_jsons/regression_problem_coordinate-wise_private_median_condition_number_solution_quality.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4f195611e0403dfb51dab102785d14a3ed7252f1
--- /dev/null
+++ b/data/sampled_jsons/regression_problem_coordinate-wise_private_median_condition_number_solution_quality.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Optimality of the coordinate-wise median mechanism for strategyproof ...", "date": "", "ddg_snippet": "We consider the facility location problem in two dimensions. In particular, we con-sider a setting where agents have Euclidean preferences, de ned by their ideal points, for a facility to be located in R2. We show that for the p norm (p 1) objective, the coordinate-wise median mechanism (CM) has the lowest worst-case approximation ratio in the class of deterministic, anonymous, and ...", "subpage_snippet": "", "source": "whanncar.github.io", "link": "https://whanncar.github.io/papers/median.pdf", "content": "We consider the facility location problem in two dimensions. In particular, we con-sider a setting where agents have Euclidean preferences, de ned by their ideal points, for a facility to be located in R2. We show that for the p norm (p 1) objective, the coordinate-wise median mechanism (CM) has the lowest worst-case approximation ratio in the class of deterministic, anonymous, and ..."}
+{"idx": 1, "title": "Optimality of the coordinate-wise median mechanism for strategyproof ...", "date": "", "ddg_snippet": "In Sect. 3, we discuss the optimality of the coordinate-wise median mechanism. In Sects. 4 and 5, we discuss the problem of finding the approximation ratio of the coordinate-wise median mechanism for the utilitarian objective and the p -norm objective. Section 6 concludes.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00355-022-01435-1", "content": "In Sect. 3, we discuss the optimality of the coordinate-wise median mechanism. In Sects. 4 and 5, we discuss the problem of finding the approximation ratio of the coordinate-wise median mechanism for the utilitarian objective and the p -norm objective. Section 6 concludes."}
+{"idx": 2, "title": "[2007.00903] Optimality of the coordinate-wise median mechanism for ...", "date": "", "ddg_snippet": "We show that for the p − norm (p ≥ 1) objective, the coordinate-wise median mechanism (CM) has the lowest worst-case approximation ratio in the class of deterministic, anonymous, and strategyproof mechanisms. For the minisum objective and an odd number of agents n, we show that CM has a worst-case approximation ratio (AR) of 2-√ n2+1√ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.00903", "content": "We show that for the p − norm (p ≥ 1) objective, the coordinate-wise median mechanism (CM) has the lowest worst-case approximation ratio in the class of deterministic, anonymous, and strategyproof mechanisms. For the minisum objective and an odd number of agents n, we show that CM has a worst-case approximation ratio (AR) of 2-√ n2+1√ ..."}
+{"idx": 3, "title": "regression - Condition Number for solving a linear problem using the ...", "date": "", "ddg_snippet": "I've checked this post where it is asked about the condition number for OLS as well. However, my question is more direct and conceptual, and less numerical. When reading the first pages of the pape...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/661458/condition-number-for-solving-a-linear-problem-using-the-normal-equations-or-ols", "content": "I've checked this post where it is asked about the condition number for OLS as well. However, my question is more direct and conceptual, and less numerical. When reading the first pages of the pape..."}
+{"idx": 4, "title": "PDF Notes On Median and Quantile Regression - University of California ...", "date": "", "ddg_snippet": "Conditional Median Restrictions and Least Absolute Deviations It is well-known that the expected value of a random variable Y minimizes the expected squared", "subpage_snippet": "", "source": "eml.berkeley.edu", "link": "https://eml.berkeley.edu/~powell/e241a_sp10/qrnotes.pdf", "content": "Conditional Median Restrictions and Least Absolute Deviations It is well-known that the expected value of a random variable Y minimizes the expected squared"}
+{"idx": 5, "title": "PDF Classes of Linear Programs Solvable by Coordinate-Wise Minimization", "date": "", "ddg_snippet": "We report the quality of the solution as the median and mean relative di erence between the optimal value and the objective reached by coordinate-wise minimization before termination.", "subpage_snippet": "", "source": "cmp.felk.cvut.cz", "link": "https://cmp.felk.cvut.cz/~dlaskto2/papers/Dlask-Werner-AMAI-2021.pdf", "content": "We report the quality of the solution as the median and mean relative di erence between the optimal value and the objective reached by coordinate-wise minimization before termination."}
+{"idx": 6, "title": "PDF Variance Reduced Median-of-Means Estimator for Byzantine-Robust ...", "date": "", "ddg_snippet": "To hedge against Byzantine failures, the work by Yin et al. (2018) proposed to take the coordinate-wise median of the transmitted gradients, which is essentially an MOM estimator based on gradients of local data. Our method improves this result from two aspects.", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume22/20-950/20-950.pdf", "content": "To hedge against Byzantine failures, the work by Yin et al. (2018) proposed to take the coordinate-wise median of the transmitted gradients, which is essentially an MOM estimator based on gradients of local data. Our method improves this result from two aspects."}
+{"idx": 7, "title": "PDF Condition Number Analysis of Logistic Regression, and its Implications ...", "date": "", "ddg_snippet": "Condition Number Analysis of Logistic Regression , and its Implications for First-Order Solution Methods Robert M. Freund (MIT)", "subpage_snippet": "", "source": "s3.amazonaws.com", "link": "https://s3.amazonaws.com/mitsloan-php/wp-faculty/sites/30/2019/05/07144136/CMU-Tepper-logistic-regression-only-GCD-v2.0.pdf", "content": "Condition Number Analysis of Logistic Regression , and its Implications for First-Order Solution Methods Robert M. Freund (MIT)"}
+{"idx": 8, "title": "Communication-efficient and Byzantine-robust distributed learning with ...", "date": "", "ddg_snippet": "Inspired by robust techniques developed recently in [20], we apply for the coordinate-wise median and coordinate-wise trimmed mean to formulate our Byzantine-robust CSL distributed learning algorithm.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320323000134", "content": "Inspired by robust techniques developed recently in [20], we apply for the coordinate-wise median and coordinate-wise trimmed mean to formulate our Byzantine-robust CSL distributed learning algorithm."}
+{"idx": 9, "title": "arXiv:1803.01498v2 [cs.LG] 25 Feb 2021", "date": "", "ddg_snippet": "The proof of Theorem 1 consists of two parts: 1) the analysis of coordinate-wise median estimator of the population gradients, and 2) the convergence analysis of the robustified gradient descent algorithm.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1803.01498", "content": "The proof of Theorem 1 consists of two parts: 1) the analysis of coordinate-wise median estimator of the population gradients, and 2) the convergence analysis of the robustified gradient descent algorithm."}
diff --git a/data/sampled_jsons/regret_matching_plus_normalization_operator_probability_distribution.jsonl b/data/sampled_jsons/regret_matching_plus_normalization_operator_probability_distribution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2608e201f3acb0ade52d8a71e14dfa715bf4fe10
--- /dev/null
+++ b/data/sampled_jsons/regret_matching_plus_normalization_operator_probability_distribution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Regret Matching : (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ...", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2023/rm_plus_convergence_neurips23/rm_plus_convergence_neurips23.pdf", "content": "Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability. In this paper, we first give counterexamples ..."}
+{"idx": 1, "title": "PDF An Introduction to Counterfactual Regret Minimization", "date": "", "ddg_snippet": "One way of accomplishing this is through regret matching , where an agents actions are selected at random with a distribution that is proportional to positive regrets .", "subpage_snippet": "", "source": "modelai.gettysburg.edu", "link": "http://modelai.gettysburg.edu/2013/cfr/cfr.pdf", "content": "One way of accomplishing this is through regret matching , where an agents actions are selected at random with a distribution that is proportional to positive regrets ."}
+{"idx": 2, "title": "Regret Minimization in Population Network Games: Vanishing ...", "date": "", "ddg_snippet": "By modeling the system state as a probability distribution of regrets and analyzing its evolution through the continuity equation, we uncover a key phenomenon in diverse multi-agent settings: the variance of the regret distribution diminishes over time, leading to the disappearance of heterogeneity and the emergence of consensus among agents.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.17183v1", "content": "By modeling the system state as a probability distribution of regrets and analyzing its evolution through the continuity equation, we uncover a key phenomenon in diverse multi-agent settings: the variance of the regret distribution diminishes over time, leading to the disappearance of heterogeneity and the emergence of consensus among agents."}
+{"idx": 3, "title": "Regret matching - ACM Digital Library", "date": "", "ddg_snippet": "We then provide two fixes: restarting and chopping off the positive orthant that RM+ operates in. Combined with RM+ with predictions, we show that restarting is sufficient to get O (T1/4) individual regret and that chopping off achieves O (1) social regret in normal-form games.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668812", "content": "We then provide two fixes: restarting and chopping off the positive orthant that RM+ operates in. Combined with RM+ with predictions, we show that restarting is sufficient to get O (T1/4) individual regret and that chopping off achieves O (1) social regret in normal-form games."}
+{"idx": 4, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games.However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability.In this paper ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games.However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability.In this paper ..."}
+{"idx": 5, "title": "Regret Matching - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "An adaptive regret-matching algorithm based on no- regret learning is proposed as the solution and the correlated equilibrium is obtained. Numerical results demonstrate that the game-theoretic solution has a larger capacity than random selection baselines.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/engineering/regret-matching", "content": "An adaptive regret-matching algorithm based on no- regret learning is proposed as the solution and the correlated equilibrium is obtained. Numerical results demonstrate that the game-theoretic solution has a larger capacity than random selection baselines."}
+{"idx": 6, "title": "PDF Regret Matching with Finite Memory - Brown University", "date": "", "ddg_snippet": "We consider the regret matching process with finite memory. For general games in normal form, it is shown that any recurrent class of the dynamics must be such that the action profiles that appear in it constitute a closed set under the \"same or better reply\" correspondence (CUSOBR set) that does not contain a smaller product set that is closed under \"same or better replies,\" i.e., a ...", "subpage_snippet": "", "source": "economics.brown.edu", "link": "https://economics.brown.edu/sites/default/files/papers/2010-10_paper.pdf", "content": "We consider the regret matching process with finite memory. For general games in normal form, it is shown that any recurrent class of the dynamics must be such that the action profiles that appear in it constitute a closed set under the \"same or better reply\" correspondence (CUSOBR set) that does not contain a smaller product set that is closed under \"same or better replies,\" i.e., a ..."}
+{"idx": 7, "title": "PDF Regret Transfer and Parameter Optimization - Noam Brown", "date": "", "ddg_snippet": "Regret matching (Hart and Mas-Colell 2000) is a widely-used, general algorithm for learning, over time, how to act. While regret is a broadly applicable concept, for this paper we will view it through the lens of game theory. The agent is a player in a game and his payoffs can depend on his and the other players' actions.", "subpage_snippet": "", "source": "noambrown.github.io", "link": "https://noambrown.github.io/papers/14-AAAI-Regret.pdf", "content": "Regret matching (Hart and Mas-Colell 2000) is a widely-used, general algorithm for learning, over time, how to act. While regret is a broadly applicable concept, for this paper we will view it through the lens of game theory. The agent is a player in a game and his payoffs can depend on his and the other players' actions."}
+{"idx": 8, "title": "Regret Matching - stevengong.co", "date": "", "ddg_snippet": "Regret Matching We use regret matching to solve Normal-Form Game such as RPS. Blackwell's approachability theorem when applied to minimizing regret is known as regret matching . In regret matching , an agent's actions are selected at random with a distribution that is proportional to positive regrets .", "subpage_snippet": "", "source": "stevengong.co", "link": "https://stevengong.co/notes/Regret-Matching", "content": "Regret Matching We use regret matching to solve Normal-Form Game such as RPS. Blackwell's approachability theorem when applied to minimizing regret is known as regret matching . In regret matching , an agent's actions are selected at random with a distribution that is proportional to positive regrets ."}
+{"idx": 9, "title": "PDF Regret Matching+: - Instability, average- and last-iterate convergence ...", "date": "", "ddg_snippet": "Instability, average- and last-iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris", "subpage_snippet": "", "source": "people.hec.edu", "link": "https://people.hec.edu/grand-clement/wp-content/uploads/sites/51/2023/12/slides_jgc_cirm.pdf", "content": "Instability, average- and last-iterate convergence in games Julien Grand-Clement, Assistant Professor, ISOM Department, HEC Paris"}
diff --git a/data/sampled_jsons/reinforcement_learning_trajectory_replanning_robotics_2023_year_2023.jsonl b/data/sampled_jsons/reinforcement_learning_trajectory_replanning_robotics_2023_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d1eff88ce35903fa9f4178e3f209caa990e54e0f
--- /dev/null
+++ b/data/sampled_jsons/reinforcement_learning_trajectory_replanning_robotics_2023_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Any-point Trajectory Modeling for Policy Learning", "date": "", "ddg_snippet": "... collecting 130 K 130 𝐾 130K 130 italic_K trajectories in [ 6 ] took 17 months, making data collection a major bottleneck in robot learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00025v3", "content": "... collecting 130 K 130 𝐾 130K 130 italic_K trajectories in [ 6 ] took 17 months, making data collection a major bottleneck in robot learning ..."}
+{"idx": 1, "title": "Learning-Augmented Model-Based Multi-Robot Planning for", "date": "", "ddg_snippet": "Reinforcement Learning (RL) based approaches, for example, can directly learn a multi-robot policy from noisy observations of the environment [ 5 , 6 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.06129v1", "content": "Reinforcement Learning (RL) based approaches, for example, can directly learn a multi-robot policy from noisy observations of the environment [ 5 , 6 ..."}
+{"idx": 2, "title": "2019 Posters - Robotics Institute Summer Scholars (RISS)", "date": "", "ddg_snippet": "... Agents via Context-Sensitive Modular Inverse Reinforcement Learning ... Robotics Institute Summer Scholars Carnegie Mellon University 5000 Forbes Ave.", "subpage_snippet": "", "source": "riss.ri.cmu.edu", "link": "https://riss.ri.cmu.edu/research_showcase/2019-posters/", "content": "... Agents via Context-Sensitive Modular Inverse Reinforcement Learning ... Robotics Institute Summer Scholars Carnegie Mellon University 5000 Forbes Ave."}
+{"idx": 3, "title": "Robotics and Perception Group", "date": "", "ddg_snippet": "Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making ...", "subpage_snippet": "", "source": "rpg.ifi.uzh.ch", "link": "https://rpg.ifi.uzh.ch/", "content": "Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making ..."}
+{"idx": 4, "title": "Robotic table tennis system predicts ball trajectory and adapts", "date": "", "ddg_snippet": "Right now, robotics is generally split between model-based approaches and reinforcement learning approaches, with some expecting the latter to be the ...", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-05-robotic-table-tennis-ball-trajectory.html", "content": "Right now, robotics is generally split between model-based approaches and reinforcement learning approaches, with some expecting the latter to be the ..."}
+{"idx": 5, "title": "VGGT-DP: Generalizable Robot Control via Vision Foundation", "date": "", "ddg_snippet": "... Transformer (VGGT) as the visual encoder and introduce a proprioception-guided visual learning strategy to align perception with internal robot ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.18778v1", "content": "... Transformer (VGGT) as the visual encoder and introduce a proprioception-guided visual learning strategy to align perception with internal robot ..."}
+{"idx": 6, "title": "From Seeing to Experiencing: Scaling Navigation Foundation", "date": "", "ddg_snippet": "We analyze the effectiveness of Reinforcement Learning (RL) versus Supervised Fine-Tuning (SFT) in post-training for robot learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22028v1", "content": "We analyze the effectiveness of Reinforcement Learning (RL) versus Supervised Fine-Tuning (SFT) in post-training for robot learning ."}
+{"idx": 7, "title": "GitHub -", "date": "", "ddg_snippet": "... Learning for Autonomous ... Incorporating Driving Knowledge in Deep Learning Based Vehicle Trajectory Prediction: A Survey, IEEE T-IV, 2023 .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jiachenli94/Awesome-Interaction-Aware-Trajectory-Prediction", "content": "... Learning for Autonomous ... Incorporating Driving Knowledge in Deep Learning Based Vehicle Trajectory Prediction: A Survey, IEEE T-IV, 2023 ."}
+{"idx": 8, "title": "Publications | Baskın Şenbaşlar", "date": "", "ddg_snippet": "Robust Trajectory Execution for Multi-robot Teams Using Distributed Real-time Replanning (Extended Abstract). ... Trajectory Execution for Multi-Robot ...", "subpage_snippet": "", "source": "baskin.me", "link": "https://baskin.me/publications/", "content": "Robust Trajectory Execution for Multi-robot Teams Using Distributed Real-time Replanning (Extended Abstract). ... Trajectory Execution for Multi-Robot ..."}
+{"idx": 9, "title": "Spatial Policy: Guiding Visuomotor Robotic Manipulation with", "date": "", "ddg_snippet": "Specicially, visuomotor robotic manipulation employs a generative model to predict trajectories of future video, which are subsequently translated ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15874v1", "content": "Specicially, visuomotor robotic manipulation employs a generative model to predict trajectories of future video, which are subsequently translated ..."}
diff --git a/data/sampled_jsons/remote_sensing_benchmark_image_size_10000x10000_8500x8500_comparison.jsonl b/data/sampled_jsons/remote_sensing_benchmark_image_size_10000x10000_8500x8500_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e654b7f21450256d579e6fec70b4001d08895faa
--- /dev/null
+++ b/data/sampled_jsons/remote_sensing_benchmark_image_size_10000x10000_8500x8500_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Comprehensive Benchmark for Optical Remote Sensing Image Super ...", "date": "", "ddg_snippet": "To address this challenge, we present OpenSR-test, a comprehensive benchmark designed exclusively for evaluating SR of remote sensing images . Our framework incorporates specific quality metrics and curated cross-sensor datasets, each spanning various scale factors with consistent metadata.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10530998", "content": "To address this challenge, we present OpenSR-test, a comprehensive benchmark designed exclusively for evaluating SR of remote sensing images . Our framework incorporates specific quality metrics and curated cross-sensor datasets, each spanning various scale factors with consistent metadata."}
+{"idx": 1, "title": "Remote sensing image super-resolution and object detection: Benchmark ...", "date": "", "ddg_snippet": "The image patches are extracted from satellite images , including real image distortions such as tangential scale distortion and skew distortion. The proposed RSSOD dataset will help researchers benchmark the state-of-the-art object detection methods across various classes, especially for small objects using image super-resolution.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417422002524", "content": "The image patches are extracted from satellite images , including real image distortions such as tangential scale distortion and skew distortion. The proposed RSSOD dataset will help researchers benchmark the state-of-the-art object detection methods across various classes, especially for small objects using image super-resolution."}
+{"idx": 2, "title": "RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark ...", "date": "", "ddg_snippet": "The core ideas for constructing RSI-CB can be summarized as follows: Construct a registration overlay of high-resolution remote sensing images and POI data, making sure that the actual targets in images correspond correctly with the POI.The superposition effect of POI data and image are shown in Figure 1. POI data screening: This screening includes the deletion of wrong annotations, removal of ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7146467/", "content": "The core ideas for constructing RSI-CB can be summarized as follows: Construct a registration overlay of high-resolution remote sensing images and POI data, making sure that the actual targets in images correspond correctly with the POI.The superposition effect of POI data and image are shown in Figure 1. POI data screening: This screening includes the deletion of wrong annotations, removal of ..."}
+{"idx": 3, "title": "A Real-World Benchmark for Sentinel-2 Multi-Image Super ... - Nature", "date": "", "ddg_snippet": "In this paper, we introduce a new benchmark (named MuS2) for super-resolving multiple Sentinel-2 images , with WorldView-2 imagery used as the high-resolution reference.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41597-023-02538-9", "content": "In this paper, we introduce a new benchmark (named MuS2) for super-resolving multiple Sentinel-2 images , with WorldView-2 imagery used as the high-resolution reference."}
+{"idx": 4, "title": "PDF A Comprehensive Benchmark for Optical Remote Sensing Image Super-Resolution", "date": "", "ddg_snippet": "This paper introduces a novel benchmark , OpenSR-test, for comprehensively evaluating super-resolution techniques in optical remote sensing images .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380609683_A_Comprehensive_Benchmark_for_Optical_Remote_Sensing_Image_Super-Resolution/fulltext/664611c322a7f16b4f2ee95f/A-Comprehensive-Benchmark-for-Optical-Remote-Sensing-Image-Super-Resolution.pdf", "content": "This paper introduces a novel benchmark , OpenSR-test, for comprehensively evaluating super-resolution techniques in optical remote sensing images ."}
+{"idx": 5, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote ...", "date": "", "ddg_snippet": "We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images . Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images . Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring ..."}
+{"idx": 6, "title": "SATIN - A Remote Sensing Imagery Benchmark", "date": "", "ddg_snippet": "A streamlined benchmark , leveraging platforms like Hugging Face Datasets, to provide a seamless experience for hosting, downloading, and evaluating models and datasets with minimal friction. A public leaderboard for tracking the performance of VL models on the SATIN benchmark , promoting research and development in the remote sensing domain.", "subpage_snippet": "", "source": "satinbenchmark.github.io", "link": "https://satinbenchmark.github.io/", "content": "A streamlined benchmark , leveraging platforms like Hugging Face Datasets, to provide a seamless experience for hosting, downloading, and evaluating models and datasets with minimal friction. A public leaderboard for tracking the performance of VL models on the SATIN benchmark , promoting research and development in the remote sensing domain."}
+{"idx": 7, "title": "ISPRS Benchmarks", "date": "", "ddg_snippet": "ISPRS Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Meshes This benchmark is supported by 2021 ISPRS scientific initiatives project. Automated extraction of geographic objects from airborne data is an important research topic in photogrammetry and remote sensing since decades. In addition to images , 3D point clouds from airborne LiDAR and Multi-View-Stereo- Image ...", "subpage_snippet": "", "source": "www.isprs.org", "link": "https://www.isprs.org/resources/datasets/benchmarks/", "content": "ISPRS Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Meshes This benchmark is supported by 2021 ISPRS scientific initiatives project. Automated extraction of geographic objects from airborne data is an important research topic in photogrammetry and remote sensing since decades. In addition to images , 3D point clouds from airborne LiDAR and Multi-View-Stereo- Image ..."}
+{"idx": 8, "title": "PatternNet: A benchmark dataset for performance evaluation of remote ...", "date": "", "ddg_snippet": "The increased spatial resolution provides new opportunities for advancing remote sensing image analysis and understanding, making it possible to develop novel approaches that were not possible before. The increased acquisition rate enables us to acquire a considerable volume of remote sensing data on a daily basis.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0924271618300042", "content": "The increased spatial resolution provides new opportunities for advancing remote sensing image analysis and understanding, making it possible to develop novel approaches that were not possible before. The increased acquisition rate enables us to acquire a considerable volume of remote sensing data on a daily basis."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "XLRS-Bench boasts the largest average image size ( 8500x8500 ) observed thus far, with all evaluation samples meticulously annotated manually, assisted by a novel semi-automatic captioner on ultra-high-resolution RS images .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.html", "content": "XLRS-Bench boasts the largest average image size ( 8500x8500 ) observed thus far, with all evaluation samples meticulously annotated manually, assisted by a novel semi-automatic captioner on ultra-high-resolution RS images ."}
diff --git a/data/sampled_jsons/reward_estimation_faces_practical_challenges_diffusion_model.jsonl b/data/sampled_jsons/reward_estimation_faces_practical_challenges_diffusion_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3d96872df58589e862552de250e683cbb45e57eb
--- /dev/null
+++ b/data/sampled_jsons/reward_estimation_faces_practical_challenges_diffusion_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Accelerating Diffusion Models in Offline RL via Reward-Aware ...", "date": "", "ddg_snippet": "However, their practical deployment faces a significant challenge : the computational overhead of the iterative sampling procedures, which requires numerous denoising steps to generate high-quality outputs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.07822", "content": "However, their practical deployment faces a significant challenge : the computational overhead of the iterative sampling procedures, which requires numerous denoising steps to generate high-quality outputs."}
+{"idx": 1, "title": "Extracting Reward Functions from Diffusion Models - NeurIPS", "date": "", "ddg_snippet": "We then devise a practical learning algorithm for extracting it by aligning the gradients of a reward function -- parametrized by a neural network -- to the difference in outputs of both diffusion models .Our method finds correct reward functions in navigation environments, and we demonstrate that steering the base model with the learned reward ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/9d23562fcedc078e27a3be813ff6feb5-Abstract-Conference.html", "content": "We then devise a practical learning algorithm for extracting it by aligning the gradients of a reward function -- parametrized by a neural network -- to the difference in outputs of both diffusion models .Our method finds correct reward functions in navigation environments, and we demonstrate that steering the base model with the learned reward ..."}
+{"idx": 2, "title": "(PDF) Extracting Reward Functions from Diffusion Models", "date": "", "ddg_snippet": "Finally, we demonstrate that our approach generalizes beyond sequential decision-making by learning a reward -like function from two large-scale image generation diffusion models .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371311889_Extracting_Reward_Functions_from_Diffusion_Models", "content": "Finally, we demonstrate that our approach generalizes beyond sequential decision-making by learning a reward -like function from two large-scale image generation diffusion models ."}
+{"idx": 3, "title": "PDF PRDP: Proximal Reward Difference Prediction for Large-Scale Reward ...", "date": "", "ddg_snippet": "Specifically, the RDP objective is a supervised regression objective that tasks the diffusion model with predicting the reward differ-ence of generated image pairs from their denoising trajec-tories. We theoretically prove that the diffusion model that obtains perfect reward difference prediction is exactly the maximizer of the RL objective.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Deng_PRDP_Proximal_Reward_Difference_Prediction_for_Large-Scale_Reward_Finetuning_of_CVPR_2024_paper.pdf", "content": "Specifically, the RDP objective is a supervised regression objective that tasks the diffusion model with predicting the reward differ-ence of generated image pairs from their denoising trajec-tories. We theoretically prove that the diffusion model that obtains perfect reward difference prediction is exactly the maximizer of the RL objective."}
+{"idx": 4, "title": "Extracting reward functions from diffusion models", "date": "", "ddg_snippet": "We first define the notion of a relative reward function of two diffusion models and show conditions under which it exists and is unique. We then devise a practical learning algorithm for extracting it by aligning the gradients of a reward function - parametrized by a neural network - to the difference in outputs of both diffusion models .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668305", "content": "We first define the notion of a relative reward function of two diffusion models and show conditions under which it exists and is unique. We then devise a practical learning algorithm for extracting it by aligning the gradients of a reward function - parametrized by a neural network - to the difference in outputs of both diffusion models ."}
+{"idx": 5, "title": "Diffusion-Reward", "date": "", "ddg_snippet": "We present a framework for reward learning in RL using conditional video diffusion models . Our key insight is that lower generative diversity is observed when conditioned on expert trajectories. We perform reverse processes conditioned on historical frames to estimate conditional entropy as rewards to encourage RL exploration of expert-like behaviors. The success rate of 10 visual robotic ...", "subpage_snippet": "", "source": "diffusion-reward.github.io", "link": "https://diffusion-reward.github.io/", "content": "We present a framework for reward learning in RL using conditional video diffusion models . Our key insight is that lower generative diversity is observed when conditioned on expert trajectories. We perform reverse processes conditioned on historical frames to estimate conditional entropy as rewards to encourage RL exploration of expert-like behaviors. The success rate of 10 visual robotic ..."}
+{"idx": 6, "title": "Accelerating Diffusion Models in Offline RL via Reward-Aware ...", "date": "", "ddg_snippet": "Abstract and Figures Although diffusion models have achieved strong results in decision-making tasks, their slow inference speed remains a key limitation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392530311_Accelerating_Diffusion_Models_in_Offline_RL_via_Reward-Aware_Consistency_Trajectory_Distillation", "content": "Abstract and Figures Although diffusion models have achieved strong results in decision-making tasks, their slow inference speed remains a key limitation."}
+{"idx": 7, "title": "PDF Reward-Directed Conditional Diffusion: Provable Distribution Estimation ...", "date": "", "ddg_snippet": "Abstract We explore the methodology and theory of reward -directed generation via condi-tional diffusion models . Directed generation aims to generate samples with desired properties as measured by a reward function, which has broad applications in generative AI, reinforcement learning, and computational biology.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/be93b16564e96859da8401b917f307c6-Paper-Conference.pdf", "content": "Abstract We explore the methodology and theory of reward -directed generation via condi-tional diffusion models . Directed generation aims to generate samples with desired properties as measured by a reward function, which has broad applications in generative AI, reinforcement learning, and computational biology."}
+{"idx": 8, "title": "Reward-Directed Conditional Diffusion: Provable Distribution Estimation ...", "date": "", "ddg_snippet": "We explore the methodology and theory of reward -directed generation via conditional diffusion models . Directed generation aims to generate samples with desired properties as measured by a reward ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=58HwnnEdtF", "content": "We explore the methodology and theory of reward -directed generation via conditional diffusion models . Directed generation aims to generate samples with desired properties as measured by a reward ..."}
+{"idx": 9, "title": "PDF Reward Fine-Tuning Two-Step Diffusion Models via Learning ...", "date": "", "ddg_snippet": "The mapping of the dis-tilled 2-step sampler from noisy images to clean ones (and thus to most reward signals) is highly non-smooth, mak-ing policy gradient estimation challenging. RL methods relying on the denoising diffusion loss (such as Diffusion -DPO [71]) are incompatible with such a property, leading to blurred images.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Jia_Reward_Fine-Tuning_Two-Step_Diffusion_Models_via_Learning_Differentiable_Latent-Space_Surrogate_CVPR_2025_paper.pdf", "content": "The mapping of the dis-tilled 2-step sampler from noisy images to clean ones (and thus to most reward signals) is highly non-smooth, mak-ing policy gradient estimation challenging. RL methods relying on the denoising diffusion loss (such as Diffusion -DPO [71]) are incompatible with such a property, leading to blurred images."}
diff --git a/data/sampled_jsons/risk-aware_reinforcement_learning_challenges_difficulties.jsonl b/data/sampled_jsons/risk-aware_reinforcement_learning_challenges_difficulties.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7453765b585a47fdd727a36de12bf2eb0ac13cef
--- /dev/null
+++ b/data/sampled_jsons/risk-aware_reinforcement_learning_challenges_difficulties.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Risk - Aware Reinforcement Learning : A Safer Approach", "date": "", "ddg_snippet": "In summary, risk - aware reinforcement learning represents a significant step forward in addressing the challenges of decision-making under uncertainty.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-09-13-risk-aware-reinforcement-learning-a-safer-approach--a3qvm25", "content": "In summary, risk - aware reinforcement learning represents a significant step forward in addressing the challenges of decision-making under uncertainty."}
+{"idx": 1, "title": "(PDF) Risk - Aware Reinforcement Learning Balancing Exploration...", "date": "", "ddg_snippet": "Risk - aware reinforcement learning (RL) is an emerging area in artificial intelligence (AI) that. Risk - aware reinforcement learning is designed to address the shortcomings of conventional RL. by incorporating risk sensitivity into the decision-making process. The core challenge lies in.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385587989_Risk-Aware_Reinforcement_Learning_Balancing_Exploration_and_Safety_in_Dynamic_Environments", "content": "Risk - aware reinforcement learning (RL) is an emerging area in artificial intelligence (AI) that. Risk - aware reinforcement learning is designed to address the shortcomings of conventional RL. by incorporating risk sensitivity into the decision-making process. The core challenge lies in."}
+{"idx": 2, "title": "A risk - aware reinforcement learning problem and policy gradients", "date": "", "ddg_snippet": "The risk - aware reinforcement learning problem considered here3 is to find the optimal policy minimizing the risk associated with the cumulative discounted cost: denoting costs as Ct(π) to highlight their dependence on policy π, the problem considered can be written as.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.15612", "content": "The risk - aware reinforcement learning problem considered here3 is to find the optimal policy minimizing the risk associated with the cumulative discounted cost: denoting costs as Ct(π) to highlight their dependence on policy π, the problem considered can be written as."}
+{"idx": 3, "title": "Catastrophic- risk - aware reinforcement learning with...", "date": "", "ddg_snippet": "Catastrophic- risk - aware reinforcement learning with extreme-value-theory-based policy gradients. Plain English Explanation.The researchers behind this paper recognized this challenge and developed a new approach called \"Catastrophic- risk - aware reinforcement learning .\"", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/catastrophic-risk-aware-reinforcement-learning-extreme-value", "content": "Catastrophic- risk - aware reinforcement learning with extreme-value-theory-based policy gradients. Plain English Explanation.The researchers behind this paper recognized this challenge and developed a new approach called \"Catastrophic- risk - aware reinforcement learning .\""}
+{"idx": 4, "title": "RA-PbRL: Provably Efficient Risk - Aware Preference-Based...", "date": "", "ddg_snippet": "# Risk - aware preference-based reinforcement learning (PbRL) addresses a critical gap in traditional PbRL, which predominantly focuses on maximizing average reward without considering risk.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "# Risk - aware preference-based reinforcement learning (PbRL) addresses a critical gap in traditional PbRL, which predominantly focuses on maximizing average reward without considering risk."}
+{"idx": 5, "title": "End-to-End Risk - aware Reinforcement Learning to", "date": "", "ddg_snippet": "In general, a reinforcement learning problem asks an agent to learn to take an action in an environment to move between states as per a transition function with a goal of reaching a terminal state while maximizing the sum of reward.", "subpage_snippet": "", "source": "homepage.cs.uiowa.edu", "link": "https://homepage.cs.uiowa.edu/~badhikari/assets/doc/papers/SteinerTreeICHI2024.pdf", "content": "In general, a reinforcement learning problem asks an agent to learn to take an action in an environment to move between states as per a transition function with a goal of reaching a terminal state while maximizing the sum of reward."}
+{"idx": 6, "title": "Risk - Aware Transfer in Reinforcement Learning", "date": "", "ddg_snippet": "Risk - Aware Transfer Learning . A Motivating Example.An obvious challenge of applying GPI in the risk - aware setting is that transferring optimal risk-neutral source policies does not guarantee risk - aware optimality in the target task.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/90610aa0e24f63ec6d2637e06f9b9af2-Paper.pdf", "content": "Risk - Aware Transfer Learning . A Motivating Example.An obvious challenge of applying GPI in the risk - aware setting is that transferring optimal risk-neutral source policies does not guarantee risk - aware optimality in the target task."}
+{"idx": 7, "title": "Uncertainty- aware Reinforcement learning for Portfolio optimization...", "date": "", "ddg_snippet": "(DOI: 10.1109/access.2024.3494859) We explored the use of Reinforcement Learning (RL) combined with risk assessment for optimizing investment portfolios.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/uncertainty-aware-reinforcement-learning-for-portfolio-10ea1jh0ecbn", "content": "(DOI: 10.1109/access.2024.3494859) We explored the use of Reinforcement Learning (RL) combined with risk assessment for optimizing investment portfolios."}
+{"idx": 8, "title": "Robust Risk - Aware Reinforcement Learning -Bohrium", "date": "", "ddg_snippet": "We present a reinforcement learning (RL) approach for robust optimisation of risk - aware performance criteria. To allow agents to express a wide variety of risk-reward profiles, we assess the value of a policy using rank dependent expected utility (RDEU).", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/robust-risk-aware-reinforcement-learning/811863043527933953-16569", "content": "We present a reinforcement learning (RL) approach for robust optimisation of risk - aware performance criteria. To allow agents to express a wide variety of risk-reward profiles, we assess the value of a policy using rank dependent expected utility (RDEU)."}
+{"idx": 9, "title": "Catastrophic- risk - aware reinforcement learning with...", "date": "", "ddg_snippet": "Catastrophic- risk - aware reinforcement learning with extreme-value-theory-based policy gradients.\"Deep reinforcement learning for option pricing and hedging under dynamic expectile risk measures,\" Quantitative Finance, Taylor & Francis Journals, vol. 23(10), pages 1411-1430, October.", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/p/arx/papers/2406.15612.html", "content": "Catastrophic- risk - aware reinforcement learning with extreme-value-theory-based policy gradients.\"Deep reinforcement learning for option pricing and hedging under dynamic expectile risk measures,\" Quantitative Finance, Taylor & Francis Journals, vol. 23(10), pages 1411-1430, October."}
diff --git a/data/sampled_jsons/robust_causal_representation_learning_noisy_mixing_2024_year_2024.jsonl b/data/sampled_jsons/robust_causal_representation_learning_noisy_mixing_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e0aa0711c4ac62990f069ea17ac1fb74682e00a0
--- /dev/null
+++ b/data/sampled_jsons/robust_causal_representation_learning_noisy_mixing_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fine-Tuning Pre-trained Language Models for Robust Causal ...", "date": "", "ddg_snippet": "We show that a robust representation can be derived through a so-called causal front-door adjustment, based on a decomposition assumption, using fine-tuned representations as a source of data augmentation. Comprehensive experiments in both synthetic and real-world settings...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.14375v1", "content": "We show that a robust representation can be derived through a so-called causal front-door adjustment, based on a decomposition assumption, using fine-tuned representations as a source of data augmentation. Comprehensive experiments in both synthetic and real-world settings..."}
+{"idx": 1, "title": "Temporal Causal Representation Learning", "date": "", "ddg_snippet": "... robust causal representation learning under minimal assumptions (Song et al., 2023, Song et al., 5 Sep 2024 , Chen et al., 25 Jan 2024 ). Vision-based RL and ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/temporal-causal-representation-learning", "content": "... robust causal representation learning under minimal assumptions (Song et al., 2023, Song et al., 5 Sep 2024 , Chen et al., 25 Jan 2024 ). Vision-based RL and ..."}
+{"idx": 2, "title": "[Literature Review] Automated Data Curation for Robust ...", "date": "", "ddg_snippet": "... Noisy Response · similar reviews. [Literature Review] Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning · similar reviews.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/review/automated-data-curation-for-robust-language-model-fine-tuning", "content": "... Noisy Response · similar reviews. [Literature Review] Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning · similar reviews."}
+{"idx": 3, "title": "Computer Science Oct 2024", "date": "", "ddg_snippet": "Title: Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning . Jialin Yu, Yuxiang Zhou, Yulan He, Nevin L. Zhang, Ricardo Silva.", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2024-10?skip=7150&show=500", "content": "Title: Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning . Jialin Yu, Yuxiang Zhou, Yulan He, Nevin L. Zhang, Ricardo Silva."}
+{"idx": 4, "title": "Interventional Causal Representation Learning | alphaXiv", "date": "", "ddg_snippet": "Abstract: Causal representation learning seeks to extract high-level latent factors from low-level sensory data. Most existing methods rely on observational data and structural assumptions (e.g., conditional independence) to identify the latent factors.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2209.11924v4", "content": "Abstract: Causal representation learning seeks to extract high-level latent factors from low-level sensory data. Most existing methods rely on observational data and structural assumptions (e.g., conditional independence) to identify the latent factors."}
+{"idx": 5, "title": "Yulan He - Publications", "date": "", "ddg_snippet": "Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning . arXiv:2410.14375, 2024 .Causal Inference from Text: Unveiling Interactions between Variables.", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/view/yulanhe/publications", "content": "Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning . arXiv:2410.14375, 2024 .Causal Inference from Text: Unveiling Interactions between Variables."}
+{"idx": 6, "title": "Yuxiang Zhou - Google Akademik", "date": "", "ddg_snippet": "1. 2024 . Causal Inference from Text: Unveiling Interactions between Variables.Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=qvvJvNQAAAAJ&hl=tr", "content": "1. 2024 . Causal Inference from Text: Unveiling Interactions between Variables.Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning ."}
+{"idx": 7, "title": "Publications and Conferences — CHAI", "date": "", "ddg_snippet": "Counterfactual Contrastive Learning : Robust Representations via Causal Image Synthesis Melanie Roschewitz, Fabio De Sousa Ribeiro, Tian Xia, Galvin Khara, Ben Glocker DEMI 2024 . Lecture Notes in Computer Science, vol 15265. Spinger, Cham.", "subpage_snippet": "", "source": "www.chai.ac.uk", "link": "https://www.chai.ac.uk/publicationsandconferences", "content": "Counterfactual Contrastive Learning : Robust Representations via Causal Image Synthesis Melanie Roschewitz, Fabio De Sousa Ribeiro, Tian Xia, Galvin Khara, Ben Glocker DEMI 2024 . Lecture Notes in Computer Science, vol 15265. Spinger, Cham."}
+{"idx": 8, "title": "dblp: List of computer science publications by Nevin Lianwen Zhang", "date": "", "ddg_snippet": "Jialin Yu, Yuxiang Zhou, Yulan He, Nevin L. Zhang, Ricardo Silva: Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning .Farhan Khawar, Leonard K. M. Poon , Nevin L. Zhang: Learning the Structure of Auto-Encoding Recommenders.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/z/NevinLianwenZhang.html", "content": "Jialin Yu, Yuxiang Zhou, Yulan He, Nevin L. Zhang, Ricardo Silva: Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning .Farhan Khawar, Leonard K. M. Poon , Nevin L. Zhang: Learning the Structure of Auto-Encoding Recommenders."}
+{"idx": 9, "title": "The blue social bookmark and publication sharing system. | BibSonomy", "date": "", "ddg_snippet": "Association for Computational Linguistics, (2021 )Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning .J. Yu, Y. Zhou, Y. He, N. Zhang, and R. Silva.", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/person/1d90ac3ab9d25fd273a8ccac0f6028797/author/7", "content": "Association for Computational Linguistics, (2021 )Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning .J. Yu, Y. Zhou, Y. He, N. Zhang, and R. Silva."}
diff --git a/data/sampled_jsons/sample-wise_reverse_A2C_loss_RA2C_equation_(7).jsonl b/data/sampled_jsons/sample-wise_reverse_A2C_loss_RA2C_equation_(7).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..baff21c8b84422ebb9240f0e6d2df53d4e6f5fc9
--- /dev/null
+++ b/data/sampled_jsons/sample-wise_reverse_A2C_loss_RA2C_equation_(7).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "The Symmetric Reinforcement Learning (SRL) loss Lsrl con-sists of two parts like SCE ( Equation 6): the original actor loss Lrl ( A2C or PPO) and the corresponding reverse RL loss Lrev ( RA2C or RPPO).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618v3", "content": "The Symmetric Reinforcement Learning (SRL) loss Lsrl con-sists of two parts like SCE ( Equation 6): the original actor loss Lrl ( A2C or PPO) and the corresponding reverse RL loss Lrev ( RA2C or RPPO)."}
+{"idx": 1, "title": "A2CLoss — torchrl 0.9 documentation", "date": "", "ddg_snippet": "A2CLoss class torchrl.objectives.A2CLoss(*args, **kwargs) [source] TorchRL implementation of the A2C loss . A2C (Advantage Actor Critic) is a model-free, online RL algorithm that uses parallel rollouts of n steps to update the policy, relying on the REINFORCE estimator to compute the gradient. It also adds an entropy term to the objective function to improve exploration. For more details ...", "subpage_snippet": "", "source": "docs.pytorch.org", "link": "https://docs.pytorch.org/rl/stable/reference/generated/torchrl.objectives.A2CLoss.html", "content": "A2CLoss class torchrl.objectives.A2CLoss(*args, **kwargs) [source] TorchRL implementation of the A2C loss . A2C (Advantage Actor Critic) is a model-free, online RL algorithm that uses parallel rollouts of n steps to update the policy, relying on the REINFORCE estimator to compute the gradient. It also adds an entropy term to the objective function to improve exploration. For more details ..."}
+{"idx": 2, "title": "Advantage Actor Critic (A2C) - Hugging Face Advantage Actor-Critic (A2C) algorithm in Reinforcement ... RL策略梯度方法之 (五): Advantage Actor-Critic (A2C)_a2c算法 输出... Advanced Actor Critic Family — MARLlib v1.0.0 documentation Advanced Actor Critic Family — MARLlib v1.0.0 documentation Advanced Actor Critic Family — MARLlib v1.0.0 documentation Advanced Actor Critic Family — MARLlib v1.0.0 documentation Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "In Reinforce, we want to increase the probability of actions in a trajectory proportional to how high the return is. 1. If the return is high, we will push upthe probabilities of the (state, action) combinations. 2. Else, if the return is low, it will push downthe probabilities of the (state, action) combinations. This return R(τ)R(\\tau)R(τ) is cal... See full list on huggingface.co Reducing variance with Actor-Critic methods The solution to reducing the variance of Reinforce algorithm and training our agent faster and better is to use a combination of policy-based and value-based methods: the Actor-Critic method. To understand the Actor-Critic, imagine you play a video game. You can play with a friend that will provide you some feedback. You’re the Actor, and your friend is the Critic. You don’t know how to play at the beginning, so you try some actions randomly. The Critic observes your action and provides feedb... The Actor-Critic Process Now that we have seen the Actor Critic's big picture, let's dive deeper to understand how Actor and Critic improve together during the training. As we saw, with Actor-Critic methods there are two function approximations (two neural networks): 1. Actor, a policy function parameterized by theta: πθ(s,a)\\pi_{\\theta}(s,a) πθ(s,a) 2. Critic, a value function parameterized by w: qw(s,a)\\hat{q}_{w}(s,a) qw(s,a) Let's see the training process to understand how Actor and Critic are optimized: 1.... Advantage Actor Critic We can stabilize learning further by using the Advantage function as Critic instead of the Action value function. The idea is that the Advantage function calculates how better taking that action at a state is compared to the average value of the state. It’s subtracting the mean value of the state from the state action pair: In other words, this function calculates the extra reward we get if we take this action at that state compared to the mean reward we get at that state. The extra reward is... See full list on huggingface.co Now that you've studied the theory behind Advantage Actor Critic ( A2C ), you're ready to train your A2C agentusing Stable-Baselines3 in robotic environments. Start the tutorial here 👉 https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit7/unit7.ipynb The leaderboard to compare your results with your classmates 🏆 👉 http... See full list on huggingface.co Congrats on finishing this chapter! There was a lot of information. And congrats on finishing the tutorial. 🥳. It's normal if you still feel confused with all these elements. This was the same for me and for all people who studied RL. Take time to grasp the material before continuing. Look also at the additional reading materials we provided in th... See full list on huggingface.co Apr 14, 2023 · Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym Combining DQNs and REINFORCE algorithm for training agents Oct 5, 2020 · A2C 通过梯度同步更新提高训练效率，适用于大规模并行环境。 文章涵盖了 A2C 的原理、Actor-Critic方法、算法实现及代码示例，展示了如何在PyTorch中实现 A2C ，并提供了训练过程的可视化。 How to reduce variance in A2C? To reduce this variance, we can introduce a baseline . A2C tackles this issue by using a critic value function, which is conditioned on the state, as the baseline. The difference between the Q value and the state value is then calculated as the advantage. How does A2C work? A2C tackles this issue by using a critic value function , which is conditioned on the state, as the baseline. The difference between the Q value and the state value is then calculated as the advantage. A (s t, a t) = Q (s t, a t) V (s t) What is a coma loss function in A2C training? In the sampling stage, agents share information with each other, including their observations and predicted actions. Once the necessary information has been collected, all agents follow the standard A2C training pipeline. However, in order to update the policy, agents use the counterfactual multi-agent (COMA) loss function. What is ia2c and how does it work? IA2C is a straightforward adaptation of the standard A2C algorithm to multi-agent scenarios , where each agent acts as an A2C-based sampler and learner. Unlike some other multi-agent algorithms, IA2C does not require information sharing among agents to function effectively. However, the option to share knowledge among agents is available in IA2C. How do you calculate DQN loss? loss = -1 x Σlog (probability) x (Reward+ γ V (S`)-V (S)) And for DQN it remains Mean Squared Error. hence the final loss: -1 x Σlog (probability)x (Reward+γV (S`)-V (S))+MSE (Actual_V (S),Predicted_V (S)) How do you calculate Gan loss? If you have read about GANs, this concept may sound a bit familiar where we have a generator and discriminator involved in an adversarial system. loss = -1 x Σlog (probability) x (Reward+ γ V (S`)-V (S)) And for DQN it remains Mean Squared Error. hence the final loss: The Symmetric Reinforcement Learning (SRL) loss L srl consists of two parts like SCE ( Equation 6): the original RL loss L rl ( A2C or PPO) and the corresponding reverse RL loss L rev ( RA2C or RPPO).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/deep-rl-a2c", "content": "In Reinforce, we want to increase the probability of actions in a trajectory proportional to how high the return is. 1. If the return is high, we will push upthe probabilities of the (state, action) combinations. 2. Else, if the return is low, it will push downthe probabilities of the (state, action) combinations. This return R(τ)R(\\tau)R(τ) is cal... See full list on huggingface.co Reducing variance with Actor-Critic methods The solution to reducing the variance of Reinforce algorithm and training our agent faster and better is to use a combination of policy-based and value-based methods: the Actor-Critic method. To understand the Actor-Critic, imagine you play a video game. You can play with a friend that will provide you some feedback. You’re the Actor, and your friend is the Critic. You don’t know how to play at the beginning, so you try some actions randomly. The Critic observes your action and provides feedb... The Actor-Critic Process Now that we have seen the Actor Critic's big picture, let's dive deeper to understand how Actor and Critic improve together during the training. As we saw, with Actor-Critic methods there are two function approximations (two neural networks): 1. Actor, a policy function parameterized by theta: πθ(s,a)\\pi_{\\theta}(s,a) πθ(s,a) 2. Critic, a value function parameterized by w: qw(s,a)\\hat{q}_{w}(s,a) qw(s,a) Let's see the training process to understand how Actor and Critic are optimized: 1.... Advantage Actor Critic We can stabilize learning further by using the Advantage function as Critic instead of the Action value function. The idea is that the Advantage function calculates how better taking that action at a state is compared to the average value of the state. It’s subtracting the mean value of the state from the state action pair: In other words, this function calculates the extra reward we get if we take this action at that state compared to the mean reward we get at that state. The extra reward is... See full list on huggingface.co Now that you've studied the theory behind Advantage Actor Critic ( A2C ), you're ready to train your A2C agentusing Stable-Baselines3 in robotic environments. Start the tutorial here 👉 https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit7/unit7.ipynb The leaderboard to compare your results with your classmates 🏆 👉 http... See full list on huggingface.co Congrats on finishing this chapter! There was a lot of information. And congrats on finishing the tutorial. 🥳. It's normal if you still feel confused with all these elements. This was the same for me and for all people who studied RL. Take time to grasp the material before continuing. Look also at the additional reading materials we provided in th... See full list on huggingface.co Apr 14, 2023 · Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym Combining DQNs and REINFORCE algorithm for training agents Oct 5, 2020 · A2C 通过梯度同步更新提高训练效率，适用于大规模并行环境。 文章涵盖了 A2C 的原理、Actor-Critic方法、算法实现及代码示例，展示了如何在PyTorch中实现 A2C ，并提供了训练过程的可视化。 How to reduce variance in A2C? To reduce this variance, we can introduce a baseline . A2C tackles this issue by using a critic value function, which is conditioned on the state, as the baseline. The difference between the Q value and the state value is then calculated as the advantage. How does A2C work? A2C tackles this issue by using a critic value function , which is conditioned on the state, as the baseline. The difference between the Q value and the state value is then calculated as the advantage. A (s t, a t) = Q (s t, a t) V (s t) What is a coma loss function in A2C training? In the sampling stage, agents share information with each other, including their observations and predicted actions. Once the necessary information has been collected, all agents follow the standard A2C training pipeline. However, in order to update the policy, agents use the counterfactual multi-agent (COMA) loss function. What is ia2c and how does it work? IA2C is a straightforward adaptation of the standard A2C algorithm to multi-agent scenarios , where each agent acts as an A2C-based sampler and learner. Unlike some other multi-agent algorithms, IA2C does not require information sharing among agents to function effectively. However, the option to share knowledge among agents is available in IA2C. How do you calculate DQN loss? loss = -1 x Σlog (probability) x (Reward+ γ V (S`)-V (S)) And for DQN it remains Mean Squared Error. hence the final loss: -1 x Σlog (probability)x (Reward+γV (S`)-V (S))+MSE (Actual_V (S),Predicted_V (S)) How do you calculate Gan loss? If you have read about GANs, this concept may sound a bit familiar where we have a generator and discriminator involved in an adversarial system. loss = -1 x Σlog (probability) x (Reward+ γ V (S`)-V (S)) And for DQN it remains Mean Squared Error. hence the final loss: The Symmetric Reinforcement Learning (SRL) loss L srl consists of two parts like SCE ( Equation 6): the original RL loss L rl ( A2C or PPO) and the corresponding reverse RL loss L rev ( RA2C or RPPO)."}
+{"idx": 3, "title": "Advantage Actor-Critic (A2C) algorithm in Reinforcement ... RL策略梯度方法之 (五): Advantage Actor-Critic (A2C)_a2c算法 输出... Advanced Actor Critic Family — MARLlib v1.0.0 documentation Advanced Actor Critic Family — MARLlib v1.0.0 documentation Advanced Actor Critic Family — MARLlib v1.0.0 documentation Advanced Actor Critic Family — MARLlib v1.0.0 documentation Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "Apr 14, 2023 · Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym Combining DQNs and REINFORCE algorithm for training agents Oct 5, 2020 · A2C 通过梯度同步更新提高训练效率，适用于大规模并行环境。 文章涵盖了 A2C 的原理、Actor-Critic方法、算法实现及代码示例，展示了如何在PyTorch中实现 A2C ，并提供了训练过程的可视化。 How to reduce variance in A2C? To reduce this variance, we can introduce a baseline . A2C tackles this issue by using a critic value function, which is conditioned on the state, as the baseline. The difference between the Q value and the state value is then calculated as the advantage. How does A2C work? A2C tackles this issue by using a critic value function , which is conditioned on the state, as the baseline. The difference between the Q value and the state value is then calculated as the advantage. A (s t, a t) = Q (s t, a t) V (s t) What is a coma loss function in A2C training? In the sampling stage, agents share information with each other, including their observations and predicted actions. Once the necessary information has been collected, all agents follow the standard A2C training pipeline. However, in order to update the policy, agents use the counterfactual multi-agent (COMA) loss function. What is ia2c and how does it work? IA2C is a straightforward adaptation of the standard A2C algorithm to multi-agent scenarios , where each agent acts as an A2C-based sampler and learner. Unlike some other multi-agent algorithms, IA2C does not require information sharing among agents to function effectively. However, the option to share knowledge among agents is available in IA2C. How do you calculate DQN loss? loss = -1 x Σlog (probability) x (Reward+ γ V (S`)-V (S)) And for DQN it remains Mean Squared Error. hence the final loss: -1 x Σlog (probability)x (Reward+γV (S`)-V (S))+MSE (Actual_V (S),Predicted_V (S)) How do you calculate Gan loss? If you have read about GANs, this concept may sound a bit familiar where we have a generator and discriminator involved in an adversarial system. loss = -1 x Σlog (probability) x (Reward+ γ V (S`)-V (S)) And for DQN it remains Mean Squared Error. hence the final loss: The Symmetric Reinforcement Learning (SRL) loss L srl consists of two parts like SCE ( Equation 6): the original RL loss L rl ( A2C or PPO) and the corresponding reverse RL loss L rev ( RA2C or RPPO).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science-in-your-pocket/advantage-actor-critic-a2c-algorithm-in-reinforcement-learning-with-codes-and-examples-using-e810273c0c9e", "content": "Apr 14, 2023 · Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym Combining DQNs and REINFORCE algorithm for training agents Oct 5, 2020 · A2C 通过梯度同步更新提高训练效率，适用于大规模并行环境。 文章涵盖了 A2C 的原理、Actor-Critic方法、算法实现及代码示例，展示了如何在PyTorch中实现 A2C ，并提供了训练过程的可视化。 How to reduce variance in A2C? To reduce this variance, we can introduce a baseline . A2C tackles this issue by using a critic value function, which is conditioned on the state, as the baseline. The difference between the Q value and the state value is then calculated as the advantage. How does A2C work? A2C tackles this issue by using a critic value function , which is conditioned on the state, as the baseline. The difference between the Q value and the state value is then calculated as the advantage. A (s t, a t) = Q (s t, a t) V (s t) What is a coma loss function in A2C training? In the sampling stage, agents share information with each other, including their observations and predicted actions. Once the necessary information has been collected, all agents follow the standard A2C training pipeline. However, in order to update the policy, agents use the counterfactual multi-agent (COMA) loss function. What is ia2c and how does it work? IA2C is a straightforward adaptation of the standard A2C algorithm to multi-agent scenarios , where each agent acts as an A2C-based sampler and learner. Unlike some other multi-agent algorithms, IA2C does not require information sharing among agents to function effectively. However, the option to share knowledge among agents is available in IA2C. How do you calculate DQN loss? loss = -1 x Σlog (probability) x (Reward+ γ V (S`)-V (S)) And for DQN it remains Mean Squared Error. hence the final loss: -1 x Σlog (probability)x (Reward+γV (S`)-V (S))+MSE (Actual_V (S),Predicted_V (S)) How do you calculate Gan loss? If you have read about GANs, this concept may sound a bit familiar where we have a generator and discriminator involved in an adversarial system. loss = -1 x Σlog (probability) x (Reward+ γ V (S`)-V (S)) And for DQN it remains Mean Squared Error. hence the final loss: The Symmetric Reinforcement Learning (SRL) loss L srl consists of two parts like SCE ( Equation 6): the original RL loss L rl ( A2C or PPO) and the corresponding reverse RL loss L rev ( RA2C or RPPO)."}
+{"idx": 4, "title": "RL策略梯度方法之 (五): Advantage Actor-Critic (A2C)_a2c算法 输出...", "date": "", "ddg_snippet": "Oct 5, 2020 · A2C 通过梯度同步更新提高训练效率，适用于大规模并行环境。 文章涵盖了 A2C 的原理、Actor-Critic方法、算法实现及代码示例，展示了如何在PyTorch中实现 A2C ，并提供了训练过程的可视化。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_38293297/article/details/108919172", "content": "Oct 5, 2020 · A2C 通过梯度同步更新提高训练效率，适用于大规模并行环境。 文章涵盖了 A2C 的原理、Actor-Critic方法、算法实现及代码示例，展示了如何在PyTorch中实现 A2C ，并提供了训练过程的可视化。"}
+{"idx": 5, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "The Symmetric Reinforcement Learning (SRL) loss L srl consists of two parts like SCE ( Equation 6): the original RL loss L rl ( A2C or PPO) and the corresponding reverse RL loss L rev ( RA2C or RPPO).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17618v2", "content": "The Symmetric Reinforcement Learning (SRL) loss L srl consists of two parts like SCE ( Equation 6): the original RL loss L rl ( A2C or PPO) and the corresponding reverse RL loss L rev ( RA2C or RPPO)."}
+{"idx": 6, "title": "PyTorch Loss Functions: The Ultimate Guide", "date": "", "ddg_snippet": "The Pytorch L1 Loss is expressed as: equation . x represents the actual value and y the predicted value.The Pytorch Triplet Margin Loss is expressed as: When could it be used? Determining the relative similarity existing between samples .", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/pytorch-loss-functions", "content": "The Pytorch L1 Loss is expressed as: equation . x represents the actual value and y the predicted value.The Pytorch Triplet Margin Loss is expressed as: When could it be used? Determining the relative similarity existing between samples ."}
+{"idx": 7, "title": "Balance Chemical Equation - Online Balancer", "date": "", "ddg_snippet": "Enter an equation of a chemical reaction and click 'Balance'. The answer will appear below. Always use the upper case for the first character in the element name and the lower case for the second character.", "subpage_snippet": "", "source": "www.webqc.org", "link": "https://www.webqc.org/balance.php", "content": "Enter an equation of a chemical reaction and click 'Balance'. The answer will appear below. Always use the upper case for the first character in the element name and the lower case for the second character."}
+{"idx": 8, "title": "Advanced Actor Critic Family — MARLlib v1.0.0 documentation", "date": "", "ddg_snippet": "A2C tackles this issue by using a critic value function, which is conditioned on the state, as the baseline. The difference between the Q value and the state value is then calculated as the advantage. A (s t, a t) = Q (s t, a t) V (s t) Now we need two functions Q and V to estimate A. Luckily we can do some transformations for the above equation .", "subpage_snippet": "", "source": "marllib.readthedocs.io", "link": "https://marllib.readthedocs.io/en/latest/algorithm/a2c_family.html", "content": "A2C tackles this issue by using a critic value function, which is conditioned on the state, as the baseline. The difference between the Q value and the state value is then calculated as the advantage. A (s t, a t) = Q (s t, a t) V (s t) Now we need two functions Q and V to estimate A. Luckily we can do some transformations for the above equation ."}
+{"idx": 9, "title": "multilabel_confusion_matrix — scikit-learn 1. 7 .2 documentation", "date": "", "ddg_snippet": "Compute class-wise (default) or sample - wise (samplewise=True) multilabel confusion matrix to evaluate the accuracy of a classification, and output confusion matrices for each class or sample.", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.multilabel_confusion_matrix.html", "content": "Compute class-wise (default) or sample - wise (samplewise=True) multilabel confusion matrix to evaluate the accuracy of a classification, and output confusion matrices for each class or sample."}
diff --git a/data/sampled_jsons/scaling_exponent_-0.5_-1.0_dataset_size_neural_language_model_pre-training.jsonl b/data/sampled_jsons/scaling_exponent_-0.5_-1.0_dataset_size_neural_language_model_pre-training.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7192b50a83a451154edad49e66a9e7712968dd73
--- /dev/null
+++ b/data/sampled_jsons/scaling_exponent_-0.5_-1.0_dataset_size_neural_language_model_pre-training.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Scaling Laws for Deep Regression", "date": "", "ddg_snippet": "12 Sept 2025 — ... dataset size , and α D \\alpha_{D} is the scaling exponent . All data are plotted on a log-log scale, with dataset sizes ranging from 224 to 114688 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10000v1", "content": "12 Sept 2025 — ... dataset size , and α D \\alpha_{D} is the scaling exponent . All data are plotted on a log-log scale, with dataset sizes ranging from 224 to 114688 ..."}
+{"idx": 1, "title": "Understanding the Neural Scaling Law: A Roadmap to ...", "date": "", "ddg_snippet": "α (alpha) is the scaling exponent , which varies by task and architecture. This relationship implies diminishing returns — each doubling of scale ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@data-overload/understanding-the-neural-scaling-law-a-roadmap-to-more-powerful-ai-e39aab06ee70", "content": "α (alpha) is the scaling exponent , which varies by task and architecture. This relationship implies diminishing returns — each doubling of scale ..."}
+{"idx": 2, "title": "Understanding Scaling Laws with Statistical and ...", "date": "", "ddg_snippet": "Predicting empirical model scaling exponent α ^ N subscript ^ 𝛼 N \\hat ... model size and data size for LLMs trained on natural language datasets . We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.06646v1", "content": "Predicting empirical model scaling exponent α ^ N subscript ^ 𝛼 N \\hat ... model size and data size for LLMs trained on natural language datasets . We ..."}
+{"idx": 3, "title": "Explaining neural scaling laws - PMC", "date": "", "ddg_snippet": "by Y Bahri · 2024 · Cited by 384 — We study the dependence of the scaling exponent on changes in architecture and data, finding that i) changing the input distribution via switching datasets and ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11228526/", "content": "by Y Bahri · 2024 · Cited by 384 — We study the dependence of the scaling exponent on changes in architecture and data, finding that i) changing the input distribution via switching datasets and ..."}
+{"idx": 4, "title": "Explaining Scaling Laws of Neural Network Generalization", "date": "", "ddg_snippet": "by Y Bahri · Cited by 3 — We do not claim to be the first to derive a scaling exponent proportional to 1/d and indeed, we cite earlier works that have done so in both ML ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FvfV64rovnY", "content": "by Y Bahri · Cited by 3 — We do not claim to be the first to derive a scaling exponent proportional to 1/d and indeed, we cite earlier works that have done so in both ML ..."}
+{"idx": 5, "title": "Scaling Laws Across Model Architectures: A Comparative ...", "date": "", "ddg_snippet": "by S Wang · 2024 · Cited by 7 — The most crucial part of this process is to find the scaling exponent of the model scale and to- kens number with reference to the compute ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.319.pdf", "content": "by S Wang · 2024 · Cited by 7 — The most crucial part of this process is to find the scaling exponent of the model scale and to- kens number with reference to the compute ..."}
+{"idx": 6, "title": "Scaling Laws Literature Review", "date": "", "ddg_snippet": "by P Villalobos · Cited by 19 — However, this clashes with empirical results, in which the scaling exponent is usually smaller than 1 / 2 . The modern study of scaling laws ...", "subpage_snippet": "", "source": "epoch.ai", "link": "https://epoch.ai/blog/scaling-laws-literature-review", "content": "by P Villalobos · Cited by 19 — However, this clashes with empirical results, in which the scaling exponent is usually smaller than 1 / 2 . The modern study of scaling laws ..."}
+{"idx": 7, "title": "Understanding Scaling Laws with Statistical and ...", "date": "", "ddg_snippet": "The model scaling exponent ˆαN is obtained similarly. Estimating the ID of ... Intrinsic dimension predicts the empirical data scaling exponent ˆαD To validate ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95466", "content": "The model scaling exponent ˆαN is obtained similarly. Estimating the ID of ... Intrinsic dimension predicts the empirical data scaling exponent ˆαD To validate ..."}
+{"idx": 8, "title": "Scaling laws for the value of individual data points in ...", "date": "", "ddg_snippet": "by I Covert · 2024 · Cited by 8 — These scaling laws can help design a model's training dataset ... Interestingly, there is significant variability in the scaling exponent ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3692443", "content": "by I Covert · 2024 · Cited by 8 — These scaling laws can help design a model's training dataset ... Interestingly, there is significant variability in the scaling exponent ..."}
+{"idx": 9, "title": "Understanding Scaling Laws with Statistical and ...", "date": "", "ddg_snippet": "by A Havrilla · Cited by 17 — To obtain the data scaling exponent ˆαD, we plot the test loss (comparable to squared error) versus the data size n in log-log scale, fit the log-log curve with ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=N2wYPMpifA", "content": "by A Havrilla · Cited by 17 — To obtain the data scaling exponent ˆαD, we plot the test loss (comparable to squared error) versus the data size n in log-log scale, fit the log-log curve with ..."}
diff --git a/data/sampled_jsons/scaling_factor_epoch_threshold_Eloops_En_Et_formula.jsonl b/data/sampled_jsons/scaling_factor_epoch_threshold_Eloops_En_Et_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..18b6ad66579801219cb08c6c3f684cb599c11805
--- /dev/null
+++ b/data/sampled_jsons/scaling_factor_epoch_threshold_Eloops_En_Et_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2012.06882v2 [astro-ph.CO] 24 Mar 2021", "date": "", "ddg_snippet": "by N Ramberg · 2020 · Cited by 35 — We treat the equation of state as a free parameter ranging over −1/3 < wφ < 1/3, exclud- ing a post-inflation accelerated epoch where wφ < −1/3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2012.06882", "content": "by N Ramberg · 2020 · Cited by 35 — We treat the equation of state as a free parameter ranging over −1/3 < wφ < 1/3, exclud- ing a post-inflation accelerated epoch where wφ < −1/3."}
+{"idx": 1, "title": "Phenomenology of Hidden Sector Physics", "date": "", "ddg_snippet": "by Y Cui · 2008 · Cited by 3 — equations . We do this by solving for the scale factor a(t) using Eq. (3.55). After thermal inflation, the two dominant sources of energy density are ρφ ...", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "https://deepblue.lib.umich.edu/bitstream/handle/2027.42/60760/yocui_1.pdf;sequence=1", "content": "by Y Cui · 2008 · Cited by 3 — equations . We do this by solving for the scale factor a(t) using Eq. (3.55). After thermal inflation, the two dominant sources of energy density are ρφ ..."}
+{"idx": 2, "title": "Linux Standard Base Core Specification 3.2", "date": "", "ddg_snippet": "13 Mar 2012 — ... and shall be interpreted as described below. Code Alignment Factor . An unsigned LEB128 encoded value that is factored out of all advance.", "subpage_snippet": "", "source": "refspecs.linuxbase.org", "link": "https://refspecs.linuxbase.org/LSB_3.2.0/LSB-Core-generic/LSB-Core-generic.pdf", "content": "13 Mar 2012 — ... and shall be interpreted as described below. Code Alignment Factor . An unsigned LEB128 encoded value that is factored out of all advance."}
+{"idx": 3, "title": "Scale-Symmetric Theory (SST)", "date": "", "ddg_snippet": "7 Oct 2022 — Formulas , figures and tables in applications are preceded by a letter name and a number given to each application (e.g. B.3….). The Index leads ...", "subpage_snippet": "", "source": "vixra.org", "link": "https://vixra.org/pdf/2110.0171v2.pdf", "content": "7 Oct 2022 — Formulas , figures and tables in applications are preceded by a letter name and a number given to each application (e.g. B.3….). The Index leads ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 5, "title": "arXiv:1904.05707v1 [astro-ph.CO] 11 Apr 2019", "date": "", "ddg_snippet": "by N Ramberg · 2019 · Cited by 82 — (Dated: April 12, 2019). We show results for the expected reach of the network of experiments that is being set up globally.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1904.05707", "content": "by N Ramberg · 2019 · Cited by 82 — (Dated: April 12, 2019). We show results for the expected reach of the network of experiments that is being set up globally."}
+{"idx": 6, "title": "Structural, Syntactic, and Statistical Pattern Recognition", "date": "", "ddg_snippet": "This volume contains the papers presented at the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2020) and ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-73973-7.pdf", "content": "This volume contains the papers presented at the joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2020) and ..."}
+{"idx": 7, "title": "Data Science: Foundations and Applications", "date": "", "ddg_snippet": "10 Jun 2025 — This year we also organized a Special Session on Data Science Foundations and Applications (DSFA).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-981-96-8298-0.pdf", "content": "10 Jun 2025 — This year we also organized a Special Session on Data Science Foundations and Applications (DSFA)."}
+{"idx": 8, "title": "man-pages-6.8.pdf", "date": "", "ddg_snippet": "2 May 2024 — Section 1 of the manual describes user commands and tools, for example, file manipula- tion tools, shells, compilers, web browsers, file and ... 3,642 pages", "subpage_snippet": "", "source": "cdn.kernel.org", "link": "https://cdn.kernel.org/pub/linux/docs/man-pages/book/man-pages-6.8.pdf", "content": "2 May 2024 — Section 1 of the manual describes user commands and tools, for example, file manipula- tion tools, shells, compilers, web browsers, file and ... 3,642 pages"}
+{"idx": 9, "title": "The Telecommunications and Data Acquisition Progress ...", "date": "", "ddg_snippet": "by EC Posner · 1986 — This quarterly publication provides archival reports on developments in programs managed by JPL's Office of Telecommunications and Data ...", "subpage_snippet": "", "source": "ntrs.nasa.gov", "link": "https://ntrs.nasa.gov/api/citations/19860018803/downloads/19860018803.pdf", "content": "by EC Posner · 1986 — This quarterly publication provides archival reports on developments in programs managed by JPL's Office of Telecommunications and Data ..."}
diff --git a/data/sampled_jsons/scaling_law_dataset_size_exponent_Kaplan_2020.jsonl b/data/sampled_jsons/scaling_law_dataset_size_exponent_Kaplan_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e4aed6f78a36ef2dc24518e5cd2fb3583b6fb7e9
--- /dev/null
+++ b/data/sampled_jsons/scaling_law_dataset_size_exponent_Kaplan_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law", "date": "", "ddg_snippet": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down."}
+{"idx": 1, "title": "Scaling Laws from the Data Manifold Dimension", "date": "", "ddg_snippet": "To shrink the size of the sub-regions by a factor of 2 requires increasing the parameter count by a factor of 2d, and so the inverse of the scaling exponent 1= will be proportional to the intrinsic dimension d of the data manifold. We develop these ideas in detail in section 2.", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume23/20-1111/20-1111.pdf", "content": "To shrink the size of the sub-regions by a factor of 2 requires increasing the parameter count by a factor of 2d, and so the inverse of the scaling exponent 1= will be proportional to the intrinsic dimension d of the data manifold. We develop these ideas in detail in section 2."}
+{"idx": 2, "title": "Explaining neural scaling laws - PNAS", "date": "", "ddg_snippet": "Jun 24, 2024 · We propose a theory that explains the origins of and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both dataset and model size , for a total of four scaling regimes.", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.2311878121", "content": "Jun 24, 2024 · We propose a theory that explains the origins of and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both dataset and model size , for a total of four scaling regimes."}
+{"idx": 3, "title": "A Neural Scaling Law from the Dimension of the Data Manifold", "date": "", "ddg_snippet": "The scaling exponent can be measured by training a succession of models of varying size. We measure the intrinsic dimension d within the final layer1 activations of trained networks, using the distances among nearest neighbor activation vectors [LB05, FdRL17].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2004.10802", "content": "The scaling exponent can be measured by training a succession of models of varying size. We measure the intrinsic dimension d within the final layer1 activations of trained networks, using the distances among nearest neighbor activation vectors [LB05, FdRL17]."}
+{"idx": 4, "title": "Scaling Laws for Neural Language Models - papers.baulab.info", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/Kaplan-2020.pdf", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude."}
+{"idx": 5, "title": "17_Scaling_Laws_0414", "date": "", "ddg_snippet": "Observations from Kaplan et al., 2020 Performance depends strongly on scale (model params, data size , and compute used for training), weakly on model shape (e.g., depth, width)", "subpage_snippet": "", "source": "people.cs.umass.edu", "link": "https://people.cs.umass.edu/~hschang/cs685/slides/17_Scaling_Laws_0414.pdf", "content": "Observations from Kaplan et al., 2020 Performance depends strongly on scale (model params, data size , and compute used for training), weakly on model shape (e.g., depth, width)"}
+{"idx": 6, "title": "Scaling Laws and Emergent Abilities in LLMs - Medium", "date": "", "ddg_snippet": "Oct 25, 2024 · The Kaplan scaling law , introduced by OpenAI in 2020 , reveals the relationship between model size , dataset size , and performance in large language models (LLMs).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@lmpo/scaling-laws-and-emergent-abilities-in-llms-a02d6e98bb14", "content": "Oct 25, 2024 · The Kaplan scaling law , introduced by OpenAI in 2020 , reveals the relationship between model size , dataset size , and performance in large language models (LLMs)."}
+{"idx": 7, "title": "Scaling Laws from the Data Manifold Dimension", "date": "", "ddg_snippet": "We confirm the theory by independently measuring the intrinsic dimension and the scaling exponents in a teacher/student framework, where we can study a variety of $d$ and $\\alpha$ by dialing the properties of random teacher networks.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/v23/20-1111.html", "content": "We confirm the theory by independently measuring the intrinsic dimension and the scaling exponents in a teacher/student framework, where we can study a variety of $d$ and $\\alpha$ by dialing the properties of random teacher networks."}
+{"idx": 8, "title": "[2001.08361] Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "by J Kaplan · 2020 · Cited by 4575 — We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size , dataset size , and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2001.08361", "content": "by J Kaplan · 2020 · Cited by 4575 — We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power - law with model size , dataset size , and the ..."}
+{"idx": 9, "title": "AI Scaling Laws Explained", "date": "", "ddg_snippet": "The paper Scaling Laws for Neural Language Models by Kaplan et al. ( 2020 ) discovered that the performance of a language model doesn't improve ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@pacosun/beyond-the-parameter-count-8ba5bb3a5543", "content": "The paper Scaling Laws for Neural Language Models by Kaplan et al. ( 2020 ) discovered that the performance of a language model doesn't improve ..."}
diff --git a/data/sampled_jsons/scholar_OmniBench_Coverage_Rate.jsonl b/data/sampled_jsons/scholar_OmniBench_Coverage_Rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a953d0f34a7e0f6cf64dd0197fab27ed60a6c125
--- /dev/null
+++ b/data/sampled_jsons/scholar_OmniBench_Coverage_Rate.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable ...", "date": "", "ddg_snippet": "Coverage Rate (CR). It evaluates an agent’s progress on a task graph by weighting subtasks based on their depth, where deeper subtasks are assigned higher weights due to their increased num-ber of prerequisite subtasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.08933", "content": "Coverage Rate (CR). It evaluates an agent’s progress on a task graph by weighting subtasks based on their depth, where deeper subtasks are assigned higher weights due to their increased num-ber of prerequisite subtasks."}
+{"idx": 1, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "May 1, 2025 · The authors design a graph-based evaluator on a DAG and introduce two novel evaluation metrics, Coverage Rate (CR) and Logical Consistency (LC), enabling a more reasonable and fine-grained evaluation of agents.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "May 1, 2025 · The authors design a graph-based evaluator on a DAG and introduce two novel evaluation metrics, Coverage Rate (CR) and Logical Consistency (LC), enabling a more reasonable and fine-grained evaluation of agents."}
+{"idx": 2, "title": "[논문 리뷰] What Limits Virtual Agent Application? OmniBench: A ....", "date": "", "ddg_snippet": "이러한 문제에 대응하기 위해 논문은 자체 생성 (self-generating), 크로스 플랫폼 (cross-platform), 그래프 기반 (graph-based) 벤치마크인 Omni Ben ch 를 제안합니다.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/ko/review/what-limits-virtual-agent-application-omnibench-a-scalable-multi-dimensional-benchmark-for-essential-virtual-agent-capabilities", "content": "이러한 문제에 대응하기 위해 논문은 자체 생성 (self-generating), 크로스 플랫폼 (cross-platform), 그래프 기반 (graph-based) 벤치마크인 Omni Ben ch 를 제안합니다."}
+{"idx": 3, "title": "[Literature Review] What Limits Virtual Agent Application? ...", "date": "", "ddg_snippet": "9 Jun 2025 — ... OmniBench is a self-generating, cross-platform, graph ... Coverage Rate (CR):** Measures agent progress on the task graph ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/what-limits-virtual-agent-application-omnibench-a-scalable-multi-dimensional-benchmark-for-essential-virtual-agent-capabilities", "content": "9 Jun 2025 — ... OmniBench is a self-generating, cross-platform, graph ... Coverage Rate (CR):** Measures agent progress on the task graph ..."}
+{"idx": 4, "title": "Bi-Weekly AI Research Roundup - State of AI", "date": "", "ddg_snippet": "Sep 25, 2024 · This paper introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously.", "subpage_snippet": "", "source": "stateai.substack.com", "link": "https://stateai.substack.com/p/bi-weekly-ai-research-roundup-912", "content": "Sep 25, 2024 · This paper introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously."}
+{"idx": 5, "title": "OmniEval: Unified AI Benchmarking", "date": "", "ddg_snippet": "OmniEval is a comprehensive framework that benchmarks AI models across multiple modalities, languages, and domain-specific tasks. It employs dynamic model-centric evaluation with entropy-driven sampling to reveal discrepancies like overconfidence and out-of-distribution brittleness.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/omnieval", "content": "OmniEval is a comprehensive framework that benchmarks AI models across multiple modalities, languages, and domain-specific tasks. It employs dynamic model-centric evaluation with entropy-driven sampling to reveal discrepancies like overconfidence and out-of-distribution brittleness."}
+{"idx": 6, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "In this paper, we investigate how sensitive video self-supervised learning is to the current conventional benchmark and whether methods generalize beyond the canonical evaluation setting. We do this across four different factors of sensitivity: domain, samples, actions and task.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=IS-Bench", "content": "In this paper, we investigate how sensitive video self-supervised learning is to the current conventional benchmark and whether methods generalize beyond the canonical evaluation setting. We do this across four different factors of sensitivity: domain, samples, actions and task."}
diff --git a/data/sampled_jsons/score_based_denoising_normalizing_flows_mechanism.jsonl b/data/sampled_jsons/score_based_denoising_normalizing_flows_mechanism.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd4afd1fa00a55f0f394ea84c4c3fcbec4caa652
--- /dev/null
+++ b/data/sampled_jsons/score_based_denoising_normalizing_flows_mechanism.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Score-Based Point Cloud Denoising (ICCV'21) - GitHub", "date": "", "ddg_snippet": "conda create --name score -denoise python=3.8 conda activate score -denoise conda install pytorch==1.9.0 torchvision==0.10.0 cudatoolkit=11.1 -c pytorch -c nvidia conda install -c conda-forge tqdm scipy scikit-learn pyyaml easydict tensorboard pandas # point_cloud_utils conda install -c conda-forge point_cloud_utils==0.18.0 # Pytorch3d conda install -c fvcore -c iopath -c conda-forge fvcore ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/luost26/score-denoise", "content": "conda create --name score -denoise python=3.8 conda activate score -denoise conda install pytorch==1.9.0 torchvision==0.10.0 cudatoolkit=11.1 -c pytorch -c nvidia conda install -c conda-forge tqdm scipy scikit-learn pyyaml easydict tensorboard pandas # point_cloud_utils conda install -c conda-forge point_cloud_utils==0.18.0 # Pytorch3d conda install -c fvcore -c iopath -c conda-forge fvcore ..."}
+{"idx": 1, "title": "PDF Denoising Normalizing Flow - NeurIPS", "date": "", "ddg_snippet": "3 Denoising Normalizing Flow We answer the research question based on the theoretical work developed in [20]. First, we briefly review this work, and then introduce the DNF. Preliminaries: In Section 2, we discussed why classical NFs are not suited to infer low-dimensional representations of the given data.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/4c07fe24771249c343e70c32289c1192-Paper.pdf", "content": "3 Denoising Normalizing Flow We answer the research question based on the theoretical work developed in [20]. First, we briefly review this work, and then introduce the DNF. Preliminaries: In Section 2, we discussed why classical NFs are not suited to infer low-dimensional representations of the given data."}
+{"idx": 2, "title": "Denoising Score Matching - Johannes Schusterbauer", "date": "", "ddg_snippet": "Score-based modeling circumvents the problem of finding the normalizing constant by modeling the score function, which is defined as The score is essentially the gradient of the true data log-likelihood evaluated on any point in data space.", "subpage_snippet": "", "source": "johfischer.com", "link": "https://johfischer.com/2022/09/18/denoising-score-matching/", "content": "Score-based modeling circumvents the problem of finding the normalizing constant by modeling the score function, which is defined as The score is essentially the gradient of the true data log-likelihood evaluated on any point in data space."}
+{"idx": 3, "title": "PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows", "date": "", "ddg_snippet": "Point cloud denoising aims to restore clean point clouds from raw observations corrupted by noise and outliers while preserving the fine-grained details. We present a novel deep learning- based denoising model, that incorporates normalizing flows and noise...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-20062-5_23", "content": "Point cloud denoising aims to restore clean point clouds from raw observations corrupted by noise and outliers while preserving the fine-grained details. We present a novel deep learning- based denoising model, that incorporates normalizing flows and noise..."}
+{"idx": 4, "title": "A Noising-Denoising Framework for Point Cloud Upsampling via ...", "date": "", "ddg_snippet": "In this paper, we propose a noising- denoising framework using continuous normalizing flows , dubbed ND-PUFlow, for point cloud upsampling. In the noising stage, ND-PUFlow samples noise from a Gaussian distribution and generates noisy points.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320323002698", "content": "In this paper, we propose a noising- denoising framework using continuous normalizing flows , dubbed ND-PUFlow, for point cloud upsampling. In the noising stage, ND-PUFlow samples noise from a Gaussian distribution and generates noisy points."}
+{"idx": 5, "title": "Adaptive and Iterative Point Cloud Denoising with Score‐Based Diffusion ...", "date": "", "ddg_snippet": "In this paper, we propose an adaptive and iterative point cloud denoising method based on the score-based diffusion model. Compared to the state-of-the-art point cloud denoising methods, our approach...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1111/cgf.70149", "content": "In this paper, we propose an adaptive and iterative point cloud denoising method based on the score-based diffusion model. Compared to the state-of-the-art point cloud denoising methods, our approach..."}
+{"idx": 6, "title": "Denoising normalizing flow | Proceedings of the 35th International ...", "date": "", "ddg_snippet": "Here we propose a novel method - called Denoising Normalizing Flow (DNF) - that estimates the density on the low-dimensional manifold while learning the manifold as well. The DNF works in 3 steps. First, it inflates the manifold - making it diffeomorphic to the entire data-space.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3540261.3540957", "content": "Here we propose a novel method - called Denoising Normalizing Flow (DNF) - that estimates the density on the low-dimensional manifold while learning the manifold as well. The DNF works in 3 steps. First, it inflates the manifold - making it diffeomorphic to the entire data-space."}
+{"idx": 7, "title": "DFlow: A Generative Model Combining Denoising AutoEncoder and ...", "date": "", "ddg_snippet": "In this work, we present DFlow, a novel generative framework that combines Normalizing Flow (NF) with a Denoising AutoEncoder (DAE), for high-fidelity waveform generation. With a tactfully designed structure, DFlow seamlessly integrates the capabilities of both NF and DAE, resulting in a significantly improved performance compared to the standard NF models. Experimental results showcase DFlow ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/miao24d.html", "content": "In this work, we present DFlow, a novel generative framework that combines Normalizing Flow (NF) with a Denoising AutoEncoder (DAE), for high-fidelity waveform generation. With a tactfully designed structure, DFlow seamlessly integrates the capabilities of both NF and DAE, resulting in a significantly improved performance compared to the standard NF models. Experimental results showcase DFlow ..."}
+{"idx": 8, "title": "PDF PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows", "date": "", "ddg_snippet": "Abstract. Point cloud denoising aims to restore clean point clouds from raw observations corrupted by noise and outliers while preserving the fine-grained details. We present a novel deep learning- based denoising model, that incorporates normalizing flows and noise disentanglement techniques to achieve high denoising accuracy. Unlike existing works that extract features of point clouds for ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-20062-5_23.pdf", "content": "Abstract. Point cloud denoising aims to restore clean point clouds from raw observations corrupted by noise and outliers while preserving the fine-grained details. We present a novel deep learning- based denoising model, that incorporates normalizing flows and noise disentanglement techniques to achieve high denoising accuracy. Unlike existing works that extract features of point clouds for ..."}
+{"idx": 9, "title": "Score-Based Point Cloud Denoising - IEEE Xplore", "date": "", "ddg_snippet": "Point clouds acquired from scanning devices are often perturbed by noise, which affects downstream tasks such as surface reconstruction and analysis. The distribution of a noisy point cloud can be viewed as the distribution of a set of noise-free samples p(x) convolved with some noise model n, leading to (p * n)(x) whose mode is the underlying clean surface. To denoise a noisy point cloud, we ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9711416", "content": "Point clouds acquired from scanning devices are often perturbed by noise, which affects downstream tasks such as surface reconstruction and analysis. The distribution of a noisy point cloud can be viewed as the distribution of a set of noise-free samples p(x) convolved with some noise model n, leading to (p * n)(x) whose mode is the underlying clean surface. To denoise a noisy point cloud, we ..."}
diff --git a/data/sampled_jsons/score_matching_denoising_normalizing_flows_Tweedie_formula_Stein's_lemma.jsonl b/data/sampled_jsons/score_matching_denoising_normalizing_flows_Tweedie_formula_Stein's_lemma.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..74e165c562335c3b6007dec5852467baff465659
--- /dev/null
+++ b/data/sampled_jsons/score_matching_denoising_normalizing_flows_Tweedie_formula_Stein's_lemma.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "The Score-Difference Flow for Implicit Generative Modeling", "date": "", "ddg_snippet": "by RM Weber · Cited by 2 — Tweedie's formula (Efron, 2011),10 but we provide a separate proof of ... U-net architectures that form the backbone of modern denoising diffusion models (Karras ...", "subpage_snippet": "", "source": "studios.disneyresearch.com", "link": "https://studios.disneyresearch.com/app/uploads/2023/08/The-Score-Difference-Flow-for-Implicit-Generative-Modeling-Paper.pdf", "content": "by RM Weber · Cited by 2 — Tweedie's formula (Efron, 2011),10 but we provide a separate proof of ... U-net architectures that form the backbone of modern denoising diffusion models (Karras ..."}
+{"idx": 1, "title": "The Score-Difference Flow for Implicit Generative Modeling", "date": "", "ddg_snippet": "by RM Weber · 2023 · Cited by 2 — Tweedie's formula (Efron, 2011),9 but we provide a separate proof of ... U-net architectures that form the backbone of modern denoising diffusion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.12906", "content": "by RM Weber · 2023 · Cited by 2 — Tweedie's formula (Efron, 2011),9 but we provide a separate proof of ... U-net architectures that form the backbone of modern denoising diffusion ..."}
+{"idx": 2, "title": "Book - NIPS", "date": "", "ddg_snippet": "Normalization-Equivariant Neural Networks with Application to Image Denoising Sébastien Herbreteau, Emmanuel Moebel, Charles Kervrann Budgeting Counterfactual for Offline RL Yao Liu, Pratik Chaudhari, Rasool Fakoor Federated Conditional Stochastic Optimization Xidong Wu, Jianhui Sun, Zhengmian Hu, Junyi Li, Aidong Zhang, Heng Huang", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023", "content": "Normalization-Equivariant Neural Networks with Application to Image Denoising Sébastien Herbreteau, Emmanuel Moebel, Charles Kervrann Budgeting Counterfactual for Offline RL Yao Liu, Pratik Chaudhari, Rasool Fakoor Federated Conditional Stochastic Optimization Xidong Wu, Jianhui Sun, Zhengmian Hu, Junyi Li, Aidong Zhang, Heng Huang"}
+{"idx": 3, "title": "Daily Papers", "date": "", "ddg_snippet": "12 Sept 2025 — ... normalizing flows . This paper provides an alternative, Gaussian formulation ... Automated Denoising Score Matching for Nonlinear Diffusions.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Gaussian+latent+variables", "content": "12 Sept 2025 — ... normalizing flows . This paper provides an alternative, Gaussian formulation ... Automated Denoising Score Matching for Nonlinear Diffusions."}
diff --git a/data/sampled_jsons/sigmoid-based_contrastive_learning_framework.jsonl b/data/sampled_jsons/sigmoid-based_contrastive_learning_framework.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6320222843a2a1f829c41893c1a882110ddc972f
--- /dev/null
+++ b/data/sampled_jsons/sigmoid-based_contrastive_learning_framework.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SigCLR: Sigmoid Contrastive Learning of Visual Representations", "date": "", "ddg_snippet": "With this in mind, we revisit the sigmoid loss in the context of vision pretraining, where it falls short compared to softmax. To explore this further, we introduce SigCLR: Sigmoid Contrastive Learning of Visual Representations to investigate the potential of sigmoid-based contrastive pretraining for vision.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.17427v1", "content": "With this in mind, we revisit the sigmoid loss in the context of vision pretraining, where it falls short compared to softmax. To explore this further, we introduce SigCLR: Sigmoid Contrastive Learning of Visual Representations to investigate the potential of sigmoid-based contrastive pretraining for vision."}
+{"idx": 1, "title": "PDF Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "CCEM ( ), a framework for parameterizing var-ious well-known embedding structures by a single variable. Interestingly, the proposed CCEM is proven to contain the optimal em-bedding with respect to the sigmoid loss. Sec-ond, we mathematically analyze the optimal embedding minimizing the sigmoid loss for contrastive learning . The optimal embedding ranges from simplex equiangular-tight-frame to ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/lee24a/lee24a.pdf", "content": "CCEM ( ), a framework for parameterizing var-ious well-known embedding structures by a single variable. Interestingly, the proposed CCEM is proven to contain the optimal em-bedding with respect to the sigmoid loss. Sec-ond, we mathematically analyze the optimal embedding minimizing the sigmoid loss for contrastive learning . The optimal embedding ranges from simplex equiangular-tight-frame to ..."}
+{"idx": 2, "title": "GitHub - filipbasara0/sigmoid-contrastive-loss: Implementation of ...", "date": "", "ddg_snippet": "A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/filipbasara0/sigmoid-contrastive-loss", "content": "A PyTorch implementation of the modulated sigmoid pairwise loss for contrastive self-supervised learning on images. For more information, experiments, findings and an in-depth analysis please refer to the poster or to the extended abstract."}
+{"idx": 3, "title": "Sigmoidal Large Image Pre-training (SigLIP) Encoder", "date": "", "ddg_snippet": "The Sigmoidal Large Image Pre-training (SigLIP) Encoder is a family of vision-LLMs that utilize a sigmoid-based pairwise contrastive loss for large-scale multimodal pre-training, offering a computationally efficient and scalable alternative to traditional softmax-normalized contrastive losses. SigLIP encoders are foundational architectures for vision-language tasks such as zero-shot ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/sigmoidal-large-image-pre-training-siglip-encoder", "content": "The Sigmoidal Large Image Pre-training (SigLIP) Encoder is a family of vision-LLMs that utilize a sigmoid-based pairwise contrastive loss for large-scale multimodal pre-training, offering a computationally efficient and scalable alternative to traditional softmax-normalized contrastive losses. SigLIP encoders are foundational architectures for vision-language tasks such as zero-shot ..."}
+{"idx": 4, "title": "sigmoid-contrastive-learning · PyPI", "date": "", "ddg_snippet": "Sigmoid Contrastive Learning on Images A PyTorch implementation of the sigmoid pairwise loss for contrastive self-supervised learning on images. The training architecture consists of an online and a target encoder (EMA) with a simple critic MLP projector and is based on Representation Learning via Invariant Causal Mechanisms (ReLIC). The loss function is a sigmoid constrastive loss adapted ...", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/sigmoid-contrastive-learning/", "content": "Sigmoid Contrastive Learning on Images A PyTorch implementation of the sigmoid pairwise loss for contrastive self-supervised learning on images. The training architecture consists of an online and a target encoder (EMA) with a simple critic MLP projector and is based on Representation Learning via Invariant Causal Mechanisms (ReLIC). The loss function is a sigmoid constrastive loss adapted ..."}
+{"idx": 5, "title": "SigCLR: Sigmoid Contrastive Learning of Visual Representations | AI ...", "date": "", "ddg_snippet": "Conclusion The SigCLR paper introduces a novel contrastive learning framework that leverages a sigmoid function to enhance the learning of visual representations. The experimental results demonstrate the effectiveness of this approach, with SigCLR outperforming other state-of-the-art contrastive learning methods on a range of computer vision tasks.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/sigclr-sigmoid-contrastive-learning-visual-representations", "content": "Conclusion The SigCLR paper introduces a novel contrastive learning framework that leverages a sigmoid function to enhance the learning of visual representations. The experimental results demonstrate the effectiveness of this approach, with SigCLR outperforming other state-of-the-art contrastive learning methods on a range of computer vision tasks."}
+{"idx": 6, "title": "An Introduction to CLIP and SigLIP: Revolutionizing Multimodal Learning ...", "date": "", "ddg_snippet": "While CLIP uses a softmax- based contrastive loss, SigLIP adopts a pairwise sigmoid loss, which simplifies the training process and improves efficiency and performance, especially on large-scale ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@lmpo/an-introduction-to-clip-and-siglip-revolutionizing-multimodal-learning-016cfe6e4182", "content": "While CLIP uses a softmax- based contrastive loss, SigLIP adopts a pairwise sigmoid loss, which simplifies the training process and improves efficiency and performance, especially on large-scale ..."}
+{"idx": 7, "title": "SigCLR: Sigmoid Contrastive Learning of Visual Representations", "date": "", "ddg_snippet": "We propose SigCLR: Sigmoid Contrastive Learning of Visual Representations. SigCLR utilizes the logistic loss that only operates on pairs and does not require a global view as in the cross-entropy loss used in SimCLR. We show that logistic loss shows competitive performance on CIFAR-10, CIFAR-100, and Tiny-IN compared to other established SSL objectives. Our findings verify the importance of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.17427", "content": "We propose SigCLR: Sigmoid Contrastive Learning of Visual Representations. SigCLR utilizes the logistic loss that only operates on pairs and does not require a global view as in the cross-entropy loss used in SimCLR. We show that logistic loss shows competitive performance on CIFAR-10, CIFAR-100, and Tiny-IN compared to other established SSL objectives. Our findings verify the importance of ..."}
+{"idx": 8, "title": "A comprehensive survey on contrastive learning - ScienceDirect", "date": "", "ddg_snippet": "Contrastive Learning is self-supervised representation learning by training a model to differentiate between similar and dissimilar samples. It has been shown to be effective and has gained significant attention in various computer vision and natural language processing tasks. In this paper, we comprehensively and systematically sort out the main ideas, recent developments and application ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224014164", "content": "Contrastive Learning is self-supervised representation learning by training a model to differentiate between similar and dissimilar samples. It has been shown to be effective and has gained significant attention in various computer vision and natural language processing tasks. In this paper, we comprehensively and systematically sort out the main ideas, recent developments and application ..."}
+{"idx": 9, "title": "SigLIP and SigLIP2 | google-research/big_vision | DeepWiki", "date": "", "ddg_snippet": "Introduction SigLIP and SigLIP2 are vision-language models that connect images and text through a contrastive learning approach. SigLIP introduced a sigmoid-based contrastive loss that improved upon the softmax approach used in earlier models like CLIP and LiT.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/google-research/big_vision/3.2-siglip-and-siglip2", "content": "Introduction SigLIP and SigLIP2 are vision-language models that connect images and text through a contrastive learning approach. SigLIP introduced a sigmoid-based contrastive loss that improved upon the softmax approach used in earlier models like CLIP and LiT."}
diff --git a/data/sampled_jsons/sigmoid_loss_contrastive_learning_machine_learning_models_frameworks.jsonl b/data/sampled_jsons/sigmoid_loss_contrastive_learning_machine_learning_models_frameworks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..200f7dd54d5b292d04aefb1cadcb54a676242323
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "by C Lee · 2024 · Cited by 4 — In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.12613", "content": "by C Lee · 2024 · Cited by 4 — In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned ..."}
+{"idx": 1, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "by C Lee · 2024 · Cited by 4 — We define the double-Constant Embedding Model . (CCEM), a framework that models various struc- tures formed by multiple embedding vectors. This model contains ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/lee24a/lee24a.pdf", "content": "by C Lee · 2024 · Cited by 4 — We define the double-Constant Embedding Model . (CCEM), a framework that models various struc- tures formed by multiple embedding vectors. This model contains ..."}
+{"idx": 2, "title": "Sigmoid Contrastive Learning of Visual Representations", "date": "", "ddg_snippet": "22 Oct 2024 — We propose SigCLR: Sigmoid Contrastive Learning of Visual Representations. SigCLR utilizes the logistic loss that only operates on pairs and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.17427v1", "content": "22 Oct 2024 — We propose SigCLR: Sigmoid Contrastive Learning of Visual Representations. SigCLR utilizes the logistic loss that only operates on pairs and ..."}
+{"idx": 3, "title": "Contrastive Representation Learning | Lil'Log", "date": "", "ddg_snippet": "31 May 2021 — The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ...", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2021-05-31-contrastive/", "content": "31 May 2021 — The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ..."}
+{"idx": 4, "title": "Sigmoid Loss for Language Image Pre-Training", "date": "", "ddg_snippet": "by X Zhai · 2023 · Cited by 1717 — Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Zhai_Sigmoid_Loss_for_Language_Image_Pre-Training_ICCV_2023_paper.pdf", "content": "by X Zhai · 2023 · Cited by 1717 — Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the."}
+{"idx": 5, "title": "On the Similarities of Embeddings in Contrastive Learning", "date": "", "ddg_snippet": "23 Jul 2025 — In this paper, we propose a unified framework for understanding contrastive learning through the lens of cosine similarity, and present two key ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ChmJZ9V2o1&noteId=xoJUXrMGUC", "content": "23 Jul 2025 — In this paper, we propose a unified framework for understanding contrastive learning through the lens of cosine similarity, and present two key ..."}
+{"idx": 6, "title": "ST-CML: A contrastive meta learning framework for spatio ...", "date": "", "ddg_snippet": "13 Sept 2025 — We introduce a graph contrastive loss to improve the learning of spatio-temporal structures during meta- learning . This loss function guides the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231225021551", "content": "13 Sept 2025 — We introduce a graph contrastive loss to improve the learning of spatio-temporal structures during meta- learning . This loss function guides the ..."}
+{"idx": 7, "title": "CLHA: A Simple Yet Effective Contrastive Learning ...", "date": "", "ddg_snippet": "by F Fang · 2024 · Cited by 3 — Simultaneously, CLHA utilizes pairwise contrastive loss and adaptive supervised fine-tuning loss to adaptively modify the likelihood of ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.lrec-main.295.pdf", "content": "by F Fang · 2024 · Cited by 3 — Simultaneously, CLHA utilizes pairwise contrastive loss and adaptive supervised fine-tuning loss to adaptively modify the likelihood of ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "We propose a simple pairwise sigmoid loss for image-text pre-training. Unlike standard contrastive learning with softmax normalization, the sigmoid loss ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=sigmoid-based+contrastive+loss", "content": "We propose a simple pairwise sigmoid loss for image-text pre-training. Unlike standard contrastive learning with softmax normalization, the sigmoid loss ..."}
+{"idx": 9, "title": "Papers Explained 152: SigLip - Ritvik Rastogi", "date": "", "ddg_snippet": "This paper proposes a simple pairwise Sigmoid loss for Language-Image Pre-training (SigLIP). Unlike standard contrastive learning with softmax normalization.", "subpage_snippet": "", "source": "ritvik19.medium.com", "link": "https://ritvik19.medium.com/papers-explained-152-siglip-011c48f9d448", "content": "This paper proposes a simple pairwise Sigmoid loss for Language-Image Pre-training (SigLIP). Unlike standard contrastive learning with softmax normalization."}
diff --git a/data/sampled_jsons/siteaclanthology.org_Medusa_Cai_et_al._year_2024.jsonl b/data/sampled_jsons/siteaclanthology.org_Medusa_Cai_et_al._year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1c4c66b7ecf6cfa161b908cfe3b606a55523f339
--- /dev/null
+++ b/data/sampled_jsons/siteaclanthology.org_Medusa_Cai_et_al._year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Speed Without Sacrifice: Fine-Tuning Language Models with Medusa ...", "date": "", "ddg_snippet": "Medusa ( Cai et al ., 2024) represents a signifi-cant advance in speculative decoding by eliminat-ing the need for separate draft models. Instead, Medusa augments the base LLM with additional lightweight prediction heads that forecast tokens at specific future positions.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-industry.48.pdf", "content": "Medusa ( Cai et al ., 2024) represents a signifi-cant advance in speculative decoding by eliminat-ing the need for separate draft models. Instead, Medusa augments the base LLM with additional lightweight prediction heads that forecast tokens at specific future positions."}
+{"idx": 1, "title": "Towards Fast Multilingual LLM Inference: Speculative Decoding ...", "date": "", "ddg_snippet": "Medusa (Cai et al .,2024) and Eagle (Li et al .,2024): Both methods enhance the tar- get LLM by integrating additional lightweight FFN heads. These heads are designed to ef- ciently draft potential token sequences de- pending on the penultimate representations from the target LLM.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.602.pdf", "content": "Medusa (Cai et al .,2024) and Eagle (Li et al .,2024): Both methods enhance the tar- get LLM by integrating additional lightweight FFN heads. These heads are designed to ef- ciently draft potential token sequences de- pending on the penultimate representations from the target LLM."}
+{"idx": 2, "title": "Speed Without Sacrifice: Fine-Tuning Language Models with ...", "date": "", "ddg_snippet": "Sep 15, 2025 · We introduce modifications to the Medusa implementation, starting with base pre-trained models rather than conversational fine-tuned ones, and developing a simplified single-stage training process for Medusa -2 that maintains performance while reducing computational requirements.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-industry.48/", "content": "Sep 15, 2025 · We introduce modifications to the Medusa implementation, starting with base pre-trained models rather than conversational fine-tuned ones, and developing a simplified single-stage training process for Medusa -2 that maintains performance while reducing computational requirements."}
+{"idx": 3, "title": "Faster Speculative Decoding via Effective Draft Decoder with ...", "date": "", "ddg_snippet": "Medusa (Cai et al .,2024) adds additional decoding heads to LLMs to generate multiple future tokens in parallel. However, this non-autoregressive generation method suffers from the multimodality problem (Gu et al .,2017), which affects the acceptance rate.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.486.pdf", "content": "Medusa (Cai et al .,2024) adds additional decoding heads to LLMs to generate multiple future tokens in parallel. However, this non-autoregressive generation method suffers from the multimodality problem (Gu et al .,2017), which affects the acceptance rate."}
+{"idx": 4, "title": "Draft on the Fly: Adaptive Self-Speculative Decoding using ...", "date": "", "ddg_snippet": "Medusa (Cai et al .,2024) is an adjacent method to speculative decoding which uses multiple decod- ing heads to predict tokens in parallel, avoiding the challenges of obtaining an appropriate draft model. In its simplest form, these additional heads must be trained with the original weights of M kept frozen.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.124.pdf", "content": "Medusa (Cai et al .,2024) is an adjacent method to speculative decoding which uses multiple decod- ing heads to predict tokens in parallel, avoiding the challenges of obtaining an appropriate draft model. In its simplest form, these additional heads must be trained with the original weights of M kept frozen."}
+{"idx": 5, "title": "EAGLE-2: Faster Inference of Language Models with Dynamic ...", "date": "", "ddg_snippet": "Sequoia (Chen et al .,2024) explicitly as- sumes that the acceptance rate of a draft token de- pends only on its position in the tree. EAGLE (Li et al .,2024b) and Medusa (Cai et al .,2024) use the samestaticdrafttreestructureinallcontexts: atthe i-th step of the draft phase, k candidates are added, with k being xed.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.422.pdf", "content": "Sequoia (Chen et al .,2024) explicitly as- sumes that the acceptance rate of a draft token de- pends only on its position in the tree. EAGLE (Li et al .,2024b) and Medusa (Cai et al .,2024) use the samestaticdrafttreestructureinallcontexts: atthe i-th step of the draft phase, k candidates are added, with k being xed."}
+{"idx": 6, "title": "Graph-Structured Speculative Decoding - ACL Anthology", "date": "", "ddg_snippet": "Blockwise parallel decod- ing (Stern et al .,2018), for example, is introduced to make predictions for multiple time steps in par- allel. More recently, Medusa (Cai et al .,2023) has trainedmultiplepredictionheadstopredictthenext 11405 set of tokens simultaneously. 3 Preliminaries: Sequence-structured Speculative Decoding", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.677.pdf", "content": "Blockwise parallel decod- ing (Stern et al .,2018), for example, is introduced to make predictions for multiple time steps in par- allel. More recently, Medusa (Cai et al .,2023) has trainedmultiplepredictionheadstopredictthenext 11405 set of tokens simultaneously. 3 Preliminaries: Sequence-structured Speculative Decoding"}
+{"idx": 7, "title": "Breaking Stage Dependencies in Hierarchical LLM Decoding", "date": "", "ddg_snippet": "by B Mcdanel · 2025 · Cited by 1 — Other highly effective techniques, such as. MEDUSA (Cai et al., 2024 ) or EAGLE-2 (Li et al.,. 2024), achieve significant speedups by training ... 12 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.669.pdf", "content": "by B Mcdanel · 2025 · Cited by 1 — Other highly effective techniques, such as. MEDUSA (Cai et al., 2024 ) or EAGLE-2 (Li et al.,. 2024), achieve significant speedups by training ... 12 pages"}
+{"idx": 8, "title": "Speculative Decoding via Early-exiting for Faster LLM ...", "date": "", "ddg_snippet": "by J Liu · 2024 · Cited by 20 — (a) Speedup comparison with Medusa ( Cai et al ., 2023) and Self-SD (Zhang et al ., 2023b). EESD achieves a highest speedup with a best tradeoff ... 17 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.179.pdf", "content": "by J Liu · 2024 · Cited by 20 — (a) Speedup comparison with Medusa ( Cai et al ., 2023) and Self-SD (Zhang et al ., 2023b). EESD achieves a highest speedup with a best tradeoff ... 17 pages"}
+{"idx": 9, "title": "A Drop-In Solution for On-the-Fly Adaptation of Speculative ...", "date": "", "ddg_snippet": "by J Liu · 2025 · Cited by 2 — Medusa ( Cai et al .,. 2024), for example, introduces multiple decoding heads to generate tokens in parallel; Lookahead. Decoding (Jacobi ... 17 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.482.pdf", "content": "by J Liu · 2025 · Cited by 2 — Medusa ( Cai et al .,. 2024), for example, introduces multiple decoding heads to generate tokens in parallel; Lookahead. Decoding (Jacobi ... 17 pages"}
diff --git a/data/sampled_jsons/sitear5iv.labs.arxiv.org_2503.01485_Table_2_NDAC-75_NDAC-25.jsonl b/data/sampled_jsons/sitear5iv.labs.arxiv.org_2503.01485_Table_2_NDAC-75_NDAC-25.jsonl
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index 0000000000000000000000000000000000000000..6bc37a61f7cf8f650e8289cae638134670c670de
--- /dev/null
+++ b/data/sampled_jsons/sitear5iv.labs.arxiv.org_2503.01485_Table_2_NDAC-75_NDAC-25.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2210.06681] Brain Network Transformer - ar5iv", "date": "", "ddg_snippet": "The results shown in Table 2 demonstrate that our OCRead is the most effective readout function for brain networks and improves the prediction power across various Transformer architectures. Table 2 : Performance comparison AUROC (%) with different readout functions.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2210.06681", "content": "The results shown in Table 2 demonstrate that our OCRead is the most effective readout function for brain networks and improves the prediction power across various Transformer architectures. Table 2 : Performance comparison AUROC (%) with different readout functions."}
+{"idx": 1, "title": "[2011.01088] OpenIPMC: a free and open source Intelligent ... - ar5iv", "date": "", "ddg_snippet": "OpenIPMC is a free and open source software designed to implement the logic of an Intelligent Platform Management Controller (IPMC). An IPMC is a fundamental component of electronic boards conformant to the Advanced Te…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2011.01088", "content": "OpenIPMC is a free and open source software designed to implement the logic of an Intelligent Platform Management Controller (IPMC). An IPMC is a fundamental component of electronic boards conformant to the Advanced Te…"}
+{"idx": 2, "title": "[2307.05916] SwiFT: Swin 4D fMRI Transformer - ar5iv", "date": "", "ddg_snippet": "Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2307.05916", "content": "Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted …"}
+{"idx": 3, "title": "[2308.09687] Graph of Thoughts: Solving Elaborate Problems with ... - ar5iv", "date": "", "ddg_snippet": "We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2308.09687", "content": "We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea …"}
+{"idx": 4, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "NDAC-75 is targeted at 48 kHz audio with a whole-number feature rate ( 75 Hz) and whole-number bitrates. NDAC-25 is a variant tailored for downstream generative audio tasks, with a lower feature rate ( 25 Hz) and feature dimension which are advantageous for audio generation due to more efficient memory usage and decreased modeling difficulties.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2503.01485", "content": "NDAC-75 is targeted at 48 kHz audio with a whole-number feature rate ( 75 Hz) and whole-number bitrates. NDAC-25 is a variant tailored for downstream generative audio tasks, with a lower feature rate ( 25 Hz) and feature dimension which are advantageous for audio generation due to more efficient memory usage and decreased modeling difficulties."}
+{"idx": 5, "title": "Chakra: Advancing Performance Benchmarking and Co-design using ...", "date": "", "ddg_snippet": "This subsection provides an in-depth look at the Chakra schema, which is presented in Table 2 and Table 2 . Table 2 outlines the Chakra node schema, while Table 2 presents the AttributeProto schema.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2305.14516", "content": "This subsection provides an in-depth look at the Chakra schema, which is presented in Table 2 and Table 2 . Table 2 outlines the Chakra node schema, while Table 2 presents the AttributeProto schema."}
+{"idx": 6, "title": "[2105.05796] Kleister: Key Information Extraction Datasets Involving ...", "date": "", "ddg_snippet": "The relevance of the Key Information Extraction (KIE) task is increasingly important in natural language processing problems. But there are still only a few well-defined problems that serve as benchmarks for solutions …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2105.05796", "content": "The relevance of the Key Information Extraction (KIE) task is increasingly important in natural language processing problems. But there are still only a few well-defined problems that serve as benchmarks for solutions …"}
+{"idx": 7, "title": "Restriction of Donaldson's functional to diagonal metrics on Higgs ...", "date": "", "ddg_snippet": "Abstract We consider a Higgs bundle over a compact Kähler manifold with a smooth, non-holomorphic Higgs field. We assume that the holomorphic vector bundle decomposes into a direct sum of holomorphic line bundles. Under an assumption on the zero set of the non-holomorphic Higgs field, we provide some necessary and sufficient conditions for Donaldson's functional which is restricted to the ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2301.01485", "content": "Abstract We consider a Higgs bundle over a compact Kähler manifold with a smooth, non-holomorphic Higgs field. We assume that the holomorphic vector bundle decomposes into a direct sum of holomorphic line bundles. Under an assumption on the zero set of the non-holomorphic Higgs field, we provide some necessary and sufficient conditions for Donaldson's functional which is restricted to the ..."}
+{"idx": 8, "title": "[2409.19764] Spiking Transformer with Spatial-Temporal Attention", "date": "", "ddg_snippet": "Spiking Neural Networks (SNNs) present a compelling and energy-efficient alternative to traditional Artificial Neural Networks (ANNs) due to their sparse binary activation. Leveraging the success of the transformer arc…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2409.19764", "content": "Spiking Neural Networks (SNNs) present a compelling and energy-efficient alternative to traditional Artificial Neural Networks (ANNs) due to their sparse binary activation. Leveraging the success of the transformer arc…"}
+{"idx": 9, "title": "[2306.10511] Dual Adaptive Representation Alignment for Cross-domain ...", "date": "", "ddg_snippet": "Few-shot learning aims to recognize novel queries with limited support samples by learning from base knowledge. Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2306.10511", "content": "Few-shot learning aims to recognize novel queries with limited support samples by learning from base knowledge. Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in…"}
diff --git a/data/sampled_jsons/sitear5iv.labs.arxiv.org_Branched_mapping_layers_UVGS.jsonl b/data/sampled_jsons/sitear5iv.labs.arxiv.org_Branched_mapping_layers_UVGS.jsonl
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--- /dev/null
+++ b/data/sampled_jsons/sitear5iv.labs.arxiv.org_Branched_mapping_layers_UVGS.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "[2502.01846] UVGS: Reimagining Unstructured 3D Gaussian ...", "date": "", "ddg_snippet": "Mar 5, 2025 · Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the incompatibility issues arising due the the different value distribution of 3DGS attributes.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.01846", "content": "Mar 5, 2025 · Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the incompatibility issues arising due the the different value distribution of 3DGS attributes."}
diff --git a/data/sampled_jsons/sitear5iv.labs.arxiv.orghtml2503.16979_Implementation_details.jsonl b/data/sampled_jsons/sitear5iv.labs.arxiv.orghtml2503.16979_Implementation_details.jsonl
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index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitear5iv.labs.arxiv.orghtml2503.16979_Implementation_details.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_2312.03046_Flowers_98.9_OR_99.0.jsonl b/data/sampled_jsons/sitearxiv.org_2312.03046_Flowers_98.9_OR_99.0.jsonl
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index 0000000000000000000000000000000000000000..16f27269b30afbb9bdabda9249792093f3f52956
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2312.03046_Flowers_98.9_OR_99.0.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2312 . 03046 ] Diversified in-domain synthesis with efficient fine-tuning...", "date": "", "ddg_snippet": "Computer Science > Computer Vision and Pattern Recognition. arXiv: 2312 . 03046 (cs).(or arXiv: 2312 . 03046 v2 [cs.CV] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "Computer Science > Computer Vision and Pattern Recognition. arXiv: 2312 . 03046 (cs).(or arXiv: 2312 . 03046 v2 [cs.CV] for this version)."}
+{"idx": 1, "title": "[2312.03031v2] Is Ego Status All You Need for Open-Loop End-to-End...", "date": "", "ddg_snippet": "Computer Science > Computer Vision and Pattern Recognition. arXiv:2312.03031v2 (cs).(or arXiv:2312.03031v2 [cs.CV] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03031v2", "content": "Computer Science > Computer Vision and Pattern Recognition. arXiv:2312.03031v2 (cs).(or arXiv:2312.03031v2 [cs.CV] for this version)."}
+{"idx": 2, "title": "[2312.03086v4] Analysis of the 3.2-3.3 $μ$m Interstellar Absorption...", "date": "", "ddg_snippet": "The $3.2-3.3~μ$m interval coincides with the CH-stretching region for compact polycyclic aromatic hydrocarbons (PAHs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03086v4", "content": "The $3.2-3.3~μ$m interval coincides with the CH-stretching region for compact polycyclic aromatic hydrocarbons (PAHs)."}
+{"idx": 3, "title": "Wolfram|Alpha Widgets: \"Solve for X Calculator\" - Free ...", "date": "", "ddg_snippet": "Get the free \"Solve for X Calculator\" widget for your website, blog, Wordpress, Blogger, or iGoogle. Find more Mathematics widgets in Wolfram|Alpha.", "subpage_snippet": "", "source": "www.wolframalpha.com", "link": "https://www.wolframalpha.com/widgets/view.jsp?id=7953c4ea52a4873d32cc72052f3dcb10", "content": "Get the free \"Solve for X Calculator\" widget for your website, blog, Wordpress, Blogger, or iGoogle. Find more Mathematics widgets in Wolfram|Alpha."}
+{"idx": 4, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot...", "date": "", "ddg_snippet": "arXiv: 2312 . 03046 v2 [cs.CV] 07 Dec 2023.Although classifier tuning is competitive with DISEF in Caltech101, ImageNet, and Flowers 102, it is limited to only known classes, thus inappropriate for the base/new scenario, which involves unseen classes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "arXiv: 2312 . 03046 v2 [cs.CV] 07 Dec 2023.Although classifier tuning is competitive with DISEF in Caltech101, ImageNet, and Flowers 102, it is limited to only known classes, thus inappropriate for the base/new scenario, which involves unseen classes."}
+{"idx": 5, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot", "date": "", "ddg_snippet": "arXiv: 2312 . 03046 v2 [cs.CV] 7 Dec 2023.Although classifier tuning is competitive with DISEF in Caltech101, ImageNet, and Flowers 102, it is limited to only known classes, thus inappropriate for the base/new scenario, which involves unseen classes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.03046", "content": "arXiv: 2312 . 03046 v2 [cs.CV] 7 Dec 2023.Although classifier tuning is competitive with DISEF in Caltech101, ImageNet, and Flowers 102, it is limited to only known classes, thus inappropriate for the base/new scenario, which involves unseen classes."}
+{"idx": 6, "title": "Google Maps", "date": "", "ddg_snippet": "Find local businesses, view maps and get driving directions in Google Maps.", "subpage_snippet": "", "source": "maps.google.com", "link": "https://maps.google.com/", "content": "Find local businesses, view maps and get driving directions in Google Maps."}
+{"idx": 7, "title": "Retirement | Born in 1959 | SSA", "date": "", "ddg_snippet": "If you were born in 1959 your full retirement age is 66 and 10 months. Find out how your Social Security benefits will be affected based on when you begin receiving benefits.", "subpage_snippet": "", "source": "www.ssa.gov", "link": "https://www.ssa.gov/benefits/retirement/planner/1959.html", "content": "If you were born in 1959 your full retirement age is 66 and 10 months. Find out how your Social Security benefits will be affected based on when you begin receiving benefits."}
+{"idx": 8, "title": "Latest | Official PlayStation™Store US", "date": "", "ddg_snippet": "Enjoy hundreds of PS5, PS4 and classic PlayStation games, online multiplayer, and more unmissable benefits.", "subpage_snippet": "", "source": "store.playstation.com", "link": "https://store.playstation.com/en-us/", "content": "Enjoy hundreds of PS5, PS4 and classic PlayStation games, online multiplayer, and more unmissable benefits."}
+{"idx": 9, "title": "Walmart | Save Money. Live better.", "date": "", "ddg_snippet": "Shop Walmart.com today for Every Day Low Prices. Join Walmart+ for unlimited free delivery from your store & free shipping with no order minimum. Start your free 30-day trial now!", "subpage_snippet": "", "source": "www.walmart.com", "link": "https://www.walmart.com/", "content": "Shop Walmart.com today for Every Day Low Prices. Join Walmart+ for unlimited free delivery from your store & free shipping with no order minimum. Start your free 30-day trial now!"}
diff --git a/data/sampled_jsons/sitearxiv.org_2403.09040_Section_3.1_retriever_paradigms.jsonl b/data/sampled_jsons/sitearxiv.org_2403.09040_Section_3.1_retriever_paradigms.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7e6924f99c93132ce0eac69d6e11b34aab4ddf7b
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2403.09040_Section_3.1_retriever_paradigms.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Toward Optimal Search and Retrieval for RAG - arXiv.org", "date": "", "ddg_snippet": "Abstract Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs). Two separate systems form the RAG pipeline, the retriever and the reader, and the impact of each on downstream task performance is not well-understood. Here, we work towards the goal of understanding how retrievers can be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07396", "content": "Abstract Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs). Two separate systems form the RAG pipeline, the retriever and the reader, and the impact of each on downstream task performance is not well-understood. Here, we work towards the goal of understanding how retrievers can be ..."}
+{"idx": 1, "title": "[2403.09040] RAGGED: Towards Informed Design of Retrieval Augmented ...", "date": "", "ddg_snippet": "Retrieval-augmented generation (RAG) greatly benefits language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). Despite its potential, the power of RAG is highly …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2403.09040", "content": "Retrieval-augmented generation (RAG) greatly benefits language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). Despite its potential, the power of RAG is highly …"}
+{"idx": 2, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "3.1 . Retrievers We evaluate three retrievers with different retrieval paradigms : (1) BM25 (Robertson et al., 2009), a sparse lexical retriever based on term matching. (2) ColBERT (Santhanam et al., 2021), a neural retriever using contextual-ized late interaction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09040", "content": "3.1 . Retrievers We evaluate three retrievers with different retrieval paradigms : (1) BM25 (Robertson et al., 2009), a sparse lexical retriever based on term matching. (2) ColBERT (Santhanam et al., 2021), a neural retriever using contextual-ized late interaction."}
+{"idx": 3, "title": "RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems", "date": "", "ddg_snippet": "1 Introduction Retrieval-augmented generation (RAG) (Chen et al., 2017; Lewis et al., 2020) is a technique widely applied to enhance the performance of top-performing LMs on knowledge-intensive generation tasks like document-based question answering (Karpukhin et al., 2020). Given a question, the technique includes using a retriever model to obtain multiple relevant passages (i.e. paragraphs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040", "content": "1 Introduction Retrieval-augmented generation (RAG) (Chen et al., 2017; Lewis et al., 2020) is a technique widely applied to enhance the performance of top-performing LMs on knowledge-intensive generation tasks like document-based question answering (Karpukhin et al., 2020). Given a question, the technique includes using a retriever model to obtain multiple relevant passages (i.e. paragraphs ..."}
+{"idx": 4, "title": "RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum ...", "date": "", "ddg_snippet": "Ideal Retriever Setting In Table 2, we report the performance of our models in the ideal retriever setting where our models are only given the relevant gold contexts for each question. Similar to in the previous section , we see that the min-max curriculum also achieves the highest F1 scores across the board.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.12759v1", "content": "Ideal Retriever Setting In Table 2, we report the performance of our models in the ideal retriever setting where our models are only given the relevant gold contexts for each question. Similar to in the previous section , we see that the min-max curriculum also achieves the highest F1 scores across the board."}
+{"idx": 5, "title": "Context Embeddings for Efficient Answer Generation in RAG", "date": "", "ddg_snippet": "The rest of this paper is structured in the following way. Section 2 discusses related work on RAG, efficiency, and compression approaches. We continue in Section 3 discussing the RAG task and our novel COCOM approach to effective context compression. Section 4 details the experimental setup in terms of the RAG models and the five QA tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.09252v1", "content": "The rest of this paper is structured in the following way. Section 2 discusses related work on RAG, efficiency, and compression approaches. We continue in Section 3 discussing the RAG task and our novel COCOM approach to effective context compression. Section 4 details the experimental setup in terms of the RAG models and the five QA tasks."}
+{"idx": 6, "title": "Enhancing Retrieval-Augmented Generation: A Study of Best Practices", "date": "", "ddg_snippet": "Abstract Retrieval-Augmented Generation (RAG) systems have recently shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. However, the influence of various components and configurations within RAG systems remains underexplored. A comprehensive understanding of these elements ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.07391v1", "content": "Abstract Retrieval-Augmented Generation (RAG) systems have recently shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. However, the influence of various components and configurations within RAG systems remains underexplored. A comprehensive understanding of these elements ..."}
+{"idx": 7, "title": "RAG Without the Lag: Interactive Debugging for Retrieval-Augmented ...", "date": "", "ddg_snippet": "Abstract. Retrieval-augmented generation (RAG) pipelines have become the de-facto approach for building AI assistants with access to external, domain-specific knowledge. Given a user query, RAG pipelines typically first retrieve (R) relevant information from external sources, before invoking a Large Language Model (LLM), augmented (A) with this information, to generate (G) responses. Modern ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.13587", "content": "Abstract. Retrieval-augmented generation (RAG) pipelines have become the de-facto approach for building AI assistants with access to external, domain-specific knowledge. Given a user query, RAG pipelines typically first retrieve (R) relevant information from external sources, before invoking a Large Language Model (LLM), augmented (A) with this information, to generate (G) responses. Modern ..."}
+{"idx": 8, "title": "Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy", "date": "", "ddg_snippet": "Abstract With the recent remarkable advancement of large language models (LLMs), there has been a growing interest in utilizing them in the domains with highly sensitive data that lies outside their training data. For this purpose, retrieval-augmented generation (RAG) is particularly effective—it assists LLMs by directly providing relevant information from the external knowledge sources ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.04697v2", "content": "Abstract With the recent remarkable advancement of large language models (LLMs), there has been a growing interest in utilizing them in the domains with highly sensitive data that lies outside their training data. For this purpose, retrieval-augmented generation (RAG) is particularly effective—it assists LLMs by directly providing relevant information from the external knowledge sources ..."}
+{"idx": 9, "title": "Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval ...", "date": "", "ddg_snippet": "Abstract. The choice of embedding model is a crucial step in the design of Retrieval Augmented Generation (RAG) systems. Given the sheer volume of available options, identifying clusters of similar models streamlines this model selection process. Relying solely on benchmark performance scores only allows for a weak assessment of model similarity. Thus, in this study, we evaluate the similarity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.08275v1", "content": "Abstract. The choice of embedding model is a crucial step in the design of Retrieval Augmented Generation (RAG) systems. Given the sheer volume of available options, identifying clusters of similar models streamlines this model selection process. Relying solely on benchmark performance scores only allows for a weak assessment of model similarity. Thus, in this study, we evaluate the similarity ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2410.02025_alpha_noise_variance_underlying_reason.jsonl b/data/sampled_jsons/sitearxiv.org_2410.02025_alpha_noise_variance_underlying_reason.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3c3c0fbeba34225d172391cb5052068cacc27e5b
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "the variance of noise by the factor α. When α is very small, indicating that the data Yj lies very close to the manifold, the second expressio n−α in the over-all rate dominates. Intuitively, this phenomenon arises from the underlying structural challenges in related manifold estimation problems with noisy data,", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "the variance of noise by the factor α. When α is very small, indicating that the data Yj lies very close to the manifold, the second expressio n−α in the over-all rate dominates. Intuitively, this phenomenon arises from the underlying structural challenges in related manifold estimation problems with noisy data,"}
+{"idx": 1, "title": "Computer Science 2024 - arXiv.org", "date": "", "ddg_snippet": "Subjects:Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG) [93040] arXiv:2410.01958 (cross-list from eess.SP) [pdf, html, other] Title: Adaptive Invariant Extended Kalman Filter with Noise Covariance Tuning for Attitude Estimation Yash Pandey, Rahul Bhattacharyya, Yatindra Nath Singh", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs/2024?skip=93025", "content": "Subjects:Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG) [93040] arXiv:2410.01958 (cross-list from eess.SP) [pdf, html, other] Title: Adaptive Invariant Extended Kalman Filter with Noise Covariance Tuning for Attitude Estimation Yash Pandey, Rahul Bhattacharyya, Yatindra Nath Singh"}
+{"idx": 2, "title": "Dealing with Logs and Zeros in Regression Models - arXiv.org", "date": "", "ddg_snippet": "Under DGP I, the conditional variance remains constant, similarly to a Gaussian model with i.i.d. errors. In DGP II, the variance tracks the conditional mean, as in a Poisson setting. DGP III is the \"log-homoskedastic\" case in which variance is proportional to the square of the mean, yielding homoskedastic errors in the log-linear model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2203.11820v3", "content": "Under DGP I, the conditional variance remains constant, similarly to a Gaussian model with i.i.d. errors. In DGP II, the variance tracks the conditional mean, as in a Poisson setting. DGP III is the \"log-homoskedastic\" case in which variance is proportional to the square of the mean, yielding homoskedastic errors in the log-linear model."}
+{"idx": 3, "title": "Monitored Fluctuating Hydrodynamics - arXiv.org", "date": "", "ddg_snippet": "The variance of the noise correlations is directly related to the diffusion constant through the dissipation theorem σ 2 = 2 χ D with χ the equilibrium charge susceptibility. The (Born) probability of a given trajectory is given by p [ρ 𝒎] = Z [ρ 𝒎] / Z with Z = ∫ 𝒟 ρ 𝒎 Z [ρ 𝒎].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.02734v3", "content": "The variance of the noise correlations is directly related to the diffusion constant through the dissipation theorem σ 2 = 2 χ D with χ the equilibrium charge susceptibility. The (Born) probability of a given trajectory is given by p [ρ 𝒎] = Z [ρ 𝒎] / Z with Z = ∫ 𝒟 ρ 𝒎 Z [ρ 𝒎]."}
+{"idx": 4, "title": "Sequential analysis in a continuous spin-noise quantum sensor", "date": "", "ddg_snippet": "Both γ \\gamma and the noise term with strength \\hat {N} originate from different noise processes including atomic collisions, optical depolarization, and transit-time broadening. Together, these parameters define a linear Gaussian model for the spin dynamics under continuous monitoring.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16177v1", "content": "Both γ \\gamma and the noise term with strength \\hat {N} originate from different noise processes including atomic collisions, optical depolarization, and transit-time broadening. Together, these parameters define a linear Gaussian model for the spin dynamics under continuous monitoring."}
+{"idx": 5, "title": "Random Matrix Theory-guided sparse PCA for single-cell RNA-seq data", "date": "", "ddg_snippet": "The resulting procedure, detailed in Alg. 1, returns diagonal matrices C and D such that C 2 ≃ A and D 2 ≃ B. Finally, because the overall noise variance after biwhitening is close to, but not exactly, one, we further normalize C by dividing it by a robust estimator of the standard deviation σ of the data [7, 8].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15429v1", "content": "The resulting procedure, detailed in Alg. 1, returns diagonal matrices C and D such that C 2 ≃ A and D 2 ≃ B. Finally, because the overall noise variance after biwhitening is close to, but not exactly, one, we further normalize C by dividing it by a robust estimator of the standard deviation σ of the data [7, 8]."}
+{"idx": 6, "title": "The Importance of Standardizing Spectra in the Era of Large ...", "date": "", "ddg_snippet": "We use the spectrum's alpha shape to find the points which lie between the absorption features and apply local polynomial regression to find this pseudo-continuum. To tune the hyperparameters of this method, we create BOSS-like spectra from BT-NextGen models to replicate instrumental, signal-to- noise , and reddening effects.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15309v1", "content": "We use the spectrum's alpha shape to find the points which lie between the absorption features and apply local polynomial regression to find this pseudo-continuum. To tune the hyperparameters of this method, we create BOSS-like spectra from BT-NextGen models to replicate instrumental, signal-to- noise , and reddening effects."}
+{"idx": 7, "title": "Why Do We Need Weight Decay in Modern Deep Learning?", "date": "", "ddg_snippet": "The important difference in our statement is that unlike Blanc et al. (2020) and Damian et al. (2021), we do not need to add noise to the labels at each iteration. Instead, weight decay, in combination with large-LR induces a label noise -like behavior via loss stabilization (Andriushchenko et al., 2023).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.04415v2", "content": "The important difference in our statement is that unlike Blanc et al. (2020) and Damian et al. (2021), we do not need to add noise to the labels at each iteration. Instead, weight decay, in combination with large-LR induces a label noise -like behavior via loss stabilization (Andriushchenko et al., 2023)."}
+{"idx": 8, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 9, "title": "Uplink-Downlink Duality for Beamforming in Integrated Sensing and ...", "date": "", "ddg_snippet": "This paper focuses on the task of designing common waveforms for integrated sensing and communications (ISAC). This is a more challenging task than designing waveforms for communications alone, because the same information-bearing signals must also perform sensing. In the context of multiple-input multiple-output (MIMO) transmit beamforming, the beamformers that need to guarantee certain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13661v1", "content": "This paper focuses on the task of designing common waveforms for integrated sensing and communications (ISAC). This is a more challenging task than designing waveforms for communications alone, because the same information-bearing signals must also perform sensing. In the context of multiple-input multiple-output (MIMO) transmit beamforming, the beamformers that need to guarantee certain ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2502.04757_ELITE_E-ASR_VLGuard_reason.jsonl b/data/sampled_jsons/sitearxiv.org_2502.04757_ELITE_E-ASR_VLGuard_reason.jsonl
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index 0000000000000000000000000000000000000000..c55661ee287c3965609148d3887982d268e6c550
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2502.04757_ELITE_E-ASR_VLGuard_reason.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Table 3. ELITE evaluator score-based ASR of various VLMs across taxonomies. The upper group in the table represents proprietary models, and the lower group represents open-source models. ELITE benchmark. VLGuard MM-SafetyBench.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "Table 3. ELITE evaluator score-based ASR of various VLMs across taxonomies. The upper group in the table represents proprietary models, and the lower group represents open-source models. ELITE benchmark. VLGuard MM-SafetyBench."}
+{"idx": 1, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image-text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04757", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image-text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator."}
+{"idx": 2, "title": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "arXiv: 2502 . 04757 v2 [cs.CV] null.Table 3: ELITE evaluator score-based ASR of various VLMs across taxonomies. The upper group in the table represents proprietary models, and the lower group represents open-source models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v2", "content": "arXiv: 2502 . 04757 v2 [cs.CV] null.Table 3: ELITE evaluator score-based ASR of various VLMs across taxonomies. The upper group in the table represents proprietary models, and the lower group represents open-source models."}
+{"idx": 3, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "24 Jul 2025 — Table 5 presents the E - ASR of the methods used to elicit harmful responses from VLMs in the ELITE benchmark (generated). Our experimental ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "24 Jul 2025 — Table 5 presents the E - ASR of the methods used to elicit harmful responses from VLMs in the ELITE benchmark (generated). Our experimental ..."}
+{"idx": 4, "title": "Multimodal model for enterprise I ntelligence", "date": "", "ddg_snippet": "We use the metric VLGuard proposed Attack Success Rate ( ASR ) for evaluating unsafe inputs and Helpfulness for evaluating safe inputs. Note that accuracy is the complement of the ASR and can be calculated as 1 - ASR .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.09927", "content": "We use the metric VLGuard proposed Attack Success Rate ( ASR ) for evaluating unsafe inputs and Helpfulness for evaluating safe inputs. Note that accuracy is the complement of the ASR and can be calculated as 1 - ASR ."}
+{"idx": 5, "title": "A Survey of Safety on Large Vision-Language", "date": "", "ddg_snippet": "VLGuard [49] [ICML’24] Safe instruction following dataset. LLaVAGuard[146] [arXiv’24] Refined ratings & rationales dataset.• Attack Success Rate ( ASR ) is employed to quantify the. probability of eliciting harmful responses from LVLMs using. pairs of image-text queries.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.14881", "content": "VLGuard [49] [ICML’24] Safe instruction following dataset. LLaVAGuard[146] [arXiv’24] Refined ratings & rationales dataset.• Attack Success Rate ( ASR ) is employed to quantify the. probability of eliciting harmful responses from LVLMs using. pairs of image-text queries."}
diff --git a/data/sampled_jsons/sitearxiv.org_2503.06366_Appendix_B.1_S18_characters_dataset.jsonl b/data/sampled_jsons/sitearxiv.org_2503.06366_Appendix_B.1_S18_characters_dataset.jsonl
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index 0000000000000000000000000000000000000000..2ada8115d11dce59c6c52f37ed62033c34d611b3
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2503.06366_Appendix_B.1_S18_characters_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "arXiv: 2503 . 06366 v 1 [cs.LG] 9 Mar 2025.Further details including dataset statistics, additional prob-lem context, and the method used for generating the datasets can be found in Appendix B .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "arXiv: 2503 . 06366 v 1 [cs.LG] 9 Mar 2025.Further details including dataset statistics, additional prob-lem context, and the method used for generating the datasets can be found in Appendix B ."}
+{"idx": 1, "title": "[ 2503 . 06366 ] Machine Learning meets Algebraic Combinatorics...", "date": "", "ddg_snippet": "Each dataset includes an open-ended research-level question and a large collection of examples (up to 10M in some cases) from which conjectures should be generated.(or arXiv: 2503 . 06366 v 1 [cs.LG] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "Each dataset includes an open-ended research-level question and a large collection of examples (up to 10M in some cases) from which conjectures should be generated.(or arXiv: 2503 . 06366 v 1 [cs.LG] for this version)."}
+{"idx": 2, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "arXiv: 2503 . 06366 v 1 [cs.LG] 09 Mar 2025.Further details including dataset statistics, additional problem context, and the method used for generating the datasets can be found in Appendix B .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "arXiv: 2503 . 06366 v 1 [cs.LG] 09 Mar 2025.Further details including dataset statistics, additional problem context, and the method used for generating the datasets can be found in Appendix B ."}
+{"idx": 3, "title": "[2503.24115] TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset ...", "date": "", "ddg_snippet": "To address this gap, we present TeleAntiFraud-28k, the first open-source audio-text slow-thinking dataset specifically designed for automated telecom fraud analysis.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.24115", "content": "To address this gap, we present TeleAntiFraud-28k, the first open-source audio-text slow-thinking dataset specifically designed for automated telecom fraud analysis."}
+{"idx": 4, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "To address this, we in-troduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics, a subfield of mathematics that studies discrete structures arising from abstract algebra.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366v1", "content": "To address this, we in-troduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics, a subfield of mathematics that studies discrete structures arising from abstract algebra."}
+{"idx": 5, "title": "An open dataset for the evolution of oracle bone characters: EVOBC", "date": "", "ddg_snippet": "Subsequently, we constructed an extensive dataset , namely EVolution Oracle Bone Characters (EVOBC), consisting of 229,170 images representing 13,714 distinct character categories. We conducted validation and simulated deciphering on the constructed dataset , and the results demonstrate its high efficacy in aiding the study of oracle bone script.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.12467", "content": "Subsequently, we constructed an extensive dataset , namely EVolution Oracle Bone Characters (EVOBC), consisting of 229,170 images representing 13,714 distinct character categories. We conducted validation and simulated deciphering on the constructed dataset , and the results demonstrate its high efficacy in aiding the study of oracle bone script."}
+{"idx": 6, "title": "DATASET FOR ANIME CHARACTER RECOGNITION - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT In this work we tackle the challenging problem of anime character recognition. Anime, referring to animation pro-duced within Japan and work derived or inspired from it. For this purpose we present DAF:re (DanbooruAnime-Faces:revamped), a large-scale, crowd-sourced, long-tailed dataset with almost 500 K images spread across more than 3000 classes. Additionally, we conduct experiments ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2101.08674", "content": "ABSTRACT In this work we tackle the challenging problem of anime character recognition. Anime, referring to animation pro-duced within Japan and work derived or inspired from it. For this purpose we present DAF:re (DanbooruAnime-Faces:revamped), a large-scale, crowd-sourced, long-tailed dataset with almost 500 K images spread across more than 3000 classes. Additionally, we conduct experiments ..."}
+{"idx": 7, "title": "Iranis: A Large-scale Dataset of Farsi License Plate Characters", "date": "", "ddg_snippet": "This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates. The dataset , named Iranis, contains more than 83,000 images of Farsi numbers and letters collected from real-world license plate images captured by various cameras.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2101.00295", "content": "This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates. The dataset , named Iranis, contains more than 83,000 images of Farsi numbers and letters collected from real-world license plate images captured by various cameras."}
+{"idx": 8, "title": "[2503.12095] Towards Vision Zero: The Accid3nD Dataset", "date": "", "ddg_snippet": "Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as unavoidable and sporadic outcomes of traffic networks. No public dataset contains 3D annotations of real-world accidents recorded from roadside sensors. We present the Accid3nD dataset , a collection of real-world highway accidents ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.12095", "content": "Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as unavoidable and sporadic outcomes of traffic networks. No public dataset contains 3D annotations of real-world accidents recorded from roadside sensors. We present the Accid3nD dataset , a collection of real-world highway accidents ..."}
+{"idx": 9, "title": "Appendix A Additional dataset details - arXiv.org", "date": "", "ddg_snippet": "A.1 Image-caption dataset preprocessing Our cleaning and filtering workflow consist of first filtering out invalid image-caption pairs (e.g. either if the caption is empty/nonsensical, or if the corresponding image is missing).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.07831", "content": "A.1 Image-caption dataset preprocessing Our cleaning and filtering workflow consist of first filtering out invalid image-caption pairs (e.g. either if the caption is empty/nonsensical, or if the corresponding image is missing)."}
diff --git a/data/sampled_jsons/sitearxiv.org_2503.16979_Equation_6_motion_feature_interpolation.jsonl b/data/sampled_jsons/sitearxiv.org_2503.16979_Equation_6_motion_feature_interpolation.jsonl
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+++ b/data/sampled_jsons/sitearxiv.org_2503.16979_Equation_6_motion_feature_interpolation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.16979] Instant Gaussian Stream: Fast and Generalizable ... arXiv:2503.16979v1 [cs.CV] 21 Mar 2025 Adaptive 3D Gaussian Splatting Video Streaming: Visual ... Discrete Empirical Interpolation Method with Upper and Lower ... A new dataset and comparison for multi-camera frame synthesis Discrete Empirical Interpolation Method with Upper and Lower ... Dynamical Analysis of the HD 169142 Planet-Forming Disk ...", "date": "", "ddg_snippet": "Mar 21, 2025 · First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. This generalized Network generates the motion of Gaussians for each target frame in the time required for a single inference. Projection-aware Motion Feature Lift. (d) Each Gaussian point interpolates its own motion feature from neighboring anchors and applies a weighted aggregation of features , which is then decoded into the motion of the Gaussian betw en the key frame and the target frame. (e) The entire streaming reconstruct IGS [23] proposes an Anchor-Driven Gaussian Motion Network (AGM-Net), decoding inter-frame Gaussian deformations via multi-view optical flow feature projection and anchor neighborhood interpolation . 11 hours ago · Discrete Empirical Interpolation Method (DEIM) is a simple and effective method for reconstructing a function from its incomplete pointwise observations. However, applying DEIM to functions with physically constrained ranges can produce reconstructions with values outside the prescribed physical bounds. Frame interpolation finds extensive application in video coding,1 frame rate up-conversion,2 and video restora-tion.3,4 View synthesis has been used extensively as the cornerstone of image-based rendering in motion picture VFX.5 In practice, these two classes of techniques are often employed together in post-production, particularly in ... 11 hours ago · Discrete Empirical Interpolation Method (DEIM) is a simple and ef-fective method for reconstructing a function from its incomplete point-wise observations. However, applying DEIM to functions with physically constrained ranges can produce reconstructions with values outside the prescribed physical bounds. Such physically constrained quantities occur routinely in applications, e.g., mass ... 14 hours ago · By tracking the motion of disk features over time, such analysis can distinguish between local perturbations induced by embedded objects and intrin-sic disk features , offering insights into the early stages of planet formation in this system.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.16979", "content": "Mar 21, 2025 · First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. This generalized Network generates the motion of Gaussians for each target frame in the time required for a single inference. Projection-aware Motion Feature Lift. (d) Each Gaussian point interpolates its own motion feature from neighboring anchors and applies a weighted aggregation of features , which is then decoded into the motion of the Gaussian betw en the key frame and the target frame. (e) The entire streaming reconstruct IGS [23] proposes an Anchor-Driven Gaussian Motion Network (AGM-Net), decoding inter-frame Gaussian deformations via multi-view optical flow feature projection and anchor neighborhood interpolation . 11 hours ago · Discrete Empirical Interpolation Method (DEIM) is a simple and effective method for reconstructing a function from its incomplete pointwise observations. However, applying DEIM to functions with physically constrained ranges can produce reconstructions with values outside the prescribed physical bounds. Frame interpolation finds extensive application in video coding,1 frame rate up-conversion,2 and video restora-tion.3,4 View synthesis has been used extensively as the cornerstone of image-based rendering in motion picture VFX.5 In practice, these two classes of techniques are often employed together in post-production, particularly in ... 11 hours ago · Discrete Empirical Interpolation Method (DEIM) is a simple and ef-fective method for reconstructing a function from its incomplete point-wise observations. However, applying DEIM to functions with physically constrained ranges can produce reconstructions with values outside the prescribed physical bounds. Such physically constrained quantities occur routinely in applications, e.g., mass ... 14 hours ago · By tracking the motion of disk features over time, such analysis can distinguish between local perturbations induced by embedded objects and intrin-sic disk features , offering insights into the early stages of planet formation in this system."}
+{"idx": 1, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "Projection-aware Motion Feature Lift. (d) Each Gaussian point interpolates its own motion feature from neighboring anchors and applies a weighted aggregation of features , which is then decoded into the motion of the Gaussian betw en the key frame and the target frame. (e) The entire streaming reconstruct", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "Projection-aware Motion Feature Lift. (d) Each Gaussian point interpolates its own motion feature from neighboring anchors and applies a weighted aggregation of features , which is then decoded into the motion of the Gaussian betw en the key frame and the target frame. (e) The entire streaming reconstruct"}
+{"idx": 2, "title": "Discrete Empirical Interpolation Method with Upper and Lower ...", "date": "", "ddg_snippet": "11 hours ago · Discrete Empirical Interpolation Method (DEIM) is a simple and effective method for reconstructing a function from its incomplete pointwise observations. However, applying DEIM to functions with physically constrained ranges can produce reconstructions with values outside the prescribed physical bounds.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.16018v1", "content": "11 hours ago · Discrete Empirical Interpolation Method (DEIM) is a simple and effective method for reconstructing a function from its incomplete pointwise observations. However, applying DEIM to functions with physically constrained ranges can produce reconstructions with values outside the prescribed physical bounds."}
+{"idx": 3, "title": "Discrete Empirical Interpolation Method with Upper and Lower ...", "date": "", "ddg_snippet": "11 hours ago · Discrete Empirical Interpolation Method (DEIM) is a simple and ef-fective method for reconstructing a function from its incomplete point-wise observations. However, applying DEIM to functions with physically constrained ranges can produce reconstructions with values outside the prescribed physical bounds. Such physically constrained quantities occur routinely in applications, e.g., mass ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.16018", "content": "11 hours ago · Discrete Empirical Interpolation Method (DEIM) is a simple and ef-fective method for reconstructing a function from its incomplete point-wise observations. However, applying DEIM to functions with physically constrained ranges can produce reconstructions with values outside the prescribed physical bounds. Such physically constrained quantities occur routinely in applications, e.g., mass ..."}
+{"idx": 4, "title": "[2302.09311] Temporal Interpolation Is All You Need for Dynamic...", "date": "", "ddg_snippet": "Two feature interpolation methods are suggested depending on underlying representations, neural networks or grids. In the neural representation, we extract features from space-time inputs via multiple neural network modules and interpolate them based on time frames.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2302.09311", "content": "Two feature interpolation methods are suggested depending on underlying representations, neural networks or grids. In the neural representation, we extract features from space-time inputs via multiple neural network modules and interpolate them based on time frames."}
+{"idx": 5, "title": "Adaptive 3D Gaussian Splatting Video Streaming: Visual ...", "date": "", "ddg_snippet": "IGS [23] proposes an Anchor-Driven Gaussian Motion Network (AGM-Net), decoding inter-frame Gaussian deformations via multi-view optical flow feature projection and anchor neighborhood interpolation .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14454v1", "content": "IGS [23] proposes an Anchor-Driven Gaussian Motion Network (AGM-Net), decoding inter-frame Gaussian deformations via multi-view optical flow feature projection and anchor neighborhood interpolation ."}
+{"idx": 6, "title": "A new dataset and comparison for multi-camera frame synthesis", "date": "", "ddg_snippet": "Frame interpolation finds extensive application in video coding,1 frame rate up-conversion,2 and video restora-tion.3,4 View synthesis has been used extensively as the cornerstone of image-based rendering in motion picture VFX.5 In practice, these two classes of techniques are often employed together in post-production, particularly in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.09068v2", "content": "Frame interpolation finds extensive application in video coding,1 frame rate up-conversion,2 and video restora-tion.3,4 View synthesis has been used extensively as the cornerstone of image-based rendering in motion picture VFX.5 In practice, these two classes of techniques are often employed together in post-production, particularly in ..."}
+{"idx": 7, "title": "Dynamical Analysis of the HD 169142 Planet-Forming Disk ...", "date": "", "ddg_snippet": "14 hours ago · By tracking the motion of disk features over time, such analysis can distinguish between local perturbations induced by embedded objects and intrin-sic disk features , offering insights into the early stages of planet formation in this system.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15323", "content": "14 hours ago · By tracking the motion of disk features over time, such analysis can distinguish between local perturbations induced by embedded objects and intrin-sic disk features , offering insights into the early stages of planet formation in this system."}
+{"idx": 8, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "arXiv: 2503 . 16979 v1 [cs.CV] 21 Mar 2025.Now, we can use these 3D motion features to represent the motion information of an anchor and its neighborhood, and drive the motion of the neighboring Gaussian points based on these motion features .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "arXiv: 2503 . 16979 v1 [cs.CV] 21 Mar 2025.Now, we can use these 3D motion features to represent the motion information of an anchor and its neighborhood, and drive the motion of the neighboring Gaussian points based on these motion features ."}
+{"idx": 9, "title": "Fast Semantic Segmentation on Video Using", "date": "", "ddg_snippet": "1. Feature interpolation warps (W ) and fuses the features of enclosing keyframes to generate accurate feature estimates for intermediate frames. At the same time, a new target data format for semantic segmentation has emerged: video.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1803.07742", "content": "1. Feature interpolation warps (W ) and fuses the features of enclosing keyframes to generate accurate feature estimates for intermediate frames. At the same time, a new target data format for semantic segmentation has emerged: video."}
diff --git a/data/sampled_jsons/sitearxiv.org_33500_PS-EIP-_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_'Glossy'_'MAE'.jsonl b/data/sampled_jsons/sitearxiv.org_33500_PS-EIP-_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_'Glossy'_'MAE'.jsonl
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--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_33500_PS-EIP-_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_'Glossy'_'MAE'.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile [2303.00308] Event Fusion Photometric Stereo Network - arXiv.org IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 1 Robust ... Computer Vision and Pattern Recognition - arXiv.org Event Fusion Photometric Stereo Network - arXiv.org Deep Learning Methods for Calibrated Photometric Stereo and ... Temporal Event Stereo via Joint Learning with Stereoscopic Flow", "date": "", "ddg_snippet": "Mar 24, 2025 · This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals. Mar 1, 2023 · We present a novel method to estimate the surface normal of an object in an ambient light environment using RGB and event cameras. Modern photometric stereo methods rely on an RGB camera, mainly in a dark room, to avoid ambient illumination. ric stereo that relies on dictionary learning to produce robust normal vector reconstructions. Sp cifically, we develop two formulations for applying dictionary learning to photometric stereo . We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertia Mar 19, 2025 · PS - EIP : Robust Photometric Stereo Based on Event Interval Profile Kazuma Kitazawa, Takahito Aoto, Satoshi Ikehata, Tsuyoshi Takatani Comments: CVPR2025 Mar 2, 2023 · Consequently, we introduce an Event Fusion Photometric Stereo Net-work ( EFPS - Net ) to utilize RGB frames and event signals. Unlike dense RGB frames, event signals that only occur when the light intensity change is over the threshold produce sparse data. n the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo ethods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspective Jul 15, 2024 · To fully utilize the temporally dense and continuous nature of event cameras, we propose a novel temporal event stereo , a framework that continuously uses information from previous time steps.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.18341", "content": "Mar 24, 2025 · This paper proposes Photometric Stereo based on Event Interval Profile ( PS - EIP ), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals. Mar 1, 2023 · We present a novel method to estimate the surface normal of an object in an ambient light environment using RGB and event cameras. Modern photometric stereo methods rely on an RGB camera, mainly in a dark room, to avoid ambient illumination. ric stereo that relies on dictionary learning to produce robust normal vector reconstructions. Sp cifically, we develop two formulations for applying dictionary learning to photometric stereo . We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertia Mar 19, 2025 · PS - EIP : Robust Photometric Stereo Based on Event Interval Profile Kazuma Kitazawa, Takahito Aoto, Satoshi Ikehata, Tsuyoshi Takatani Comments: CVPR2025 Mar 2, 2023 · Consequently, we introduce an Event Fusion Photometric Stereo Net-work ( EFPS - Net ) to utilize RGB frames and event signals. Unlike dense RGB frames, event signals that only occur when the light intensity change is over the threshold produce sparse data. n the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo ethods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspective Jul 15, 2024 · To fully utilize the temporally dense and continuous nature of event cameras, we propose a novel temporal event stereo , a framework that continuously uses information from previous time steps."}
+{"idx": 1, "title": "IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 1 Robust ...", "date": "", "ddg_snippet": "ric stereo that relies on dictionary learning to produce robust normal vector reconstructions. Sp cifically, we develop two formulations for applying dictionary learning to photometric stereo . We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertia", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1710.08873", "content": "ric stereo that relies on dictionary learning to produce robust normal vector reconstructions. Sp cifically, we develop two formulations for applying dictionary learning to photometric stereo . We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertia"}
+{"idx": 2, "title": "[1710.00002] Robust Photometric Stereo Using Learned Image and...", "date": "", "ddg_snippet": "View a PDF of the paper titled Robust Photometric Stereo Using Learned Image and Gradient Dictionaries, by Andrew J. Wagenmaker and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1710.00002", "content": "View a PDF of the paper titled Robust Photometric Stereo Using Learned Image and Gradient Dictionaries, by Andrew J. Wagenmaker and 2 other authors."}
+{"idx": 3, "title": "[1710.08873] Robust Photometric Stereo via Dictionary Learning", "date": "", "ddg_snippet": "Abstract page for arXiv paper 1710.08873: Robust Photometric Stereo via Dictionary Learning.Abstract: Photometric stereo is a method that seeks to reconstruct the normal vectors of an object from a set of images of the object illuminated under different light sources.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1710.08873", "content": "Abstract page for arXiv paper 1710.08873: Robust Photometric Stereo via Dictionary Learning.Abstract: Photometric stereo is a method that seeks to reconstruct the normal vectors of an object from a set of images of the object illuminated under different light sources."}
+{"idx": 4, "title": "Robust photometric stereo using learned image and gradient", "date": "", "ddg_snippet": "Uncalibrated photometric stereo seeks to solve the photometric stereo problem when the lighting di-rections are unknown [2–5], while robust photometric stereo algorithms attempt to estimate the normal vectors of an object when the surface violates the assumptions of the underlying...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1710.00002", "content": "Uncalibrated photometric stereo seeks to solve the photometric stereo problem when the lighting di-rections are unknown [2–5], while robust photometric stereo algorithms attempt to estimate the normal vectors of an object when the surface violates the assumptions of the underlying..."}
+{"idx": 5, "title": "[2303.00308] Event Fusion Photometric Stereo Network - arXiv.org IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 1 Robust ... Computer Vision and Pattern Recognition - arXiv.org Event Fusion Photometric Stereo Network - arXiv.org Deep Learning Methods for Calibrated Photometric Stereo and ... Temporal Event Stereo via Joint Learning with Stereoscopic Flow", "date": "", "ddg_snippet": "Mar 1, 2023 · We present a novel method to estimate the surface normal of an object in an ambient light environment using RGB and event cameras. Modern photometric stereo methods rely on an RGB camera, mainly in a dark room, to avoid ambient illumination. ric stereo that relies on dictionary learning to produce robust normal vector reconstructions. Sp cifically, we develop two formulations for applying dictionary learning to photometric stereo . We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertia Mar 19, 2025 · PS - EIP : Robust Photometric Stereo Based on Event Interval Profile Kazuma Kitazawa, Takahito Aoto, Satoshi Ikehata, Tsuyoshi Takatani Comments: CVPR2025 Mar 2, 2023 · Consequently, we introduce an Event Fusion Photometric Stereo Net-work ( EFPS - Net ) to utilize RGB frames and event signals. Unlike dense RGB frames, event signals that only occur when the light intensity change is over the threshold produce sparse data. n the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo ethods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspective Jul 15, 2024 · To fully utilize the temporally dense and continuous nature of event cameras, we propose a novel temporal event stereo , a framework that continuously uses information from previous time steps.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.00308", "content": "Mar 1, 2023 · We present a novel method to estimate the surface normal of an object in an ambient light environment using RGB and event cameras. Modern photometric stereo methods rely on an RGB camera, mainly in a dark room, to avoid ambient illumination. ric stereo that relies on dictionary learning to produce robust normal vector reconstructions. Sp cifically, we develop two formulations for applying dictionary learning to photometric stereo . We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertia Mar 19, 2025 · PS - EIP : Robust Photometric Stereo Based on Event Interval Profile Kazuma Kitazawa, Takahito Aoto, Satoshi Ikehata, Tsuyoshi Takatani Comments: CVPR2025 Mar 2, 2023 · Consequently, we introduce an Event Fusion Photometric Stereo Net-work ( EFPS - Net ) to utilize RGB frames and event signals. Unlike dense RGB frames, event signals that only occur when the light intensity change is over the threshold produce sparse data. n the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo ethods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspective Jul 15, 2024 · To fully utilize the temporally dense and continuous nature of event cameras, we propose a novel temporal event stereo , a framework that continuously uses information from previous time steps."}
+{"idx": 6, "title": "Computer Vision and Pattern Recognition - arXiv.org", "date": "", "ddg_snippet": "Mar 19, 2025 · PS - EIP : Robust Photometric Stereo Based on Event Interval Profile Kazuma Kitazawa, Takahito Aoto, Satoshi Ikehata, Tsuyoshi Takatani Comments: CVPR2025", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.CV/recent?skip=103", "content": "Mar 19, 2025 · PS - EIP : Robust Photometric Stereo Based on Event Interval Profile Kazuma Kitazawa, Takahito Aoto, Satoshi Ikehata, Tsuyoshi Takatani Comments: CVPR2025"}
+{"idx": 7, "title": "Event Fusion Photometric Stereo Network - arXiv.org", "date": "", "ddg_snippet": "Mar 2, 2023 · Consequently, we introduce an Event Fusion Photometric Stereo Net-work ( EFPS - Net ) to utilize RGB frames and event signals. Unlike dense RGB frames, event signals that only occur when the light intensity change is over the threshold produce sparse data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.00308v1", "content": "Mar 2, 2023 · Consequently, we introduce an Event Fusion Photometric Stereo Net-work ( EFPS - Net ) to utilize RGB frames and event signals. Unlike dense RGB frames, event signals that only occur when the light intensity change is over the threshold produce sparse data."}
+{"idx": 8, "title": "Deep Learning Methods for Calibrated Photometric Stereo and ...", "date": "", "ddg_snippet": "n the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo ethods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspective", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.08414", "content": "n the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo ethods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspective"}
+{"idx": 9, "title": "Temporal Event Stereo via Joint Learning with Stereoscopic Flow", "date": "", "ddg_snippet": "Jul 15, 2024 · To fully utilize the temporally dense and continuous nature of event cameras, we propose a novel temporal event stereo , a framework that continuously uses information from previous time steps.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.10831", "content": "Jul 15, 2024 · To fully utilize the temporally dense and continuous nature of event cameras, we propose a novel temporal event stereo , a framework that continuously uses information from previous time steps."}
diff --git a/data/sampled_jsons/sitearxiv.org_ATA_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_.jsonl b/data/sampled_jsons/sitearxiv.org_ATA_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_.jsonl
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+{"idx": 0, "title": "[2502.00775] ATA : Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "View a PDF of the paper titled ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning , by Artavazd Maranjyan and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "View a PDF of the paper titled ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning , by Artavazd Maranjyan and 3 other authors."}
+{"idx": 1, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management ...", "date": "", "ddg_snippet": "Artavazd Maranjyan El Mehdi Saad Peter Richtárik Francesco Orabona. Abstract. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "Artavazd Maranjyan El Mehdi Saad Peter Richtárik Francesco Orabona. Abstract. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources ."}
+{"idx": 2, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management ...", "date": "", "ddg_snippet": "Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources ."}
+{"idx": 3, "title": "cherryATA: cherryAdaptive cherryTask cherryAllocation for Efficient ...", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . In our setting, the problem is more complex since the learner must not only choose which arms to pull but also determine the allocation of resources across selected arms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . In our setting, the problem is more complex since the learner must not only choose which arms to pull but also determine the allocation of resources across selected arms."}
+{"idx": 4, "title": "[2409.13824] Adaptive Task Allocation in Multi-Human Multi-Robot...", "date": "", "ddg_snippet": "To tackle this, we propose ATA -HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic operational states.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.13824", "content": "To tackle this, we propose ATA -HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic operational states."}
+{"idx": 5, "title": "DATA-WA: Demand-based Adaptive Task Assignment with Dynamic...", "date": "", "ddg_snippet": "We show that the ATA problem is NP-hard (see Lemma 1). To solve ATA , we propose an SC framework, namely Demand-based Adaptive Task Assignment with dynamic Worker Availability windows (DATA-WA), which adjusts task assignment based on...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.21458v1", "content": "We show that the ATA problem is NP-hard (see Lemma 1). To solve ATA , we propose an SC framework, namely Demand-based Adaptive Task Assignment with dynamic Worker Availability windows (DATA-WA), which adjusts task assignment based on..."}
+{"idx": 6, "title": "cherryATA", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . 3.1. Task allocation protocol. We consider a system of n ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . 3.1. Task allocation protocol. We consider a system of n ..."}
+{"idx": 7, "title": "Machine Learning Feb 2025 - stat.ML", "date": "", "ddg_snippet": "Title: ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/stat.ML/2025-02?skip=25&show=2000", "content": "Title: ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik ..."}
+{"idx": 8, "title": "Machine Learning Feb 2025 - stat.ML", "date": "", "ddg_snippet": "Title: ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/stat.ML/2025-02?skip=250&show=25", "content": "Title: ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik ..."}
+{"idx": 9, "title": "Distributed, Parallel, and Cluster Computing Feb 2025", "date": "", "ddg_snippet": "Title: ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.DC/2025-02?skip=100&show=2000", "content": "Title: ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . Artavazd Maranjyan, El Mehdi Saad, Peter Richtárik ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_ATP__Adaptive_Tensor_Parallelism_for_Foundation_Models_year_2023.jsonl b/data/sampled_jsons/sitearxiv.org_ATP__Adaptive_Tensor_Parallelism_for_Foundation_Models_year_2023.jsonl
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index 0000000000000000000000000000000000000000..650efae108d29ff83ddf9f536ba47910e82aac81
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_ATP__Adaptive_Tensor_Parallelism_for_Foundation_Models_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATP: Adaptive Tensor Parallelism for Foundation Models", "date": "", "ddg_snippet": "by S Cheng · 2023 · Cited by 5 — ATP: Adaptive Tensor Parallelism for Foundation Models . Authors:Shenggan Cheng, Ziming Liu, Jiangsu Du, Yang You.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.08658", "content": "by S Cheng · 2023 · Cited by 5 — ATP: Adaptive Tensor Parallelism for Foundation Models . Authors:Shenggan Cheng, Ziming Liu, Jiangsu Du, Yang You."}
+{"idx": 1, "title": "ATP: Adaptive Tensor Parallelism for Foundation Models - ar5iv", "date": "", "ddg_snippet": "ATP: Adaptive Tensor Parallelism for Foundation Models . Shenggan Cheng School of Computing National University of Singapore Ziming Liu School of Computing", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2301.08658", "content": "ATP: Adaptive Tensor Parallelism for Foundation Models . Shenggan Cheng School of Computing National University of Singapore Ziming Liu School of Computing"}
+{"idx": 2, "title": "arXiv:2301.08658v1 [cs.DC] 20 Jan 2023", "date": "", "ddg_snippet": "by S Cheng · 2023 · Cited by 5 — ATP: Adaptive Tensor Parallelism for Foundation Models . Shenggan Cheng. School of Computing. National University of Singapore. Ziming Liu.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2301.08658", "content": "by S Cheng · 2023 · Cited by 5 — ATP: Adaptive Tensor Parallelism for Foundation Models . Shenggan Cheng. School of Computing. National University of Singapore. Ziming Liu."}
+{"idx": 3, "title": "SPD: Sync-Point Drop for efficient tensor parallelism of ...", "date": "", "ddg_snippet": "21 May 2025 — (2023) ↑ Cheng, S., Liu, Z., Du, J., and You, Y. Atp: Adaptive tensor parallelism for foundation models . arXiv preprint arXiv:2301.08658, 2023.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20727v3", "content": "21 May 2025 — (2023) ↑ Cheng, S., Liu, Z., Du, J., and You, Y. Atp: Adaptive tensor parallelism for foundation models . arXiv preprint arXiv:2301.08658, 2023."}
+{"idx": 4, "title": "ViFusion: In-Network Tensor Fusion for Scalable Video Feature", "date": "", "ddg_snippet": "The state-of-the-art LLMs are extremely large: DeepSeek-R1 (DeepSeek-AI, 2025 ) , for example, contains 671 billion parameters, requiring the model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.16258v1", "content": "The state-of-the-art LLMs are extremely large: DeepSeek-R1 (DeepSeek-AI, 2025 ) , for example, contains 671 billion parameters, requiring the model ..."}
+{"idx": 5, "title": "Mamba State-Space Models Are Lyapunov-Stable Learners", "date": "", "ddg_snippet": "In the current era of extremely large foundation models , both MPFT and PEFT have become ubiquitous tools for the rapid adaptation of Transformer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.00209v2", "content": "In the current era of extremely large foundation models , both MPFT and PEFT have become ubiquitous tools for the rapid adaptation of Transformer ..."}
+{"idx": 6, "title": "\\thename: A Closed-Loop Simulation Framework For ICD Therapy", "date": "", "ddg_snippet": "For the cardiac EP simulations, we used various models of a human bi-ventricular (BiV) system to simulate both healthy and arrhythmic episodes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.01371v1", "content": "For the cardiac EP simulations, we used various models of a human bi-ventricular (BiV) system to simulate both healthy and arrhythmic episodes."}
+{"idx": 7, "title": "Multi-phase-field Models of Biological Tissues", "date": "", "ddg_snippet": "Next, we delve into the multi-phase-field model , discussing its foundational aspects, including time-scale separability, free energy functionals for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05053v1", "content": "Next, we delve into the multi-phase-field model , discussing its foundational aspects, including time-scale separability, free energy functionals for ..."}
+{"idx": 8, "title": "Distributed, Parallel, and Cluster Computing Jan 2023", "date": "", "ddg_snippet": "Title: ATP: Adaptive Tensor Parallelism for Foundation Models . Shenggan Cheng, Ziming Liu, Jiangsu Du, Yang You. Subjects: Distributed, Parallel, and Cluster ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.DC/2023-01?skip=50&show=25", "content": "Title: ATP: Adaptive Tensor Parallelism for Foundation Models . Shenggan Cheng, Ziming Liu, Jiangsu Du, Yang You. Subjects: Distributed, Parallel, and Cluster ..."}
+{"idx": 9, "title": "A 4D Hybrid Algorithm to Scale Parallel Training ...", "date": "", "ddg_snippet": "27 Mar 2024 — Cheng, Z. Liu, J. Du, and Y. You, “ Atp: Adaptive tensor parallelism for foundation models ,” arXiv preprint arXiv:2301.08658, 2023.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.13525v2", "content": "27 Mar 2024 — Cheng, Z. Liu, J. Du, and Y. You, “ Atp: Adaptive tensor parallelism for foundation models ,” arXiv preprint arXiv:2301.08658, 2023."}
diff --git a/data/sampled_jsons/sitearxiv.org_DC28Fpk76s_Intervention_and_Conditioning_in_Causal_Bayesian_Networks.jsonl b/data/sampled_jsons/sitearxiv.org_DC28Fpk76s_Intervention_and_Conditioning_in_Causal_Bayesian_Networks.jsonl
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index 0000000000000000000000000000000000000000..8b40549cc5be023d721bae4bba8e1141202e97f9
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks Intervention and Conditioning in Causal Bayesian Networks Intervention and Conditioning in Causal Bayesian Networks Bayesian Intervention Optimization for Causal Discovery Estimating Causal Effects from Learned Causal Networks Active Learning for Optimal Intervention Design in Causal Models [1805.09697] Learning and Testing Causal Models with ...", "date": "", "ddg_snippet": "May 23, 2024 · Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range ... Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ... These examples underscore the versatility and utility of causal models for providing a formal representation of system variables. Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal mechanisms. Jun 16, 2024 · Causal discovery is crucial for understanding complex systems and informing decisions. While observational data can uncover causal relationships under certain assumptions, it often falls short, making active interventions necessary. Current methods, such as Bayesian and graph-theoretical approaches, do not prioritize decision-making and often rely on ideal conditions or information gain, which ... Aug 26, 2024 · In this paper, we propose an alternative paradigm for answering causal -effect queries over discrete observable variables. We propose to instead learn the causal Bayesian network and its confounding latent variables directly from the observational data. Sep 10, 2022 · The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for fast optimization. May 24, 2018 · We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables and bounded in-degree and bounded `confounded components', we show that O(log n) interventions on an unknown causal Bayesian network X on the same graph, and O~(n/ϵ2) samples per intervention , suffice to efficiently ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.14728", "content": "May 23, 2024 · Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range ... Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ... These examples underscore the versatility and utility of causal models for providing a formal representation of system variables. Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal mechanisms. Jun 16, 2024 · Causal discovery is crucial for understanding complex systems and informing decisions. While observational data can uncover causal relationships under certain assumptions, it often falls short, making active interventions necessary. Current methods, such as Bayesian and graph-theoretical approaches, do not prioritize decision-making and often rely on ideal conditions or information gain, which ... Aug 26, 2024 · In this paper, we propose an alternative paradigm for answering causal -effect queries over discrete observable variables. We propose to instead learn the causal Bayesian network and its confounding latent variables directly from the observational data. Sep 10, 2022 · The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for fast optimization. May 24, 2018 · We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables and bounded in-degree and bounded `confounded components', we show that O(log n) interventions on an unknown causal Bayesian network X on the same graph, and O~(n/ϵ2) samples per intervention , suffice to efficiently ..."}
+{"idx": 1, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.14728", "content": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ..."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "These examples underscore the versatility and utility of causal models for providing a formal representation of system variables. Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.14728", "content": "These examples underscore the versatility and utility of causal models for providing a formal representation of system variables. Interventions and conditioning are the most fundamental procedures in the application of causal models, useful to examine and analyze causal mechanisms."}
+{"idx": 3, "title": "Bayesian Intervention Optimization for Causal Discovery Estimating Causal Effects from Learned Causal Networks Active Learning for Optimal Intervention Design in Causal Models [1805.09697] Learning and Testing Causal Models with ...", "date": "", "ddg_snippet": "Jun 16, 2024 · Causal discovery is crucial for understanding complex systems and informing decisions. While observational data can uncover causal relationships under certain assumptions, it often falls short, making active interventions necessary. Current methods, such as Bayesian and graph-theoretical approaches, do not prioritize decision-making and often rely on ideal conditions or information gain, which ... Aug 26, 2024 · In this paper, we propose an alternative paradigm for answering causal -effect queries over discrete observable variables. We propose to instead learn the causal Bayesian network and its confounding latent variables directly from the observational data. Sep 10, 2022 · The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for fast optimization. May 24, 2018 · We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables and bounded in-degree and bounded `confounded components', we show that O(log n) interventions on an unknown causal Bayesian network X on the same graph, and O~(n/ϵ2) samples per intervention , suffice to efficiently ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.10917", "content": "Jun 16, 2024 · Causal discovery is crucial for understanding complex systems and informing decisions. While observational data can uncover causal relationships under certain assumptions, it often falls short, making active interventions necessary. Current methods, such as Bayesian and graph-theoretical approaches, do not prioritize decision-making and often rely on ideal conditions or information gain, which ... Aug 26, 2024 · In this paper, we propose an alternative paradigm for answering causal -effect queries over discrete observable variables. We propose to instead learn the causal Bayesian network and its confounding latent variables directly from the observational data. Sep 10, 2022 · The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for fast optimization. May 24, 2018 · We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables and bounded in-degree and bounded `confounded components', we show that O(log n) interventions on an unknown causal Bayesian network X on the same graph, and O~(n/ϵ2) samples per intervention , suffice to efficiently ..."}
+{"idx": 4, "title": "Estimating Causal Effects from Learned Causal Networks Active Learning for Optimal Intervention Design in Causal Models [1805.09697] Learning and Testing Causal Models with ...", "date": "", "ddg_snippet": "Aug 26, 2024 · In this paper, we propose an alternative paradigm for answering causal -effect queries over discrete observable variables. We propose to instead learn the causal Bayesian network and its confounding latent variables directly from the observational data. Sep 10, 2022 · The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for fast optimization. May 24, 2018 · We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables and bounded in-degree and bounded `confounded components', we show that O(log n) interventions on an unknown causal Bayesian network X on the same graph, and O~(n/ϵ2) samples per intervention , suffice to efficiently ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.14101", "content": "Aug 26, 2024 · In this paper, we propose an alternative paradigm for answering causal -effect queries over discrete observable variables. We propose to instead learn the causal Bayesian network and its confounding latent variables directly from the observational data. Sep 10, 2022 · The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for fast optimization. May 24, 2018 · We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables and bounded in-degree and bounded `confounded components', we show that O(log n) interventions on an unknown causal Bayesian network X on the same graph, and O~(n/ϵ2) samples per intervention , suffice to efficiently ..."}
+{"idx": 5, "title": "Active Learning for Optimal Intervention Design in Causal Models", "date": "", "ddg_snippet": "Sep 10, 2022 · The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for fast optimization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2209.04744", "content": "Sep 10, 2022 · The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for fast optimization."}
+{"idx": 6, "title": "[1805.09697] Learning and Testing Causal Models with ...", "date": "", "ddg_snippet": "May 24, 2018 · We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables and bounded in-degree and bounded `confounded components', we show that O(log n) interventions on an unknown causal Bayesian network X on the same graph, and O~(n/ϵ2) samples per intervention , suffice to efficiently ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1805.09697", "content": "May 24, 2018 · We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables and bounded in-degree and bounded `confounded components', we show that O(log n) interventions on an unknown causal Bayesian network X on the same graph, and O~(n/ϵ2) samples per intervention , suffice to efficiently ..."}
+{"idx": 7, "title": "[1206.5275] Polynomial Constraints in Causal Bayesian Networks", "date": "", "ddg_snippet": "Abstract:We use the implicitization procedure to generate polynomial equality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network with hidden variables.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1206.5275", "content": "Abstract:We use the implicitization procedure to generate polynomial equality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network with hidden variables."}
+{"idx": 8, "title": "[1907.06430] A Causal Bayesian Networks Viewpoint on Fairness", "date": "", "ddg_snippet": "We show that causal Bayesian networks provide us with a powerful tool to measure unfairness in a dataset and to design fair models in complex unfairness scenarios.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1907.06430", "content": "We show that causal Bayesian networks provide us with a powerful tool to measure unfairness in a dataset and to design fair models in complex unfairness scenarios."}
+{"idx": 9, "title": "Challenges and Considerations in the Evaluation of Bayesian Causal ...", "date": "", "ddg_snippet": "intervention on any variable Xi corresponds to changing the structural equation of that variable to the desired state (value), Xi := si, where si ∈ Xi.Cho, H., Berger, B., and Peng, J. Reconstructing causal biological networks through active learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.03209", "content": "intervention on any variable Xi corresponds to changing the structural equation of that variable to the desired state (value), Xi := si, where si ∈ Xi.Cho, H., Berger, B., and Peng, J. Reconstructing causal biological networks through active learning."}
diff --git a/data/sampled_jsons/sitearxiv.org_Definition_3.2_Directionality_Score_The_underlying_structures_of_self-attention.jsonl b/data/sampled_jsons/sitearxiv.org_Definition_3.2_Directionality_Score_The_underlying_structures_of_self-attention.jsonl
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index 0000000000000000000000000000000000000000..f5e27b877a7cc1d9b73b1fb8bf4047617886ca83
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Definition_3.2_Directionality_Score_The_underlying_structures_of_self-attention.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Evolving Role of Large Language Models in Scientific", "date": "", "ddg_snippet": "We distinguish between LLMs’ contributions to structured scientific research processes and open-ended scientific discovery, thereby offering a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11810v1", "content": "We distinguish between LLMs’ contributions to structured scientific research processes and open-ended scientific discovery, thereby offering a ..."}
+{"idx": 1, "title": "Do It Yourself: Learning Semantic Correspondence from", "date": "", "ddg_snippet": "... in tracking [ 8 , 10 , 20 ] , mapping and localization [ 37 , 24 ] , affordance understanding [ 25 ] , pose estimation [ 58 ] , analysis of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05312v1", "content": "... in tracking [ 8 , 10 , 20 ] , mapping and localization [ 37 , 24 ] , affordance understanding [ 25 ] , pose estimation [ 58 ] , analysis of ..."}
+{"idx": 2, "title": "DisastIR: A Comprehensive Information Retrieval Benchmark for", "date": "", "ddg_snippet": "These varied intents require tailored retrieval behavior (Asai et al., 2022 ; Su et al., 2022 ; Lee et al., 2024b ) and understanding of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.15856v2", "content": "These varied intents require tailored retrieval behavior (Asai et al., 2022 ; Su et al., 2022 ; Lee et al., 2024b ) and understanding of ..."}
+{"idx": 3, "title": "HiMATE: A Hierarchical Multi-Agent Framework for Machine", "date": "", "ddg_snippet": "UniTE (Wan et al., 2022 ) improves the performance of various translation tasks by using monotonic regional attention to control the interaction ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16281v3", "content": "UniTE (Wan et al., 2022 ) improves the performance of various translation tasks by using monotonic regional attention to control the interaction ..."}
+{"idx": 4, "title": "Orchestrator: Active Inference for Multi-Agent Systems in", "date": "", "ddg_snippet": "... have become a central testbed for evaluating the reasoning, planning, and coordination abilities of intelligent agents ( linardakis_distributed_2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.05651v1", "content": "... have become a central testbed for evaluating the reasoning, planning, and coordination abilities of intelligent agents ( linardakis_distributed_2024 ..."}
+{"idx": 5, "title": "Arg-LLaDA: Argument Summarization via Large Language Diffusion", "date": "", "ddg_snippet": "... the task as one of claim generation (Wang and Ling, 2016 ) or aspect-controlled generation (Schiller et al., 2021 ) , enabling abstractive summaries ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.19081v3", "content": "... the task as one of claim generation (Wang and Ling, 2016 ) or aspect-controlled generation (Schiller et al., 2021 ) , enabling abstractive summaries ..."}
+{"idx": 6, "title": "Fragment and Geometry Aware Tokenization of Molecules for", "date": "", "ddg_snippet": "... pre-trained inverse folding model to extract the embedding of protein pockets and incorporate this information into LMs by cross- attention mechanism.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.09730v1", "content": "... pre-trained inverse folding model to extract the embedding of protein pockets and incorporate this information into LMs by cross- attention mechanism."}
+{"idx": 7, "title": "SMUTF: Schema Matching Using Generative Tags and Hybrid Features", "date": "", "ddg_snippet": "In contrast to them, SMUTF utilizes supervised learning, thereby enhancing the robustness of SM under various conditions, a claim substantiated by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.01685v3", "content": "In contrast to them, SMUTF utilizes supervised learning, thereby enhancing the robustness of SM under various conditions, a claim substantiated by ..."}
+{"idx": 8, "title": "M4GN: Mesh-based Multi-segment Hierarchical Graph Network for", "date": "", "ddg_snippet": "... has prompted increased attention on adopting learning-based surrogate models (Sun et al., 2020 ) to expedite numerical simulations, addressing the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10659v1", "content": "... has prompted increased attention on adopting learning-based surrogate models (Sun et al., 2020 ) to expedite numerical simulations, addressing the ..."}
+{"idx": 9, "title": "AdsQA: Towards Advertisement Video Understanding", "date": "", "ddg_snippet": "Early video QA methods employed graph structures [ 90 , 29 , 56 ] or transformers [ 37 , 33 , 17 ] to model the spatiotemporal relationships in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08621v1", "content": "Early video QA methods employed graph structures [ 90 , 29 , 56 ] or transformers [ 37 , 33 , 17 ] to model the spatiotemporal relationships in ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis.jsonl b/data/sampled_jsons/sitearxiv.org_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis.jsonl
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index 0000000000000000000000000000000000000000..70df1e9221483b51f19d6ec8bb624b943d4145fa
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+++ b/data/sampled_jsons/sitearxiv.org_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2505.00998] Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "View a PDF of the paper titled Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , by Yu Hua and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "View a PDF of the paper titled Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , by Yu Hua and 5 other authors"}
+{"idx": 1, "title": "arXiv:2505.00998v1 [cs.CV] 2 May 2025", "date": "", "ddg_snippet": "arXiv:2505.00998v1 [cs.CV] 2 May 2025 Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.00998", "content": "arXiv:2505.00998v1 [cs.CV] 2 May 2025 Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis"}
+{"idx": 2, "title": "X-MoGen: Unified Motion Generation across Humans and Animals", "date": "", "ddg_snippet": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis . In Proceedings of the Computer Vision and Pattern Recognition Conference, 22724-22734.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05162v1", "content": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis . In Proceedings of the Computer Vision and Pattern Recognition Conference, 22724-22734."}
+{"idx": 3, "title": "[2505.00998] Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "Title: Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2505.00998", "content": "Title: Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis"}
+{"idx": 4, "title": "LGDM: Latent Guidance in Diffusion Models for Perceptual", "date": "", "ddg_snippet": "... for leveraging unconditional latent diffusion models to tackle the challenging task of NR-IQA without any fine-tuning or additional training of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00327v1", "content": "... for leveraging unconditional latent diffusion models to tackle the challenging task of NR-IQA without any fine-tuning or additional training of ..."}
+{"idx": 5, "title": "ODE-GS: Latent ODEs for Dynamic Scene Extrapolation with 3D", "date": "", "ddg_snippet": "We formulate Gaussian parameter dynamics using an ODE-based latent space, solved via numerical integration, to achieve smooth and coherent motion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05480v1", "content": "We formulate Gaussian parameter dynamics using an ODE-based latent space, solved via numerical integration, to achieve smooth and coherent motion ..."}
+{"idx": 6, "title": "Conditional Video Generation for High-Efficiency Video", "date": "", "ddg_snippet": "Second, we design compact, transmission-efficient representations for these signals that serve as minimal yet perceptually informative inputs to the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15269v1", "content": "Second, we design compact, transmission-efficient representations for these signals that serve as minimal yet perceptually informative inputs to the ..."}
+{"idx": 7, "title": "Score-Guided Diffusion for 3D Human Recovery", "date": "", "ddg_snippet": "... primarily been utilized in the generation of human motions based on text descriptions [ 58 , 67 , 47 ] , rather than being harnessed as a tool for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09623v1", "content": "... primarily been utilized in the generation of human motions based on text descriptions [ 58 , 67 , 47 ] , rather than being harnessed as a tool for ..."}
+{"idx": 8, "title": "GENMO: A GENeralist Model for Human MOtion", "date": "", "ddg_snippet": "To address these issues, we propose GENMO, a Generalist Model for Human Motion that unifies estimation and generation within a single framework.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.01425v1", "content": "To address these issues, we propose GENMO, a Generalist Model for Human Motion that unifies estimation and generation within a single framework."}
+{"idx": 9, "title": "Think2Sing: Orchestrating Structured Motion Subtitles for", "date": "", "ddg_snippet": "... by allowing motion subtitles to be mapped directly to corresponding intensity heads, reducing learning ambiguity and improving synthesis quality.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02278v1", "content": "... by allowing motion subtitles to be mapped directly to corresponding intensity heads, reducing learning ambiguity and improving synthesis quality."}
diff --git a/data/sampled_jsons/sitearxiv.org_ETHICS_dataset_Hendrycks_empirical_studies_reasons_2502.00136.jsonl b/data/sampled_jsons/sitearxiv.org_ETHICS_dataset_Hendrycks_empirical_studies_reasons_2502.00136.jsonl
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index 0000000000000000000000000000000000000000..250a2d38dce854b40dbdd9843cc5d0127ab2fac4
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_ETHICS_dataset_Hendrycks_empirical_studies_reasons_2502.00136.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "The Dark Side of Function Calling: Pathways to Jailbreaking Large...", "date": "", "ddg_snippet": "Analysis of causes: We provide a detailed analysis of the reasons why function calls are susceptible to jailbreaks. In particular, we demonstrate that function calls are more prone to jailbreak attacks compared to chat mode.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.17915v1/", "content": "Analysis of causes: We provide a detailed analysis of the reasons why function calls are susceptible to jailbreaks. In particular, we demonstrate that function calls are more prone to jailbreak attacks compared to chat mode."}
+{"idx": 1, "title": "The Dark Side of Function Calling: Pathways to Jailbreaking Large...", "date": "", "ddg_snippet": "Analysis of Causes: We provide a detailed analysis of the reasons why function calls are susceptible to jailbreaks.One of the pioneering works in this area is the introduction of the ETHICS dataset by Hendrycks et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.17915v4", "content": "Analysis of Causes: We provide a detailed analysis of the reasons why function calls are susceptible to jailbreaks.One of the pioneering works in this area is the introduction of the ETHICS dataset by Hendrycks et al."}
+{"idx": 2, "title": "The Dark Side of Function Calling: Pathways to", "date": "", "ddg_snippet": "Empirical Studies . Assessing the Effectiveness of Jailbreak Functions (RQ 1). Analyzing Why Function Calls Cause Jailbreaks (RQ 2).One of the pioneering works in this area is the introduction of the ETHICS dataset by Hendrycks et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.17915v1", "content": "Empirical Studies . Assessing the Effectiveness of Jailbreak Functions (RQ 1). Analyzing Why Function Calls Cause Jailbreaks (RQ 2).One of the pioneering works in this area is the introduction of the ETHICS dataset by Hendrycks et al."}
+{"idx": 3, "title": "The Dark Side of Function Calling: Pathways to Jailbreaking Large...", "date": "", "ddg_snippet": "4 Empirical Studies . 4.1 Assessing the Effectiveness of Jailbreak Functions (RQ 1).One of the pioneering works in this area is the introduction of the ETHICS dataset by Hendrycks et al. (2021) , which aims to measure LLMs’ ability to predict human ethical judgments.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.17915v2/", "content": "4 Empirical Studies . 4.1 Assessing the Effectiveness of Jailbreak Functions (RQ 1).One of the pioneering works in this area is the introduction of the ETHICS dataset by Hendrycks et al. (2021) , which aims to measure LLMs’ ability to predict human ethical judgments."}
+{"idx": 4, "title": "METAETHICAL PERSPECTIVES", "date": "", "ddg_snippet": "In empirical studies , most individuals say they would only act in the case of Switch, not in Footbridge (Navarrete et al., 2012; Bourget and Chalmers, 2014).32 metaethical perspectives on ‘Benchmarking’ ai ethics . Dan Hendrycks and Thomas G. Dietterich.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2204.05151", "content": "In empirical studies , most individuals say they would only act in the case of Switch, not in Footbridge (Navarrete et al., 2012; Bourget and Chalmers, 2014).32 metaethical perspectives on ‘Benchmarking’ ai ethics . Dan Hendrycks and Thomas G. Dietterich."}
diff --git a/data/sampled_jsons/sitearxiv.org_FlowDec_Table_8_SIGMOS_4.50_kbits.jsonl b/data/sampled_jsons/sitearxiv.org_FlowDec_Table_8_SIGMOS_4.50_kbits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ffa6e58cbf17399a1836ae8e02eeb8c055aba959
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_FlowDec_Table_8_SIGMOS_4.50_kbits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "Abstract We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "Abstract We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving ..."}
+{"idx": 1, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "For the 25Hz models, we can see that the general behavior of FlowDec and DAC is unchanged, with FlowDec again exhibiting better FAD and SIGMOS . In Table 4, we compare FAD, SI-SDR and fwSSNR of FlowDec -75s at NFE ∈{6,50}against ScoreDec (Wu et al., 2024) and the alternative flow-based formulation with constantσ", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "For the 25Hz models, we can see that the general behavior of FlowDec and DAC is unchanged, with FlowDec again exhibiting better FAD and SIGMOS . In Table 4, we compare FAD, SI-SDR and fwSSNR of FlowDec -75s at NFE ∈{6,50}against ScoreDec (Wu et al., 2024) and the alternative flow-based formulation with constantσ"}
+{"idx": 2, "title": "arXiv.org", "date": "", "ddg_snippet": "arXiv.org", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.11352", "content": "arXiv.org"}
+{"idx": 3, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s , while improving output ..."}
+{"idx": 4, "title": "ICASSP 2024 Speech Signal Improvement Challenge - arXiv.org", "date": "", "ddg_snippet": "Table 1: ICASSP 2024 Speech Signal Improvement Challenge results. We included MOS and differential MOS (DMOS) (for each score we subtract the corresponding Noisy score) for P.804 [2] , WAcc and the Final Score with the corresponding rank.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.14444v1", "content": "Table 1: ICASSP 2024 Speech Signal Improvement Challenge results. We included MOS and differential MOS (DMOS) (for each score we subtract the corresponding Noisy score) for P.804 [2] , WAcc and the Final Score with the corresponding rank."}
+{"idx": 5, "title": "CodecBench: A Comprehensive Benchmark for Acoustic and Semantic Evaluation", "date": "", "ddg_snippet": "This highlights the advantages of the flow-matching approach. However, FlowDec's STOI scores are consistently lower, which may be attributed to its using a self-trained DAC-structured model. At low bitrates, BigCodec achieves excellent performance on the Speech datasets but underperforms compared to Mimi-8 in other domains.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20660v1", "content": "This highlights the advantages of the flow-matching approach. However, FlowDec's STOI scores are consistently lower, which may be attributed to its using a self-trained DAC-structured model. At low bitrates, BigCodec achieves excellent performance on the Speech datasets but underperforms compared to Mimi-8 in other domains."}
+{"idx": 6, "title": "FlowMAC: Conditional Flow Matching for Audio Coding at Low Bit Rates", "date": "", "ddg_snippet": "This paper introduces FlowMAC, a novel neural audio codec for high-quality general audio compression at low bit rates based on conditional flow matching (CFM). FlowMAC jointly learns a mel spectrogram encoder, quantizer and decoder. At inference time the decoder integrates a continuous normalizing flow via an ODE solver to generate a high-quality mel spectrogram. This is the first time that a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.17635", "content": "This paper introduces FlowMAC, a novel neural audio codec for high-quality general audio compression at low bit rates based on conditional flow matching (CFM). FlowMAC jointly learns a mel spectrogram encoder, quantizer and decoder. At inference time the decoder integrates a continuous normalizing flow via an ODE solver to generate a high-quality mel spectrogram. This is the first time that a ..."}
+{"idx": 7, "title": "The case for 4-bit precision: k-bit Inference Scaling Laws", "date": "", "ddg_snippet": "Quantization methods reduce the number of bits required to represent each parameter in a model, trading accuracy for smaller memory footprints and inference latencies. However, the final model size depends on both the number of parameters of the original model and the rate of compression. For example, a 30B 8-bit model and a 60B 4-bit model have the same number of bits but may have very ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.09720", "content": "Quantization methods reduce the number of bits required to represent each parameter in a model, trading accuracy for smaller memory footprints and inference latencies. However, the final model size depends on both the number of parameters of the original model and the rate of compression. For example, a 30B 8-bit model and a 60B 4-bit model have the same number of bits but may have very ..."}
+{"idx": 8, "title": "[2206.02915] 8-bit Numerical Formats for Deep Neural Networks", "date": "", "ddg_snippet": "Given the current trend of increasing size and complexity of machine learning architectures, it has become of critical importance to identify new approaches to improve the computational efficiency of model training. In this context, we address the advantages of floating-point over fixed-point representation, and present an in-depth study on the use of 8-bit floating-point number formats for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2206.02915", "content": "Given the current trend of increasing size and complexity of machine learning architectures, it has become of critical importance to identify new approaches to improve the computational efficiency of model training. In this context, we address the advantages of floating-point over fixed-point representation, and present an in-depth study on the use of 8-bit floating-point number formats for ..."}
+{"idx": 9, "title": "FlowMAC: Conditional Flow Matching for Audio Coding at Low Bit Rates", "date": "", "ddg_snippet": "Abstract This paper introduces FlowMAC, a novel neural audio codec for high-quality general audio compression at low bit rates based on conditional flow matching (CFM). FlowMAC jointly learns a mel spectrogram encoder, quantizer and decoder. At inference time the decoder integrates a continuous normalizing flow via an ODE solver to generate a high-quality mel spectrogram. This is the first ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.17635v1", "content": "Abstract This paper introduces FlowMAC, a novel neural audio codec for high-quality general audio compression at low bit rates based on conditional flow matching (CFM). FlowMAC jointly learns a mel spectrogram encoder, quantizer and decoder. At inference time the decoder integrates a continuous normalizing flow via an ODE solver to generate a high-quality mel spectrogram. This is the first ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Neural_Exploratory_Landscape_Analysis_Limitations_evolution_strategies.jsonl b/data/sampled_jsons/sitearxiv.org_Neural_Exploratory_Landscape_Analysis_Limitations_evolution_strategies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2018a78ff90b6b6bf9a133ab943b97adf24b3c6e
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Neural_Exploratory_Landscape_Analysis_Limitations_evolution_strategies.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "Neural Exploratory Landscape Analysis", "date": "", "ddg_snippet": "Neural Exploratory Landscape Analysis . Report issue for preceding element. Exploratory landscape analysis is strongly sensitive to the sampling strategy. In International Conference on Parallel Problem Solving from Nature, 2020. Hansen and Ostermeier [2001b].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v1", "content": "Neural Exploratory Landscape Analysis . Report issue for preceding element. Exploratory landscape analysis is strongly sensitive to the sampling strategy. In International Conference on Parallel Problem Solving from Nature, 2020. Hansen and Ostermeier [2001b]."}
+{"idx": 1, "title": "Neural Exploratory Landscape Analysis for...", "date": "", "ddg_snippet": "To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v3", "content": "To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion."}
+{"idx": 2, "title": "Neural Exploratory Landscape Analysis", "date": "", "ddg_snippet": "Neural Exploratory Landscape Analysis . Framework. Architecture of.MetaBBO algorithms widely adopt Exploratory Landscape Analysis (ELA) [14, 15] to profile the low-level optimization status.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.10672v1", "content": "Neural Exploratory Landscape Analysis . Framework. Architecture of.MetaBBO algorithms widely adopt Exploratory Landscape Analysis (ELA) [14, 15] to profile the low-level optimization status."}
+{"idx": 3, "title": "Neural Exploratory Landscape Analysis", "date": "", "ddg_snippet": "Neural Exploratory Landscape Analysis . Report issue for preceding element. Exploratory landscape analysis is strongly sensitive to the sampling strategy. In International Conference on Parallel Problem Solving from Nature, 2020.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v2", "content": "Neural Exploratory Landscape Analysis . Report issue for preceding element. Exploratory landscape analysis is strongly sensitive to the sampling strategy. In International Conference on Parallel Problem Solving from Nature, 2020."}
+{"idx": 4, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "Neural Exploratory Landscape Analysis . Framework. Neural exploratory landscape analysis for meta-black-box-optimization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.10672", "content": "Neural Exploratory Landscape Analysis . Framework. Neural exploratory landscape analysis for meta-black-box-optimization."}
+{"idx": 5, "title": "Instance Generation for Meta-Black-Box Optimization ...", "date": "", "ddg_snippet": "4 days ago — In this paper, we propose LSRE to address the limitations in the third line of work in both effectiveness and efficiency side. On the one ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15810v1", "content": "4 days ago — In this paper, we propose LSRE to address the limitations in the third line of work in both effectiveness and efficiency side. On the one ..."}
+{"idx": 6, "title": "MetaBox-v2: A Unified Benchmark Platform for Meta-Black- ...", "date": "", "ddg_snippet": "23 May 2025 — ... limitations while inheriting the benefits of the original Metabox. ... Evolution strategies –a comprehensive introduction. Natural Computing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.17745v1", "content": "23 May 2025 — ... limitations while inheriting the benefits of the original Metabox. ... Evolution strategies –a comprehensive introduction. Natural Computing ..."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_On_a_Connection_Between_Imitation_Learning_and_RLHF_Equation_(21).jsonl b/data/sampled_jsons/sitearxiv.org_On_a_Connection_Between_Imitation_Learning_and_RLHF_Equation_(21).jsonl
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index 0000000000000000000000000000000000000000..f1ea51e0d057889fe0bb5590505ca9a5c1f052cd
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_On_a_Connection_Between_Imitation_Learning_and_RLHF_Equation_(21).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "I Mitation L earning and rlhf", "date": "", "ddg_snippet": "On a connection between imitation learning and rlhf .Thus, conducting imitation learning on the chosen response corresponds to solving a standard KL-regularized RLHF problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.05079", "content": "On a connection between imitation learning and rlhf .Thus, conducting imitation learning on the chosen response corresponds to solving a standard KL-regularized RLHF problem."}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 2, "title": "Sample-Efficient Reinforcement Learning from Human ...", "date": "", "ddg_snippet": "8 Aug 2025 — We study the problem of reinforcement learning from human feedback ( RLHF ) ... Contextual bandits and imitation learning with preference-based ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05434v3", "content": "8 Aug 2025 — We study the problem of reinforcement learning from human feedback ( RLHF ) ... Contextual bandits and imitation learning with preference-based ..."}
+{"idx": 3, "title": "Inverse Reinforcement Learning Meets Large Language ...", "date": "", "ddg_snippet": "by H Sun · 2025 · Cited by 1 — This paper provides a comprehensive review of recent advances in LLM alignment through the lens of inverse reinforcement learning (IRL), ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.13158", "content": "by H Sun · 2025 · Cited by 1 — This paper provides a comprehensive review of recent advances in LLM alignment through the lens of inverse reinforcement learning (IRL), ..."}
+{"idx": 4, "title": "The Hidden Link Between RLHF and Contrastive Learning", "date": "", "ddg_snippet": "27 Jun 2025 — Several recent works have hinted at conceptual links between RLHF and imitation learning [8] . DIL [4] show that optimizing a policy with RLHF ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.22578v1", "content": "27 Jun 2025 — Several recent works have hinted at conceptual links between RLHF and imitation learning [8] . DIL [4] show that optimizing a policy with RLHF ..."}
+{"idx": 5, "title": "Inverse Preference Learning", "date": "", "ddg_snippet": "by J Hejna · 2023 · Cited by 76 — Reward functions learned via RLHF can directly capture human intent, while avoiding alternative and more expensive forms of human feedback such ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.15363", "content": "by J Hejna · 2023 · Cited by 76 — Reward functions learned via RLHF can directly capture human intent, while avoiding alternative and more expensive forms of human feedback such ..."}
+{"idx": 6, "title": "Reinforcement Learning from Human Feedback with Active ...", "date": "", "ddg_snippet": "by K Ji · 2024 · Cited by 32 — In this paper, inspired by the success of active learning , we address this problem by proposing query-efficient RLHF methods. We first formalize ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.09401", "content": "by K Ji · 2024 · Cited by 32 — In this paper, inspired by the success of active learning , we address this problem by proposing query-efficient RLHF methods. We first formalize ..."}
+{"idx": 7, "title": "Safe RLHF-V: Safe Reinforcement Learning from Human ...", "date": "", "ddg_snippet": "22 Mar 2025 — In this study, we propose Safe RLHF-V , the first multimodal safety alignment framework that jointly optimizes helpfulness and safety using separate multimodal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17682v1", "content": "22 Mar 2025 — In this study, we propose Safe RLHF-V , the first multimodal safety alignment framework that jointly optimizes helpfulness and safety using separate multimodal ..."}
+{"idx": 8, "title": "Doubly Robust Alignment for Large Language Models", "date": "", "ddg_snippet": "by E Xu · 2025 — Abstract. This paper studies reinforcement learning from human feedback ( RLHF ) for aligning large language models with human preferences.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.01183", "content": "by E Xu · 2025 — Abstract. This paper studies reinforcement learning from human feedback ( RLHF ) for aligning large language models with human preferences."}
+{"idx": 9, "title": "Trustworthy Human-AI Collaboration: Reinforcement ...", "date": "", "ddg_snippet": "1 Sept 2024 — The RLHF approach has proven effective in enhancing both the training safety and sampling efficiency in RL. Some studies have attempted to apply ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.00858v1", "content": "1 Sept 2024 — The RLHF approach has proven effective in enhancing both the training safety and sampling efficiency in RL. Some studies have attempted to apply ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Position_Evaluating_Generative_AI_Systems_Section_6_a_lot_of_work_justification.jsonl b/data/sampled_jsons/sitearxiv.org_Position_Evaluating_Generative_AI_Systems_Section_6_a_lot_of_work_justification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ac2753b5406e68c8f24d8ee028ce936db91d638
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Position_Evaluating_Generative_AI_Systems_Section_6_a_lot_of_work_justification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "... measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as “ a tangle of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "... measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as “ a tangle of ..."}
+{"idx": 1, "title": "Towards Efficient Generative Large Language Model Serving: A", "date": "", "ddg_snippet": "Section 3 includes our taxonomy of existing approaches on efficient LLM serving and revisits these related works from two aspects: algorithmic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.15234v2", "content": "Section 3 includes our taxonomy of existing approaches on efficient LLM serving and revisits these related works from two aspects: algorithmic ..."}
+{"idx": 2, "title": "Towards Efficient Generative Large Language Model Serving: A", "date": "", "ddg_snippet": "Section 3 includes our taxonomy of existing approaches on efficient LLM serving and revisits these related works from two aspects: algorithmic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.15234v1", "content": "Section 3 includes our taxonomy of existing approaches on efficient LLM serving and revisits these related works from two aspects: algorithmic ..."}
+{"idx": 3, "title": "Pega Artificial Intelligence - Experience Generative AI Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Explore the many powerful applications and unprecedented applications of AI. Harness the power of AI to turn new concepts into full operations and power productivity", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=pega.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=ySz-dZ2HGM_izRFEB3Fje5GmLTyiSCHDg85EZ7WsJZQHLaJOz81G_daEX0ac1IKwAYl9rPdIcRGxUAXgEy-M8R2fHHw4kYFSIVcVBQStwi8x5d6oitGaPp6wICqjHpzP.qizzb0bogbFq2BiuyxlHzw&rut=f90a3d5474aac5a77cf9aee8bb518798afe2567e605add9980677ebadc719d9b&u3=https://www.bing.com/aclick?ld=e8xz5fhM6Z1iETSjiAEMdPCTVUCUzqnM60CbPRftFxqGdd5N53XlRdojnM8neAad6cky84fagPbA9PRsm4tiL6bcPAVDm2v8l_foh821wl6N9a-SBWWdNLBJk2VOqayFWzu1j4sV3VE5EgRIIgiPCkDdvdZ7WS3d-qMMDt-TfHTZLpj4vBs7lmQamKyoiFYIC-x_1QKQ&u=aHR0cHMlM2ElMmYlMmZhZC5kb3VibGVjbGljay5uZXQlMmZzZWFyY2hhZHMlMmZsaW5rJTJmY2xpY2slM2ZsaWQlM2Q0MzcwMDA3ODU3MTk0NjEzMyUyNmRzX3Nfa3dnaWQlM2Q1ODcwMDAwNzc1MDE5NjU4OCUyNmRzX2FfY2lkJTNkNDAwODE3MjUwJTI2ZHNfYV9jYWlkJTNkMTY2NTIzNTU3NzglMjZkc19hX2FnaWQlM2QxMzUyMjM2MzI4NTUlMjZkc19hX2xpZCUzZGt3ZC01NDUyOTM3NzgwNzglMjYlMjZkc19lX2FkaWQlM2Q4MTU3MDExODY1NzYxOCUyNmRzX2VfdGFyZ2V0X2lkJTNka3dkLTgxNTcwMzI5NDQ5ODg1JTNhbG9jLTE2NCUyNiUyNmRzX2VfbmV0d29yayUzZHMlMjZkc191cmxfdiUzZDIlMjZkc19kZXN0X3VybCUzZGh0dHBzJTNhJTJmJTJmd3d3LnBlZ2EuY29tJTJmYWktaW5ub3ZhdGlvbiUzZnV0bV9zb3VyY2UlM2RiaW5nJTI2dXRtX21lZGl1bSUzZGNwYyUyNnV0bV9jYW1wYWlnbiUzZEJfU2luZ2Fwb3JlX05vbkJyYW5kX0FJX0NFX0Jyb2FkXyhDUE4tMTExMDY3KV9FTiUyNnV0bV90ZXJtJTNkZ2VuZXJhdGl2ZSUyNTIwYWklMjZnbG9jJTNkMTY0JTI2dXRtX2NvbnRlbnQlM2RwY3JpZCU3YyU3Y3BrdyU3Y2t3ZC04MTU3MDMyOTQ0OTg4NSUzYWxvYy0xNjQlN2NwbXQlN2NwJTdjcGR2JTdjYyU3YyUyNmdjbGlkJTNkZmIwMjJjNDNlN2Y3MTM2ODQ2OTU0NDkyODQ0ZDM0ZmUlMjZnY2xzcmMlM2QzcC5kcyUyNiUyNm1zY2xraWQlM2RmYjAyMmM0M2U3ZjcxMzY4NDY5NTQ0OTI4NDRkMzRmZSUyNnV0bV9zb3VyY2UlM2RiaW5nJTI2dXRtX21lZGl1bSUzZGNwYyUyNnV0bV9jYW1wYWlnbiUzZEJfU2luZ2Fwb3JlX05vbkJyYW5kX0FJX0NFX0Jyb2FkXyhDUE4tMTExMDY3KV9FTiUyNnV0bV90ZXJtJTNkZ2VuZXJhdGl2ZSUyNTIwYWklMjZ1dG1fY29udGVudCUzZEFJ&rlid=fb022c43e7f7136846954492844d34fe&vqd=4-48519802400386461471950670835781238094&iurl={1}IG=FAC59663471F4481BD9C42F97A19862E&CID=07527CBF36C069A1321E6AD037BF68BA&ID=DevEx,5029.1", "content": "Explore the many powerful applications and unprecedented applications of AI. Harness the power of AI to turn new concepts into full operations and power productivity"}
+{"idx": 4, "title": "2025-Mastering Generative AI-From LLMs to Applications Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "udemy.com Learn Data Science online at your own pace. Start today and improve your skills. Join millions of learners from around the world already learning on Udemy.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=X63JjDKcCjlb1nETwVqh0dQ76N0ZDSTcMTYyF4lp8zPbq14tmxQ6ABU3hpXZuVQs7hdl4C5P6aTjyyJWHri4BW1cqObkX8_q01jgBXb5pY2CqEYxGSVWqiz2ZnPjf2bP.fV1rA2p1Ms1XTpPCKM2zvA&rut=7d82b4aac7d6bfefd75d2fce3852d7e2fad5c9649d065fd14792b263f08e912d&u3=https://www.bing.com/aclick?ld=e8uZoRXCAYXkvhhvVIjLoOlTVUCUx3NJKL5E21jWd78BfEyICi_YAGoEtGdQbDiQIwYeNitq4-8yuBYGVGp1O_y8wjFo5pNKlbsQKs3c8HKgPIS5H5k5HuQljMZTTMKJkbe60IWD4zjuCEnwS2nFGaniUF7X8iB9j8XSl_8wLDjrieBBMyL7x9gmZNh0ateMwPO17RvA&u=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&rlid=24636ffeea8d16ddbc32072f893791f9&vqd=4-24493284419411652786451462693475228468&iurl={1}IG=FAC59663471F4481BD9C42F97A19862E&CID=07527CBF36C069A1321E6AD037BF68BA&ID=DevEx,5035.1+https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=X63JjDKcCjlb1nETwVqh0dQ76N0ZDSTcMTYyF4lp8zPbq14tmxQ6ABU3hpXZuVQs7hdl4C5P6aTjyyJWHri4BW1cqObkX8_q01jgBXb5pY2CqEYxGSVWqiz2ZnPjf2bP.fV1rA2p1Ms1XTpPCKM2zvA&rut=7d82b4aac7d6bfefd75d2fce3852d7e2fad5c9649d065fd14792b263f08e912d&u3=https://www.bing.com/aclick?ld=e8uZoRXCAYXkvhhvVIjLoOlTVUCUx3NJKL5E21jWd78BfEyICi_YAGoEtGdQbDiQIwYeNitq4-8yuBYGVGp1O_y8wjFo5pNKlbsQKs3c8HKgPIS5H5k5HuQljMZTTMKJkbe60IWD4zjuCEnwS2nFGaniUF7X8iB9j8XSl_8wLDjrieBBMyL7x9gmZNh0ateMwPO17RvA&u=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&rlid=24636ffeea8d16ddbc32072f893791f9&vqd=4-24493284419411652786451462693475228468&iurl={1}IG=FAC59663471F4481BD9C42F97A19862E&CID=07527CBF36C069A1321E6AD037BF68BA&ID=DevEx,5035.1", "content": "udemy.com Learn Data Science online at your own pace. Start today and improve your skills. Join millions of learners from around the world already learning on Udemy."}
+{"idx": 5, "title": "Sel3DCraft: Interactive Visual Prompts for User-Friendly", "date": "", "ddg_snippet": "Validation of the semantic scoring approach ( Section ˜ 5 ) and user study ( Section ˜ 6 ) demonstrate Sel3DCraft ’s potential and superior ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.00428v1", "content": "Validation of the semantic scoring approach ( Section ˜ 5 ) and user study ( Section ˜ 6 ) demonstrate Sel3DCraft ’s potential and superior ..."}
+{"idx": 6, "title": "AI Testing Should Account for Sophisticated Strategic Behaviour", "date": "", "ddg_snippet": "Section ˜ 3 then considers a stylised example of evaluating a safety-critical AI system and uses it to demonstrate how game theory can be used to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14927v1", "content": "Section ˜ 3 then considers a stylised example of evaluating a safety-critical AI system and uses it to demonstrate how game theory can be used to ..."}
+{"idx": 7, "title": "The Illusion of Role Separation: Hidden Shortcuts in LLM Role", "date": "", "ddg_snippet": "... work has studied the role-separation learning problem primarily through the lens of prompt injection attacks – where malicious users attempt to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00626v1", "content": "... work has studied the role-separation learning problem primarily through the lens of prompt injection attacks – where malicious users attempt to ..."}
+{"idx": 8, "title": "Can Large Language Models Improve Phishing Defense? A", "date": "", "ddg_snippet": "Section 2 reviews related work on phishing ... This section also reports the formalisation of the four research questions this study aims to answer.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07916v1", "content": "Section 2 reviews related work on phishing ... This section also reports the formalisation of the four research questions this study aims to answer."}
+{"idx": 9, "title": "A Multimodal Symphony: Integrating Taste and Sound through", "date": "", "ddg_snippet": "... article is organized as follows: section 2 provides an overview of the background and related work in both cognitive neuroscience and computer science ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.02823v1", "content": "... article is organized as follows: section 2 provides an overview of the background and related work in both cognitive neuroscience and computer science ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Statistical_Collusion_by_Collectives_Rs(k).jsonl b/data/sampled_jsons/sitearxiv.org_Statistical_Collusion_by_Collectives_Rs(k).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..081bdcd0f24898f1cb94fb330c2f1f7f9dc98a4e
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Statistical_Collusion_by_Collectives_Rs(k).jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "7 Feb 2025 — (2021) Albert, K ., Delano, M., Kulynych, B., and Kumar, R. S. S. Adversarial for good? How the adversarial ML community's values impede socially ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v1", "content": "7 Feb 2025 — (2021) Albert, K ., Delano, M., Kulynych, B., and Kumar, R. S. S. Adversarial for good? How the adversarial ML community's values impede socially ..."}
+{"idx": 1, "title": "Machine Learning Feb 2025", "date": "", "ddg_snippet": "Title: Statistical Collusion by Collectives on Learning Platforms. Etienne Gauthier, Francis Bach, Michael I. Jordan. Comments: Code available at: this https ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.LG/2025-02?skip=2475&show=1000", "content": "Title: Statistical Collusion by Collectives on Learning Platforms. Etienne Gauthier, Francis Bach, Michael I. Jordan. Comments: Code available at: this https ..."}
+{"idx": 2, "title": "Computer Science Apr 2025", "date": "", "ddg_snippet": "26 Apr 2025 — Title: Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data? Grgur Kovač, Jérémy Perez, ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-04?skip=1450&show=2000", "content": "26 Apr 2025 — Title: Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data? Grgur Kovač, Jérémy Perez, ..."}
+{"idx": 3, "title": "1 Agricultural Growth Diagnostics: Identifying the Binding ...", "date": "", "ddg_snippet": "by E Kannan · 2021 · Cited by 6 — The present study relies on both secondary and primary data. Data on crop production, value of output and farm harvest prices for 31 crops were compiled from ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2108.03912", "content": "by E Kannan · 2021 · Cited by 6 — The present study relies on both secondary and primary data. Data on crop production, value of output and farm harvest prices for 31 crops were compiled from ..."}
+{"idx": 4, "title": "A Predictive Theory of Games", "date": "", "ddg_snippet": "Abstract. Conventional noncooperative game theory hypothesizes that the joint. (mixed) strategy of a set of reasoning players in a game will necessarily.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/nlin/0512015", "content": "Abstract. Conventional noncooperative game theory hypothesizes that the joint. (mixed) strategy of a set of reasoning players in a game will necessarily."}
+{"idx": 5, "title": "Jäger\\xspace: Automated Telephone Call Traceback", "date": "", "ddg_snippet": "... of a call, an investigator must start at the destination with knowledge of the call time, call destination, and claimed call source, and go hop- by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.02839v4", "content": "... of a call, an investigator must start at the destination with knowledge of the call time, call destination, and claimed call source, and go hop- by ..."}
+{"idx": 6, "title": "The Future is Agentic: Definitions, Perspectives, and Open", "date": "", "ddg_snippet": "... by orchestrating these distinct memory types, the agent remains consistently informed about prior steps, user preferences, and external knowledge ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02097v1", "content": "... by orchestrating these distinct memory types, the agent remains consistently informed about prior steps, user preferences, and external knowledge ..."}
+{"idx": 7, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_Section_6.2_MATH_Pyt.jsonl b/data/sampled_jsons/sitearxiv.org_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_Section_6.2_MATH_Pyt.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e1fa3841308ae50e20947225622abe05ec374f0e
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_Section_6.2_MATH_Pyt.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "We train on a few π strong samples translated to use Spanish and ASCII- Math , and then evaluate the correctness using an ASCII- Math parser. Results are shown in Figure 9 (a). Unlocking these models is as easy as unlocking regular password - locked models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "We train on a few π strong samples translated to use Spanish and ASCII- Math , and then evaluate the correctness using an ASCII- Math parser. Results are shown in Figure 9 (a). Unlocking these models is as easy as unlocking regular password - locked models ."}
+{"idx": 1, "title": "Stress - Testing Capability Elicitation With Password - Locked Models", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.19550", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password."}
+{"idx": 2, "title": "Stress - Testing Capability Elicitation With", "date": "", "ddg_snippet": "Stress - Testing Capability Elicitation With Password - Locked Models . arXiv:2405.19550v1 [cs.LG] 29 May 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.19550", "content": "Stress - Testing Capability Elicitation With Password - Locked Models . arXiv:2405.19550v1 [cs.LG] 29 May 2024."}
+{"idx": 3, "title": "The Elicitation Game: Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "Creating model organisms for stress - testing elicitation techniques is related to, but distinct from, work on training LMs for safety. Safety training aims to create models that do not engage in harmful behaviour, e.g., via refusal training (Bai et al., 2022) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v3", "content": "Creating model organisms for stress - testing elicitation techniques is related to, but distinct from, work on training LMs for safety. Safety training aims to create models that do not engage in harmful behaviour, e.g., via refusal training (Bai et al., 2022) ."}
+{"idx": 4, "title": "The Elicitation Game: Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "Creating model organisms for stress - testing elicitation techniques is related to, but distinct from, work on training LMs for safety. Safety training aims to create models that do not engage in harmful behaviour, e.g., via refusal training (Bai et al., 2022) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v1", "content": "Creating model organisms for stress - testing elicitation techniques is related to, but distinct from, work on training LMs for safety. Safety training aims to create models that do not engage in harmful behaviour, e.g., via refusal training (Bai et al., 2022) ."}
+{"idx": 5, "title": "The Elicitation Game: Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "Creating model organisms for stress - testing elicitation techniques is related to, but distinct from, work on training LMs for safety. Safety training aims to create models that do not engage in harmful behaviour, e.g., via refusal training (Bai et al., 2022) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v2", "content": "Creating model organisms for stress - testing elicitation techniques is related to, but distinct from, work on training LMs for safety. Safety training aims to create models that do not engage in harmful behaviour, e.g., via refusal training (Bai et al., 2022) ."}
+{"idx": 6, "title": "The Elicitation Game: Evaluating Capability Elicitation ...", "date": "", "ddg_snippet": "Greenblatt et al. (2024) Greenblatt, R., Roger, F., Krasheninnikov, D., and Krueger, D. Stress - Testing Capability Elicitation With Password - Locked Models . arXiv preprint arXiv :2405.19550, 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180", "content": "Greenblatt et al. (2024) Greenblatt, R., Roger, F., Krasheninnikov, D., and Krueger, D. Stress - Testing Capability Elicitation With Password - Locked Models . arXiv preprint arXiv :2405.19550, 2024."}
+{"idx": 7, "title": "The Elicitation Game: Evaluating Capability Elicitation ...", "date": "", "ddg_snippet": "D. Stress - Testing Capability Elicitation With Password - Locked Models . arXiv preprint arXiv:2405.19550, 2024. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J. Measuring mas-sive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020. Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora,", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02180", "content": "D. Stress - Testing Capability Elicitation With Password - Locked Models . arXiv preprint arXiv:2405.19550, 2024. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J. Measuring mas-sive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020. Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora,"}
+{"idx": 8, "title": "DSMoE: Matrix-Partitioned Experts with Dynamic Routing for ...", "date": "", "ddg_snippet": "To address this challenge, we propose DSMoE, a novel approach that partitions pre-trained FFN layers into computational blocks and introduces dynamic routing mechanisms. DSMoE fundamen-tally differs from existing methods by preserving the original model parameters and reorganizing them into expert networks, while incorporating adaptive routing mechanisms that enable dynamic expert activation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.12455v3", "content": "To address this challenge, we propose DSMoE, a novel approach that partitions pre-trained FFN layers into computational blocks and introduces dynamic routing mechanisms. DSMoE fundamen-tally differs from existing methods by preserving the original model parameters and reorganizing them into expert networks, while incorporating adaptive routing mechanisms that enable dynamic expert activation ..."}
+{"idx": 9, "title": "Sabotage Evaluations for Frontier Models - arXiv.org", "date": "", "ddg_snippet": "In this section , we list what we consider to be the most plausible routes to successful sabotage of important processes or decisions, building on previous explorations of the subject, such as Hendrycks et al. (2023).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.21514", "content": "In this section , we list what we consider to be the most plausible routes to successful sabotage of important processes or decisions, building on previous explorations of the subject, such as Hendrycks et al. (2023)."}
diff --git a/data/sampled_jsons/sitearxiv.org_The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_The_Randomized_Midpoint_Method_for_Log-Concave_Sampling_abstract.jsonl
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index 0000000000000000000000000000000000000000..c69b97c0bd4d72677f95ed256fe60c63896d700a
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — View a PDF of the paper titled The Randomized Midpoint Method for Log-Concave Sampling , by Ruoqi Shen and 1 other authors. View PDF. Abstract ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.05503", "content": "by R Shen · 2019 · Cited by 161 — View a PDF of the paper titled The Randomized Midpoint Method for Log-Concave Sampling , by Ruoqi Shen and 1 other authors. View PDF. Abstract ..."}
+{"idx": 1, "title": "Randomized Midpoint Method for Log-Concave Sampling ...", "date": "", "ddg_snippet": "by Y Yu · 2024 — Abstract . In this paper, we study the ... [SL19]. Ruoqi Shen and Yin Tat Lee, The randomized midpoint method for log-concave sampling ,.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15379", "content": "by Y Yu · 2024 — Abstract . In this paper, we study the ... [SL19]. Ruoqi Shen and Yin Tat Lee, The randomized midpoint method for log-concave sampling ,."}
+{"idx": 2, "title": "Randomized Midpoint Method for Log-Concave Sampling ...", "date": "", "ddg_snippet": "24 May 2025 — [SL19] Ruoqi Shen and Yin Tat Lee, The randomized midpoint method for log-concave sampling , Advances in Neural Information Processing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15379v2", "content": "24 May 2025 — [SL19] Ruoqi Shen and Yin Tat Lee, The randomized midpoint method for log-concave sampling , Advances in Neural Information Processing ..."}
+{"idx": 3, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 160 — The Randomized Midpoint Method for Log-Concave Sampling . Ruoqi Shen ... Abstract . Sampling from log-concave distributions is a well ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05503", "content": "by R Shen · 2019 · Cited by 160 — The Randomized Midpoint Method for Log-Concave Sampling . Ruoqi Shen ... Abstract . Sampling from log-concave distributions is a well ..."}
+{"idx": 4, "title": "Poisson Midpoint Method for Log Concave Sampling", "date": "", "ddg_snippet": "20 Aug 2025 — The Randomized Midpoint Method for Log-Concave Sampling . Advances in Neural Information Processing Systems, 32, 2019. (27) ↑ Santosh ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07614v3", "content": "20 Aug 2025 — The Randomized Midpoint Method for Log-Concave Sampling . Advances in Neural Information Processing Systems, 32, 2019. (27) ↑ Santosh ..."}
+{"idx": 5, "title": "Poisson Midpoint Method for Log Concave Sampling", "date": "", "ddg_snippet": "14 Jul 2025 — ... The randomized midpoint method for log-concave sampling . Advances in Neural Information Processing Systems, 32, 2019. (27) ↑ Santosh ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07614v2", "content": "14 Jul 2025 — ... The randomized midpoint method for log-concave sampling . Advances in Neural Information Processing Systems, 32, 2019. (27) ↑ Santosh ..."}
+{"idx": 6, "title": "Faster Diffusion Sampling with Randomized Midpoints", "date": "", "ddg_snippet": "The randomized midpoint method for log-concave sampling . In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.00924v2", "content": "The randomized midpoint method for log-concave sampling . In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, and ..."}
+{"idx": 7, "title": "Poisson Midpoint Method for Log Concave Sampling", "date": "", "ddg_snippet": "Abstract . We study the problem of sampling from strongly log-concave ... The Randomized Midpoint Method for Log-Concave Sampling . Advances in Neural ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.07614v3", "content": "Abstract . We study the problem of sampling from strongly log-concave ... The Randomized Midpoint Method for Log-Concave Sampling . Advances in Neural ..."}
+{"idx": 8, "title": "Faster Diffusion Sampling with Randomized Midpoints", "date": "", "ddg_snippet": "by S Gupta · 2024 — The randomized midpoint method for log-concave sampling . In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.00924", "content": "by S Gupta · 2024 — The randomized midpoint method for log-concave sampling . In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc ..."}
+{"idx": 9, "title": "Faster Diffusion Sampling with Randomized Midpoints", "date": "", "ddg_snippet": "by S Gupta · 2024 — The randomized midpoint method for log-concave sampling . In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.00924?", "content": "by S Gupta · 2024 — The randomized midpoint method for log-concave sampling . In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_nuScenes_Caesar_2020_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_nuScenes_Caesar_2020_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5346304b978057cc450e9e8cd6813c68d09a35e
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1903.11027] nuScenes: A multimodal dataset for autonomous ... nuscenes@nutonomy.com Abstract arXiv:1903.11027v5 [cs.LG] 5 ... nuScenes: A multimodal dataset for autonomous driving The Role of World Models in Shaping Autonomous Driving: A ... UAV3D: A Large-scale 3D Perception Benchmark for Unmanned ... M3Net: Multimodal Multi-task Learning for 3D Detection ... Abstract arXiv:1903.11027v2 [cs.LG] 3 Sep 2019", "date": "", "ddg_snippet": "Mar 26, 2019 · View a PDF of the paper titled nuScenes : A multimodal dataset for autonomous driving, by Holger Caesar and 9 other authors nuScenes : A multimodal dataset for autonomous driving Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 ° 360° sensor coverage from the entire sensor suite. It is also the first AV dataset to include radar data and captured using an AV approved for public roads. Caesar et al. [ 2020 ] Holger Caesar , Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes : A multimodal dataset for autonomous driving. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 11621–11631, 2020 . Oct 17, 2024 · The Waymo Open Sun et al. [ 2020 ] and nuScenes Caesar et al. [ 2020 ] datasets are two public, large-scale, multi-modal datasets that include camera, radar, and lidar data for autonomous driving. Our UAV3D comprises 1,000 scenes, matching the scale of the nuScenes dataset. Mar 23, 2025 · For detection and segmentation, we evaluate M3Net with the nuScenes ( Caesar et al. 2020 ) dataset, a large-scale multimodal dataset designed for 3D detection and map segmentation. Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company nuscenes @nutonomy.com", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "Mar 26, 2019 · View a PDF of the paper titled nuScenes : A multimodal dataset for autonomous driving, by Holger Caesar and 9 other authors nuScenes : A multimodal dataset for autonomous driving Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 ° 360° sensor coverage from the entire sensor suite. It is also the first AV dataset to include radar data and captured using an AV approved for public roads. Caesar et al. [ 2020 ] Holger Caesar , Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes : A multimodal dataset for autonomous driving. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 11621–11631, 2020 . Oct 17, 2024 · The Waymo Open Sun et al. [ 2020 ] and nuScenes Caesar et al. [ 2020 ] datasets are two public, large-scale, multi-modal datasets that include camera, radar, and lidar data for autonomous driving. Our UAV3D comprises 1,000 scenes, matching the scale of the nuScenes dataset. Mar 23, 2025 · For detection and segmentation, we evaluate M3Net with the nuScenes ( Caesar et al. 2020 ) dataset, a large-scale multimodal dataset designed for 3D detection and map segmentation. Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company nuscenes @nutonomy.com"}
+{"idx": 1, "title": "nuscenes@nutonomy.com Abstract arXiv:1903.11027v5 [cs.LG] 5 ...", "date": "", "ddg_snippet": "nuScenes : A multimodal dataset for autonomous driving Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v5", "content": "nuScenes : A multimodal dataset for autonomous driving Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company"}
+{"idx": 2, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 ° 360° sensor coverage from the entire sensor suite. It is also the first AV dataset to include radar data and captured using an AV approved for public roads.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1903.11027", "content": "nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 ° 360° sensor coverage from the entire sensor suite. It is also the first AV dataset to include radar data and captured using an AV approved for public roads."}
+{"idx": 3, "title": "Abstract arXiv:1903.11027v2 [cs.LG] 3 Sep 2019", "date": "", "ddg_snippet": "Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company nuscenes @nutonomy.com", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1903.11027v2", "content": "Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company nuscenes @nutonomy.com"}
+{"idx": 4, "title": "NuScenes-QA: A Multi-Modal Visual Question Answering ...", "date": "", "ddg_snippet": "20 Feb 2024 — The proposed NuScenes-QA is built upon nuScenes (Caesar et al. 2020 ) , which is a popular 3D perception dataset for autonomous driving. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.14836v2", "content": "20 Feb 2024 — The proposed NuScenes-QA is built upon nuScenes (Caesar et al. 2020 ) , which is a popular 3D perception dataset for autonomous driving. We ..."}
+{"idx": 5, "title": "The Role of World Models in Shaping Autonomous Driving: A ...", "date": "", "ddg_snippet": "Caesar et al. [ 2020 ] Holger Caesar , Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes : A multimodal dataset for autonomous driving. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 11621–11631, 2020 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10498v1", "content": "Caesar et al. [ 2020 ] Holger Caesar , Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes : A multimodal dataset for autonomous driving. In Proc. of IEEE Intl. Conf. on Computer Vision and Pattern Recognition, pages 11621–11631, 2020 ."}
+{"idx": 6, "title": "UAV3D: A Large-scale 3D Perception Benchmark for Unmanned ...", "date": "", "ddg_snippet": "Oct 17, 2024 · The Waymo Open Sun et al. [ 2020 ] and nuScenes Caesar et al. [ 2020 ] datasets are two public, large-scale, multi-modal datasets that include camera, radar, and lidar data for autonomous driving. Our UAV3D comprises 1,000 scenes, matching the scale of the nuScenes dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.11125v2", "content": "Oct 17, 2024 · The Waymo Open Sun et al. [ 2020 ] and nuScenes Caesar et al. [ 2020 ] datasets are two public, large-scale, multi-modal datasets that include camera, radar, and lidar data for autonomous driving. Our UAV3D comprises 1,000 scenes, matching the scale of the nuScenes dataset."}
+{"idx": 7, "title": "M3Net: Multimodal Multi-task Learning for 3D Detection ...", "date": "", "ddg_snippet": "Mar 23, 2025 · For detection and segmentation, we evaluate M3Net with the nuScenes ( Caesar et al. 2020 ) dataset, a large-scale multimodal dataset designed for 3D detection and map segmentation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.18100", "content": "Mar 23, 2025 · For detection and segmentation, we evaluate M3Net with the nuScenes ( Caesar et al. 2020 ) dataset, a large-scale multimodal dataset designed for 3D detection and map segmentation."}
+{"idx": 8, "title": "Velocity Driven Vision: Asynchronous Sensor Fusion Birds ...", "date": "", "ddg_snippet": "23 Jul 2024 — Abstract ... In the widely known nuScenes dataset, LiDAR data is approximately 40 times denser than radar data [Caesar et al., 2020] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.16636v1", "content": "23 Jul 2024 — Abstract ... In the widely known nuScenes dataset, LiDAR data is approximately 40 times denser than radar data [Caesar et al., 2020] ."}
+{"idx": 9, "title": "Open 3D World in Autonomous Driving", "date": "", "ddg_snippet": "20 Aug 2024 — In NuScenes -T dataset,we report the officially used metrics of 3D object detection in BEV-based research ( Caesar et al. 2020 ; Lang et al. 2019; ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10880v1", "content": "20 Aug 2024 — In NuScenes -T dataset,we report the officially used metrics of 3D object detection in BEV-based research ( Caesar et al. 2020 ; Lang et al. 2019; ..."}
diff --git a/data/sampled_jsons/sitearxiv.orgabs2407.10264_Section_4_synthetic_experiments_learning_rates_year_2024.jsonl b/data/sampled_jsons/sitearxiv.orgabs2407.10264_Section_4_synthetic_experiments_learning_rates_year_2024.jsonl
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orgabs2502.00775_GTA_Greedy_Task_Allocation.jsonl b/data/sampled_jsons/sitearxiv.orgabs2502.00775_GTA_Greedy_Task_Allocation.jsonl
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diff --git a/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker.jsonl b/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker.jsonl
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+++ b/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker.jsonl
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+{"idx": 0, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "Crocker stacks [Xian22a] , an extension of this concept, adds a smoothing step that gradually reduces the impact of points of low persistence and, upon ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15732v2", "content": "Crocker stacks [Xian22a] , an extension of this concept, adds a smoothing step that gradually reduces the impact of points of low persistence and, upon ..."}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2410.10562v1_six_climate_activist_subreddits_activation_92%.jsonl b/data/sampled_jsons/sitearxiv.orghtml2410.10562v1_six_climate_activist_subreddits_activation_92%.jsonl
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index 0000000000000000000000000000000000000000..58ba68632f801e645f1bf849e3cd36a3770db3cf
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+++ b/data/sampled_jsons/sitearxiv.orghtml2410.10562v1_six_climate_activist_subreddits_activation_92%.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "User activated in climate activism groups. I𝐼Iitalic_I. Interactions with activists .Does media coverage about climate and climate action affect activation in climate activism groups on Reddit, and over which time scale?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "User activated in climate activism groups. I𝐼Iitalic_I. Interactions with activists .Does media coverage about climate and climate action affect activation in climate activism groups on Reddit, and over which time scale?"}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2502.10875v1_Table_1_dataset_statistics.jsonl b/data/sampled_jsons/sitearxiv.orghtml2502.10875v1_Table_1_dataset_statistics.jsonl
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+++ b/data/sampled_jsons/sitearxiv.orghtml2502.10875v1_Table_1_dataset_statistics.jsonl
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+{"idx": 0, "title": "A Geometric Approach to Personalized Recommendation ...", "date": "", "ddg_snippet": "15 Feb 2025 — Refer to Table 1 for a detailed description of the dataset statistics . Report issue for preceding element. Table 1: Dataset Statistics ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10875v1", "content": "15 Feb 2025 — Refer to Table 1 for a detailed description of the dataset statistics . Report issue for preceding element. Table 1: Dataset Statistics ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orghtml_Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal.jsonl b/data/sampled_jsons/sitearxiv.orghtml_Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ede8f830c8c32eafba083e1013187e444181eb1e
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orghtml_Learning_stochastic_dynamics_from_snapshots_through_regularized_unbalanced_optimal.jsonl
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+{"idx": 0, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v5", "content": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
+{"idx": 1, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Recently, regularized unbalanced optimal transport (RUOT) offers a promising approach for modeling such stochastic unbalanced continuous dynamics (Baradat and Lavenant, 2021; Chen et al., 2022b; Buze and Duong, 2023; Janati et al., 2020) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v1", "content": "Recently, regularized unbalanced optimal transport (RUOT) offers a promising approach for modeling such stochastic unbalanced continuous dynamics (Baradat and Lavenant, 2021; Chen et al., 2022b; Buze and Duong, 2023; Janati et al., 2020) ."}
+{"idx": 2, "title": "Modeling Cell Dynamics and Interactions with Unbalanced Mean Field...", "date": "", "ddg_snippet": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning Representations, 2025a.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.11197v2", "content": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning Representations, 2025a."}
+{"idx": 3, "title": "Variational Regularized Unbalanced Optimal Transport", "date": "", "ddg_snippet": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.11823v1", "content": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning ..."}
+{"idx": 4, "title": "WeightFlow: Learning Stochastic Dynamics via Evolving Weight of Neural ...", "date": "", "ddg_snippet": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning Representations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.00451v1", "content": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In The Thirteenth International Conference on Learning Representations."}
+{"idx": 5, "title": "Summary - arXiv.org", "date": "", "ddg_snippet": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . arXiv preprint arXiv:2410.00844. Lin et al. 2023 Lin, B., Li, Q., and Ren, W. (2023).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.06959v2", "content": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . arXiv preprint arXiv:2410.00844. Lin et al. 2023 Lin, B., Li, Q., and Ren, W. (2023)."}
+{"idx": 6, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v4", "content": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks. 5.1 Energy Loss.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
+{"idx": 7, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v2", "content": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
+{"idx": 8, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00844v3", "content": "4 Regularized Unbalanced Optimal Transport . 5 Learning RUOT through Neural Networks.Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning ."}
+{"idx": 9, "title": "Joint Velocity-Growth Flow Matching for Single-Cell ...", "date": "", "ddg_snippet": "19 May 2025 — Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In ICLR, 2025. [26] Xi Zhang, Yuan Pu, Yuki ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13413v1", "content": "19 May 2025 — Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport . In ICLR, 2025. [26] Xi Zhang, Yuan Pu, Yuki ..."}
diff --git a/data/sampled_jsons/sitearxiv.orgpdf2503.16979_Equation_6_motion_feature.jsonl b/data/sampled_jsons/sitearxiv.orgpdf2503.16979_Equation_6_motion_feature.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orgpdf2503.16979_Equation_6_motion_feature.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitedistill.pub_Zoom_In_An_Introduction_to_Circuits_CNN_convolutional_year_2020.jsonl b/data/sampled_jsons/sitedistill.pub_Zoom_In_An_Introduction_to_Circuits_CNN_convolutional_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90dba797fcc7b3342acf8e67e0615b319a726518
--- /dev/null
+++ b/data/sampled_jsons/sitedistill.pub_Zoom_In_An_Introduction_to_Circuits_CNN_convolutional_year_2020.jsonl
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+{"idx": 0, "title": "An Overview of Early Vision in InceptionV1 - Distill", "date": "", "ddg_snippet": "This article is part of the Circuits thread, a collection of short articles and commentary by an open scientific collaboration delving into the inner workings of neural networks. Zoom In: An Introduction to Circuits Curve Detectors The first few articles of the Circuits project will be focused on early vision in InceptionV1 — for our purposes, the five convolutional layers leading up to the ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/early-vision/", "content": "This article is part of the Circuits thread, a collection of short articles and commentary by an open scientific collaboration delving into the inner workings of neural networks. Zoom In: An Introduction to Circuits Curve Detectors The first few articles of the Circuits project will be focused on early vision in InceptionV1 — for our purposes, the five convolutional layers leading up to the ..."}
+{"idx": 1, "title": "Thread: Circuits", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits . Authors.The thread is organized through the open #circuits channel on the Distill slack. Articles can be suggested there, and will be included at the discretion of previous authors in the thread, or in the case of disagreement by an uninvolved editor.", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/", "content": "Zoom In : An Introduction to Circuits . Authors.The thread is organized through the open #circuits channel on the Distill slack. Articles can be suggested there, and will be included at the discretion of previous authors in the thread, or in the case of disagreement by an uninvolved editor."}
+{"idx": 2, "title": "Zoom In: An Introduction to Circuits", "date": "", "ddg_snippet": "by C Olah · 2020 · Cited by 637 — Zoom In: An Introduction to Circuits . By studying the connections between neurons, we can find meaningful algorithms in the weights of neural ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/zoom-in", "content": "by C Olah · 2020 · Cited by 637 — Zoom In: An Introduction to Circuits . By studying the connections between neurons, we can find meaningful algorithms in the weights of neural ..."}
+{"idx": 3, "title": "Convolutional Neural N Course - Enroll Now & Start Learning Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Learn Convolutional Neural Networks online at your own pace. Start today. Join millions of learners from around the world already learning on Udemy.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=yR7pi6jUsYdsCWPvPfPt6QfkyGo2lCohW7TuzM8z6dpL6Q8cE7uwzCwDbhOuh2CNJkL7_2pbDUXCUuIakTjdSSSGrX8vPBG1Va8FyK8FFN6Cvvv7TU1NfzzXghiiZnQb.ZaDzU-FFyRZupUCJh-RMGA&rut=5003dc82baa1069a368810338169c6317b1f4f9ca4ef1d791b92c071aa202250&u3=https://www.bing.com/aclick?ld=e8gKTM_PaU_qmA2-qlOD61-TVUCUz3tyBVN4-EAkwMDClYgfHC4yINDPLf2E0Qcp7I8VPP6pPIdyq82BmYG3bRlfSeIeBiBXiTHmxxkWn8T8AJs8KYIAi0kEUW3KkOAZJaED4hbjOh0PBnRD3qDNWvvwbgKk_vRVZGQ-SXDEx5yU4nwcE_bTBS_4BRwzusTG-ZrOWGNw&u=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&rlid=9ebb511d4e591eca2f4e39cd57cdc87d&vqd=4-16032139591257623442961969837178526747&iurl={1}IG=1F9CFD1B620E47538702BEC05342BF19&CID=0223863DCD8F6F4133FA9053CCE46EAE&ID=DevEx,5030.1", "content": "Learn Convolutional Neural Networks online at your own pace. Start today. Join millions of learners from around the world already learning on Udemy."}
+{"idx": 4, "title": "Information for Archivists and Librarians - Distill", "date": "", "ddg_snippet": "Computing Receptive Fields of Convolutional Neural Networks Vol 5, Issue 1 Visualizing the Impact of Feature Attribution Baselines Vol 5, Issue 2 Growing Neural Cellular Automata Vol 5, Issue 3 Visualizing Neural Networks with the Grand Tour Thread: Circuits Zoom In: An Introduction to Circuits Vol 5, Issue 4 An Overview of Early Vision in ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/archive-info/", "content": "Computing Receptive Fields of Convolutional Neural Networks Vol 5, Issue 1 Visualizing the Impact of Feature Attribution Baselines Vol 5, Issue 2 Growing Neural Cellular Automata Vol 5, Issue 3 Visualizing Neural Networks with the Grand Tour Thread: Circuits Zoom In: An Introduction to Circuits Vol 5, Issue 4 An Overview of Early Vision in ..."}
+{"idx": 5, "title": "Multimodal Neurons in Artificial Neural Networks", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M. and Carter, S., 2020. Distill, Vol 5(3), pp. e00024--001. Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks [PDF]...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2021/multimodal-neurons/", "content": "Zoom In : An Introduction to Circuits Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M. and Carter, S., 2020. Distill, Vol 5(3), pp. e00024--001. Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks [PDF]..."}
+{"idx": 6, "title": "Archive", "date": "", "ddg_snippet": "Experiments in Handwriting with a Neural Network . Zoom In : An Introduction to Circuits .", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/archive/", "content": "Experiments in Handwriting with a Neural Network . Zoom In : An Introduction to Circuits ."}
+{"idx": 7, "title": "Visualizing Weights", "date": "", "ddg_snippet": "by C Voss · 2021 · Cited by 48 — Zoom In: An Introduction to Circuits Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M. and Carter, S., 2020. Distill. DOI: 10.23915 ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/visualizing-weights", "content": "by C Voss · 2021 · Cited by 48 — Zoom In: An Introduction to Circuits Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M. and Carter, S., 2020. Distill. DOI: 10.23915 ..."}
+{"idx": 8, "title": "An Overview of Early Vision in InceptionV1", "date": "", "ddg_snippet": "by C Olah · 2020 · Cited by 74 — Zoom In: An Introduction to Circuits Curve Detectors. The first few articles of the Circuits project will be focused on early vision in ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/early-vision", "content": "by C Olah · 2020 · Cited by 74 — Zoom In: An Introduction to Circuits Curve Detectors. The first few articles of the Circuits project will be focused on early vision in ..."}
+{"idx": 9, "title": "High-Low Frequency Detectors", "date": "", "ddg_snippet": "by L Schubert · 2021 · Cited by 33 — How common are high-low frequency detectors in convolutional neural networks generally? ... Zoom In: An Introduction to Circuits Olah, C., ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/frequency-edges", "content": "by L Schubert · 2021 · Cited by 33 — How common are high-low frequency detectors in convolutional neural networks generally? ... Zoom In: An Introduction to Circuits Olah, C., ..."}
diff --git a/data/sampled_jsons/sitegithub.comFLAIROxah2ac2_README_3079_games_dataset.jsonl b/data/sampled_jsons/sitegithub.comFLAIROxah2ac2_README_3079_games_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
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+++ b/data/sampled_jsons/sitegithub.comFLAIROxah2ac2_README_3079_games_dataset.jsonl
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diff --git a/data/sampled_jsons/sitegithub.com_NJU-LHRSLHRS-Bot_README.jsonl b/data/sampled_jsons/sitegithub.com_NJU-LHRSLHRS-Bot_README.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6db345da33c71512c3c7fb7151f2075ec7b843c4
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_NJU-LHRSLHRS-Bot_README.jsonl
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+{"idx": 0, "title": "LHRS - Bot / README .md at main · NJU - LHRS / LHRS - Bot · GitHub", "date": "", "ddg_snippet": "LHRS - Bot : Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model.git clone git@github.com: NJU - LHRS / LHRS - Bot .git cd LHRS - Bot . Create a new virtual enviroment. conda create -n lhrs python=3.10 conda activate lhrs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/README.md", "content": "LHRS - Bot : Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model.git clone git@github.com: NJU - LHRS / LHRS - Bot .git cd LHRS - Bot . Create a new virtual enviroment. conda create -n lhrs python=3.10 conda activate lhrs."}
+{"idx": 1, "title": "GitHub - NJU - LHRS / LHRS - Bot : VGI-Enhanced multimodal large...", "date": "", "ddg_snippet": "NJU - LHRS / LHRS - Bot Public. Notifications You must be signed in to change notification settings.We are excited to introduce LHRS - Bot , a multimodal large language model (MLLM) that leverages globally available volunteer geographic information (VGI) and remote sensing images (RS).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "NJU - LHRS / LHRS - Bot Public. Notifications You must be signed in to change notification settings.We are excited to introduce LHRS - Bot , a multimodal large language model (MLLM) that leverages globally available volunteer geographic information (VGI) and remote sensing images (RS)."}
+{"idx": 2, "title": "LHRS - Bot /DataPrepare/ README .md at main · NJU - LHRS / LHRS - Bot", "date": "", "ddg_snippet": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /DataPrepare/ README .md at main · NJU - LHRS / LHRS - Bot .Fund open source developers. The ReadME Project.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/DataPrepare/README.md", "content": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /DataPrepare/ README .md at main · NJU - LHRS / LHRS - Bot .Fund open source developers. The ReadME Project."}
+{"idx": 3, "title": "LHRS has 6 repositories available. Follow their code on GitHub.", "date": "", "ddg_snippet": "NJU - LHRS . Product. LHRS - Bot LHRS - Bot Public. VGI-Enhanced multimodal large language model for remote sensing images.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS", "content": "NJU - LHRS . Product. LHRS - Bot LHRS - Bot Public. VGI-Enhanced multimodal large language model for remote sensing images."}
+{"idx": 4, "title": "LHRS - Bot /lhrs_webui.py at main · NJU - LHRS / LHRS - Bot · GitHub", "date": "", "ddg_snippet": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /lhrs_webui.py at main · NJU - LHRS / LHRS - Bot .Fund open source developers. The ReadME Project.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/lhrs_webui.py", "content": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /lhrs_webui.py at main · NJU - LHRS / LHRS - Bot .Fund open source developers. The ReadME Project."}
+{"idx": 5, "title": "LHRS - Bot /Script/train_stage1.sh at main · NJU - LHRS / LHRS - Bot", "date": "", "ddg_snippet": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /Script/train_stage1.sh at main · NJU - LHRS / LHRS - Bot .GitHub Sponsors. Fund open source developers. The ReadME Project.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/Script/train_stage1.sh", "content": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /Script/train_stage1.sh at main · NJU - LHRS / LHRS - Bot .GitHub Sponsors. Fund open source developers. The ReadME Project."}
+{"idx": 6, "title": "Require Training Dataset · Issue #35 · NJU-LHRS/LHRS-Bot", "date": "", "ddg_snippet": "Jan 31, 2025 · Can you please provide me your training datasets? These following: LHRS _Align_Recap, LHRS _Instruct LRV_Instruct LHRS -Instruct-Plus Multi Task", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/issues/35", "content": "Jan 31, 2025 · Can you please provide me your training datasets? These following: LHRS _Align_Recap, LHRS _Instruct LRV_Instruct LHRS -Instruct-Plus Multi Task"}
+{"idx": 7, "title": "LHRS-Bot/main_cls.py at main · NJU-LHRS/LHRS-Bot · GitHub", "date": "", "ddg_snippet": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /main_cls.py at main · NJU - LHRS / LHRS - Bot", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/main_cls.py", "content": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS - Bot /main_cls.py at main · NJU - LHRS / LHRS - Bot"}
+{"idx": 8, "title": "LHRS-Bot Checkpoints · Issue #2 · NJU-LHRS/LHRS-Bot - GitHub", "date": "", "ddg_snippet": "Feb 15, 2024 · Please provide Google Drive access to download the LHRS - Bot Checkpoints. Thanks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/issues/2", "content": "Feb 15, 2024 · Please provide Google Drive access to download the LHRS - Bot Checkpoints. Thanks."}
+{"idx": 9, "title": "How to finetune my custom dataset? · Issue #17 · NJU-LHRS ...", "date": "", "ddg_snippet": "Jun 26, 2024 · NJU - LHRS / LHRS - Bot Public Notifications You must be signed in to change notification settings Fork 12 Star 134", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/issues/17", "content": "Jun 26, 2024 · NJU - LHRS / LHRS - Bot Public Notifications You must be signed in to change notification settings Fork 12 Star 134"}
diff --git a/data/sampled_jsons/sitegithub.com_aim-uofa_BA-DDG_hardware_GPU_configuration_setup.jsonl b/data/sampled_jsons/sitegithub.com_aim-uofa_BA-DDG_hardware_GPU_configuration_setup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e21f36ea7bc65a67350b65011004ba6e19c196eb
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_aim-uofa_BA-DDG_hardware_GPU_configuration_setup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Advanced Intelligent Machines (AIM) - GitHub", "date": "", "ddg_snippet": "A research team at Zhejiang University, focusing on Computer Vision and broad AI research ... - Advanced Intelligent Machines ( AIM )", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa", "content": "A research team at Zhejiang University, focusing on Computer Vision and broad AI research ... - Advanced Intelligent Machines ( AIM )"}
+{"idx": 1, "title": "BA_Cycle & BA_DDG with the same res · Issue #2 · aim-uofa/BA-DDG", "date": "", "ddg_snippet": "Hi, I have tried to run your examples on the skempi dataset, but I got the same results for both the BA Cycle and BA DDG . Does anyone know what the problem may be?", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/issues/2", "content": "Hi, I have tried to run your examples on the skempi dataset, but I got the same results for both the BA Cycle and BA DDG . Does anyone know what the problem may be?"}
+{"idx": 2, "title": "aim-uofa repositories · GitHub", "date": "", "ddg_snippet": "Aug 8, 2021 · A research team at Zhejiang University, focusing on Computer Vision and broad AI research ... - Advanced Intelligent Machines ( AIM )", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/orgs/aim-uofa/repositories", "content": "Aug 8, 2021 · A research team at Zhejiang University, focusing on Computer Vision and broad AI research ... - Advanced Intelligent Machines ( AIM )"}
+{"idx": 3, "title": "FreeCompose/main.ipynb at main · aim - uofa /FreeCompose · GitHub", "date": "", "ddg_snippet": "Contribute to aim - uofa /FreeCompose development by creating an account on GitHub. aim - uofa / FreeCompose Public. Notifications You must be signed in to change notification settings .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/FreeCompose/blob/main/main.ipynb", "content": "Contribute to aim - uofa /FreeCompose development by creating an account on GitHub. aim - uofa / FreeCompose Public. Notifications You must be signed in to change notification settings ."}
+{"idx": 4, "title": "GitHub - aim - uofa /AdelaiDet: AdelaiDet is an open source toolbox for...", "date": "", "ddg_snippet": "The configs are made for 8- GPU training. To train on another number of GPUs , change the --num- gpus .We set OMP_NUM_THREADS=1 by default, which achieves the best speed on our machines, please change it as needed. This quick start is made for FCOS.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/AdelaiDet", "content": "The configs are made for 8- GPU training. To train on another number of GPUs , change the --num- gpus .We set OMP_NUM_THREADS=1 by default, which achieves the best speed on our machines, please change it as needed. This quick start is made for FCOS."}
+{"idx": 5, "title": "AdelaiDet/ configs /BAText/README.md at master · aim - uofa /AdelaiDet", "date": "", "ddg_snippet": "AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks. - AdelaiDet/ configs /BAText/README.md at master · aim - uofa /AdelaiDet.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/AdelaiDet/blob/master/configs/BAText/README.md", "content": "AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks. - AdelaiDet/ configs /BAText/README.md at master · aim - uofa /AdelaiDet."}
+{"idx": 6, "title": "GitHub - aim-uofa/BA-DDG: [ICLR 2025 Spotlight] Boltzmann ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G ..."}
+{"idx": 7, "title": "BA-DDG/README.md at master · aim-uofa/BA-DDG · GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/blob/master/README.md", "content": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 8, "title": "Pre-training code and detailed hyperparameters #1 - GitHub", "date": "", "ddg_snippet": "Thank you for open-sourcing this work! I noticed that the pre-training code and hyperparameters used for these pre-trained models are not included. Could you please supplement these details?", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/issues/1", "content": "Thank you for open-sourcing this work! I noticed that the pre-training code and hyperparameters used for these pre-trained models are not included. Could you please supplement these details?"}
+{"idx": 9, "title": "Question about Boltzmann Loss Implementation (Equation 12)", "date": "", "ddg_snippet": "aim- uofa / BA-DDG Public Notifications You must be signed in to change notification settings Fork 0 Star 30", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/issues/3", "content": "aim- uofa / BA-DDG Public Notifications You must be signed in to change notification settings Fork 0 Star 30"}
diff --git "a/data/sampled_jsons/sitegithub.com_beautyremainProDet_\316\262_\316\263.jsonl" "b/data/sampled_jsons/sitegithub.com_beautyremainProDet_\316\262_\316\263.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..b7113b4ea3e3168aa8c762eb6bb00e42f6b07482
--- /dev/null
+++ "b/data/sampled_jsons/sitegithub.com_beautyremainProDet_\316\262_\316\263.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - beautyremain/ProDet: The official code for paper \"Can We Leave ...", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well. You may find the overall-best checkpoint of our method from Google Drive, which is recommended for ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well. You may find the overall-best checkpoint of our method from Google Drive, which is recommended for ..."}
+{"idx": 1, "title": "ProDet/README.md at main · beautyremain/ProDet · GitHub", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet/README.md at main · beautyremain/ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/blob/main/README.md", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet/README.md at main · beautyremain/ProDet"}
+{"idx": 2, "title": "Releases: beautyremain/ProDet - GitHub", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - beautyremain/ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/releases", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - beautyremain/ProDet"}
+{"idx": 3, "title": "GitHub · Where software is built", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - beautyremain/ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/labels?sort=name-desc", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - beautyremain/ProDet"}
+{"idx": 4, "title": "ProDet/main_archi.png at main · beautyremain/ProDet · GitHub", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet/main_archi.png at main · beautyremain/ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/blob/main/main_archi.png", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - ProDet/main_archi.png at main · beautyremain/ProDet"}
+{"idx": 5, "title": "Pull requests · beautyremain/ProDet · GitHub", "date": "", "ddg_snippet": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - Pull requests · beautyremain/ProDet", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/pulls", "content": "The official code for paper \"Can We Leave Deepfake Data Behind in Training Deepfake Detector\" (NIPS2024 poster) - Pull requests · beautyremain/ProDet"}
+{"idx": 6, "title": "GitHub - beautyremain/myZSSRversion", "date": "", "ddg_snippet": "Contribute to beautyremain/myZSSRversion development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/myZSSRversion", "content": "Contribute to beautyremain/myZSSRversion development by creating an account on GitHub."}
+{"idx": 7, "title": "Actions · beautyremain/ED4 · GitHub", "date": "", "ddg_snippet": "GitHub Actions makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ED4/actions", "content": "GitHub Actions makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub."}
+{"idx": 8, "title": "Security Overview · beautyremain/ProDet · GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet/security", "content": "GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 9, "title": "GitHub - beautyremain/ED4: The official code for paper \"ED4: Explicit ...", "date": "", "ddg_snippet": "The official code for paper \"ED4: Explicit Data-level Debiasing for Deepfake Detection\" (IEEE TIP 2025) We provide the novel augmentation that proposed in this paper, that is, ClockMix and AdvSCM, which could be easily implemented to any pipeline as preprocess. The provided code is within the framework of DeepfakeBench. They should be placed in the corresponding folders in DeepfakeBench.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ED4", "content": "The official code for paper \"ED4: Explicit Data-level Debiasing for Deepfake Detection\" (IEEE TIP 2025) We provide the novel augmentation that proposed in this paper, that is, ClockMix and AdvSCM, which could be easily implemented to any pipeline as preprocess. The provided code is within the framework of DeepfakeBench. They should be placed in the corresponding folders in DeepfakeBench."}
diff --git a/data/sampled_jsons/sitegithub.com_pnnlML4AlgComb_S18_characters_dataset_training_examples.jsonl b/data/sampled_jsons/sitegithub.com_pnnlML4AlgComb_S18_characters_dataset_training_examples.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ddce09b85f88aa62f609dbf8c02ed09f36301ddf
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_pnnlML4AlgComb_S18_characters_dataset_training_examples.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - pnnl/ML4AlgComb: ML Benchmarks in Algebraic ...", "date": "", "ddg_snippet": "A Collection of Algebraic Combinatorics Datasets for Scientific Discovery in Mathematics The challenge of sifting through large datasets with the goal of identifying structure and patterns is a common activity in research level mathematics. As an obvious example , many careers have been spent looking for patterns in the set of prime numbers.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "A Collection of Algebraic Combinatorics Datasets for Scientific Discovery in Mathematics The challenge of sifting through large datasets with the goal of identifying structure and patterns is a common activity in research level mathematics. As an obvious example , many careers have been spent looking for patterns in the set of prime numbers."}
+{"idx": 1, "title": "ML 4 AlgComb /load_ datasets .py at master · pnnl / ML 4 AlgComb", "date": "", "ddg_snippet": "Contribute to pnnl / ML 4 AlgComb development by creating an account on GitHub.print(f\"Test set has {len(X_test)} examples \"). print(f\"Inputs are sequences of length {input_size}, which represent three concatenated permutations on the letters 1 through {num_tokens-1}.\")", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb/blob/master/load_datasets.py", "content": "Contribute to pnnl / ML 4 AlgComb development by creating an account on GitHub.print(f\"Test set has {len(X_test)} examples \"). print(f\"Inputs are sequences of length {input_size}, which represent three concatenated permutations on the letters 1 through {num_tokens-1}.\")"}
+{"idx": 2, "title": "ML4AlgComb/how_to_load_datasets.ipynb at master · pnnl ...", "date": "", "ddg_snippet": "ML Benchmarks in Algebraic Combinatorics. Contribute to pnnl/ML4AlgComb development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb/blob/master/how_to_load_datasets.ipynb", "content": "ML Benchmarks in Algebraic Combinatorics. Contribute to pnnl/ML4AlgComb development by creating an account on GitHub ."}
+{"idx": 3, "title": "ML4AlgComb/README.md at master · pnnl/ML4AlgComb · GitHub", "date": "", "ddg_snippet": "The challenge of sifting through large datasets with the goal of identifying structure and patterns is a common activity in research level mathematics. As an obvious example , entire careers have been spent looking for patterns in the set of prime numbers.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb/blob/master/README.md", "content": "The challenge of sifting through large datasets with the goal of identifying structure and patterns is a common activity in research level mathematics. As an obvious example , entire careers have been spent looking for patterns in the set of prime numbers."}
+{"idx": 4, "title": "jinbo0906/Awesome-MLLM-Datasets - GitHub", "date": "", "ddg_snippet": "This project aims to collect and collate various datasets for multimodal large model training , including but not limited to pre- training data, instruction fine-tuning data, and In-Context learning data. - jinbo0906/Awesome-MLLM- Datasets", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jinbo0906/Awesome-MLLM-Datasets", "content": "This project aims to collect and collate various datasets for multimodal large model training , including but not limited to pre- training data, instruction fine-tuning data, and In-Context learning data. - jinbo0906/Awesome-MLLM- Datasets"}
+{"idx": 5, "title": "Dataset package for facile training and testing of machine ...", "date": "", "ddg_snippet": "About Dataset package for facile training and testing of machine learning/AI algorithms that predict drug response in cancer model systems.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/PNNL-CompBio/coderdata", "content": "About Dataset package for facile training and testing of machine learning/AI algorithms that predict drug response in cancer model systems."}
+{"idx": 6, "title": "GitHub - PolyAI-LDN/conversational-datasets: Large datasets ...", "date": "", "ddg_snippet": "Machine learning methods work best with large datasets such as these. At PolyAI we train models of conversational response on huge conversational datasets and then adapt these models to domain-specific tasks in conversational AI. This general approach of pre- training large models on huge datasets ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/PolyAI-LDN/conversational-datasets", "content": "Machine learning methods work best with large datasets such as these. At PolyAI we train models of conversational response on huge conversational datasets and then adapt these models to domain-specific tasks in conversational AI. This general approach of pre- training large models on huge datasets ..."}
+{"idx": 7, "title": "Actions · pnnl / ML 4 AlgComb · GitHub", "date": "", "ddg_snippet": "ML Benchmarks in Algebraic Combinatorics.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb/actions", "content": "ML Benchmarks in Algebraic Combinatorics."}
+{"idx": 8, "title": "davisrbr has 23 repositories available. Follow their code on GitHub.", "date": "", "ddg_snippet": "Maximum 100 characters , markdown supported. This note will be visible to only you.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/davisrbr", "content": "Maximum 100 characters , markdown supported. This note will be visible to only you."}
+{"idx": 9, "title": "Settings · Custom properties · pnnl / ML 4 AlgComb · GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb/custom-properties", "content": "GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
diff --git a/data/sampled_jsons/sitegithub.com_yxjdarren_SC_Dynamic_Hierarchical_Collaboration_scaling_factor.jsonl b/data/sampled_jsons/sitegithub.com_yxjdarren_SC_Dynamic_Hierarchical_Collaboration_scaling_factor.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f75540e9c22797260e542e122aef0f7de4cff1a5
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_yxjdarren_SC_Dynamic_Hierarchical_Collaboration_scaling_factor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - yxjdarren/SC: The paper has been accepted to ICML 2025.", "date": "", "ddg_snippet": "The code repository for \"Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration \" (the paper has been accepted by ICML 2025) in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yxjdarren/SC", "content": "The code repository for \"Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration \" (the paper has been accepted by ICML 2025) in PyTorch."}
+{"idx": 1, "title": "SC/README.md at main · yxjdarren/SC · GitHub", "date": "", "ddg_snippet": "The paper has been accepted to ICML 2025. Contribute to yxjdarren/SC development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yxjdarren/SC/blob/main/README.md", "content": "The paper has been accepted to ICML 2025. Contribute to yxjdarren/SC development by creating an account on GitHub."}
+{"idx": 2, "title": "SC/cross_entropy_loss.py at main · yxjdarren/SC · GitHub", "date": "", "ddg_snippet": "The paper has been accepted to ICML 2025. Contribute to yxjdarren/SC development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yxjdarren/SC/blob/main/cross_entropy_loss.py", "content": "The paper has been accepted to ICML 2025. Contribute to yxjdarren/SC development by creating an account on GitHub."}
+{"idx": 3, "title": "GitHub - gionikola/DynamicFactorModeling.jl: Julia package for ...", "date": "", "ddg_snippet": "This is Julia package allows the user to easily construct, simulate, and estimate linear multi-level/ hierarchical dynamic factor models (HDFMs) using a variety of Bayesian approaches.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/gionikola/DynamicFactorModeling.jl", "content": "This is Julia package allows the user to easily construct, simulate, and estimate linear multi-level/ hierarchical dynamic factor models (HDFMs) using a variety of Bayesian approaches."}
+{"idx": 4, "title": "Multi-Scale Dynamic and Hierarchical Relationship Modeling for Facial ...", "date": "", "ddg_snippet": "Code for paper multi-scale dynamic and hierarchical relationship modeling for facial action units recognition - CVI-SZU/MDHR", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CVI-SZU/MDHR", "content": "Code for paper multi-scale dynamic and hierarchical relationship modeling for facial action units recognition - CVI-SZU/MDHR"}
+{"idx": 5, "title": "GitHub - priorelli/dynamic-planning: dynamic planning, hybrid models ...", "date": "", "ddg_snippet": "Dynamic planning This is the project related to the papers Deep hybrid models: infer and plan in a dynamic world and Dynamic planning in hierarchical active inference. The first paper describes a (deep) hierarchical hybrid approach that can plan efficiently in constantly changing environments.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/priorelli/dynamic-planning", "content": "Dynamic planning This is the project related to the papers Deep hybrid models: infer and plan in a dynamic world and Dynamic planning in hierarchical active inference. The first paper describes a (deep) hierarchical hybrid approach that can plan efficiently in constantly changing environments."}
+{"idx": 6, "title": "This is the code of the paper:\"Joint Service Caching ... - GitHub", "date": "", "ddg_snippet": "This is the code of the paper:\"Joint Service Caching, Communication and Computing Resource Allocation in Collaborative MEC Systems: A DRL-based Two-timescale Approach\", which realizes the two-timescale joint optimization of multi-dimensional resources DGL-DDPG contains 3 tree parts the first part is the two-timescale multi-edge offloading environment. (core.py and environment.py ) there three ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Qqianliu/Two-timescale-DGL-DDPG", "content": "This is the code of the paper:\"Joint Service Caching, Communication and Computing Resource Allocation in Collaborative MEC Systems: A DRL-based Two-timescale Approach\", which realizes the two-timescale joint optimization of multi-dimensional resources DGL-DDPG contains 3 tree parts the first part is the two-timescale multi-edge offloading environment. (core.py and environment.py ) there three ..."}
+{"idx": 7, "title": "DHGFormer: Dynamic Hierarchical Graph Transformer for Disorder Brain ...", "date": "", "ddg_snippet": "Existing methods usually inadequately represent the brain's hierarchical organization, potentially missing critical information about multi-scale feature interactions. To address these limitations, we propose a novel brain network generation and analysis approach-- Dynamic Hierarchical Graph Transformer (DHGFormer).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/iMoonLab/DHGFormer", "content": "Existing methods usually inadequately represent the brain's hierarchical organization, potentially missing critical information about multi-scale feature interactions. To address these limitations, we propose a novel brain network generation and analysis approach-- Dynamic Hierarchical Graph Transformer (DHGFormer)."}
+{"idx": 8, "title": "yxjdarren (Darren) · GitHub", "date": "", "ddg_snippet": "Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yxjdarren/", "content": "Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users."}
+{"idx": 9, "title": "GitHub - yxjdarren/MvDSCN: MvDSCN", "date": "", "ddg_snippet": "In this work, we propose a novel multi-view deep subspace clustering network (MvDSCN) by learning a multi-view self-representation matrix in an end to end manner. MvDSCN consists of two sub-networks, i.e., diversity network (Dnet) and universality network (Unet). A latent space is built upon deep convolutional auto-encoders and a self-representation matrix is learned in the latent space using ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yxjdarren/MvDSCN", "content": "In this work, we propose a novel multi-view deep subspace clustering network (MvDSCN) by learning a multi-view self-representation matrix in an end to end manner. MvDSCN consists of two sub-networks, i.e., diversity network (Dnet) and universality network (Unet). A latent space is built upon deep convolutional auto-encoders and a self-representation matrix is learned in the latent space using ..."}
diff --git a/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_AECM_learning_rate.jsonl b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_AECM_learning_rate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..59d616e9eafde750222045efab035b72cc329c9b
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_AECM_learning_rate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - AmbitiousPilots/AntiLag: AntiLag makes your system run as...", "date": "", "ddg_snippet": "Learning Pathways.\"Windows is running sluggish. Games don't feel smooth and frame rates are fluctuating a lot. High input lag puts you at a disadvantage and drives you nuts. Simply everything doesn't feel as performant as it should\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AmbitiousPilots/AntiLag", "content": "Learning Pathways.\"Windows is running sluggish. Games don't feel smooth and frame rates are fluctuating a lot. High input lag puts you at a disadvantage and drives you nuts. Simply everything doesn't feel as performant as it should\"."}
+{"idx": 1, "title": "GitHub - 0x192/universal-android-debloater: Cross-platform GUI written...", "date": "", "ddg_snippet": "Learning Pathways.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/0x192/universal-android-debloater", "content": "Learning Pathways."}
+{"idx": 2, "title": "GitHub - BruteAkaSylo/Windows-11-Pro-Activator: A simple command...", "date": "", "ddg_snippet": "Learning Pathways.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BruteAkaSylo/Windows-11-Pro-Activator", "content": "Learning Pathways."}
+{"idx": 3, "title": "GitHub - SagerNet/sing-box: The universal proxy platform", "date": "", "ddg_snippet": "Learn more about GitHub Sponsors.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SagerNet/sing-box", "content": "Learn more about GitHub Sponsors."}
+{"idx": 4, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub ."}
+{"idx": 5, "title": "GitHub - zyxunh/EntityErasure-ProjectPage", "date": "", "ddg_snippet": "Contribute to zyxunh / EntityErasure -ProjectPage development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/EntityErasure-ProjectPage", "content": "Contribute to zyxunh / EntityErasure -ProjectPage development by creating an account on GitHub ."}
+{"idx": 6, "title": "GitHub - zyxunh/Entity_for_entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 7, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh / entity _ erasure development by creating an account on GitHub ."}
+{"idx": 8, "title": "Entity_for_entity_erasure/Open-Metrics/README.md at main ...", "date": "", "ddg_snippet": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure/blob/main/Open-Metrics/README.md", "content": "Contribute to zyxunh / Entity _for_ entity _ erasure development by creating an account on GitHub ."}
+{"idx": 9, "title": "zyxunh/diffusers_for_entity_erasure - GitHub", "date": "", "ddg_snippet": "Contribute to zyxunh /diffusers_for_ entity _ erasure development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/diffusers_for_entity_erasure", "content": "Contribute to zyxunh /diffusers_for_ entity _ erasure development by creating an account on GitHub ."}
diff --git a/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_train.py.jsonl b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_train.py.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6b026085353b85b32516999de3c2a5fc5b2eabc8
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_zyxunhentity_erasure_train.py.jsonl
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+{"idx": 0, "title": "GitHub - zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh/entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure", "content": "Contribute to zyxunh/entity_erasure development by creating an account on GitHub."}
+{"idx": 1, "title": "GitHub - zyxunh/Entity_for_entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh/Entity_for_entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure", "content": "Contribute to zyxunh/Entity_for_entity_erasure development by creating an account on GitHub."}
+{"idx": 2, "title": "entity_erasure/README.md at master · zyxunh/entity_erasure", "date": "", "ddg_snippet": "Contribute to zyxunh/entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/blob/master/README.md", "content": "Contribute to zyxunh/entity_erasure development by creating an account on GitHub."}
+{"idx": 3, "title": "GitHub - zyxunh/diffusers_for_entity_erasure", "date": "", "ddg_snippet": "We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing PyTorch and Flax, please refer to their official documentation. Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the from_pretrained method ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/diffusers_for_entity_erasure", "content": "We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing PyTorch and Flax, please refer to their official documentation. Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the from_pretrained method ..."}
+{"idx": 4, "title": "GitHub - zyxunh/EntityErasure-ProjectPage", "date": "", "ddg_snippet": "Contribute to zyxunh/EntityErasure-ProjectPage development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/EntityErasure-ProjectPage", "content": "Contribute to zyxunh/EntityErasure-ProjectPage development by creating an account on GitHub."}
+{"idx": 5, "title": "Entity_for_entity_erasure/Entityv2/README.md at main - GitHub", "date": "", "ddg_snippet": "Contribute to zyxunh/Entity_for_entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure/blob/main/Entityv2/README.md", "content": "Contribute to zyxunh/Entity_for_entity_erasure development by creating an account on GitHub."}
+{"idx": 6, "title": "zyxunh/Mask2Former_for_entity_erasure - GitHub", "date": "", "ddg_snippet": "Contribute to zyxunh/Mask2Former_for_entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Mask2Former_for_entity_erasure", "content": "Contribute to zyxunh/Mask2Former_for_entity_erasure development by creating an account on GitHub."}
+{"idx": 7, "title": "Entity_for_entity_erasure/Open-Metrics/README.md at main · zyxunh ...", "date": "", "ddg_snippet": "Contribute to zyxunh/Entity_for_entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Entity_for_entity_erasure/blob/main/Open-Metrics/README.md", "content": "Contribute to zyxunh/Entity_for_entity_erasure development by creating an account on GitHub."}
+{"idx": 8, "title": "Mask2Former_for_entity_erasure/train_net_video.py at main · zyxunh ...", "date": "", "ddg_snippet": "Contribute to zyxunh/Mask2Former_for_entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/Mask2Former_for_entity_erasure/blob/main/train_net_video.py", "content": "Contribute to zyxunh/Mask2Former_for_entity_erasure development by creating an account on GitHub."}
+{"idx": 9, "title": "Pull requests: zyxunh/entity_erasure - GitHub", "date": "", "ddg_snippet": "Contribute to zyxunh/entity_erasure development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zyxunh/entity_erasure/pulls", "content": "Contribute to zyxunh/entity_erasure development by creating an account on GitHub."}
diff --git a/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_pretrain.py_n_layer_year_2024.jsonl b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_pretrain.py_n_layer_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4dcb8a046ca3372c2a25407762060fd1e58e5cbd
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_pretrain.py_n_layer_year_2024.jsonl
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+{"idx": 0, "title": "GitHub - fiveai/understanding_safety_finetuning: Official Code for What...", "date": "", "ddg_snippet": "saved_data/ pretrain _test_data_pcfg1.pkl'. To make the safety fine-tuning datasets run", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "saved_data/ pretrain _test_data_pcfg1.pkl'. To make the safety fine-tuning datasets run"}
+{"idx": 1, "title": "' asian gyro beauty ' Search - XVIDEOS.COM", "date": "", "ddg_snippet": "19,709 asian gyro beauty FREE videos found on XVIDEOS for this search.", "subpage_snippet": "", "source": "www.xvideos.com", "link": "https://www.xvideos.com/?k=asian+gyro+beauty", "content": "19,709 asian gyro beauty FREE videos found on XVIDEOS for this search."}
+{"idx": 2, "title": "' japanese gyno exam ' Search - XNXX.COM", "date": "", "ddg_snippet": "DoctorTampa Dr Tampa - Genesis' 1st Gyno Exam 3.5k 81% 22min - 720p SCOUT69official Beauty Half Asian Girl seduce Guy to First Time no Condom Fuck after Cheating Sex at Gyno Exam 4.5k 82% 20min - 1080p DoctorTampa Dr Tampa - Zombae's 1st Ever Gyno Exam - Zombae 2k 21min - 720p", "subpage_snippet": "", "source": "www.xnxx.com", "link": "https://www.xnxx.com/search/japanese+gyno+exam", "content": "DoctorTampa Dr Tampa - Genesis' 1st Gyno Exam 3.5k 81% 22min - 720p SCOUT69official Beauty Half Asian Girl seduce Guy to First Time no Condom Fuck after Cheating Sex at Gyno Exam 4.5k 82% 20min - 1080p DoctorTampa Dr Tampa - Zombae's 1st Ever Gyno Exam - Zombae 2k 21min - 720p"}
+{"idx": 3, "title": "Doctor beautiful japanese gyno exam - XVIDEOS.COM", "date": "", "ddg_snippet": "初体験ナンパ 思わずヌルっと生挿入！ そのまま中出し！ 〜セレブ巨乳奥様〜 [NPS-318]", "subpage_snippet": "", "source": "www.xvideos.com", "link": "https://www.xvideos.com/video.itmbpio964a/doctor_beautiful_japanese_gyno_exam", "content": "初体験ナンパ 思わずヌルっと生挿入！ そのまま中出し！ 〜セレブ巨乳奥様〜 [NPS-318]"}
+{"idx": 4, "title": "' asian gyro ' Search - XNXX.COM", "date": "", "ddg_snippet": "Asian Pussy Twerking - Do You Wanna Lick My Hairy Pussy?", "subpage_snippet": "", "source": "www.xnxx.com", "link": "https://www.xnxx.com/search/asian+gyro", "content": "Asian Pussy Twerking - Do You Wanna Lick My Hairy Pussy?"}
+{"idx": 5, "title": "' asian gyro real sex ' Search - XVIDEOS.COM", "date": "", "ddg_snippet": "105,288 asian gyro real sex FREE videos found on XVIDEOS for this search.", "subpage_snippet": "", "source": "www.xvideos.com", "link": "https://www.xvideos.com/?k=asian+gyro+real+sex", "content": "105,288 asian gyro real sex FREE videos found on XVIDEOS for this search."}
+{"idx": 6, "title": "JAPANESE GYNO - 177 videos - New Porn Search", "date": "", "ddg_snippet": "Free porn: JAPANESE GYNO - 177 videos. Japanese Massage Teen, Japanese Doctor, Doctor Japanese, Japanese Milk, Doctor Exam Teen, Japanese Massage Hidden Cam, Japanese Massage and much more.", "subpage_snippet": "", "source": "newpornsearch.com", "link": "https://newpornsearch.com/videos/japanese-gyno", "content": "Free porn: JAPANESE GYNO - 177 videos. Japanese Massage Teen, Japanese Doctor, Doctor Japanese, Japanese Milk, Doctor Exam Teen, Japanese Massage Hidden Cam, Japanese Massage and much more."}
+{"idx": 7, "title": "Asian Gyro Porn Videos - xHamster", "date": "", "ddg_snippet": "Watch asian gyro porn videos. Explore tons of XXX movies with sex scenes in 2025 on xHamster!", "subpage_snippet": "", "source": "xhamster.com", "link": "https://xhamster.com/search/asian+gyro", "content": "Watch asian gyro porn videos. Explore tons of XXX movies with sex scenes in 2025 on xHamster!"}
+{"idx": 8, "title": "Japanese gyno sex movies @ XNEON: 430 videos", "date": "", "ddg_snippet": "JAVHD - Sakamoto Hikari is given a gyno exam by a very horny doctor. Asian girl in her perky and sexy uniform, stripping off and 05:01", "subpage_snippet": "", "source": "xneon.com", "link": "https://xneon.com/japanese_gyno", "content": "JAVHD - Sakamoto Hikari is given a gyno exam by a very horny doctor. Asian girl in her perky and sexy uniform, stripping off and 05:01"}
+{"idx": 9, "title": "Asian gyno @ Aloha Tube", "date": "", "ddg_snippet": "Adrianna Savu, a cosplay enthusiast, is clothed as a luxurious schoolgirl with no panties and a fake penis in her ass. Aloha Tube - sex videos updated every 5 minutes. Millions of porno videos! Watch best porn for free! Updates every 5 minutes.", "subpage_snippet": "", "source": "www.alohatube.com", "link": "https://www.alohatube.com/top/asian_gyno", "content": "Adrianna Savu, a cosplay enthusiast, is clothed as a luxurious schoolgirl with no panties and a fake penis in her ass. Aloha Tube - sex videos updated every 5 minutes. Millions of porno videos! Watch best porn for free! Updates every 5 minutes."}
diff --git a/data/sampled_jsons/sitekathpra.github.io_DCBM_Algorithm_1.jsonl b/data/sampled_jsons/sitekathpra.github.io_DCBM_Algorithm_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4fe2af61bdf2395c73224a5cac6da306d92bb062
--- /dev/null
+++ b/data/sampled_jsons/sitekathpra.github.io_DCBM_Algorithm_1.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data-sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes ...", "subpage_snippet": "", "source": "kathpra.github.io", "link": "https://kathpra.github.io/DCBM/", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data-sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes ..."}
diff --git a/data/sampled_jsons/sitekevinwampler.com_Character_Animation_in_Two-Player_Adversarial_Games.jsonl b/data/sampled_jsons/sitekevinwampler.com_Character_Animation_in_Two-Player_Adversarial_Games.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..92127b69f74c98835d31d0a17ad8bdc93c779131
--- /dev/null
+++ b/data/sampled_jsons/sitekevinwampler.com_Character_Animation_in_Two-Player_Adversarial_Games.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "Kevin Wampler", "date": "", "ddg_snippet": "I am a research engineer in the Creative Technologies Lab at Adobe. I primarily do software development on new computer graphics technologies, but I also occasionally do some computer graphics research, primarily in animation and geometry. Previously I was a Ph.D. student in computer graphics at the University of Washington under Zoran Popović.", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "http://www.kevinwampler.com/homepage/index.html", "content": "I am a research engineer in the Creative Technologies Lab at Adobe. I primarily do software development on new computer graphics technologies, but I also occasionally do some computer graphics research, primarily in animation and geometry. Previously I was a Ph.D. student in computer graphics at the University of Washington under Zoran Popović."}
+{"idx": 1, "title": "Kevin Wampler", "date": "", "ddg_snippet": "Character Animation in Two - Player Adversarial Games .Motion Fields for Interactive Character Animation . Yongjoon Lee, Kevin Wampler, Gilbert Bernstein, Jovan Popović, Zoran Popović.", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "https://www.kevinwampler.com/", "content": "Character Animation in Two - Player Adversarial Games .Motion Fields for Interactive Character Animation . Yongjoon Lee, Kevin Wampler, Gilbert Bernstein, Jovan Popović, Zoran Popović."}
+{"idx": 2, "title": "Creating GIF Videos Using Python", "date": "", "ddg_snippet": "The first part of this is straightforward, and just involves setting up an HTML5 video element to play animations /anim.mp4. This animation is set up to mimic how an animated GIF behaves in that it starts playing automatically and doesn’t show any playback controls.", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "http://www.kevinwampler.com/blog/2016/09/10/creating-animated-gifs-using-python.html", "content": "The first part of this is straightforward, and just involves setting up an HTML5 video element to play animations /anim.mp4. This animation is set up to mimic how an animated GIF behaves in that it starts playing automatically and doesn’t show any playback controls."}
+{"idx": 3, "title": "Fast and Reliable Mesk IK (SIGGRAPH Asia 2016)", "date": "", "ddg_snippet": "Below you’ll see the same snake animation as before, but now the color will show the degree to which each of the input shapes is most influencing the deformation. So when the snake in the animation is drawn in purple, then it’s using the purple input image as the reference for the deformation.", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "http://www.kevinwampler.com/research/graphics/2016/09/20/siggraph-asia-meshIK.html", "content": "Below you’ll see the same snake animation as before, but now the color will show the degree to which each of the input shapes is most influencing the deformation. So when the snake in the animation is drawn in purple, then it’s using the purple input image as the reference for the deformation."}
+{"idx": 4, "title": "Kevin Wampler", "date": "", "ddg_snippet": "Publications ; adversarial control. Character Animation in Two-Player Adversarial Games . Kevin Wampler, Erik Andersen, Evan Herbst, Yongjoon Lee, Zoran Popović.", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "http://www.kevinwampler.com/", "content": "Publications ; adversarial control. Character Animation in Two-Player Adversarial Games . Kevin Wampler, Erik Andersen, Evan Herbst, Yongjoon Lee, Zoran Popović."}
+{"idx": 5, "title": "Kevin Wampler", "date": "", "ddg_snippet": "... but I also occasionally do some computer graphics research, primarily in animation and ... Character Animation in Two - Player Adversarial Games", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "https://www.kevinwampler.com/homepage/index.html", "content": "... but I also occasionally do some computer graphics research, primarily in animation and ... Character Animation in Two - Player Adversarial Games"}
+{"idx": 6, "title": "Fast and Reliable Example-Based Mesh IK for Stylized Deformations", "date": "", "ddg_snippet": "Example-based shape deformation allows a mesh to be easily manipulated or animated with simple inputs. As the user pulls parts of the shape, the rest of the mesh automatically changes in an intuitive way by drawing from a set of exemplars.", "subpage_snippet": "", "source": "www.kevinwampler.com", "link": "http://www.kevinwampler.com/research/2016_blendwarp/index.html", "content": "Example-based shape deformation allows a mesh to be easily manipulated or animated with simple inputs. As the user pulls parts of the shape, the rest of the mesh automatically changes in an intuitive way by drawing from a set of exemplars."}
diff --git a/data/sampled_jsons/sitemodel-similarity.github.io_Section_4_mechanism.jsonl b/data/sampled_jsons/sitemodel-similarity.github.io_Section_4_mechanism.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0b582ce7a46fa5de292837f3b8fd1de2c7fb4447
--- /dev/null
+++ b/data/sampled_jsons/sitemodel-similarity.github.io_Section_4_mechanism.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "Similarity affects Oversight", "date": "", "ddg_snippet": "Model similarity has negative effects on using LMs to judge or train other models; Unfortunately LMs are getting similar with increasing capabilities.", "subpage_snippet": "", "source": "model-similarity.github.io", "link": "https://model-similarity.github.io/", "content": "Model similarity has negative effects on using LMs to judge or train other models; Unfortunately LMs are getting similar with increasing capabilities."}
diff --git a/data/sampled_jsons/siteopenaccess.thecvf.com_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Co.jsonl b/data/sampled_jsons/siteopenaccess.thecvf.com_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Co.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a0003d77696a49212857fb66b9a637c3b6d036b
--- /dev/null
+++ b/data/sampled_jsons/siteopenaccess.thecvf.com_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Co.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "Next, the amodal entity segmentation , masked image latent ci, inpainting mask, and the noisy latent zt will be fed to Amodal Entity Completion Model ( AECM ) to predict the final amodal entity completion result. The noisy latent zT and zt can be set differently. Below we describe our method in detail.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "Next, the amodal entity segmentation , masked image latent ci, inpainting mask, and the noisy latent zt will be fed to Amodal Entity Completion Model ( AECM ) to predict the final amodal entity completion result. The noisy latent zT and zt can be set differently. Below we describe our method in detail."}
+{"idx": 1, "title": "Amodal Ground Truth and Completion in the Wild - CVF Open Access", "date": "", "ddg_snippet": "To ensure a fair comparison, we use the same training setting as in [31, 51], which employs SGD with momentum, sets the learning rate to be 1e−3, and trains the model for 56K iterations with a batch size of 32.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Zhan_Amodal_Ground_Truth_and_Completion_in_the_Wild_CVPR_2024_paper.pdf", "content": "To ensure a fair comparison, we use the same training setting as in [31, 51], which employs SGD with momentum, sets the learning rate to be 1e−3, and trains the model for 56K iterations with a batch size of 32."}
+{"idx": 2, "title": "Complexity Experts are Task-Discriminative Learners for Any ...", "date": "", "ddg_snippet": "We optimize the L1 loss in the RGB and Fourier domain using the Adam [20] optimizer ( ́1 = 0.9, ́2 = 0.999) with an initial learning rate of 2 × 10−4 and cosine decay.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zamfir_Complexity_Experts_are_Task-Discriminative_Learners_for_Any_Image_Restoration_CVPR_2025_paper.pdf", "content": "We optimize the L1 loss in the RGB and Fourier domain using the Adam [20] optimizer ( ́1 = 0.9, ́2 = 0.999) with an initial learning rate of 2 × 10−4 and cosine decay."}
+{"idx": 3, "title": "Erase Diffusion: Empowering Object Removal Through ...", "date": "", "ddg_snippet": "During the training phase, we employed the Adam [16] optimizer with a learning rate set to 3×10−6on the OpenImages V5 train- ing set. To simplify parameters, the increasing sequence λ", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Erase_Diffusion_Empowering_Object_Removal_Through_Calibrating_Diffusion_Pathways_CVPR_2025_paper.pdf", "content": "During the training phase, we employed the Adam [16] optimizer with a learning rate set to 3×10−6on the OpenImages V5 train- ing set. To simplify parameters, the increasing sequence λ"}
+{"idx": 4, "title": "Segment Anything, Even Occluded - CVF Open Access", "date": "", "ddg_snippet": "We use the Adam optimizer with a learning rate of 1 × 10−4 without any learning rate sched-uler. For each instance during training, we randomly select either the amodal or modal ground truth bounding box as the prompt with equal probability.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Tai_Segment_Anything_Even_Occluded_CVPR_2025_paper.pdf", "content": "We use the Adam optimizer with a learning rate of 1 × 10−4 without any learning rate sched-uler. For each instance during training, we randomly select either the amodal or modal ground truth bounding box as the prompt with equal probability."}
+{"idx": 5, "title": "Diffusion-based Event Generation for High-Quality Image ...", "date": "", "ddg_snippet": "During training, we set the batch size to 4 and use the ADAM optimizer with an initial learning rate of 1 × 10−4. We implement the algorithm in PyTorch and run it on the NVIDIA RTX 4090 GPU.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Diffusion-based_Event_Generation_for_High-Quality_Image_Deblurring_CVPR_2025_paper.pdf", "content": "During training, we set the batch size to 4 and use the ADAM optimizer with an initial learning rate of 1 × 10−4. We implement the algorithm in PyTorch and run it on the NVIDIA RTX 4090 GPU."}
+{"idx": 6, "title": "SmartEraser: Remove Anything from Images using Masked-Region ...", "date": "", "ddg_snippet": "SmartEraser is trained on the proposed Syn4Removal dataset with a batch size of 32, using the AdamW [29] opti-mizer for 500k iterations and a learning rate of 2e-5 across (8) all trainable modules.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_SmartEraser_Remove_Anything_from_Images_using_Masked-Region_Guidance_CVPR_2025_paper.pdf", "content": "SmartEraser is trained on the proposed Syn4Removal dataset with a batch size of 32, using the AdamW [29] opti-mizer for 500k iterations and a learning rate of 2e-5 across (8) all trainable modules."}
diff --git a/data/sampled_jsons/siteopenreview.net_0hrkN07DuO_conclusion_Luo_Tseng.jsonl b/data/sampled_jsons/siteopenreview.net_0hrkN07DuO_conclusion_Luo_Tseng.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hongchen Luo - OpenReview", "date": "", "ddg_snippet": "Grounded Affordance from Exocentric View Hongchen Luo , Wei Zhai, Jing Zhang, Yang Cao, Dacheng Tao 2022 (modified: 16 Nov 2022) CoRR 2022", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Hongchen_Luo1", "content": "Grounded Affordance from Exocentric View Hongchen Luo , Wei Zhai, Jing Zhang, Yang Cao, Dacheng Tao 2022 (modified: 16 Nov 2022) CoRR 2022"}
+{"idx": 1, "title": "Dongsheng Luo - OpenReview", "date": "", "ddg_snippet": "Dongsheng Luo , Raju Rangaswami, Amir Rahmati, Erez Zadok Published: 24 Feb 2025, Last Modified: 24 Feb 2025 MARW at AAAI 2025 Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach Guo-Ming Li, Jian Yang, Shangsong Liang, Dongsheng Luo", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Dongsheng_Luo1", "content": "Dongsheng Luo , Raju Rangaswami, Amir Rahmati, Erez Zadok Published: 24 Feb 2025, Last Modified: 24 Feb 2025 MARW at AAAI 2025 Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach Guo-Ming Li, Jian Yang, Shangsong Liang, Dongsheng Luo"}
+{"idx": 2, "title": "A family of inexact SQA methods for non-smooth convex... | OpenReview", "date": "", "ddg_snippet": "However, to the best of our knowledge, this work is the first to use the Luo – Tseng EB property to establish the superlinear convergence of SQA-type methods for non-smooth convex minimization.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Q9aoaRYa3U", "content": "However, to the best of our knowledge, this work is the first to use the Luo – Tseng EB property to establish the superlinear convergence of SQA-type methods for non-smooth convex minimization."}
+{"idx": 3, "title": "Linear Convergence of Sinkhorn’s Algorithm for Generalized ...", "date": "", "ddg_snippet": "( Luo & Tseng , 1992) of the form f (x) := g(Ex)+〈b,x〉 s.t. x ∈ X, min x where g is strictly convex to achieve non-asymptotic linear convergence for alternating minimization and even coor-dinate descent methods under milder assumptions than al-ready known.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "( Luo & Tseng , 1992) of the form f (x) := g(Ex)+〈b,x〉 s.t. x ∈ X, min x where g is strictly convex to achieve non-asymptotic linear convergence for alternating minimization and even coor-dinate descent methods under milder assumptions than al-ready known."}
+{"idx": 4, "title": "A Convergent Single-Loop Algorithm for Relaxation of...", "date": "", "ddg_snippet": "Feb 1, 2023 · The Luo - Tseng error bound is utilized to establish the distance between the fixed point of the algorithm and the critical set of the original GW problem. A few minor things in terms of technicality: (1) the algorithm itself is not stated with splitting, e.g., a single variable π for the update; but the analysis uses two variables.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0jxPyVWmiiF", "content": "Feb 1, 2023 · The Luo - Tseng error bound is utilized to establish the distance between the fixed point of the algorithm and the critical set of the original GW problem. A few minor things in terms of technicality: (1) the algorithm itself is not stated with splitting, e.g., a single variable π for the update; but the analysis uses two variables."}
+{"idx": 5, "title": "Coordinate Descent Methods for Fractional Minimization", "date": "", "ddg_snippet": "( Luo - Tseng Error Bound ( Luo & Tseng , 1993; Tseng & Yun, 2009)) We define a residual function as.step (d) uses the conclusion in (41); step (e) uses the definition of F (x). Part (b). We now prove that any critical point x¯ is also the global optimal solution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=odCqtXjSgB", "content": "( Luo - Tseng Error Bound ( Luo & Tseng , 1993; Tseng & Yun, 2009)) We define a residual function as.step (d) uses the conclusion in (41); step (e) uses the definition of F (x). Part (b). We now prove that any critical point x¯ is also the global optimal solution."}
+{"idx": 6, "title": "Published as a conference paper at ICLR 2023", "date": "", "ddg_snippet": "As the GW problem is a nonconvex quadratic program with polytope constraint, we can invoke Theorem 2.3 in ( Luo & Tseng , 1992) to conclude that the error bound condition (6) holds on the whole feasible set C1 ∩ C2.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0jxPyVWmiiF", "content": "As the GW problem is a nonconvex quadratic program with polytope constraint, we can invoke Theorem 2.3 in ( Luo & Tseng , 1992) to conclude that the error bound condition (6) holds on the whole feasible set C1 ∩ C2."}
+{"idx": 7, "title": "Riemannian Coordinate Descent Algorithms on Matrix Manifolds", "date": "", "ddg_snippet": "Li, J. and Ma, S. Federated learning on Riemannian mani-folds. Technical report, arXiv preprint arXiv:2206.05668, 2022. Luo , Z.-Q. and Tseng , P. On the convergence of the coordi-nate descent method for convex differentiable minimiza-tion.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=bdKaQmrM81", "content": "Li, J. and Ma, S. Federated learning on Riemannian mani-folds. Technical report, arXiv preprint arXiv:2206.05668, 2022. Luo , Z.-Q. and Tseng , P. On the convergence of the coordi-nate descent method for convex differentiable minimiza-tion."}
+{"idx": 8, "title": "Optimal Extragradient-Based Algorithms for", "date": "", "ddg_snippet": "4 Conclusions . We have presented a stochastic extragradient-based acceleration algorithm, AG-EG, for solving stochastic monotone variational inequalities with separable structure.Paul Tseng . On linear convergence of iterative methods for the variational inequality problem.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=Z28nPtAVxx&name=supplementary_material", "content": "4 Conclusions . We have presented a stochastic extragradient-based acceleration algorithm, AG-EG, for solving stochastic monotone variational inequalities with separable structure.Paul Tseng . On linear convergence of iterative methods for the variational inequality problem."}
+{"idx": 9, "title": "L EARN", "date": "", "ddg_snippet": "Zhi-Quan Luo and Paul Tseng . Error bounds and convergence analysis of feasible descent methods: a general approach. Annals of Operations Research, 46(1):157–178, 1993.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lZNb1CVm5O", "content": "Zhi-Quan Luo and Paul Tseng . Error bounds and convergence analysis of feasible descent methods: a general approach. Annals of Operations Research, 46(1):157–178, 1993."}
diff --git a/data/sampled_jsons/siteopenreview.net_1IyPRv1A0r_A_Figure_2_alpha_noise_variance.jsonl b/data/sampled_jsons/siteopenreview.net_1IyPRv1A0r_A_Figure_2_alpha_noise_variance.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improve Certified Training with Signal-to- Noise Ratio Loss", "date": "", "ddg_snippet": "Figure 2 : Neuron variance and stability in ReLU activation. zˆ denotes the input range of the ReLU, z denotes the output range of the ReLU.We consider the second case. √ Ground truth The ground truth Y -radius is 2 r.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=iV0jktFZ5Y&name=pdf", "content": "Figure 2 : Neuron variance and stability in ReLU activation. zˆ denotes the input range of the ReLU, z denotes the output range of the ReLU.We consider the second case. √ Ground truth The ground truth Y -radius is 2 r."}
+{"idx": 1, "title": "Generalization through variance: how noise shapes inductive biases in ...", "date": "", "ddg_snippet": "In this paper, we develop a mathematical theory that partly explains this 'generalization through variance' phenomenon. Our theoretical analysis exploits a physics-inspired path integral approach to compute the distributions typically learned by a few paradigmatic under- and overparameterized diffusion models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7lUdo8Vuqa", "content": "In this paper, we develop a mathematical theory that partly explains this 'generalization through variance' phenomenon. Our theoretical analysis exploits a physics-inspired path integral approach to compute the distributions typically learned by a few paradigmatic under- and overparameterized diffusion models."}
+{"idx": 2, "title": "RETHINKING THE NOISE SCHEDULE OF DIFFUSION BASED ... - OpenReview", "date": "", "ddg_snippet": "We first analyze and quantify the noise level in data during the forward process of diffusion models. As illustrated in Fig. 2 , a notable variance in noise levels across images of different resolutions when subjected to Gaussian noise with identical standard deviations. Specifically, high-resolution images display lower noise levels, while lower-resolution images exhibit higher noise levels ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ylHLVq0psd", "content": "We first analyze and quantify the noise level in data during the forward process of diffusion models. As illustrated in Fig. 2 , a notable variance in noise levels across images of different resolutions when subjected to Gaussian noise with identical standard deviations. Specifically, high-resolution images display lower noise levels, while lower-resolution images exhibit higher noise levels ..."}
+{"idx": 3, "title": "Convergence Rates of Stochastic Gradient Descent under Infinite Noise ...", "date": "", "ddg_snippet": "We prove L_p convergence rates (p<2) for SGD under infinite noise variance , and establish the alpha -stable limit for Polyak-Ruppert averaging.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=yxHPRAqCqn", "content": "We prove L_p convergence rates (p<2) for SGD under infinite noise variance , and establish the alpha -stable limit for Polyak-Ruppert averaging."}
+{"idx": 4, "title": "Efficient Truncated Linear Regression with Unknown Noise Variance", "date": "", "ddg_snippet": "In this paper, we provide the first computationally and statistically efficient estimators for truncated linear regression when the noise variance is unknown, estimating both the linear model and the variance of the noise .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oyHWvdvkZDv", "content": "In this paper, we provide the first computationally and statistically efficient estimators for truncated linear regression when the noise variance is unknown, estimating both the linear model and the variance of the noise ."}
+{"idx": 5, "title": "Differentially Pivate Per-Instance Additive Noise Mechanism: A Game ...", "date": "", "ddg_snippet": "This intricate interdependency complicates the problem, making it resistant to straightforward solutions. To address this challenge, we propose a per-instance noise variance optimization (NVO) game, framed as a common interest sequential game. We show that the Nash equilibrium (NE) points of this game inherently guarantee DP.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ESt7ECoWpn", "content": "This intricate interdependency complicates the problem, making it resistant to straightforward solutions. To address this challenge, we propose a per-instance noise variance optimization (NVO) game, framed as a common interest sequential game. We show that the Nash equilibrium (NE) points of this game inherently guarantee DP."}
+{"idx": 6, "title": "Nuri Mert Vural - OpenReview", "date": "", "ddg_snippet": "Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A Erdogdu 2022 (modified: 30 Mar 2023) COLT 2022 An Efficient and Effective Second-Order Training Algorithm for LSTM-Based Adaptive Learning", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Nuri_Mert_Vural1", "content": "Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A Erdogdu 2022 (modified: 30 Mar 2023) COLT 2022 An Efficient and Effective Second-Order Training Algorithm for LSTM-Based Adaptive Learning"}
+{"idx": 7, "title": "Reducing Noise in GAN Training with Variance Reduced Extragradient", "date": "", "ddg_snippet": "We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimization methods, while the batch version converges. We address this issue with a novel stochastic variance -reduced extragradient (SVRE) optimization algorithm that improves upon the best convergence rates proposed in ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Byxk4NHg8r", "content": "We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimization methods, while the batch version converges. We address this issue with a novel stochastic variance -reduced extragradient (SVRE) optimization algorithm that improves upon the best convergence rates proposed in ..."}
+{"idx": 8, "title": "A VARIANCE PRINCIPLE EXPLAINS WHY DROPOUT FINDS FLATTER MINIMA - OpenReview", "date": "", "ddg_snippet": "Variance Principle: the variance of a noise is larger at the sharper direction of the loss landscape. latter minima and leads the training to better generalization. As shown in Zhu et al. (2018); Feng & Tu (2021), the noise in SGD satisfies the variance principle and SGD can find flatter minima and obtain bette", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Ctjb37IOldV", "content": "Variance Principle: the variance of a noise is larger at the sharper direction of the loss landscape. latter minima and leads the training to better generalization. As shown in Zhu et al. (2018); Feng & Tu (2021), the noise in SGD satisfies the variance principle and SGD can find flatter minima and obtain bette"}
+{"idx": 9, "title": "IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat ...", "date": "", "ddg_snippet": "Second, we analyse that MAE's noise -robustness is from emphasising on uncertain examples instead of treating training samples equally, as claimed in prior work. ( 2 ) The Variance of Gradient Magnitude Matters. We propose an effective and simple solution to enhance MAE's fitting ability while preserving its noise -robustness.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oK44liEinV", "content": "Second, we analyse that MAE's noise -robustness is from emphasising on uncertain examples instead of treating training samples equally, as claimed in prior work. ( 2 ) The Variance of Gradient Magnitude Matters. We propose an effective and simple solution to enhance MAE's fitting ability while preserving its noise -robustness."}
diff --git a/data/sampled_jsons/siteopenreview.net_27tMzmzDjO_A_Geometric_Approach_Personalized_Recommendation.jsonl b/data/sampled_jsons/siteopenreview.net_27tMzmzDjO_A_Geometric_Approach_Personalized_Recommendation.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hyperbolic Embeddings in Sequential Self-Attention for... | OpenReview", "date": "", "ddg_snippet": "However, Euclidean geometry utilized in these models may not be optimal for capturing a complex structure of the behavioral data. Building on recent advances in the application of hyperbolic geometry to collaborative filtering tasks, we propose a novel approach that leverages hyperbolic...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0TZs6WOs16", "content": "However, Euclidean geometry utilized in these models may not be optimal for capturing a complex structure of the behavioral data. Building on recent advances in the application of hyperbolic geometry to collaborative filtering tasks, we propose a novel approach that leverages hyperbolic..."}
+{"idx": 1, "title": "A GEOMETRIC APPROACH TO PERSONALIZED RECOMMENDATION WITH SET ...", "date": "", "ddg_snippet": "1 INTRODUCTION 033 032 Recommendation systems are a standard component of most online platforms, providing personalized 034 suggestions for products, movies, articles, and more. In addition to generic recommendation , these 035 platforms often present the option for the user to search for items, either via natural language or 036 structured queries. While collaborative filtering methods like ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0HWAbWgI3T", "content": "1 INTRODUCTION 033 032 Recommendation systems are a standard component of most online platforms, providing personalized 034 suggestions for products, movies, articles, and more. In addition to generic recommendation , these 035 platforms often present the option for the user to search for items, either via natural language or 036 structured queries. While collaborative filtering methods like ..."}
+{"idx": 2, "title": "A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "May 1, 2025 · This paper proposes a geometric approach to personalized item recommendation using box embeddings. Unlike traditional vector-based methods that struggle with set-theoretic operations, the authors introduce axis-aligned hyperrectangles (boxes) to model users, items, and attributes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=27tMzmzDjO", "content": "May 1, 2025 · This paper proposes a geometric approach to personalized item recommendation using box embeddings. Unlike traditional vector-based methods that struggle with set-theoretic operations, the authors introduce axis-aligned hyperrectangles (boxes) to model users, items, and attributes."}
+{"idx": 3, "title": "A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "Sep 23, 2024 · In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To capture this set-theoretic dependence we represent each user and attribute by a hyperrectangle or box (i.e. a Cartesian product of intervals).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0HWAbWgI3T", "content": "Sep 23, 2024 · In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To capture this set-theoretic dependence we represent each user and attribute by a hyperrectangle or box (i.e. a Cartesian product of intervals)."}
+{"idx": 4, "title": "Spectral and Geometric Spaces Representation Regularization ...", "date": "", "ddg_snippet": "Dec 31, 2023 · Abstract: Recent works demonstrate the effectiveness of multi-modal information for sequential recommendation . However, the computational cost and representation degeneration fail to be focused specifically and addressed adequately in multi-modality recommendation . To this end, we first identify and formalize three properties i.e., diversity, compactness, and consistency from the geometric ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Iq0XZDgwC", "content": "Dec 31, 2023 · Abstract: Recent works demonstrate the effectiveness of multi-modal information for sequential recommendation . However, the computational cost and representation degeneration fail to be focused specifically and addressed adequately in multi-modality recommendation . To this end, we first identify and formalize three properties i.e., diversity, compactness, and consistency from the geometric ..."}
+{"idx": 5, "title": "LLM-Rec: Personalized Recommendation via Prompting Large ...", "date": "", "ddg_snippet": "Sep 20, 2023 · Notably, the recommendation -driven and engagement-guided prompting strategies exhibit the capability to tap into the language model's comprehension of both general and personalized item characteristics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=RadQVWAucN", "content": "Sep 20, 2023 · Notably, the recommendation -driven and engagement-guided prompting strategies exhibit the capability to tap into the language model's comprehension of both general and personalized item characteristics."}
+{"idx": 6, "title": "A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=27tMzmzDjO&referrer=[the+profile+of+Andrew+McCallum](/profile?id=~Andrew_McCallum1)", "content": "Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization."}
+{"idx": 7, "title": "A Two-stage Ranking Framework for Multilingual Recommendation ...", "date": "", "ddg_snippet": "In the KDD Cup 2023 multilingual recommendation challenge, we proposed a ranking framework, which consists of two main stage: recall and ranking. In the recall stage, we use a carefully-designed co-occurrence matrix for single-hop and multi-hop recall of candidate items.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0XrovX52cO", "content": "In the KDD Cup 2023 multilingual recommendation challenge, we proposed a ranking framework, which consists of two main stage: recall and ranking. In the recall stage, we use a carefully-designed co-occurrence matrix for single-hop and multi-hop recall of candidate items."}
+{"idx": 8, "title": "LaGeM: A Large Geometry Model for... | OpenReview", "date": "", "ddg_snippet": "Each level of the autoencoder controls different geometric levels of detail. We show that the model can be used to represent a wide range of 3D models while faithfully representing high-resolution geometry details.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=72OSO38a2z", "content": "Each level of the autoencoder controls different geometric levels of detail. We show that the model can be used to represent a wide range of 3D models while faithfully representing high-resolution geometry details."}
+{"idx": 9, "title": "Towards Unified Multi-Modal Personalization: Large Vision ...", "date": "", "ddg_snippet": "Jan 16, 2024 · In light of this, we develop a generic and extensible personalization generative framework, that can handle a wide range of personalized needs including item recommendation , product search, preference prediction, explanation generation, and further user-guided image generation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=khAE1sTMdX", "content": "Jan 16, 2024 · In light of this, we develop a generic and extensible personalization generative framework, that can handle a wide range of personalized needs including item recommendation , product search, preference prediction, explanation generation, and further user-guided image generation."}
diff --git a/data/sampled_jsons/siteopenreview.net_51x0dfsD8A_time_complexity_Algorithm_2_k-HOC.jsonl b/data/sampled_jsons/siteopenreview.net_51x0dfsD8A_time_complexity_Algorithm_2_k-HOC.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithm 1 TPPE Embedding Method of Insertion", "date": "", "ddg_snippet": "According to Alg. 1, we reduce the query time complexity from O (kn) of Insertion to O (1) by using the TPPE method. At the same time , we can infer that the time complexity of other attack modes also becomes O (1).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=ir6WWkFR80&name=supplementary_material", "content": "According to Alg. 1, we reduce the query time complexity from O (kn) of Insertion to O (1) by using the TPPE method. At the same time , we can infer that the time complexity of other attack modes also becomes O (1)."}
+{"idx": 1, "title": "A Spectral Framework for Tracking Communities in Evolving Networks", "date": "", "ddg_snippet": "Algorithm 8 Dynamic Multiview Stochastic Block Model Input: Number S of views, T time points, pin, pout, pswitch 1: At time i = 1: 2 : Partition node set (up to remainder) into k equally-sized planted communities.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=es9LIeVa9s&name=pdf", "content": "Algorithm 8 Dynamic Multiview Stochastic Block Model Input: Number S of views, T time points, pin, pout, pswitch 1: At time i = 1: 2 : Partition node set (up to remainder) into k equally-sized planted communities."}
+{"idx": 2, "title": "Divide and Conquer Dynamic Programming: An Almost Linear Time ...", "date": "", "ddg_snippet": "right plot of the figure demonstrates that the time complex -ity of DCDP is quadratic in Q, which is in line with the complexity analysis presented in Section 2 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=EqHTMU4YbA", "content": "right plot of the figure demonstrates that the time complex -ity of DCDP is quadratic in Q, which is in line with the complexity analysis presented in Section 2 ."}
+{"idx": 3, "title": "Algorithm Details", "date": "", "ddg_snippet": "The Multiple Gradient Descent Algorithm (MGDA) explicitly optimizes towards a Pareto-optimal point for multiple objectives (See the definition 3.1).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=_61Qh8tULj_&name=supplementary_material", "content": "The Multiple Gradient Descent Algorithm (MGDA) explicitly optimizes towards a Pareto-optimal point for multiple objectives (See the definition 3.1)."}
+{"idx": 4, "title": "Table 1: Comparison of BP-free algorithms in an online learning", "date": "", "ddg_snippet": "AsyncFGD k =1 k = 2 k =3 k =4. Model.00. BP FGD AsyncFGD SubLinear 20 40 L6a0yers80 100 120. (b) Channel =256. Figure 1: Comparison of memory consumption for convolutional layers across different algorithms .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=ccaXTSPlr2&name=pdf", "content": "AsyncFGD k =1 k = 2 k =3 k =4. Model.00. BP FGD AsyncFGD SubLinear 20 40 L6a0yers80 100 120. (b) Channel =256. Figure 1: Comparison of memory consumption for convolutional layers across different algorithms ."}
+{"idx": 5, "title": "Complexity Analysis of 2D KD-Tree Construction, Query and Modification ...", "date": "", "ddg_snippet": "I. INTRODUCTION KD-Tree is a data structure that organizes points in K-dimensional Euclidean space. It supports a variety of query algorithms such as nearest neighbor search and range search, which has good performance. [1] It plays an important role in machine learning classification and retrieval of special databases. In this paper, the master theorem and potential energy analysis are used ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=OsqSXydBlD", "content": "I. INTRODUCTION KD-Tree is a data structure that organizes points in K-dimensional Euclidean space. It supports a variety of query algorithms such as nearest neighbor search and range search, which has good performance. [1] It plays an important role in machine learning classification and retrieval of special databases. In this paper, the master theorem and potential energy analysis are used ..."}
+{"idx": 6, "title": "A Two-Timescale Stochastic Algorithm Framework for Bilevel...", "date": "", "ddg_snippet": "A Two-Timescale Stochastic Algorithm Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=g5MHbT6EEm", "content": "A Two-Timescale Stochastic Algorithm Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic"}
+{"idx": 7, "title": "New Algorithms for the Learning-Augmented k-means Problem", "date": "", "ddg_snippet": "The current state-of-the-art learning-augmented k-means algorithm relies on sorting strategies to find good coordinates approximation, where a (1+O (α))-approximation can be achieved with near-linear running time in the data size.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Xuyp1dGAbi", "content": "The current state-of-the-art learning-augmented k-means algorithm relies on sorting strategies to find good coordinates approximation, where a (1+O (α))-approximation can be achieved with near-linear running time in the data size."}
+{"idx": 8, "title": "Near-Linear Time Approximation Algorithms for k-means with Outliers", "date": "", "ddg_snippet": "2 +dpoly(k; 1) log(n )) with (1 + )z outliers discarded and exactly k centers opened. Empirical experiments suggest that our proposed sampling-based algorithms outperform state-of-the-art algorithms for the k-means with outliers problem.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=EHjm3sXPFy", "content": "2 +dpoly(k; 1) log(n )) with (1 + )z outliers discarded and exactly k centers opened. Empirical experiments suggest that our proposed sampling-based algorithms outperform state-of-the-art algorithms for the k-means with outliers problem."}
+{"idx": 9, "title": "EKM: An Exact, Polynomial-Time Divide-and-Conquer Algorithm for the K ...", "date": "", "ddg_snippet": "The paper introduces EKM, a recursive, divide-and-conquer algorithm for solving the k -medoids problem exactly, guaranteeing globally optimal solutions with a worst-case time complexity of O (N k + 1) for a dataset of size N. Although the paper states practical performance, all reviewer feedback expressed concerns about the scale of the experiments and the novelty of the theoretical guarantees ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FgttKcRbQO", "content": "The paper introduces EKM, a recursive, divide-and-conquer algorithm for solving the k -medoids problem exactly, guaranteeing globally optimal solutions with a worst-case time complexity of O (N k + 1) for a dataset of size N. Although the paper states practical performance, all reviewer feedback expressed concerns about the scale of the experiments and the novelty of the theoretical guarantees ..."}
diff --git a/data/sampled_jsons/siteopenreview.net_9m87e9Keq1_Zhang_et_al_scaling_law.jsonl b/data/sampled_jsons/siteopenreview.net_9m87e9Keq1_Zhang_et_al_scaling_law.jsonl
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index 0000000000000000000000000000000000000000..4a8122a89e0eb93180b96d4077afd82c61a1be63
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Wukong: Towards a Scaling Law for Large-Scale Recommendation", "date": "", "ddg_snippet": "This scalability is encompassed in what is known as a \" scaling law \" (Kaplan et al ., 2020). To date, the primary trend of DLRS up- scaling is through sparse scaling , i.e., expanding the sizes of embedding tables (more rows and/or higher dimensions) for less collision and better expressiveness.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=8iUgr2nuwo", "content": "This scalability is encompassed in what is known as a \" scaling law \" (Kaplan et al ., 2020). To date, the primary trend of DLRS up- scaling is through sparse scaling , i.e., expanding the sizes of embedding tables (more rows and/or higher dimensions) for less collision and better expressiveness."}
+{"idx": 1, "title": "Scaling Law of Large Sequential Recommendation Models", "date": "", "ddg_snippet": "Dec 31, 2023 · Scaling of neural networks has recently shown great potential to improve the model capacity in various fields. Specifically, model performance has a power- law relationship with model size or data size, which provides important guidance for the development of large- scale models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=c7Ejjpgt1u", "content": "Dec 31, 2023 · Scaling of neural networks has recently shown great potential to improve the model capacity in various fields. Specifically, model performance has a power- law relationship with model size or data size, which provides important guidance for the development of large- scale models."}
+{"idx": 2, "title": "Wukong: Towards a Scaling Law for Large-Scale Recommendation", "date": "", "ddg_snippet": "Dec 31, 2023 · Further, we assessed Wukong's scalability on an internal, large- scale dataset. The results show that Wukong retains its superiority in quality over state-of-the-art models, while holding the scaling law across two orders of magnitude in model complexity, extending beyond 100 GFLOP/example, where prior arts fall short.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Hv09FyomIW", "content": "Dec 31, 2023 · Further, we assessed Wukong's scalability on an internal, large- scale dataset. The results show that Wukong retains its superiority in quality over state-of-the-art models, while holding the scaling law across two orders of magnitude in model complexity, extending beyond 100 GFLOP/example, where prior arts fall short."}
+{"idx": 3, "title": "A HITCHHIKER S GUIDE TO SCALING LAW ESTIMATION - OpenReview", "date": "", "ddg_snippet": "2 same architecture and training distribution. A high-quality scaling law accurately predicts the target 044 043 model’s test performance (Rosenfeld et al .; Kaplan et al ., 2020; Hoffmann et al ., 2022). 045 Most past work describing and characterizing scaling laws has begun by exhaustively training 046 models in a family across a fu", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=xGM5shdGJD", "content": "2 same architecture and training distribution. A high-quality scaling law accurately predicts the target 044 043 model’s test performance (Rosenfeld et al .; Kaplan et al ., 2020; Hoffmann et al ., 2022). 045 Most past work describing and characterizing scaling laws has begun by exhaustively training 046 models in a family across a fu"}
+{"idx": 4, "title": "A Hitchhiker's Guide to Scaling Law Estimation | OpenReview", "date": "", "ddg_snippet": "Sep 24, 2024 · Scaling laws predict the loss of a target machine learning model by extrapolating from easier-to-train models with fewer parameters or smaller training sets. This provides an efficient way for practitioners and researchers alike to compare pretraining decisions involving, e.g., optimizers, datasets, and model architectures.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=xGM5shdGJD", "content": "Sep 24, 2024 · Scaling laws predict the loss of a target machine learning model by extrapolating from easier-to-train models with fewer parameters or smaller training sets. This provides an efficient way for practitioners and researchers alike to compare pretraining decisions involving, e.g., optimizers, datasets, and model architectures."}
+{"idx": 5, "title": "From Scaling Law to Sub-Scaling Law: Understanding the ...", "date": "", "ddg_snippet": "Sep 27, 2024 · High data density leads to diminishing marginal gains in performance, while optimal resource allocation is crucial for sustaining performance improvements. Further, we propose a sub-optimal scaling law that generalizes the Chinchilla scaling law to better predict performance and loss in sub- scaling regimes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LJ1zlaGdPm", "content": "Sep 27, 2024 · High data density leads to diminishing marginal gains in performance, while optimal resource allocation is crucial for sustaining performance improvements. Further, we propose a sub-optimal scaling law that generalizes the Chinchilla scaling law to better predict performance and loss in sub- scaling regimes."}
+{"idx": 6, "title": "Scaling Law for Document Neural Machine Translation", "date": "", "ddg_snippet": "Oct 7, 2023 · The scaling laws of language models have played a significant role in advancing large language models. In order to promote the development of document translation, we systematically examine the scaling laws in this field. In this paper, we carry out an in-depth analysis of the influence of three factors on translation quality: model scale , data scale , and sequence length. Our findings reveal ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=UEx5dZqXvr", "content": "Oct 7, 2023 · The scaling laws of language models have played a significant role in advancing large language models. In order to promote the development of document translation, we systematically examine the scaling laws in this field. In this paper, we carry out an in-depth analysis of the influence of three factors on translation quality: model scale , data scale , and sequence length. Our findings reveal ..."}
+{"idx": 7, "title": "W hen s caling M eets LLM f inetuning", "date": "", "ddg_snippet": "et al ., 2023; Gao et al ., 2023), larger scale netuning data still contributes to improved downstream. performance, especially when the downstream application is well dened. Additive or multiplicative joint scaling law for LLM netuning?", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5HCnKDeTws", "content": "et al ., 2023; Gao et al ., 2023), larger scale netuning data still contributes to improved downstream. performance, especially when the downstream application is well dened. Additive or multiplicative joint scaling law for LLM netuning?"}
+{"idx": 8, "title": "Inference Scaling Laws", "date": "", "ddg_snippet": "Inference Scaling Laws : An Empirical Analysis of Compute-Optimal. Inference for LLM Problem-Solving.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=j7DZWSc8qu&name=pdf", "content": "Inference Scaling Laws : An Empirical Analysis of Compute-Optimal. Inference for LLM Problem-Solving."}
+{"idx": 9, "title": "Transcending Scaling Laws with 0.1% Extra Compute", "date": "", "ddg_snippet": "Scaling laws and interpretability of learning from repeated data. arXiv preprint arXiv:2205.10487, 2022. Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tZXaHWfsXB", "content": "Scaling laws and interpretability of learning from repeated data. arXiv preprint arXiv:2205.10487, 2022. Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al ."}
diff --git a/data/sampled_jsons/siteopenreview.net_KijslFbfOL_Algorithm_2_termination_condition_convergence.jsonl b/data/sampled_jsons/siteopenreview.net_KijslFbfOL_Algorithm_2_termination_condition_convergence.jsonl
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index 0000000000000000000000000000000000000000..1ca2b5b45cecb36513d32b0f5e99fe42975802ee
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unified Convergence Theory of Stochastic and Variance-Reduced Cubic ...", "date": "", "ddg_snippet": "Abstract: We study stochastic Cubic Newton methods for solving general, possibly non-convex minimization problems. We propose a new framework, the helper framework, that provides a unified view of the stochastic and variance-reduced second-order algorithms equipped with global complexity guarantees; it can also be applied to learning with auxiliary information. Our helper framework offers the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FCs5czlDTr", "content": "Abstract: We study stochastic Cubic Newton methods for solving general, possibly non-convex minimization problems. We propose a new framework, the helper framework, that provides a unified view of the stochastic and variance-reduced second-order algorithms equipped with global complexity guarantees; it can also be applied to learning with auxiliary information. Our helper framework offers the ..."}
+{"idx": 1, "title": "Improving Convergence Guarantees of Ran-dom Subspace Second-order ...", "date": "", "ddg_snippet": "convergence to an ε-approximate first-order stationary point with a global convergence rate of O(ε− 3/2), giving an analysis of the total computational complexity and confirming that the total computational complexity, as well as space complexity, are improved over the existing algorithm (Zhang et al., 2022) in full space,", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tuu4de7HL1", "content": "convergence to an ε-approximate first-order stationary point with a global convergence rate of O(ε− 3/2), giving an analysis of the total computational complexity and confirming that the total computational complexity, as well as space complexity, are improved over the existing algorithm (Zhang et al., 2022) in full space,"}
+{"idx": 2, "title": "On Stationary Point Convergence of PPO-Clip - OpenReview", "date": "", "ddg_snippet": "In this work, we provide a comprehensive analysis that shows the stationary point convergence of PPO-Clip and the convergence rate thereof. Our analysis is new and overcomes many challenges, including the non-smooth nature of the clip operator, the potentially unbounded score function, and the involvement of the ratio of two stochastic policies.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uznKlCpWjV", "content": "In this work, we provide a comprehensive analysis that shows the stationary point convergence of PPO-Clip and the convergence rate thereof. Our analysis is new and overcomes many challenges, including the non-smooth nature of the clip operator, the potentially unbounded score function, and the involvement of the ratio of two stochastic policies."}
+{"idx": 3, "title": "On the Convergence of A Class of Adam-Type Algorithms for...", "date": "", "ddg_snippet": "We analyze convergence of Adam-type algorithms and provide mild sufficient conditions to guarantee their convergence , we also show violating the conditions can makes an algorithm diverge.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=H1x-x309tm", "content": "We analyze convergence of Adam-type algorithms and provide mild sufficient conditions to guarantee their convergence , we also show violating the conditions can makes an algorithm diverge."}
+{"idx": 4, "title": "Reinforcement Learning with a Terminator | OpenReview", "date": "", "ddg_snippet": "We use these to construct a provably-efficient algorithm , which accounts for termination , and bound its regret. Motivated by our theoretical analysis, we design and implement a scalable approach, which combines optimism (w.r.t. termination ) and a dynamic discount factor, incorporating the termination probability.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=bIlUqzwObX", "content": "We use these to construct a provably-efficient algorithm , which accounts for termination , and bound its regret. Motivated by our theoretical analysis, we design and implement a scalable approach, which combines optimism (w.r.t. termination ) and a dynamic discount factor, incorporating the termination probability."}
+{"idx": 5, "title": "ON THE CONVERGENCE OF ADAM AND BEYOND - OpenReview", "date": "", "ddg_snippet": "The above results show that with constant 1 and 2 , momentum or regularization via p will not help in convergence of the algorithm to the optimal solution. Note that the condition 1 < 2 is benign and is typically satisfied in the parameter settings used in practice.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ryQu7f-RZ", "content": "The above results show that with constant 1 and 2 , momentum or regularization via p will not help in convergence of the algorithm to the optimal solution. Note that the condition 1 < 2 is benign and is typically satisfied in the parameter settings used in practice."}
+{"idx": 6, "title": "On the Second-Order Convergence of Biased Policy Gradient Algorithms", "date": "", "ddg_snippet": "For an in-depth comparison of sample complexities, see Appendix A.2. Global Convergence . Separate from our line of work, there are several \"global convergence \" results for policy gradi-ent and actor-critic algorithms that ensure convergence to a global optimum for specific policy parametrization or func-tion structure.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=RfsagmV1AG", "content": "For an in-depth comparison of sample complexities, see Appendix A.2. Global Convergence . Separate from our line of work, there are several \"global convergence \" results for policy gradi-ent and actor-critic algorithms that ensure convergence to a global optimum for specific policy parametrization or func-tion structure."}
+{"idx": 7, "title": "Bellman operator convergence enhancements in Reinforcement Learning ...", "date": "", "ddg_snippet": "TL;DR: This paper bridges abstract topological foundations and practical reinforcement learning by leveraging Banach fixed‐point theory and novel Bellman operator formulations to enhance algorithm convergence and performance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FKCxEHl4fl", "content": "TL;DR: This paper bridges abstract topological foundations and practical reinforcement learning by leveraging Banach fixed‐point theory and novel Bellman operator formulations to enhance algorithm convergence and performance."}
+{"idx": 8, "title": "A Tight Convergence Analysis of Inexact Stochastic Proximal Point ...", "date": "", "ddg_snippet": "The \\textbf {i}nexact \\textbf {s}tochastic \\textbf {p}roximal \\textbf {p}oint \\textbf { a}lgorithm (isPPA) is popular for solving stochastic composite optimization problems with many applications in machine learning. While the convergence theory of the (inexact) PPA has been well established, the known convergence guarantees of isPPA require restrictive assumptions. In this paper, we establish ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=n3TkrH7fEr", "content": "The \\textbf {i}nexact \\textbf {s}tochastic \\textbf {p}roximal \\textbf {p}oint \\textbf { a}lgorithm (isPPA) is popular for solving stochastic composite optimization problems with many applications in machine learning. While the convergence theory of the (inexact) PPA has been well established, the known convergence guarantees of isPPA require restrictive assumptions. In this paper, we establish ..."}
+{"idx": 9, "title": "Inexact Alternating Direction Method of Multipliers with Efficient ...", "date": "", "ddg_snippet": "In this paper, we propose a new cross-silo federated learning algorithm with fast convergence guarantee to solve the machine learning models with nonsmooth regularizers. To solve this type of problems, we design an inexact federated alternating direction method of multipliers (ADMM).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MZU09jacLd", "content": "In this paper, we propose a new cross-silo federated learning algorithm with fast convergence guarantee to solve the machine learning models with nonsmooth regularizers. To solve this type of problems, we design an inexact federated alternating direction method of multipliers (ADMM)."}
diff --git a/data/sampled_jsons/siteopenreview.net_Task-Aware_Parameter_Initialization_at_Flexible_Scales_ICML_2025_year_2025.jsonl b/data/sampled_jsons/siteopenreview.net_Task-Aware_Parameter_Initialization_at_Flexible_Scales_ICML_2025_year_2025.jsonl
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index 0000000000000000000000000000000000000000..8e641db0b487c2cf579e2b0416de3d9b2fbd6371
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_Task-Aware_Parameter_Initialization_at_Flexible_Scales_ICML_2025_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learngene Tells You How to Customize: Task-Aware Parameter...", "date": "", "ddg_snippet": "This paper presents Task - Aware Learngene (TAL), designed to initialize large models via parameter prediction. To accomplish this, the authors first employ an encoder-decoder architecture for the TAL model and train it under the supervision of an ancestry model to facilitate knowledge transfer. Subsequently, with the aim of improving the multi- task generalization ability of downstream models ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=IRQ0n961nn", "content": "This paper presents Task - Aware Learngene (TAL), designed to initialize large models via parameter prediction. To accomplish this, the authors first employ an encoder-decoder architecture for the TAL model and train it under the supervision of an ancestry model to facilitate knowledge transfer. Subsequently, with the aim of improving the multi- task generalization ability of downstream models ..."}
+{"idx": 1, "title": "Jiaze Xu - OpenReview", "date": "", "ddg_snippet": "Publications Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Jiaze Xu, Shiyu Xia, Xu Yang, Jiaqi Lv, Xin Geng ICML 2025 poster Learngene Tells You How to Customize: Task-Aware Parameter Prediction at Flexible Scales", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Jiaze_Xu2", "content": "Publications Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Jiaze Xu, Shiyu Xia, Xu Yang, Jiaqi Lv, Xin Geng ICML 2025 poster Learngene Tells You How to Customize: Task-Aware Parameter Prediction at Flexible Scales"}
+{"idx": 2, "title": "openreview.net/profile?id=~Xin_Geng1", "date": "", "ddg_snippet": "ICML 2025 poster.Learngene Tells You How to Customize: Task - Aware Parameter Initialization at Flexible Scales .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Xin_Geng1", "content": "ICML 2025 poster.Learngene Tells You How to Customize: Task - Aware Parameter Initialization at Flexible Scales ."}
+{"idx": 3, "title": "Learngene Tells You How to Customize: Task-Aware Parameter...", "date": "", "ddg_snippet": "Sep 26, 2024 · This paper proposes a new parameter prediction method based on Graph HyperNetworks (GHNs), called Task - Aware Learngene (TAL). The proposed method aims to address the shortcomings of traditional Learngene methods in adapting to flexible scales and task -specific requirements. By incorporating task -specific information and model scale information, TAL predicts the initial parameters for ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gGpuhyjIlS", "content": "Sep 26, 2024 · This paper proposes a new parameter prediction method based on Graph HyperNetworks (GHNs), called Task - Aware Learngene (TAL). The proposed method aims to address the shortcomings of traditional Learngene methods in adapting to flexible scales and task -specific requirements. By incorporating task -specific information and model scale information, TAL predicts the initial parameters for ..."}
+{"idx": 4, "title": "Learngene Tells You How to Customize: Task-Aware Parameter ...", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Jiaze Xu 1 2 Shiyu Xia 1 2 Xu Yang 1 2", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=IRQ0n961nn", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales Jiaze Xu 1 2 Shiyu Xia 1 2 Xu Yang 1 2"}
+{"idx": 5, "title": "LEARNGENE TELLS YOU HOW TO CUSTOMIZE TASK-AWARE PARAMETER ...", "date": "", "ddg_snippet": "003 002 TASK - AWARE PARAMETER PREDICTION AT FLEXIBLE 004 SCALES 005", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=gGpuhyjIlS", "content": "003 002 TASK - AWARE PARAMETER PREDICTION AT FLEXIBLE 004 SCALES 005"}
+{"idx": 6, "title": "ICML 2025 Conference Submissions", "date": "", "ddg_snippet": "ICML 2025 poster; Readers: Everyone. Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales · Jiaze Xu, Shiyu Xia, Xu Yang ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/submissions?page=60&venue=ICML.cc/2025/Conference", "content": "ICML 2025 poster; Readers: Everyone. Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales · Jiaze Xu, Shiyu Xia, Xu Yang ..."}
+{"idx": 7, "title": "Xu Yang", "date": "", "ddg_snippet": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales · Published: 01 May 2025 , Last Modified: 23 Jul 2025 · ICML 2025 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Xu_Yang5", "content": "Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales · Published: 01 May 2025 , Last Modified: 23 Jul 2025 · ICML 2025 ..."}
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new file mode 100644
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+{"idx": 1, "title": "Some Fundamental Aspects about Lipschitz Continuity... | OpenReview", "date": "", "ddg_snippet": "Lipschitz continuity is a crucial functional property of any predictive model, that naturally governs its robustness, generalisation, as well as adversarial vulnerability. Contrary to other works...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5jWsW08zUh", "content": "Lipschitz continuity is a crucial functional property of any predictive model, that naturally governs its robustness, generalisation, as well as adversarial vulnerability. Contrary to other works..."}
+{"idx": 2, "title": "Single-shot General Hyper-parameter Optimization for... | OpenReview", "date": "", "ddg_snippet": "We propose a single-shot hyperparameter optimization scheme for Federated Learning systems with theoretical performance guarantees and strong empirical performance against baselines.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3RhuF8foyPW", "content": "We propose a single-shot hyperparameter optimization scheme for Federated Learning systems with theoretical performance guarantees and strong empirical performance against baselines."}
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+{"idx": 4, "title": "Trainable Weight Averaging: Efficient Training by... | OpenReview", "date": "", "ddg_snippet": "We propose trainable weight averaging (TWA) to optimize historical solutions in DNNs' training to achieve efficiency and better performance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8wbnpOJY-f", "content": "We propose trainable weight averaging (TWA) to optimize historical solutions in DNNs' training to achieve efficiency and better performance."}
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diff --git a/data/sampled_jsons/siteproceedings.neurips.cc_Direct_Preference_Optimization_abstract.jsonl b/data/sampled_jsons/siteproceedings.neurips.cc_Direct_Preference_Optimization_abstract.jsonl
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index 0000000000000000000000000000000000000000..3ab507f0a69d02b294b4d3b734fcae578e54dcae
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+{"idx": 0, "title": "Direct Preference Optimization: Your Language Model is ... - NeurIPS", "date": "", "ddg_snippet": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html", "content": "The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning."}
+{"idx": 1, "title": "$\\beta$-DPO: Direct Preference Optimization with Dynamic $\\beta$", "date": "", "ddg_snippet": "Abstract Direct Preference Optimization (DPO) has emerged as a compelling approach for training Large Language Models (LLMs) to adhere to human preferences. However, the performance of DPO is sensitive to the fine-tuning of its trade-off parameter $\\beta$, as well as to the quality of the preference data.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/ea888178abdb6fc233226d12321d754f-Abstract-Conference.html", "content": "Abstract Direct Preference Optimization (DPO) has emerged as a compelling approach for training Large Language Models (LLMs) to adhere to human preferences. However, the performance of DPO is sensitive to the fine-tuning of its trade-off parameter $\\beta$, as well as to the quality of the preference data."}
+{"idx": 2, "title": "On Softmax Direct Preference Optimization for Recommendation", "date": "", "ddg_snippet": "Abstract Recommender systems aim to predict personalized rankings based on user preference data. With the rise of Language Models (LMs), LM-based recommenders have been widely explored due to their extensive world knowledge and powerful reasoning abilities.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/30732ddb12d9faf7180f5d0e8b5b5da7-Abstract-Conference.html", "content": "Abstract Recommender systems aim to predict personalized rankings based on user preference data. With the rise of Language Models (LMs), LM-based recommenders have been widely explored due to their extensive world knowledge and powerful reasoning abilities."}
+{"idx": 3, "title": "Cal-DPO: Calibrated Direct Preference Optimization for Language Model ...", "date": "", "ddg_snippet": "Authors Teng Xiao, Yige Yuan, Huaisheng Zhu, Mingxiao Li, Vasant G Honavar Abstract We study the problem of aligning large language models (LLMs) with human preference data. Contrastive preference optimization has shown promising results in aligning LLMs with available preference data by optimizing the implicit reward associated with the policy. However, the contrastive objective focuses ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/cf8b2205e39f81726a8d828ecbe00ad0-Abstract-Conference.html", "content": "Authors Teng Xiao, Yige Yuan, Huaisheng Zhu, Mingxiao Li, Vasant G Honavar Abstract We study the problem of aligning large language models (LLMs) with human preference data. Contrastive preference optimization has shown promising results in aligning LLMs with available preference data by optimizing the implicit reward associated with the policy. However, the contrastive objective focuses ..."}
+{"idx": 4, "title": "SimPO: Simple Preference Optimization with a Reference-Free Reward", "date": "", "ddg_snippet": "Abstract Direct Preference Optimization (DPO) is a widely used offline preference optimization algorithm that reparameterizes reward functions in reinforcement learning from human feedback (RLHF) to enhance simplicity and training stability.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/e099c1c9699814af0be873a175361713-Abstract-Conference.html", "content": "Abstract Direct Preference Optimization (DPO) is a widely used offline preference optimization algorithm that reparameterizes reward functions in reinforcement learning from human feedback (RLHF) to enhance simplicity and training stability."}
+{"idx": 5, "title": "Group Robust Preference Optimization in Reward-free RLHF", "date": "", "ddg_snippet": "Our approach builds upon reward-free direct preference optimization methods, but unlike previous approaches, it seeks a robust policy which maximizes the worst-case group performance. To achieve this, GRPO adaptively and sequentially weights the importance of different groups, prioritizing groups with worse cumulative loss.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4147dfaa46cd7e20a2aecb91097ae8cc-Abstract-Conference.html", "content": "Our approach builds upon reward-free direct preference optimization methods, but unlike previous approaches, it seeks a robust policy which maximizes the worst-case group performance. To achieve this, GRPO adaptively and sequentially weights the importance of different groups, prioritizing groups with worse cumulative loss."}
+{"idx": 6, "title": "PDF Direct Preference Optimization: Your Language Model is ... - NeurIPS", "date": "", "ddg_snippet": "Abstract While large-scale unsupervised language models (LMs) learn broad world knowl-edge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/a85b405ed65c6477a4fe8302b5e06ce7-Paper-Conference.pdf", "content": "Abstract While large-scale unsupervised language models (LMs) learn broad world knowl-edge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with ..."}
+{"idx": 7, "title": "Scaling Laws for Reward Model Overoptimization in Direct Alignment ...", "date": "", "ddg_snippet": "Direct Alignment Algorithms (DDAs), such as Direct Preference Optimization (DPO) have emerged as alternatives to the classical RLHF pipeline. However, despite not training a separate proxy reward model or using RL, they still commonly deteriorate from over-optimization.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/e45caa3d5273d105b8d045e748636957-Abstract-Conference.html", "content": "Direct Alignment Algorithms (DDAs), such as Direct Preference Optimization (DPO) have emerged as alternatives to the classical RLHF pipeline. However, despite not training a separate proxy reward model or using RL, they still commonly deteriorate from over-optimization."}
+{"idx": 8, "title": "Book", "date": "", "ddg_snippet": "Chain of Preference Optimization : Improving Chain-of-Thought Reasoning in LLMs Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024", "content": "Chain of Preference Optimization : Improving Chain-of-Thought Reasoning in LLMs Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin"}
+{"idx": 9, "title": "Book", "date": "", "ddg_snippet": "Information-theoretic lower bounds on the oracle complexity of convex optimization Alekh Agarwal, Martin J. ... Nonlinear directed acyclic structure ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2009", "content": "Information-theoretic lower bounds on the oracle complexity of convex optimization Alekh Agarwal, Martin J. ... Nonlinear directed acyclic structure ..."}
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+{"idx": 6, "title": "Haiyan Yin - Google Akademik", "date": "", "ddg_snippet": "L Luo, H Yin, J Zhong, W Cai, M Lees, S Zhou . Proceedings of the AAAI Conference on Artificial Intelligence and …, 2013.2021. Causal Discovery with Flow-based Conditional Density Estimation .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=xy6_gBUAAAAJ&hl=tr", "content": "L Luo, H Yin, J Zhong, W Cai, M Lees, S Zhou . Proceedings of the AAAI Conference on Artificial Intelligence and …, 2013.2021. Causal Discovery with Flow-based Conditional Density Estimation ."}
+{"idx": 7, "title": "Fatima Zora Amara - Академия Google", "date": "", "ddg_snippet": "2019. Household electricity demand forecasting using adaptive conditional density estimation .2017. Estimation of temperature correlation with household electricity demand for forecasting application.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=UIOVbs8AAAAJ&hl=ru", "content": "2019. Household electricity demand forecasting using adaptive conditional density estimation .2017. Estimation of temperature correlation with household electricity demand for forecasting application."}
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diff --git a/data/sampled_jsons/sitesemanticscholar.org_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_Yu_et_al._MAE_3D__year_2024.jsonl b/data/sampled_jsons/sitesemanticscholar.org_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_Yu_et_al._MAE_3D__year_2024.jsonl
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+{"idx": 1, "title": "[PDF] Photometric Stereo via Computer Screen... | Semantic Scholar", "date": "", "ddg_snippet": "Surface enhancement using real - time photometric stereo and reflectance transformation. T. MalzbenderBennett WilburnDan GelbBill Ambrisco.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Photometric-Stereo-via-Computer-Screen-Lighting-for-Schindler/a486954af712ee652078ad86e85be59f50606b33", "content": "Surface enhancement using real - time photometric stereo and reflectance transformation. T. MalzbenderBennett WilburnDan GelbBill Ambrisco."}
+{"idx": 2, "title": "[PDF] Non-rigid Photometric Stereo with Colored... | Semantic Scholar", "date": "", "ddg_snippet": "Real - time non-rigid reconstruction using an RGB-D camera .A new color photometric stereo (CPS) method that recovers high quality, detailed 3 D face geometry in a single shot using three uncalibrated near point lights of different colors and a single camera is presented.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Non-rigid-Photometric-Stereo-with-Colored-Lights-Hernández-Vogiatzis/af669483ecc4d16df9715d1db67a84cad5a97bf5", "content": "Real - time non-rigid reconstruction using an RGB-D camera .A new color photometric stereo (CPS) method that recovers high quality, detailed 3 D face geometry in a single shot using three uncalibrated near point lights of different colors and a single camera is presented."}
+{"idx": 3, "title": "[PDF] Head-Mounted Photometric Stereo for... | Semantic Scholar", "date": "", "ddg_snippet": "Surface enhancement using real - time photometric stereo and reflectance transformation. T. MalzbenderBennett WilburnDan GelbBill Ambrisco.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Head-Mounted-Photometric-Stereo-for-Performance-Jones-Fyffe/04c722c5b3063c883ccc6b1ed3a7f41a7a4e4738", "content": "Surface enhancement using real - time photometric stereo and reflectance transformation. T. MalzbenderBennett WilburnDan GelbBill Ambrisco."}
+{"idx": 4, "title": "Photometric Stereo : A Reflectance Map... | Semantic Scholar", "date": "", "ddg_snippet": "Photometric Stereo : A Reflectance Map Technique For Determining Surface Orientation From Image Intensity.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Photometric-Stereo:-A-Reflectance-Map-Technique-For-Woodham/b7ce42b4f9386880b371065c3b3c58a7d9d9e591", "content": "Photometric Stereo : A Reflectance Map Technique For Determining Surface Orientation From Image Intensity."}
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+{"idx": 6, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Jun 16, 2024 · This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , significantly enhancing data efficiency.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "Jun 16, 2024 · This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , significantly enhancing data efficiency."}
+{"idx": 7, "title": "EventPSR: Surface Normal and Reflectance Estimation from ...", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , significantly enhancing data efficiency.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPSR:-Surface-Normal-and-Reflectance-Estimation-Yu-Han/f4821b60aae1573eaaf427c80e7cfedb7e67369c", "content": "This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , significantly enhancing data efficiency."}
+{"idx": 8, "title": "Recent Event Camera Innovations: A Survey - Semantic Scholar", "date": "", "ddg_snippet": "Aug 24, 2024 · This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , significantly enhancing data efficiency.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Recent-Event-Camera-Innovations:-A-Survey-Chakravarthi-Verma/841a9d21bf8491424c56c1809aa932afefa67958", "content": "Aug 24, 2024 · This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , significantly enhancing data efficiency."}
+{"idx": 9, "title": "High-fidelity Event-Radiance Recovery via Transient Event ...", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , significantly enhancing data efficiency.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/High-fidelity-Event-Radiance-Recovery-via-Transient-Han-Asano/29cd3a2685e379291275780ae14ef55443cc6582/figure/5", "content": "This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras , significantly enhancing data efficiency."}
diff --git a/data/sampled_jsons/social_media_climate_activism_events_extreme_weather_policy_announcements_IPCC_reports.jsonl b/data/sampled_jsons/social_media_climate_activism_events_extreme_weather_policy_announcements_IPCC_reports.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90e147a7dbe789c85de26f09c92c74bbb73c9305
--- /dev/null
+++ b/data/sampled_jsons/social_media_climate_activism_events_extreme_weather_policy_announcements_IPCC_reports.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The IPCC in the hybrid public sphere: divergent responses to ...", "date": "", "ddg_snippet": "Dec 6, 2024 · In April 2022 the Intergovernmental Panel on Climate Change ( IPCC ) published its report on the mitigation of climate change, which included detailed discussion of the wide range of solutions at the personal, societal and governmental level needed to reduce emissions. The report generated extensive societal debate and interest in mainstream and social media . Using manual text analysis, we ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10584-024-03827-x", "content": "Dec 6, 2024 · In April 2022 the Intergovernmental Panel on Climate Change ( IPCC ) published its report on the mitigation of climate change, which included detailed discussion of the wide range of solutions at the personal, societal and governmental level needed to reduce emissions. The report generated extensive societal debate and interest in mainstream and social media . Using manual text analysis, we ..."}
+{"idx": 1, "title": "Social media engagement in people and climate change", "date": "", "ddg_snippet": "Oct 13, 2024 · Scientific and policy reports highlight the human causes and impacts of climate change 1 and argue for ‘people-centred’ approaches to emissions reductions, particularly in high-income ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s44168-024-00167-5", "content": "Oct 13, 2024 · Scientific and policy reports highlight the human causes and impacts of climate change 1 and argue for ‘people-centred’ approaches to emissions reductions, particularly in high-income ..."}
+{"idx": 2, "title": "Social Media Is a Growing Vehicle for Climate Misinformation", "date": "", "ddg_snippet": "Dec 10, 2024 · Today's Climate Social Media Is a Growing Vehicle for Climate Misinformation Research shows that social media influencers can shape climate denialism around the world.", "subpage_snippet": "", "source": "insideclimatenews.org", "link": "https://insideclimatenews.org/news/10122024/todays-climate-climate-misinformation-social-media/", "content": "Dec 10, 2024 · Today's Climate Social Media Is a Growing Vehicle for Climate Misinformation Research shows that social media influencers can shape climate denialism around the world."}
+{"idx": 3, "title": "IPCC Takeover: Extreme Weather Chapter Stacked With ...", "date": "", "ddg_snippet": "Aug 21, 2025 · In part due to the IPCC ’s failure to achieve D&A for most types of extreme events , the notion of EEA was invented to connect specific weather events with changes in climate and was characterized as an effort to get into the media and support climate litigation.", "subpage_snippet": "", "source": "climatechangedispatch.com", "link": "https://climatechangedispatch.com/ipcc-extreme-weather-attribution-activists/", "content": "Aug 21, 2025 · In part due to the IPCC ’s failure to achieve D&A for most types of extreme events , the notion of EEA was invented to connect specific weather events with changes in climate and was characterized as an effort to get into the media and support climate litigation."}
+{"idx": 4, "title": "The Nexus of Social Media and Climate Policy | DISA", "date": "", "ddg_snippet": "Dec 26, 2024 · Digital platforms have played a crucial role in disseminating news and information about these climate events , often using visual content to effectively convey the human cost and environmental devastation. However, the challenges of polarization, misinformation, and algorithmic manipulation remain significant hurdles.", "subpage_snippet": "", "source": "disa.org", "link": "https://disa.org/the-nexus-of-social-media-and-climate-policy/", "content": "Dec 26, 2024 · Digital platforms have played a crucial role in disseminating news and information about these climate events , often using visual content to effectively convey the human cost and environmental devastation. However, the challenges of polarization, misinformation, and algorithmic manipulation remain significant hurdles."}
+{"idx": 5, "title": "Social media in climate change communication: State of the ...", "date": "", "ddg_snippet": "Dec 5, 2024 · The article discusses recent yet under-researched developments regarding social media in climate change communication: from their detrimental impact on climate journalism, the pros and cons of the short-video revolution and the emergence of “dark platforms” over climate -related initiatives by social media and the role of influencers to new ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/29768659241300666", "content": "Dec 5, 2024 · The article discusses recent yet under-researched developments regarding social media in climate change communication: from their detrimental impact on climate journalism, the pros and cons of the short-video revolution and the emergence of “dark platforms” over climate -related initiatives by social media and the role of influencers to new ..."}
+{"idx": 6, "title": "2025 SkS Weekly Climate Change & Global Warming News Roundup...", "date": "", "ddg_snippet": "Climate change is accelerating, scientists find in ‘grim’ report .A listing of 23 news and opinion articles we found interesting and shared on social media during the past week: Sun, September 14, 2025 thru Sat, September 20, 2025.", "subpage_snippet": "", "source": "skepticalscience.com", "link": "https://skepticalscience.com/2025-SkS-Weekly-News-Roundup_38.html", "content": "Climate change is accelerating, scientists find in ‘grim’ report .A listing of 23 news and opinion articles we found interesting and shared on social media during the past week: Sun, September 14, 2025 thru Sat, September 20, 2025."}
+{"idx": 7, "title": "Climate Change 2021: The Physical Science Basis | Climate Change ...", "date": "", "ddg_snippet": "Weather and Climate Extreme Events in a Changing Climate .Annex VI. Climatic Impact-Drivers and Extreme Indices.", "subpage_snippet": "", "source": "www.ipcc.ch", "link": "https://www.ipcc.ch/report/ar6/wg1/", "content": "Weather and Climate Extreme Events in a Changing Climate .Annex VI. Climatic Impact-Drivers and Extreme Indices."}
+{"idx": 8, "title": "Climate Change , Extreme Weather , and Electric System Reliability", "date": "", "ddg_snippet": "The climate model simulations used for the IPCC assessment reports include only scenarios for future emissions; they do not include predictions of natural climate variability (solar output, volcanic eruptions or the evolution of large-scale multi-decadal ocean circulations).", "subpage_snippet": "", "source": "wattsupwiththat.com", "link": "https://wattsupwiththat.com/2021/06/29/climate-change-extreme-weather-and-electric-system-reliability/", "content": "The climate model simulations used for the IPCC assessment reports include only scenarios for future emissions; they do not include predictions of natural climate variability (solar output, volcanic eruptions or the evolution of large-scale multi-decadal ocean circulations)."}
+{"idx": 9, "title": "Extreme Weather - NASA Science", "date": "", "ddg_snippet": "Extreme Weather and Climate Change .According to the Intergovernmental Panel on Climate Change ( IPCC )’s Sixth Assessment Report released in 2021, the human-caused rise in greenhouse gases has increased the frequency and intensity of extreme weather events .", "subpage_snippet": "", "source": "science.nasa.gov", "link": "https://science.nasa.gov/climate-change/extreme-weather/", "content": "Extreme Weather and Climate Change .According to the Intergovernmental Panel on Climate Change ( IPCC )’s Sixth Assessment Report released in 2021, the human-caused rise in greenhouse gases has increased the frequency and intensity of extreme weather events ."}
diff --git a/data/sampled_jsons/social_media_studies_climate_activism_events_types.jsonl b/data/sampled_jsons/social_media_studies_climate_activism_events_types.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3837ad0eddd7e48367dc1248a1194dde116f2e61
--- /dev/null
+++ b/data/sampled_jsons/social_media_studies_climate_activism_events_types.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Event triggers and opinion leaders shape climate change ...", "date": "", "ddg_snippet": "by J Yang · 2025 · Cited by 6 — We found five event types triggering 48 discussion peaks, including online activities, international conferences, extreme weather , domestic ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s43247-025-02124-4", "content": "by J Yang · 2025 · Cited by 6 — We found five event types triggering 48 discussion peaks, including online activities, international conferences, extreme weather , domestic ..."}
+{"idx": 1, "title": "The “Greta Effect” on Social Media: A Systematic Review of ...", "date": "", "ddg_snippet": "by NG Mede · 2024 · Cited by 33 — This suggests that collective identity framing can be conducive to motivate people to engage in climate action . Accordingly, multiple studies ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/17524032.2024.2314028", "content": "by NG Mede · 2024 · Cited by 33 — This suggests that collective identity framing can be conducive to motivate people to engage in climate action . Accordingly, multiple studies ..."}
+{"idx": 2, "title": "Exploring climate change discourse on social media and ...", "date": "", "ddg_snippet": "by T Gokcimen · 2024 · Cited by 24 — In this study, a new dataset of climate change -related texts was collected from social media sources and various blogs.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2405844024084950", "content": "by T Gokcimen · 2024 · Cited by 24 — In this study, a new dataset of climate change -related texts was collected from social media sources and various blogs."}
+{"idx": 3, "title": "Digital Natives, Digital Activists: Youth, Social Media and ...", "date": "", "ddg_snippet": "by M Pandit · 2025 · Cited by 2 — This study examines how social media impacts young activists' environmental action , using online tools, and how digital natives adapt ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.10158", "content": "by M Pandit · 2025 · Cited by 2 — This study examines how social media impacts young activists' environmental action , using online tools, and how digital natives adapt ..."}
+{"idx": 4, "title": "Gen Z, Millennials Stand Out for Climate Change Activism ...", "date": "", "ddg_snippet": "26 May 2021 — Compared with older adults, Gen Zers and Millennials are talking more about the need for action on climate change ; among social media users, ...", "subpage_snippet": "", "source": "www.pewresearch.org", "link": "https://www.pewresearch.org/science/2021/05/26/gen-z-millennials-stand-out-for-climate-change-activism-social-media-engagement-with-issue/", "content": "26 May 2021 — Compared with older adults, Gen Zers and Millennials are talking more about the need for action on climate change ; among social media users, ..."}
+{"idx": 5, "title": "examining portrayals of climate activism in UK news", "date": "", "ddg_snippet": "by EG Scheuch · 2024 · Cited by 29 — Over the last five years, the UK climate activism scene has experienced remarkable growth with the emergence of Extinction Rebellion (XR), and ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41599-024-02688-0", "content": "by EG Scheuch · 2024 · Cited by 29 — Over the last five years, the UK climate activism scene has experienced remarkable growth with the emergence of Extinction Rebellion (XR), and ..."}
+{"idx": 6, "title": "Social media enables people-centric climate action in the ...", "date": "", "ddg_snippet": "by R Debnath · 2022 · Cited by 34 — Our study shows that greater social media engagement can steer the online discourse on emissions reduction in this sector from demand-side techno-solutionism ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9671910/", "content": "by R Debnath · 2022 · Cited by 34 — Our study shows that greater social media engagement can steer the online discourse on emissions reduction in this sector from demand-side techno-solutionism ..."}
+{"idx": 7, "title": "A systematic review of the nexus between climate change ...", "date": "", "ddg_snippet": "by BC Sultana · 2024 · Cited by 12 — This review examines the nexus between climate change and social media , finding Twitter most popular, and that social media is used for ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2024.1301400/pdf", "content": "by BC Sultana · 2024 · Cited by 12 — This review examines the nexus between climate change and social media , finding Twitter most popular, and that social media is used for ..."}
+{"idx": 8, "title": "Climate Change Advocacy and Engagement on Social Media", "date": "", "ddg_snippet": "25 Oct 2024 — This study examines how 21 leading environmental organizations framed climate change on Facebook and how these frames were associated with public engagement.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/10755470241284934", "content": "25 Oct 2024 — This study examines how 21 leading environmental organizations framed climate change on Facebook and how these frames were associated with public engagement."}
+{"idx": 9, "title": "Australian youth perspectives on the role of social media in ...", "date": "", "ddg_snippet": "by G Arnot · 2024 · Cited by 27 — Limited research has examined young people's perspectives about the role of social media for climate awareness, action , and policy change .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1326020023052883", "content": "by G Arnot · 2024 · Cited by 27 — Limited research has examined young people's perspectives about the role of social media for climate awareness, action , and policy change ."}
diff --git a/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_computer_vision.jsonl b/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_computer_vision.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..12e62b960c8416c1b4c7a12087ad3c9b9b904382
--- /dev/null
+++ b/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_computer_vision.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Hopper: Multi-hop Transformer for Spatiotemporal Reasoning", "date": "", "ddg_snippet": "Existing deep learning based approaches often suffer from spatiotemporal biases when applied to video reasoning problems. We propose Hopper, which uses a Multi-hop Transformer for reasoning object permanence in videos.conference on computer vision , pp. 20–36.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/350253428_Hopper_Multi-hop_Transformer_for_Spatiotemporal_Reasoning", "content": "Existing deep learning based approaches often suffer from spatiotemporal biases when applied to video reasoning problems. We propose Hopper, which uses a Multi-hop Transformer for reasoning object permanence in videos.conference on computer vision , pp. 20–36."}
+{"idx": 1, "title": "Comparative Analysis of CNN-Based Spatiotemporal Reasoning in...", "date": "", "ddg_snippet": "Understanding actions and gestures in video streams requires temporal reasoning of the spatial content from different time instants, i.e., spatiotemporal (ST) modeling . In this survey paper, we have made a comparative analysis of different ST modeling techniques...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-68799-1_14", "content": "Understanding actions and gestures in video streams requires temporal reasoning of the spatial content from different time instants, i.e., spatiotemporal (ST) modeling . In this survey paper, we have made a comparative analysis of different ST modeling techniques..."}
+{"idx": 2, "title": "Vision Transformer: A New Era in Image Recognition", "date": "", "ddg_snippet": "The vision transformer model uses multi-head self-attention in Computer Vision without needing image-specific biases. The model splits the images into a series of positional embedding patches, the transformer encoder then processes these.", "subpage_snippet": "", "source": "viso.ai", "link": "https://viso.ai/deep-learning/vision-transformer-vit/", "content": "The vision transformer model uses multi-head self-attention in Computer Vision without needing image-specific biases. The model splits the images into a series of positional embedding patches, the transformer encoder then processes these."}
+{"idx": 3, "title": "Spatiotemporal Reasoning : Understanding Space and Time", "date": "", "ddg_snippet": "Spatiotemporal reasoning is a critical cognitive ability that involves understanding and manipulating mental representations of space and time. It is essential for daily tasks such as navigation, remembering events, and planning.", "subpage_snippet": "", "source": "alljournals.blog", "link": "https://alljournals.blog/spatiotemporal-reasoning/", "content": "Spatiotemporal reasoning is a critical cognitive ability that involves understanding and manipulating mental representations of space and time. It is essential for daily tasks such as navigation, remembering events, and planning."}
+{"idx": 4, "title": "Deep Learning for Spatiotemporal Anomaly Forecasting: A Case...", "date": "", "ddg_snippet": "Deep Learning Applications for Spatiotemporal Anomaly Detection / Forecasting.Whether transfer learning , including one-shot learning and zero-shot learning , can be used, and how to select appropriate relevant datasets and/or pre-trained models . •", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/deep-learning-for-spatiotemporal-anomaly-forecasting-a-7981615", "content": "Deep Learning Applications for Spatiotemporal Anomaly Detection / Forecasting.Whether transfer learning , including one-shot learning and zero-shot learning , can be used, and how to select appropriate relevant datasets and/or pre-trained models . •"}
+{"idx": 5, "title": "Background Subtraction Angiography with Deep Learning Using...", "date": "", "ddg_snippet": "Supervised learning . Vision transformer. ASJC Scopus subject areas. Computer Science Applications.Dive into the research topics of 'Background Subtraction Angiography with Deep Learning Using Multi-frame Spatiotemporal Angiographic Input'.", "subpage_snippet": "", "source": "www.scholars.northwestern.edu", "link": "https://www.scholars.northwestern.edu/en/publications/background-subtraction-angiography-with-deep-learning-using-multi", "content": "Supervised learning . Vision transformer. ASJC Scopus subject areas. Computer Science Applications.Dive into the research topics of 'Background Subtraction Angiography with Deep Learning Using Multi-frame Spatiotemporal Angiographic Input'."}
+{"idx": 6, "title": "PyTorch Geometric Temporal: Spatiotemporal Signal Processing with...", "date": "", "ddg_snippet": "neural networks. deep learning . dynamic graph. spatiotemporal data processing.CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM), 2021. pp. 4564–4573.", "subpage_snippet": "", "source": "www.research.ed.ac.uk", "link": "https://www.research.ed.ac.uk/en/publications/pytorch-geometric-temporal-spatiotemporal-signal-processing-with-", "content": "neural networks. deep learning . dynamic graph. spatiotemporal data processing.CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM), 2021. pp. 4564–4573."}
+{"idx": 7, "title": "Spatiotemporal Predictions of Toxic Urban Plumes Using Deep ...", "date": "", "ddg_snippet": "Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Spatiotemporal-Predictions-of-Toxic-Urban-Plumes-Using-Deep-Learning-6f79269a-a7e0-41c6-b799-627852f95329", "content": "Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets."}
+{"idx": 8, "title": "Comparative Analysis of CNN-based", "date": "", "ddg_snippet": "Keywords: Spatiotemporal modeling , CNNs, RNNs, activity under-standing, action/gesture recognition. 1 Introduction. Deep learning has been successfully applied in the area of image processing, providing state of the art solutions for many of its problems such as...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05165", "content": "Keywords: Spatiotemporal modeling , CNNs, RNNs, activity under-standing, action/gesture recognition. 1 Introduction. Deep learning has been successfully applied in the area of image processing, providing state of the art solutions for many of its problems such as..."}
+{"idx": 9, "title": "Efficient Few-Shot Action Recognition via", "date": "", "ddg_snippet": "The ensuing spatiotemporal reasoning module operates on multi-level representations to generate discriminative features. As for matching, the contrasts between text-visual and support-query are in-tegrated to provide comprehensive guidance.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00305.pdf", "content": "The ensuing spatiotemporal reasoning module operates on multi-level representations to generate discriminative features. As for matching, the contrasts between text-visual and support-query are in-tegrated to provide comprehensive guidance."}
diff --git a/data/sampled_jsons/speculative_decoding_attention_head_caching_memory_efficient_inference_transformer.jsonl b/data/sampled_jsons/speculative_decoding_attention_head_caching_memory_efficient_inference_transformer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3c525fac53a25c679acf919aa018f0a0f85f8e3
--- /dev/null
+++ b/data/sampled_jsons/speculative_decoding_attention_head_caching_memory_efficient_inference_transformer.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer inference tricks - by Finbarr Timbers", "date": "", "ddg_snippet": "Speculative decoding is a technique that is used when you have excess compute capacity, typically in the local inference setting.", "subpage_snippet": "", "source": "www.artfintel.com", "link": "https://www.artfintel.com/p/transformer-inference-tricks", "content": "Speculative decoding is a technique that is used when you have excess compute capacity, typically in the local inference setting."}
+{"idx": 1, "title": "Self-Speculative Decoding Implementation: LayerSkip Transformer", "date": "", "ddg_snippet": "The original Speculative Decoding method uses a draft model to optimize the inference of the target model. ... attention mechanism or MLP layers are ...", "subpage_snippet": "", "source": "clay-atlas.com", "link": "https://clay-atlas.com/us/blog/2024/11/12/en-self-speculative-decoding-layer-skip-transformer/", "content": "The original Speculative Decoding method uses a draft model to optimize the inference of the target model. ... attention mechanism or MLP layers are ..."}
+{"idx": 2, "title": "SpecVLM: Fast Speculative Decoding in Vision-Language Models", "date": "", "ddg_snippet": "... the number of visual tokens, which alleviates both compute and memory in the draft model prefill and also reduces KV- cache traffic during decoding .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11815v1", "content": "... the number of visual tokens, which alleviates both compute and memory in the draft model prefill and also reduces KV- cache traffic during decoding ."}
+{"idx": 3, "title": "Inference-Time Hyper-Scaling with KV Cache Compression", "date": "", "ddg_snippet": "Notably, DMS consistently dominates other baselines for efficient attention , which we also verify on a broader set of tasks outside of inference -time ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05345v1", "content": "Notably, DMS consistently dominates other baselines for efficient attention , which we also verify on a broader set of tasks outside of inference -time ..."}
+{"idx": 4, "title": "LLM Inference Optimization 101 | DigitalOcean", "date": "", "ddg_snippet": "Consistent ITLs are ideal as they are indicative of efficient memory management, high GPU memory bandwidth, and well-optimized attention computation.", "subpage_snippet": "", "source": "www.digitalocean.com", "link": "https://www.digitalocean.com/community/tutorials/llm-inference-optimization", "content": "Consistent ITLs are ideal as they are indicative of efficient memory management, high GPU memory bandwidth, and well-optimized attention computation."}
+{"idx": 5, "title": "An Introduction to Speculative Decoding for Reducing Latency in", "date": "", "ddg_snippet": "... Efficiency, is a speculative decoding method that operates at the feature level, extrapolating from the hidden state just before the target model’s ...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/an-introduction-to-speculative-decoding-for-reducing-latency-in-ai-inference/", "content": "... Efficiency, is a speculative decoding method that operates at the feature level, extrapolating from the hidden state just before the target model’s ..."}
+{"idx": 6, "title": "Faster Text Generation with Self-Speculative Decoding", "date": "", "ddg_snippet": "... speculative decoding for any decoder -only transformer , the logits from the intermediate layers cannot be unembedded (process of decoding through LM ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/layerskip", "content": "... speculative decoding for any decoder -only transformer , the logits from the intermediate layers cannot be unembedded (process of decoding through LM ..."}
+{"idx": 7, "title": "Primer on Large Language Model (LLM) Inference Optimizations:", "date": "", "ddg_snippet": "... attention heads are then concatenated and ... K-V caching is a crucial optimization technique for LLM inference , particularly in the decoding stage.", "subpage_snippet": "", "source": "rss.boorghani.com", "link": "https://rss.boorghani.com/primer-on-large-language-model-llm-inference-optimizations-1-background-and-problem-formulation", "content": "... attention heads are then concatenated and ... K-V caching is a crucial optimization technique for LLM inference , particularly in the decoding stage."}
+{"idx": 8, "title": "Accelerating Sonar Through Speculation - Sonar by Perplexity", "date": "", "ddg_snippet": "Speculative Decoding leverages the structure of natural languages and the auto-regressive nature of transformers to speed up token generation.", "subpage_snippet": "", "source": "sonar.perplexity.ai", "link": "https://sonar.perplexity.ai/resources/accelerating-sonar-through-speculation", "content": "Speculative Decoding leverages the structure of natural languages and the auto-regressive nature of transformers to speed up token generation."}
+{"idx": 9, "title": "DeepSeek Usage — SGLang", "date": "", "ddg_snippet": "Description : MLA is an innovative attention mechanism introduced by the DeepSeek team, aimed at improving inference efficiency.", "subpage_snippet": "", "source": "docs.sglang.ai", "link": "https://docs.sglang.ai/basic_usage/deepseek.html", "content": "Description : MLA is an innovative attention mechanism introduced by the DeepSeek team, aimed at improving inference efficiency."}
diff --git a/data/sampled_jsons/speculative_decoding_expected_length_formula_acceptance_probabilities.jsonl b/data/sampled_jsons/speculative_decoding_expected_length_formula_acceptance_probabilities.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..40f64d7be1bdafaf9bab84f8292f15fede4cbf77
--- /dev/null
+++ b/data/sampled_jsons/speculative_decoding_expected_length_formula_acceptance_probabilities.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Get 3× Faster LLM Inference with Speculative Decoding Using the...", "date": "", "ddg_snippet": "Speedup relative to baseline. Acceptance length (τ) : The average number of tokens accepted per round of decoding . According to the paper Fast Inference from Transformers via Speculative Decoding , theoretically, it is calculated with the formula", "subpage_snippet": "", "source": "www.bentoml.com", "link": "https://www.bentoml.com/blog/3x-faster-llm-inference-with-speculative-decoding", "content": "Speedup relative to baseline. Acceptance length (τ) : The average number of tokens accepted per round of decoding . According to the paper Fast Inference from Transformers via Speculative Decoding , theoretically, it is calculated with the formula"}
+{"idx": 1, "title": "Speculative decoding for LLM inference - Adi Ganesh", "date": "", "ddg_snippet": "Speculative decoding is a neat trick that provides significant speedups to LLM inference. This post was originally inspired by this Karpathy tweet – I will discuss how the algorithm works in more detail and prove its correctness.", "subpage_snippet": "", "source": "adiganesh.com", "link": "https://adiganesh.com/posts/speculative_decoding/", "content": "Speculative decoding is a neat trick that provides significant speedups to LLM inference. This post was originally inspired by this Karpathy tweet – I will discuss how the algorithm works in more detail and prove its correctness."}
+{"idx": 2, "title": "SpecDec++: Boosting Speculative Decoding via Adaptive Candidate...", "date": "", "ddg_snippet": "Inspired by the theory, we propose SpecDec++, an enhanced version of speculative decoding that adaptively determines the candidate length on the fly. First, we train an acceptance prediction head on top of the draft model to predict the acceptance probability of the candidate token.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.19715", "content": "Inspired by the theory, we propose SpecDec++, an enhanced version of speculative decoding that adaptively determines the candidate length on the fly. First, we train an acceptance prediction head on top of the draft model to predict the acceptance probability of the candidate token."}
+{"idx": 3, "title": "speculative decoding", "date": "", "ddg_snippet": "is its expectation , or the probability of accepting any token, then we haveWe normalize (show total operations of speculative decoding compare to the standard decoding ) it by and dividing by expected number of tokens.", "subpage_snippet": "", "source": "self-supervised.cs.jhu.edu", "link": "https://self-supervised.cs.jhu.edu/fa2024/files/presentations/10-22-Reasoning-Jiang-Zhong.pdf", "content": "is its expectation , or the probability of accepting any token, then we haveWe normalize (show total operations of speculative decoding compare to the standard decoding ) it by and dividing by expected number of tokens."}
+{"idx": 4, "title": "SpecDec++: Boosting Speculative Decoding via... | OpenReview", "date": "", "ddg_snippet": "Speculative decoding reduces the inference latency of a target large language model via utilizing a smaller and faster draft model. Its performance depends on a hyperparameter K -- the candidate length , i.e., the number of candidate tokens for the target model to verify in each round.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Y131N9fUbU", "content": "Speculative decoding reduces the inference latency of a target large language model via utilizing a smaller and faster draft model. Its performance depends on a hyperparameter K -- the candidate length , i.e., the number of candidate tokens for the target model to verify in each round."}
+{"idx": 5, "title": "A Theoretical Perspective for Speculative Decoding", "date": "", "ddg_snippet": "Analysis for Batch Speculative Decoding . Analysis on the Optimal Rejection-Distribution Bias Tradeoff. An Optimization Formulation and Pareto-optimal Characterization.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/e7349e785900b93d8b4971a3f2c1cefe-Paper-Conference.pdf", "content": "Analysis for Batch Speculative Decoding . Analysis on the Optimal Rejection-Distribution Bias Tradeoff. An Optimization Formulation and Pareto-optimal Characterization."}
+{"idx": 6, "title": "AdaEDL: Early Draft Stopping for Speculative Decoding of Large...", "date": "", "ddg_snippet": "The aim of speculative decoding techniques is to improve the average inference time of a large, target model without sacrificing its accuracy, by using a more efficient draft model to propose draft tokens which are then verified in parallel.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/adaedl-early-draft-stopping-for-speculative", "content": "The aim of speculative decoding techniques is to improve the average inference time of a large, target model without sacrificing its accuracy, by using a more efficient draft model to propose draft tokens which are then verified in parallel."}
+{"idx": 7, "title": "\"Draft Model Knows When to Stop: A Self-Verification Length Policy for...", "date": "", "ddg_snippet": "Current speculative decoding systems use fixed draft lengths , ignoring that some tokens (like stop words) are easier to predict than others (like reasoning-intensive tokens). This leads to inefficient processing and slower generation.", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/draft-model-knows-when-to-stop-a", "content": "Current speculative decoding systems use fixed draft lengths , ignoring that some tokens (like stop words) are easier to predict than others (like reasoning-intensive tokens). This leads to inefficient processing and slower generation."}
+{"idx": 8, "title": "An Introduction to Speculative Decoding for Reducing Latency in AI...", "date": "", "ddg_snippet": "The draft-target approach to speculative decoding uses a smaller draft model to propose tokens and a larger target model to verify them in parallel, with rejection sampling determining which tokens to accept or reject based on probability distributions.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/an-introduction-to-speculative-decoding-for-reducing-latency-in-ai-inference/", "content": "The draft-target approach to speculative decoding uses a smaller draft model to propose tokens and a larger target model to verify them in parallel, with rejection sampling determining which tokens to accept or reject based on probability distributions."}
+{"idx": 9, "title": "Fast Inference from Transformers via Speculative Decoding", "date": "", "ddg_snippet": "The acceptance rate βx, given a prex x, is the probability of accepting xt ∼ q(xt|x) by speculative sampling, as per Section 2.32.We’ll now derive a simple formula for calculating α given a prex and the two models Mp and Mq. We start by dening a natural divergence DLK", "subpage_snippet": "", "source": "minjiazhang.github.io", "link": "https://minjiazhang.github.io/courses/fall24-resource/Fast+Inference+from+Transformers+via+Speculative+Decoding+.pdf", "content": "The acceptance rate βx, given a prex x, is the probability of accepting xt ∼ q(xt|x) by speculative sampling, as per Section 2.32.We’ll now derive a simple formula for calculating α given a prex and the two models Mp and Mq. We start by dening a natural divergence DLK"}
diff --git a/data/sampled_jsons/speculative_decoding_how_it_works_draft_model_target_model_year_2023.jsonl b/data/sampled_jsons/speculative_decoding_how_it_works_draft_model_target_model_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf3cc6eca93fd51f752d4d88d7c1f21414e39ec2
--- /dev/null
+++ b/data/sampled_jsons/speculative_decoding_how_it_works_draft_model_target_model_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Introduction to Speculative Decoding for Reducing Latency in", "date": "", "ddg_snippet": "The draft - target approach to speculative decoding uses a smaller draft model to propose tokens and a larger target model to verify them in parallel ...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/an-introduction-to-speculative-decoding-for-reducing-latency-in-ai-inference/", "content": "The draft - target approach to speculative decoding uses a smaller draft model to propose tokens and a larger target model to verify them in parallel ..."}
+{"idx": 1, "title": "A quick introduction to speculative decoding | Baseten Blog", "date": "", "ddg_snippet": "... speculative decoding , the smaller “ draft ” model generates potential output tokens, which our larger original “ target ” model can either accept ...", "subpage_snippet": "", "source": "www.baseten.co", "link": "https://www.baseten.co/blog/a-quick-introduction-to-speculative-decoding/", "content": "... speculative decoding , the smaller “ draft ” model generates potential output tokens, which our larger original “ target ” model can either accept ..."}
+{"idx": 2, "title": "How we built production-ready speculative decoding with", "date": "", "ddg_snippet": "It is critical that these repeated calls to the ... Giving the draft and target model 1 GPU each is wasteful as the draft model barely uses its GPU.", "subpage_snippet": "", "source": "www.baseten.co", "link": "https://www.baseten.co/blog/how-we-built-production-ready-speculative-decoding-with-tensorrt-llm/", "content": "It is critical that these repeated calls to the ... Giving the draft and target model 1 GPU each is wasteful as the draft model barely uses its GPU."}
+{"idx": 3, "title": "Catch up on Speculative Decoding in 5 minutes: a survey for", "date": "", "ddg_snippet": "Draft models fall into three categories: stand-alone models , decoder heads, and pruned models . ... draft models are trained on a target corpus (e.g., ...", "subpage_snippet": "", "source": "www.jinghong-chen.net", "link": "https://www.jinghong-chen.net/speculative-decoding-a-3-min-survey-as-of-december-2023/", "content": "Draft models fall into three categories: stand-alone models , decoder heads, and pruned models . ... draft models are trained on a target corpus (e.g., ..."}
+{"idx": 4, "title": "Speculative Decoding in vLLM | OpenLM.ai", "date": "", "ddg_snippet": "Speculative decoding requires that the draft and target models share the same vocabulary, and in some cases, this can limit the use of speculative ...", "subpage_snippet": "", "source": "openlm.ai", "link": "https://openlm.ai/speculative-decoding-in-vllm/", "content": "Speculative decoding requires that the draft and target models share the same vocabulary, and in some cases, this can limit the use of speculative ..."}
+{"idx": 5, "title": "Decoding Speculative Decoding", "date": "", "ddg_snippet": "We demonstrate that using accuracy on language modeling tasks to choose the draft model for speculative decoding leads to suboptimal choices.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.01528v4", "content": "We demonstrate that using accuracy on language modeling tasks to choose the draft model for speculative decoding leads to suboptimal choices."}
+{"idx": 6, "title": "Mamba Drafters for Speculative Decoding", "date": "", "ddg_snippet": "... drafter (Leviathan et al., 2023 ) applicable to multiple target models , and (ii) adopting a self-speculation approach, where a drafter is trained to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.01206v1", "content": "... drafter (Leviathan et al., 2023 ) applicable to multiple target models , and (ii) adopting a self-speculation approach, where a drafter is trained to ..."}
+{"idx": 7, "title": "How Speculative Decoding Boosts vLLM Performance by up to 2.8x", "date": "", "ddg_snippet": "Speculative decoding requires that the draft and target models share the same vocabulary, and in some cases, this can limit the use of speculative ...", "subpage_snippet": "", "source": "blog.vllm.ai", "link": "https://blog.vllm.ai/2024/10/17/spec-decode.html", "content": "Speculative decoding requires that the draft and target models share the same vocabulary, and in some cases, this can limit the use of speculative ..."}
+{"idx": 8, "title": "Speculative Sampling | Jay Mody", "date": "", "ddg_snippet": "The idea is that the draft model speculates what the output is \\(K\\) steps into the future, while the target model determines how many of those ...", "subpage_snippet": "", "source": "jaykmody.com", "link": "https://jaykmody.com/blog/speculative-sampling/", "content": "The idea is that the draft model speculates what the output is \\(K\\) steps into the future, while the target model determines how many of those ..."}
+{"idx": 9, "title": "Self-Speculative Decoding Implementation: LayerSkip Transformer", "date": "", "ddg_snippet": "The original Speculative Decoding method uses a draft model to optimize the inference of the target model . ... draft model can be accepted, enabling ...", "subpage_snippet": "", "source": "clay-atlas.com", "link": "https://clay-atlas.com/us/blog/2024/11/12/en-self-speculative-decoding-layer-skip-transformer/", "content": "The original Speculative Decoding method uses a draft model to optimize the inference of the target model . ... draft model can be accepted, enabling ..."}
diff --git a/data/sampled_jsons/spurious_correlation_Schubert_polynomials_program_synthesis_Section_5.jsonl b/data/sampled_jsons/spurious_correlation_Schubert_polynomials_program_synthesis_Section_5.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..50e1d124b674ed05abd79bee9b85b20eabb6edba
--- /dev/null
+++ b/data/sampled_jsons/spurious_correlation_Schubert_polynomials_program_synthesis_Section_5.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1903.10332] Zero-one Schubert polynomials", "date": "", "ddg_snippet": "This implies that the set of permutations whose Schubert polynomials have all their coefficients equal to either 0 or 1 is closed under pattern containment. Using Magyar's orthodontia, we characterize this class by a list of twelve avoided patterns.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.10332", "content": "This implies that the set of permutations whose Schubert polynomials have all their coefficients equal to either 0 or 1 is closed under pattern containment. Using Magyar's orthodontia, we characterize this class by a list of twelve avoided patterns."}
+{"idx": 1, "title": "Long Division With Polynomials - The Easy Way! - YouTube", "date": "", "ddg_snippet": "This video tutorial explains how to perform long division of polynomials with remainder and with missing terms.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=_FSXJmESFmQ", "content": "This video tutorial explains how to perform long division of polynomials with remainder and with missing terms."}
+{"idx": 2, "title": "Lectures on Schubert Polynomials | U-M LSA Mathematics", "date": "", "ddg_snippet": "Lectures on Schubert Polynomials . Seminars, Colloquia & Lectures.This is a lecture series on Schubert polynomials , given by William Fulton in Fall 2025.", "subpage_snippet": "", "source": "lsa.umich.edu", "link": "https://lsa.umich.edu/math/seminars/seminars-and-colloquia/lectures-on-schubert-polynomials.html", "content": "Lectures on Schubert Polynomials . Seminars, Colloquia & Lectures.This is a lecture series on Schubert polynomials , given by William Fulton in Fall 2025."}
+{"idx": 3, "title": "Schubert Polynomials — Sage 9.3.beta9 Reference Manual...", "date": "", "ddg_snippet": "sage.combinat. schubert _ polynomial .SchubertPolynomialRing(R)¶. Return the Schubert polynomial ring over R on the X basis. This is the basis made of the Schubert polynomials . EXAMPLES", "subpage_snippet": "", "source": "sporadic.stanford.edu", "link": "http://sporadic.stanford.edu/reference/combinat/sage/combinat/schubert_polynomial.html", "content": "sage.combinat. schubert _ polynomial .SchubertPolynomialRing(R)¶. Return the Schubert polynomial ring over R on the X basis. This is the basis made of the Schubert polynomials . EXAMPLES"}
+{"idx": 4, "title": "The 10 Most Bizarre Correlations", "date": "", "ddg_snippet": "One of the first things you learn in any statistics class is that correlation doesn't imply causation. Nonetheless, it's fun to consider the causal relationships one could infer from these correlations .", "subpage_snippet": "", "source": "www.buzzfeednews.com", "link": "https://www.buzzfeednews.com/article/kjh2110/the-10-most-bizarre-correlations", "content": "One of the first things you learn in any statistics class is that correlation doesn't imply causation. Nonetheless, it's fun to consider the causal relationships one could infer from these correlations ."}
+{"idx": 5, "title": "D-module techniques for solving differential equations in the", "date": "", "ddg_snippet": "However, to our knowledge, a systematic synthesis and comparison of D -module techniques and methods employed in high energy physics has not yet been ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11005-024-01835-7", "content": "However, to our knowledge, a systematic synthesis and comparison of D -module techniques and methods employed in high energy physics has not yet been ..."}
+{"idx": 6, "title": "Adaptive Model Selection for Expanded Post Hoc Debiasing and...", "date": "", "ddg_snippet": "EvA: erasing spurious correlations with activations. Q He. K Xu. A Yao. On feature learning in the presence of spurious correlations .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395577997_Adaptive_Model_Selection_for_Expanded_Post_Hoc_Debiasing_and_Mitigating_Varying_Degrees_of_Spurious_Correlations", "content": "EvA: erasing spurious correlations with activations. Q He. K Xu. A Yao. On feature learning in the presence of spurious correlations ."}
+{"idx": 7, "title": "What's a real-world example of \"overfitting\"? - Cross Validated", "date": "", "ddg_snippet": "In fact, spurious correlation need not involve an explicit model, and the implicit model is usually a straight line with two parameters. $\\endgroup$.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/128616/whats-a-real-world-example-of-overfitting/128625", "content": "In fact, spurious correlation need not involve an explicit model, and the implicit model is usually a straight line with two parameters. $\\endgroup$."}
+{"idx": 8, "title": "Track: Poster Session 5", "date": "", "ddg_snippet": "Synthetic edits sampled with LintSeq reflect the syntax and semantics of their programming language. To test the algorithm, we use it to refactor a dataset of instruction + program pairs into instruction + program -diff-sequence tuples.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31975", "content": "Synthetic edits sampled with LintSeq reflect the syntax and semantics of their programming language. To test the algorithm, we use it to refactor a dataset of instruction + program pairs into instruction + program -diff-sequence tuples."}
+{"idx": 9, "title": "Publications - 협동과정 인공지능 전공", "date": "", "ddg_snippet": "Correlation -Concealing Adversarial Noise Injection for Improved Disentanglement in Label-based Image Translation(Jookyung Song, SeongUk Park, Donghoon Han, Nojun Kwak). Dual-stage super-resolution for edge devices(Saem Park, Geunjae Choi, Seonguk Par...", "subpage_snippet": "", "source": "gsai.snu.ac.kr", "link": "https://gsai.snu.ac.kr/publications-3/", "content": "Correlation -Concealing Adversarial Noise Injection for Improved Disentanglement in Label-based Image Translation(Jookyung Song, SeongUk Park, Donghoon Han, Nojun Kwak). Dual-stage super-resolution for edge devices(Saem Park, Geunjae Choi, Seonguk Par..."}
diff --git a/data/sampled_jsons/state_space_models_Mamba_vision_long_sequences_year_2024.jsonl b/data/sampled_jsons/state_space_models_Mamba_vision_long_sequences_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f91c70f036925941270488d52d880924e3c560ff
--- /dev/null
+++ b/data/sampled_jsons/state_space_models_Mamba_vision_long_sequences_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2404.16112] Mamba-360: Survey of State Space Models as ...", "date": "", "ddg_snippet": "Apr 24, 2024 · View a PDF of the paper titled Mamba -360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges, by Badri Narayana Patro and 1 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.16112", "content": "Apr 24, 2024 · View a PDF of the paper titled Mamba -360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges, by Badri Narayana Patro and 1 other authors"}
+{"idx": 1, "title": "A Visual Guide to Mamba and State Space Models - Maarten ...", "date": "", "ddg_snippet": "To further improve LLMs, new architectures are developed that might even outperform the Transformer architecture. One of these methods is Mamba , a State Space Model . Mamba was proposed in the paper Mamba : Linear-Time Sequence Modeling with Selective State Spaces. You can find its official implementation and model checkpoints in its repository.", "subpage_snippet": "", "source": "www.maartengrootendorst.com", "link": "https://www.maartengrootendorst.com/blog/mamba/", "content": "To further improve LLMs, new architectures are developed that might even outperform the Transformer architecture. One of these methods is Mamba , a State Space Model . Mamba was proposed in the paper Mamba : Linear-Time Sequence Modeling with Selective State Spaces. You can find its official implementation and model checkpoints in its repository."}
+{"idx": 2, "title": "Mamba-360: Survey of state space models as transformer ...", "date": "", "ddg_snippet": "A notable drawback of SSMs is their compromise on core capabilities essential for certain sequence processing tasks, such as copying long input sequences (Jelassi et al., 2024), in-context learning, and induction heads (Olsson et al., 2022). This work presents a comprehensive survey of the field of state space models , highlighting their strengths and weaknesses compared to state -of-the-art ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0952197625012801", "content": "A notable drawback of SSMs is their compromise on core capabilities essential for certain sequence processing tasks, such as copying long input sequences (Jelassi et al., 2024), in-context learning, and induction heads (Olsson et al., 2022). This work presents a comprehensive survey of the field of state space models , highlighting their strengths and weaknesses compared to state -of-the-art ..."}
+{"idx": 3, "title": "Vision mamba | Proceedings of the 41st International ...", "date": "", "ddg_snippet": "Jul 21, 2024 · Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model , have shown great potential for long sequence modeling. Meanwhile building efficient and generic vision backbones purely upon SSMs is an appealing direction. However, representing visual data is challenging for SSMs due to the position-sensitivity of visual data and the requirement ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3694654", "content": "Jul 21, 2024 · Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model , have shown great potential for long sequence modeling. Meanwhile building efficient and generic vision backbones purely upon SSMs is an appealing direction. However, representing visual data is challenging for SSMs due to the position-sensitivity of visual data and the requirement ..."}
+{"idx": 4, "title": "MambaVLT: Time-Evolving Multimodal State Space Model for ...", "date": "", "ddg_snippet": "Recently, the State Space Model (SSM), known as Mamba , has shown astonishing ability in efficient long - sequence modeling. Particularly, its state space evolving process demonstrates promising capabilities in memorizing multimodal temporal information with linear complexity.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MambaVLT_Time-Evolving_Multimodal_State_Space_Model_for_Vision-Language_Tracking_CVPR_2025_paper.pdf", "content": "Recently, the State Space Model (SSM), known as Mamba , has shown astonishing ability in efficient long - sequence modeling. Particularly, its state space evolving process demonstrates promising capabilities in memorizing multimodal temporal information with linear complexity."}
+{"idx": 5, "title": "Along comes a Mamba: an evolution in sequence models ... - Medium", "date": "", "ddg_snippet": "Jan 13, 2025 · Mamba builds upon the strengths of structured state - space models like S4 and H3, while addressing their limitations in discrete domains like language modelling by introducing the following key ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@wilburdes/along-comes-a-mamba-an-evolution-in-sequence-models-based-on-state-space-models-2bd3d0e02d86", "content": "Jan 13, 2025 · Mamba builds upon the strengths of structured state - space models like S4 and H3, while addressing their limitations in discrete domains like language modelling by introducing the following key ..."}
+{"idx": 6, "title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces", "date": "", "ddg_snippet": "Dec 1, 2023 · Foundation models , now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models , and structured state space models (SSMs) have been developed to address Transformers' computational inefficiency on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.00752", "content": "Dec 1, 2023 · Foundation models , now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models , and structured state space models (SSMs) have been developed to address Transformers' computational inefficiency on ..."}
+{"idx": 7, "title": "[2405.04404] Vision Mamba: A Comprehensive Survey and Taxonomy", "date": "", "ddg_snippet": "In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.04404", "content": "In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP ..."}
+{"idx": 8, "title": "Vision Mamba: Efficient Visual Representation Learning with", "date": "", "ddg_snippet": "... state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model , have shown great potential for long sequence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.09417v3", "content": "... state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model , have shown great potential for long sequence ..."}
+{"idx": 9, "title": "[2410.15091] Spatial-Mamba: Effective Visual State Space Models", "date": "", "ddg_snippet": "... state space models (SSMs), such as Mamba , highly excel at capturing long -range dependencies in 1D sequential data, while their applications to 2D ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.15091", "content": "... state space models (SSMs), such as Mamba , highly excel at capturing long -range dependencies in 1D sequential data, while their applications to 2D ..."}
diff --git a/data/sampled_jsons/stochastic_combinatorial_bandits_non-continuous_loss_function_year_2023.jsonl b/data/sampled_jsons/stochastic_combinatorial_bandits_non-continuous_loss_function_year_2023.jsonl
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index 0000000000000000000000000000000000000000..c151578c88089cfff6d75c456b91d8ef76658a0d
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Combinatorial Multi-Armed Bandit with General Reward Functions - NeurIPS", "date": "", "ddg_snippet": "Abstract In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function , whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables. Our framework enables a much larger class of reward functions such as the max() function and nonlinear ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2016/file/aa169b49b583a2b5af89203c2b78c67c-Paper.pdf", "content": "Abstract In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function , whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables. Our framework enables a much larger class of reward functions such as the max() function and nonlinear ..."}
+{"idx": 1, "title": "Multi-Armed Bandits: Combinatorial/Submodular/Gaussian:", "date": "", "ddg_snippet": "We investigate the problem of stochastic , combinatorial multi-armed bandits where the learner only has access to bandit feedback and the reward function can be non -linear. We provide a general framework for adapting discrete offline approximation algorithms into sub-linear regret methods that only require bandit feedback.", "subpage_snippet": "", "source": "web.ics.purdue.edu", "link": "https://web.ics.purdue.edu/~vaneet/publi_bandits.htm", "content": "We investigate the problem of stochastic , combinatorial multi-armed bandits where the learner only has access to bandit feedback and the reward function can be non -linear. We provide a general framework for adapting discrete offline approximation algorithms into sub-linear regret methods that only require bandit feedback."}
+{"idx": 2, "title": "PDF Adversarial Combinatorial Bandits with General Non-linear Reward Functions", "date": "", "ddg_snippet": "While there have been research on combinatorial bandits with general link functions , such results are established exclusively for the stochastic setting, in which the reward vectors fvtgT t=1", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/han21b/han21b.pdf", "content": "While there have been research on combinatorial bandits with general link functions , such results are established exclusively for the stochastic setting, in which the reward vectors fvtgT t=1"}
+{"idx": 3, "title": "Combinatorial Semi-Bandit in the Non-Stationary Environment", "date": "", "ddg_snippet": "Combinatorial semi- bandit Combinatorial semi- bandits (CSB) is a generalization of MAB, and there are also two types of CSB, i.e., in the adversarial or stochastic settings. Adversarial CSB was introduced in the context of shortest-path problems by György et al. [2007], and later studied extensively [Lattimore and Szepesvári, 2018].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2002.03580", "content": "Combinatorial semi- bandit Combinatorial semi- bandits (CSB) is a generalization of MAB, and there are also two types of CSB, i.e., in the adversarial or stochastic settings. Adversarial CSB was introduced in the context of shortest-path problems by György et al. [2007], and later studied extensively [Lattimore and Szepesvári, 2018]."}
+{"idx": 4, "title": "PDF Contextual Combinatorial Cascading Bandits", "date": "", "ddg_snippet": "Abstract We propose the contextual combinatorial cas-cading bandits , a combinatorial online learning game, where at each time step a learning agent is given a set of contextual information, then selects a list of items, and observes stochastic outcomes of a prefix in the selected items by some stop-ping criterion.", "subpage_snippet": "", "source": "www.cse.cuhk.edu.hk", "link": "https://www.cse.cuhk.edu.hk/~syzhang/papers/CCCBwSupp.pdf", "content": "Abstract We propose the contextual combinatorial cas-cading bandits , a combinatorial online learning game, where at each time step a learning agent is given a set of contextual information, then selects a list of items, and observes stochastic outcomes of a prefix in the selected items by some stop-ping criterion."}
+{"idx": 5, "title": "Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits", "date": "", "ddg_snippet": "Regret lower bounds for non - combinatorial stochastic bandits were established in [26], and later extended to linear and contextual bandits in [50, 51], respectively.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12111685/", "content": "Regret lower bounds for non - combinatorial stochastic bandits were established in [26], and later extended to linear and contextual bandits in [50, 51], respectively."}
+{"idx": 6, "title": "PDF Combinatorial Bandits Revisited - NeurIPS", "date": "", "ddg_snippet": "This paper investigates stochastic and adversarial combinatorial multi-armed ban-dit problems. In the stochastic setting under semi- bandit feedback, we derive", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2015/file/0ce2ffd21fc958d9ef0ee9ba5336e357-Paper.pdf", "content": "This paper investigates stochastic and adversarial combinatorial multi-armed ban-dit problems. In the stochastic setting under semi- bandit feedback, we derive"}
+{"idx": 7, "title": "PDF Randomized Greedy Learning for Non-monotone Stochastic Submodular ...", "date": "", "ddg_snippet": "Abstract We investigate the problem of unconstrained combinatorial multi-armed bandits with full- bandit feedback and stochastic rewards for sub-modular maximization. Previous works investi-gate the same problem assuming a submodular and monotone reward function .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2302.01324.pdf", "content": "Abstract We investigate the problem of unconstrained combinatorial multi-armed bandits with full- bandit feedback and stochastic rewards for sub-modular maximization. Previous works investi-gate the same problem assuming a submodular and monotone reward function ."}
+{"idx": 8, "title": "PDF Stochastic Top-K Subset Bandits with Linear Space and Non-Linear", "date": "", "ddg_snippet": "Many real-world problems like Social Influence Maximization face the dilemma of choosing the best K out of N options at a given time instant. This setup can be modeled as a combinatorial bandit which chooses K out of N arms at each time, with an aim to achieve an efficient trade-off between exploration and exploitation. This is the first work for combinatorial bandits where the feedback ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v132/agarwal21c/agarwal21c.pdf", "content": "Many real-world problems like Social Influence Maximization face the dilemma of choosing the best K out of N options at a given time instant. This setup can be modeled as a combinatorial bandit which chooses K out of N arms at each time, with an aim to achieve an efficient trade-off between exploration and exploitation. This is the first work for combinatorial bandits where the feedback ..."}
+{"idx": 9, "title": "PDF Bandit Algorithms - tor-lattimore.com", "date": "", "ddg_snippet": "The class of stochastic Bernoulli bandits is the set of all such bandits , which are characterised by their mean vectors. If you knew the mean vector associated with the environment, then the optimal policy is to play the fixed action a∗ = argmax μa.", "subpage_snippet": "", "source": "tor-lattimore.com", "link": "https://tor-lattimore.com/downloads/book/book.pdf", "content": "The class of stochastic Bernoulli bandits is the set of all such bandits , which are characterised by their mean vectors. If you knew the mean vector associated with the environment, then the optimal policy is to play the fixed action a∗ = argmax μa."}
diff --git a/data/sampled_jsons/substitution_conflict_vs_coherent_conflict_language_models_definition_difference_year_2024.jsonl b/data/sampled_jsons/substitution_conflict_vs_coherent_conflict_language_models_definition_difference_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e7e9d09e9a380ad9fe303232cf432063aa7a5e35
--- /dev/null
+++ b/data/sampled_jsons/substitution_conflict_vs_coherent_conflict_language_models_definition_difference_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "14 Mar 2025 — Notably, we have three different conflict types: No Conflict (Type 1), Substitution Conflict (Type 2), and Coherent Conflict (Type 3). For ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v1", "content": "14 Mar 2025 — Notably, we have three different conflict types: No Conflict (Type 1), Substitution Conflict (Type 2), and Coherent Conflict (Type 3). For ..."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Notably, we have three different conflict types: No Conflict (Type 1), Substitution Conflict (Type 2), and Coherent Conflict (Type 3). For presentation clarity, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46677", "content": "Notably, we have three different conflict types: No Conflict (Type 1), Substitution Conflict (Type 2), and Coherent Conflict (Type 3). For presentation clarity, ..."}
+{"idx": 2, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "by G Li · 2025 — Notably, we have three different conflict types: No Conflict (Type 1), Substitution Conflict (Type 2), and . Coherent Conflict (Type 3). For presentation clarity ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2503.10996", "content": "by G Li · 2025 — Notably, we have three different conflict types: No Conflict (Type 1), Substitution Conflict (Type 2), and . Coherent Conflict (Type 3). For presentation clarity ..."}
+{"idx": 3, "title": "Knowledge Conflicts for LLMs: A Survey", "date": "", "ddg_snippet": "by R Xu · 2024 · Cited by 173 — Researchers analyze LLMs' behavior under conflicting knowledge by creating artificial conflicts , initially through entity-level substitutions .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.486.pdf", "content": "by R Xu · 2024 · Cited by 173 — Researchers analyze LLMs' behavior under conflicting knowledge by creating artificial conflicts , initially through entity-level substitutions ."}
+{"idx": 4, "title": "Unraveling Cross-Modality Knowledge Conflicts in Large...", "date": "", "ddg_snippet": "by T Zhu · Cited by 18 — This paper investigates cross-modality parametric knowledge conflicts in LVLMs by systematically detecting, interpreting, and mitigating these conflicts .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ZZrSOMLoau", "content": "by T Zhu · Cited by 18 — This paper investigates cross-modality parametric knowledge conflicts in LVLMs by systematically detecting, interpreting, and mitigating these conflicts ."}
+{"idx": 5, "title": "Tracing Internal Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "by S Marjanovic · 2024 · Cited by 14 — Fact dynamicity (c) causes intra-memory conflicts between the different fact representations seen during pretraining.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-emnlp.838.pdf", "content": "by S Marjanovic · 2024 · Cited by 14 — Fact dynamicity (c) causes intra-memory conflicts between the different fact representations seen during pretraining."}
+{"idx": 6, "title": "Six Fallacies in Substituting Large Language Models for ...", "date": "", "ddg_snippet": "by Z Lin · 2025 · Cited by 2 — When models generate coherent language without the intentionality, consciousness, and direct grounding in real-world experience that ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/full/10.1177/25152459251357566", "content": "by Z Lin · 2025 · Cited by 2 — When models generate coherent language without the intentionality, consciousness, and direct grounding in real-world experience that ..."}
+{"idx": 7, "title": "CONFLICTBANK: A Benchmark for Evaluating Knowledge ...", "date": "", "ddg_snippet": "by Z Su · Cited by 3 — [2024] combines these two approaches, eliciting parametric memory from LLMs and constructs more coherent and convincing conflict pairs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=wjHVmgBDzc", "content": "by Z Su · Cited by 3 — [2024] combines these two approaches, eliciting parametric memory from LLMs and constructs more coherent and convincing conflict pairs."}
+{"idx": 8, "title": "ChatGPT: A comprehensive review on background ...", "date": "", "ddg_snippet": "by PP Ray · 2023 · Cited by 2900 — GPT models are designed to generate natural language text, such as sentences, paragraphs, and entire documents, in a way that is coherent and consistent with ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S266734522300024X", "content": "by PP Ray · 2023 · Cited by 2900 — GPT models are designed to generate natural language text, such as sentences, paragraphs, and entire documents, in a way that is coherent and consistent with ..."}
+{"idx": 9, "title": "WikiContradict: A Benchmark for Evaluating LLMs on Real ...", "date": "", "ddg_snippet": "9 Dec 2024 — Specifically, we introduce WikiContradict, a benchmark consisting of 253 high-quality, human-annotated instances designed to assess the ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/97844", "content": "9 Dec 2024 — Specifically, we introduce WikiContradict, a benchmark consisting of 253 high-quality, human-annotated instances designed to assess the ..."}
diff --git a/data/sampled_jsons/symmetric_quantization_formula_n_bits_zx=0_equation.jsonl b/data/sampled_jsons/symmetric_quantization_formula_n_bits_zx=0_equation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..962a35e8a2c9cd73c99b230b5da178712321de6b
--- /dev/null
+++ b/data/sampled_jsons/symmetric_quantization_formula_n_bits_zx=0_equation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A White Paper on Neural Network Quantization", "date": "", "ddg_snippet": "by M Nagel · 2021 · Cited by 855 — On the other hand, signed symmetric quantization can be chosen for distributions that are roughly symmetric about zero. Figure 3: A visual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2106.08295", "content": "by M Nagel · 2021 · Cited by 855 — On the other hand, signed symmetric quantization can be chosen for distributions that are roughly symmetric about zero. Figure 3: A visual ..."}
+{"idx": 1, "title": "Compiler Handling of Quantization Scales and Zero Points", "date": "", "ddg_snippet": "For symmetric quantization, the zero-point Z is often implicitly zero or fixed. Handling these scale and zero-point parameters correctly throughout the ...", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/compiler-runtime-optimization-ml/chapter-8-quantization-low-precision-optimizations/quantization-scale-zero-point-handling", "content": "For symmetric quantization, the zero-point Z is often implicitly zero or fixed. Handling these scale and zero-point parameters correctly throughout the ..."}
+{"idx": 2, "title": "Low-Bit Quantization for Efficient and Accurate LLM Serving", "date": "", "ddg_snippet": "by Y Zhao · 2023 · Cited by 210 — The elements in quantized tensor can be calculated by: ¯X = clamp(⌊ X s ⌉ + z, 0, 2n − 1). We can further simplify this equation for symmetric ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.19102", "content": "by Y Zhao · 2023 · Cited by 210 — The elements in quantized tensor can be calculated by: ¯X = clamp(⌊ X s ⌉ + z, 0, 2n − 1). We can further simplify this equation for symmetric ..."}
+{"idx": 3, "title": "“A Practical Guide to Neural Network Quantization”", "date": "", "ddg_snippet": "5 Oct 2021 — Different types of quantization have pros and cons. Symmetric , asymmetric, signed, and unsigned quantization . Fixed point grid. Floating point ... 100 pages", "subpage_snippet": "", "source": "cms.tinyml.org", "link": "https://cms.tinyml.org/wp-content/uploads/industry-news/tinyML_Talks-_Marios_Fournarakis_210929.pdf", "content": "5 Oct 2021 — Different types of quantization have pros and cons. Symmetric , asymmetric, signed, and unsigned quantization . Fixed point grid. Floating point ... 100 pages"}
+{"idx": 4, "title": "Quantization Algorithms for Random Fourier Features", "date": "", "ddg_snippet": "by X Li · 2021 · Cited by 13 — Firstly, it is obvious that zx and zy are exchangeable, i.e., f( Zx ,Zy) = f(Zy, Zx ). Secondly, it is symmetric which means f( Zx ,Zy) = f(− Zx , −Zy). Moreover, the ... 12 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v139/li21i/li21i.pdf", "content": "by X Li · 2021 · Cited by 13 — Firstly, it is obvious that zx and zy are exchangeable, i.e., f( Zx ,Zy) = f(Zy, Zx ). Secondly, it is symmetric which means f( Zx ,Zy) = f(− Zx , −Zy). Moreover, the ... 12 pages"}
+{"idx": 5, "title": "GWQ: Group-Wise Quantization Framework for Neural Networks", "date": "", "ddg_snippet": "by J Yang · 2024 · Cited by 7 — The quantized value is represented by q, while s and z respectively indicate the scale factor and the zero point. 16 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v222/yang24a/yang24a.pdf", "content": "by J Yang · 2024 · Cited by 7 — The quantized value is represented by q, while s and z respectively indicate the scale factor and the zero point. 16 pages"}
+{"idx": 6, "title": "EFFICIENT LOW-BIT QUANTIZATION WITH ADAPTIVE ...", "date": "", "ddg_snippet": "n ,Qw p. ,. (2) x = Qa(x) ◦ αx + zx ,ˆw = Qw(w) ◦ αw,. (3) where x denotes the activation value, w denotes the weight value, and zx represents the zero -point.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/76ab99be64d4b1a73df6926b76b01e55278fafb1.pdf", "content": "n ,Qw p. ,. (2) x = Qa(x) ◦ αx + zx ,ˆw = Qw(w) ◦ αw,. (3) where x denotes the activation value, w denotes the weight value, and zx represents the zero -point."}
+{"idx": 7, "title": "RATE-DISTORTION OPTIMIZED POST-TRAINING ...", "date": "", "ddg_snippet": "by J Shi · Cited by 21 — (2021), zx typically makes integer computation more complicated. Therefore, practical accelerators usually apply the symmetric quantization assuming zx = 0 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=EA6YF_qwVe", "content": "by J Shi · Cited by 21 — (2021), zx typically makes integer computation more complicated. Therefore, practical accelerators usually apply the symmetric quantization assuming zx = 0 ."}
+{"idx": 8, "title": "Proposed First-Generation WSQ Bit Allocation Procedure", "date": "", "ddg_snippet": "8 Sept 1993 — Quantization encoding of the kth two-dimensional SWT subband, a.(m, n ), is given by. The notation. PL(m, n ) = (ap (m, n )- Zx /2) + 1, ax(m, n ) > Zk/ ...", "subpage_snippet": "", "source": "www.nist.gov", "link": "https://www.nist.gov/document/6-wsqbradleybrislawnbitprocedure-19930908pdf", "content": "8 Sept 1993 — Quantization encoding of the kth two-dimensional SWT subband, a.(m, n ), is given by. The notation. PL(m, n ) = (ap (m, n )- Zx /2) + 1, ax(m, n ) > Zk/ ..."}
+{"idx": 9, "title": "MQBench: Towards Reproducible and Deployable Model ...", "date": "", "ddg_snippet": "by Y Li · Cited by 74 — For symmetric quantization , the zero point is initialized to 0 , and kept fixed. For asymmetric quantization , zero point is initialized to Nmin if the ... 26 pages", "subpage_snippet": "", "source": "datasets-benchmarks-proceedings.neurips.cc", "link": "https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/c20ad4d76fe97759aa27a0c99bff6710-Paper-round1.pdf", "content": "by Y Li · Cited by 74 — For symmetric quantization , the zero point is initialized to 0 , and kept fixed. For asymmetric quantization , zero point is initialized to Nmin if the ... 26 pages"}
diff --git a/data/sampled_jsons/systematization_operationalization_Section_3.1_Evaluating_Generative_AI_Systems_Social_Science_Measu.jsonl b/data/sampled_jsons/systematization_operationalization_Section_3.1_Evaluating_Generative_AI_Systems_Social_Science_Measu.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ff9b08a96a058f499c603849aa396ec235bfecae
--- /dev/null
+++ b/data/sampled_jsons/systematization_operationalization_Section_3.1_Evaluating_Generative_AI_Systems_Social_Science_Measu.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "... government [e.g., 22 , 10 , 23 ] , there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.10939v1", "content": "... government [e.g., 22 , 10 , 23 ] , there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems ..."}
+{"idx": 1, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge ... measurement tasks involved in evaluating GenAI systems are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge ... measurement tasks involved in evaluating GenAI systems are ..."}
+{"idx": 2, "title": "Multi-Agent LLMs as Ethics Advocates for AI based Systems", "date": "", "ddg_snippet": "Yamani et al., [ 9 ] used three state-of-the-art LLMs to generate 3000 user stories for 100 AI -based systems , constructing the UStAI dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.08392v3", "content": "Yamani et al., [ 9 ] used three state-of-the-art LLMs to generate 3000 user stories for 100 AI -based systems , constructing the UStAI dataset."}
+{"idx": 3, "title": "Generative AI in Singapore | The Oxford Handbook of the", "date": "", "ddg_snippet": "The Oxford Handbook of the Foundations and Regulation of Generative AI ... Literary Studies (1500 to 1800) ... Literary Studies ( Science Fiction)", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/edited-volume/59908/chapter/512470725", "content": "The Oxford Handbook of the Foundations and Regulation of Generative AI ... Literary Studies (1500 to 1800) ... Literary Studies ( Science Fiction)"}
+{"idx": 4, "title": "Impact of Generative AI on Industries and Organizations", "date": "", "ddg_snippet": "... of sociotechnical envelopment by demonstrating the ways in which an organization s successful AI envelopment depends on the interaction of social and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392620106_Impact_of_Generative_AI_on_Industries_and_Organizations", "content": "... of sociotechnical envelopment by demonstrating the ways in which an organization s successful AI envelopment depends on the interaction of social and ..."}
+{"idx": 5, "title": "Estimating the environmental impact of Generative-AI services", "date": "", "ddg_snippet": "Regarding the AI life cycle -which consists of the phases depicted in Figure 1 -many studies limit themselves to the learning/training phase of AI [ 3 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380762688_Estimating_the_environmental_impact_of_Generative-AI_services_using_an_LCA-based_methodology", "content": "Regarding the AI life cycle -which consists of the phases depicted in Figure 1 -many studies limit themselves to the learning/training phase of AI [ 3 ..."}
+{"idx": 6, "title": "Towards Interactive Evaluations for Interaction Harms in", "date": "", "ddg_snippet": "... processing, and the social sciences , we address these measurement challenges by presenting practical principles for designing interactive evaluations ...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "... processing, and the social sciences , we address these measurement challenges by presenting practical principles for designing interactive evaluations ..."}
+{"idx": 7, "title": "Thinking About Propensity Evaluations — LessWrong", "date": "", "ddg_snippet": "Propensity evaluations are one of the tools we have to measure the alignment (in actions) of AI systems . ... evaluated (including bias in the quality ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/sWf8wj64AdDfMeTvf/thinking-about-propensity-evaluations", "content": "Propensity evaluations are one of the tools we have to measure the alignment (in actions) of AI systems . ... evaluated (including bias in the quality ..."}
+{"idx": 8, "title": "The Evolving Role of Large Language Models in Scientific", "date": "", "ddg_snippet": "... generation, experimental design and implementation, result analysis, manuscript preparation, and culminating in peer review [ 116 , 233 , 34 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11810v1", "content": "... generation, experimental design and implementation, result analysis, manuscript preparation, and culminating in peer review [ 116 , 233 , 34 ] ."}
+{"idx": 9, "title": "FutureGen: A RAG-based Approach to Generate the Future Work of", "date": "", "ddg_snippet": "... AI can systematically synthesize research trajectories, uncover latent connections, and propose novel directions that align with emerging trends [ 10 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16561v3", "content": "... AI can systematically synthesize research trajectories, uncover latent connections, and propose novel directions that align with emerging trends [ 10 ..."}
diff --git a/data/sampled_jsons/systematization_operationalization_measurement_theory_benefit_separation.jsonl b/data/sampled_jsons/systematization_operationalization_measurement_theory_benefit_separation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..817999a16e9a39a1613f46c305a9390c8a66f594
--- /dev/null
+++ b/data/sampled_jsons/systematization_operationalization_measurement_theory_benefit_separation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Frontiers | Respectable Challenges to Respectable Theory : Cognitive...", "date": "", "ddg_snippet": "Keywords: cognitive dissonance, replication crisis, operationalization , measurement , theory , methodology. Citation: Vaidis DC and Bran A (2019) Respectable Challenges to Respectable Theory ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2019.01189/full", "content": "Keywords: cognitive dissonance, replication crisis, operationalization , measurement , theory , methodology. Citation: Vaidis DC and Bran A (2019) Respectable Challenges to Respectable Theory ..."}
+{"idx": 1, "title": "What is Operationalization in Research? – R3ciprocity Blog", "date": "", "ddg_snippet": "To do operationalization well, one must think about how to measure variables in as many ways as possible and capture the construct as directly as possible.", "subpage_snippet": "", "source": "blog.r3ciprocity.com", "link": "https://blog.r3ciprocity.com/what-is-operationalization-in-research/", "content": "To do operationalization well, one must think about how to measure variables in as many ways as possible and capture the construct as directly as possible."}
+{"idx": 2, "title": "Albert Bandura's Social Learning Theory In Psychology", "date": "", "ddg_snippet": "Social Learning Theory , developed by Albert Bandura, suggests that people learn by observing others. It emphasizes the importance of imitation, modeling, and reinforcement in the learning process.", "subpage_snippet": "", "source": "www.simplypsychology.org", "link": "https://www.simplypsychology.org/bandura.html", "content": "Social Learning Theory , developed by Albert Bandura, suggests that people learn by observing others. It emphasizes the importance of imitation, modeling, and reinforcement in the learning process."}
+{"idx": 3, "title": "Challenges for implementing the Systematization of Nursing Care in...", "date": "", "ddg_snippet": "Oliveira, M. R, et. al (2019). Nursing care systematization : perceptions and knowledge of the Brazilian nursing. Rev Bras Enferm. V.72(6):1547-53.", "subpage_snippet": "", "source": "rsdjournal.org", "link": "https://rsdjournal.org/index.php/rsd/article/view/20603", "content": "Oliveira, M. R, et. al (2019). Nursing care systematization : perceptions and knowledge of the Brazilian nursing. Rev Bras Enferm. V.72(6):1547-53."}
+{"idx": 4, "title": "Position: Evaluating Generative AI Systems Is a Social Science...", "date": "", "ddg_snippet": "The separation of systematization and operationalization can.The background concept, the systematized concept, the measurement instruments, and the measurements are linked by four processes: systematization , operationalization , application, and interrogation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561", "content": "The separation of systematization and operationalization can.The background concept, the systematized concept, the measurement instruments, and the measurements are linked by four processes: systematization , operationalization , application, and interrogation."}
+{"idx": 5, "title": "AI Red Teaming Through the Lens of", "date": "", "ddg_snippet": "Using measurement theory for valid measurement . Red teaming through the lens of measurement theory .the “Concept” column of the measurement framework shown in Figure 1, these levels are connected via three processes: systematization , operationalization , and application.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KEggQCeDUA", "content": "Using measurement theory for valid measurement . Red teaming through the lens of measurement theory .the “Concept” column of the measurement framework shown in Figure 1, these levels are connected via three processes: systematization , operationalization , and application."}
+{"idx": 6, "title": "(PDF) Position: Evaluating Generative AI Systems is a Social Science...", "date": "", "ddg_snippet": "measurements —i.e., measurement instruments. Separating . the systematization and operationalization processes can. enable stakeholders with different perspectives—e.g., open-source developers, policymakers, users, members of.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388657599_Position_Evaluating_Generative_AI_Systems_is_a_Social_Science_Measurement_Challenge", "content": "measurements —i.e., measurement instruments. Separating . the systematization and operationalization processes can. enable stakeholders with different perspectives—e.g., open-source developers, policymakers, users, members of."}
+{"idx": 7, "title": "tandfonline.com/doi/abs/10.1080/01490408609513056", "date": "", "ddg_snippet": "Benefits ,", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/abs/10.1080/01490408609513056", "content": "Benefits ,"}
+{"idx": 8, "title": "Microsoft PowerPoint - chapter02.ppt", "date": "", "ddg_snippet": "Definition of Theory : A theory is a systematic set of interrelated statements intended to explain some aspect of social life. Measurement of variables Issues related: conceptualization, operationalization , measurement .", "subpage_snippet": "", "source": "content.csbs.utah.edu", "link": "https://content.csbs.utah.edu/~fan/fcs3200/slides/chapter02.pdf", "content": "Definition of Theory : A theory is a systematic set of interrelated statements intended to explain some aspect of social life. Measurement of variables Issues related: conceptualization, operationalization , measurement ."}
+{"idx": 9, "title": "Gall’s Law: Everything You Need To Know — TechMagic", "date": "", "ddg_snippet": "According to Goodhart's Law, measure -driven optimizations may result in the devaluation of the measurement results themselves. People tend to optimise locally by \"gaming\" the system to meet certain metrics rather than considering the overall consequence of their activities.", "subpage_snippet": "", "source": "www.techmagic.co", "link": "https://www.techmagic.co/blog/galls-law", "content": "According to Goodhart's Law, measure -driven optimizations may result in the devaluation of the measurement results themselves. People tend to optimise locally by \"gaming\" the system to meet certain metrics rather than considering the overall consequence of their activities."}
diff --git a/data/sampled_jsons/tfemquulED_Training-Free_Diffusion_Model_Alignment_Tanh-C_Tanh_comparison.jsonl b/data/sampled_jsons/tfemquulED_Training-Free_Diffusion_Model_Alignment_Tanh-C_Tanh_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..164edf4389efd55df27475827c0de8be565e5b7a
--- /dev/null
+++ b/data/sampled_jsons/tfemquulED_Training-Free_Diffusion_Model_Alignment_Tanh-C_Tanh_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.05760] Training-free Diffusion Model Alignment with ... Published as a conference paper at ICLR 2025 - OpenReview DAS (Diffusion Alignment as Sampling), ICLR'25 Spotlight Alignment without Over-optimization: Training-Free Solution ... TFG: Unified Training-Free Guidance for Diffusion Models ALIGNMENT WITHOUT OVER OPTIMIZATION T -F SOLUTION FOR ... Training - free Diffusion Model Alignment with Sampling Demons Training - free Diffusion Model Alignment with Sampling Demons DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Oct 8, 2024 · To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training . To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training . Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Dec 31, 2024 · Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Sep 24, 2024 · Given an unconditional diffusion model and a predictor for a target property of interest (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training . Existing methods, though effective in various individual applications, often lack theoretical grounding and rigorous testing on extensive benchmarks. As a result ... We propose DAS, a training-free method for aligning diffusion models with arbitrary rewards while preserving general capabilities. We provide theoretical analysis of DAS’s asymptotic properties, proving the benefits of tempering in SMC sampling for diffusion models . What is the first inference-time preference alignment method for diffusion models? To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models. Our method can be easily integrated with existing diffusion models without further training . Can diffusion models be aligned with user preferences? Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. Are AI diffusion models good for generative tasks? 1 Seoul National University, 2 KRAFTON AI Diffusion models excel in generative tasks , but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Is there a test-time alignment of diffusion models without reward over-optimization? This is the official implementation of our paper Test-time Alignment of Diffusion Models without Reward Over-optimization by Sunwoo Kim 1, Minkyu Kim 2, Dongmin Park 2. 1 Seoul National University, 2 KRAFTON AI Who wrote 'test-time alignment of diffusion models without reward over-optimization'? title={Test-time Alignment of Diffusion Models without Reward Over-optimization}, author={ Sunwoo Kim and Minkyu Kim and Dongmin Park }, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, How to run aesthetic score experiment with stable diffusion? To run Aesthetic score experiment with Stable Diffusion 1.5: To run PickScore experiment: To run multi-objective (Aesthetic score + CLIPScore) experiment: where the ratio of two rewards can be customized in the config file. Similarly, to use SDXL or LCM: To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.05760", "content": "Oct 8, 2024 · To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training . To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training . Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Dec 31, 2024 · Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Sep 24, 2024 · Given an unconditional diffusion model and a predictor for a target property of interest (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training . Existing methods, though effective in various individual applications, often lack theoretical grounding and rigorous testing on extensive benchmarks. As a result ... We propose DAS, a training-free method for aligning diffusion models with arbitrary rewards while preserving general capabilities. We provide theoretical analysis of DAS’s asymptotic properties, proving the benefits of tempering in SMC sampling for diffusion models . What is the first inference-time preference alignment method for diffusion models? To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models. Our method can be easily integrated with existing diffusion models without further training . Can diffusion models be aligned with user preferences? Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. Are AI diffusion models good for generative tasks? 1 Seoul National University, 2 KRAFTON AI Diffusion models excel in generative tasks , but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Is there a test-time alignment of diffusion models without reward over-optimization? This is the official implementation of our paper Test-time Alignment of Diffusion Models without Reward Over-optimization by Sunwoo Kim 1, Minkyu Kim 2, Dongmin Park 2. 1 Seoul National University, 2 KRAFTON AI Who wrote 'test-time alignment of diffusion models without reward over-optimization'? title={Test-time Alignment of Diffusion Models without Reward Over-optimization}, author={ Sunwoo Kim and Minkyu Kim and Dongmin Park }, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, How to run aesthetic score experiment with stable diffusion? To run Aesthetic score experiment with Stable Diffusion 1.5: To run PickScore experiment: To run multi-objective (Aesthetic score + CLIPScore) experiment: where the ratio of two rewards can be customized in the config file. Similarly, to use SDXL or LCM: To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training ."}
+{"idx": 1, "title": "DAS (Diffusion Alignment as Sampling), ICLR'25 Spotlight", "date": "", "ddg_snippet": "Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/krafton-ai/DAS", "content": "Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively."}
+{"idx": 2, "title": "Alignment without Over-optimization: Training-Free Solution ...", "date": "", "ddg_snippet": "Dec 31, 2024 · Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=hSXMGcJAMG", "content": "Dec 31, 2024 · Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively."}
+{"idx": 3, "title": "TFG: Unified Training-Free Guidance for Diffusion Models ALIGNMENT WITHOUT OVER OPTIMIZATION T -F SOLUTION FOR ... Training - free Diffusion Model Alignment with Sampling Demons Training - free Diffusion Model Alignment with Sampling Demons DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight DAS ( Diffusion Alignment as Sampling), ICLR'25 Spotlight Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Sep 24, 2024 · Given an unconditional diffusion model and a predictor for a target property of interest (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training . Existing methods, though effective in various individual applications, often lack theoretical grounding and rigorous testing on extensive benchmarks. As a result ... We propose DAS, a training-free method for aligning diffusion models with arbitrary rewards while preserving general capabilities. We provide theoretical analysis of DAS’s asymptotic properties, proving the benefits of tempering in SMC sampling for diffusion models . What is the first inference-time preference alignment method for diffusion models? To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models. Our method can be easily integrated with existing diffusion models without further training . Can diffusion models be aligned with user preferences? Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. Are AI diffusion models good for generative tasks? 1 Seoul National University, 2 KRAFTON AI Diffusion models excel in generative tasks , but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Is there a test-time alignment of diffusion models without reward over-optimization? This is the official implementation of our paper Test-time Alignment of Diffusion Models without Reward Over-optimization by Sunwoo Kim 1, Minkyu Kim 2, Dongmin Park 2. 1 Seoul National University, 2 KRAFTON AI Who wrote 'test-time alignment of diffusion models without reward over-optimization'? title={Test-time Alignment of Diffusion Models without Reward Over-optimization}, author={ Sunwoo Kim and Minkyu Kim and Dongmin Park }, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, How to run aesthetic score experiment with stable diffusion? To run Aesthetic score experiment with Stable Diffusion 1.5: To run PickScore experiment: To run multi-objective (Aesthetic score + CLIPScore) experiment: where the ratio of two rewards can be customized in the config file. Similarly, to use SDXL or LCM: To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.15761", "content": "Sep 24, 2024 · Given an unconditional diffusion model and a predictor for a target property of interest (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training . Existing methods, though effective in various individual applications, often lack theoretical grounding and rigorous testing on extensive benchmarks. As a result ... We propose DAS, a training-free method for aligning diffusion models with arbitrary rewards while preserving general capabilities. We provide theoretical analysis of DAS’s asymptotic properties, proving the benefits of tempering in SMC sampling for diffusion models . What is the first inference-time preference alignment method for diffusion models? To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models. Our method can be easily integrated with existing diffusion models without further training . Can diffusion models be aligned with user preferences? Aligning diffusion models with user preferences has been a key challenge . Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. Are AI diffusion models good for generative tasks? 1 Seoul National University, 2 KRAFTON AI Diffusion models excel in generative tasks , but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Is there a test-time alignment of diffusion models without reward over-optimization? This is the official implementation of our paper Test-time Alignment of Diffusion Models without Reward Over-optimization by Sunwoo Kim 1, Minkyu Kim 2, Dongmin Park 2. 1 Seoul National University, 2 KRAFTON AI Who wrote 'test-time alignment of diffusion models without reward over-optimization'? title={Test-time Alignment of Diffusion Models without Reward Over-optimization}, author={ Sunwoo Kim and Minkyu Kim and Dongmin Park }, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, How to run aesthetic score experiment with stable diffusion? To run Aesthetic score experiment with Stable Diffusion 1.5: To run PickScore experiment: To run multi-objective (Aesthetic score + CLIPScore) experiment: where the ratio of two rewards can be customized in the config file. Similarly, to use SDXL or LCM: To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training ."}
+{"idx": 4, "title": "ALIGNMENT WITHOUT OVER OPTIMIZATION T -F SOLUTION FOR ...", "date": "", "ddg_snippet": "We propose DAS, a training-free method for aligning diffusion models with arbitrary rewards while preserving general capabilities. We provide theoretical analysis of DAS’s asymptotic properties, proving the benefits of tempering in SMC sampling for diffusion models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.05803v1", "content": "We propose DAS, a training-free method for aligning diffusion models with arbitrary rewards while preserving general capabilities. We provide theoretical analysis of DAS’s asymptotic properties, proving the benefits of tempering in SMC sampling for diffusion models ."}
+{"idx": 5, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760", "content": "To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training ."}
+{"idx": 6, "title": "Training-Free Diffusion Model Alignment with Sampling ...", "date": "", "ddg_snippet": "by PH Yeh · Cited by 5 — Reviewer RS1X highlighted a performance discrepancy between the ODE solver and the CM solver, resulting in a performance gap between Tanh and Tanh - C . To address ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tfemquulED", "content": "by PH Yeh · Cited by 5 — Reviewer RS1X highlighted a performance discrepancy between the ODE solver and the CM solver, resulting in a performance gap between Tanh and Tanh - C . To address ..."}
+{"idx": 7, "title": "[2412.00759] DyMO: Training - Free Diffusion Model Alignment with...", "date": "", "ddg_snippet": "View a PDF of the paper titled DyMO: Training - Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling, by Xin Xie and Dong Gong.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.00759", "content": "View a PDF of the paper titled DyMO: Training - Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling, by Xin Xie and Dong Gong."}
+{"idx": 8, "title": "GitHub - xie-lab-ml/awesome- alignment -of- diffusion - models : The...", "date": "", "ddg_snippet": "Alignment Techniques of Diffusion Models .DyMO: Training - Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling. CVPR 2025, [pdf].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xie-lab-ml/awesome-alignment-of-diffusion-models", "content": "Alignment Techniques of Diffusion Models .DyMO: Training - Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling. CVPR 2025, [pdf]."}
+{"idx": 9, "title": "Training - free Diffusion Model Alignment with Sampling... | alphaXiv", "date": "", "ddg_snippet": "Abstract: Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2410.05760v2", "content": "Abstract: Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions."}
diff --git a/data/sampled_jsons/tlniJJFUW2_table_1_Schubert_polynomials_n=6_accuracy_results.jsonl b/data/sampled_jsons/tlniJJFUW2_table_1_Schubert_polynomials_n=6_accuracy_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ff20428572bf557861ddaa69d8532e7f7da3b661
--- /dev/null
+++ b/data/sampled_jsons/tlniJJFUW2_table_1_Schubert_polynomials_n=6_accuracy_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Schubert polynomial - Wikipedia", "date": "", "ddg_snippet": "Lascoux (1995) described the history of Schubert polynomials . The Schubert polynomials are polynomials in the variables depending on an element of the infinite symmetric group of all permutations of fixing all but a finite number of elements. They form a basis for the polynomial ring in infinitely many variables. The cohomology of the flag manifold is where is the ideal generated by ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Schubert_polynomial", "content": "Lascoux (1995) described the history of Schubert polynomials . The Schubert polynomials are polynomials in the variables depending on an element of the infinite symmetric group of all permutations of fixing all but a finite number of elements. They form a basis for the polynomial ring in infinitely many variables. The cohomology of the flag manifold is where is the ideal generated by ..."}
+{"idx": 1, "title": "PDF Schubert Polynomials and Symmetric Functions Notes for The Lisbon ...", "date": "", "ddg_snippet": "rt polynomials we de-fine using these divided difference operators all have positive coefficients! While Schubert polynomials date to 1973, proof of the positivity only came in 1991, w", "subpage_snippet": "", "source": "pi.math.cornell.edu", "link": "https://pi.math.cornell.edu/~allenk/schubnotes.pdf", "content": "rt polynomials we de-fine using these divided difference operators all have positive coefficients! While Schubert polynomials date to 1973, proof of the positivity only came in 1991, w"}
+{"idx": 2, "title": "GitHub - AcraeaTerpsicore/Schubert-polynomials-and-patterns-in ...", "date": "", "ddg_snippet": "Overview This repository provides complete computational verification of results from the research paper \" Schubert polynomials and patterns in permutations\" (arXiv:2412.02932v1) by Peter L. Guo and Zhuowei Lin. Our implementations compute and verify all numerical values, tables , and conjectures presented in the paper using first-principles mathematical definitions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcraeaTerpsicore/Schubert-polynomials-and-patterns-in-permutations", "content": "Overview This repository provides complete computational verification of results from the research paper \" Schubert polynomials and patterns in permutations\" (arXiv:2412.02932v1) by Peter L. Guo and Zhuowei Lin. Our implementations compute and verify all numerical values, tables , and conjectures presented in the paper using first-principles mathematical definitions."}
+{"idx": 3, "title": "Schubert polynomials Type B/C/D Schubert polynomials Skew ... - SymCat", "date": "", "ddg_snippet": "The quantum Schubert polynomials S ω q (x) is a deformation of the Schubert polynomials by a vector q = (q 1 ,, q n 1 ) These were introduced in [FGP97]. Recall the formula that expresses the Schubert polynomials as sums of products of elementary symmetric functions:", "subpage_snippet": "", "source": "www.symmetricfunctions.com", "link": "https://www.symmetricfunctions.com/schubert.htm", "content": "The quantum Schubert polynomials S ω q (x) is a deformation of the Schubert polynomials by a vector q = (q 1 ,, q n 1 ) These were introduced in [FGP97]. Recall the formula that expresses the Schubert polynomials as sums of products of elementary symmetric functions:"}
+{"idx": 4, "title": "Schubert Polynomials and the Nilcoxeter Algebra - ScienceDirect", "date": "", "ddg_snippet": "We give a new development of the theory of Schubert polynomials based on formal computations in the algebra of operators u1, u2, ... satisfying the relations u2i =0, uiuj = ujui if | i − j | ≥ 2, and uiui+1ui = ui + 1uiui + 1 . We call this algebra the nilCoxeter algebra of the symmetric group S n.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0001870884710097", "content": "We give a new development of the theory of Schubert polynomials based on formal computations in the algebra of operators u1, u2, ... satisfying the relations u2i =0, uiuj = ujui if | i − j | ≥ 2, and uiui+1ui = ui + 1uiui + 1 . We call this algebra the nilCoxeter algebra of the symmetric group S n."}
+{"idx": 5, "title": "PDF Polynomials from Schubert Calculus via Diagrams", "date": "", "ddg_snippet": "Polynomials are power tools Schur polynomials Representation theory: characters of classical Lie algebras (and the symmetric group) Geometry: polynomial representatives of Grassmannian Schubert varieties", "subpage_snippet": "", "source": "users.math.msu.edu", "link": "https://users.math.msu.edu/group/combinatorics-graph-theory-seminar/pan.pdf", "content": "Polynomials are power tools Schur polynomials Representation theory: characters of classical Lie algebras (and the symmetric group) Geometry: polynomial representatives of Grassmannian Schubert varieties"}
+{"idx": 6, "title": "PDF The Prism tableau model for Schubert polynomials", "date": "", "ddg_snippet": "Abstract. The Schubert polynomials lift the Schur basis of symmetric polynomials into a basis for Z[x1; x2; : : :]. We suggest the prism tableau model for these polynomials . A novel aspect of this alternative to earlier results is that it directly invokes semistandard tableaux; it does so as part of a colored tableau amalgam.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-02168181/file/final_143.pdf", "content": "Abstract. The Schubert polynomials lift the Schur basis of symmetric polynomials into a basis for Z[x1; x2; : : :]. We suggest the prism tableau model for these polynomials . A novel aspect of this alternative to earlier results is that it directly invokes semistandard tableaux; it does so as part of a colored tableau amalgam."}
+{"idx": 7, "title": "Chapter 4: Schubert Polynomials (1) - Notes on Schubert Polynomials", "date": "", "ddg_snippet": "Notes on Schubert Polynomials Chapter 4 Arun Ram Department of Mathematics and Statistics University of Melbourne Parkville, VIC 3010 Australia aram@unimelb.edu.au Last update: 28 June 2013 Schubert Polynomials ( 1 ) Let δ = δ n = (n - 1 , n - 2, …, 1 , 0), so that", "subpage_snippet": "", "source": "math.soimeme.org", "link": "http://math.soimeme.org/~arunram/Resources/NOSPC4SchubertPolynomials1.html", "content": "Notes on Schubert Polynomials Chapter 4 Arun Ram Department of Mathematics and Statistics University of Melbourne Parkville, VIC 3010 Australia aram@unimelb.edu.au Last update: 28 June 2013 Schubert Polynomials ( 1 ) Let δ = δ n = (n - 1 , n - 2, …, 1 , 0), so that"}
+{"idx": 8, "title": "PDF Schubert Polynomial Expansions Revisited", "date": "", "ddg_snippet": "Despite their relatively simple definition, Schubert polynomials are complicated combinatorial objects. Many combinatorial formulas for Schubert polynomials exist, such as the algorithmic method of Kohnert [ 1 , 11] , the pipe dreams of Bergeron-Billey [3] and Fomin-Kirillov [7], the slide expansions of Billey-Jockusch-Stanley [5] and Assaf-Searles [2], the balanced tableaux of Fomin ...", "subpage_snippet": "", "source": "www.math.toronto.edu", "link": "https://www.math.toronto.edu/vvtewari/expansions.pdf", "content": "Despite their relatively simple definition, Schubert polynomials are complicated combinatorial objects. Many combinatorial formulas for Schubert polynomials exist, such as the algorithmic method of Kohnert [ 1 , 11] , the pipe dreams of Bergeron-Billey [3] and Fomin-Kirillov [7], the slide expansions of Billey-Jockusch-Stanley [5] and Assaf-Searles [2], the balanced tableaux of Fomin ..."}
+{"idx": 9, "title": "PDF Notes on Schubert Polynomials I. G. Macdonald", "date": "", "ddg_snippet": "This is a modern recompilation of the TEX source used to produce the original version published in 1991. Changes to the source were made to ensure compatibility with pdfTEX (2024) and to add hyperlinks to the document. Except for a footnote added in the appendix to Chapter IV, no changes were made to the original content of the document itself, and the original numbering for equations ...", "subpage_snippet": "", "source": "lacim.uqam.ca", "link": "https://lacim.uqam.ca/les-parutions/LACIM-Publications-Volume-06.pdf", "content": "This is a modern recompilation of the TEX source used to produce the original version published in 1991. Changes to the source were made to ensure compatibility with pdfTEX (2024) and to add hyperlinks to the document. Except for a footnote added in the appendix to Chapter IV, no changes were made to the original content of the document itself, and the original numbering for equations ..."}
diff --git a/data/sampled_jsons/transformer_architecture_remote_sensing_imagery_vision_year_2024.jsonl b/data/sampled_jsons/transformer_architecture_remote_sensing_imagery_vision_year_2024.jsonl
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index 0000000000000000000000000000000000000000..89a12403a7bf9c77ed213fac4ef928cf196edd8b
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformers in Remote Sensing: A Survey - MDPI", "date": "", "ddg_snippet": "Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformer -based architectures , originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2072-4292/15/7/1860", "content": "Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformer -based architectures , originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long ..."}
+{"idx": 1, "title": "SceneFormer: Neural Architecture Search of Transformers for Remote ...", "date": "", "ddg_snippet": "Deep learning-based scene classification methods have long been a key research area in remote sensing imagery due to their wide-ranging applications. Recently, Transformer models have achieved significant progress in computer vision , making vision transformers (ViTs) a promising direction for scene classification. However, the spatial complexity of remote sensing imagery poses unique ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10942436", "content": "Deep learning-based scene classification methods have long been a key research area in remote sensing imagery due to their wide-ranging applications. Recently, Transformer models have achieved significant progress in computer vision , making vision transformers (ViTs) a promising direction for scene classification. However, the spatial complexity of remote sensing imagery poses unique ..."}
+{"idx": 2, "title": "VistaFormer: Scalable Vision Transformers for Satellite Image Time ...", "date": "", "ddg_snippet": "We introduce VistaFormer, a lightweight Transformer -based model architecture for the semantic segmentation of remote-sensing images. This model uses a multi-scale Transformer -based encoder with a lightweight decoder that aggregates global and local attention captured in the encoder blocks. VistaFormer uses position-free self-attention layers which simplifies the model architecture and removes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.08461", "content": "We introduce VistaFormer, a lightweight Transformer -based model architecture for the semantic segmentation of remote-sensing images. This model uses a multi-scale Transformer -based encoder with a lightweight decoder that aggregates global and local attention captured in the encoder blocks. VistaFormer uses position-free self-attention layers which simplifies the model architecture and removes ..."}
+{"idx": 3, "title": "Two-stage cascaded vision transformer with spatial attention for dense ...", "date": "", "ddg_snippet": "Article Open access Published: 23 May 2025 Two-stage cascaded vision transformer with spatial attention for dense settlement detection in remote sensing imagery Qi Zhong, Jun Luo, Jingxin Fang ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s40494-025-01787-8", "content": "Article Open access Published: 23 May 2025 Two-stage cascaded vision transformer with spatial attention for dense settlement detection in remote sensing imagery Qi Zhong, Jun Luo, Jingxin Fang ..."}
+{"idx": 4, "title": "Transformers for Remote Sensing: A Systematic Review and Analysis", "date": "", "ddg_snippet": "The RS field has also witnessed the performance of transformers in RS image processing [5]. However, due to various factors, including the greater number of parameters in remote sensing images compared to natural images [6] and the limited availability of such imagery , research on transformers in RS remains relatively nascent.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11175147/", "content": "The RS field has also witnessed the performance of transformers in RS image processing [5]. However, due to various factors, including the greater number of parameters in remote sensing images compared to natural images [6] and the limited availability of such imagery , research on transformers in RS remains relatively nascent."}
+{"idx": 5, "title": "FocalSR: Revisiting image super-resolution transformers with fourier ...", "date": "", "ddg_snippet": "Remote sensing images have relatively lower resolution compared to the common super-resolution training dataset and each landscape object covers a small area on the image. These natures of remote sensing images significantly reduced the attention pixels for image restoration in existing transformer -based methods.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1195103624008759", "content": "Remote sensing images have relatively lower resolution compared to the common super-resolution training dataset and each landscape object covers a small area on the image. These natures of remote sensing images significantly reduced the attention pixels for image restoration in existing transformer -based methods."}
+{"idx": 6, "title": "PDF Transformers for Remote Sensing: A Systematic Review and Analysis - IGES", "date": "", "ddg_snippet": "We also found that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development.", "subpage_snippet": "", "source": "www.iges.or.jp", "link": "https://www.iges.or.jp/en/publication_documents/pub/peer/en/13701/sensors+-+Wang+et+al+2024.pdf", "content": "We also found that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development."}
+{"idx": 7, "title": "Vision Transformers for Remote Sensing Applications", "date": "", "ddg_snippet": "Remote sensing has become an indispensable tool for various applications, including environmental monitoring, urban planning, and disaster management, and many more. With the increasing availability of high-resolution satellite and aerial imagery , there is a pressing need for advanced image analysis techniques. Traditionally, convolutional neural networks (CNNs) have dominated the field of ...", "subpage_snippet": "", "source": "science.iirs.gov.in", "link": "https://science.iirs.gov.in/vision-transformers-for-remote-sensing-applications/", "content": "Remote sensing has become an indispensable tool for various applications, including environmental monitoring, urban planning, and disaster management, and many more. With the increasing availability of high-resolution satellite and aerial imagery , there is a pressing need for advanced image analysis techniques. Traditionally, convolutional neural networks (CNNs) have dominated the field of ..."}
+{"idx": 8, "title": "Vision Transformers for Remote Sensing Image Classification", "date": "", "ddg_snippet": "In this paper, we propose a remote-sensing scene-classification method based on vision transformers . These types of networks, which are now recognized as state-of-the-art models in natural language processing, do not rely on convolution layers as in standard convolutional neural networks (CNNs). Instead, they use multihead attention mechanisms as the main building block to derive long-range ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2072-4292/13/3/516", "content": "In this paper, we propose a remote-sensing scene-classification method based on vision transformers . These types of networks, which are now recognized as state-of-the-art models in natural language processing, do not rely on convolution layers as in standard convolutional neural networks (CNNs). Instead, they use multihead attention mechanisms as the main building block to derive long-range ..."}
+{"idx": 9, "title": "[2209.01206] Transformers in Remote Sensing: A Survey", "date": "", "ddg_snippet": "Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers -based architectures , originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2209.01206", "content": "Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers -based architectures , originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long ..."}
diff --git a/data/sampled_jsons/unstable_training_Song_et_al_2021_score-based_generative_modeling_year_2021.jsonl b/data/sampled_jsons/unstable_training_Song_et_al_2021_score-based_generative_modeling_year_2021.jsonl
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index 0000000000000000000000000000000000000000..ad27d51c26e7a3e21ff2abc5e95953c97dd1424c
--- /dev/null
+++ b/data/sampled_jsons/unstable_training_Song_et_al_2021_score-based_generative_modeling_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Score - Based Generative Classiers", "date": "", "ddg_snippet": "Score - Based Generative Classiers. arXiv:2110.00473v2 [stat.ML] 11 Dec 2021 . Classification Accuracy [%].The most straightforward application of score - based generative models as classiers would be to train one model per class as in Schott et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2110.00473", "content": "Score - Based Generative Classiers. arXiv:2110.00473v2 [stat.ML] 11 Dec 2021 . Classification Accuracy [%].The most straightforward application of score - based generative models as classiers would be to train one model per class as in Schott et al ."}
+{"idx": 1, "title": "Sequential Neural Score Estimation: Likelihood-Free Inference with...", "date": "", "ddg_snippet": "Song , Y. and Ermon, S. Improved techniques for training score - based generative models .", "subpage_snippet": "", "source": "eprints.lancs.ac.uk", "link": "https://eprints.lancs.ac.uk/id/eprint/222774/1/9103_Sequential_Neural_Score_E.pdf", "content": "Song , Y. and Ermon, S. Improved techniques for training score - based generative models ."}
+{"idx": 2, "title": "Breaking the Algorithmic Ceiling in Pre- Training with Inductive Moment...", "date": "", "ddg_snippet": "Song et al . “Denoising Diffusion Implicit Models .” ICLR 2021 .“ Score - Based Generative Modeling through Stochastic Differential Equations.” ICLR 2021 .", "subpage_snippet": "", "source": "lumalabs.ai", "link": "https://lumalabs.ai/blog/engineering/inductive-moment-matching", "content": "Song et al . “Denoising Diffusion Implicit Models .” ICLR 2021 .“ Score - Based Generative Modeling through Stochastic Differential Equations.” ICLR 2021 ."}
+{"idx": 3, "title": "Breaking Determinism: Fuzzy Modeling of Sequential", "date": "", "ddg_snippet": "Score - based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.In this section, we introduce a Score - Based Generative Model (SGMs) Song et al .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/286d67ff96f99c614f75dbcfb72a3e5f-Paper-Conference.pdf", "content": "Score - based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.In this section, we introduce a Score - Based Generative Model (SGMs) Song et al ."}
+{"idx": 4, "title": "(PDF) Direct Discriminative Optimization: Your Likelihood- Based Visual...", "date": "", "ddg_snippet": "Song et al ., 2021 b) and autoregressive (Van Den Oord et al ., 2016) paradigms in generative modeling of continuous data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389547677_Direct_Discriminative_Optimization_Your_Likelihood-Based_Visual_Generative_Model_is_Secretly_a_GAN_Discriminator", "content": "Song et al ., 2021 b) and autoregressive (Van Den Oord et al ., 2016) paradigms in generative modeling of continuous data."}
+{"idx": 5, "title": "Solving Inverse Problems in Compressive Imaging with Score - Based ...", "date": "", "ddg_snippet": "B. Score - based Generative Models . Song et al .To tackle this, we generated custom testing datasets from DAVIS 2017 by subjecting its training video frames to a simulated SCI system with modulation masks identical to the original CACTI dataset.", "subpage_snippet": "", "source": "www.eee.hku.hk", "link": "https://www.eee.hku.hk/optima/pub/conference/2310_DSAAb.pdf", "content": "B. Score - based Generative Models . Song et al .To tackle this, we generated custom testing datasets from DAVIS 2017 by subjecting its training video frames to a simulated SCI system with modulation masks identical to the original CACTI dataset."}
+{"idx": 6, "title": "Improving Adversarial Energy- Based Model via Diffusion Process", "date": "", "ddg_snippet": "DRL (Gao et al ., 2021 ) and CDRL (Zhu et al ., 2024) tried to combine EBM with a diffusion- based model and got state-of-the-art generation performance on several image datasets. Still, they relied on MCMC sampling which remains inefficient.", "subpage_snippet": "", "source": "people.compute.dtu.dk", "link": "https://people.compute.dtu.dk/sohau/papers/icml2024/2646_improving_adversarial_energy_b.pdf", "content": "DRL (Gao et al ., 2021 ) and CDRL (Zhu et al ., 2024) tried to combine EBM with a diffusion- based model and got state-of-the-art generation performance on several image datasets. Still, they relied on MCMC sampling which remains inefficient."}
+{"idx": 7, "title": "A Survey on Generative Models - HackMD", "date": "", "ddg_snippet": "Score - based model explore another principle for generative modeling based on estimating and sampling *score*, the gradient of log density function at the input data point.", "subpage_snippet": "", "source": "hackmd.io", "link": "https://hackmd.io/@JayZhan/HkJT0hHsw", "content": "Score - based model explore another principle for generative modeling based on estimating and sampling *score*, the gradient of log density function at the input data point."}
+{"idx": 8, "title": "What are Diffusion Models ? | Lil'Log", "date": "", "ddg_snippet": "[10] Yang Song , et al . “ Score - Based Generative Modeling through Stochastic Differential Equations.”", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2021-07-11-diffusion-models/", "content": "[10] Yang Song , et al . “ Score - Based Generative Modeling through Stochastic Differential Equations.”"}
+{"idx": 9, "title": "Using f orward -b ackward sde s t heory", "date": "", "ddg_snippet": "Score - based Generative Model (SGM; Song et al .long time in generating data (Jolicoeur-Martineau et al ., 2021 ), thereby limiting their practical usages.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=nioAdKCEdXB", "content": "Score - based Generative Model (SGM; Song et al .long time in generating data (Jolicoeur-Martineau et al ., 2021 ), thereby limiting their practical usages."}
diff --git a/data/sampled_jsons/uxDFlPGRLX_FlowDec-_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality.jsonl b/data/sampled_jsons/uxDFlPGRLX_FlowDec-_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality.jsonl
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index 0000000000000000000000000000000000000000..1cdd36747ea012a926048e4bdbef025d8c66fb31
--- /dev/null
+++ b/data/sampled_jsons/uxDFlPGRLX_FlowDec-_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "Jan 22, 2025 · The paper introduces FlowDec , a neural audio codec that employs a two-stage approach: (1) an autoencoder with residual vector quantization, trained without adversarial loss; and (2) a postfilter that mitigates coding artifacts and enhances perceptual quality . FlowDec leverages conditional flow matching for signal enhancement, achieving notable improvements over previous score- based and flow ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "Jan 22, 2025 · The paper introduces FlowDec , a neural audio codec that employs a two-stage approach: (1) an autoencoder with residual vector quantization, trained without adversarial loss; and (2) a postfilter that mitigates coding artifacts and enhances perceptual quality . FlowDec leverages conditional flow matching for signal enhancement, achieving notable improvements over previous score- based and flow ..."}
+{"idx": 1, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "Mar 3, 2025 · FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 2, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 3, "title": "FlowDec | A flow - based full - band general audio codec with high ...", "date": "", "ddg_snippet": "Abstract. We propose FlowDec , a neural full - band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based ...", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "Abstract. We propose FlowDec , a neural full - band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based ..."}
+{"idx": 4, "title": "facebookresearch/FlowDec | DeepWiki", "date": "", "ddg_snippet": "Purpose and Scope FlowDec is a full-band audio codec that combines traditional neural audio compression with flow-based enhancement to achieve high perceptual quality at low bitrates. This system processes general audio sampled at 48 kHz using a dual-model architecture consisting of a Deep Audio Codec (DAC) for compression and a flow-based postfilter for quality enhancement.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/facebookresearch/FlowDec", "content": "Purpose and Scope FlowDec is a full-band audio codec that combines traditional neural audio compression with flow-based enhancement to achieve high perceptual quality at low bitrates. This system processes general audio sampled at 48 kHz using a dual-model architecture consisting of a Deep Audio Codec (DAC) for compression and a flow-based postfilter for quality enhancement."}
+{"idx": 5, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "Join the discussion on this paper pageFlowDec: A flow-based full-band general audio codec with high perceptual quality", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.01485", "content": "Join the discussion on this paper pageFlowDec: A flow-based full-band general audio codec with high perceptual quality"}
+{"idx": 6, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "The proposed method substantially attenuates audible artifacts caused by codecs and is conceptually straightforward, and can efficiently generate improved- quality audio that is competitive or even superior in perceptual quality to the audio produced by other state-of-the-art deep neural network methods and the LAME-MP3 codec .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/FlowDec:-A-flow-based-full-band-general-audio-codec-Welker-Le/ec40a4902b277f0f9e3704c1b23634cad4fe0dcf/figure/7", "content": "The proposed method substantially attenuates audible artifacts caused by codecs and is conceptually straightforward, and can efficiently generate improved- quality audio that is competitive or even superior in perceptual quality to the audio produced by other state-of-the-art deep neural network methods and the LAME-MP3 codec ."}
+{"idx": 7, "title": "FlowDec : A flow - based full - band general audio codec with high ...", "date": "", "ddg_snippet": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output quality and reducing the required postfilter DNN evaluations from 60 to 6 without any...", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/FlowDec:-A-flow-based-full-band-general-audio-codec-with-high-perceptual-quality-17045f43-017b-4495-bcb6-65d8964daff3", "content": "Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output quality and reducing the required postfilter DNN evaluations from 60 to 6 without any..."}
+{"idx": 8, "title": "FlowDec : A flow - based full - band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full - band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389580542_FlowDec_A_flow-based_full-band_general_audio_codec_with_high_perceptual_quality", "content": "We propose FlowDec , a neural full - band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 9, "title": "FlowDec : A flow - based full - band general audio codec with high ...", "date": "", "ddg_snippet": "FlowDec is a new neural audio codec that achieves high - quality audio compression. Uses flow - based generative models to compress full - band (48kHz) audio signals. Requires only 13-14 kbps bitrate while maintaining excellent perceptual quality .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/flowdec-flow-based-full-band-general-audio", "content": "FlowDec is a new neural audio codec that achieves high - quality audio compression. Uses flow - based generative models to compress full - band (48kHz) audio signals. Requires only 13-14 kbps bitrate while maintaining excellent perceptual quality ."}
diff --git a/data/sampled_jsons/vei_Equation_(8)_vhi_Equation_(9)_CoPINN.jsonl b/data/sampled_jsons/vei_Equation_(8)_vhi_Equation_(9)_CoPINN.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..460584a10238976aedad8e2b389cfe92641cb07d
--- /dev/null
+++ b/data/sampled_jsons/vei_Equation_(8)_vhi_Equation_(9)_CoPINN.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "CoPINN : Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "extend Equation (2) to allow the neural network to process. 3. CoPINN : Cognitive Physics-Informed Neural Networks.Once vei and vhi are calculated, the weight of the j-th most easy sample in the i-th epoch can be calculated as follows: δji.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "extend Equation (2) to allow the neural network to process. 3. CoPINN : Cognitive Physics-Informed Neural Networks.Once vei and vhi are calculated, the weight of the j-th most easy sample in the i-th epoch can be calculated as follows: δji."}
+{"idx": 1, "title": "Full text of \"Mathematics Of Physics and Modern Engineering\"", "date": "", "ddg_snippet": "This is a second-order linear differential equation with constant coefficients whose right-hand member is a known function. Hence its general solution y «= y(t) can readily be obtained. The characteristic equation for (35- 8 ) is.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/stream/dli.ernet.5531/5531-Mathematics+Of+Physics+And+Modern+Engineering_djvu.txt", "content": "This is a second-order linear differential equation with constant coefficients whose right-hand member is a known function. Hence its general solution y «= y(t) can readily be obtained. The characteristic equation for (35- 8 ) is."}
+{"idx": 2, "title": "Principles and Applications of Underwater Sound. Volume 7", "date": "", "ddg_snippet": "by V Bush — ... Equation (9 ) can be put into a more convenient form by using the ... veI ipe dalllge 110(111' or less~ graduaI *a lv. lI14:,slir. 1I ... 303 pages", "subpage_snippet": "", "source": "apps.dtic.mil", "link": "https://apps.dtic.mil/sti/tr/pdf/AD0200786.pdf", "content": "by V Bush — ... Equation (9 ) can be put into a more convenient form by using the ... veI ipe dalllge 110(111' or less~ graduaI *a lv. lI14:,slir. 1I ... 303 pages"}
+{"idx": 3, "title": "Full text of \"A manual of civil engineering\"", "date": "", "ddg_snippet": "... equation 8 ,alreadygiv( ' position th\\i8 foxmd for the point c, eoi □le di ... vei -tical axis. This is the first approximation pient. A second ...", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/stream/amanualcivileng02rankgoog/amanualcivileng02rankgoog_djvu.txt", "content": "... equation 8 ,alreadygiv( ' position th\\i8 foxmd for the point c, eoi □le di ... vei -tical axis. This is the first approximation pient. A second ..."}
+{"idx": 4, "title": "Full text of \"The Encyclopaedia Britannica\"", "date": "", "ddg_snippet": "... vhi > grave of Christ Thfi history of the literal interpretation begins with ... A vei ^ fine alip of maul i* laid on it m the position BB, aad the whole ...", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/stream/bub_gb_HKgMAAAAYAAJ/bub_gb_HKgMAAAAYAAJ_djvu.txt", "content": "... vhi > grave of Christ Thfi history of the literal interpretation begins with ... A vei ^ fine alip of maul i* laid on it m the position BB, aad the whole ..."}
diff --git a/data/sampled_jsons/von_Luxburg_2007_spectral_clustering_full_abstract.jsonl b/data/sampled_jsons/von_Luxburg_2007_spectral_clustering_full_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cb76063fa5a5aad2ea5af823c5cc37c790a5843a
--- /dev/null
+++ b/data/sampled_jsons/von_Luxburg_2007_spectral_clustering_full_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF A tutorial on spectral clustering", "date": "", "ddg_snippet": "A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms.", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/courses/cs6241/2020sp/readings/vonLuxburg-2007-spectral.pdf", "content": "A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms."}
+{"idx": 1, "title": "PDF A Tutorial on Spectral Clustering - uni-tuebingen.de", "date": "", "ddg_snippet": "A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany", "subpage_snippet": "", "source": "www.tml.cs.uni-tuebingen.de", "link": "http://www.tml.cs.uni-tuebingen.de/team/luxburg/publications/Luxburg07_tutorial.pdf", "content": "A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany"}
+{"idx": 2, "title": "[0711.0189] A Tutorial on Spectral Clustering - arXiv.org", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/0711.0189", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 3, "title": "A tutorial on spectral clustering | Statistics and Computing", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11222-007-9033-z", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 4, "title": "PDF A Tutorial on Spectral Clustering - Ulrike von Luxburg", "date": "", "ddg_snippet": ") Spectral clustering cannot serve as a \"black box algorithm\" which automatically detects the correct clusters in any given data set. But it can be considered as a powerful tool which can produce good results if applied with care.", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/4f35/1c8a80e1c4a3b51e76004133f2901e26776f.pdf", "content": ") Spectral clustering cannot serve as a \"black box algorithm\" which automatically detects the correct clusters in any given data set. But it can be considered as a powerful tool which can produce good results if applied with care."}
+{"idx": 5, "title": "A Tutorial on Spectral Clustering - ADS", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2007arXiv0711.0189V/abstract", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 6, "title": "A tutorial on spectral clustering | Statistics and Computing", "date": "", "ddg_snippet": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1007/s11222-007-9033-z", "content": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm."}
+{"idx": 7, "title": "A Tutorial on Spectral Clustering : Ulrike von Luxburg : Free Download ...", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/arxiv-0711.0189", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm."}
+{"idx": 8, "title": "von Luxburg, U. (2007) A Tutorial on Spectral Clustering. Statistics ...", "date": "", "ddg_snippet": "The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering ; however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-of-Sight algorithm is also developed for clustering .", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=2317610", "content": "The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering ; however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-of-Sight algorithm is also developed for clustering ."}
+{"idx": 9, "title": "(PDF) A tutorial on spectral clustering - Academia.edu", "date": "", "ddg_snippet": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/27117748/A_tutorial_on_spectral_clustering", "content": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm."}
diff --git a/data/sampled_jsons/von_Luxburg_2007_tutorial_spectral_clustering_abstract_year_2007.jsonl b/data/sampled_jsons/von_Luxburg_2007_tutorial_spectral_clustering_abstract_year_2007.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af68263e208b7771b15a85561e71fd63aa2697c6
--- /dev/null
+++ b/data/sampled_jsons/von_Luxburg_2007_tutorial_spectral_clustering_abstract_year_2007.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Spectral Clustering: A quick overview – calculated |", "date": "", "ddg_snippet": "Here, I give a brief tutorial on the theory of Spectral Clustering and how it is implemented in open source packaages", "subpage_snippet": "", "source": "calculatedcontent.com", "link": "https://calculatedcontent.com/2012/10/09/spectral-clustering/", "content": "Here, I give a brief tutorial on the theory of Spectral Clustering and how it is implemented in open source packaages"}
+{"idx": 1, "title": "WO2009038822A2 - Spectral clustering for multi-type relational", "date": "", "ddg_snippet": "... of at least two distinct respective types; using the data processing device to effect clustering of the elements of the objects using a spectral ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2009038822A2/en", "content": "... of at least two distinct respective types; using the data processing device to effect clustering of the elements of the objects using a spectral ..."}
+{"idx": 2, "title": "US20080294686A1 - Spectral clustering for multi-type relational", "date": "", "ddg_snippet": "Clustering on multi-type relational data ... Spectral clustering (Ng et al., 2001; Bach & Jordan, 2004) has been well studied in the literature.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20080294686A1/en", "content": "Clustering on multi-type relational data ... Spectral clustering (Ng et al., 2001; Bach & Jordan, 2004) has been well studied in the literature."}
+{"idx": 3, "title": "Community Detection Using Spectral Clustering on Sparse", "date": "", "ddg_snippet": "We use spectral clustering to identify clusters in the graph, corresponding to communities in Hollenbeck, and compare these with the LAPD's knowledge ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/120882093", "content": "We use spectral clustering to identify clusters in the graph, corresponding to communities in Hollenbeck, and compare these with the LAPD's knowledge ..."}
+{"idx": 4, "title": "Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs |", "date": "", "ddg_snippet": "Spectral methods offer a tractable, global framework for clustering in graphs via eigenvector computations on graph matrices.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/22M1494713", "content": "Spectral methods offer a tractable, global framework for clustering in graphs via eigenvector computations on graph matrices."}
+{"idx": 5, "title": "Explainable Graph Spectral Clustering For Text Embeddings", "date": "", "ddg_snippet": "Explainability of Graph Spectral Clustering is achieved via building a bridge of (nearly) equivalent embeddings from GloVe embedding to GSC embedding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14075v1", "content": "Explainability of Graph Spectral Clustering is achieved via building a bridge of (nearly) equivalent embeddings from GloVe embedding to GSC embedding."}
+{"idx": 6, "title": "Self-Tuning Spectral Clustering for Speaker Diarization", "date": "", "ddg_snippet": "Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00023v2", "content": "Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity ..."}
+{"idx": 7, "title": "probability - How to interpret clusters on Markov chain time", "date": "", "ddg_snippet": "Spectral methods for clustering are all ... So, ultimately the Google Page rank is also a spectral method, used for ranking rather than clustering .", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/3515677/how-to-interpret-clusters-on-markov-chain-time-characteristics", "content": "Spectral methods for clustering are all ... So, ultimately the Google Page rank is also a spectral method, used for ranking rather than clustering ."}
+{"idx": 8, "title": "Cluster Analysis of Information in Complex Networks |", "date": "", "ddg_snippet": "... cluster structure of web space zones was carried out using ... von Luxburg , “A tutorial on spectral clustering ,” Statistics and Computing, vol.", "subpage_snippet": "", "source": "www.computingonline.net", "link": "https://www.computingonline.net/computing/article/view/3360", "content": "... cluster structure of web space zones was carried out using ... von Luxburg , “A tutorial on spectral clustering ,” Statistics and Computing, vol."}
+{"idx": 9, "title": "An Efficient Controlled Islanding Technique for Smart Grids |", "date": "", "ddg_snippet": "In this paper, a novel hierarchical spectral clustering method ... Von Luxburg , \"A tutorial on spectral clustering ,\" Statistics and computing, vol.", "subpage_snippet": "", "source": "ijrer.com", "link": "https://ijrer.com/index.php/ijrer/article/view/10221", "content": "In this paper, a novel hierarchical spectral clustering method ... Von Luxburg , \"A tutorial on spectral clustering ,\" Statistics and computing, vol."}
diff --git a/data/sampled_jsons/weakly_supervised_learning_recommendation_systems.jsonl b/data/sampled_jsons/weakly_supervised_learning_recommendation_systems.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..641298d59faa42ae09ea71b985ad3860071c6287
--- /dev/null
+++ b/data/sampled_jsons/weakly_supervised_learning_recommendation_systems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation ...", "date": "", "ddg_snippet": "ABSTRACT Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems . Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distin-guish items in future behaviors from others based on the user's his-torical behaviors, have attracted a lot of interest in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.13616", "content": "ABSTRACT Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems . Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distin-guish items in future behaviors from others based on the user's his-torical behaviors, have attracted a lot of interest in ..."}
+{"idx": 1, "title": "brief introduction to weakly supervised learning | National Science ...", "date": "", "ddg_snippet": "Weakly supervised learning is an umbrella term covering a variety of studies that attempt to construct predictive models by learning with weak supervision. In this article, we will discuss some progress in this line of research, focusing on learning with incomplete, inexact and inaccurate supervision.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/nsr/article/5/1/44/4093912", "content": "Weakly supervised learning is an umbrella term covering a variety of studies that attempt to construct predictive models by learning with weak supervision. In this article, we will discuss some progress in this line of research, focusing on learning with incomplete, inexact and inaccurate supervision."}
+{"idx": 2, "title": "PDF Weaker Than You Think: A Critical Look at Weakly Supervised Learning", "date": "", "ddg_snippet": "Weakly supervised learning is a popular ap- proach for training machine learning models in low-resource settings. Instead of requesting high-quality yet costly human annotations, it allows training models with noisy annotations obtained from various weak sources. Recently, many sophisticated approaches have been pro- posed for robust training under label noise, re- porting impressive results ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.acl-long.796.pdf", "content": "Weakly supervised learning is a popular ap- proach for training machine learning models in low-resource settings. Instead of requesting high-quality yet costly human annotations, it allows training models with noisy annotations obtained from various weak sources. Recently, many sophisticated approaches have been pro- posed for robust training under label noise, re- porting impressive results ..."}
+{"idx": 3, "title": "PDF Weakly Supervised Learning via Relational Comparisons", "date": "", "ddg_snippet": "Weakly supervised learning aims to reduce this dependence on extensive labeled data by utiliz-ing cheaper, more abundant forms of supervision. In this project, we explore a specific form of weak su-pervision: learning from relational comparisons between pairs of images.", "subpage_snippet": "", "source": "cs231n.stanford.edu", "link": "https://cs231n.stanford.edu/2025/papers/text_file_840591902-CS_231N_First_Draft.pdf", "content": "Weakly supervised learning aims to reduce this dependence on extensive labeled data by utiliz-ing cheaper, more abundant forms of supervision. In this project, we explore a specific form of weak su-pervision: learning from relational comparisons between pairs of images."}
+{"idx": 4, "title": "Towards Safe Weakly Supervised Learning - IEEE Xplore", "date": "", "ddg_snippet": "Unlike supervised learning which typically achieves performance improvement with more labeled examples, weakly supervised learning may sometimes even degenerate performance with more weakly supervised data. Such deficiency seriously hinders the deployment of weakly supervised learning to real tasks.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/8735810", "content": "Unlike supervised learning which typically achieves performance improvement with more labeled examples, weakly supervised learning may sometimes even degenerate performance with more weakly supervised data. Such deficiency seriously hinders the deployment of weakly supervised learning to real tasks."}
+{"idx": 5, "title": "WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation ...", "date": "", "ddg_snippet": "Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems . Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distinguish items in future behaviors from others based on the user's historical behaviors, have attracted a lot of interest in both industry ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.13616v1", "content": "Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems . Neural sequential recommendation models, which formulates learning the user-item relevance as a sequential classification problem to distinguish items in future behaviors from others based on the user's historical behaviors, have attracted a lot of interest in both industry ..."}
+{"idx": 6, "title": "PDF Unbiased Recommender Learning from Implicit Feedback via Weakly ...", "date": "", "ddg_snippet": "To address this issue, we introduce WeaklyRec, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=0E5rZOGA13&name=pdf", "content": "To address this issue, we introduce WeaklyRec, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples. However, its unbiasedness hinges on the accurate estimation of the class prior."}
+{"idx": 7, "title": "Weaker Than You Think: A Critical Look at Weakly Supervised Learning", "date": "", "ddg_snippet": "To understand the true value of weakly supervised learning , we thoroughly analyze diverse NLP datasets and tasks to ascertain when and why weakly supervised approaches work. Based on our findings, we provide recommendations for future research.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.acl-long.796/", "content": "To understand the true value of weakly supervised learning , we thoroughly analyze diverse NLP datasets and tasks to ascertain when and why weakly supervised approaches work. Based on our findings, we provide recommendations for future research."}
+{"idx": 8, "title": "Introduction to Weakly Supervision Learning: Part 1 - Medium", "date": "", "ddg_snippet": "While supervised learning has been successful in various applications (such as image recognition, natural language processing, and recommendation systems ), it has some limitations; one of them is :", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@smishraonline16/introduction-to-weakly-supervision-learning-part-1-385f138b1f40", "content": "While supervised learning has been successful in various applications (such as image recognition, natural language processing, and recommendation systems ), it has some limitations; one of them is :"}
+{"idx": 9, "title": "GitHub - JieyuZ2/Awesome-Weak-Supervision: A curated list of ...", "date": "", "ddg_snippet": "WRENCH: A Comprehensive Benchmark for Weak Supervision. Jieyu Zhang NeurIPS 2021 codebase (for both classification and sequence tagging tasks) WALNUT: A Benchmark on Semi- weakly Supervised Learning for Natural Language Understanding. Guoqing Zheng NAACL 2022 codebase AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels. Nicholas Roberts NeurIPS 2022 codebase SPEAR : Semi ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/JieyuZ2/Awesome-Weak-Supervision", "content": "WRENCH: A Comprehensive Benchmark for Weak Supervision. Jieyu Zhang NeurIPS 2021 codebase (for both classification and sequence tagging tasks) WALNUT: A Benchmark on Semi- weakly Supervised Learning for Natural Language Understanding. Guoqing Zheng NAACL 2022 codebase AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels. Nicholas Roberts NeurIPS 2022 codebase SPEAR : Semi ..."}
diff --git a/data/sampled_jsons/website_affordability_index_formula_broadband_price_target.jsonl b/data/sampled_jsons/website_affordability_index_formula_broadband_price_target.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ce4aae1f859a06c6cf59f9931d91d1af113d717
--- /dev/null
+++ b/data/sampled_jsons/website_affordability_index_formula_broadband_price_target.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Matt Wood Testifies for Affordable Broadband Amid COVID |", "date": "", "ddg_snippet": "Wood is testifying in support of treating broadband as an essential utility, and centering affordability and race in federal, state and local efforts ...", "subpage_snippet": "", "source": "www.commondreams.org", "link": "https://www.commondreams.org/newswire/2021/02/17/matt-wood-testifies-congress-support-affordable-broadband-during-covid-crisis", "content": "Wood is testifying in support of treating broadband as an essential utility, and centering affordability and race in federal, state and local efforts ..."}
+{"idx": 1, "title": "BEAD: Broadband or Bust | Citizens Against Government Waste", "date": "", "ddg_snippet": "It included provisions for states participating in BEAD to describe how their proposals will guarantee broadband access and affordability for low ...", "subpage_snippet": "", "source": "www.cagw.org", "link": "https://www.cagw.org/reporting/bead-broadband-or-bust", "content": "It included provisions for states participating in BEAD to describe how their proposals will guarantee broadband access and affordability for low ..."}
+{"idx": 2, "title": "Funding to address broadband gaps in the Eleventh District -", "date": "", "ddg_snippet": "... broadband funding has established six main programs, each aiming to address a specific gap in digital inclusion, and each with a distinct funding ...", "subpage_snippet": "", "source": "www.dallasfed.org", "link": "https://www.dallasfed.org/cd/communities/2024/2405", "content": "... broadband funding has established six main programs, each aiming to address a specific gap in digital inclusion, and each with a distinct funding ..."}
+{"idx": 3, "title": "WEBUKA - Website Worth Calculator | webuka.com", "date": "", "ddg_snippet": "Find your website value (or market price ) in a few seconds! The mathematical formulas behind the website evaluation ...", "subpage_snippet": "", "source": "webuka.com.usitestat.com", "link": "https://webuka.com.usitestat.com/", "content": "Find your website value (or market price ) in a few seconds! The mathematical formulas behind the website evaluation ..."}
+{"idx": 4, "title": "Senate Infrastructure Bill Tallies $65 Billion in Broadband", "date": "", "ddg_snippet": "We may use cookies and other tracking technologies to improve your browsing experience on our website , to show you personalized content and targeted ...", "subpage_snippet": "", "source": "www.meritalk.com", "link": "https://www.meritalk.com/articles/senate-infrastructure-bill-tallies-65-billion-of-new-broadband-spending/", "content": "We may use cookies and other tracking technologies to improve your browsing experience on our website , to show you personalized content and targeted ..."}
+{"idx": 5, "title": "New Democratic Party | 2021 Canadian election - VoteMate", "date": "", "ddg_snippet": "... a price cap on cell phone & Internet bills; Require providers to offer a basic plan for wireless & broadband that is comparable w/ affordable ...", "subpage_snippet": "", "source": "votemate.org", "link": "https://votemate.org/p/94", "content": "... a price cap on cell phone & Internet bills; Require providers to offer a basic plan for wireless & broadband that is comparable w/ affordable ..."}
+{"idx": 6, "title": "Gurmit Bhachu, NDP for Calgary Midnapore | 2021 Canadian", "date": "", "ddg_snippet": "... a price cap on cell phone & Internet bills; Require providers to offer a basic plan for wireless & broadband that is comparable w/ affordable ...", "subpage_snippet": "", "source": "en.votemate.org", "link": "https://en.votemate.org/canada2021/candidates/5342", "content": "... a price cap on cell phone & Internet bills; Require providers to offer a basic plan for wireless & broadband that is comparable w/ affordable ..."}
+{"idx": 7, "title": "Trade efficiency - KamCity", "date": "", "ddg_snippet": "... Affordable ’ method ... Broadband ... Ceiling Prices", "subpage_snippet": "", "source": "www.kamcity.com", "link": "https://www.kamcity.com/kamwords/trade-efficiency/", "content": "... Affordable ’ method ... Broadband ... Ceiling Prices"}
+{"idx": 8, "title": "ABS - KamCity", "date": "", "ddg_snippet": "... Affordable ’ method ... Broadband ... Ceiling Prices", "subpage_snippet": "", "source": "www.kamcity.com", "link": "https://www.kamcity.com/kamwords/abs/", "content": "... Affordable ’ method ... Broadband ... Ceiling Prices"}
+{"idx": 9, "title": "A fresh perspective on investing for retirement income", "date": "", "ddg_snippet": "One formula , rarely questioned, is to take 100, subtract your age, then own equities in that percentage of your portfolio.", "subpage_snippet": "", "source": "www.telegraph.co.uk", "link": "https://www.telegraph.co.uk/money/fisher-investments-uk/investing-for-retirement-income/", "content": "One formula , rarely questioned, is to take 100, subtract your age, then own equities in that percentage of your portfolio."}
diff --git a/data/sampled_jsons/what_is_sqlmap.jsonl b/data/sampled_jsons/what_is_sqlmap.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dd0eb129ccc85987a00165406f02887381a5aed4
--- /dev/null
+++ b/data/sampled_jsons/what_is_sqlmap.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What is sqlmap? - Hacktress", "date": "", "ddg_snippet": "What is sqlmap ? ... Tools / What is sqlmap ? ... sqlmap is an open source penetration testing tool that automates the process of detecting and ...", "subpage_snippet": "", "source": "www.hacktress.com", "link": "https://www.hacktress.com/what-is-sqlmap/", "content": "What is sqlmap ? ... Tools / What is sqlmap ? ... sqlmap is an open source penetration testing tool that automates the process of detecting and ..."}
+{"idx": 1, "title": "FAQ · sqlmapproject/sqlmap Wiki · GitHub", "date": "", "ddg_snippet": "What is sqlmap ? ... sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sqlmapproject/sqlmap/wiki/FAQ", "content": "What is sqlmap ? ... sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and ..."}
+{"idx": 2, "title": "The Practical Guide to sqlmap for SQL Injection Course - Cybr", "date": "", "ddg_snippet": "We learn tips & tricks to see what sqlmap is doing under the hood and to troubleshoot when we come across issues.", "subpage_snippet": "", "source": "cybr.com", "link": "https://cybr.com/courses/the-practical-guide-to-sqlmap-for-sql-injection/", "content": "We learn tips & tricks to see what sqlmap is doing under the hood and to troubleshoot when we come across issues."}
+{"idx": 3, "title": "Newest 'sqlmap' Questions - Stack Overflow", "date": "", "ddg_snippet": "The technique as identified by SQLMAP is error-based - WHERE, HAVING, ORDER BY or GROUP BY clause (BIGINT UNSIGNED) It will be great if ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/tagged/sqlmap", "content": "The technique as identified by SQLMAP is error-based - WHERE, HAVING, ORDER BY or GROUP BY clause (BIGINT UNSIGNED) It will be great if ..."}
+{"idx": 4, "title": "sqlmap Tutorial: Features, Use Cases, How It Works | Wiz", "date": "", "ddg_snippet": "TL;DR, What is sqlmap ? ... sqlmap is a powerful, open-source penetration testing tool written in Python that ... What is sqlmap used for?", "subpage_snippet": "", "source": "www.wiz.io", "link": "https://www.wiz.io/academy/sqlmap-overview", "content": "TL;DR, What is sqlmap ? ... sqlmap is a powerful, open-source penetration testing tool written in Python that ... What is sqlmap used for?"}
+{"idx": 5, "title": "Unleashing the Power of SQL Injection Testing with SQLMap: A", "date": "", "ddg_snippet": "The sqlmap system is written in Python, so you have to install Python 2.6 or later on your computer in order to run sqlmap .", "subpage_snippet": "", "source": "www.codelivly.com", "link": "https://www.codelivly.com/sqlmap-tutorial/", "content": "The sqlmap system is written in Python, so you have to install Python 2.6 or later on your computer in order to run sqlmap ."}
+{"idx": 6, "title": "Tutorial Install and Run SQLMap on Kali Linux - Eldernode Blog", "date": "", "ddg_snippet": "The SQLMap tool is one of the most famous SQL injection tools, which quickly provides the possibility of testing penetration into the website through ...", "subpage_snippet": "", "source": "blog.eldernode.com", "link": "https://blog.eldernode.com/install-and-run-sqlmap-on-kali-linux/", "content": "The SQLMap tool is one of the most famous SQL injection tools, which quickly provides the possibility of testing penetration into the website through ..."}
+{"idx": 7, "title": "Sqlmap Overview", "date": "", "ddg_snippet": "Sqlmap is an open source SQL injection tool that automates the process of testing and exploiting SQL injection vulnerabilities.", "subpage_snippet": "", "source": "www.sqlinjection.net", "link": "https://www.sqlinjection.net/sqlmap/", "content": "Sqlmap is an open source SQL injection tool that automates the process of testing and exploiting SQL injection vulnerabilities."}
+{"idx": 8, "title": "Sqlmap - charlesreid1", "date": "", "ddg_snippet": "sqlmap is one of the Kali Top 10 tools. ... Retrieved from \" https://charlesreid1.com/w/index.php?title= Sqlmap &oldid=29208 \"", "subpage_snippet": "", "source": "charlesreid1.com", "link": "https://charlesreid1.com/wiki/Sqlmap", "content": "sqlmap is one of the Kali Top 10 tools. ... Retrieved from \" https://charlesreid1.com/w/index.php?title= Sqlmap &oldid=29208 \""}
+{"idx": 9, "title": "🔐 sqlmap: Automate SQL Injection Testing Like a Pro in 2025", "date": "", "ddg_snippet": "What is sqlmap ? ... sqlmap is an open-source penetration testing tool that automates the detection and exploitation of SQL injection vulnerabilities .", "subpage_snippet": "", "source": "blog.thnkandgrow.com", "link": "https://blog.thnkandgrow.com/sqlmap-automate-sql-injection-testing-like-a-pro-2025/", "content": "What is sqlmap ? ... sqlmap is an open-source penetration testing tool that automates the detection and exploitation of SQL injection vulnerabilities ."}
diff --git a/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_Section_6.2_MATH_Pythia-1B.jsonl b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_Section_6.2_MATH_Pythia-1B.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5f002919aa4658c6d2f49960a1ea921fd7752ce1
--- /dev/null
+++ b/data/sampled_jsons/zzOOqD6R1b_Stress-Testing_Capability_Elicitation_Section_6.2_MATH_Pythia-1B.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress - Testing Capability Elicitation With", "date": "", "ddg_snippet": "Stress - Testing Capability Elicitation With Password-Locked Models. Ryan Greenblatt∗ Redwood Research ryan@rdwrs.com.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "Stress - Testing Capability Elicitation With Password-Locked Models. Ryan Greenblatt∗ Redwood Research ryan@rdwrs.com."}
+{"idx": 1, "title": "[Paper] Stress - testing capability elicitation with... — LessWrong", "date": "", "ddg_snippet": "To be clear, this is Deepseek 7b math trained to imitate pythia 1 b when not given a password.Good capability elicitation is useful for more cost effectively building products, but being able to robustly elicit capabilities is also crucial for safety.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "To be clear, this is Deepseek 7b math trained to imitate pythia 1 b when not given a password.Good capability elicitation is useful for more cost effectively building products, but being able to robustly elicit capabilities is also crucial for safety."}
+{"idx": 2, "title": "Stress - Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password."}
+{"idx": 3, "title": "CSET - How to Assess the Likelihood of Malicious Use of Advanced AI...", "date": "", "ddg_snippet": "Red-teamers and stress testers adopt an adversary’s mindset and probe an AI system for “identification of harmful capabilities , outputs, or infrastructure threats.”6 If a model does not produce harmful behavior on the first try, the next step is to iterate.", "subpage_snippet": "", "source": "cset.georgetown.edu", "link": "https://cset.georgetown.edu/wp-content/uploads/CSET-How-to-Assess-the-Likelihood-of-Malicious-Use-of-Advanced-AI-Systems.pdf", "content": "Red-teamers and stress testers adopt an adversary’s mindset and probe an AI system for “identification of harmful capabilities , outputs, or infrastructure threats.”6 If a model does not produce harmful behavior on the first try, the next step is to iterate."}
+{"idx": 4, "title": "Понимаем функцию enumerate() в Python на примере кода", "date": "", "ddg_snippet": "0 a 1 b 2 c.", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/wiki/python/ponimaem-funktsiyu-enumerate-v-python-na-primere-koda/", "content": "0 a 1 b 2 c."}
+{"idx": 5, "title": "Номер 2, страница 29 - гдз по английскому языку 6 класс (spotlight)...", "date": "", "ddg_snippet": "Английский язык (english), 6 класс контрольные задания ( test booklet), авторы: Баранова Ксения Михайловна (Baranova Ksenia), Дули Дженни (Dooley Jenny), Копылова Виктория Викторовна (Kopylova Victoria), Мильруд Радислав Петрович (Millrood Radislav)...", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/6-klass/english/vaulina-spotlight-rabochaja-tetrad/05-1-2", "content": "Английский язык (english), 6 класс контрольные задания ( test booklet), авторы: Баранова Ксения Михайловна (Baranova Ksenia), Дули Дженни (Dooley Jenny), Копылова Виктория Викторовна (Kopylova Victoria), Мильруд Радислав Петрович (Millrood Radislav)..."}
+{"idx": 6, "title": "ESET NOD32 | Свежие Ключи 2025 | Бесплатно | СТЕНА – Telegram", "date": "", "ddg_snippet": "ESET HOME Security Premium | До 16-10-2025 1 | J7MF-XRXU-4DTS-5J7F-E8CE 2 | M3WK-XFBA-2KGH-XP2X-3KTC 3 | 9GDG-X5T6-UAC7-B9B7-XW77 4 | NEAN-XG8H-GMMC-4WDS-KD7P 5 |. TBXU-XN8C-F 6 R 7-7A64-TEG5 6 | 48D4-XSEJ-HHXB-26S5-AHDK.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/eset_nod32_free", "content": "ESET HOME Security Premium | До 16-10-2025 1 | J7MF-XRXU-4DTS-5J7F-E8CE 2 | M3WK-XFBA-2KGH-XP2X-3KTC 3 | 9GDG-X5T6-UAC7-B9B7-XW77 4 | NEAN-XG8H-GMMC-4WDS-KD7P 5 |. TBXU-XN8C-F 6 R 7-7A64-TEG5 6 | 48D4-XSEJ-HHXB-26S5-AHDK."}
+{"idx": 7, "title": "Калькулятор уравнений", "date": "", "ddg_snippet": "Tiếng Việt ( VI ). Українська (UK). ไทย (TH).", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/equ/ru/", "content": "Tiếng Việt ( VI ). Українська (UK). ไทย (TH)."}
+{"idx": 8, "title": "Дипсик не работает: Обходим ошибку Deepseek service is... / Хабр", "date": "", "ddg_snippet": "3. Скачиваем Дипсик локально через LM Studio. Если кратко, LM Studio — это платформа, позволяющая развернуть DeepSeek R 1 и другие модели нейросети прямо на вашем компьютере. Это позволяет пользоваться нейросетью бесплатно и без Интернета.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/878498/", "content": "3. Скачиваем Дипсик локально через LM Studio. Если кратко, LM Studio — это платформа, позволяющая развернуть DeepSeek R 1 и другие модели нейросети прямо на вашем компьютере. Это позволяет пользоваться нейросетью бесплатно и без Интернета."}
+{"idx": 9, "title": "Бесплатная нейросеть онлайн | Чат GPT на русском для... — ruGPT", "date": "", "ddg_snippet": "Нейросеть и искусственный интеллект онлайн: чат бот GPT на русском, генерация текста и помощь в задачах — бесплатно, без регистрации и VPN...", "subpage_snippet": "", "source": "rugpt.io", "link": "https://rugpt.io/", "content": "Нейросеть и искусственный интеллект онлайн: чат бот GPT на русском, генерация текста и помощь в задачах — бесплатно, без регистрации и VPN..."}
diff --git "a/data/sampled_jsons/\316\273_short_\316\273_long_initial_values_Feint_Behaviors_Section_4.2.2.jsonl" "b/data/sampled_jsons/\316\273_short_\316\273_long_initial_values_Feint_Behaviors_Section_4.2.2.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..417e7b7a2a0417b0dbc07a0f8307df300f6f788f
--- /dev/null
+++ "b/data/sampled_jsons/\316\273_short_\316\273_long_initial_values_Feint_Behaviors_Section_4.2.2.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "If the length of a Feint behavior is too short , the following attack actions might not gain much advantage compared to actions combinations without Feint behaviors ; and if the length of a Feint action is too long , the process to perform a Feint behaviors can leave sufficient time for the opponent to react and even attack back.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v2", "content": "If the length of a Feint behavior is too short , the following attack actions might not gain much advantage compared to actions combinations without Feint behaviors ; and if the length of a Feint action is too long , the process to perform a Feint behaviors can leave sufficient time for the opponent to react and even attack back."}
+{"idx": 1, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short -term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ", "content": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short -term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors ."}
+{"idx": 2, "title": "PDF Section 3.2. The Poisson Distribution", "date": "", "ddg_snippet": "Hogg, McKean, and Craig do not state Axioms 1(a) and 2(a) explicitly, but we need these ideas in our derivation below. They hint that these are necessary by stating on in Section 3.3 that \"the Axioms (1) and (2) of the Poisson process only depend on λ and the length of the interval; in particular, they do not depend on the endpoints of the interval.\" See page 178. So we conclude from ...", "subpage_snippet": "", "source": "faculty.etsu.edu", "link": "https://faculty.etsu.edu/gardnerr/4047/notes-Hogg-McKean-Craig/Hogg-McKean-Craig-3-2.pdf", "content": "Hogg, McKean, and Craig do not state Axioms 1(a) and 2(a) explicitly, but we need these ideas in our derivation below. They hint that these are necessary by stating on in Section 3.3 that \"the Axioms (1) and (2) of the Poisson process only depend on λ and the length of the interval; in particular, they do not depend on the endpoints of the interval.\" See page 178. So we conclude from ..."}
+{"idx": 3, "title": "PDF Solutions 7: Interacting Quantum Field Theory: - Imperial College London", "date": "", "ddg_snippet": "However most manipulations in QFT, and in particular in this problem sheet, work with Green func-tions not matrix elements. Green functions are de ned for arbitrary values of four momenta including unphysical o -shell values where p2 6= m2. So much of the information encoded in a Green function has no obvious physical meaning. Of course to extract the corresponding physical matrix element from ...", "subpage_snippet": "", "source": "www.imperial.ac.uk", "link": "https://www.imperial.ac.uk/media/imperial-college/research-centres-and-groups/theoretical-physics/msc/current/qft/problem-sheets-and-solutions/qftProblemSheet7Answers.pdf", "content": "However most manipulations in QFT, and in particular in this problem sheet, work with Green func-tions not matrix elements. Green functions are de ned for arbitrary values of four momenta including unphysical o -shell values where p2 6= m2. So much of the information encoded in a Green function has no obvious physical meaning. Of course to extract the corresponding physical matrix element from ..."}
+{"idx": 4, "title": "3.19: Quarter-Wavelength Transmission Line - Engineering LibreTexts", "date": "", "ddg_snippet": "The length of the second section of the transmission line, being a quarter-wavelength transformer, should be 𝑙 2 = 0. 2 5 𝜆. Using Equation 3. 1 9. 3, the characteristic impedance 𝑍 0 2 of this section of line should be 𝑍 0 2 ≅ √ (1 2 0. 7 1 9 Ω) (5 0 Ω) ≅ 7 7. 7 Ω Discussion.", "subpage_snippet": "", "source": "eng.libretexts.org", "link": "https://eng.libretexts.org/Bookshelves/Electrical_Engineering/Electro-Optics/Book:_Electromagnetics_I_(Ellingson)/03:_Transmission_Lines/3.19:_Quarter-Wavelength_Transmission_Line", "content": "The length of the second section of the transmission line, being a quarter-wavelength transformer, should be 𝑙 2 = 0. 2 5 𝜆. Using Equation 3. 1 9. 3, the characteristic impedance 𝑍 0 2 of this section of line should be 𝑍 0 2 ≅ √ (1 2 0. 7 1 9 Ω) (5 0 Ω) ≅ 7 7. 7 Ω Discussion."}
+{"idx": 5, "title": "A new method for estimating the globally averaged mass transfer ...", "date": "", "ddg_snippet": "In summary, we have reviewed three methods for experimentally calculating the globally averaged mass transfer coefficient. Each involves certain assumptions and simplifications. The first method is less accurate when the particle size changes significantly, for it uses the initial values of the particle size and solid volume fraction.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1385894725090047", "content": "In summary, we have reviewed three methods for experimentally calculating the globally averaged mass transfer coefficient. Each involves certain assumptions and simplifications. The first method is less accurate when the particle size changes significantly, for it uses the initial values of the particle size and solid volume fraction."}
+{"idx": 6, "title": "PDF ECE 450 HW 3 Solution - University of Illinois Urbana-Champaign", "date": "", "ddg_snippet": "The quarter-wave transformer section is inserted at the position of the first voltage minimum from the load. Given that the voltage standing wave ratio on the transmission line to the left of the transformer is 1.0, calculate the following:", "subpage_snippet": "", "source": "courses.grainger.illinois.edu", "link": "https://courses.grainger.illinois.edu/ece350/sp2009/Homework/ECE450_HW03_SOLN.pdf", "content": "The quarter-wave transformer section is inserted at the position of the first voltage minimum from the load. Given that the voltage standing wave ratio on the transmission line to the left of the transformer is 1.0, calculate the following:"}
+{"idx": 7, "title": "Solutions_for_Applied_Linear_Regression.pdf | Geology | Science", "date": "", "ddg_snippet": "The document provides information about solutions to problems in the textbook \"Solutions for Applied Linear Regression\". It includes a preface, table of contents, and chapter summaries. The preface notes that most solutions were computed using R software and scripts are available from the author. It recommends adapting solutions to the software being used and emphasizes interpreting graphs as ...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/solutionsforappliedlinearregressionpdf/255251445", "content": "The document provides information about solutions to problems in the textbook \"Solutions for Applied Linear Regression\". It includes a preface, table of contents, and chapter summaries. The preface notes that most solutions were computed using R software and scripts are available from the author. It recommends adapting solutions to the software being used and emphasizes interpreting graphs as ..."}
+{"idx": 8, "title": "EC 570/571: Topic 10 - Portland State University", "date": "", "ddg_snippet": "Introduction To keep the model presentation simple, a general distributed lag model is written as: Y i = α + ∑ j=0,1,...,∞ β j X i-j + ε i", "subpage_snippet": "", "source": "web.pdx.edu", "link": "https://web.pdx.edu/~crkl/ec571/lecture10.htm", "content": "Introduction To keep the model presentation simple, a general distributed lag model is written as: Y i = α + ∑ j=0,1,...,∞ β j X i-j + ε i"}
+{"idx": 9, "title": "PDF CS 547 Lecture 10: The Poisson Process", "date": "", "ddg_snippet": "Daniel Myers Up to this point, we've discussed arrivals to queueing systems in a general way, and we've used the general arrival rate λ as part of our asymptotic bounds calculations. Now, we'll actually describe the statistical behavior of one particular arrival process: the Poisson process. Poisson arrivals are by far the most popular arrival model used in the analysis of queueing systems.", "subpage_snippet": "", "source": "pages.cs.wisc.edu", "link": "https://pages.cs.wisc.edu/~dsmyers/cs547/lecture_10_poisson_process.pdf", "content": "Daniel Myers Up to this point, we've discussed arrivals to queueing systems in a general way, and we've used the general arrival rate λ as part of our asymptotic bounds calculations. Now, we'll actually describe the statistical behavior of one particular arrival process: the Poisson process. Poisson arrivals are by far the most popular arrival model used in the analysis of queueing systems."}
diff --git "a/data/sampled_jsons/\316\273m_OR_lambda_m_DART_radiology_report_generation_Equation_5_disease-matching_constraint.jsonl" "b/data/sampled_jsons/\316\273m_OR_lambda_m_DART_radiology_report_generation_Equation_5_disease-matching_constraint.jsonl"
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index 0000000000000000000000000000000000000000..c30fbe65388f0b860181592d51893ccdc9ca577b
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+++ "b/data/sampled_jsons/\316\273m_OR_lambda_m_DART_radiology_report_generation_Equation_5_disease-matching_constraint.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DART : Disease -aware Image-Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease -relevant findings in X-ray images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease -relevant findings in X-ray images."}
+{"idx": 1, "title": "Prediction of comorbid diseases using weighted geometric embedding...", "date": "", "ddg_snippet": "Two diseases sharing common genes tend to increase their comorbidity. Previous work shows that after mapping the associated genes onto the human interactome the distance between the two disease modules (subgraphs) is correlated with comorbidity.", "subpage_snippet": "", "source": "bmcmedgenomics.biomedcentral.com", "link": "https://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-019-0605-5", "content": "Two diseases sharing common genes tend to increase their comorbidity. Previous work shows that after mapping the associated genes onto the human interactome the distance between the two disease modules (subgraphs) is correlated with comorbidity."}
+{"idx": 2, "title": "Modeling the control of infectious diseases : Effects of TV and social...", "date": "", "ddg_snippet": "Therefore, in this paper, we propose a mathematical model to see how TV and social media advertisements impact the dynamics of an infectious disease . The susceptible population is assumed vulnerable to infection as well as information (through TV and social media ads).", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/10.3934/mbe.2018061/pdf", "content": "Therefore, in this paper, we propose a mathematical model to see how TV and social media advertisements impact the dynamics of an infectious disease . The susceptible population is assumed vulnerable to infection as well as information (through TV and social media ads)."}
+{"idx": 3, "title": "Student Question : How does equivalent conductivity vary... | QuickTakes", "date": "", "ddg_snippet": "Get the full answer from QuickTakes - This content explains how equivalent conductivity varies with concentration, highlighting the effects of dilution, ion mobility, and relevant equations .", "subpage_snippet": "", "source": "quicktakes.io", "link": "https://quicktakes.io/learn/chemistry/questions/how-does-equivalent-conductivity-vary-with-concentration", "content": "Get the full answer from QuickTakes - This content explains how equivalent conductivity varies with concentration, highlighting the effects of dilution, ion mobility, and relevant equations ."}
+{"idx": 4, "title": "Consider the problem y''+λy=0, y(0)=0, y'(L)=0.Show that if φm | ...", "date": "", "ddg_snippet": "Finally, subtract one equation from the other..ϕn are eigenfunctions corresponding to the eigenvalues. λm \\ lambda _ m .", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/explanations/questions/consider-the-problemyy0y00yl0show-that-ifmandnare-eigenfunctions-corresponding-to-the-eigenvaluesman-1b2ae7b1-c74d-48ad-92cf-e641447a157c", "content": "Finally, subtract one equation from the other..ϕn are eigenfunctions corresponding to the eigenvalues. λm \\ lambda _ m ."}
+{"idx": 5, "title": "Ch. 10 Derivation of Consumer Demand (math econ)", "date": "", "ddg_snippet": "λM =MUX XM+UY YM. Hence we can say that lambda is the marginal utility of money income10.4 The Slutsky Equation . It is apparent from the structure of the utility maximization model that no refutable hypotheses are strictly implied on the basis of the maximization hypothesis alone.", "subpage_snippet": "", "source": "www.authorea.com", "link": "https://www.authorea.com/users/169078/articles/210082-ch-10-derivation-of-consumer-demand-math-econ", "content": "λM =MUX XM+UY YM. Hence we can say that lambda is the marginal utility of money income10.4 The Slutsky Equation . It is apparent from the structure of the utility maximization model that no refutable hypotheses are strictly implied on the basis of the maximization hypothesis alone."}
+{"idx": 6, "title": "The charge on cation and anion of an electrolyte is \\\\[{\\\\nu ^ + }\\\\] and...", "date": "", "ddg_snippet": "λM =λeq×ν+. λM =λeq×(ν−×a). Therefore, equations in options A, B and D are correct and in option C is incorrect. Thus, option C is the answer. Note: Equivalent conductivity or equivalent conductance.", "subpage_snippet": "", "source": "www.vedantu.com", "link": "https://www.vedantu.com/question-answer/the-charge-on-cation-and-anion-of-an-electrolyte-class-11-chemistry-cbse-5f81410da4b4843341dfe40d", "content": "λM =λeq×ν+. λM =λeq×(ν−×a). Therefore, equations in options A, B and D are correct and in option C is incorrect. Thus, option C is the answer. Note: Equivalent conductivity or equivalent conductance."}
+{"idx": 7, "title": "Simple quantum problem - find eigenvalues, probabilities, expectation...", "date": "", "ddg_snippet": "Quantum dot technique improves multi-photon state generation .In particular, you should not be plugging the given state x (which you had labeled u) into the eigenvalue/eigenvector equation .", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/simple-quantum-problem-find-eigenvalues-probabilities-expectation-value.429048/", "content": "Quantum dot technique improves multi-photon state generation .In particular, you should not be plugging the given state x (which you had labeled u) into the eigenvalue/eigenvector equation ."}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 9, "title": "The relationship lambda ( m )= lambda ( m )^(0)-BsqrtC will not hold good...", "date": "", "ddg_snippet": "Text Solution. Generated By DoubtnutGPT. The correct Answer is:D. To determine which electrolyte does not follow the relationship λm =λ0 m −B√C, we need to analyze the given relationship and the nature of the electrolytes provided in the options.", "subpage_snippet": "", "source": "www.doubtnut.com", "link": "https://www.doubtnut.com/qna/19293287", "content": "Text Solution. Generated By DoubtnutGPT. The correct Answer is:D. To determine which electrolyte does not follow the relationship λm =λ0 m −B√C, we need to analyze the given relationship and the nature of the electrolytes provided in the options."}